Revision to the Guideline on Air Quality Models: Enhancements to the AERMOD Dispersion Modeling System and Incorporation of Approaches To Address Ozone and Fine Particulate Matter, 45339-45387 [2015-18075]
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Vol. 80
Wednesday,
No. 145
July 29, 2015
Part III
Environmental Protection Agency
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40 CFR Part 51
Revision to the Guideline on Air Quality Models: Enhancements to the
AERMOD Dispersion Modeling System and Incorporation of Approaches To
Address Ozone and Fine Particulate Matter; Proposed Rule
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Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules
ENVIRONMENTAL PROTECTION
AGENCY
40 CFR Part 51
[EPA–HQ–OAR–2015–0310; FRL–9930–11–
OAR]
RIN 2060–AS54
Revision to the Guideline on Air
Quality Models: Enhancements to the
AERMOD Dispersion Modeling System
and Incorporation of Approaches To
Address Ozone and Fine Particulate
Matter
Environmental Protection
Agency (EPA).
ACTION: Proposed rule; notice of
conference.
AGENCY:
In this action, the
Environmental Protection Agency (EPA)
proposes to revise the Guideline on Air
Quality Models (‘‘Guideline’’). The
Guideline has been incorporated into
EPA’s regulations, satisfying a
requirement under the Clean Air Act
(CAA) section 165(e)(3) for the EPA to
specify, with reasonable particularity
models to be used in the Prevention of
Significant Deterioration (PSD) program.
It provides EPA-preferred models and
other recommended techniques, as well
as guidance for their use in predicting
ambient concentrations of air pollutants.
The proposed revisions to the Guideline
include enhancements to the
formulation and application of the
EPA’s AERMOD near-field dispersion
modeling system and the incorporation
of a tiered demonstration approach to
address the secondary chemical
formation of ozone and fine particulate
matter (PM2.5) associated with precursor
emissions from single sources.
Additionally, the EPA proposes various
editorial changes to update and
reorganize information throughout the
Guideline to streamline the compliance
assessment process.
Within this action, the EPA is also
announcing the Eleventh Conference on
Air Quality Modeling and invites the
public to participate in the conference.
The conference will focus on the
proposed revisions to the Guideline and
part of the conference will also serve as
the public hearing for these revisions.
DATES: Comments must be received on
or before October 27, 2015.
Public hearing and conference: The
public hearing for this action and the
Eleventh Conference on Air Quality
Modeling will be held August 12–13,
2015, from 8:30 a.m. to 5:00 p.m.
ADDRESSES: Submit your comments,
identified by Docket ID No. EPA–HQ–
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SUMMARY:
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OAR–2015–0310, by one of the
following methods:
• Federal eRulemaking Portal: https://
www.regulations.gov. Follow the online
instructions for submitting comments.
• Email: A-and-R-Docket@epa.gov.
Include docket ID No. EPA–HQ–OAR–
2015–0310 in the subject line of the
message.
• Fax: (202) 566–9744.
• Mail: Environmental Protection
Agency, Mail code 28221T, Attention
Docket No. EPA–HQ–OAR–2015–0310,
1200 Pennsylvania Ave. NW.,
Washington, DC 20460. Please include a
total of two copies.
• Hand/Courier Delivery: EPA Docket
Center, Room 3334, EPA WJC West
Building, 1301 Constitution Ave. NW.,
Washington, DC. Such deliveries are
only accepted during the Docket’s
normal hours of operation, and special
arrangements should be made for
deliveries of boxed information.
Instructions: Direct your comments to
Docket ID No. EPA–HQ–OAR–2015–
0310. The EPA’s policy is that all
comments received will be included in
the public docket without change and
may be made available online at https://
www.regulations.gov, including any
personal information provided, unless
the comment includes information
claimed to be Confidential Business
Information (CBI) or other information
whose disclosure is restricted by statute.
Do not submit information that you
consider to be CBI or otherwise
protected through https://
www.regulations.gov or email. The
www.regulations.gov Web site is an
‘‘anonymous access’’ system, which
means the EPA will not know your
identity or contact information unless
you provide it in the body of your
comment. If you send an email
comment directly to the EPA without
going through https://
www.regulations.gov, your email
address will be automatically captured
and included as part of the comment
that is placed in the public docket and
made available on the Internet. If you
submit an electronic comment, the EPA
recommends that you include your
name and other contact information in
the body of your comment and with any
disk or CD ROM you submit. If the EPA
cannot read your comment due to
technical difficulties and cannot contact
you for clarification, the EPA may not
be able to consider your comment.
Electronic files should avoid the use of
special characters, any form of
encryption, and be free of any defects or
viruses. For additional information
about the EPA’s public docket, visit the
EPA Docket Center homepage at https://
www.epa.gov/epahome/dockets.htm.
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Docket: All documents in the docket
are listed in the https://
www.regulations.gov index. Although
listed in the index, some information is
not publicly available, e.g., CBI or other
information whose disclosure is
restricted by statute. Certain other
material, such as copyrighted material,
will be publicly available only in hard
copy. Publicly available docket
materials are available either
electronically in www.regulations.gov or
in hard copy at the Air and Radiation
Docket and Information Center, EPA/
DC, Room 3334, WJC West Building,
1301 Constitution Ave. NW.,
Washington, DC. The Public Reading
Room is open from 8:30 a.m. to 4:30
p.m., Monday through Friday, excluding
legal holidays. The telephone number
for the Public Reading Room is (202)
566–1744 and the telephone number for
the Air and Radiation Docket and
Information Center is (202) 566–1742.
Public hearing and conference: The
public hearing for this action and the
Eleventh Conference on Air Quality
Modeling will be held in the EPA
Auditorium, Room C111, 109 T.W.
Alexander Drive, Research Triangle
Park, NC 27711.
FOR FURTHER INFORMATION CONTACT: Mr.
George M. Bridgers, Air Quality
Assessment Division, Office of Air
Quality Planning and Standards, U.S.
Environmental Protection Agency, Mail
code C439–01, Research Triangle Park,
NC 27711; telephone: (919) 541–5563;
fax: (919) 541–0044; email:
Bridgers.George@epa.gov.
SUPPLEMENTARY INFORMATION:
Table of Contents
The following topics are discussed in
this preamble:
I. General Information
A. Does this action apply to me?
B. What should I consider as I prepare my
comments for the EPA?
C. Where can I get a copy of this
document?
II. Background
A. The Guideline on Air Quality Models
and EPA Modeling Conferences
B. The Tenth Conference on Air Quality
Modeling
III. Public Participation Regarding Revisions
to the Guideline and Notice of Eleventh
Conference on Air Quality Modeling
IV. Proposed Changes to the Guideline
A. Proposed Actions
1. Clarifications To Distinguish
Requirements From Recommendations
2. Updates to EPA’s AERMOD Modeling
System
3. Status of AERSCREEN
4. Updates to 3-Tiered Demonstration
Approach for NO2
5. Status of CALINE3 Models
6. Addressing Single-Source Impacts on
Ozone and Secondary PM2.5
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7. Status of CALPUFF and Assessing LongRange Transport for PSD Increment and
Regional Haze
8. Role of EPA’s Model Clearinghouse
9. Updates to Modeling Procedures for
Cumulative Impact Analysis
10. Updates on Use of Meteorological Input
Data for Regulatory Dispersion Modeling
11. Transition Period for Applicability of
Revisions to the Guideline
B. Proposed Editorial Changes
V. Statutory and Executive Order Reviews
A. Executive Order 12866: Regulatory
Planning and Review and Executive
Order 13563: Improving Regulation and
Regulatory Review
B. Paperwork Reduction Act
C. Regulatory Flexibility Act
D. Unfunded Mandates Reform Act
E. Executive Order 13132: Federalism
F. Executive Order 13175: Consultation
and Coordination With Indian Tribal
Governments
G. Executive Order 13045: Protection of
Children From Environmental Health
and Safety Risks
H. Executive Order 13211: Actions
Concerning Regulations That
Significantly Affect Energy Supply,
Distribution, or Use
I. National Technology Transfer and
Advancement Act
J. Executive Order 12898: Federal Actions
To Address Environmental Justice in
Minority Populations and Low-Income
Populations
I. General Information
A. Does this action apply to me?
This action applies to federal, state,
territorial, and local air quality
management programs that conduct air
quality modeling as part of State
Implementation Plan (SIP) submittals
and revisions, New Source Review
(NSR), including new or modifying
industrial sources under Prevention of
Significant Deterioration (PSD),
Conformity, and other air quality
assessments required under EPA
regulation. Categories and entities
potentially regulated by this action
include:
electronically within the disk or CD
ROM the specific information that is
claimed as CBI. In addition to one
complete version of the comment that
includes information claimed as CBI, a
copy of the comment that does not
contain the information claimed as CBI
must be submitted for inclusion in the
public docket. Information so marked
will not be disclosed except in
accordance with procedures set forth in
40 CFR part 2.
2. Tips for preparing your comments.
When submitting comments, remember
to:
• Follow directions—The agency may
ask you to respond to specific questions
or organize comments by referencing a
CFR part or section number.
• Explain why you agree or disagree,
suggest alternatives, and substitute
language for your requested changes.
• Describe any assumptions and
provide any technical information and/
or data that you used.
• If you estimate potential costs or
burdens, explain how you arrived at
your estimate in sufficient detail to
allow for it to be reproduced.
• Provide specific examples to
illustrate your concerns and suggest
alternatives.
• Explain your views as clearly as
possible, avoiding the use of profanity
or personal threats.
• Make sure to submit your
comments by the comment period
deadline identified.
C. Where can I get a copy of this
document?
In addition to being available in the
docket, an electronic copy of this
proposed rule will also be available on
the Worldwide Web (WWW) through
the EPA’s Technology Transfer Network
(TTN). Following signature, a copy of
this proposed rule will be posted on the
TTN’s Support Center for Regulatory
Atmospheric Modeling (SCRAM) Web
site at the following address: https://
NAICS a
www.epa.gov/ttn/scram. The TTN
Category
Code
provides information and technology
exchange in various areas of air
Federal/state/territorial/local/
tribal government ..................
924110 pollution control.
a North
American Industry Classification II. Background
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System.
B. What should I consider as I prepare
my comments for the EPA?
1. Submitting CBI. Do not submit this
information to the EPA through https://
www.regulations.gov or email. Clearly
mark any of the information that you
claim to be CBI. For CBI information in
a disk or CD ROM that you mail to the
EPA, mark the outside of the disk or CD
ROM as CBI and then identify
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A. The Guideline on Air Quality Models
and EPA Modeling Conferences
The Guideline is used by the EPA,
other federal, state, territorial, and local
air quality agencies, and industry to
prepare and review new source permits,
source permit modifications, SIP
submittals and revisions, conformity,
and other air quality assessments
required under EPA regulation. The
Guideline serves as a means by which
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national consistency is maintained in
air quality analyses for regulatory
activities under 40 CFR 51.112, 51.117,
51.150, 51.160, 51.165, 51.166, 52.21,
93.116, 93.123, and 93.150.
The EPA originally published the
Guideline in April 1978 (EPA–450/2–
78–027), and it was incorporated by
reference in the regulations for the PSD
program in June 1978. The EPA revised
the Guideline in 1986 (51 FR 32176),
and updated it with supplement A in
1987 (53 FR 32081), supplement B in
July 1993 (58 FR 38816), and
supplement C in August 1995 (60 FR
40465). The EPA published the
Guideline as appendix W to 40 CFR part
51 when the EPA issued supplement B.
The EPA republished the Guideline in
August 1996 (61 FR 41838) to adopt the
CFR system for labeling paragraphs. The
publication and incorporation of the
Guideline by reference into the EPA’s
PSD regulations satisfies the
requirement under the CAA section
165(e)(3) for the EPA to promulgate
regulations that specify with reasonable
particularity models to be used under
specified sets of conditions for purposes
of the PSD program.
To support the process of developing
and revising the Guideline during the
period of 1977–1988, we held the First,
Second, and Third Conferences on Air
Quality Modeling as required by CAA
section 320 to help standardize
modeling procedures. These modeling
conferences provided a forum for
comments on the Guideline and
associated revisions, thereby helping us
introduce improved modeling
techniques into the regulatory process.
In October 1988, we held the Fourth
Conference on Air Quality Modeling to
advise the public on new modeling
techniques and to solicit comments to
guide our consideration of any
rulemaking needed to further revise the
Guideline. We held the Fifth Conference
in March 1991, which also served as a
public hearing for the proposed
revisions to the Guideline. In August
1995, we held the Sixth Conference as
a forum to update our available
modeling tools with state-of-the-science
techniques and for the public to offer
new ideas.
The Seventh Conference was held in
June 2000, and also served as a public
hearing for another round of proposed
changes to the recommended air quality
models in the Guideline. These changes
included the CALPUFF modeling
system, AERMOD modeling system, and
ISC–PRIME model.
Subsequently, the EPA revised the
Guideline on April 15, 2003 (68 FR
18440), to adopt CALPUFF as the
preferred model for long-range transport
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of emissions from 50 to several hundred
kilometers and to make various editorial
changes to update and reorganize
information and remove obsolete
models.
We held the Eighth Conference on Air
Quality Modeling in September 2005.
This conference provided details on
changes to the preferred air quality
models, including available methods for
model performance evaluation and the
notice of data availability that the EPA
published in September 2003, related to
the incorporation of the PRIME
downwash algorithm in the AERMOD
dispersion model (in response to
comments received from the Seventh
Conference). Additionally, at the Eighth
Conference, a panel of experts discussed
the use of state-of-the-science prognostic
meteorological data for informing the
dispersion models.
The EPA further revised the Guideline
on November 9, 2005 (70 FR 68218), to
adopt AERMOD as the preferred model
for near-field dispersion of emissions for
distances up to 50 kilometers.
The Ninth Conference on Air Quality
Modeling was held in October 2008, and
emphasized the following topics:
Reinstituting the Model Clearinghouse,
review of non-guideline applications of
dispersion models, regulatory status
updates of AERMOD and CALPUFF,
continued discussions on the use of
prognostic meteorological data for
informing dispersion models, and
presentations reviewing the available
model evaluation methods.
B. The Tenth Conference on Air Quality
Modeling
The most recent EPA modeling
conference was the Tenth Conference on
Air Quality Modeling held in March
2012. This conference covered multiple
topics which have been vital in the
development of the proposed revisions
to the Guideline. The conference
addressed updates on the regulatory
status and future development of
AERMOD and CALPUFF, review of the
Mesoscale Model Interface (MMIF)
prognostic meteorological data
processing tool for dispersion models,
draft modeling guidance for compliance
demonstrations of the PM2.5 National
Ambient Air Quality Standards
(NAAQS), modeling for compliance
demonstration of the 1-hour nitrogen
dioxide (NO2) and sulfur dioxide (SO2)
NAAQS, and new and emerging
models/techniques for future
consideration under the Guideline to
address single-source modeling for
ozone and secondary PM2.5, as well as
long-range transport and chemistry. A
transcript of the conference proceedings
and a document that summarizes the
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public comments received are available
at EPA’s SCRAM Web site at https://
www.epa.gov/ttn/scram/
10thmodconf.htm.
The EPA promulgated a new 1-hour
NAAQS for NO2 in January 2010, and a
new 1-hour NAAQS for SO2 in June
2010. Although AERMOD evaluations
that formed the basis of its promulgation
as the EPA’s preferred dispersion model
demonstrated that AERMOD provides
generally unbiased estimates of ambient
concentrations, the increased stringency
of these new standards resulted in
increased scrutiny by the modeling
community of AERMOD model
performance. In response, the EPA
issued several guidance memoranda to
clarify the applicability of the Guideline
and address initial issues with use of
current models and procedures under
PSD permitting.1 2 3 4 However, the
situation also necessitated the EPA and
the modeling community to more
closely evaluate the science and model
formulation of AERMOD to better
understand the issues being experienced
by stakeholders and to address
performance issues in its use for PSD
permitting under these new standards.
As part of this effort, the EPA
reconvened the AERMOD
Implementation Workgroup (AIWG)
with state and local agency modelers to
evaluate AERMOD across a variety of
hypothetical sources and results from
this assessment were also presented at
this conference to inform the modeling
community of potential implications
1 U.S. EPA, 2010. Applicability of Appendix W
Modeling Guidance for the 1-hour NO2 NAAQS.
Tyler Fox Memorandum dated June 28, 2010, Office
of Air Quality Planning & Standards, Research
Triangle Park, North Carolina 27711. https://
www.epa.gov/ttn/scram/guidance/clarification/
ClarificationMemo_AppendixW_Hourly-NO2NAAQS_FINAL_06-28-2010.pdf.
2 U.S. EPA, 2010. Applicability of Appendix W
Modeling Guidance for the 1-hour SO2 NAAQS.
Tyler Fox Memorandum dated August 23, 2010,
Office of Air Quality Planning & Standards,
Research Triangle Park, North Carolina 27711.
https://www.epa.gov/ttn/scram/guidance/
clarification/ClarificationMemo_AppendixW_
Hourly-SO2-NAAQS_FINAL_08-23-2010.pdf.
3 U.S. EPA, 2010. Guidance Concerning the
Implementation of the 1-hour SO2 NAAQS for the
Prevention of Significant Deterioration Program.
Stephen D. Page, Memorandum dated August 23,
2010, Office of Air Quality Planning & Standards,
Research Triangle Park, North Carolina 27711.
https://www.epa.gov/region07/air/nsr/nsrmemos/
appwso2.pdf.
4 U.S. EPA, 2010. Guidance Concerning the
Implementation of the 1-hour NO2 NAAQS for the
Prevention of Significant Deterioration Program.
Stephen D. Page, Memorandum dated June 29,
2010, Office of Air Quality Planning & Standards,
Research Triangle Park, North Carolina 27711.
https://www.epa.gov/ttn/scram/guidance/
clarification/ClarificationMemo_AppendixW_
Hourly-SO2-NAAQS_FINAL_08-23-2010.pdf.
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and areas for improvement in the model
and guidance on their use.
Several presentations at the Tenth
Modeling Conference addressed issues
and challenges associated with
demonstrating compliance with these
new 1-hour NAAQS for NO2 and SO2.
This included results from a study
sponsored by the American Petroleum
Institute (API) that evaluated AERMOD
model performance under low wind
speed conditions using additional
National Oceanic and Atmospheric
Administration (NOAA) field studies at
Oak Ridge, TN, and Idaho Falls, ID,
which were not included in the original
17 databases used to support
AERMOD’s promulgation in 2005. The
API low wind study 5 showed
significant overprediction of observed
concentrations, especially for the Oak
Ridge study where observed wind
speeds were below 0.5 m/s for 10 of the
11 tracer tests, and included wind
speeds as low as 0.15 m/s. The API low
wind study also included proposed
modifications to the AERMET
meteorological processor and AERMOD
model to address this bias toward
overprediction under stable/light wind
conditions.
Prior to the promulgation of the 1hour NO2 NAAQS, compliance with the
previous annual NO2 NAAQS was
routinely demonstrated based on the
Tier 1 assumption of full conversion or
a Tier 2 option based on an ambient
ratio of 75 percent conversion of
nitrogen oxides (NOX) to NO2, referred
to as the Ambient Ratio Method (ARM).
However, compliance with the new 1hour NAAQS has typically required a
more refined treatment of NOX
conversion to NO2. Therefore, several
presentations at the Tenth Modeling
Conference focused on issues associated
with demonstrating compliance with
the new 1-hour NO2 NAAQS.
These presentations included an
overview of an API funded study to
develop a Tier 2 ambient ratio method
for the 1-hour NO2 NAAQS, referred to
as ARM2. The ARM2 approach was
developed based on an extensive
analysis of ambient ratios of NO2/NOX
that were analyzed by land use (urban
vs. rural) and geographical areas. Based
on these analyses of the ambient NO2/
NOX ratios, an empirical relationship
between ambient concentrations of NO2
and NOX was developed. The EPA
subsequently reviewed and evaluated
this ARM2 approach and then
5 AECOM, 2010. AERMOD low wind speed
evaluation study results, https://
mycommittees.api.org/rasa/amp/
Modeling%20Documents/AECOM%202009%
20Low%20Wind%20Speed%20Evaluation%
20Study%20Report.pdf.
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incorporated this screening technique as
a non-Default/Beta option in version
13350 of AERMOD in December 2013.
Another issue associated with NO2
NAAQS compliance presented at this
conference focused on the use of
relative (instantaneous) dispersion
coefficients to define the plume volume
which determines the amount of ozone
available to convert nitrogen (NO) to
NO2 using the Plume Volume Molar
Ratio Method (PVMRM) option in
AERMOD. The relative dispersion
coefficients originally incorporated in
AERMOD for PVMRM are best
representative of daytime convective
conditions and may tend to
overestimate plume volumes during
stable conditions. Such overestimation
of the plume volume will tend to result
in PVMRM to overestimate
concentrations of NO2.
In addition, modeling of single-source
impacts for ozone and secondarily
formed PM2.5 was a topic of discussion
at the Tenth Modeling Conference. On
January 4, 2012, the EPA granted a
petition submitted on behalf of the
Sierra Club on July 28, 2010 6 and
committed to engage in rulemaking to
evaluate whether updates to the
Guideline are warranted and, as
appropriate, incorporate new analytical
techniques or models for ozone and
secondarily formed PM2.5. As a part of
satisfying this commitment, there were
presentations of ongoing research at the
Tenth Modeling Conference regarding
single-source plume chemistry and
photochemical grid modeling
techniques, as well as several public
forums. In addition, the EPA presented
an overview along with a panel
discussion of its Draft Guideline for
PM2.5 Permit Modeling that addressed
the need for consideration of secondary
PM2.5 in demonstrating compliance with
the PM2.5 NAAQS.7 Subsequently,
written comments pertaining to such
modeling were submitted to the EPA.
As introduced at the Tenth Modeling
Conference, the Interagency Workgroup
on Air Quality Modeling (IWAQM)
process was formally reinitiated in June
2013 to inform the EPA’s process of
updating the Guideline to address
chemically reactive pollutants in nearfield and long-range transport
applications. The IWAQM, which
6 U.S. EPA, 2012. Gina McCarthy Letter to Robert
Ukeiley dated January 4, 2012, Washington, DC
20460. https://www.epa.gov/ttn/scram/
10thmodconf/review_material/Sierra_Club_
Petition_OAR-11-002-1093.pdf.
7 U.S. EPA, 2014. Guidance for PM
2.5 Modeling.
May 20, 2014, EPA–454/B–14–001. Office of Air
Quality Planning & Standards, Research Triangle
Park, NC. https://www.epa.gov/ttn/scram/guidance/
guide/Guidance_for_PM25_Permit_Modeling.pdf.
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consists of representatives from the
EPA, the U.S. Forest Service, the
National Park Service, and the U.S. Fish
and Wildlife Service, was initially
formed to support development of
technically sound recommendations
regarding assessment of air pollutant
source impacts on Federal Class I parks
and wilderness areas. Comments
received from stakeholders at the Tenth
Modeling Conference supported
reinitiating this interagency
collaborative effort (as ‘‘Phase 3’’) to
provide additional guidance for
modeling single-source impacts on
secondarily formed pollutants (e.g.,
ozone and PM2.5) in the near-field and
for long-range transport. Stakeholder
comments also support the idea of this
collaborative effort working in parallel
with stakeholders to further model
development and evaluation. This
renewed 8 effort included the
establishment of two separate working
groups, one focused on long-range
transport of primary and secondary
pollutants and the other on near-field
single-source impacts of secondary
pollutants. The primary objectives of
this phase of IWAQM include reviewing
existing approaches for estimating
single-source secondary pollutant
impacts, developing revisions to the
Guideline, and the development of
guidance for using technical methods to
estimate downwind secondary pollutant
impacts.
III. Public Participation Regarding
Revisions to the Guideline and Notice
of Eleventh Conference on Air Quality
Modeling
Interested persons may provide the
EPA with their views on the proposed
revisions to the Guideline in several
ways. This includes submitting written
comments to the EPA, participating in
the Eleventh Conference on Air Quality
Modeling, and speaking at the public
hearing that will be conducted as part
of the conference. Additional
information on how to submit written
comments on the proposed revisions to
the Guideline is provided in the
ADDRESSES section above.
8 Phase 1 of the IWAQM effort focused on review
of respective regional modeling programs,
development of an organizational framework, and
formulating reasonable objectives and plans that
were presented to EPA management for support and
commitment. Phase 2 of the IWAQM process
continued this work and largely concluded in 1998
with the publication of the Interagency Workgroup
on Air Quality Modeling (IWAQM) Phase 2
Summary Report and Recommendations for
Modeling Long Range Transport Impacts (EPA–454/
R–98–019) (USEPA, 1998). This report provided a
series of recommendations concerning the
application of the CALPUFF model for use in
regulatory long-range transport (LRT) modeling that
supported the revisions in 2003 to the Guideline.
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The public hearing for this action and
the Eleventh Conference on Air Quality
Modeling will be held August 12–13,
2015, from 8:30 a.m. to 5:00 p.m., in the
EPA Auditorium, Room C111, 109 T.W.
Alexander Drive, Research Triangle
Park, NC 27711. On August 12, 2015,
the first half of the conference will
consist of a structured agenda with
presentations. The second half of the
first and all of the second day (August
13, 2015), is reserved for the public
hearing on this proposed rule. Advance
requests for reserved time to speak
during the public hearing should be
submitted by August 7, 2015, to Mr.
George M. Bridgers, Air Quality
Assessment Division, Office of Air
Quality Planning and Standards, U.S.
Environmental Protection Agency, Mail
code C439–01, Research Triangle Park,
NC 27711; telephone: (919) 541–5563;
fax: (919) 541–0044; email:
Bridgers.George@epa.gov. The EPA will
also provide an opportunity for oral
presentations by individuals that sign
up at the public hearing. Information
submitted to the EPA during the
conference will be placed in the docket
for this rule proposing revisions to the
Guideline.
Background information:
Preregistration details, additional
background information, and a more
detailed agenda for the Eleventh
Conference on Air Quality Modeling are
electronically available at https://
www.epa.gov/ttn/scram/
11thmodconf.htm. Preregistration for
the conference, while not required, is
strongly recommended due to
heightened security protocols at the
EPA–RTP facility.
REAL ID Act: Because this hearing is
being held at a U.S. government facility,
individuals planning to attend the
hearing should be prepared to show
valid picture identification to the
security staff in order to gain access to
the meeting room. Please note that the
REAL ID Act, passed by Congress in
2005, established new requirements for
entering federal facilities. These
requirements took effect July 21, 2014.
If your driver’s license is issued by
Alaska, American Samoa, Arizona,
Kentucky, Louisiana, Maine,
Massachusetts, Minnesota, Montana,
New York, Oklahoma, or the state of
Washington, you must present an
additional form of identification to enter
the federal buildings where the public
hearings will be held. Acceptable
alternative forms of identification
include: Federal employee badges,
passports, enhanced driver’s licenses
and military identification cards. We
will list any additional acceptable forms
of identification at: https://www.epa.gov/
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ttn/scram/11thmodconf.htm. In
addition, you will need to obtain a
property pass for any personal
belongings you bring with you. Upon
leaving the building, you will be
required to return this property pass to
the security desk. No large signs will be
allowed in the building, cameras may
only be used outside of the building and
demonstrations will not be allowed on
federal property for security reasons.
Conference and Public Hearing: The
Eleventh Conference on Air Quality
Modeling will be open to the public. No
registration fee is charged. The
conference will be conducted informally
and chaired by an EPA official. As
required under CAA section 320, a
verbatim transcript of the conference
proceedings will be produced and
placed in the docket for this proposed
rule.
The Eleventh Conference on Air
Quality Modeling will begin with
introductory remarks by the presiding
EPA official. The EPA staff will then
provide an overview of the revisions to
the Guideline as proposed in this
document and present on the research
that supports those revisions and
supports formulation updates to the
preferred models. The following topics
will be presented:
I. Overview of the Eleventh Conference
on Air Quality Modeling;
II. Review of the proposed revisions to
the Guideline; and
III. Review of the proposed revisions to
the preferred air quality models.
At the conclusion of the
presentations, the EPA will convene the
public hearing on the proposed
revisions to the Guideline. The public
hearing will span the second half of the
first day and throughout the second day
of the conference.
Those wishing to reserve time to
speak at the public hearing, whether to
volunteer a presentation on a special
topic or to offer general comment on any
of the modeling techniques scheduled
for presentation, should contact us at
the address given in the FOR FURTHER
INFORMATION CONTACT section (note the
cutoff date). Such persons should
identify the organization (if any) on
whose behalf they are speaking and the
length of the presentation. If a
scheduled presentation is projected to
be longer than 10 minutes, the presenter
should also state why a longer period is
needed. Scheduled speakers should
bring extra copies of their presentation
for inclusion in the docket and for the
convenience of the recorder. Scheduled
speakers will also be permitted to enter
additional written comments into the
record.
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Any person in attendance wishing to
speak at the public hearing who has not
reserved time prior to the conference
may provide oral comments on the
proposed revisions to the Guideline
during time allotted on the last day of
the conference. These parties will need
to sign up to speak on the second day
of the hearing and the EPA may need to
limit the duration of presentations to
allow all participants to be heard.
Additional written statements or
comments on the proposed revisions
should be sent to the OAR Regulatory
Docket (see ADDRESSES section). A
transcript of the conference proceedings
and a copy of all written comments will
be maintained in Docket ID No. EPA–
HQ–OAR–2015–0310, which will
remain open until October 27, 2015, for
the purpose of receiving additional
comments after the conference and the
public hearing on the proposed
revisions to the Guideline.
IV. Proposed Changes to the Guideline
In this action, the EPA is proposing
two type of revisions to the Guideline.
The first involve substantive changes to
address various topics, including those
presented and discussed at the Tenth
Modeling Conference. These proposed
revisions to the Guideline include
enhancements to the formulation and
application of the EPA’s preferred
dispersion modeling system, AERMOD,
and the incorporation of a tiered
demonstration approach to address the
secondary chemical formation of ozone
and PM2.5 associated with precursor
emissions from single sources. The
second type of revision involves
editorial changes to update and
reorganize information throughout the
Guideline. These revisions are not
intended to meaningfully change the
substance of the Guideline, but rather to
make the Guideline easier to use and to
streamline the compliance assessment
process.
A. Proposed Actions
This section provides a detailed
overview of the substantive proposed
changes to the Guideline that are
intended to improve the science of the
models and approaches used in
regulatory assessments.
1. Clarifications To Distinguish
Requirements From Recommendations
The EPA’s PSD permitting regulations
specify that ‘‘[a]ll applications of air
quality modeling involved in this
subpart shall be based on the applicable
models, data bases, and other
requirements specified in appendix W
of this part (Guideline on Air Quality
Models).’’ 40 CFR 51.166(l); see also 40
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CFR 52.21(l). The applicable models are
the preferred models listed in appendix
A to appendix W to 40 CFR part 51.
However, there has been some
ambiguity in the past with respect to the
‘‘other requirements’’ specified in the
Guideline that must be used in PSD
permitting analysis and other regulatory
modeling assessments.
Ambiguity can result because the
Guideline generally contains
‘‘recommendations’’ and these
recommendations are expressed in nonmandatory language. For instance, the
Guideline frequently uses ‘‘should’’ and
‘‘may’’ rather than ‘‘shall’’ and ‘‘must.’’
This approach is generally preferred
throughout the Guideline because of the
need to exercise expert judgment in air
quality analysis and the reasons
discussed in the Guideline that ‘‘dictate
against a strict modeling ‘cookbook’.’’
(40 CFR part 51, appendix W, section
1.0(c))
Considering the non-mandatory
language used throughout the Guideline,
the EPA’s Environmental Appeals Board
has correctly observed the following:
‘‘Although appendix W has been
promulgated as codified regulatory text,
appendix W provides permit issuers broad
latitude and considerable flexibility in
application of air quality modeling.
Appendix W is replete with references to
‘‘recommendations,’’ ‘‘guidelines,’’ and
reviewing authority discretion.’’
In Re Prairie State Generating Company,
13 E.A.D. 1, 99 (EAB 2005) (internal
citations omitted).
Although this approach is typical
throughout the Guideline, there are
instances where the EPA does not
believe permit issues should have broad
latitude. Some principles of air quality
modeling described in the Guideline
must always be applied to produce an
acceptable analysis. Thus, to promote
clarity in the use and interpretation of
the revised Guideline, we have, in these
cases used mandatory language, and
made specific reference to
‘‘requirements’’ throughout the
proposed text where appropriate to
distinguish requirements from
recommendations in the application of
models for regulatory purposes. We
solicit comment regarding the
appropriateness of these revisions in
providing the necessary clarity on the
requirements under the proposed
revisions to the Guideline as distinct
from the recommendations in the
revised text while noting the continued
flexibilities provided for within the
Guideline including but not limited to
use and approval of alternative models.
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2. Updates to EPA’s AERMOD Modeling
System
Based on studies presented and
discussed at the Tenth Modeling
Conference, and additional relevant
research since 2010, the EPA and other
researchers have conducted additional
model evaluations and developed
changes to the model formulation of the
AERMOD modeling system to improve
model performance in its regulatory
applications. We propose the following
updates to the AERMOD modeling
system to address a number of technical
concerns expressed by stakeholders:
1. A proposed option incorporated in
AERMET to adjust the surface friction
velocity (u*) to address issues with
AERMOD model overprediction under
stable, low wind speed conditions. This
proposed option is selected by the user
with the METHOD STABLEBL ADJ_U*
record in the AERMET Stage 3 input
file.
2. A proposed low wind option in
AERMOD to address issues with model
overprediction under low wind speed
conditions. The low wind option will
increase the minimum value of the
lateral turbulence intensity (sigma-v)
from 0.2 to 0.3 and adjusts the
dispersion coefficient to account for the
effects of horizontal plume meander on
the plume centerline concentration. It
also eliminates upwind dispersion
which is incongruous with a straightline, steady-state plume dispersion
model such as AERMOD. The proposed
option is selected by specifying
‘‘LOWWIND3’’ on the CO MODELOPT
keyword in the AERMOD input file.
3. Modifications to AERMOD
formulation to address issues with
overprediction for applications
involving relatively tall stacks located
near relatively small urban areas (no
user input is required).
4. Proposed regulatory default options
in AERMOD to address plume rise for
horizontal and capped stacks based on
the July 9, 1993, Model Clearinghouse
memorandum,9 with adjustments to
account for the PRIME algorithm for
sources subject to building downwash.
These options are selected by the model
user specifying ‘‘POINTCAP’’ or
‘‘POINTHOR’’ for source type on the SO
LOCATION keyword in the AERMOD
input file.
5. A proposed buoyant line source
option, based on the BLP model, has
been incorporated in AERMOD. This
9 U.S. EPA, 1993. ‘‘Proposal for Calculating Plume
Rise for Stacks with Horizontal Releases or Rain
Caps for Cookson Pigment, Newark, New Jersey’’,
Memorandum from Joseph A. Tikvart, U.S. EPA/
OAQPS, Research Triangle Park, NC. July 9, 1993.
https://www.epa.gov/ttn/scram/guidance/mch/new_
mch/R1076_TIKVART_9_JUL_93.pdf.
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proposed option is selected by the
model user with the SOURCE type
‘‘BOUYLINE’’ to specify the individual
buoyant line source locations and
emissions and the new ‘‘BLAVGVAL’’
keyword to specify average parameters
for a composite buoyant line.
6. Proposed updates to the NO2 Tier
2 and Tier 3 screening techniques coded
within AERMOD as described more
fully later in this preamble section.
Model performance evaluation and
peer scientific review references for the
updated AERMOD modeling system are
cited, as appropriate. An updated user’s
guide and model formulation
documents for version 15181 have been
placed in the docket. We have updated
the summary description of the
AERMOD modeling system to appendix
A of the Guideline to reflect these
proposed updates. The essential codes,
preprocessors, and test cases have been
updated and posted to the EPA’s
SCRAM Web site, https://www.epa.gov/
ttn/scram.
We invite comments on whether we
have reasonably addressed the technical
concerns expressed by the stakeholder
community and are on sound footing to
recommend these updates to the
regulatory default version of the
AERMOD modeling system which
includes its replacement of BLP as an
appendix A model for the intended
regulatory applications.
3. Status of AERSCREEN
In the preamble of the 2005 Guideline,
we stated that a screening version of
AERMOD called AERSCREEN was being
developed and, in the meantime,
SCREEN3 may be used until
AERSCREEN was available. In 2011, the
EPA released AERSCREEN, a program
that creates inputs and runs AERMOD
in screening mode. AERSCREEN also
interfaces with AERMOD’s terrain
processor, AERMAP, the building
processor for AERMOD, BPIPPRIME,
and can use AERSURFACE surface
characteristics in the generation of
meteorological data for AERMOD via
the MAKEMET utility. In an April 2011
memorandum, the EPA stated that
AERSCREEN was the recommended
screening model for simple and
complex terrain and replaced SCREEN3.
Since AERSCREEN invokes AERMOD,
AERSCREEN represents the state of the
science in screening dispersion models.
As part of this proposed update to
AERSCREEN, AERSCREEN now
includes inversion break-up and coastal
fumigation, features that were part of
SCREEN3. These fumigation algorithms
also take advantage of AERMOD’s
boundary layer parameterizations for
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45345
calculating variables needed by the
algorithms.
We invite comment on incorporation
of AERSCREEN into the Guideline as
the screening model for AERMOD that
may be applicable in applications in all
types of terrain and for applications
involving building downwash.
4. Updates to 3-Tiered Demonstration
Approach for NO2
Section 5.2.4 of the 2005 Guideline
details a 3-tiered approach for assessing
NOX sources, which was recommended
to obtain annual average estimates of
NO2 from point sources for purposes of
NSR analysis, including the PSD
program and SIP planning purposes.
This 3-tiered approach addresses the coemissions of NO and NO2 and the
subsequent conversion of NO to NO2 in
the atmosphere. The tiered levels
include: (1) Assuming that all NO is
converted to NO2 (full conversion), (2)
using the Ambient Ratio Method (ARM),
which applies an assumed equilibrium
ratio of NO2 to NOX, based on observed
ambient conditions, to the annual
results from the Tier 1 full conversion,
and (3) detailed screening options
focused on determining site-specific
ratios of NO2 to NOX.
In January 2010, a new 1-hour NO2
standard was promulgated. Prior to the
adoption of the 1-hour NO2 standard,
few PSD permit applications required
the use of Tier 3 options and guidance
available at the time did not fully
address the modeling needs for a 1-hour
standard, i.e., tiered approaches for NO2
in the 2005 Guideline specifically
targeted an annual standard. As a result,
several guidance memoranda have been
issued by the EPA to further inform
modeling procedures for sources
demonstrating compliance with the new
1-hour standard.1 2 3 4. In response to the
1-hour NO2 standard, the EPA is
proposing several modifications to the
Tier 2 and 3 NO2 screening techniques
incorporated into AERMOD.
For the Tier 2 technique, the EPA is
proposing to replace the existing ARM
with a revised Ambient Ratio Method 2
(ARM2). The existing Tier 2 technique,
ARM, was based on a study that focused
exclusively on long-term averages.10 A
recently published study 11 presented a
new analysis of national levels of
ambient ratios of NO2 to NOX based on
10 Chu, S.H. and E.L. Meyer, 1991. Use of
Ambient Ratios to Estimate Impact of NOX Sources
on Annual NO2 Concentrations. Proceedings, 84th
Annual Meeting & Exhibition of the Air & Waste
Management Association, Vancouver, B.C.; 16–21
June 1991. (16pp.) (Docket No. A–92–65, II–A–9).
11 Podrez, M. 2015. An Update to the Ambient
Ratio Method for 1-h NO2 Air Quality Standards
Dispersion Modeling. Atmospheric Environment,
103: 163–170.
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hourly data from the EPA’s Air Quality
System (AQS). Based on this analysis, a
new second tier NO2 screening
technique, ARM2, has been developed
and incorporated into AERMOD.
Because ARM2 is based on hourly
measurements of the NO2 to NOX ratios
and provides more detailed estimates of
this ratio based on the total NOX
present, the EPA is proposing to
incorporate a modified version of ARM2
as the new preferred second tier NOX
modeling approach.
For the Tier 3 technique, the EPA
proposes that the existing detailed
screening options of the Ozone Limiting
Method (OLM) 12 and PVMRM 13 be
formally incorporated into the
regulatory version of AERMOD. Both
OLM and PVMRM have been available
as non-regulatory, non-default options
in AERMOD for many years, but their
usage in a NAAQS compliance
demonstration required approval by the
reviewing authority. Based on the EPA’s
evaluation and external studies
available on their performance, which
show that OLM and PVMRM are
capable of modeling 1-hour NO2
impacts and NO and NO2 speciation
with reasonable accuracy when applied
appropriately, both OLM and PVMRM
are being proposed as preferred Tier 3
screening methods for NO2 modeling. In
addition, the EPA is proposing to
incorporate a revised version of the
PVMRM option, referred to as
PVMRM2, that utilizes relative
dispersion coefficients to estimate
plume volume during convective
conditions and total dispersion
coefficients during stable conditions.
These adjustments to the calculation of
plume volume are intended to mitigate
potential overprediction of NO2
conversion in multisource applications,
especially during stable meteorological
conditions. The EPA is proposing to
replace the existing PVMRM with the
new PVMRM2 with both versions being
made available in the proposed version
of AERMOD to facilitate testing and
evaluation of the EPA’s proposed
replacement of PVMRM option with
new PVMRMR2 option.
We invite comments on whether we
have reasonably addressed technical
concerns regarding the 3-tiered
demonstration approach and specific
NO2 screening techniques within
AERMOD and whether we are on sound
12 Cole, H.S. and J.E. Summerhays, 1979. A
Review of Techniques Available for Estimation of
Short-Term NO2 Concentrations. Journal of the Air
Pollution Control Association, 29(8): 812–817.
13 Hanrahan, P.L., 1999. The Polar Volume Polar
Ratio Method for Determining NO2/NOX Ratios in
Modeling—Part I: Methodology. Journal of the Air
& Waste Management Association, 49: 1324–1331.
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foundation to recommend the updates
described above.
5. Status of CALINE3 Models
The 2005 Guideline identified
CALINE3 14 and its variants (CAL3QHC
and CAL3QHCR) as the preferred model
for mobile source modeling for carbon
monoxide (CO), particulate matter (PM),
and lead. CALINE3 was developed in
the late 1970’s using P–G stability
classes as the basis for the dispersion
algorithms. AERMOD, on the other
hand, uses a planetary boundary layer
scaling parameter to characterize
stability and determine dispersion rates,
which has been found to be superior to
dispersion parameterizations based on
P–G stability classes.15 In addition, the
LINE and AREA source options in
AERMOD implement a full numerical
integration of emissions across the LINE
or AREA sources, whereas the CALINE3
family of models incorporate a much
less refined approach. Thus, AERMOD
provides a more scientifically credible
and accurate characterization of plume
dispersion than CALINE3. Recent model
performance studies 16 have shown that
the CALINE models performed poorly
when compared to AERMOD and other
modern dispersion models which also
employ state-of-the-science dispersion
parameters. AERMOD is also able to
model multiple years in a single model
run, while the CALINE3 variants are
limited to either a single meteorological
condition (CALINE3 and CAL3QHC) or
a single year of meteorological data
(CAL3QHCR). Additionally, AERMOD
is able to utilize more recent, and more
representative, meteorological
observations than are readily available
for modeling with CAL3QHCR. Based
on the more scientifically sound basis
for AERMOD, improved model
performance over CALINE3, and the
availability of more representative
meteorological data, the EPA proposes
replacing CALINE3 with AERMOD as
the preferred appendix A model for
determining near-field impacts for
primary emissions from mobile sources,
14 Benson, Paul E., 1979. CALINE3—A Versatile
Dispersion Model for Predicting Air Pollutant
Levels Near Highways and Arterial Streets. Interim
Report, Report Number FHWA/CA/TL–79/23.
Federal Highway Administration, Washington, DC
(NTIS No. PB 80–220841).
15 Cimorelli, A. et al., 2005. AERMOD: A
Dispersion Model for Industrial Source
Applications. Part I: General Model Formulation
and Boundary Layer Characterization. Journal of
Applied Meteorology, 44(5): 682–693.
16 Heist, D., V. Isakov, S. Perry, M. Snyder, A.
Venkatram, C. Hood, J. Stocker, D. Carruthers, S.
Arunachalam, AND C. Owen. Estimating near-road
pollutant dispersion: a model inter-comparison.
Transportation Research Part D: Transport and
Environment. Elsevier BV, AMSTERDAM,
Netherlands, 25:93–105, (2013).
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including PM2.5, PM10, and CO hot-spot
analyses.17
We solicit comments on our proposal
to identify AERMOD as a replacement
for CALINE3 as an appendix A model
for its intended regulatory applications.
6. Addressing Single-Source Impacts on
Ozone and Secondary PM2.5
On January 4, 2012, the EPA granted
a petition submitted on behalf of the
Sierra Club on July 28, 2010,18 that
requested the EPA initiate rulemaking to
establish air quality models for ozone
and PM2.5 for use by all major sources
applying for a PSD permit. In granting
that petition, the EPA explained that the
‘‘complex chemistry of ozone and
secondary formation of PM2.5 are welldocumented and have historically
presented significant challenges to the
designation of particular models for
assessing the impacts of individual
stationary sources on the formation of
these air pollutants’’ and further
explained that ‘‘[b]ecause of these
considerations, the EPA’s judgment in
the past has been that it was not
technically sound to designate with
particularity specific models that must
be used to assess the impacts of a single
source on ozone concentrations,’’ but
rather the EPA had established a process
for determining on a case-by-case basis
the analytical techniques that should be
used for ozone, as well as for secondary
formation of PM2.5.
In the petition grant, the EPA
committed to engage in rulemaking to
evaluate whether updates to the
Guideline are warranted and, as
appropriate, incorporate new analytical
techniques or models for ozone and
secondarily formed PM2.5. This
rulemaking satisfies the EPA’s
commitment in the petition grant. As a
part of this commitment and in
compliance with CAA section 320, the
EPA conducted the Tenth Modeling
Conference in March 2012, where there
were presentations of ongoing research
of single-source plume chemistry and
photochemical grid modeling
techniques, as well as several public
forums, and the EPA subsequently
received written comments pertaining to
such modeling.
17 U.S. EPA, 2013, Transportation Conformity
Guidance for Quantitative Hot-Spot Analyses in
PM2.5 and PM10 Nonattainment and Maintenance
Areas. Publication No. EPA–420–B–13–053, Office
of Transportation and Air Quality, Ann Arbor, MI.
https://www.epa.gov/otaq/stateresources/transconf/
policy/420b13053-sec.pdf.
18 U.S. EPA, 2012. Gina McCarthy Letter to Robert
Ukeiley dated January 4, 2012, Washington, DC
20460. https://www.epa.gov/ttn/scram/
10thmodconf/review_material/Sierra_Club_
Petition_OAR-11-002-1093.pdf.
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The EPA initiated Phase 3 of the
IWAQM process in June 2013 to inform
this process to update the Guideline to
address chemically reactive pollutants
for near-field and long-range transport
applications. Comments received from
stakeholders at the Tenth Modeling
Conference supported this collaborative
effort to provide additional guidance for
modeling single-source impacts of
secondarily formed pollutants in the
near-field and for long-range transport.
Stakeholder comments also supported
the idea of this collaborative effort
occurring in parallel with stakeholders’
efforts to further model development
and evaluation. The EPA’s
recommended revisions to the Guideline
are largely based on detailed review and
assessment of this input.
For this proposed revision to the
Guideline, the EPA has determined that
advances in photochemical modeling
science indicate it is now reasonable to
provide more specific, generallyapplicable guidance that identifies
particular models or analytical
techniques that may be used under
specific circumstances for assessing the
impacts of an individual source on
ozone and secondary PM2.5.
Quantifying secondary pollutant
formation requires simulating chemical
reactions and thermodynamic
partitioning in a realistic chemical and
physical environment. Chemical
transport models treat atmospheric
chemical and physical processes such as
deposition and transport. There are two
types of chemical transport models,
which are differentiated based on a
fixed frame of reference (i.e., Eulerian
models, specifically photochemical grid
models) or a frame of reference that
moves with parcels of air between the
source and receptor point (i.e.,
Lagrangian models).19
Comparing these two types of
chemical transport models,
photochemical grid models are
integrated, three-dimensional grid-based
models that treat chemical and physical
processes in each grid cell and use
Eulerian diffusion and transport
processes to move chemical species to
other grid cells.19 While some
Lagrangian models also treat in-plume
gas and particulate chemistry, to do so
these models require time and space
varying oxidant concentrations, and in
the case of PM2.5, neutralizing agents
such as ammonia, because important
secondary impacts happen when plume
edges start to interact with the
19 McMurry, P.H., Shepherd, M.F., Vickery, J.S.,
2004. Particulate matter science for policy makers:
A NARSTO assessment. Cambridge University
Press.
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surrounding chemical environment.20 21
These oxidant and neutralizing agents
are not routinely measured, but can be
generated with a three-dimensional
photochemical transport model and
subsequently input to a Lagrangian
modeling system.
In light of these differences between
photochemical grid models and
Lagrangian models that address
chemistry, the EPA believes
photochemical grid models are
generally most appropriate for
addressing ozone and secondary PM2.5
because they provide a spatially and
temporally dynamic realistic chemical
and physical environment for plume
growth and chemical
transformation.20 22 Publically available
and documented Eulerian
photochemical grid models such as the
Comprehensive Air Quality Model with
Extensions (CAMx) 23 and the
Community Multiscale Air Quality
(CMAQ) 24 model treat emissions,
chemical transformation, transport, and
deposition using time and space variant
meteorology. These modeling systems
include primarily emitted species and
secondarily formed pollutants such as
ozone and PM2.5.25 26 27 28 These models
20 Baker, K.R., Kelly, J.T., 2014. Single source
impacts estimated with photochemical model
source sensitivity and apportionment approaches.
Atmospheric Environment, 96: 266–274.
21 ENVIRON, 2012. Evaluation of chemical
dispersion models using atmospheric plume
measurements from field experiments, EPA
Contract No: EP–D–07–102. September 2012. 06–
20443M6. https://www.epa.gov/ttn/scram/reports/
Plume_Eval_Final_Sep_2012v5.pdf.
22 Zhou, W., Cohan, D.S., Pinder, R.W., Neuman,
J.A., Holloway, J.S., Peischl, J., Ryerson, T.B.,
Nowak, J.B., Flocke, F., Zheng, W.G., 2012.
Observation and modeling of the evolution of Texas
power plant plumes. Atmospheric Chemistry and
Physics, 12: 455–468.
23 ENVIRON, 2014. User’s Guide Comprehensive
Air Quality Model with Extensions version 6,
https://www.camx.com. ENVIRON International
Corporation, Novato.
24 Byun, D., Schere, K.L., 2006. Review of the
governing equations, computational algorithms, and
other components of the models-3 Community
Multiscale Air Quality (CMAQ) modeling system.
Applied Mechanics Reviews, 59: 51–77.
25 Chen, J., Lu, J., Avise, J.C., DaMassa, J.A.,
Kleeman, M.J., Kaduwela, A.P., 2014. Seasonal
modeling of PM 2.5 in California’s San Joaquin
Valley. Atmospheric Environment, 92: 182–190.
26 Civerolo, K., Hogrefe, C., Zalewsky, E., Hao, W.,
Sistla, G., Lynn, B., Rosenzweig, C., Kinney, P.L.,
2010. Evaluation of an 18-year CMAQ simulation:
Seasonal variations and long-term temporal changes
in sulfate and nitrate. Atmospheric Environment,
44: 3745–3752.
27 Russell, A.G., 2008. EPA Supersites programrelated emissions-based particulate matter
modeling: initial applications and advances.
Journal of the Air & Waste Management
Association, 58: 289–302.
28 Tesche, T., Morris, R., Tonnesen, G., McNally,
D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx
annual 2002 performance evaluation over the
eastern US. Atmospheric Environment, 40: 4906–
4919.
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have been used extensively to support
ozone and PM2.5 SIPs and to explore
relationships between inputs and air
quality impacts in the United States and
elsewhere.26 29 30
For assessing secondary pollutant
impacts from single sources, the degree
of complexity required to assess
potential impacts varies depending on
the nature of the source, its emissions,
and the background environment. In
order to provide the user community
flexibility in estimating single-source
secondary pollutant impacts and given
the emphasis on the use of
photochemical grid models for these
purposes, the EPA is proposing a twotiered demonstration approach for
addressing single-source impacts on
ozone and secondary PM2.5. The first
tier involves use of technically credible
relationships between precursor
emissions and a source’s impacts that
may be published in the peer-reviewed
literature; developed from modeling that
was previously conducted for an area by
a source, a governmental agency, or
some other entity and that is deemed
sufficient; or generated by a peerreviewed reduced form model. The
second tier involves application of more
sophisticated case-specific chemical
transport models (e.g., photochemical
grid models) to be determined in
consultation with the EPA Regional
Office and conducted consistent with
new EPA single-source modeling
guidance.31 The appropriate tier for a
given application should be selected in
consultation with the appropriate
reviewing authority and be consistent
with EPA guidance.
To fully implement these proposed
changes to the Guideline related to
addressing ozone and secondary PM2.5
impacts, the EPA intends to pursue a
separate rulemaking to establish a
technical basis and new values for PM2.5
Significant Impact Levels (SILs) and to
introduce a new demonstration tool for
ozone and PM2.5 precursors referred to
as Model Emissions Rates for Precursors
(MERP). When completed, this rule
29 Cai, C., Kelly, J.T., Avise, J.C., Kaduwela, A.P.,
Stockwell, W.R., 2011. Photochemical modeling in
California with two chemical mechanisms: model
intercomparison and response to emission
reductions. Journal of the Air & Waste Management
Association, 61: 559–572.
30 Hogrefe, C., Hao, W., Zalewsky, E., Ku, J.-Y.,
Lynn, B., Rosenzweig, C., Schultz, M., Rast, S.,
Newchurch, M., Wang, L., 2011. An analysis of
long-term regional-scale ozone simulations over the
Northeastern United States: variability and trends.
Atmospheric Chemistry and Physics, 11: 567–582.
31 U.S. EPA, 2015. Guidance on the use of models
for assessing the impacts from single sources on
secondarily formed pollutants ozone and PM2.5.
Publication No. EPA 454/P–15–001. Office of Air
Quality Planning & Standards, Research Triangle
Park, North Carolina 27711.
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would differ from the current process
recommended in the EPA’s Guidance
for PM2.5 Permit Modeling.7 A MERP
would neither replace the existing
Significant Emissions Rates (SERs) for
these pollutants nor serve as the basis
for the applicability of PSD
requirements to sources with emissions
above the SER. However, a MERP would
represent a level of emissions of
precursors that is not expected to
contribute significantly to
concentrations of ozone or secondarilyformed PM2.5. Our present
understanding of the atmospheric
science of ozone and secondary PM2.5
formation indicates that MERP values
will likely be higher than the SERs and
more appropriate for evaluating the
impacts of these criteria pollutants as
precursors to ozone and PM2.5
formation. As part of the separate
rulemaking, the EPA intends to
demonstrate that a source with
precursor emissions (e.g., NOX and SO2
for PM2.5) below the MERP level will
have ambient impacts that will be less
than the SIL and, thereby, provide a
sufficient demonstration that the source
will not cause or contribute to a
violation of the PM2.5 NAAQS or PSD
increments. The EPA’s Guidance for
PM2.5 Permit Modeling 7 provides for a
three-tiered approach to address
secondary PM2.5 with (1) a qualitative
assessment; (2) a hybrid qualitative/
quantitative assessment utilizing
existing technical work; and (3) a full
quantitative modeling exercise. The
EPA expects that MERPs as a
demonstration tool will replace the first
tier of a qualitative assessment as
sources that currently would provide a
qualitative assessment are expected to
have precursor emissions levels below
the MERP. The second and third tier of
assessment will then be consistent with
the EPA’s proposed two-tiered
demonstration approach for PM2.5
reflected in this proposed revisions to
the Guideline. To specifically assist the
public in commenting on this rule
within the overall context of the NSR
program, including PSD, the EPA has
added two separate memoranda to the
docket of this proposed rule. These
memoranda provide more details on
how this future approach to PSD
compliance demonstrations will work
for secondary PM2.5 and also describe
our expectations for how such an
approach might work for ozone based
on a future, separate action to similarly
establish a SIL and MERPs (for VOC and
NOX precursors) for ozone using
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approaches similar to those for
PM2.5.32 33
While the development of MERPs for
ozone and secondary PM2.5 precursors is
expected to address a number of PSD
permitting situations, the EPA believes
that most of the remaining situations in
which a source must demonstrate
compliance under the proposed
Guideline will be addressed sufficiently
under the proposed first tier where
existing technical information could be
used in combination with other
supportive information and analysis for
the purposes of estimating secondary
impacts from a particular source. The
existing technical information should
provide a credible and representative
estimate of the secondary impacts from
the project source. In these situations, a
more refined approach for estimating
secondary pollutant impacts from
project sources may not be necessary.
The EPA has been compiling and
reviewing screening approaches that are
based on technically credible tools (e.g.,
photochemical grid models) that relate
source precursor emissions to secondary
impacts. In review of existing
approaches detailed in peer reviewed
journal articles and non-peer reviewed
forms (e.g., technical reports, conference
presentations), it is not clear that a
single approach has been clearly
proposed to and evaluated by the
modeling community for estimating
screening level secondary impacts from
single sources. Other screening level
alternatives to photochemical grid
model application may include the use
of existing credible photochemical
model impacts for sources deemed to be
similar in terms of emission rates,
release parameters, and background
environment. The EPA will continue to
engage with the modeling community to
identify credible alternative approaches
for estimating single-source secondary
pollutant impacts which provide
flexibility and are less resource
intensive for permit demonstration
purposes.
For those situations for which existing
modeling or screening estimates are not
available or appropriate, the second tier
proposed by the EPA would apply and
involve use of more sophisticated casespecific chemical transport models (e.g.,
photochemical grid models) to be
32 U.S. EPA, 2015. ‘‘Proposed Approach for
Demonstrating PM2.5 PSD Compliance’’,
Memorandum to Docket No. EPA–HQ–OAR–2015–
0310 by Tyler J Fox, U.S. EPA/OAQPS, Research
Triangle Park, NC. June 30, 2015.
33 U.S. EPA, 2015. ‘‘Proposed Approach for
Demonstrating Ozone PSD Compliance’’,
Memorandum to Docket No. EPA–HQ–OAR–2015–
0310 by Tyler J Fox, U.S. EPA/OAQPS, Research
Triangle Park, NC. June 30, 2015.
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determined in consultation with the
appropriate EPA Regional Office based
upon new EPA single-source modeling
guidance.31 Based on several scientific
studies, the EPA proposes to determine
that photochemical grid models are
appropriate for assessment of near-field
and regional scale reactive pollutant
impacts from specific sources 20 22 34 35 or
a group of multiple sources impacting
an area.25 27 28 Even though single-source
emissions are injected into a grid
volume, photochemical transport
models have been shown to adequately
capture single-source impacts when
compared with downwind in-plume
measurements.20 22 Where set up
appropriately for the purposes of
assessing the contribution of single
sources to primary and secondarily
formed pollutants, photochemical grid
models can be used with a variety of
approaches to estimate these impacts.
These approaches generally fall into the
category of source sensitivity (how air
quality changes due to changes in
emissions) and source apportionment
(what air quality impacts are related to
certain emissions). Source
apportionment has been used to
differentiate the contribution from
single sources on model predicted
ozone and PM2.5 concentrations.20 34 The
direct decoupled method (DDM) has
also been used to estimate ozone and
PM2.5 impacts from specific sources 20 35
as well as the simpler brute-force
sensitivity approach.20 22 35 Limited
comparison of single-source impacts
between models 36 and approaches to
differentiate single-source impacts 20 36
show generally similar downwind
spatial gradients and impacts.
Near-source in-plume aircraft based
measurement field studies provide an
opportunity for evaluating model
estimates of (near-source) downwind
transport and chemical impacts from
single stationary point sources.21
Photochemical grid model source
apportionment and source sensitivity
simulation of a single source downwind
impacts compare well against field
study primary and secondary ambient
measurements made in Tennessee and
Texas.20 21 This work indicates
photochemical grid models and source
34 Baker, K.R., Foley, K.M., 2011. A nonlinear
regression model estimating single source
concentrations of primary and secondarily formed
PM2.5. Atmospheric Environment, 45: 3758–3767.
35 Bergin, M.S., Russell, A.G., Odman, M.T.,
Cohan, D.S., Chameldes, W.L., 2008. Single-Source
Impact Analysis Using Three-Dimensional Air
Quality Models. Journal of the Air & Waste
Management Association, 58: 1351–1359.
36 Baker, K.R., Kelly, J.T., Fox, T., 2013.
Estimating second pollutant impacts from single
sources (control #27). https://aqmodels.awma.org/
conference-proceedings.
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apportionment and source sensitivity
approaches provide meaningful
estimates of single-source impacts on
ozone and secondarily-formed PM2.5.
Additional evaluations for longer time
periods and more diverse environments,
both physical and chemical, would be
valuable to generate broader confidence
in these approaches for this purpose.
We invite comments on whether the
proposed two-tiered demonstration
approach and related EPA guidance is
appropriately based on sound science
and practical application of available
models and tools to address singlesource impacts on ozone and secondary
PM2.5.
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7. Status of CALPUFF and Assessing
Long-Range Transport for PSD
Increment and Regional Haze
The 2003 Guideline recommended
CALPUFF as the preferred model for
long-range transport (i.e., sourcereceptor distances of 50 to several
hundred kilometers) of emissions from
point, volume, area, and line sources for
primary criteria pollutants (e.g., PM and
SO2). Since that time, as discussed
previously in this preamble, the EPA
has received input from stakeholders
and has worked through the IWAQM
process on analytical techniques to
address chemically reactive pollutants
for near-field and long-range transport
applications. As a result, in order to
provide the user community flexibility
in estimating single-source secondary
pollutant impacts and given the
availability of more appropriate
modeling techniques, such as
photochemical transport models (which
address limitations of models like
CALPUFF 37), the EPA is proposing that
the Guideline no longer contain
language that requires the use of
CALPUFF or another Lagrangian puff
model for long-range transport
assessments. Additionally, the EPA is
proposing to remove the CALPUFF
modeling system as an EPA-preferred
model for long-range transport due to
concerns about the management and
maintenance of the model code given
the frequent change in ownership of the
model code since promulgation in the
previous version of the Guideline.38 The
37 U.S. EPA, 2009. Reassessment of the
Interagency Workgroup on Air Quality Modeling
(IWAQM) Phase 2 Summary Report; Revisions to
Phase 2 Recommendations. Draft. Office of Air
Quality Planning & Standards, Research Triangle
Park, North Carolina 27711. https://www.epa.gov/
ttn/scram/guidance/reports/Draft_IWAQM_
Reassessment_052709.pdf.
38 U.S. EPA, 2015. ‘‘Summary of CALPUFF
Ownership Since 2003 Promulgation’’,
Memorandum to Docket No. EPA–HQ–OAR–2015–
0310 by Tyler J Fox, U.S. EPA/OAQPS, Research
Triangle Park, NC. June 30, 2015.
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EPA recognizes that long-range
transport assessments may be necessary
in certain limited situations for PSD
increment. For these situations, the EPA
is proposing a screening approach
where CALPUFF along with other
appropriate screening tools and
methods may be used to support longrange transport PSD increment
assessments.
To determine if a Class I PSD
increment analyses may be necessary
beyond 50 km (i.e., long-range transport
assessment), the EPA is recommending
a screening approach to determine if a
significant impact will occur with
particular focus on Class I areas that
may be threatened at such distances.
The first step relies upon the near-field
application of the appropriate screening
and/or preferred model to determine the
significance of ambient impact at or
about 50 km from the new of modifying
source. If this initial analysis indicates
there may be significant ambient
impacts at that distance, then further
analysis is necessary. For assessment of
Class I ambient impacts, under the
proposed Guideline, there will not a
preferred model for distances beyond 50
km. Typically, a Lagrangian model is
the type of model appropriate to use for
these screening assessments; however,
applicants should establish approaches
(models and modeling parameters) on a
case-by-case basis in consultation with
the appropriate reviewing authority,
Regional Office, and the affected Federal
Land Manager(s) (FLM(s)). If a
cumulative increment analysis is
necessary, for these limited situations,
the selection and use of an alternative
model shall occur in agreement with the
appropriate reviewing authority
(paragraph 3.0(b)) and approval by the
EPA Regional Office based on the
requirements of section 3.2.2(e).
As previously noted, Phase 3 of the
IWAQM process was reinitiated in June
2013 to inform the EPA’s commitment
to update the Guideline to address
chemically reactive pollutants in nearfield and long-range transport
applications. This Phase 3 effort
included the establishment of a working
group composed of EPA and FLM
technical staff focused on long-range
transport of primary and secondary
pollutants with an emphasis on use of
consistent approaches to those being
developed and applied to meet nearfield assessment needs for ozone and
secondarily-formed PM2.5. The EPA
expects that such approaches will be
focused on state of the science chemical
transport models (CTMs) as detailed in
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IWAQM reports 39 40 and published
literature.
To inform future consideration of
visibility modeling in regulatory
applications consistent with proposed
changes for addressing chemistry for
single-source impact on ozone and
secondary PM2.5, the final report 40 of
the IWAQM long-range transport
subgroup identified that modern CTMs
have evolved sufficiently and provide a
credible platform for estimating
potential visibility impacts from a single
or small group of emission sources.
Chemical transport models are well
suited for the purpose of estimating
long-range impacts of secondary
pollutants, such as PM2.5, that
contribute to regional haze and other
secondary pollutants, such as ozone,
that contribute to negative impacts on
vegetation through deposition
processes. These multiple needs require
a full chemistry photochemical model
capable of representing both gas,
particle, and aqueous phase chemistry
for PM2.5, haze, and ozone.
Photochemical transport models are
suitable for estimating visibility and
deposition since important physical and
chemical processes related to the
formation and transport of PM are
realistically treated. Source sensitivity
and apportionment techniques
implemented in photochemical grid
models have evolved sufficiently and
provide the opportunity for estimating
potential visibility and deposition
impacts from one or a small group of
emission sources using a full science
photochemical grid model.
Photochemical grid models using
meteorology output from prognostic
meteorological models have
demonstrated skill in estimating sourcereceptor relationships in the nearfield 20 21 and over long distances.41
It is important that modeling tools
used for single-source long-range
transport impacts assessments
demonstrate skill in adequately
replicating source-receptor relationships
that are not in close proximity. For
39 U.S. EPA, 2015. Interagency Workgroup on Air
Quality Modeling Phase 3 Summary Report: NearField Single Source Secondary Impacts. Publication
No. EPA 454/P–15–002. Office of Air Quality
Planning & Standards, Research Triangle Park,
North Carolina 27711.
40 U.S. EPA, 2015. Interagency Workgroup on Air
Quality Modeling Phase 3 Summary Report: Long
Range Transport and Air Quality Related Values.
Publication No. EPA 454/P–15–003. Office of Air
Quality Planning & Standards, Research Triangle
Park, North Carolina 27711.
41 ENVIRON, 2012. Documentation of the
Evaluation of CALPUFF and Other Long Range
Transport Models using Tracer Field Experiment
Data, EPA Contract No: EP–D–07–102. February
2012. 06–20443M4. https://www.epa.gov/ttn/scram/
reports/EPA-454_R-12-003.pdf.
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source-receptor distances greater than
50 km, regional scale photochemical
grid models may be applied for the
assessment of visibility impacts due to
one or a small group of sources. Skill in
estimating source-receptor relationships
on this scale can be illustrated by
evaluating modeling systems against
regional scale inert tracer release
experiments. Historically, several
regional tracer release experiments have
been used to demonstrate skill in longrange transport of inert pollutants: 1980
Great Plains Mesoscale Tracer Field
Experiment, the 1983 CrossAppalachian Tracer Experiment
(CAPTEX), the 1987 Across North
American Tracer Experiment
(ANATEX), and 1994 European Tracer
Experiment (ETEX).41 42 Photochemical
grid models have been shown to
demonstrate similar skill to Lagrangian
models for pollutant transport when
compared to measurements made from
multiple mesoscale field experiments.41
Use of CTMs for Air Quality Related
Values (AQRV) analysis requirements,
while not subject to specific EPA model
approval requirements outlined in 40
CFR 51.166(l)(2) and 40 CFR 52.21(l)(2),
should be justified for each application
following the general recommendations
outlined in section 3.2, and concurrence
sought with the affected FLM(s).
In 2005, the EPA issued guidelines for
implementation of the best available
retrofit technology (BART) requirements
under the Regional Haze Rule. In these
BART Guidelines, the EPA addressed
the question of how states could best
predict a single source’s contribution to
visibility impairment.43 At the time, the
EPA recognized that CALPUFF had not
yet been fully evaluated for secondary
pollutant formation, but the EPA still
considered CALPUFF to be the best
application for assessing a single
source’s impact on visibility in a Class
I area for purposes of the regional haze
program. The EPA took note of the
limitations of CALPUFF for this purpose
but concluded that CALPUFF was the
best modeling application for use in
evaluating BART, especially given how
the modeling results would be used.
Based on this assessment, the EPA
recommended that the states use
CALPUFF. The EPA also made clear,
however, that states could use other
alternative approaches, including
photochemical grid models, if done in
42 Hegarty, J., Draxler, R.R., Stein, A.F., Brioude,
J., Mountain, M., Eluszkiewicz, J., Nehrkorn, T.,
Ngan, F., Andrews, A., 2013. Evaluation of
Lagrangian particle dispersion models with
measurements from controlled tracer releases.
Journal of Applied Meteorology and Climatology,
52: 2623–2637.
43 See 70 FR 39104, 39122–23 (July 6, 2005).
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consultation with the appropriate EPA
Regional Office.
The current version of the Guideline
does not contain any explicit
recommendation regarding the use of
CALPUFF in the regional haze program,
but in advising states and in making its
own BART determinations, the EPA has
looked to the Guideline to resolve
questions regarding the proper
application of the model. In particular,
the EPA has guided states to use the
applicable regulatory version of
CALPUFF for such assessments.
Following the EPA’s recommendations,
states have used the EPA-preferred
version of CALPUFF in hundreds of
BART determinations since 2005.
Although most assessments of BART are
now complete, a handful of BART
determinations remain outstanding. We
expect most of the remaining actions
addressing the BART requirements to be
completed within the next two years.
The proposed changes to the
Guideline do not affect the EPA’s
recommendation in the 2005 BART
Guidelines to use CALPUFF in the
BART determination process. Given that
the overwhelming majority of BART
determinations have been made using
CALPUFF, we consider it appropriate
for states (or the EPA) to continue to use
this application for the remaining
assessments under the current Guideline
with approved protocols. This approach
assures consistency across and within
states in the regional haze program. In
addition, in many instances, the
modeling of visibility impacts has
already been completed even though the
BART determination process is not yet
done. Allowing states to continue to rely
on CALPUFF avoids additional time
and expense in developing a new
assessment of visibility impacts for a
SIP initially due in 2007. We intend to
continue to advise states with respect to
the EPA-preferred version of CALPUFF
that should be used in specific BART
cases. Consistent with the BART
Guidelines, states may also use
alternative modeling approaches, in
consultation with the appropriate EPA
Regional Office.
The EPA is seeking comment on its
proposed screening approach to address
long-range transport for purposes of
assessing PSD increments; its decision
to remove CALPUFF as a preferred
model in appendix A for such longrange transport assessments; and its
decision to consider CALPUFF as a
screening technique along with other
Lagrangian models to be used in
consultation with the appropriate
reviewing authority. It is important to
note that the EPA’s proposed action to
remove CALPUFF as an appendix A
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model in this Guideline does not affect
its use under the FLM’s guidance
regarding AQRV assessments (FLAG
2010) nor previous use of this model as
part of regulatory modeling applications
required under the CAA. Similarly, this
proposed action does not affect EPA’s
recommendation that States use
CALPUFF to determine the applicability
and level of BART in regional haze
implementation plans.43
8. Role of EPA’s Model Clearinghouse
The EPA’s Model Clearinghouse has
been a fundamental aspect of
communication between the EPA
Region Offices and with the broader
permitting community on technical
modeling and compliance
demonstration issues for almost three
decades. The Model Clearinghouse
serves a critical role in helping resolve
issues that arise from unique situations
that are not specifically addressed in the
Guideline or necessitate the
consideration of an alternative model or
technique for a specific application or
range of applications. The Model
Clearinghouse ensures that fairness,
consistency, and transparency in
modeling decisions are fostered among
the Regional Offices and the state, local,
and tribal agencies.
In this action, we are proposing to
codify the long-standing process of the
Regional Offices consulting and
coordinating with the Model
Clearinghouse on all approvals of
alternative models or techniques. While
the Regional Administrators are the
delegated authority to issue such
approvals under section 3.2 of the
Guideline, all alternative model
approvals will only be issued after
consultation with the EPA’s Model
Clearinghouse and formal
documentation through a concurrence
memorandum which demonstrates that
the requirements within section 3.2 for
use of an alternative model have been
met.
We invite comment on our proposal
to codify existing practice of requiring
consultation and coordination between
the EPA Regional Offices and the EPA’s
Model Clearinghouse on all approvals
under section 3.2 of alternative models
or techniques.
9. Updates to Modeling Procedures for
Cumulative Impact Analysis
Based on input from the Tenth
Modeling Conference and recent permit
modeling experiences under new shortterm NAAQS for SO2 and NO2, the EPA
is proposing to make modifications to
section 8 of the Guideline regarding
model inputs and background
concentrations to provide much needed
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clarity associated with input and
database selection for use in PSD and
SIP modeling. Many of these revisions
are based on the EPA clarification
memoranda issued since 2010 that were
intended to provide the necessary
clarification regarding applicability of
the Guideline to PSD modeling for these
new standards.1 2 44 45 The EPA has
specifically cautioned against the literal
and uncritical application of very
prescriptive procedures for conducting
NAAQS and PSD modeling compliance
demonstrations as described in chapter
C of the draft New Source Review
Workshop Manual.46 Our main concern
is that following such procedures in a
literal and uncritical manner has led to
practices that are overly conservative
and unnecessarily complicate the
permitting process. The proposed
changes to section 8 are intended to
modify these practices and provide a
more appropriate basis for selection and
use of modeling inputs through the
Guideline itself and supporting
guidance.
We have provided a more definitive
definition of the appropriate modeling
domain and how to best characterize the
various contributions to air quality
concentrations within that domain.
Specifically, we provide the following
recommendations:
• Definition and/or factors to
consider in determining appropriate
modeling domain for NAAQS and PSD
increment assessments and for SIP
attainment demonstrations (see section
8.1).
• Revised requirements on how to
characterize emissions from nearby
sources to be explicitly modeled for
purposes of a cumulative impact
assessment under PSD and new
language regarding how to characterize
direct and precursor emissions from
modeled sources for SIP attainment
44 U.S. EPA, 2011. Additional Clarification
Regarding Applicability of Appendix W Modeling
Guidance for the 1-hour NO2 NAAQS. Tyler Fox
Memorandum dated March 1, 2011, Office of Air
Quality Planning & Standards, Research Triangle
Park, North Carolina 27711.https://www.epa.gov/ttn/
scram/guidance/clarification/NO2_Clarification_
Memo-20140930.pdf.
45 U.S. EPA, 2014. Clarification on the Use of
AERMOD Dispersion Modeling for Demonstrating
Compliance with the NO2 National Ambient Air
Quality Standard. R. Chris Owen and Roger Brode
Memorandum dated September 30, 2014, Office of
Air Quality Planning & Standards, Research
Triangle Park, North Carolina 27711. https://
www.epa.gov/ttn/scram/guidance/clarification/
NO2_Clarification_Memo-20140930.pdf.
46 U.S. EPA, 1990. New Source Review Workshop
Manual: Prevention of Significant Deterioration and
Nonattainment Area Permitting (Draft). Office of Air
Quality Planning & Standards, Research Triangle
Park, North Carolina 27711. https://www.epa.gov/
nsr.
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demonstrations for ozone, PM2.5, and
regional haze (see section 8.2).
• Revised recommendations on how
to determine background concentrations
in constructing the design
concentration, or total air quality
concentration, as part of a cumulative
impact analysis for NAAQS and PSD
increments. Specific recommendations
are proposed for situations involving
isolated single-source(s) and multisource areas (see section 8.3) with an
emphasis on how to determine which
nearby sources to explicitly model
based on the concept of significant
concentration gradients and the use of
monitored background to adequately
represent ‘‘other sources’’ (i.e., that
portion of the background attributable to
natural sources, other unidentified
sources in the vicinity of the project,
and regional transport contributions
from more distant sources (domestic
and international)). It is important to
note the interconnectedness of these
issues as the question of which nearby
sources to include in cumulative
modeling is inextricably linked with the
question of what ambient monitoring
data are available and what these data
represent for a specific application.
More specific data requirements and the
format required for the individual
models are described in detail in the
users’ guide and/or associated
documentation for each model.
Given the added complexity of the
technical issues that arise in the context
of demonstrating compliance with
NAAQS through dispersion modeling,
we strongly encourage adherence to the
recommendations in section 9.2.1 of the
proposed Guideline regarding
development of a modeling protocol,
i.e., that ‘‘[e]very effort should be made
by the Regional Office to meet with all
parties involved in either a SIP revision
or a PSD permit application prior to the
start of any work on such a project.
During this meeting, a protocol should
be established between the preparing
and reviewing parties to define the
procedures to be followed, the data to be
collected, the model to be used, and the
analysis of the source and concentration
data.’’ We expect by providing more
clarity in the Guideline of the factors to
be considered in the cumulative impact
assessment, permit applicants and
permitting authorities will be able to
find the proper balance of the
competing factors that contribute to
these analyses.
We invite comments on whether the
updates proposed in section 8 of the
Guideline and associated guidance are
appropriate and sufficient to provide the
necessary clarification in selecting and
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establishing the model inputs for
conducting the regulatory modeling for
PSD and SIP applications.
10. Updates on Use of Meteorological
Input Data for Regulatory Dispersion
Modeling
For near-field dispersion modeling
applications using National Weather
Service (NWS) Automated Surface
Observing Stations (ASOS), the EPA
released a pre-processor to AERMET,
called AERMINUTE, in 2011 that
calculates hourly averaged winds from
2-minute winds reported every minute
at NWS ASOS sites. AERMET
substitutes these hourly averaged winds
for the standard hourly observations,
thus reducing the number of calms and
missing winds for input into AERMOD.
The presence of calms and missing
winds were due to the METAR reporting
methodology of surface observations. In
March 2013, the EPA released a
memorandum regarding the use of
ASOS data in AERMOD as well as the
use of AERMINUTE. When using
meteorological data from ASOS sites for
input to AERMOD, hourly averaged
winds from AERMINUTE should be
used in most cases.
For a near-field dispersion modeling
application where there is no
representative NWS station, and it is
prohibitive or not feasible to collect
adequately representative site-specific
data, it may be necessary to use
prognostic meteorological data for the
application. The EPA released the
MMIF program that converts the
prognostic meteorological data into a
format suitable for dispersion modeling
applications. The most recent 3 years of
prognostic data are preferred. Use of the
prognostic data is contingent on the
concurrence of the appropriate
reviewing authorities and collaborating
agencies that the data are of acceptable
quality and representative of the
modeling application.
We solicit comments on our proposed
updates regarding use of meteorological
input data for regulatory application of
dispersion models.
11. Transition Period for Applicability
of Revisions to the Guideline
In previous rulemakings to revise the
Guideline, we have traditionally
communicated that it would be
appropriate to provide 1 year to
transition to the use of new models,
techniques and procedures in the
context of PSD permit applications and
other regulatory modeling applications.
We invite comments whether it would
be appropriate to apply a 1-year
transition after promulgation of the
revised Guideline (i.e., from its effective
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date) such that applications conducted
under the current Guideline with
approved protocols would be acceptable
during that period, but new
requirements and recommendations
should be used for applications
submitted after that period or protocols
approved after that period.
The EPA believes such a transition
period is appropriate to avoid the time
and expense of revisiting modeling that
is substantially complete, which would
cause undue delays to permit
applications that are pending when the
proposed revisions to the Guideline are
finalized. The revisions that the EPA is
proposing to the Guideline are intended
as incremental improvements to the
Guideline, and such improvements do
not necessarily invalidate past practices
under the previous edition of the
Guideline. The requirements and
recommendations in the current (2005)
version of the Guideline were
previously identified as acceptable by
the EPA, and they will continue to be
acceptable for air quality assessments
during the period of transition to the
revised version of the Guideline.
Where a proposed revision to the
Guideline does raise questions about the
acceptability of a requirement or
recommendation that it replaces, model
users and applicants are encouraged to
consult with the appropriate reviewing
authority as soon as possible to assure
the acceptability of modeling used to
support permit applications during this
period.
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B. Proposed Editorial Changes
The EPA is proposing to make
editorial changes to update and
reorganize information throughout the
Guideline. These revisions are not
intended to meaningfully change the
substance of the Guideline, but rather to
make the Guideline easier to use. One
way this is accomplished is by grouping
topics together in a more logical manner
to make related content easier to find.
This in turn should streamline the
compliance assessment process.
Editorial changes are described below
for each affected section. We invite
comment on any of the changes
proposed below for the Guideline text.
1. Preface
Only a few minor text revisions are
proposed to this section for consistency
with the remainder of the Guideline.
2. Section 1
The EPA propose to update the
introduction section to reflect the
reorganized nature of the revised
Guideline. Minor text revisions are
proposed throughout this section for
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additional clarity. Additional
information is provided regarding the
importance of CAA section 320 to
amendments of the Guideline.
3. Section 2
The EPA proposes to revise section 2
to more appropriately discuss the
process by which models are evaluated
and considered for use in particular
applications. We propose to incorporate
information from the previous section 9
pertaining to model accuracy and
uncertainty within this section to clarify
how model performance evaluation is
critical in determining the suitability of
models for particular application.
We also propose to provide a
discussion in section 2.1 (Model
Accuracy and Uncertainty) of the three
types of models historically used for
regulatory demonstrations. For each
type of model, some strengths and
weaknesses are listed to assist readers in
the understanding of the particular
regulatory applications to which they
are most appropriate.
In addition, the EPA proposes
revisions to section 2.2 with respect to
the recommended practice of
progressing from simplified and
conservative air quality analysis toward
more complex and refined analysis. In
this section, the EPA proposes to clarify
distinctions between various types of
models that have previously been
described as screening models. In
addition, this section clarifies
distinctions between models used for
screening purposes and screening
techniques and demonstration tools that
may be acceptable in certain
applications.
4. Section 3
The EPA proposes minor
modifications to section 3 to more
accurately reflect current EPA practices
and by moving the discussion of the
EPA’s Model Clearinghouse to a revised
section 3.3 for ease of reference and
prominence within the Guideline. A
change is proposed to require Regional
Office consultation with the Model
Clearinghouse on all alternative model
approvals. Previously, section 3
included various requirements under
recommendation subheading that were
not clearly identified as requirements.
Accordingly, the EPA is proposing to
modify section 3 with the incorporation
of requirement subsections to eliminate
any ambiguity.
5. Section 4
The EPA proposes to significantly
revise section 4 to incorporate the
modeling approaches recommended for
air quality impact analyses for the
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criteria pollutants of CO, lead, SO2,
NO2, and primary PM2.5 and PM10. In
many respects, the proposed revisions
to section 4 are a combination of the
previous sections 4 and 5, reflecting
inert criteria pollutants only. The EPA
also proposes to modify section 4 to
incorporate requirement subsections to
provide clarity of the various
requirements where previously sections
4 and 5 included various requirements
under recommendation subheadings.
As proposed, this section provides an
in-depth discussion of screening and
refined models, including the
introduction of AERSCREEN as the
recommended screening model for
simple and complex terrain for single
sources and options for multi-source
screening with AERMOD.47 The EPA
proposes to include a clear discussion of
each appendix A preferred model in
section 4.3 (Refined Models). The EPA
also proposes to modify the discussion
for each preferred model (i.e., AERMOD
Modeling System, CTDMPLUS, and
OCD) from the previous section 4 with
appropriate edits and some streamlining
based on information available in the
respective model formulation
documentation and users guides.
The EPA is proposing to add a
subsection specifically addressing the
modeling recommendations for SO2
where, previously, section 4 of the
Guideline was generally understood to
be applicable for SO2. Minor updates are
proposed with respect to the modeling
recommendations for each of the other
inert criteria pollutants that were
previously found in section 5. For NO2,
the ARM2 is proposed to be added as a
Tier 2 option, and the Tier 3 options of
OLM and PVMRM are proposed to
become part of the regulatory version of
AERMOD. For any pollutant that had
significant emissions from mobile
sources, our previous recommendation
to use the CALINE3 models is proposed
to be replaced with AERMOD.
6. Section 5
As already stated, much of the
previous section 5 with respect to the
inert criteria pollutants is proposed to
be incorporated into the revised section
4. As proposed, the revised section 5 is
now focused only on the modeling
approaches recommended for ozone and
secondary PM2.5.
Both ozone and secondary PM2.5 are
formed through chemical reactions in
the atmosphere and are not
47 U.S. EPA, 2015. Technical Support Document
(TSD) for Replacement of CALINE3 with AERMOD
for Transportation Related Air Quality Analyses.
Publication No. EPA–454/B–15–002. Office of Air
Quality Planning & Standards, Research Triangle
Park, NC.
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appropriately modeled with traditional
steady-state Gaussian plume models,
such as AERMOD. Chemical transport
models are necessary to appropriately
assess the single-source air quality
impacts of precursor pollutants on the
formation of ozone or secondary PM2.5.
While the proposed revision to
section 5 do not specify a particular
EPA-preferred model or technique for
use in air quality assessments, a twotiered screening approach is proposed
for ozone and secondary PM2.5 with
appropriate references to the EPA’s new
single-source modeling guidance. The
first tier consists of technically credible
and appropriate relationships between
emissions and the impacts developed
from existing modeling simulations. If
existing technical information is not
available or appropriate, then a second
tier approach would apply, involving
use of sophisticated chemical transport
models (e.g., photochemical grid
models) as determined in consultation
with the appropriate EPA Regional
Office on a case-by-case basis based
upon the EPA’s new single-source
modeling guidance.
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7. Section 6
Revisions to section 6 are proposed to
more clearly address the modeling
recommendations of other federal
agencies, such as the FLM(s), that have
been developed in response to EPA
rules or standards. While no attempt is
made to comprehensively discuss each
topic, the EPA proposes to provide
appropriate references to the respective
federal agency guidance documents.
The proposed revision to section 6
focus primarily on AQRVs, including
near-field and long-range transport
assessments for visibility impairment
and deposition. The interests of the
Bureau of Ocean Energy and
Management for Outer Continental Shelf
permitting situations and of the Federal
Aviation Administration for airport and
air base permitting situations are
represented in proposed section 6.3
(Modeling Guidance for Other
Governmental Programs).
The discussion of Good Engineering
Practices (GEP) for stack height
consideration is proposed to be
modified and moved to section 7. The
EPA proposed to remove the discussion
of long-range transport for PSD Class I
increment and references to the
previously preferred long-range
transport model, CALPUFF, in
accordance with the more detailed
discussion in the Proposed Actions
section of this Preamble.
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8. Section 7
We propose to revise section 7 to be
more streamlined and appropriate to the
variety of general modeling issues and
considerations that are not already been
covered in sections 4, 5, and 6 of the
Guideline. The EPA proposes to move
the information concerning design
concentrations and receptor sites to
section 9. The discussion of stability
categories is proposed to be removed
from section 7 since it is specifically
addressed in the model formulation
documentation and guidance for the
dispersion models that require stability
categories to be defined. As already
stated, the GEP discussion from the
previous section 6 is proposed to be
incorporated into this section.
The EPA proposes to expand the
recommendations for determining rural
or urban dispersion coefficients to
provide more clarity with respect to
appropriate characterization within
AERMOD, including a discussion on the
existence of highly industrialized areas
where population density is low that
may be best treated with urban rather
than rural dispersion coefficients.
References to CALPUFF in the Complex
Winds subsection are proposed to be
removed due to technical issues
described in the Proposed Actions
section of this preamble. As proposed,
if necessary for special complex wind
situations, the setup and application of
an alternative model should now be
determined in consultation with the
appropriate reviewing authority.
Finally, the EPA proposes to revise
section 7 to include a new discussion of
modeling considerations specific to
mobile sources.
9. Section 8
The EPA propose extensive updates
and modifications to section 8 to reflect
current EPA practices, requirements,
and recommendations for determining
the appropriate modeling domain and
model input data from new or
modifying source(s) or sources under
consideration for a revised permit limit,
from background concentrations
(including air quality monitoring data
and nearby and others sources), and
from meteorology. As with earlier
sections, the EPA proposes to modify
section 8 to incorporate requirement
subsections where previously section 8
ambiguously included various
requirements under recommendation
subheadings.
The Background Concentration
subsection is proposed to be
significantly modified from the existing
Guideline to include a more clear and
comprehensive discussion of nearby
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45353
and other sources. This is intended to
eliminate confusion of how to identify
nearby sources that should be explicitly
modeled and all other sources that
should be generally represented by air
quality monitoring data. In addition to
air quality monitoring data, a brief
discussion on the use of photochemical
grid modeling to appropriately
characterize background concentrations
has been included in this proposed
section. Updates to Tables 8–1 and 8–
2 are proposed per changes in the
considerations for nearby sources, as
discussed in the Proposed Actions
section of this Preamble.
The use of prognostic mesoscale
meteorological models to provide
meteorological input for regulatory
dispersion modeling applications is
proposed to be incorporated throughout
the Meteorological Input Data
subsection, including the introduction
of the MMIF as a tool to inform
regulatory model applications. Other
than additional minor modifications to
the recommendations through this
subsection based on current EPA
practices, the most substantive proposed
edits relate to the recommendation to
use the AERMINUTE meteorological
data processor to calculate hourly
average wind speed and direction when
processing NWS ASOS data for
developing AERMET meteorological
inputs to the AERMOD dispersion
model.
10. Section 9
The EPA proposes to move all of the
information previously in section 9
related to model accuracy and
evaluation into other sections in the
revised Guideline (primarily to the
revised section 2 and some to the
revised section 4). This provides for
greater clarity in those topics as applied
to selection of models under the
Guideline. However, the EPA proposes
to remove subsection on the ‘‘Use of
Uncertainty in Decision Making.’’. After
removing this content, the EPA
proposes to totally revise section 9 to
focus on the regulatory application of
models, which would include the
majority of the information found
previously in section 10.
The EPA proposes to revise the
discussion portion of section 9 to more
clearly summarize the general concepts
presented in earlier sections of the
Guideline and to set the stage for the
appropriate regulatory application of
models and/or, in rare circumstances,
air quality monitoring data. The
importance of developing and vetting a
modeling protocol is more prominently
presented in a separate subsection.
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The information related to design
concentrations is proposed to be
updated and unified from previous
language found in sections 7 and 10. An
expanded discussion of receptor sites is
proposed based on language from the
previous section 7 and new
considerations given past practices of
model users tending to define an
excessively large and inappropriate
number of receptors based on vague
guidance.
The recommendations for NAAQS
and PSD increment compliance
demonstrations are proposed to be
overhauled to more clearly and
accurately reflect the long-standing EPA
recommendation and practice of
performing a single-source impact
analysis as a first stage of the NAAQS
and PSD increment compliance
demonstration and, as necessary,
conducting a more comprehensive
cumulative impact analysis as the
second stage. The appropriate
considerations and applications of
screening and/or refined model are
described in each stage.
Finally, the section on Use of
Measured Data in Lieu of Model
Estimates subsection is proposed to be
revised to provide more details on the
process for determining the rare
circumstances in which air quality
monitoring data may be considered for
determining the most appropriate
emissions limit for a modification to an
existing source. As with other portions
of the revised section 9, the language
throughout this subsection is proposed
to be updated to reflect current EPA
practices, as appropriate.
11. Section 10
As discussed, the majority of the
information found previously in section
10 is proposed to be incorporated into
the revised section 9. As proposed,
section 10 consists of the references that
were in the previous section 12. We also
propose to update each reference, as
appropriate, based on the text revisions
throughout the Guideline.
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12. Section 11
In a streamlining effort, the EPA
proposes to remove this bibliography
section from the Guideline.
13. Section 12
As stated earlier, this references
section is now proposed as section 10
with appropriate updates.
14. Appendix A to the Guideline
The EPA proposes to revise appendix
A to the Guideline to remove the
Buoyant Line and Point Source
Dispersion Model (BLP), CALINE3, and
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CALPUFF as refined air quality models
preferred for specific regulator
applications. The rational for the
removal of these air quality models from
the preferred status can be found in the
Proposed Actions section of this
Preamble.
V. Statutory and Executive Order
Reviews
A. Executive Order 12866: Regulatory
Planning and Review and Executive
Order 13563: Improving Regulation and
Regulatory Review
This proposed action is not a
‘‘significant regulatory action’’ under
the terms of Executive Order 12866 (58
FR 51735, October 4, 1993) and is,
therefore not subject to OMB review
under Executive Orders 12866 and
13563 (76 FR 3821, January 21, 2011).
B. Paperwork Reduction Act
This proposed action does not impose
an information collection burden
subject to OMB review under the
provisions of the Paperwork Reduction
Act, 44 U.S.C. 3501 et seq.
C. Regulatory Flexibility Act
The Regulatory Flexibility Act (RFA)
generally requires an agency to prepare
a regulatory flexibility analysis of any
rule subject to notice and comment
rulemaking requirements under the
Administrative Procedure Act or any
other statute unless the agency certifies
that the rule will not have a significant
economic impact on a substantial
number of small entities. Small entities
include small businesses, small
organizations, and small governmental
jurisdictions.
For purposes of assessing the impacts
of this rule on small entities, small
entity is defined as (1) a small business
as defined by the Small Business
Administration’s (SBA) regulations at 13
CFR 121.201; (2) a small governmental
jurisdiction that is a government of a
city, county, town, school district or
special district with a population of less
than 50,000; and (3) a small
organization that is any not-for-profit
enterprise which is independently
owned and operated and is not
dominant in its field.
The modeling techniques described in
this proposed action are primarily used
by air agencies and by industries
owning major sources subject to NSR
permitting requirements. To the extent
that any small entities would have to
conduct air quality assessments, using
the models and/or techniques described
in this proposed action are not expect to
pose any additional burden (compared
to the existing models and/or
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techniques) on these entities. The
proposal features updates to the existing
EPA-preferred model, AERMOD, that
serves to increase efficiency and
accuracy by changing only
mathematical formulations and specific
data elements. Also, this proposed
action will streamline resources
necessary to conduct necessary
modeling with AERMOD by
incorporating model algorithms from
the BLP model and replacing CALINE3
for mobile source applications.
Although this proposed action calls for
new models and/or techniques for use
in addressing ozone and secondary
PM2.5, we expect most small entities
will generally be able to rely on existing
modeling simulations; so, we expect
minimal burden associated with these
assessments. Therefore, we do not
believe that that this proposal poses a
significant or unreasonable burden on
any small entities.
After considering the economic
impacts of this rule on small entities, I
certify that this action will not have a
significant economic impact on a
substantial number of small entities. We
continue to be interested in the
potential impacts of the proposed rule
on small entities and welcome
comments on issues related to such
impacts.
D. Unfunded Mandates Reform Act
This proposed action contains no
federal mandates under the provisions
of Title II of the Unfunded Mandates
Reform Act of 1995 (UMRA), 2 U.S.C.
1531–1538 for state, local, or tribal
governments or the private sector. This
action imposes no enforceable duty on
any state, local or tribal governments or
the private sector. Therefore, this action
is not subject to the requirements of
sections 202 or 205 of the UMRA. This
action is also not subject to the
requirements of section 203 of UMRA
because it contains no regulatory
requirements that might significantly or
uniquely affect small governments.
E. Executive Order 13132: Federalism
This proposed action does not have
federalism implications. It will not have
substantial direct effects on the states,
on the relationship between the national
government and the states, or on the
distribution of power and
responsibilities among the various
levels of government, as specified in
Executive Order 13132. This rule does
not create a mandate on state, local or
tribal governments nor does it impose
any enforceable duties on these entities.
This action would add better, more
accurate techniques for conducting air
quality assessments and does not add
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any additional requirements for any of
the affected parties covered under
Executive Order 13132. Thus, the
requirements of section 6 of the
Executive Order do not apply to this
proposal. In the spirit of Executive
Order 13132, and consistent with the
EPA policy to promote communications
between the EPA and state and local
governments, the EPA specifically
solicits comment on this proposed rule
from state and local officials.
because it does not affect the level of
protection provided to human health or
the environment.
F. Executive Order 13175: Consultation
and Coordination With Indian Tribal
Governments
This proposed action does not have
tribal implications, as specified in
Executive Order 13175 (65 FR 67249,
November 9, 2000). This proposed rule
imposes no requirements on tribal
governments. Accordingly, Executive
Order 13175 does not apply to this
action. In the spirit of Executive Order
13175, the EPA specifically solicits
additional comment on this proposed
action from tribal officials.
Dated: July 14, 2015.
Gina McCarthy,
Administrator.
G. Executive Order 13045: Protection of
Children From Environmental Health
and Safety Risks
The EPA interprets Executive Order
13045 as applying only to those
regulatory actions that concern
environmental health or safety risks that
the EPA has reason to believe may
disproportionately affect children, per
the definition of ‘‘covered regulatory
action’’ in section 2–202 of the
Executive Order. This action is not
subject to Executive Order 13045
because it does not concern an
environmental health risk or safety risk.
Authority: 23 U.S.C. 101; 42 U.S.C. 7401–
7671q.
H. Executive Order 13211: Actions
Concerning Regulations That
Significantly Affect Energy Supply,
Distribution, or Use
This action is not a ‘‘significant
energy action’’ as defined in Executive
Order 13211 (66 FR 28355 (May 22,
2001)), because it is not likely to have
a significant adverse effect on the
supply, distribution, or use of energy.
mstockstill on DSK4VPTVN1PROD with PROPOSALS2
I. National Technology Transfer and
Advancement Act
This rulemaking does not involve
technical standards.
J. Executive Order 12898: Federal
Actions To Address Environmental
Justice in Minority Populations and
Low-Income Populations
The EPA has determined that this
proposed rule will not have
disproportionately high and adverse
human health or environmental effects
on minority or low-income populations
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List of Subjects in 40 CFR Part 51
Environmental protection,
Administrative practice and procedure,
Air pollution control, Carbon monoxide,
Intergovernmental relations, Nitrogen
oxides, Ozone, Particulate Matter,
Reporting and recordkeeping
requirements, Sulfur oxides.
For the reasons stated in the
preamble, title 40, chapter I of the Code
of Federal Regulations is proposed to be
amended as follows:
PART 51—REQUIREMENTS FOR
PREPARATION, ADOPTION, AND
SUBMITTAL OF IMPLEMENTATION
PLANS
1. The authority citation for part 51
continues to read as follows:
■
2. Appendix W to part 51 is revised
to read as follows:
■
APPENDIX W TO PART 51—Guideline
on Air Quality Models Preface
a. Industry and control agencies have long
expressed a need for consistency in the
application of air quality models for
regulatory purposes. In the 1977 Clean Air
Act (CAA), Congress mandated such
consistency and encouraged the
standardization of model applications. The
Guideline on Air Quality Models (hereafter,
Guideline) was first published in April 1978
to satisfy these requirements by specifying
models and providing guidance for their use.
The Guideline provides a common basis for
estimating the air quality concentrations of
criteria pollutants used in assessing control
strategies and developing emissions limits.
b. The continuing development of new air
quality models in response to regulatory
requirements and the expanded requirements
for models to cover even more complex
problems have emphasized the need for
periodic review and update of guidance on
these techniques. Historically, three primary
activities have provided direct input to
revisions of the Guideline. The first is a series
of periodic EPA workshops and modeling
conferences conducted for the purpose of
ensuring consistency and providing
clarification in the application of models.
The second activity was the solicitation and
review of new models from the technical and
user community. In the March 27, 1980,
Federal Register, a procedure was outlined
for the submittal to the EPA of privately
developed models. After extensive evaluation
and scientific review, these models, as well
as those made available by the EPA, have
been considered for recognition in the
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Guideline. The third activity is the extensive
on-going research efforts by the EPA and
others in air quality and meteorological
modeling.
c. Based primarily on these three activities,
new sections and topics have been included
as needed. The EPA does not make changes
to the guidance on a predetermined schedule,
but rather on an as-needed basis. The EPA
believes that revisions of the Guideline
should be timely and responsive to user
needs and should involve public
participation to the greatest possible extent.
All future changes to the guidance will be
proposed and finalized in the Federal
Register. Information on the current status of
modeling guidance can always be obtained
from EPA’s Regional Offices.
Table of Contents
List of Tables
1.0 Introduction
2.0 Overview of Model Use
2.1 Suitability of Models
2.1.1 Model Accuracy and Uncertainty
2.2 Levels of Sophistication of Air Quality
Analyses and Models
2.3 Availability of Models
3.0 Preferred and Alternative Air Quality
Models
3.1 Preferred Models
3.1.1 Discussion
3.1.2 Requirements
3.2 Alternative Models
3.2.1 Discussion
3.2.2 Requirements
3.3 EPA’s Model Clearinghouse
4.0 Models for Carbon Monoxide, Lead,
Sulfur Dioxide, Nitrogen Dioxide and
Primary Particulate Matter
4.1 Discussion
4.2 Requirements
4.2.1 Screening Models and Techniques
4.2.1.1 AERSCREEN
4.2.1.2 CTSCREEN
4.2.1.3 Screening in Complex Terrain
4.2.2 Refined Models
4.2.2.1 AERMOD
4.2.2.2 CTDMPLUS
4.2.2.3 OCD
4.2.3 Pollutant Specific Modeling
Requirements
4.2.3.1 Models for Carbon Monoxide
4.2.3.2 Models for Lead
4.2.3.3 Models for Sulfur Dioxide
4.2.3.4 Models for Nitrogen Dioxide
4.2.3.5 Models for PM2.5
4.2.3.6 Models for PM10
5.0 Models for Ozone and Secondarily
Formed Particulate Matter
5.1 Discussion
5.2 Recommendations
5.3 Recommended Models and Approaches
for Ozone
5.3.1 Models for NAAQS Attainment
Demonstrations and Multi-Source Air
Quality Assessments
5.3.2 Models for Single-Source Air
Quality Assessments
5.4 Recommended Models and Approaches
for Secondarily Formed PM2.5
5.4.1 Models for NAAQS Attainment
Demonstrations and Multi-Source Air
Quality Assessments
5.4.2 Models for Single-Source Air
Quality Assessments
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6.0
Modeling for Air Quality Related Values
and Other Governmental Programs
6.1 Discussion
6.2 Air Quality Related Values
6.2.1 Visibility
6.2.1.1 Models for Estimating Near-Field
Visibility Impairment
6.2.1.2 Models for Estimating Visibility
Impairment for Long-Range Transport
6.2.2 Models for Estimating Deposition
Impacts
6.3 Modeling Guidance for Other
Governmental Programs
7.0 General Modeling Considerations
7.1 Discussion
7.2 Recommendations
7.2.1 All sources
7.2.1.1 Dispersion Coefficients
7.2.1.2 Complex Winds
7.2.1.3 Gravitational Settling and
Deposition
7.2.2 Stationary Sources
7.2.2.1 Good Engineering Practice Stack
Height
7.2.2.2 Plume Rise
7.2.3 Mobile Sources
8.0 Model Input Data
8.1 Modeling Domain
8.1.1 Discussion
8.1.2 Requirements
8.2 Source Data
8.2.1 Discussion
8.2.2 Requirements
8.3 Background Concentrations
8.3.1 Discussion
8.3.2 Recommendations for Isolated
Single Source
8.3.3 Recommendations for Multi-Source
Areas
8.4 Meteorological Input Data
8.4.1 Discussion
8.4.2 Recommendations and
Requirements
8.4.3 National Weather Service Data
8.4.3.1 Discussion
8.4.3.2 Recommendations
8.4.4 Site-specific data
8.4.4.1 Discussion
8.4.4.2 Recommendations
8.4.5 Prognostic meteorological data
8.4.5.1 Discussion
8.4.5.2 Recommendations
8.4.6 Treatment of Near-Calms and Calms
8.4.6.1 Discussion
8.4.6.2 Recommendations
9.0 Regulatory Application of Models
9.1 Discussion
9.2 Recommendations
9.2.1 Modeling Protocol
9.2.2 Design Concentration and Receptor
Sites
9.2.3 NAAQS and PSD Increments
Compliance Demonstrations for New or
Modified Sources
9.2.3.1 Considerations in Developing
Emissions Limits
9.2.4 Use of Measured Data in Lieu of
Model Estimates
10.0 References
Appendix A to Appendix W of Part 51—
Summaries of Preferred Air Quality
Models
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LIST OF TABLES
Table No.
Title
8–1 .........
Point Source Model Emission
Input for SIP Revisions of Inert
Pollutants.
Point Source Model Emission
Input for NAAQS Compliance in
PSD Demonstrations.
8–2 .........
1.0 Introduction
a. The Guideline recommends air quality
modeling techniques that should be applied
to State Implementation Plan (SIP) submittals
and revisions, to New Source Review (NSR),
including new or modifying sources under
Prevention of Significant Deterioration
(PSD),1 2 3 conformity analyses,4 and other air
quality assessments required under EPA
regulation. Applicable only to criteria air
pollutants, the Guideline is intended for use
by the EPA Regional Offices in judging the
adequacy of modeling analyses performed by
the EPA, by state, local, and tribal permitting
authorities, and by industry. It is appropriate
for use by other federal government agencies
and by state, local, and tribal agencies with
air quality and land management
responsibilities. The Guideline serves to
identify, for all interested parties, those
modeling techniques and databases that the
EPA considers acceptable. The Guideline is
not intended to be a compendium of
modeling techniques. Rather, it should serve
as a common measure of acceptable technical
analysis when supported by sound scientific
judgment.
b. Air quality measurements 5 are routinely
used to characterize ambient concentrations
of criteria pollutants throughout the nation
but are rarely sufficient for characterizing the
ambient impacts of individual sources or
demonstrating adequacy of emissions limits
for an existing source due to limitations in
spatial and temporal coverage of ambient
monitoring networks. The impacts of new
sources that do not yet exist and
modifications to existing sources that have
yet to be implemented can only be
determined through modeling. Thus, models
have become a primary analytical tool in
most air quality assessments. Air quality
measurements can be used in a
complementary manner to air quality models,
with due regard for the strengths and
weaknesses of both analysis techniques, and
are particularly useful in assessing the
accuracy of model estimates.
c. It would be advantageous to categorize
the various regulatory programs and to apply
a designated model to each proposed source
needing analysis under a given program.
However, the diversity of the nation’s
topography and climate, and variations in
source configurations and operating
characteristics dictate against a strict
modeling ‘‘cookbook.’’ There is no one model
capable of properly addressing all
conceivable situations even within a broad
category such as point sources.
Meteorological phenomena associated with
threats to air quality standards are rarely
amenable to a single mathematical treatment;
thus, case-by-case analysis and judgment are
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frequently required. As modeling efforts
become more complex, it is increasingly
important that they be directed by highly
competent individuals with a broad range of
experience and knowledge in air quality
meteorology. Further, they should be
coordinated closely with specialists in
emissions characteristics, air monitoring and
data processing. The judgment of
experienced meteorologists, atmospheric
scientists, and analysts is essential.
d. The model that most accurately
estimates concentrations in the area of
interest is always sought. However, it is clear
from the needs expressed by the EPA
Regional Offices, by state, local, and tribal
agencies, by many industries and trade
associations, and also by the deliberations of
Congress that consistency in the selection
and application of models and databases
should also be sought, even in case-by-case
analyses. Consistency ensures that air quality
control agencies and the general public have
a common basis for estimating pollutant
concentrations, assessing control strategies,
and specifying emissions limits. Such
consistency is not, however, promoted at the
expense of model and database accuracy. The
Guideline provides a consistent basis for
selection of the most accurate models and
databases for use in air quality assessments.
e. Recommendations are made in the
Guideline concerning air quality models and
techniques, model evaluation procedures,
and model input databases and related
requirements. The guidance provided here
should be followed in air quality analyses
relative to SIPs, NSR, and in supporting
analyses required by the EPA and by state,
local, and tribal permitting authorities.
Specific models are identified for particular
applications. The EPA may approve the use
of an alternative model or technique that can
be demonstrated to be more appropriate than
those recommended in the Guideline. In all
cases, the model or technique applied to a
given situation should be the one that
provides the most accurate representation of
atmospheric transport, dispersion, and
chemical transformations in the area of
interest. However, to ensure consistency,
deviations from the Guideline should be
carefully documented as part of the public
record and fully supported by the
appropriate reviewing authority, as discussed
later.
f. From time to time, situations arise
requiring clarification of the intent of the
guidance on a specific topic. Periodic
workshops are held with EPA headquarters,
EPA Regional Office, and state, local, and
tribal agency modeling representatives to
ensure consistency in modeling guidance and
to promote the use of more accurate air
quality models, techniques, and databases.
The workshops serve to provide further
explanations of Guideline requirements to
the EPA Regional Offices and workshop
materials are issued with this clarifying
information. In addition, findings from
ongoing research programs, new model
development, or results from model
evaluations and applications are
continuously evaluated. Based on this
information, changes in the applicable
guidance may be indicated and appropriate
revisions to the Guideline may be considered.
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g. All changes to the Guideline must follow
rulemaking requirements since the Guideline
is codified in appendix W to 40 Code of
Federal Regulations (CFR) part 51. The EPA
will promulgate proposed and final rules in
the Federal Register to amend this appendix.
The EPA utilizes the existing procedures
under CAA section 320 that requires EPA to
conduct a Conference on Air Quality
Modeling at least every 3 years. These
modeling conferences are intended to
develop standardized air quality modeling
procedures and form the basis for associated
revisions to this Guideline in support of the
EPA’s continuing effort to prescribe with
‘‘reasonable particularity’’ air quality models
and meteorological and emission databases
suitable for modeling National Ambient Air
Quality Standards (NAAQS) 6 and PSD
increments (CAA 320, 42 U.S.C. 7620).
Ample opportunity for public comment will
be provided for each proposed change and
public hearings scheduled.
h. A wide range of topics on modeling and
databases are discussed in the Guideline.
Section 2 gives an overview of models and
their suitability for use in regulatory
applications. Section 3 provides specific
guidance on the determination of preferred
air quality models and on the selection of
alternative models or techniques. Sections 4
through 6 provide recommendations on
modeling techniques for assessing criteria
pollutant impacts from single and multiple
sources with specific modeling requirements
for selected regulatory applications. Section
7 discusses general considerations common
to many modeling analyses for stationary and
mobile sources. Section 8 makes
recommendations for data inputs to models
including source, background air quality, and
meteorological data. Section 9 summarizes
how estimates and measurements of air
quality are used in assessing source impact
and in evaluating control strategies.
i. Appendix W to 40 CFR part 51 contains
an appendix: Appendix A. Thus, when
reference is made to ‘‘appendix A’’ in this
document, it refers to appendix A to
appendix W to 40 CFR part 51. Appendix A
contains summaries of refined air quality
models that are ‘‘preferred’’ for particular
applications; both EPA models and models
developed by others are included.
2.0 Overview of Model Use
a. Increasing reliance has been placed on
concentration estimates from air quality
models as the primary basis for regulatory
decisions concerning source permits and
emission control requirements. In many
situations, such as review of a proposed new
source, no practical alternative exists. Before
attempting to implement the guidance
contained in this document, the reader
should be aware of certain general
information concerning air quality models
and their evaluation and use. Such
information is provided in this section.
2.1 Suitability of Models
a. The extent to which a specific air quality
model is suitable for the assessment of source
impacts depends upon several factors. These
include: (1) The topographic and
meteorological complexities of the area; (2)
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the detail and accuracy of the input
databases, i.e., emissions inventory,
meteorological data, and air quality data; (3)
the manner in which complexities of
atmospheric processes are handled in the
model; (4) the technical competence of those
undertaking such simulation modeling; and
(5) the resources available to apply the
model. Any of these factors can have a
significant influence on the overall model
performance, which must be thoroughly
evaluated to determine the suitability of an
air quality model to a particular application
or range of applications.
b. Air quality models are most accurate and
reliable in areas that have gradual transitions
of land use and topography. Meteorological
conditions in these areas are spatially
uniform such that observations are broadly
representative and air quality model
projections are not further complicated by a
heterogeneous environment. Areas subject to
major topographic influences experience
meteorological complexities that are often
difficult to measure and simulate. Models
with adequate performance are available for
increasingly complex environments.
However, they are resource intensive and
frequently require site-specific observations
and formulations. Such complexities and the
related challenges for the air quality
simulation should be considered when
selecting the most appropriate air quality
model for an application.
c. Appropriate model input data should be
available before an attempt is made to
evaluate or apply an air quality model.
Assuming the data are adequate, the greater
the detail with which a model considers the
spatial and temporal variations in
meteorological conditions and permitenforceable emissions, the greater the ability
to evaluate the source impact and to
distinguish the effects of various control
strategies.
d. There are three types of models that
have historically been used in the regulatory
demonstrations applicable in the Guideline,
each having strengths and weaknesses that
lend themselves to particular regulatory
applications.
i. Gaussian plume models use a ‘‘steadystate’’ approximation, which assumes that
over the model time step, the emissions,
meteorology and other model inputs, are
constant throughout the model domain,
resulting in a resolved plume with the
emissions distributed throughout the plume
according to a Gaussian distribution. This
formulation allows Gaussian models to
estimate near-field impacts of a limited
number of sources at a relatively high
resolution, with temporal scales of an hour
and spatial scales of meters. However, this
formulation allows for only relatively inert
pollutants, with very limited considerations
of transformation and removal (e.g.,
deposition), and further limits the domain for
which the model may be used. Thus,
Gaussian models may not be appropriate if
model inputs are changing sharply over the
model time step or within the desired model
domain or if more advanced considerations
of chemistry are needed.
ii. Lagrangian puff models, on the other
hand, are non-steady-state, and assume that
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model input conditions are changing over the
model domain and model time step.
Lagrangian models can also be used to
determine near and far-field impacts from a
limited number of sources at a high
resolution. Traditionally, Lagrangian models
have been used for relatively inert pollutants,
with slightly more complex considerations of
removal than Gaussian models. Some
Lagrangian models treat in-plume gas and
particulate chemistry. However, these models
require time and space varying concentration
fields of oxidants and, in the case of fine
particulate matter (PM2.5), neutralizing
agents, such as ammonia. Reliable
background fields are critical for applications
involving secondary pollutant formation
because secondary impacts generally occur
when in-plume precursors mix and react
with species in the background
atmosphere.7 8 These oxidant and
neutralizing agents are not routinely
measured, but can be generated with a threedimensional photochemical grid model.
iii. Photochemical grid models are threedimensional Eulerian grid-based models that
treat chemical and physical processes in each
grid cell and use diffusion and transport
processes to move chemical species between
grid cells.9 Eulerian models assume that
emissions are spread evenly throughout each
model grid cell. Typically, Eulerian models
have difficulty with fine scale resolution of
individual plumes. However, these types of
models can be appropriately applied for
assessment of near-field and regional scale
reactive pollutant impacts from specific
sources 7 10 11 12 or all sources.13 14 15
Photochemical gird models simulate a more
realistic environment for chemical
transformation,7 12 but simulations can be
more resource intensive than Lagrangian or
Gaussian plume models.
e. Competent and experienced
meteorologists, atmospheric scientists, and
analysts are an essential prerequisite to the
successful application of air quality models.
The need for such specialists is critical when
the more sophisticated models are used or
the area being investigated has complicated
meteorological or topographic features. It is
important to note that a model applied
improperly or with inappropriate data can
lead to serious misjudgments regarding the
source impact or the effectiveness of a
control strategy.
f. The resource demands generated by use
of air quality models vary widely depending
on the specific application. The resources
required may be important factors in the
selection and use of a model or technique for
a specific analysis. These resources depend
on the nature of the model and its
complexity, the detail of the databases, the
difficulty of the application, the amount and
level of expertise required, and the costs of
manpower and computational facilities.
2.1.1 Model Accuracy and Uncertainty
a. The formulation and application of air
quality models are accompanied by several
sources of uncertainty. ‘‘Irreducible’’
uncertainty stems from the ‘‘unknown’’
conditions, which may not be explicitly
accounted for in the model (e.g., the
turbulent velocity field). Thus, there are
likely to be deviations from the observed
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concentrations in individual events due to
variations in the unknown conditions.
‘‘Reducible’’ uncertainties 16 are caused by:
(1) Uncertainties in the ‘‘known’’ input
conditions (e.g., emission characteristics and
meteorological data); (2) errors in the
measured concentrations; and (3) inadequate
model physics and formulation.
b. Evaluations of model accuracy should
focus on the reducible uncertainty associated
with physics and the formulation of the
model. The accuracy of the model is
normally determined by an evaluation
procedure which involves the comparison of
model concentration estimates with
measured air quality data.17 The statement of
model accuracy is based on statistical tests or
performance measures such as bias, noise,
correlation, etc.18 19
c. Since the 1980’s, the EPA has worked
with the modeling community to encourage
development of standardized model
evaluation methods and the development of
continually improved methods for the
characterization of model
performance.16 18 20 21 22 There is general
consensus on what should be considered in
the evaluation of air quality models; namely,
quality assurance planning, documentation
and scrutiny should be consistent with the
intended use and should include:
• Scientific peer review;
• Supportive analyses (diagnostic
evaluations, code verification, sensitivity
analyses);
• Diagnostic and performance evaluations
with data obtained in trial locations; and
• Statistical performance evaluations in
the circumstances of the intended
applications.
Performance evaluations and diagnostic
evaluations assess different qualities of how
well a model is performing, and both are
needed to establish credibility within the
client and scientific community.
d. Performance evaluations allow the EPA
and model users to determine the relative
performance of a model in comparison with
alternative modeling systems. Diagnostic
evaluations allow determination of a model
capability to simulate individual processes
that affect the results, and usually employ
smaller spatial/temporal scale date sets (e.g.,
field studies). Diagnostic evaluations enable
the EPA and model users to build confidence
that model predictions are accurate for the
right reasons. However, the objective
comparison of modeled concentrations with
observed field data provides only a partial
means for assessing model performance. Due
to the limited supply of evaluation datasets,
there are practical limits in assessing model
performance. For this reason, the conclusions
reached in the science peer reviews and the
supportive analyses have particular relevance
in deciding whether a model will be useful
for its intended purposes.
2.2 Levels of Sophistication of Air Quality
Analyses and Models
a. It is desirable to begin an air quality
analysis by using simplified or conservative
methods (or both) followed, as appropriate,
by more complex and refined methods. The
purpose of this approach is to streamline the
process and sufficiently address regulatory
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requirements by eliminating the need of more
detailed modeling when it is not necessary in
a specific regulatory application. For
example, in the context of a PSD permit
application, a simplified or conservative
analysis may be sufficient where it shows the
proposed construction clearly will not cause
or contribute to ambient concentrations in
excess of either the NAAQS or the PSD
increments.2 3
b. There are two general levels of
sophistication of air quality models. The first
level consists of screening models that
provide conservative modeled estimates of
the air quality impact of a specific source or
source category based on simplified
assumptions of the model inputs (e.g., preset,
worst-case meteorological conditions). In the
case of a PSD assessment, if a screening
model indicates that the concentration
contributed by the source could cause or
contribute to a violation of any NAAQS or
PSD increment, then the second level of more
sophisticated models should be applied.
c. The second level consists of refined
models that provide more detailed treatment
of physical and chemical atmospheric
processes, require more detailed and precise
input data, and provide spatially and
temporally resolved concentration estimates.
As a result they provide a more sophisticated
and, at least theoretically, a more accurate
estimate of source impact and the
effectiveness of control strategies.
d. There are situations where a screening
model or a refined model is not available
such that screening and refined modeling are
not viable options to determine sourcespecific air quality impacts. In such
situations, a screening technique or reducedform model may be viable options for
estimating source impacts.
i. Screening techniques are differentiated
from a screening model in that screening
techniques are approaches that make
simplified and conservative assumptions
about the physical and chemical atmospheric
processes important to determining source
impacts while screening models make
assumptions about conservative inputs to a
specific model. The complexity of screening
techniques ranges from simplified
assumptions of chemistry applied to refined
or screening model output to sophisticated
approximations of the chemistry applied
within a refined model.
ii. Reduced-form models are
computationally efficient simulation tools for
characterizing the pollutant response to
specific types of emission reductions for a
particular geographic area or background
environmental conditions that reflect
underlying atmospheric science of a refined
model but reduce the computational
resources of running a complex, numerical
air quality model such as a photochemical
grid model.
In such situations, an attempt should be
made to acquire or improve the necessary
databases and to develop appropriate
analytical techniques, but the screening
technique or reduced-form model may be
sufficient in conducting regulatory modeling
applications when applied in consultation
with the EPA Regional Office.
e. Consistent with the general principle
described in paragraph 2.2(a), the EPA may
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establish a demonstration tool or method as
a sufficient means for a user or applicant to
make a demonstration required by regulation,
either by itself or as part of a modeling
demonstration. To be used for such
regulatory purposes, such a tool or method
must be reflected in a codified regulation or
have a well-documented technical basis and
reasoning that is contained or incorporated in
the record of the regulatory decision in
which it is applied.
2.3 Availability of Models
a. For most of the screening and refined
models discussed in the Guideline, codes,
associated documentation and other useful
information are publicly available for
download from the EPA’s Support Center for
Regulatory Atmospheric Modeling (SCRAM)
Web site at https://www.epa.gov/ttn/scram.
This is a Web site with which air quality
modelers should become familiar and
regularly visit for important model updates
and additional clarifications and revisions to
modeling guidance documents that are
applicable to EPA programs and regulations.
Codes and documentation may also available
from the National Technical Information
Service (NTIS), https://www.ntis.gov, and,
when available, is referenced with the
appropriate NTIS accession number.
3.0 Preferred and Alternative Air Quality
Models
a. This section specifies the approach to be
taken in determining preferred models for
use in regulatory air quality programs. The
status of models developed by the EPA, as
well as those submitted to the EPA for review
and possible inclusion in this Guideline, is
discussed in this section. The section also
provides the criteria and process for
obtaining EPA approval for use of alternative
models for individual cases in situations
where the preferred models are not
applicable or available. Additional sources of
relevant modeling information are the EPA’s
Model Clearinghouse 23 (section 3.3), EPA
modeling conferences, periodic Regional,
State, and Local Modelers’ Workshops, and
the EPA’s SCRAM Web site (section 2.3).
b. When approval is required for a specific
modeling technique or analytical procedure
in this Guideline, we refer to the
‘‘appropriate reviewing authority.’’ Many
states and some local agencies administer
NSR and PSD permitting under programs
approved into SIPs. In some EPA regions,
federal authority to administer NSR and PSD
permitting and related activities has been
delegated to state or local agencies. In these
cases, such agencies ‘‘stand in the shoes’’ of
the respective EPA regions. Therefore,
depending on the circumstances, the
appropriate reviewing authority may be an
EPA Regional Office, a state, local, or tribal
agency, or perhaps the Federal Land Manager
(FLM). In some cases, the Guideline requires
review and approval of the use of an
alternative model by the EPA Regional Office
(sometimes stated as ‘‘Regional
Administrator’’). For all approvals of
alternative models or techniques, the EPA
Regional Office will coordinate and shall
seek concurrence with the EPA’s Model
Clearinghouse. If there is any question as to
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the appropriate reviewing authority, you
should contact the EPA Regional Office
modeling contact (https://www.epa.gov/ttn/
scram/guidance_cont_regions.htm), whose
jurisdiction generally includes the physical
location of the source in question and its
expected impacts.
c. In all regulatory analyses, early
discussions among the EPA Regional Office
staff, state, local, and tribal agency staff,
industry representatives, and where
appropriate, the FLM, are invaluable and are
strongly encouraged. Prior to the actual
analyses, agreement on the databases to be
used, modeling techniques to be applied, and
the overall technical approach helps avoid
misunderstandings concerning the final
results and may reduce the later need for
additional analyses. The preparation of a
written modeling protocol that is vetted with
the appropriate reviewing authority helps to
keep misunderstandings and resource
expenditures at a minimum.
d. The identification of preferred models in
this Guideline should not be construed as a
determination that the preferred models
identified here are to be permanently used to
the exclusion of all others or that they are the
only models available for relating emissions
to air quality. The model that most accurately
estimates concentrations in the area of
interest is always sought. However,
designation of specific preferred models is
needed to promote consistency in model
selection and application.
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3.1.1
Preferred Models
Discussion
a. The EPA has developed some models
suitable for regulatory application, while
other models have been submitted by private
developers for possible inclusion in the
Guideline. Refined models that are preferred
and required by the EPA for particular
applications have undergone the necessary
peer scientific reviews 24 25 and model
performance evaluation exercises 26 27 that
include statistical measures of model
performance in comparison with measured
air quality data as described in section 2.1.1.
b. An American Society for Testing and
Materials (ASTM) reference 28 provides a
general philosophy for developing and
implementing advanced statistical
evaluations of atmospheric dispersion
models, and provides an example statistical
technique to illustrate the application of this
philosophy. Consistent with this approach,
the EPA has determined and applied a
specific evaluation protocol that provides a
statistical technique for evaluating model
performance for predicting peak
concentration values, as might be observed at
individual monitoring locations.29
c. When a single model is found to perform
better than others, it is recommended for
application as a preferred model and listed
in appendix A. If no one model is found to
clearly perform better through the evaluation
exercise, then the preferred model listed in
appendix A may be selected on the basis of
other factors such as past use, public
familiarity, resource requirements, and
availability. Accordingly, the models listed
in appendix A meet these conditions:
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i. The model must be written in a common
programming language, and the executable(s)
must run on a common computer platform.
ii. The model must be documented in a
user’s guide or model formulation report
which identifies the mathematics of the
model, data requirements and program
operating characteristics at a level of detail
comparable to that available for other
recommended models in appendix A.
iii. The model must be accompanied by a
complete test dataset including input
parameters and output results. The test data
must be packaged with the model in
computer-readable form.
iv. The model must be useful to typical
users, e.g., state air agencies, for specific air
quality control problems. Such users should
be able to operate the computer program(s)
from available documentation.
v. The model documentation must include
a robust comparison with air quality data
(and/or tracer measurements) or with other
well- established analytical techniques.
vi. The developer must be willing to make
the model and source code available to users
at reasonable cost or make them available for
public access through the Internet or
National Technical Information Service. The
model and its code cannot be proprietary.
d. The EPA’s process of establishing a
preferred model includes a determination of
technical merit, in accordance with the above
six items including the practicality of the
model for use in ongoing regulatory
programs. Each model will also be subjected
to a performance evaluation for an
appropriate database and to a peer scientific
review. Models for wide use (not just an
isolated case) that are found to perform better
will be proposed for inclusion as preferred
models in future Guideline revisions.
e. No further evaluation of a preferred
model is required for a particular application
if the EPA requirements for regulatory use
specified for the model in the Guideline are
followed. Alternative models to those listed
in appendix A should generally be compared
with measured air quality data when they are
used for regulatory applications consistent
with recommendations in section 3.2.
3.1.2 Requirements
a. Appendix A identifies refined models
that are preferred for use in regulatory
applications. If a model is required for a
particular application, the user must select a
model from appendix A or follow procedures
in section 3.2.2 for use of an alternative
model or technique. Preferred models may be
used without a formal demonstration of
applicability as long as they are used as
indicated in each model summary in
appendix A. Further recommendations for
the application of preferred models to
specific source applications are found in
subsequent sections of the Guideline.
b. If changes are made to a preferred model
without affecting the modeled
concentrations, the preferred status of the
model is unchanged. Examples of
modifications that do not affect
concentrations are those made to enable use
of a different computer platform or those that
only affect the format or averaging time of the
model results. The integration of a graphical
user interface (GUI) to facilitate setting up the
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model inputs and/or analyzing the model
results without otherwise altering the model
kernel is another example of a modification
that does not affect concentrations. However,
when any changes are made, the Regional
Administrator must require a test case
example to demonstrate that the modeled
concentration are not affected.
c. A preferred model must be operated
with the options listed in appendix A for its
intended regulatory application. If other
options are exercised, the model is no longer
‘‘preferred.’’ Any other modification to a
preferred model that would result in a
change in the concentration estimates
likewise alters its status so that it is no longer
a preferred model. Use of the modified model
must then be justified as an alternative model
on a case-by-case basis to the appropriate
reviewing authority and approved by the
Regional Administrator.
d. Where the EPA has not identified a
preferred model for a particular pollutant or
situation, the EPA may establish a multitiered approach for making a demonstration
required under PSD or another CAA program.
The initial tier or tiers may involve use of
demonstration tools, screening models,
screening techniques, or reduced-form
models; while the last tier may involve the
use of demonstration tools, refinded models
or techniques, or alternative models
approved under section 3.2.
3.2 Alternative Models
3.2.1 Discussion
a. Selection of the best model or techniques
for each individual air quality analysis is
always encouraged, but the selection should
be done in a consistent manner. A simple
listing of models in this Guideline cannot
alone achieve that consistency nor can it
necessarily provide the best model for all
possible situations. As discussed in section
3.1.1, the EPA has determined and applied a
specific evaluation protocol that provides a
statistical technique for evaluating model
performance for predicting peak
concentration values, as might be observed at
individual monitoring locations.29 This
protocol is available to assist in developing
a consistent approach when justifying the use
of other-than-preferred models recommended
in the Guideline (i.e., alternative models).
The procedures in this protocol provide a
general framework for objective decisionmaking on the acceptability of an alternative
model for a given regulatory application.
These objective procedures may be used for
conducting both the technical evaluation of
the model and the field test or performance
evaluation.
b. This subsection discusses the use of
alternate models and defines three situations
when alternative models may be used. This
subsection also provides a procedure for
implementing 40 CFR 51.166(l)(2) in PSD
permitting. This provision requires written
approval of the Administrator for any
modification or substitution of an applicable
model. An applicable model for purposes of
40 CFR 51.166(l) is a preferred model in
appendix A to the Guideline. Approval to use
an alternative model under section 3.2 of the
Guideline qualifies as approval for the
modification or substitution of a model under
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40 CFR 51.166(l)(2). The Regional
Administrators are delegated authority to
issue such approvals under section 3.2 of the
Guideline, provided that such approval is
issued after consultation with EPA’s Model
Clearinghouse and formally documented in a
concurrence memorandum from EPA’s
Model Clearinghouse which demonstrates
that the requirements within section 3.2 for
use of an alternative model have been met.
3.2.2 Requirements
a. Determination of acceptability of an
alternative model is an EPA Regional Office
responsibility in consultation with EPA’s
Model Clearinghouse as discussed in
paragraphs 3.0(b) and 3.2.1(b). Where the
Regional Administrator finds that an
alternative model is more appropriate than a
preferred model, that model may be used
subject to the approval of the EPA Regional
Office based on the requirements of this
subsection. This finding will normally result
from a determination that (1) a preferred air
quality model is not appropriate for the
particular application; or (2) a more
appropriate model or technique is available
and applicable.
b. An alternative model shall be evaluated
from both a theoretical and a performance
perspective before it is selected for use. There
are three separate conditions under which
such a model may be approved for use:
1. If a demonstration can be made that the
model produces concentration estimates
equivalent to the estimates obtained using a
preferred model;
2. If a statistical performance evaluation
has been conducted using measured air
quality data and the results of that evaluation
indicate the alternative model performs
better for the given application than a
comparable model in appendix A; or
3. If there is no preferred model.
Any one of these three separate conditions
may justify use of an alternative model. Some
known alternative models that are applicable
for selected situations are listed on the EPA’s
SCRAM Web site (section 2.3). However,
inclusion there does not confer any unique
status relative to other alternative models
that are being or will be developed in the
future.
c. Equivalency, condition (1) in paragraph
(b) of this subsection, is established by
demonstrating that the maximum or highest,
second highest concentrations are within
+/¥ 2 percent of the estimates obtained from
the preferred model. The option to show
equivalency is intended as a simple
demonstration of acceptability for an
alternative model that is so nearly identical
(or contains options that can make it
identical) to a preferred model that it can be
treated for practical purposes as the preferred
model. However, notwithstanding this
demonstration, models that are not
equivalent may be used when one of the two
other conditions described in paragraphs (d)
and (e) of this subsection are satisfied.
d. For condition (2) in paragraph (b) of this
subsection, established statistical
performance evaluation procedures and
techniques 28 29 for determining the
acceptability of a model for an individual
case based on superior performance should
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be followed, as appropriate. Preparation and
implementation of an evaluation protocol
which is acceptable to both control agencies
and regulated industry is an important
element in such an evaluation.
e. Finally, for condition (3) in paragraph (b)
of this subsection, an alternative model or
technique may be approved for use provided
that:
i. The model or technique has received a
scientific peer review;
ii. The model or technique can be
demonstrated to be applicable to the problem
on a theoretical basis;
iii. The databases which are necessary to
perform the analysis are available and
adequate;
iv. Appropriate performance evaluations of
the model or technique have shown that the
model or technique is not inappropriately
biased for regulatory application; a and
v. A protocol on methods and procedures
to be followed has been established.
f. To formally document that the
requirements of section 3.2 for use of an
alternative model are satisfied for a particular
application or range of applications, a
memorandum will be prepared by the EPA’s
Model Clearinghouse through a consultative
process with the Region Office.
3.3 EPA’s Model Clearinghouse
a. The Regional Administrator has the
authority to select models that are
appropriate for use in a given situation.
However, there is a need for assistance and
guidance in the selection process so that
fairness, consistency, and transparency in
modeling decisions are fostered among the
EPA Regional Offices and the state, local, and
tribal agencies. To satisfy that need, the EPA
established the Model Clearinghouse 23 to
serve a central role of coordination and
collaboration between EPA headquarters and
the EPA Regional Offices. Additionally, the
EPA holds periodic workshops with EPA
headquarters, EPA Regional Office, and state,
local, and tribal agency modeling
representatives.
b. The EPA Regional Office should always
be consulted for information and guidance
concerning modeling methods and
interpretations of modeling guidance, and to
ensure that the air quality model user has
available the latest most up-to-date policy
and procedures. As appropriate, the EPA
Regional Office may also request assistance
from the EPA’s Model Clearinghouse on
other applications of models, analytical
techniques, or databases or to clarify
interpretation of the Guideline or related
modeling guidance.
c. The EPA Regional Office will coordinate
with the EPA’s Model Clearinghouse after an
initial evaluation and decision has been
developed concerning the application of an
alternative model. The acceptability and
formal approval process for an alternative
model is described in section 3.2.
a For PSD and other applications that use the
model results in an absolute sense, the model
should not be biased toward underestimates.
Alternatively, for ozone and PM2.5 SIP attainment
demonstrations and other applications that use the
model results in a relative sense, the model should
note be biased toward overestimates.
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4.0 Models for Carbon Monoxide, Lead,
Sulfur Dioxide, Nitrogen Dioxide and
Primary Particulate Matter
4.1 Discussion
a. This section identifies modeling
approaches generally used in the air quality
impact analysis of sources that emit the
criteria pollutants carbon monoxide (CO),
lead, sulfur dioxide (SO2), nitrogen dioxide
(NO2), and primary particulates (PM2.5 and
PM10).
b. The guidance in this section is specific
to the application of the Gaussian plume
models identified in appendix A. Gaussian
plume models assume that emissions and
meteorology are in a steady-state, which is
typically based on an hourly time step. This
approach results in a plume that has an
hourly-averaged distribution of emission
mass according to a Gaussian curve through
the plume. Though Gaussian steady-state
models conserve the mass of the primary
pollutant throughout the plume, they can
still take into account a limited consideration
of first-order removal processes (e.g., wet and
dry deposition) and limited chemical
conversion (e.g., OH oxidation).
c. Due to the steady-state assumption,
Gaussian plume models are generally
considered applicable to distances less than
50 km, beyond which, modeled predictions
of plume impact are likely conservative. The
locations of these impacts are expected to be
unreliable due to changes in meteorology that
are likely to occur during the travel time.
d. The applicability of Gaussian plume
models may vary depending on the
topography of the modeling domain, i.e.,
simple or complex. Simple terrain, as used
here, is considered to be an area where
terrain features are all lower in elevation than
the top of the stack of the source(s) in
question. Complex terrain is defined as
terrain exceeding the height of the stack
being modeled.
e. Gaussian models determine source
impacts at discrete locations (receptors) for
each meteorological and emission scenario,
and generally attempt to estimate
concentrations at specific sites that represent
an ensemble average of numerous repetitions
of the same ‘‘event.’’ Uncertainties in model
estimates are driven by this formulation, and
as noted in section 2.1.1, evaluations of
model accuracy should focus on the
reducible uncertainty associated with
physics and the formulation of the model.
The ‘‘irreducible’’ uncertainty associated
with Gaussian plume models may be
responsible for variation in concentrations of
as much as +/¥ 50 percent.30 ‘‘Reducible’’
uncertainties 16 can be on a similar scale. For
example, Pasquill 31 estimates that, apart
from data input errors, maximum groundlevel concentrations at a given hour for a
point source in flat terrain could be in error
by 50 percent due to these uncertainties.
Errors of 5 to 10 degrees in the measured
wind direction can result in concentration
errors of 20 to 70 percent for a particular time
and location, depending on stability and
station location. Such uncertainties do not
indicate that an estimated concentration does
not occur, only that the precise time and
locations are in doubt. Composite errors in
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highest estimated concentrations of 10 to 40
percent are found to be typical.32 33 However,
estimates of concentrations paired in time
and space with observed concentrations are
less certain.
f. Model evaluations and inter-comparisons
should take these aspects of uncertainty into
account. For a regulatory application of a
model, the emphasis of model evaluations is
generally placed on the highest modeled
impacts. Thus, the Cox-Tikvart model
evaluation approach, which compares the
highest modeled impacts on several
timescales, is recommended for comparisons
of models and measurements and model
inter-comparisons. The approach includes
bootstrap techniques to determine the
significance of various modeled predictions
and increases the robustness of such
comparisons when the number of available
measurements are limited.34 35 Because of the
uncertainty in paired modeled and observed
concentrations, any attempts at calibration of
models based on these comparisons is of
questionable benefit and shall not be done.
4.2 Requirements
a. For NAAQS compliance demonstrations
under PSD, use of the screening and
preferred models for the pollutants listed in
this subsection shall be limited to the nearfield at a nominal distance of 50 km or less.
Near-field application is consistent with
capabilities of Gaussian plume models and,
based on the EPA’s assessment, is sufficient
to address whether a source will cause or
contribution to ambient concentrations in
excess to a NAAQS. In most cases, maximum
source impacts of inert pollutant are
anticipated to occur within 10 to 20 km from
the source. Therefore, the EPA does not
consider a long-range transport assessment
beyond 50 km necessary for these
pollutants.36
b. For assessment of PSD increments
within the near-field nominal distance of 50
km or less, use of the screening and preferred
models for the pollutants listed in this
subsection shall be limited to the same
screening and preferred models approved for
NAAQS compliance demonstrations.
c. To determine if a Class I PSD increment
analyses may be necessary beyond 50 km
(i.e., long-range transport assessment), the
following screening approach shall be used
to determine if a significant impact will
occur with particular focus on Class I areas
that may be threatened at such distances.
i. Based on application in the near-field of
the appropriate screening and/or preferred
model, determine the significance of the
ambient impacts at or about 50 km from the
new or modifying source. If this initial step
indicates there may be significant ambient
impacts at that distance or such near-field
assessment is not available, then further
assessment is necessary.
ii. For assessment of Class I significance of
ambient impacts and cumulative increment
analyses, there is not a preferred model or
screening approach for distances beyond 50
km. Thus, the EPA Regional Office shall be
consulted in determining the appropriate and
agreed upon modeling approach to conduct
the second level assessment. Typically a
Lagrangian model may be the type of model
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used for this second level assessment, but
applicants shall reach agreed upon
approaches (models and modeling
parameters) on a case-by-case basis. When
Lagrangian models are used in this manner,
they shall not include plume-depleting
reactions, such that model estimates are
considered conservative, as is generally
appropriate for screening assessments.
d. In those limited situations where a
cumulative increment analysis beyond 50 km
is necessary, the selection and use of an
alternative model shall occur in agreement
with the appropriate reviewing authority
(paragraph 3.0(b)) and approval by the EPA
Regional Office based on the requirements of
paragraph 3.2.2(e).
4.2.1 Screening Models and Techniques
a. Where a preliminary or conservative
estimate is desired, point source screening
techniques are an acceptable approach to air
quality analyses.
b. As discussed in paragraph 2.2(a),
screening models or techniques are designed
to provide a conservative estimate of
concentrations. The screening models used
in most applications are the screening
versions of the preferred models for refined
applications. The two screening models,
AERSCREEN 37 38 and CTSCREEN, are
screening versions of AERMOD (American
Meteorological Society (AMS)/EPA
Regulatory Model) and CTDMPLUS
(Complex Terrain Dispersion Model Plus
Algorithms for Unstable Situations),
respectively. AERSCREEN is the preferred
screening model for most applications in all
types of terrain and for applications
involving building downwash. For those
applications in complex terrain where the
application involves a well-defined hill or
ridge, CTSCREEN 39 can be used.
c. Although AERSCREEN and CTSCREEN
are designed to address a single-source
scenario, there are approaches that can be
used on a case-by-case basis to address multisource situations using screening
meteorology or other conservative model
assumptions. However, the appropriate
reviewing authority (paragraph 3.0(b)) shall
be consulted, and concurrence obtained, on
the protocol for modeling multiple sources
with AERSCREEN or CTSCREEN to ensure
that the worst case is identified and assessed.
d. As discussed in section 4.2.3.4, there are
also screening techniques built into
AERMOD that use simplified or limited
chemistry assumptions for determining the
partitioning of NO and NO2 for NO2
modeling. These screening techniques are
part of the EPA’s preferred modeling
approach for NO2 and do not need to be
approved as an alternative model. However,
as with other screening models and
techniques, their usage shall occur in
agreement with the appropriate reviewing
authority (paragraph 3.0(b)).
e. All screening models and techniques
shall be configured to appropriately address
the site and problem at hand. Close attention
must be paid to whether the area should be
classified urban or rural in accordance with
section 7.2.1.1. The climatology of the area
must be studied to help define the worst-case
meteorological conditions. Agreement shall
be reached between the model user and the
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appropriate reviewing authority (paragraph
3.0(b)) on the choice of the screening model
or technique for each analysis, on the input
data and model settings, and the appropriate
metric for satisfying regulatory requirements.
4.2.1.1 AERSCREEN
a. Released in 2011, AERSCREEN is the
EPA’s recommended screening model for
simple and complex terrain for single sources
including point sources, area sources,
horizontal stacks, capped stacks, and flares.
AERSCREEN runs AERMOD in a screening
mode and consists of two main components:
(1) The MAKEMET program which generates
a site-specific matrix of meteorological
conditions for input into the AERMOD
model; and (2) the AERSCREEN commandprompt interface.
b. The MAKEMET program generates a
matrix of meteorological conditions, in the
form of AERMOD-ready surface and profile
files, based on user-specified surface
characteristics, ambient temperatures,
minimum wind speed, and anemometer
height. The meteorological matrix is
generated based on looping through a range
of wind speeds, cloud covers, ambient
temperatures, solar elevation angles, and
convective velocity scales (w*, for convective
conditions only) based on user-specified
surface characteristics (Zo, Bo, r). For
unstable cases, the convective mixing height
(Zic) is calculated based on w*, and the
mechanical mixing height (Zim) is calculated
for unstable and stable conditions based on
the friction velocity, u*.
c. For applications involving simple or
complex terrain, AERSCREEN interfaces with
AERMAP. AERSCREEN also interfaces with
BIPPRM to provide the necessary building
parameters for applications involving
building downwash using the PRIME
downwash algorithm. AERSCREEN generates
inputs to AERMOD via MAKEMET,
AERMAP, and BPIPPRM and invokes
AERMOD in a screening mode. The screening
mode of AERMOD forces the AERMOD
model calculations to represent values for the
plume centerline, regardless of the sourcereceptor-wind direction orientation. The
maximum concentration output from
AERSCREEN represents a worst-case 1-hour
concentration. Averaging-time scaling factors
of 0.9 for 3-hour, 0.7 for 8-hour, 0.40 for 24hour, and 0.08 for annual concentration
averages are applied internally by
AERSCREEN to the highest 1-hour
concentration calculated by the model for
non-area type sources. For area type source
concentrations for averaging times greater
than one hour, the concentrations are equal
to the 1-hour estimates.37 40
4.2.1.2 CTSCREEN
a. CTSCREEN 39 41 can be used to obtain
conservative, yet realistic, worst-case
estimates for receptors located on terrain
above stack height. CTSCREEN accounts for
the three-dimensional nature of plume and
terrain interaction and requires detailed
terrain data representative of the modeling
domain. The terrain data must be digitized in
the same manner as for CTDMPLUS and a
terrain processor is available.42 CTSCREEN is
designed to execute a fixed matrix of
meteorological values for wind speed (u),
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standard deviation of horizontal and vertical
wind speeds (sv, sw), vertical potential
temperature gradient (dq/dz), friction
velocity (u*), Monin-Obukhov length (L),
mixing height (zi) as a function of terrain
height, and wind directions for both neutral/
stable conditions and unstable convective
conditions. The maximum concentration
output from CTSCREEN represents a worstcase 1-hour concentration. Time-scaling
factors of 0.7 for 3-hour, 0.15 for 24-hour and
0.03 for annual concentration averages are
applied internally by CTSCREEN to the
highest 1-hour concentration calculated by
the model.
4.2.1.3 Screening in Complex Terrain
a. For applications utilizing AERSCREEN,
AERSCREEN automatically generates a polargrid receptor network with spacing
determined by the maximum distance to
model. If the application warrants a different
receptor network than that generated by
AERSCREEN, it may be necessary to run
AERMOD in screening mode with a userdefined network. For CTSCREEN
applications or AERMOD in screening mode
outside of AERSCREEN, placement of
receptors requires very careful attention
when modeling in complex terrain. Often the
highest concentrations are predicted to occur
under very stable conditions, when the
plume is near, or impinges on, the terrain.
The plume under such conditions may be
quite narrow in the vertical, so that even
relatively small changes in a receptor’s
location may substantially affect the
predicted concentration. Receptors within
about a kilometer of the source may be even
more sensitive to location. Thus, a dense
array of receptors may be required in some
cases.
b. For applications involving AERSCREEN,
AERSCREEN interfaces with AERMAP to
generate the receptor elevations. For
applications involving CTSCREEN, digitized
contour data must be preprocessed 42 to
provide hill shape parameters in suitable
input format. The user then supplies
receptors either through an interactive
program that is part of the model or directly,
by using a text editor; using both methods to
select receptors will generally be necessary to
assure that the maximum concentrations are
estimated by either model. In cases where a
terrain feature may ‘‘appear to the plume’’ as
smaller, multiple hills, it may be necessary
to model the terrain both as a single feature
and as multiple hills to determine design
concentrations.
c. Other screening techniques may be
acceptable for complex terrain cases where
established procedures 43 are used. The user
is encouraged to confer with the appropriate
reviewing authority (paragraph 3.0(b)) if any
unresolvable problems are encountered, e.g.,
applicability, meteorological data, receptor
siting, or terrain contour processing issues.
4.2.2 Refined Models
a. A brief description of each preferred
model for refined applications is found in
appendix A. Also listed in that appendix are
availability, the model input requirements,
the standard options that shall be selected
when running the program, and output
options.
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4.2.2.1
AERMOD
a. For a wide range of regulatory
applications in all types of terrain, and for
aerodynamic building downwash, the
recommended model is AERMOD.44 45 The
AERMOD regulatory modeling system
consists of the AERMOD dispersion model,
the AERMET meteorological processor, and
the AERMAP terrain processor. AERMOD is
a steady-state Gaussian plume model
applicable to directly emitted air pollutants
that employs best state-of-practice
parameterizations for characterizing the
meteorological influences and dispersion.
Differentiation of simple versus complex
terrain is unnecessary with AERMOD. In
complex terrain, AERMOD employs the wellknown dividing-streamline concept in a
simplified simulation of the effects of plumeterrain interactions.
b. The AERMOD modeling system has been
extensively evaluated across a wide range of
scenarios based on numerous field studies,
including tall stacks in flat and complex
terrain settings, sources subject to building
downwash influences, and low-level nonbuoyant sources.27 These evaluations
included several long-term field studies
associated with operating plants as well as
several intensive tracer studies. Based on
these evaluations, AERMOD has shown
consistently good performance, with ‘‘errors’’
in predicted vs. observed peak
concentrations, based on the Robust Highest
Concentration (RHC) metric, consistently
within the range of 10 to 40 percent cited in
paragraph 4.1(g).
c. AERMOD incorporates the Plume Rise
Model Enhancements (PRIME) algorithm to
account for enhanced plume growth and
restricted plume rise for plumes affected by
building wake effects.46 The PRIME
algorithm accounts for entrainment of plume
mass into the cavity recirculation region,
including re-entrainment of plume mass into
the wake region beyond the cavity.
d. AERMOD incorporates the Buoyant Line
and Point Source (BLP) Dispersion model to
account for buoyant plume rise from line
sources. The BLP option within AERMOD
utilizes the standard meteorological inputs
provided by the AERMET meteorological
processor.
e. The state-of-the-science for modeling
atmospheric deposition is evolving and new
modeling techniques are continually being
assessed and their results are being compared
with observations. Consequently, while
deposition treatment is available in
AERMOD, the approach taken for any
purpose shall be coordinated with the
appropriate reviewing authority (paragraph
3.0(b)).
4.2.2.2
CTDMPLUS
a. If the modeling application involves an
elevated point source with a well-defined hill
or ridge and a detailed dispersion analysis of
the spatial pattern of plume impacts is of
interest, CTDMPLUS is available.
CTDMPLUS provides greater resolution of
concentrations about the contour of the hill
feature than does AERMOD through a
different plume-terrain interaction algorithm.
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4.2.2.3 OCD
a. If the modeling application involves
determining the impact of offshore emissions
from point, area, or line sources on the air
quality of coastal regions, the recommended
model is the OCD (Offshore and Coastal
Dispersion) Model. OCD is a straight-line
Gaussian model that incorporates overwater
plume transport and dispersion as well as
changes that occur as the plume crosses the
shoreline. OCD is also applicable for
situations that involve platform building
downwash.
4.2.3 Pollutant Specific Modeling
Requirements
4.2.3.1 Models for Carbon Monoxide
a. Models for assessing the impact of CO
emissions are needed to meet NSR
requirements, including PSD, to address
compliance with the CO NAAQS and to
determine localized impacts from
transportations projects. Examples include
evaluating effects of point sources, congested
roadway intersections, and highways, as well
as the cumulative effect of numerous sources
of CO in an urban area.
b. The general modeling recommendations
and requirements for screening models in
section 4.2.1 and refined models in section
4.2.2 shall be applied for CO modeling. Given
the relatively low CO background
concentrations, screening techniques are
likely to be adequate in most cases. However,
since the screening model specified in
section 4.2.1 (AERSCREEN) can only handle
one source at a time, a section 4.2.2 model
may be used with screening meteorology
(e.g., generated with MAKEMET) to conduct
screening assessments of CO projects
involving more than one source (e.g.,
roadway hotspot assessments).47
4.2.3.2 Models for Lead
a. In January 1999 (40 CFR part 58,
appendix D), the EPA gave notice that
concern about ambient lead impacts was
being shifted away from roadways and
toward a focus on stationary point sources.
Thus, models for assessing the impact of lead
emissions are needed to meet NSR
requirements, including PSD, to address
compliance with the lead NAAQS and for
SIP attainment demonstrations. The EPA has
also issued guidance on siting ambient
monitors in the vicinity of stationary point
sources.48 For lead, the SIP should contain an
air quality analysis to determine the
maximum rolling 3-month average lead
concentration resulting from major lead point
sources, such as smelters, gasoline additive
plants, etc. The EPA has developed a postprocessor to calculate rolling 3-month
average concentrations from model output.49
General guidance for lead SIP development is
also available.50
b. For major lead point sources, such as
smelters, which contribute fugitive emissions
and for which deposition is important,
professional judgment should be used, and
there shall be coordination with the
appropriate reviewing authority (paragraph
3.0(b)). For most applications, the general
requirements for screening and refined
models of section 4.2.1 and 4.2.2 are
applicable to lead modeling.
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4.2.3.3 Models for Sulfur Dioxide
a. Models for SO2 are needed to meet NSR
requirements, including PSD, to address
compliance with the SO2 NAAQS and PSD
increments, for SIP attainment
demonstrations,51 and for characterizing
current air quality via modeling.52 SO2 is one
of a group of highly reactive gasses known as
‘‘oxides of sulfur’’ with largest emissions
sources being fossil fuel combustion at power
plants and other industrial facilities.
b. Given the relatively inert nature of SO2
on the short-term time scales of interest (i.e.,
1-hour) and the sources of SO2 (i.e.,
stationary point sources), the general
modeling requirements for screening models
in section 4.2.1 and refined models in section
4.2.2 are applicable for SO2 modeling
applications. For urban areas, AERMOD
automatically invokes a half-life of 4 hours 53
to SO2. Therefore, care must be taken when
determining whether a source is urban or
rural (see section 7.2.1.1 for urban/rural
determination methodology).
4.2.3.4 Models for Nitrogen Dioxide
a. Models for assessing the impact of
sources on ambient NO2 concentrations are
needed to meet NSR requirements, including
PSD, to address compliance with the NO2
NAAQS and PSD increments. Impact of an
individual source on ambient NO2 depends,
in part, on the chemical environment into
which the source’s plume is to be emitted.
This is due to the fact that NO2 sources coemit NO along with NO2 and any emitted NO
may react with ambient ozone to convert to
additional NO2 downwind. Thus,
comprehensive modeling of NO2 would need
to consider the ratio of emitted NO and NO2,
the ambient levels of ozone and subsequent
reactions between ozone and NO, and the
photolysis of NO2 to NO.
b. Due to the complexity of NO2 modeling,
a multi-tiered approach is required to obtain
hourly and annual average estimates of
NO2.54 Since these methods are considered
screening, their usage shall occur in
agreement with the appropriate reviewing
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authority (paragraph 3.0(b)). Additionally,
since screening techniques are conservative
by their nature, there are limitations to how
these options can be used. Specifically,
negative emissions should not be modeled
because decreases in concentrations would
be overestimated. Each tiered approach (see
Figure 4–1) accounts for increasing complex
considerations of NO2 chemistry and is
described in paragraphs b through d of this
subsection. The tiers of NO2 modeling
include:
i. A first-tier (most conservative) ‘‘full’’
conversion approach;
ii. A second-tier approach that assumes
ambient equilibrium between NO and NO2;
and
iii. A third-tier consisting of several
detailed screening techniques that account
for ambient ozone and the relative amount of
NO and NO2 emitted from a source.
c. For Tier 1, use an appropriate section
4.2.2 refined model to estimate nitrogen
oxides (NOX) concentrations and assume a
total conversion of NO to NO2. If the
resulting design concentrations exceed the
NAAQS or PSD increments for NO2, proceed
to Tier 2.
d. For Tier 2, multiply the Tier 1 result(s)
by the Ambient Ratio Method 2 (ARM2),
which provides estimates of representative
equilibrium ratios of NO2/NOX value based
ambient levels of NO2 and NOX derived from
national data from the EPA’s Air Quality
System (AQS).55 The national default for
ARM2 will include a minimum NO2/NOX
ratio of 0.5 and a maximum ratio of 0.9. The
reviewing agency may establish alternative
default minimum NO2/NOX values based on
the source’s in-stack emissions ratios, with
alternative minimum values reflecting the
source’s in-stack NO2/NOX ratios. Preferably,
alternative default NO2/NOX values should
be based on source-specific data which
satisfies all quality assurance procedures that
ensure data accuracy for both NO2 and NOX
within the typical range of measured values.
However, alternate information may be used
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to justify a source’s anticipated NO2/NOX instack ratios, such as manufacturer test data,
state or local agency guidance, peer-reviewed
literature, the EPA’s NO2/NOX ratio database.
e. For Tier 3, a detailed screening
technique shall be applied on a case-by-case
basis. Because of the additional input data
requirements and complexities associated
with the Tier 3 options, their usage shall
occur in consultation with the EPA Regional
Office in addition to the appropriate
reviewing authority. The Ozone Limiting
Method (OLM) 56 and the Plume Volume
Molar Ratio Method (PVMRM) 57 are two
detailed screening techniques that may be
used for most sources. These two techniques
use an appropriate section 4.2.2 model to
estimate NOX concentrations and then
estimate the conversion of primary NO
emissions to NO2 based on the ambient levels
of ozone and the plume characteristics. OLM
only accounts for NO2 formation based on the
ambient levels of ozone while PVMRM also
accommodates distance-dependent
conversion ratios based on ambient ozone.
Both PVMRM and OLM require that ambient
ozone concentrations be provided on an
hourly basis and explicit specification of the
speciation of the NO2/NOX in-stack ratios.
PVMRM works best for relatively isolated
and elevated point source modeling while
OLM works best for large groups of sources,
area sources, and near-surface releases,
including road-way sources.
f. Alternative models or techniques may be
considered on a case-by-case basis and their
usage shall be approved by the EPA Regional
Office (section 3.2). Such techniques should
consider individual quantities of NO and
NO2 emissions, atmospheric transport and
dispersion, and atmospheric transformation
of NO to NO2. Dispersion models that
account for more explicit photochemistry
may also be applied to estimate ambient
impacts of NOX sources.
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4.2.3.5 Models for PM2.5
a. The PM2.5 NAAQS, promulgated on July
18, 1997, includes particles with an
aerodynamic diameter nominally less than or
equal to 2.5 micrometers. PM2.5 is a mixture
consisting of several diverse components58.
Ambient PM2.5 generally consists of two
components, the primary component,
emitted directly from a source, and the
secondary component, which is formed in
the atmosphere from other pollutants emitted
from the source. Models for PM2.5 are needed
to meet NSR requirements, including PSD, to
address compliance with the PM2.5 NAAQS
and PSD increments and for SIP attainment
demonstrations.
b. For NSR, including PSD, modeling
assessments, the refined methods in section
4.2.2 are required for modeling the primary
component of PM2.5, while the methods in
section 5.4 are recommended for addressing
the secondary component of PM2.5. Guidance
for PSD assessments is available for
determining the best approach to handling
sources of primary and secondary PM2.5.59
c. For SIP attainment demonstrations and
regional haze reasonable progress goal
analyses, effects of a control strategy on PM2.5
are estimated from the sum of the effects on
the primary and secondary components
composing PM2.5. Model users should refer to
section 5.4.1 and associated SIP modeling
guidance 60 for further details concerning
appropriate modeling approaches.
d. The general modeling requirements for
the refined models discussed in section 4.2.2
should be applied for PM2.5 hot-spot
modeling for mobile sources. Specific
guidance is available for analyzing direct
PM2.5 impacts from highways, terminals, and
other projects.61
4.2.3.6 Models for PM10
a. The NAAQS for PM10 was promulgated
on July 1, 1987. The EPA promulgated
regulations for PSD increment measured as
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PM10 in a document published on June 3,
1993. Models for PM10 are needed to meet
NSR requirements, including PSD, to address
compliance with the PM10 NAAQS and PSD
increments and for SIP attainment
demonstrations.
b. For most sources, the general modeling
requirements for screening models in section
4.2.1 and refined models in section 4.2.2
shall be applied for PM10 modeling. In cases
where the particle size and its effect on
ambient concentrations need to be
considered, particle deposition may be used
in on a case-by-case basis and their usage
shall be approved by the EPA Regional Office
(section 3.2). A SIP development guide 62 is
also available to assist in PM10 analyses and
control strategy development.
c. Fugitive dust usually refers to dust put
into the atmosphere by the wind blowing
over plowed fields, dirt roads or desert or
sandy areas with little or no vegetation.
Fugitive emissions include the emissions
resulting from the industrial process that are
not captured and vented through a stack but
may be released from various locations
within the complex. In some unique cases, a
model developed specifically for the
situation may be needed. Due to the difficult
nature of characterizing and modeling
fugitive dust and fugitive emissions, the
proposed procedure shall be determined in
consultation with the appropriate reviewing
authority (paragraph 3.0(b)) for each specific
situation before the modeling exercise is
begun. Re-entrained dust is created by
vehicles driving over dirt roads (e.g., haul
roads) and dust-covered roads typically
found in arid areas. Such sources can be
characterized as line, area or volume
sources.61 63 Emission rates may be based on
site-specific data or values from the general
literature.
d. Under certain conditions, recommended
dispersion models may not be suitable to
appropriately address the nature of ambient
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PM10. In these circumstances, the alternative
modeling approach shall be approved by the
EPA Regional Office (section 3.2).
e. The general modeling requirements for
the refined models discussed in section 4.2.2
should be applied for PM10 hot-spot
modeling for mobile sources. Specific
guidance is available for analyzing direct
PM10 impacts from highways, terminals, and
other projects.61
5.0 Models for Ozone and Secondarily
Formed Particulate Matter
5.1 Discussion
a. Air pollutants formed through chemical
reactions in the atmosphere are referred to as
secondary pollutants. For example, groundlevel ozone and a portion of particulate
matter with aerodynamic diameter less than
2.5 m m (PM2.5 or fine PM) are secondary
pollutants formed through photochemical
reactions. Ozone and secondarily formed
particulate matter are closely related to each
other in that they share common sources of
emissions or are formed in the atmosphere
from chemical reactions with similar
precursors.
b. Ozone formation is driven by emissions
of NOX and volatile organic compounds
(VOCs). Ozone formation is a complicated
nonlinear process that requires favorable
meteorological conditions in addition to VOC
and NOX emissions. Sometimes complex
terrain features also contribute to the buildup of precursors and subsequent ozone
formation or destruction.
c. PM2.5 can be either primary (i.e., emitted
directly from sources) or secondary in nature.
The fraction of PM2.5 which is primary versus
secondary varies by location and season. In
the United States, PM2.5 is dominated by a
variety of chemical species or components of
atmospheric particles, such as ammonium
sulfate, ammonium nitrate, organic carbon
(OC) mass, elemental carbon (EC), and other
soil compounds and oxidized metals. PM2.5
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sulfate, nitrate, and ammonium ions are
predominantly the result of chemical
reactions of the oxidized products of sulfur
dioxide (SO2) and NOX emissions with direct
ammonia (NH3) emissions.64
d. Modeled strategies designed to reduce
ozone or PM2.5 levels typically need to
consider the chemical coupling between
these pollutants. Control measures reducing
ozone and PM2.5 precursor emissions may
not lead to proportional reductions in ozone
and PM2.5. This coupling is important in
understanding processes that control the
levels of both pollutants. Thus, when
feasible, it is important to use models that
take into account the chemical coupling
between ozone and PM2.5. In addition, using
such a multi-pollutant modeling system can
reduce the resource burden associated with
applying and evaluating separate models for
each pollutant and promotes consistency
among the strategies themselves.
e. PM2.5 is a mixture consisting of several
diverse chemical species or components of
atmospheric particles. Because chemical and
physical properties and origins of each
component differ, it may be appropriate to
use either a single model capable of
addressing several of the important
components or to model primary and
secondary components using different
models. Effects of a control strategy on PM2.5
is estimated from the sum of the effects on
the specific components composing PM2.5.
5.2 Recommendations
a. Chemical transformations can play an
important role in defining the concentrations
and properties of certain air pollutants.
Models that take into account chemical
reactions and physical processes of various
pollutants (including precursors) are needed
for determining the current state of air
quality, as well as predicting and projecting
the future evolution of these pollutants. It is
important that a modeling system provide a
realistic representation of chemical and
physical processes leading to secondary
pollutant formation and removal from the
atmosphere.
b. Chemical transport models treat
atmospheric chemical and physical processes
such as deposition and motion. There are two
types of chemical transport models, Eulerian
(grid based) and Lagrangian. These types of
models are differentiated from each other by
their frame of reference. Eulerian models are
based on a fixed frame of reference and
Lagrangian models use a frame of reference
that moves with parcels of air between the
source and receptor point.9 Photochemical
grid models are three-dimensional Eulerian
grid-based models that treat chemical and
physical processes in each grid cell and use
diffusion and transport processes to move
chemical species between grid cells. These
types of models are appropriate for
assessment of near-field and regional scale
reactive pollutant impacts from specific
sources7 10 11 12 or all sources.13 14 15 In some
limited cases, the secondary processes can be
treated with a box model, potentially in
combination with a number of other
modeling techniques and/or analyses to treat
individual source sectors.
c. Regardless of the modeling system used
to estimate secondary impacts of ozone and/
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or PM2.5, model results should be compared
to observation data to generate confidence
that the modeling system is representative of
the local and regional air quality. For ozone
related projects, model estimates of ozone
should be compared with observations in
both time and space. For PM2.5, model
estimates of speciated PM2.5 components
(such as sulfate ion, nitrate ion, etc.) should
be compared with observations in both time
and space.65
d. Model performance metrics comparing
observations and predictions are often used
to summarize model performance. These
metrics include mean bias, mean error,
fractional bias, fractional error, and
correlation coefficient.65 There are no
specific levels of any model performance
metric that indicate ‘‘acceptable’’ model
performance. The EPA’s preferred approach
for providing context about model
performance is to compare model
performance metrics with similar
contemporary applications.60 65 Because
model application purpose and scope vary,
model users should consult with the
appropriate reviewing authority (paragraph
3.0(b)) to determine what model performance
elements should be emphasized and
presented to provide confidence in the
regulatory model application.
e. There is no preferred modeling system
or technique for estimating ozone or
secondary PM2.5 for specific source impacts
or to assess impacts from multiple sources.
For assessing secondary pollutant impacts
from single sources, the degree of complexity
required to assess potential impacts varies
depending on the nature of the source, its
emissions, and the background environment.
The EPA recommends a two-tiered approach
where the first tier consists of using existing
technically credible and appropriate
relationships between emissions and impacts
developed from previous modeling that is
deemed sufficient for evaluating a source’s
impacts. The second tier consists of more
sophisticated case-specific modeling
analyses. The appropriate tier for a given
application should be selected in
consultation with the appropriate reviewing
authority (paragraph 3.0(b)) and be consistent
with EPA guidance.66
5.3 Recommended Models and Approaches
for Ozone
a. Models that estimate ozone
concentrations are needed to guide the
choice of strategies for the purposes of a
nonattainment area demonstrating future
year attainment of the ozone NAAQS.
Additionally, models that estimate ozone
concentrations are needed to assess impacts
from specific sources or source complexes to
satisfy requirements for NSR, including PSD,
and other regulatory programs. Other
purposes for ozone modeling include
estimating the impacts of specific events on
air quality, ozone deposition impacts, and
planning for areas that may be attaining the
ozone NAAQS.
5.3.1 Models for NAAQS Attainment
Demonstrations and Multi-Source Air
Quality Assessments
a. Simulation of ozone formation and
transport is a complex exercise. Control
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agencies with jurisdiction over areas with
ozone problems should use photochemical
grid models to evaluate the relationship
between precursor species and ozone. Use of
photochemical grid models is the
recommended means for identifying control
strategies needed to address high ozone
concentrations in such areas. Judgment on
the suitability of a model for a given
application should consider factors that
include use of the model in an attainment
test, development of emissions and
meteorological inputs to the model, and
choice of episodes to model. Guidance on the
use of models and other analyses for
demonstrating attainment of the air quality
goals for ozone is available.60 Users should
consult with the appropriate reviewing
authority (paragraph 3.0(b)) to ensure the
most current modeling guidance is applied.
5.3.2 Models for Single-Source Air Quality
Assessments
a. Depending on the magnitude of
emissions, estimating the impact of an
individual source’s emissions of NOX and
VOC on ozone concentrations is necessary for
obtaining a permit. The simulation of ozone
formation and transport requires realistic
treatment of atmospheric chemistry and
deposition. Models should be applied which
integrate chemical and physical processes
important in the formation, decay, and
transport of ozone and important precursor
species (e.g., Lagrangian and photochemical
grid models). Photochemical grid models are
primarily designed to characterize precursor
emissions and impacts from a wide variety of
sources over a large geographic area but can
also be used to assess the impacts from
specific sources.7 11 12
b. The first tier of assessment for ozone
impacts involves those situations where
existing technical information is available
(e.g., results from existing photochemical
grid modeling, published empirical estimates
of source specific impacts, or reduced-form
models) in combination with other
supportive information and analysis for the
purposes of estimating secondary impacts
from a particular source. The existing
technical information should provide a
credible and representative estimate of the
secondary impacts from the project source.
The appropriate reviewing authority
(paragraph 3.0(b)) and appropriate EPA
guidance 66 should be consulted to determine
what types of assessments may be
appropriate on a case-by-case basis.
c. The second tier of assessment for ozone
impacts involves those situations where
existing technical information is not
available such that chemical transport
models (e.g., photochemical grid models)
should be used to address single-source
impacts. Special considerations are needed
when using these models to evaluate the
ozone impact from an individual source.
Guidance on the use of models and other
analyses for demonstrating the impacts of
single sources for ozone is available.66 This
document provides a more detailed
discussion of the appropriate approaches to
obtaining estimates of ozone impacts from a
single source. Model users should use the
latest version of this guidance in consultation
with the appropriate reviewing authority
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(paragraph 3.0(b)) to determine the most
suitable single-source ozone modeling
approach on a case-by-case basis.
5.4 Recommended Models and Approaches
for Secondarily Formed PM 2.5
a. Models are needed to guide the choice
of strategies to address an observed PM2.5
problem in an area not attaining the PM2.5
NAAQS. Additionally, models are needed to
assess PM2.5 impacts from specific sources or
industrial source complexes to satisfy
requirements for NSR, including PSD, and
other regulatory programs. Other purposes
for PM2.5 modeling include estimating the
impacts of specific events on air quality,
visibility, deposition impacts, and planning
for areas that may be attaining the PM2.5
NAAQS.
5.4.1 Models for NAAQS Attainment
Demonstrations and Multi-Source Air
Quality Assessments
a. Models for PM2.5 are needed to assess the
adequacy of a proposed strategy for meeting
the annual and/or 24-hour PM2.5 NAAQS.
Modeling primary and secondary PM2.5 can
be a multi-faceted and complex problem,
especially for secondary components of PM2.5
such as sulfates and nitrates. Control
agencies with jurisdiction over areas with
secondary PM2.5 problems should use models
which integrate chemical and physical
processes important in the formation, decay,
and transport of these species (e.g.,
photochemical grid models). Suitability of a
modeling approach or mix of modeling
approaches for a given application requires
technical judgment as well as professional
experience in choice of models, use of the
model(s) in an attainment test, development
of emissions and meteorological inputs to the
model, and selection of days to model.
Guidance on the use of models and other
analyses for demonstrating attainment of the
air quality goals for PM2.5 is available.59 60
Users should consult with the appropriate
reviewing authority (paragraph 3.0(b)) to
ensure the most current modeling guidance
is applied.
5.4.2 Models for Single-Source Air Quality
Assessments
a. Depending on the magnitude of
emissions, estimating the impact of an
individual source’s emissions on secondary
particulate matter concentrations is necessary
for obtaining a permit. Primary PM2.5
components shall be simulated using
AERMOD (see section 4.2.2). The simulation
of secondary particulate matter formation
and transport is a complex exercise requiring
realistic treatment of atmospheric chemistry
and deposition. Models should be applied
which integrate chemical and physical
processes important in the formation, decay,
and transport of these species (e.g.,
Lagrangian and photochemical grid models).
Photochemical grid models are primarily
designed to characterize precursor emissions
and impacts from a wide variety of sources
over a large geographic area and can also be
used to assess the impacts from specific
sources.7 10
b. The first tier of assessment for secondary
PM2.5 impacts involves those situations
where existing technical information is
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available (e.g., results from existing
photochemical grid modeling, published
empirical estimates of source specific
impacts, or reduced-form models) in
combination with other supportive
information and analysis for the purposes of
estimating secondary impacts from a
particular source. The existing technical
information should provide a credible and
representative estimate of the secondary
impacts from the project source. The
appropriate reviewing authority (paragraph
3.0(b)) and appropriate EPA guidance 66
should be consulted to determine what types
of assessments may be appropriate on a caseby-case basis.
c. The second tier of assessment for
secondary PM2.5 impacts involves those
situations where existing technical
information is not available such that
chemical transport models (e.g.,
photochemical grid models) should be used
for assessments of single-source impacts.
Special considerations are needed when
using these models to evaluate the secondary
particulate matter impact from an individual
source. Guidance on the use of models and
other analyses for demonstrating the impacts
of single sources for secondary PM2.5 is
available.66 This document provides a more
detailed discussion of the appropriate
approaches to obtaining estimates of
secondary particulate matter concentrations
from a single source. Model users should use
the latest version of this guidance in
consultation with the appropriate reviewing
authority (paragraph 3.0(b)) to determine the
most suitable single-source modeling
approach for secondary PM2.5 on a case-bycase basis.
6.0 Modeling for Air Quality Related
Values and Other Governmental Programs
6.1 Discussion
a. Other federal agencies have also
developed specific modeling approaches for
their own regulatory or other requirements.
Although such regulatory requirements and
guidance have come about because of EPA
rules or standards, the implementation of
such regulations and the use of the modeling
techniques is under the jurisdiction of the
agency issuing the guidance or directive.
This section covers such situations with
reference to those guidance documents, when
they are available.
b. When using the model recommended or
discussed in the Guideline in support of
programmatic requirements not specifically
covered by EPA regulations, the model user
should consult the appropriate federal or
state agency to ensure the proper application
and use of the models and/or techniques.
Other federal agencies have developed
specific modeling approaches for their own
regulatory or other requirements. Most of the
programs have, or will have when fully
developed, separate guidance documents that
cover the program and a discussion of the
tools that are needed. The following
paragraphs reference those guidance
documents, when they are available. No
attempt has been made to provide a
comprehensive discussion of each topic since
the reference documents were designed to do
that.
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6.2 Air Quality Related Values
a. The 1997 CAA Amendments give FLMs
an ‘‘affirmative responsibility’’ to protect the
natural and cultural resources of Class I areas
from the adverse impacts of air pollution and
to provide the appropriate procedures and
analysis techniques. The Act identifies the
FLM as the Secretary of the department, or
their designee, with authority over these
lands. Mandatory Federal Class I areas are
defined in the CAA as international parks,
national parks over 6,000 acres and
wilderness areas and memorial parks over
5,000 acres, established as of 1977. The FLMs
are also concerned with the protection of
resources in federally managed Class II areas
because of other statutory mandates to
protect these areas.
b. The FLM agency responsibilities include
the review of air quality permit applications
from proposed new or modified major
pollution sources that may affect these Class
I areas to determine if emissions from a
proposed or modified source will cause or
contribute to adverse impacts on air quality
related values (AQRVs) of a Class I area and
making recommendations to the FLM.
AQRVs are resources identified by the FLM
agencies, which have the potential to be
affected by air pollution. These resources
may include visibility, scenic, cultural,
physical, or ecological resources for a
particular area. The FLM agencies take into
account the particular resources and AQRVs
that would be affected; the frequency and
magnitude of any potential impacts; and the
direct, indirect, and cumulative effects of any
potential impacts in making their
recommendations.
c. While the AQRV notification and impact
analysis requirements are outlined in the
PSD regulations at 40 CFR 51.166(p) and 40
CFR 52.21(p), determination of appropriate
analytical methods and metrics for AQRV’s
are determined by the FLM agencies and are
published in guidance external to the general
recommendations of this paragraph.
d. To develop greater consistency in the
application of air quality models to assess
potential AQRV impacts in both Class I areas
and protected Class II areas, the FLM
agencies have developed the Federal Land
Managers’ Air Quality Related Values Work
Group Phase I Report (FLAG) 67. FLAG
focuses upon specific technical and policy
issues associated with visibility impairment,
effects of pollutant deposition on soils and
surface waters, and ozone effects on
vegetation. Model users should consult the
latest version of the FLAG report for current
modeling guidance and with affected FLM
agency representatives for any application
specific guidance which is beyond the scope
of the Guideline.
6.2.1 Visibility
a. Visibility in important natural areas (e.g.,
Federal Class I areas) is protected under a
number of provisions of the CAA, including
sections 169A and 169B (addressing impacts
primarily from existing sources) and section
165 (new source review). Visibility
impairment is caused by light scattering and
light absorption associated with particles and
gases in the atmosphere. In most areas of the
country, light scattering by PM2.5 is the most
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significant component of visibility
impairment. The key components of PM2.5
contributing to visibility impairment include
sulfates, nitrates, organic carbon, elemental
carbon, and crustal material.67
b. Visibility regulations (40 CFR 51.300
through 51.309) require state, local, and tribal
agencies to mitigate current and prevent
future visibility impairment in any of the 156
mandatory Federal Class I areas where
visibility is considered an important
attribute. In 1999, the EPA issued revisions
to the regulations to address visibility
impairment in the form of regional haze,
which is caused by numerous, diverse
sources (e.g., stationary, mobile, and area
sources) located across a broad region (40
CFR 51.308 through 51.309). The state of
relevant scientific knowledge has expanded
significantly since the 1997 CAA
Amendments. A number of studies and
reports 68 69 have concluded that long-range
transport (e.g., up to hundreds of kilometers)
of fine particulate matter plays a significant
role in visibility impairment across the
country. CAA section 169A requires states to
develop SIPs containing long-term strategies
for remedying existing and preventing future
visibility impairment in the 156 mandatory
Class I Federal areas, where visibility is
considered an important attribute. In order to
develop long-term strategies to address
regional haze, many state, local, and tribal
agencies will need to conduct regional-scale
modeling of fine particulate concentrations
and associated visibility impairment.
c. The FLAG visibility modeling
recommendations are divided into two
distinct sections to address different
requirements for (1) near field modeling
where plumes or layers are compared against
a viewing background and (2) distant/multisource modeling for plumes and aggregations
of plumes that affect the general appearance
of a scene.67 The recommendations
separately address visibility assessments for
sources proposing to locate relatively near
and at farther distances from these areas.67
6.2.1.1 Models for Estimating Near-Field
Visibility Impairment
a. To calculate the potential impact of a
plume of specified emissions for specific
transport and dispersion conditions (‘‘plume
blight’’) for source-receptor distances less
than 50 km, a screening model and guidance
are available.67 70 If a more comprehensive
analysis is necessary, a refined model should
be selected. The model selection, procedures,
and analyses should be determined in
consultation with the appropriate reviewing
authority (paragraph 3.0(b)) and the affected
FLM(s).
6.2.1.2 Models for Estimating Visibility
Impairment for Long-Range Transport
a. Chemical transformations can play an
important role in defining the concentrations
and properties of certain air pollutants.
Models that take into account chemical
reactions and physical processes of various
pollutants (including precursors) are needed
for determining the current state of air
quality, as well as predicting and projecting
the future evolution of these pollutants. It is
important that a modeling system provide a
realistic representation of chemical and
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physical processes leading to secondary
pollutant formation and removal from the
atmosphere.
b. Chemical transport models treat
atmospheric chemical and physical processes
such as deposition and motion. There are two
types of chemical transport models, Eulerian
(grid based) and Lagrangian. These types of
models are differentiated from each other by
their frame of reference. Eulerian models are
based on a fixed frame of reference and
Lagrangian models use a frame of reference
that moves with parcels of air between the
source and receptor point.9 Photochemical
grid models are three-dimensional Eulerian
grid-based models that treat chemical and
physical processes in each grid cell and use
diffusion and transport processes to move
chemical species between grid cells.9 These
types of models are appropriate for
assessment of near-field and regional scale
reactive pollutant impacts from specific
sources 7 10 11 12 or all sources.13 14 15
c. Development of the requisite
meteorological and emissions databases
necessary for use of photochemical grid
models to estimate AQRVs should conform to
recommendations in section 8 and those
outlined in the EPA’s Modeling Guidance for
Demonstrating Attainment of Air Quality
Goals for Ozone, PM2.5, and Regional Haze.60
Demonstration of the adequacy of prognostic
meteorological fields can be established
through appropriate diagnostic and statistical
performance evaluations consistent with
recommendations provided in the
appropriate guidance.60 Model users should
consult the latest version of this guidance
and with the appropriate reviewing authority
(paragraph 3.0(b)) for any application specific
guidance which is beyond the scope of this
subsection.
6.2.2 Models for Estimating Deposition
Impacts
a. For many Class I areas, AQRVs have
been identified that are sensitive to
atmospheric deposition of air pollutants.
Emissions of NOX, sulfur oxides, NH3,
mercury, and secondary pollutants such as
ozone and particulate matter affect
components of ecosystems. In sensitive
ecosystems, these compounds can acidify
soils and surface waters, add nutrients that
change biodiversity, and affect the ecosystem
services provided by forests and natural
areas.67 To address the relationship between
deposition and ecosystem effects the FLM
agencies have developed estimates of critical
loads. A critical load is defined as ‘‘A
quantitative estimate of an exposure to one
or more pollutants below which significant
harmful effects on specified sensitive
elements of the environment do not occur
according to present knowledge.’’ 71
b. The FLM deposition modeling
recommendations are divided into two
distinct sections to address different
requirements for (1) near field modeling, and
(2) distant/multi-source modeling for
cumulative effects. The recommendations
separately address deposition assessments for
sources proposing to locate relatively near
and at farther distances from these areas.67
Where the source and receptors are not in
close proximity, chemical transport (e.g.,
photochemical grid) models generally should
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be applied for an assessment of deposition
impacts due to one or a small group of
sources. Over these distances chemical and
physical transformations can change
atmospheric residence time due to different
propensity for deposition to the surface of
different forms of nitrate and sulfate. Users
should consult the latest version of the FLAG
report 67 and relevant FLM representatives for
guidance on the use of models for deposition.
Where source and receptors are in close
proximity, users should contact the
appropriate FLM for application specific
guidance.
6.3 Modeling Guidance for Other
Governmental Programs
a. Dispersion and photochemical grid
modeling need to be conducted to ensure that
individual and cumulative offshore oil and
gas exploration, development, and
production plans and activities do not
significantly affect the air quality of any state
as required under the Outer Continental
Shelf Lands Act (OCSLA). Air quality
modeling requires various input datasets,
including emissions sources, meteorology,
and pre-existing pollutant concentrations.
For sources under the reviewing authority of
the Department of Interior, Bureau of Ocean
Energy Management (BOEM), guidance for
the development of all necessary Outer
Continental Shelf (OCS) air quality modeling
inputs and appropriate model selection and
application is available from the BOEMS’s
Web site: https://www.boem.gov/
Environmental-Stewardship/EnvironmentalStudies/Gulf-of-Mexico-Region/ApprovedAir-Quality-Models-for-the-GOMR.aspx.
b. The Federal Aviation Administration
(FAA) is the appropriate reviewing authority
for air quality assessments of primary
pollutant impacts at airports and air bases.
Air quality application for this purpose is
intended for estimating the collective impact
of changes in aircraft operations, point
source, and mobile source emissions at
airports on pollutant concentrations. The
latest version of the Aviation Environmental
Design Tool (AEDT), is developed and is
supported by the FAA, and is appropriate for
air quality assessment of primary pollutant
impacts at airports or air bases. AEDT has
adopted AERMOD for treating dispersion.
Application of AEDT is intended for
estimating the collective impact of changes in
aircraft operations, point source, and mobile
source emissions on pollutant
concentrations. It is not intended for PSD,
SIP, or other regulatory air quality analyses
of point or mobile sources at or peripheral to
airport property that are unrelated to airport
operations. The latest version of AEDT may
be obtained from FAA at its Web site:
https://aedt.faa.gov.
7.0
General Modeling Considerations
7.1 Discussion
a. This section contains recommendations
concerning a number of different issues not
explicitly covered in other sections of the
Guideline. The topics covered here are not
specific to any one program or modeling area
but are common to dispersion modeling
analyses for criteria pollutants.
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7.2 Recommendations
7.2.1 All Sources
7.2.1.1 Dispersion Coefficients
a. For any dispersion modeling exercise,
the urban or rural determination of a source
is critical in determining the boundary layer
characteristics that affect the model’s
prediction of downwind concentrations.
Historically, steady-state Gaussian plume
models used in most applications have
employed dispersion coefficients based on
Pasquill-Gifford 72 in rural areas and
McElroy- Pooler 73 in urban areas. These
coefficients are still incorporated in the BLP
and OCD models. However, the AERMOD
model incorporates a more up-to-date
characterization of the atmospheric boundary
layer using continuous functions of
parameterized horizontal and vertical
turbulence based on Monin-Obukhov
similarity (scaling) relationships.44 Another
key feature of AERMOD’s formulation is the
option to use directly observed variables of
the boundary layer to parameterize
dispersion.44 45
b. The selection of rural or urban
dispersion coefficients in a specific
application should follow one of the
procedures suggested by Irwin 74 to
determine whether the character of an area is
primarily urban or rural:
i. Land Use Procedure: (1) Classify the land
use within the total area, Ao, circumscribed
by a 3km radius circle about the source using
the meteorological land use typing scheme
proposed by Auer; 75 (2) if land use types I1,
I2, C1, R2, and R3 account for 50 percent or
more of Ao, use urban dispersion coefficients;
otherwise, use appropriate rural dispersion
coefficients.
ii. Population Density Procedure: (1)
¯
Compute the average population density, p
per square kilometer with Ao as defined
¯
above; (2) If p is greater than 750 people/km2,
use urban dispersion coefficients; otherwise
use appropriate rural dispersion coefficients.
(Of the two methods, the land use procedure
is considered more definitive.)
c. Population density should be used with
caution and generally not be applied to
highly industrialized areas where the
population density may be low and thus a
rural classification would be indicated.
However, the area is likely to be sufficiently
built-up so that the urban land use criteria
would be satisfied. Therefore, in this case,
the classification should be ‘‘urban’’ and
urban dispersion parameters should be used.
d. For applications of AERMOD in urban
areas, under either the Land Use Procedure
or the Population Density Procedure, the user
needs to estimate the population of the urban
area affecting the modeling domain because
the urban influence in AERMOD is scaled
based on a user-specified population. For
non-population oriented urban areas, or areas
influenced by both population and industrial
activity, the user will need to estimate an
equivalent population to adequately account
for the combined effects of industrialized
areas and populated areas within the
modeling domain. Selection of the
appropriate population for these applications
should be determined in consultation with
the appropriate reviewing authority
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(paragraph 3.0(b)) and the latest version of
the AERMOD Implementation Guide.76
e. It should be noted that AERMOD allows
for modeling rural and urban sources in a
single model run. For analyses of whole
urban complexes, the entire area should be
modeled as an urban region if most of the
sources are located in areas classified as
urban. For tall stacks located within or
adjacent to small or moderate sized urban
areas, the stack height or effective plume
height may extend above the urban boundary
layer and, therefore, may be more
appropriately modeled using rural
coefficients. Model users should consult with
the appropriate reviewing authority
(paragraph 3.0(b)) when evaluating this
situation and the latest version of the
AERMOD Implementation Guide.76
f. Buoyancy-induced dispersion (BID), as
identified by Pasquill,77 is included in the
preferred models and should be used where
buoyant sources, e.g., those involving fuel
combustion, are involved.
7.2.1.2 Complex Winds
a. Inhomogeneous local winds. In many
parts of the United States, the ground is
neither flat nor is the ground cover (or land
use) uniform. These geographical variations
can generate local winds and circulations,
and modify the prevailing ambient winds
and circulations. Geographic effects are most
apparent when the ambient winds are light
or calm.78 In general these geographically
induced wind circulation effects are named
after the source location of the winds, e.g.,
lake and sea breezes, and mountain and
valley winds. In very rugged hilly or
mountainous terrain, along coastlines, or
near large land use variations, the
characterization of the winds is a balance of
various forces, such that the assumptions of
steady-state straight-line transport both in
time and space are inappropriate. In such
cases, a model should be chosen to fully treat
the time and space variations of meteorology
effects on transport and dispersion. The
setup and application of such a model should
be determined in consultation with the
appropriate reviewing authority (paragraph
3.0(b)) consistent with limitations of
paragraph 3.2.2(e). The meteorological input
data requirements for developing the time
and space varying three-dimensional winds
and dispersion meteorology for these
situations are discussed in paragraph
8.4.1.2(c). Examples of inhomogeneous
winds include, but are not limited to,
situations described in the following
paragraphs:
i. Inversion breakup fumigation. Inversion
breakup fumigation occurs when a plume (or
multiple plumes) is emitted into a stable
layer of air and that layer is subsequently
mixed to the ground through convective
transfer of heat from the surface or because
of advection to less stable surroundings.
Fumigation may cause excessively high
concentrations but is usually rather shortlived at a given receptor. There are no
recommended refined techniques to model
this phenomenon. There are, however,
screening procedures 40 that may be used to
approximate the concentrations.
Considerable care should be exercised in
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using the results obtained from the screening
techniques.
ii. Shoreline fumigation. Fumigation can be
an important phenomenon on and near the
shoreline of bodies of water. This can affect
both individual plumes and area-wide
emissions. When fumigation conditions are
expected to occur from a source or sources
with tall stacks located on or just inland of
a shoreline, this should be addressed in the
air quality modeling analysis. EPA has
evaluated several coastal fumigation models,
and the evaluation results of these models are
available for their possible application on a
case-by-case basis when air quality estimates
under shoreline fumigation conditions are
needed.79 Selection of the appropriate model
for applications where shoreline fumigation
is of concern should be determined in
consultation with the appropriate reviewing
authority (paragraph 3.0(b)).
iii. Stagnation. Stagnation conditions are
characterized by calm or very low wind
speeds, and variable wind directions. These
stagnant meteorological conditions may
persist for several hours to several days.
During stagnation conditions, the dispersion
of air pollutants, especially those from lowlevel emissions sources, tends to be
minimized, potentially leading to relatively
high ground-level concentrations. If point
sources are of interest, users should note the
guidance provided in paragraph (a) of this
subsection. Selection of the appropriate
model for applications where stagnation is of
concern should be determined in
consultation with the appropriate reviewing
authority (paragraph 3.0(b)).
7.2.1.3 Gravitational Settling and
Deposition
a. Gravitational settling and deposition
may be directly included in a model if either
is a significant factor. When particulate
matter sources can be quantified and settling
and dry deposition are problems,
professional judgment should be used, and
there should be coordination with the
appropriate reviewing authority (paragraph
3.0(b)). AERMOD contains algorithms for dry
and wet deposition of gases and particles.80
For other Gaussian plume models, an
‘‘infinite half-life’’ may be used for estimates
of particle concentrations when only
exponential decay terms are used for treating
settling and deposition. Lagrangian models
have varying degrees of complexity for
dealing with settling and deposition and the
selection of a parameterization for such
should be included in the approval process
for selecting a Lagrangian model. Eulerian
grid models tend to have explicit
parameterizations for gravitational settling
and deposition as well as wet deposition
parameters already included as part of the
chemistry scheme.
7.2.2 Stationary Sources
7.2.2.1 Good Engineering Practice Stack
Height
a. The use of stack height credit in excess
of Good Engineering Practice (GEP) stack
height or credit resulting from any other
dispersion technique is prohibited in the
development of emissions limits by 40 CFR
51.118 and 40 CFR 51.164. The definition of
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GEP stack height and dispersion technique
are contained in 40 CFR 51.100. Methods and
procedures for making the appropriate stack
height calculations, determining stack height
credits and an example of applying those
techniques are found in several
references,81 82 83 84 which provide a great
deal of additional information for evaluating
and describing building cavity and wake
effects.
b. If stacks for new or existing major
sources are found to be less than the height
defined by the EPA’s refined formula for
determining GEP height, then air quality
impacts associated with cavity or wake
effects due to the nearby building structures
should be determined. The EPA refined
formula height is defined as H + 1.5L.83 Since
the definition of GEP stack height defines
excessive concentrations as a maximum
ground-level concentration due in whole or
in part to downwash of at least 40 percent
in excess of the maximum concentration
without downwash, the potential air quality
impacts associated with cavity and wake
effects should also be considered for stacks
that equal or exceed the EPA formula height
for GEP. The AERSCREEN model can be used
to obtain screening estimates of potential
downwash influences, based on the PRIME
downwash algorithm incorporated in the
AERMOD model. If more refined
concentration estimates are required, the
recommended steady-state plume dispersion
model in section 4.2.2, AERMOD, should be
used.
7.2.2.2 Plume Rise
a. The plume rise methods of Briggs 85 86
are incorporated in many of the preferred
models and are recommended for use in
many modeling applications. In
AERMOD,44 45 for the stable boundary layer,
plume rise is estimated using an iterative
approach, similar to that in the CTDMPLUS
model. In the convective boundary layer,
plume rise is superposed on the
displacements by random convective
velocities.87 In AERMOD, plume rise is
computed using the methods of Briggs except
cases involving building downwash, in
which a numerical solution of the mass,
energy, and momentum conservation laws is
performed.88 No explicit provisions in these
models are made for multistack plume rise
enhancement or the handling of such special
plumes as flares; these problems should be
considered on a case-by-case basis.
b. Gradual plume rise is generally
recommended where its use is appropriate:
(1) In AERMOD; (2) in complex terrain
screening procedures to determine close-in
impacts and (3) when calculating the effects
of building wakes. The building wake
algorithm in AERMOD incorporates and
exercises the thermodynamically based
gradual plume rise calculations as described
in paragraph (a) of this subsection. If the
building wake is calculated to affect the
plume for any hour, gradual plume rise is
also used in downwind dispersion
calculations to the distance of final plume
rise, after which final plume rise is used.
Plumes captured by the near wake are reemitted to the far wake as a ground-level
volume source.
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c. Stack tip downwash generally occurs
with poorly constructed stacks and when the
ratio of the stack exit velocity to wind speed
is small. An algorithm developed by Briggs 86
is the recommended technique for this
situation and is used in preferred models for
point sources.
7.2.3 Mobile Sources
a. Emissions of primary pollutants from
mobile sources can be modeled with an
appropriate model identified in section 4.2.
Screening of mobile sources can be
accomplished by using screening
meteorology, such as that generated by the
MAKEMET component of AERSCREEN,
which can generate a range of meteorological
scenarios using site-specific characteristics,
such as albedo, Bowen ratio, and surface
roughness. Maximum hourly concentrations
computed from screening runs can be
converted to longer averaging periods using
the scaling ratios specific in the AERSCREEN
User’s Guide.37
b. Mobile sources can be modeled in
AERMOD as either line (i.e., elongated area)
sources or as a series of volume sources.
However, since mobile source modeling
usually includes an analysis of very nearsource impacts (e.g., hot-spot modeling,
which can include receptors within 5–10
meters of the roadway), the results can be
highly sensitive to the characterization of the
mobile emissions. When modeling roadway
links, such as highway and arterial links, the
EPA recommends that line/area sources
instead of volume sources be used whenever
possible, as it is easier to characterize them
correctly. Important characteristics for both
line/area and volume sources include the
plume release height, source width, and
initial dispersion characteristics, which
should also take into account the impact of
traffic-induced turbulence, which can cause
roadway sources to have larger initial
dimensions than might normally be used for
representing line sources.
c. The EPA’s quantitative PM hot-spot
guidance 61 and Haul Road Workgroup Final
Report 63 provide guidance on the
appropriate characterization of mobile
sources as a function of the roadway and
vehicle characteristics. The EPA’s
quantitative PM hot-spot guidance includes
important considerations and should be
consulted when modeling roadway links.
Line or area sources are recommended for
mobile sources. However, if volume sources
are used, it is particularly important to insure
that roadway emissions are appropriately
spaced when using volume source so that the
emissions field is uniform across the
roadway. Additionally, receptor placement is
particularly important for volume sources,
which have ‘‘exclusion zones’’, where
concentrations are not calculated for
receptors located ‘‘within’’ the volume
sources, i.e., less than 2.15 times the initial
lateral dispersion coefficient from the center
of the volume.61 Placing receptors in these
‘‘exclusion zones’’ will result in
underestimates of roadway impacts.
8.0 Model Input Data
a. Databases and related procedures for
estimating input parameters are an integral
part of the modeling process. The most
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appropriate input data available should
always be selected for use in modeling
analyses. Modeled concentrations can vary
widely depending on the source data or
meteorological data used. This section
attempts to minimize the uncertainty
associated with database selection and use by
identifying requirements for input data used
in modeling. More specific data requirements
and the format required for the individual
models are described in detail in the users’
guide and/or associated documentation for
each model.
8.1 Modeling Domain
8.1.1 Discussion
a. The modeling domain is the geographic
area for which the required air quality
analyses for the NAAQS and PSD increments
are conducted.
8.1.2 Requirements
a. For a NAAQS or PSD increment
assessment, the modeling domain or project’s
impact area shall include all locations where
the emissions of a pollutant from the new or
modifying source(s) may cause a significant
ambient impact. This impact area is defined
as an area with a radius extending from the
new or modifying source to: (1) The most
distant point source where air quality
modeling predicts a significant ambient
impact will occur, or (2) the nominal 50 km
distance considered applicable for Gaussian
dispersion models, whichever is less. The
required air quality analysis shall be carried
out within this geographical area with
characterization of source impacts, nearby
source impacts, and background
concentrations, as recommended later in this
section.
b. For SIP attainment demonstrations for
ozone and PM2.5, or regional haze reasonable
progress goal analyses, the modeling domain
is determined by the nature of the problem
being modeled and the spatial scale of the
emissions which impact the nonattainment
or Class I area(s). The modeling domain shall
be designed so that all major upwind source
areas that influence the downwind
nonattainment area are included in addition
to all monitor locations that are currently or
recently violating the NAAQS or close to
violating the NAAQS in the nonattainment
area. Similarly, all Class I areas to be
evaluated in a regional haze modeling
application shall be included and sufficiently
distant from the edge of the modeling
domain. Guidance on the determination of
the appropriate modeling domain for
photochemical grid models in demonstrating
attainment of these air quality goals is
available.60 Users should consult the latest
version of this guidance for the most current
modeling guidance and with the appropriate
reviewing authority (paragraph 3.0(b)) for any
application specific guidance which is
beyond the scope of this section.
8.2 Source Data
8.2.1 Discussion
a. Sources of pollutants can be classified as
point, line, area, and volume sources. Point
sources are defined in terms of size and may
vary between regulatory programs. The line
sources most frequently considered are
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roadways and streets along which there are
well-defined movements of motor vehicles.
They may also be lines of roof vents or
stacks, such as in aluminum refineries. Area
and volume sources are often collections of
a multitude of minor sources with
individually small emissions that are
impractical to consider as separate point or
line sources. Large area sources are typically
treated as a grid network of square areas,
with pollutant emissions distributed
uniformly within each grid square. Generally,
input data requirements for air quality
models necessitate the use of metric units. As
necessary, any English units common to
engineering applications should be
appropriately converted to metric.
b. For point sources, there are many source
characteristics and operating conditions that
may be needed to appropriately model the
facility. For example, the plant layout (e.g.,
location of stacks and buildings), stack
parameters (e.g., height and diameter), boiler
size and type, potential operating conditions,
and pollution control equipment parameters.
Such details are required inputs to air quality
models and are needed to determine
maximum potential impacts.
c. Modeling mobile emissions from streets
and highways requires data on the road
layout, including the width of each traveled
lane, the number of lanes, and the width of
the median strip. Additionally, traffic
patterns should be taken into account (e.g.,
daily cycles of rush hour, differences in
weekday and weekend traffic volumes, and
changes in the distribution of heavy-duty
trucks and light-duty passenger vehicles), as
these patterns will affect the types and
amounts of pollutant emissions allocated to
each lane, and the height of emissions.
d. Emission factors can be determined
through source specific testing and
measurements (e.g., stack test data) from
existing sources or provided from a
manufacturing association or vendor.
Additionally, emissions factors for a variety
of source types are compiled in an EPA
publication commonly known as AP–42.89
AP–42 also provides an indication of the
quality and amount of data on which many
of the factors are based. Other information
concerning emissions is available in EPA
publications relating to specific source
categories. The appropriate reviewing
authority (paragraph 3.0(b)) should be
consulted to determine appropriate source
definitions and for guidance concerning the
determination of emissions from and
techniques for modeling the various source
types.
8.2.2 Requirements
a. For SIP attainment demonstrations for
the purpose of projecting future year NAAQS
attainment for ozone, PM2.5, and regional
haze reasonable progress goal analyses,
emissions which reflect actual emissions
during the base modeling year time period
should be input to models for base year
modeling. Emissions projections to future
years should account for key variables such
as growth due to increased or decreased
activity, expected emissions controls due to
regulations, settlement agreements or consent
decrees, fuel switches, and any other relevant
information. Guidance on emissions
estimation techniques (including future year
projections) for SIP attainment
demonstrations is available.60 90
b. For the purpose of SIP revisions for
stationary point sources, the regulatory
modeling of inert pollutants shall use the
emissions input data shown in Table 8–1 for
short-term and long-term NAAQS. To
demonstrate compliance and/or establish the
appropriate SIP emissions limits, Table 8–1
generally provides for the use of ‘‘allowable’’
emissions in the regulatory dispersion
modeling of the stationary point source(s) of
interest. In such modeling, these source(s)
should be modeled sequentially with these
loads for every hour of the year. As part of
a cumulative impact analysis, Table 8–1
allows for the model user to account for
actual operations in developing the
emissions inputs for dispersion modeling of
nearby sources, while other sources are best
represented by air quality monitoring data.
Consultation with the appropriate reviewing
authority (paragraph 3.0(b)) is advisable on
the establishment of the appropriate
emissions inputs for regulatory modeling
applications with respect to SIP revisions for
stationary point sources.
c. For the purposes of demonstrating
NAAQS compliance in a PSD assessment, the
regulatory modeling of inert pollutants shall
use the emissions input data shown in Table
8–2 for short and long-term NAAQS. The
new or modifying stationary point source
shall be modeled with ‘‘allowable’’ emission
in the regulatory dispersion modeling. As
part of a cumulative impact analysis, Table
8–2 allows for the model user to account for
actual operations in developing the
emissions inputs for dispersion modeling of
nearby sources, while other sources are best
represented by air quality monitoring data.
For purposes of situations involving
emissions trading refer to current EPA policy
and guidance to establish input data.
Consultation with the appropriate reviewing
authority (paragraph 3.0(b)) is advisable on
the establishment of the appropriate
emissions inputs for regulatory modeling
applications with respect to PSD assessments
for a proposed new or modifying source.
d. For stationary source applications,
changes in operating conditions that affect
the physical emission parameters (e.g.,
release height, initial plume volume, and exit
velocity) shall be considered to ensure that
maximum potential impacts are
appropriately determined in the assessment.
For example, the load or operating condition
for point sources that causes maximum
ground-level concentrations shall be
established. As a minimum, the source
should be modeled using the design capacity
(100 percent load). If a source operates at
greater than design capacity for periods that
could result in violations of the NAAQS or
PSD increment, this load should be modeled.
Where the source operates at substantially
less than design capacity, and the changes in
the stack parameters associated with the
operating conditions could lead to higher
ground level concentrations, loads such as 50
percent and 75 percent of capacity should
also be modeled. Malfunctions which may
result in excess emissions are not considered
to be a normal operating condition. They
generally should not be considered in
determining allowable emissions. However,
if the excess emissions are the result of poor
maintenance, careless operation, or other
preventable conditions, it may be necessary
to consider them in determining source
impact. A range of operating conditions
should be considered in screening analyses;
the load causing the highest concentration, in
addition to the design load, should be
included in refined modeling.
e. Emissions from mobile sources also have
physical and temporal characteristics that
should be appropriately accounted for. For
example, an appropriate emissions model
shall be used to determine emissions profiles.
Such emissions should include speciation
specific for the vehicle types used on the
roadway (e.g., light duty and heavy duty
trucks) and subsequent parameterizations of
the physical emissions characteristics (e.g.,
release height) should reflect those emissions
sources. For long-term standards, annual
average emissions may be appropriate, but
for short-term standards, discrete temporal
representation of emissions should be used
(e.g., variations in weekday and weekend
traffic or the diurnal rush-hour profile typical
of many cities). Detailed information and
data requirements for modeling mobile
sources of pollution are provided in the
user’s manuals for each of the models
applicable to mobile sources.61 63
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TABLE 8–1—POINT SOURCE MODEL EMISSION INPUT FOR SIP REVISIONS OF INERT POLLUTANTS 1
Emissions limit
(lb/MMBtu) 2
Averaging time
Operating level
(lb/MMBtu) 2
×
×
Operating factor
(e.g., hr/yr. hr/day)
Stationary Point Source(s) Subject to SIP Emissions Limit(s) Evaluation for Compliance With Ambient Standards
(Including Areawide Demonstrations)
Annual & quarterly .....................
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Maximum allowable emission
limit or federal enforceable
permit limit.
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Actual or design capacity
(whichever is greater), or federally permit enforceable permit condition.
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Actual operating factor averaged over the most recent 2
years.3
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TABLE 8–1—POINT SOURCE MODEL EMISSION INPUT FOR SIP REVISIONS OF INERT POLLUTANTS 1—Continued
Averaging time
Emissions limit
(lb/MMBtu) 2
Short term (≤24 hours) ..............
Maximum allowable emission
limit or federally enforceable
permit limit.
Operating level
(lb/MMBtu) 2
×
×
Operating factor
(e.g., hr/yr. hr/day)
Continuous operation, i.e., all
hours of each time period
under consideration (for all
hours of the meteorological
database).5
Actual or design capacity
(whichever is greater), or federally enforceable permit condition.4
Nearby Source(s).6
Annual & quarterly .....................
Short term (≤24 hours) ..............
Maximum allowable emission
limit or federal enforceable
permit limit.5
Maximum allowable emission
limit or federal enforceable
permit limit.6
Annual level when actually operating, averaged over the
most recent 2 years.3
Temporally representative level
when actually operating, reflective of the most recent 2
years.3 7
Actual operating factor averaged over the most recent 2
years.3 8
Continuous operation, i.e., all
hours of each time period
under consideration (for all
hours of the meteorological
database).5
Other Source(s) 8 9
The ambient impacts from Non-nearby or Other Sources (e.g., natural sources, minor sources and, distant major source and unidentified
sources) can be represented by air quality monitoring data unless adequate data do not exist.
1 For purposes of emissions trading, NSR, or PSD, other model input criteria may apply. See Section 8.2 for more information regarding attainment demonstrations of primary PM2.5.
2 Terminology applicable to fuel burning sources; analogous terminology (e.g., lb/throughput) may be used for other types of sources.
3 Unless it is determined that this period is not representative.
4 Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load causing the highest concentration.
5 If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24–hours) and the source operation is constrained by a
federally enforceable permit condition, an appropriate adjustment to the modeled emission rate may be made (e.g., if operation is only 8 a.m. to
4 p.m. each day, only these hours will be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating
6 See Section 8.3.3.
7 Temporally representative operating level could be based on Continuous Emissions Monitoring (CEM) data or other information and should
be determined through consultation with the appropriate reviewing authority (Paragraph 3.0(b)).
8 For those permitted sources not in operation or that have not established an appropriate factor, continuous operation (i.e., 8760) should be
used.
9 See Section 8.3.2.
TABLE 8–2—POINT SOURCE MODEL EMISSION INPUT FOR NAAQS COMPLIANCE IN PSD DEMONSTRATIONS
Emissions limit
(lb/MMBtu) 1
Averaging time
Operating level
(lb/MMBtu) 2
×
×
Operating factor
(e.g., hr/yr. hr/day)
Proposed Major New or Modified Source
Annual & quarterly .....................
Short term (≤24 hours) ..............
Maximum allowable emission
limit or federal enforceable
permit limit.
Maximum allowable emission
limit or federal enforceable
permit limit.
Design capacity or federally enforceable permit condition.
Continuous operation (i.e., 8760
hours).2
Design capacity or federally enforceable permit condition.3
Continuous operation, i.e., all
hours of each time period
under consideration (for all
hours of the meteorological
database).2
Nearby Source(s) 4 5
Annual & quarterly .....................
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Short term (≤24 hours) ..............
Maximum allowable emission
limit or federal enforceable
permit limit.5
Maximum allowable emission
limit or federal enforceable
permit limit.5
Annual level when actually
erating, averaged over
most recent 2 years.6
Annual level when actually
erating, averaged over
most recent 2 years.6 7
opthe
opthe
Actual operating factor averaged over the most recent 2
years.6 8
Continuous operation, i.e., all
hours of each time period
under consideration (for all
hours of the meteorological
database).2
Other Source(s) 5 9
The ambient impacts from Non-nearby or Other Sources (e.g., natural sources, minor sources and,distant major sources, and unidentified
sources) can be represented by air quality monitoring data unless adequate data do not exist.
1 Terminology
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2 If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24–hours) and the source operation is constrained by a
federally enforceable permit condition, an appropriate adjustment to the modeled emission rate may be made (e.g., if operation is only 8 a.m. to
4 p.m. each day, only these hours will be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating
3 Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load causing the highest concentration.
4 Includes existing facility to which modification is proposed if the emissions from the existing facility will not be affected by the modification.
Otherwise use the same parameters as for major modification.
5 See Section 8.3.3.
6 Unless it is determined that this period is not representative.
7 Temporally representative operating level could be based on Continuous Emissions Monitoring (CEM) data or other information and should
be determined through consultation with the appropriate reviewing authority (Paragraph 3.0(b)).
8 For those permitted sources not in operation or that have not established an appropriate factor, continuous operation (i.e., 8760) should be
used.
9 See Section 8.3.2.
8.3 Background Concentrations
8.3.1 Discussion
a. Background concentrations are essential
in constructing the design concentration, or
total air quality concentration, as part of a
cumulative impact analysis for NAAQS and
PSD increments (section 9.2.4). Background
air quality should not include the ambient
impacts of the project source under
consideration. Instead, it should include:
i. Nearby sources: These are individual
sources in the vicinity of the source(s) under
consideration for emissions limits that are
not adequately represented by ambient
monitoring data. Typically, sources that
cause a significant concentration gradient in
the vicinity of the source(s) under
consideration for emissions limits are not
adequately represented by background
ambient monitoring. The ambient
contributions from these nearby sources are
thereby accounted for by explicitly modeling
their emissions (section 8.2).
ii. Other sources: That portion of the
background attributable to natural sources,
other unidentified sources in the vicinity of
the project, and regional transport
contributions from more distant sources
(domestic and international). The ambient
contributions from these sources are typically
accounted for through use of ambient
monitoring data or, in some cases, regionalscale photochemical grid modeling results.
b. The monitoring network used for
developing background concentrations is
expected to conform to the same quality
assurance and other requirements as those
networks established for PSD purposes.91
Accordingly, the air quality monitoring data
should be of sufficient completeness and
follow appropriate data validation
procedures. These data should be adequately
representative of the area to inform
calculation of the design concentration for
comparison to the applicable NAAQS
(section 9.2.2)
c. For photochemical grid modeling
conducted in SIP attainment demonstrations
for ozone, PM2.5 and regional haze, the
emissions from nearby and other sources are
included as model inputs and fully
accounted for in the modeling application
and predicted concentrations. The concept of
adding individual components to develop a
design concentration, therefore, do not apply
in these SIP applications. However, such
modeling results may then be appropriate for
consideration in characterizing background
concentrations for other regulatory
applications. Also, as noted in section 5, this
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modeling approach does provide for an
appropriate atmospheric environment to
assess single-sources impacts for ozone and
secondary PM2.5.
d. For PSD assessments in general and SIP
attainment demonstrations for inert
pollutants, the development of the
appropriate background concentration for a
cumulative impact analysis involves proper
accounting of each contribution to the design
concentration and will depend upon whether
the project area’s situation consists of either
an isolated single source(s) or a multitude of
sources.
8.3.2 Recommendations for Isolated Single
Source
a. In areas with an isolated source(s),
determining the appropriate background
concentration should focus on
characterization of contributions from all
other sources through adequately
representative ambient monitoring data.
b. The EPA recommends use of the most
recent quality assured air quality monitoring
data collected in the vicinity of the source to
determine the background concentration for
the averaging times of concern. In most cases,
the EPA recommends using data from the
monitor closest to and upwind of the project
area. If several monitors are available,
preference should be given to the monitor
with the most similar characteristics as the
project area. If there are no monitors located
in the vicinity of the new or modify source,
a ‘‘regional site’’ may be used to determine
background concentrations. A regional site is
one that is located away from the area of
interest but is impacted by similar or
adequately representative sources.
c. Many of the challenges related to
cumulative impact analyses arise in the
context of defining the appropriate metric to
characterize background concentrations from
ambient monitoring data and determining the
appropriate method for combining this
monitor-based background contribution to
the modeled impact of the project and other
nearby sources. For many cases, the best
starting point would be use of the current
design value for the applicable NAAQS as a
uniform monitored background contribution
across the project area. However, there are
cases in which the current design value may
not be appropriate. Such cases include but
are not limited to:
i. For situations involving a modifying
source where the existing facility is
determined to impact the ambient monitor,
the background concentration at each
monitor can be determined by excluding
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values when the source in question is
impacting the monitor. In such cases,
monitoring sites inside a 90° sector
downwind of the source may be used to
determine the area of impact.
ii. There may be other circumstances
which would necessitate modifications to the
ambient data record. Such cases could
include removal of data from specific days or
hours when a monitor is being impacted
activities that are not typical or expected to
occur again in the future (e.g., construction,
roadway repairs, forest fires, or unusual
agricultural activities). There may also be
cases where scaling (multiplying the
monitored concentrations with a scaling
factor) or adjusting (adding or subtracting a
constant value the monitored concentrations)
of data from specific days or hours. Such
adjustments would make the monitored
background concentrations more temporally
and/or spatially representative of area around
the new or modifying source for the purposes
of the regulator assessment.
iii. For short-term standards, the diurnal or
seasonal patterns of the air quality
monitoring data may differ significantly from
the patterns associated with the modeled
concentrations. When this occurs, it may be
appropriate to pair the air quality monitoring
data in a temporal manner that reflects these
patterns (e.g., pairing by season and/or hour
of day).92
iv. For situations where monitored air
quality concentrations vary across the
modeling domain, it may be appropriate to
consider air quality monitoring data from
multiple monitors within the project area.
d. Determination of the appropriate
background concentrations should be
consistent with appropriate EPA modeling
guidance 59 92 and justified in the modeling
protocol that is vetted with the appropriate
reviewing authority (paragraph 3.0(b)).
e. Considering the spatial and temporal
variability throughout a typical modeling
domain on an hourly basis and the
complexities and limitations of hourly
observations from the ambient monitoring
network, the EPA does not recommend
hourly or daily pairing of monitored
background and modeled concentrations
except in rare cases of relatively isolated
sources where the available monitor can be
shown to be representative of the ambient
concentration levels in the areas of maximum
impact from the proposed new source. The
implicit assumption underlying hourly
pairing is that the background monitored
levels for each hour are spatially uniform and
that the monitored values are fully
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representative of background levels at each
receptor for each hour. Such an assumption
clearly ignores the many factors that
contribute to the temporal and spatial
variability of ambient concentrations across a
typical modeling domain on an hourly basis.
In most cases, the seasonal (or quarterly)
pairing of monitored and modeled
concentrations should sufficiently address
situations to which the impacts from
modeled emissions are not temporally
correlated with background monitored levels.
f. In those cases where adequately
representative monitoring data to
characterize background concentrations are
not available, it may be appropriate to use
results from a regional-scale photochemical
grid model or other representative model
application as background concentrations
consistent with the considerations discussed
above and in consultation with the
appropriate reviewing authority (paragraph
3.0(b)).
8.3.3 Recommendations for Multi-Source
Areas
a. In multi-source areas, determining the
appropriate background concentration
involves: (1) identification and
characterization of contributions from nearby
sources through explicit modeling, and (2)
characterization of contributions from other
sources through adequately representative
ambient monitoring data. A key point here is
the interconnectedness of each component in
that the question of which nearby sources to
include in the cumulative modeling is
inextricably linked to the question of what
the ambient monitoring data represents
within the project area.
b. Nearby sources: All sources in the
vicinity of the source(s) under consideration
for emissions limits that are not adequately
represented by ambient monitoring data
should be explicitly modeled. Since an
ambient monitor is limited to characterizing
air quality at a fixed location, sources that
causes a significant concentration gradient in
the vicinity of the source(s) under
consideration for emissions limits are not
likely to be adequately characterized by the
monitored data due to the high degree of
variability of the source’s impact.
i. The pattern of concentration gradients
can vary significantly based on the averaging
period being assessed. In general,
concentration gradients will be smaller and
more spatially uniform for annual averages
than for short-term averages, especially for
hourly averages. The spatial distribution of
annual impacts around a source will often
have a single peak downwind of the source
based on the prevailing wind direction,
except in cases where terrain or other
geographic effects are important. By contrast,
the spatial distribution of peak short-term
impacts will typically show several localized
concentration peaks with more significant
gradient.
ii. Concentration gradients associated with
a particular source will generally be largest
between that source’s location and the
distance to the maximum ground-level
concentrations from that source. Beyond the
maximum impact distance, concentration
gradients will generally be much smaller and
more spatially uniform. Thus, the magnitude
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of a concentration gradient will be greatest in
the proximity of the source and will
generally not be significant at distances
greater than 10 times the height of the
stack(s) at that source without consideration
of terrain influences.
iii. The number of nearby sources to be
explicitly modeled in the air quality analysis
is expected to be few except in unusual
situations. In most cases, the few nearby
sources will be located within 10 to 20 km
from the source(s) under consideration.
Owing to both the uniqueness of each
modeling situation and the large number of
variables involved in identifying nearby
sources, no attempt is made here to
comprehensively define a ‘‘significant
concentration gradient.’’ Rather,
identification of nearby sources calls for the
exercise of professional judgement by the
appropriate reviewing authority (paragraph
3.0(b)). This guidance is not intended to alter
the exercise of that judgement or to
comprehensively prescribe which sources
should be included as nearby sources.
c. For cumulative impact analyses of shortterm and annual ambient standards, the
nearby sources as well as the project
source(s) must be evaluated using an
appropriate appendix A model or approved
alternative model with the emission input
data shown in Table 8–1 or 8–2.
i. When modeling a nearby source that
does not have a permit and the emissions
limits contained in the SIP for a particular
source category is greater than the emissions
possible given the source’s maximum
physical capacity to emit, the ‘‘maximum
allowable emissions limit’’ for such a nearby
source may be calculated as the emissions
rate representative of the nearby source’s
maximum physical capacity to emit,
considering its design specifications and
allowable fuels and process materials.
However, the burden is on the permit
applicant to sufficiently document what the
maximum physical capacity to emit is for
such a nearby source.
ii. It is appropriate to model nearby sources
only during those times when they, by their
nature, operate at the same time as the
primary source(s). Accordingly, it is not
necessary to model impacts of a nearby
source that does not, by its nature, operate at
the same time as the primary source,
regardless of an identified significant
concentration gradient from the nearby
source. The burden is on the permit
applicant to adequately justify the exclusion
of nearby sources to the satisfaction of the
appropriate reviewing authority (paragraph
3.0(b)). The following examples illustrate two
cases in which a nearby source may be
shown not to operate at the same time as the
primary source(s) being modeled: (1)
Seasonal sources (only used during certain
seasons of the year). Such sources would not
be modeled as nearby sources during times
in which they do not operate; and (2)
Emergency backup generators, to the extent
that they do not operate simultaneously with
the sources that they back up. Such
emergency equipment would not be modeled
as nearby sources.
d. Other sources. That portion of the
background attributable to all other sources
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(e.g., natural sources, minor and distance
major sources) should be accounted for
through use of ambient monitoring data and
determined by the procedures found in
section 8.3.2 in keeping with eliminating or
reducing the source-oriented impacts from
nearby sources to avoid potential doublecounting of modeled and monitored
contributions.
8.4 Meteorological Input Data
8.4.1 Discussion
a. This subsection covers meteorological
input data for use in dispersion modeling for
regulatory applications and is separate from
recommendations made for photochemical
grid modeling. Recommendations for
meteorological data for photochemical grid
modeling applications are outlined in the
latest version of EPA’s Guidance on the Use
of Models and Other Analyses for
Demonstrating Attainment of Air Quality
Goals for Ozone, PM2.5, and Regional Haze 93.
In cases where Lagrangian models are
applied for regulatory purposes, appropriate
meteorological inputs should be determined
in consultation with the appropriate
reviewing authority (paragraph 3.0(b)).
b. The meteorological data used as input to
a dispersion model should be selected on the
basis of spatial and climatological (temporal)
representativeness as well as the ability of
the individual parameters selected to
characterize the transport and dispersion
conditions in the area of concern. The
representativeness of the measured data is
dependent on numerous factors including
but not limited to: (1) The proximity of the
meteorological monitoring site to the area
under consideration; (2) The complexity of
the terrain; (3) The exposure of the
meteorological monitoring site; and (4) The
period of time during which data are
collected. The spatial representativeness of
the data can be adversely affected by large
distances between the source and receptors
of interest and the complex topographic
characteristics of the area. Temporal
representativeness is a function of the yearto-year variations in weather conditions.
Where appropriate, data representativeness
should be viewed in terms of the
appropriateness of the data for constructing
realistic boundary layer profiles and, where
applicable, three-dimensional meteorological
fields, as described in paragraphs (c) and (d)
of this subsection.
c. The meteorological data should be
adequately representative and may be sitespecific data, data from a nearby National
Weather Service (NWS) or comparable
station, or prognostic meteorological data.
The implementation of ASOS (automated
surface observing stations) in recent years
should not preclude the use of NWS–ASOS
data if such a station is determined to be
representative of the modeled area.94
d. Model input data are normally obtained
either from the NWS or as part of a sitespecific measurement program. State
climatology offices, local universities, FAA,
military stations, industry and pollution
control agencies may also be sources of such
data. In specific cases, prognostic
meteorological data may be appropriate for
use and obtained from similar sources. Some
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recommendations and requirements for the
use of each type of data are included in this
subsection.
8.4.2 Recommendations and Requirements
a. AERMET 95 shall be used to preprocess
all meteorological data, be it observed or
prognostic, for use with AERMOD in
regulatory applications. The AERMINUTE 96
processor, in most cases, should be used to
process 1-minute ASOS wind data for input
into AERMET when processing NWS ASOS
sites in AERMET. When processing
prognostic meteorological data for AERMOD,
the Mesoscale Model Interface Program
(MMIF) 93 should be used to process data for
input into AERMET. Other methods of
processing prognostic meteorological data for
input into AERMET should be approved by
the appropriate reviewing authority.
Additionally, the following meteorological
preprocessors are recommended by the EPA:
PCRAMMET 97, MPRM 98, and METPRO 99.
PCRAMMET is the recommended
meteorological data preprocessor for use in
applications of OCD employing hourly NWS
data. MPRM is the recommended
meteorological data preprocessor for
applications of OCD employing site-specific
meteorological data. METPRO is the
recommended meteorological data
preprocessor for use with CTDMPLUS.100
b. Regulatory application of AERMOD
necessitates careful consideration of the
meteorological data for input to AERMET.
Data representativeness, in the case of
AERMOD, means utilizing data of an
appropriate type for constructing realistic
boundary layer profiles. Of particular
importance is the requirement that all
meteorological data used as input to
AERMOD should be adequately
representative of the transport and dispersion
within the analysis domain. Where surface
conditions vary significantly over the
analysis domain, the emphasis in assessing
representativeness should be given to
adequate characterization of transport and
dispersion between the source(s) of concern
and areas where maximum design
concentrations are anticipated to occur. The
EPA recommends that the surface
characteristics input to AERMET should be
representative of the land cover in the
vicinity of the meteorological data, i.e., the
location of the meteorological tower for
measured data or the representative grid cell
for prognostic data. Therefore, the model user
should apply the latest version
AERSURFACE 101 102, where applicable, for
determining surface characteristics when
processing measured meteorological data
through AERMET. In areas where it is not
possible to use AERSURFACE output, surface
characteristics can determined using
techniques that apply the same analysis as
AERSURFACE. In the case of prognostic
meteorological data, the surface
characteristics associated with the prognostic
meteorological model output for the
representative grid cell should be used.103 104
Furthermore, since the spatial scope of each
variable could be different,
representativeness should be judged for each
variable separately. For example, for a
variable such as wind direction, the data
should ideally be collected near plume
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height to be adequately representative,
especially for sources located in complex
terrain. Whereas, for a variable such as
temperature, data from a station several
kilometers away from the source may be
considered to be adequately representative.
More information about meteorological data,
representativeness, and surface
characteristics can be found in the AERMOD
Implementation Guide 76.
c. Regulatory application of CTDMPLUS
requires the input of multi-level
measurements of wind speed, direction,
temperature, and turbulence from an
appropriately sited meteorological tower. The
measurements should be obtained up to the
representative plume height(s) of interest.
Plume heights of interest can be determined
by use of screening procedures such as
CTSCREEN.
d. Regulatory application of OCD requires
meteorological data over land and over water.
The over land or surface data processed
through PCRAMMET 97 which provides
hourly stability class, wind direction and
speed, ambient temperature, and mixing
height are required. Data over water requires
hourly mixing height, relative humidity, air
temperature, and water surface temperature.
Missing winds are substituted with the
surface winds. Vertical wind direction shear,
vertical temperature gradient, and turbulence
intensities are optional.
e. The model user should acquire enough
meteorological data to ensure that worst-case
meteorological conditions are adequately
represented in the model results. The use of
5 years of adequately representative NWS
meteorological data, at least 1 year of sitespecific, or at least 3 years of prognostic
meteorological data are required. If 1 year or
more, up to 5 years, of site-specific data is
available, these data are preferred for use in
air quality analyses. Such data should have
been subjected to quality assurance
procedures as described in section 8.4.4.2.
f. Objective analysis in meteorological
modeling is to improve meteorological
analyses (the ‘‘first guess field’’) used as
initial conditions for prognostic
meteorological models by incorporating
information from meteorological
observations. Direct and indirect (using
remote sensing techniques) observations of
temperature, humidity, and wind from
surface and radiosonde reports are commonly
employed to improve these analysis fields.
For LRT applications, it is recommended that
objective analysis procedures using direct
and indirect meteorological observations be
employed in preparing input fields to
produce prognostic meteorological datasets.
The length of record of observations should
conform to recommendations outlined in
paragraph 8.4.2(e) for prognostic
meteorological model datasets.
8.4.3 National Weather Service Data
8.4.3.1 Discussion
a. The NWS meteorological data are
routinely available and familiar to most
model users. Although the NWS does not
provide direct measurements of all the
needed dispersion model input variables,
methods have been developed and
successfully used to translate the basic NWS
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data to the needed model input. Site-specific
measurements of model input parameters
have been made for many modeling studies,
and those methods and techniques are
becoming more widely applied, especially in
situations such as complex terrain
applications, where available NWS data are
not adequately representative. However,
there are many modeling applications where
NWS data are adequately representative, and
the applications still rely heavily on the NWS
data.
b. Many models use the standard hourly
weather observations available from the
National Centers for Environmental
Information (NCEI) b. These observations are
then preprocessed before they can be used in
the models. Prior to the advent of ASOS in
the early 1990’s, the ‘‘hourly’’ weather
observation was a human observer-based
observation reflecting a single 2-minute
average generally taken about 10 minutes
before the hour. However, beginning with
January 2000 for first-order stations and
March 2005 for all stations, NCEI has
archived the rolling 2-minute average winds
at every minute for ASOS sites. The
AERMINUTE processor 96 was developed to
reduce calm and missing hours by taking
advantage of the availability of the 1-minute
ASOS wind data to calculate full hourly
average winds to replace standard hourly
observations and reduce the number of calm
and missing winds in AERMET processing.
8.4.3.2 Recommendations
a. The preferred models listed in appendix
A all accept as input the NWS meteorological
data preprocessed into model compatible
form. If NWS data are judged to be
adequately representative for a specific
modeling application, they may be used.
NEIS makes available surface 105 106 and
upper air 107 meteorological data online and
in CD–ROM format. Upper air data are also
available at the Earth System Research
Laboratory Global Systems Divisions Web
site (https://esrl.noaa.gov/gsd).
b. Although most NWS wind
measurements are made at a standard height
of 10 meters, the actual anemometer height
should be used as input to the preferred
meteorological processor and model.
c. Standard hourly NWS wind directions
are reported to the nearest 10 degrees. A
specific set of randomly generated numbers
has been developed for use with the
preferred EPA models and should be used
with standard NWS data to ensure a lack of
bias in wind direction assignments within
the models.
d. Beginning with year 2000, NCDC began
archiving 2-minute winds, reported every
minute for NWS ASOS sites. The
AERMINUTE processor was developed to
read those winds and calculate hourly
average winds for input into AERMET. When
such data are available for the NWS ASOS
site being processed, the AERMINUTE
processor should be used in most cases to
calculate hourly average wind speed and
direction when processing NWS ASOS data
for input to AERMOD.94
b Formerly the National Climatic Data Center
(NCDC).
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e. Data from universities, FAA, military
stations, industry and pollution control
agencies may be used if such data are
equivalent in accuracy and detail (e.g., siting
criteria, frequency of observations, data
completeness, etc.) to the NWS data, they are
judged to be adequately representative for the
particular application and have undergone
quality assurance checks.
f. After valid data retrieval requirements
have been met,108 large number of hours in
the record having missing data should be
treated according to an established data
substitution protocol provided that
adequately representative alternative data are
available. Data substitution guidance is
provided in section 5.3 of reference 108. If no
representative alternative data are available
for substitution, the absent data should be
coded as missing using missing data codes
appropriate to the applicable meteorological
pre-processor. Appropriate model options for
treating missing data, if available in the
model, should be employed.
8.4.4 Site-Specific data
8.4.4.1 Discussion
a. Spatial or geographical
representativeness is best achieved by
collection of all of the needed model input
data in close proximity to the actual site of
the source(s). Site-specific measured data are
therefore preferred as model input, provided
that appropriate instrumentation and quality
assurance procedures are followed and that
the data collected are adequately
representative (free from inappropriate local
or microscale influences) and compatible
with the input requirements of the model to
be used. It should be noted that, while sitespecific measurements are frequently made
‘‘on-property’’ (i.e., on the source’s premises),
acquisition of adequately representative sitespecific data does not preclude collection of
data from a location off property. Conversely,
collection of meteorological data on a
source’s property does not of itself guarantee
adequate representativeness. For help in
determining representativeness of sitespecific measurements, technical
guidance 108 is available. Site-specific data
should always be reviewed for
representativeness and adequacy by an
experienced meteorologist, atmospheric
scientist, or other qualified scientist.
8.4.4.2 Recommendations
a. The EPA guidance 108 provides
recommendations on the collection and use
of site-specific meteorological data.
Recommendations on characteristics, siting,
and exposure of meteorological instruments
and on data recording, processing,
completeness requirements, reporting, and
archiving are also included. This publication
should be used as a supplement to other
limited guidance on these subjects.5 91 109 110
Detailed information on quality assurance is
also available.111 As a minimum, site-specific
measurements of ambient air temperature,
transport wind speed and direction, and the
variables necessary to estimate atmospheric
dispersion should be available in
meteorological datasets to be used in
modeling. Care should be taken to ensure
that meteorological instruments are located
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to provide an adequately representative
characterization of pollutant transport
between sources and receptors of interest.
The appropriate reviewing authority
(paragraph 3.0(b)) is available to help
determine the appropriateness of the
measurement locations.
b. All processed site-specific data should
be in the form of hourly averages for input
into the dispersion model. These data
include surface wind speed, transport
direction, dilution wind speed, and
turbulence measurements sA and sE (for use
in stability determinations and direct input
into the dispersion model). The hourly
average turbulence measurements should be
the square root of the arithmetic average of
the 15-minute average variances (square of sA
or sE).
c. Missing data substitution. After valid
data retrieval requirements have been met,108
hours in the record having missing data
should be treated according to an established
data substitution protocol provided that
adequately representative alternative data are
available. Such protocols are usually part of
the approved monitoring program plan. Data
substitution guidance is provided in section
5.3 of reference 108. If no representative
alternative data are available for substitution,
the absent data should be coded as missing
using missing data codes appropriate to the
applicable meteorological pre-processor.
Appropriate model options for treating
missing data, if available in the model,
should be employed.
d. Solar radiation measurements. Total
solar radiation or net radiation should be
measured with a reliable pyranometer or net
radiometer, sited and operated in accordance
with established site-specific meteorological
guidance.108 111
e. Temperature measurements.
Temperature measurements should be made
at standard shelter height (2m) in accordance
with established site-specific meteorological
guidance.108
f. Temperature difference measurements.
Temperature difference (DT) measurements
should be obtained using matched
thermometers or a reliable thermocouple
system to achieve adequate accuracy. Siting,
probe placement, and operation of DT
systems should be based on guidance found
in Chapter 3 of reference 108 and such
guidance should be followed when obtaining
vertical temperature gradient data. AERMET
may employ the Bulk Richardson scheme,
which requires measurements of temperature
difference, in lieu of cloud cover or
insolation data. To ensure correct application
and acceptance, AERMOD users should
consult with the appropriate reviewing
authority (paragraph 3.0(b)) before using the
Bulk Richardson scheme for their analysis.
g. Wind measurements. For simulation of
plume rise and dispersion of a plume emitted
from a stack, characterization of the wind
profile up through the layer in which the
plume disperses is desirable. This is
especially important in complex terrain and/
or complex wind situations where wind
measurements at heights up to hundreds of
meters above stack base may be required in
some circumstances. For tall stacks when
site-specific data are needed, these winds
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have been obtained traditionally using
meteorological sensors mounted on tall
towers. A feasible alternative to tall towers is
the use of meteorological remote sensing
instruments (e.g., acoustic sounders or radar
wind profilers) to provide winds aloft,
coupled with 10-meter towers to provide the
near-surface winds. Note that when sitespecific wind measurements are used,
AERMOD, at a minimum, requires wind
observations at a height above ground
between seven times the local surface
roughness height and 100 meters. (For
additional requirements for AERMOD and
CTDMPLUS, see appendix A.) Specifications
for wind measuring instruments and systems
are contained in reference 108.
h. Turbulence. There are several dispersion
models that are capable of using direct
measurements of turbulence (wind
fluctuations) in the characterization of the
vertical and lateral dispersion (e.g.,
CTDMPLUS, AERMOD). For specific
requirements for CTDMPLUS, AERMOD, see
appendix A. For technical guidance on
measurement and processing of turbulence
parameters, see reference 108. When
turbulence data are used in this manner to
directly characterize the vertical and lateral
dispersion, the averaging time for the
turbulence measurements should be 1 hour.
However, since AERMOD incorporates an
algorithm to account for horizontal plume
meander under low wind conditions, the
methodology outlined in paragraph 8.4.4.2(b)
should be used to calculate hourly averages
of sq, based on four 15-minuite values, to
minimize ‘‘double counting’’ of plume spread
associated with meander. The calculation of
hourly sq discussed above is automatically
applied within AERMET when sub-hourly
data are processed. There are other
dispersion models that employ P–G stability
categories for the characterization of the
vertical and lateral dispersion. Methods for
using site-specific turbulence data for the
characterization of P–G stability categories
are discussed in reference 108. When
turbulence data are used in this manner to
determine the P–G stability category, the
averaging time for the turbulence
measurements should be 15 minutes, with
hourly averaged values based on
methodology in paragraph 8.4.4.2(b).
i. Stability categories. For dispersion
models that employ P–G stability categories
for the characterization of the vertical and
lateral dispersion, the P–G stability
categories, as originally defined, couple nearsurface measurements of wind speed with
subjectively determined insolation
assessments based on hourly cloud cover and
ceiling height observations. The wind speed
measurements are made at or near 10m. The
insolation rate is typically assessed using
observations of cloud cover and ceiling
height based on criteria outlined by Turner.72
It is recommended that the P–G stability
category be estimated using the Turner
method with site-specific wind speed
measured at or near 10m and representative
cloud cover and ceiling height.
Implementation of the Turner method, as
well as considerations in determining
representativeness of cloud cover and ceiling
height in cases for which site-specific cloud
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observations are unavailable, may be found
in section 6 of reference 108. In the absence
of requisite data to implement the Turner
method, the solar radiation/delta-T (SRDT)
method or wind fluctuation statistics (i.e., the
sE and sA methods) may be used.
j. The SRDT method, described in section
6.4.4.2 of reference 108, is modified slightly
from that published from earlier work 112 and
has been evaluated with three site-specific
databases.113 The two methods of stability
classification which use wind fluctuation
statistics, the sE and sA methods, are also
described in detail in section 6.4.4 of
reference 108 (note applicable tables in
section 6). For additional information on the
wind fluctuation methods, several references
are available.114 115 116 117
8.4.5 Prognostic Meteorological Data
8.4.5.1 Discussion
a. For some modeling applications, there
may not be a representative NWS or
comparable meteorological station available
(e.g., complex terrain), and it may be cost
prohibitive or infeasible to collect adequately
representative site-specific data. For these
cases, it may be necessary to use prognostic
meteorological data in a regulatory modeling
application.
b. The EPA has developed a processor, the
MMIF (Mesoscale Model Interface Program)
to process MM5 (Mesoscale Model 5) or WRF
(Weather Research and Forecasting) model
data for input into various models including
AERMOD. MMIF can process data for input
into AERMET or AERMOD for a single grid
cell or multiple grid cells. MMIF output has
been found to compare favorably against
observed data (site-specific or NWS).118
Specific guidance on processing MMIF for
AERMOD can be found in reference 104.
When using MMIF to process prognostic data
for regulatory applications, the data should
be processed to generate AERMET inputs and
the data subsequently processed through
AERMET for input into AERMOD. If an
alternative method of processing data for
input into AERMET is used, it must be
approved by the appropriate reviewing
authority (paragraph 3.0(b)).
8.4.5.2 Recommendations
a. Prognostic model evaluation.
Appropriate effort should be devoted to the
process of evaluating the prognostic
meteorological data. The modeling data
should be compared to NWS observational
data in an effort to show that the data are
accurately replicating the observed
meteorological conditions of the time periods
modeled. An operational evaluation of the
modeling data for all model years (i.e.,
statistical, graphical) should be completed.93
The use of output from prognostic mesoscale
meteorological models is contingent upon the
concurrence with the appropriate reviewing
authority (paragraph 3.0(b)) that the data are
of acceptable quality, which can be
demonstrated through statistical comparisons
with meteorological observations aloft and at
the surface at several appropriate locations.93
b. Representativeness. When processing
MMIF data for use with AERMOD, the grid
cell used for the dispersion modeling should
be adequately spatially representative of the
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analysis domain. In most cases, this may be
the grid cell containing the emission source
of interest. Since the dispersion modeling
may involve multiple sources and the
domain may cover several grid cells,
depending on grid resolution of the
prognostic model, professional judgement
may be needed to select the appropriate grid
cell to use. In such cases, the selected grid
cell should be adequately representative of
the entire domain.
c. Grid resolution. The grid resolution of
the prognostic meteorological data should be
considered and evaluated appropriately,
particularly for projects involving complex
terrain. The operational evaluation of the
modeling data should consider whether a
finer grid resolution is needed to ensure that
the data are representative. The use of output
from prognostic mesoscale meteorological
models is contingent upon the concurrence
with the appropriate reviewing authority
(paragraph 3.0(b)) that the data are of
acceptable quality.
8.4.6 Treatment of Near-Calms and Calms
8.4.6.1 Discussion
a. Treatment of calm or light and variable
wind poses a special problem in modeling
applications since steady-state Gaussian
plume models assume that concentration is
inversely proportional to wind speed,
depending on model formulations.
Procedures have been developed to prevent
the occurrence of overly conservative
concentration estimates during periods of
calms. These procedures acknowledge that a
steady-state Gaussian plume model does not
apply during calm conditions, and that our
knowledge of wind patterns and plume
behavior during these conditions does not, at
present, permit the development of a better
technique. Therefore, the procedures
disregard hours which are identified as calm.
The hour is treated as missing and a
convention for handling missing hours is
recommended. With the advent of the
AERMINUTE processor, when processing
NWS ASOS data, the inclusion of hourly
averaged winds from AERMINUTE will, in
some instances, dramatically reduce the
number of calm and missing hours,
especially when the ASOS wind are derived
from a sonic anemometer. To alleviate
concerns about low winds, especially those
introduced with AERMINUTE, the EPA
implemented a wind speed threshold in
AERMET for use with ASOS derived
winds.96 Winds below the threshold will be
treated as calms.
b. AERMOD, while fundamentally a
steady-state Gaussian plume model, contains
algorithms for dealing with low wind speed
(near calm) conditions. As a result, AERMOD
can produce model estimates for conditions
when the wind speed may be less than
1 m/s, but still greater than the instrument
threshold. Required input to AERMET for
site-specific data, the meteorological
processor for AERMOD, includes a threshold
wind speed and a reference wind speed. The
threshold wind speed is typically the
threshold of the instrument used to collect
the wind speed data. The reference wind
speed is selected by the model as the lowest
level of non-missing wind speed and
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direction data where the speed is greater than
the wind speed threshold, and the height of
the measurement is between seven times the
local surface roughness and 100 meters. If the
only valid observation of the reference wind
speed between these heights is less than the
threshold, the hour is considered calm, and
no concentration is calculated. None of the
observed wind speeds in a measured wind
profile that are less than the threshold speed
are used in construction of the modeled wind
speed profile in AERMOD.
8.4.6.2
Recommendations
a. Hourly concentrations calculated with
steady-state Gaussian plume models using
calms should not be considered valid; the
wind and concentration estimates for these
hours should be disregarded and considered
to be missing. Critical concentrations for
3-, 8-, and 24-hour averages should be
calculated by dividing the sum of the hourly
concentrations for the period by the number
of valid or non-missing hours. If the total
number of valid hours is less than 18 for 24hour averages, less than 6 for 8-hour averages
or less than 3 for 3-hour averages, the total
concentration should be divided by 18 for the
24-hour average, 6 for the 8-hour average and
3 for the 3-hour average. For annual averages,
the sum of all valid hourly concentrations is
divided by the number of non-calm hours
during the year. AERMOD has been coded to
implement these instructions. For hours that
are calm or missing, the AERMOD hourly
concentrations will be zero. For other models
listed in appendix A, a post-processor
computer program, CALMPRO 119 has been
prepared, is available on the EPA’s SCRAM
Web site (section 2.3), and should be used.
b. Stagnant conditions that include
extended periods of calms often produce
high concentrations over wide areas for
relatively long averaging periods. The
standard steady-state Gaussian plume models
are often not applicable to such situations.
When stagnation conditions are of concern,
other modeling techniques should be
considered on a case-by-case basis (see also
section 7.2.1.2).
c. When used in steady-state Gaussian
plume models, measured site-specific wind
speeds of less than 1 m/s but higher than the
response threshold of the instrument should
be input as 1 m/s; the corresponding wind
direction should also be input. Wind
observations below the response threshold of
the instrument should be set to zero, with the
input file in ASCII format. For input to
AERMOD, no adjustment should be made to
the site-specific wind data. For NWS ASOS
data, especially data using the 1-minute
ASOS winds, a wind speed threshold option
is allowed with a recommended speed of 0.5
m/s. 94 When using prognostic data processed
by MMIF, a 0.5 m/s threshold is also invoked
by MMIF for input into AERMET.
Observations with wind speeds less than the
threshold are considered calm, and no
concentration is calculated. In all cases
involving steady-state Gaussian plume
models, calm hours should be treated as
missing, and concentrations should be
calculated as in paragraph (a) of this
subsection.
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9.0
Regulatory Application of Models
9.1 Discussion
a. Standardized procedures are valuable in
the review of air quality modeling and data
analyses conducted to support SIP submittals
and revisions, NSR, including PSD, or other
EPA requirements to ensure consistency in
their regulatory application. This section
recommends procedures specific to NSR,
including PSD, that facilitate some degree of
standardization while at the same time
allowing the flexibility needed to assure the
technically best analysis for each regulatory
application. For SIP attainment
demonstrations, refer to the appropriate EPA
guidance 51 60 for the recommended
procedures.
b. Air quality model estimates, especially
with the support of measured air quality
data, are the preferred basis for air quality
demonstrations. A number of actions have
been taken to ensure that the best air quality
model is used correctly for each regulatory
application and that it is not arbitrarily
imposed.
• First, the Guideline clearly recommends
that the most appropriate model be used in
each case. Preferred models are identified,
based on a number of factors, for many uses.
• Second, the preferred models have been
subjected to a systematic performance
evaluation and a peer scientific review.
Statistical performance measures, including
measures of difference (or residuals) such as
bias, variance of difference and gross
variability of the difference, and measures of
correlation such as time, space, and time and
space combined as described in section 2.1.1,
were generally followed.
• Third, more specific information has
been provided for considering the
incorporation of new models into the
Guideline (section 3.1) and the Guideline
contains procedures for justifying the caseby-case use of alternative models and
obtaining EPA approval (section 3.2).
The Guideline, therefore, provides
objective methods that allow a determination
to be made as to what air quality model or
technique is most appropriate for a particular
application.
c. Air quality modeling is the preferred
basis for air quality demonstrations.
Nevertheless, there are rare circumstances
where the performance of the preferred air
quality model may be shown to be less than
reasonably acceptable or where no preferred
air quality model, screening model or
technique, or alternative model are suitable
for the situation. In these unique instances,
there is the possibility of assuring
compliance and establishing emissions limits
for an existing source solely on the basis of
observed air quality data in lieu of an air
quality modeling analysis. Comprehensive
air quality monitoring in the vicinity of the
existing source with proposed modifications
will be necessary in these cases. The same
attention should be given to the detailed
analyses of the air quality data as would be
applied to a model performance evaluation.
d. The current levels and forms of the
NAAQS for the six criteria pollutants can be
found on the EPA’s NAAQS Web site at
https://www.epa.gov/air/criteria.html. Under
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the CAA, the NAAQS are subjected to
extensive review every 5 years and the
standards, including the level and the form,
may be revised as part of that review. The
criteria pollutants have either long-term
(annual or quarterly) and/or short-term (24hour or less) forms that are not to be
exceeded more than a certain frequency over
a period of time (e.g., no exceedance on a
rolling 3-month average, no more than once
per year, or no more than once per year
averaged over 3 years), are averaged over a
period of time (e.g., an annual mean or an
annual mean averaged over 3 years), or are
some percentile that is averaged over a
period of time (e.g., annual 99th or 98th
percentile averaged over 3 years). The 3-year
period for ambient monitoring design values
does not dictate the length of the data periods
recommended for modeling (i.e., 5 years of
NWS meteorological data, at least 1 year of
site-specific, or at least 3 years of prognostic
meteorological data).
e. This section discusses general
recommendations on the regulatory
application of models for the purposes of
NSR, including PSD permitting, and
particularly for estimating design
concentration(s), appropriately comparing
these estimates to NAAQS and PSD
increment, and developing emissions limits.
Lastly, this section provides the criteria
necessary for considering use of analysis
based on measured ambient data in lieu of
modeling as the sole basis for demonstrating
compliance with NAAQS and PSD
increments.
9.2 Recommendations
9.2.1 Modeling Protocol
a. Every effort should be made by the
appropriate reviewing authority (paragraph
3.0(b)) to meet with all parties involved in
either a SIP submission or revision or a PSD
permit application prior to the start of any
work on such a project. During this meeting,
a protocol should be established between the
preparing and reviewing parties to define the
procedures to be followed, the data to be
collected, the model to be used, and the
analysis of the source and concentration data
to be performed. An example of the content
for such an effort is contained in the Air
Quality Analysis Checklist posted on the
EPA’s SCRAM Web site (section 2.3). This
checklist suggests the appropriate level of
detail to assess the air quality resulting from
the proposed action. Special cases may
require additional data collection or analysis
and this should be determined and agreed
upon at this pre-application meeting. The
protocol should be written and agreed upon
by the parties concerned, although it is not
intended that this protocol be a binding,
formal legal document. Changes in such a
protocol or deviations from the protocol are
often necessary as the data collection and
analysis progresses. However, the protocol
establishes a common understanding of how
the demonstration required to meet
regulatory requirements will be made.
9.2.2 Design Concentration and Receptor
Sites
a. Under the PSD permitting program, an
air quality analysis for criteria pollutants is
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required to demonstrate that emissions from
the construction or operation of a proposed
new source or modification will not cause or
contribute to a violation of the NAAQS or
PSD increments.
i. For a NAAQS assessment, the design
concentration is the combination of the
appropriate background concentration
(section 8.3) with the estimated modeled
impact of the source. The NAAQS design
concentration is then compared to the
applicable NAAQS.
ii. For a PSD increment assessment, the
design concentration includes impacts after
the appropriate baseline date from all
increment consuming and increment
expanding sources. The PSD increment
design concentration is then compared to the
applicable PSD increment.
b. The specific form of the NAAQS for the
pollutant(s) of concern will also influence
how the background and modeled data
should be combined for appropriate
comparison with the respective NAAQS in
such a modeling demonstration. Given the
potential for revision of the form of the
NAAQS and the complexities of combining
background and modeled data, specific
details on this process can be found in
applicable modeling guidance available on
the EPA’s SCRAM Web site (section 2.3).
Modeled concentrations should not be
rounded before comparing the resulting
design concentration to the NAAQS or PSD
increments. Ambient monitoring and
dispersion modeling address different issues
and needs relative to each aspect of the
overall air quality assessment.
c. The PSD increments for criteria
pollutants are listed in 40 CFR 52.21(c) and
40 CFR 51.166(c). For short-term increments,
these maximum allowable increases in
pollutant concentrations may be exceeded
once per year at each site, while the annual
increment may not be exceeded. The highest,
second-highest increase in estimated
concentrations for the short-term averages as
determined by a model should be less than
or equal to the permitted increment. The
modeled annual averages should not exceed
the increment.
d. Receptor sites for refined dispersion
modeling should be located within the
modeling domain (section 8.1). In designing
a receptor network, the emphasis should be
placed on receptor density and location, not
total number of receptors. Typically, the
density of receptor sites should be
progressively more resolved near the new or
modifying source, areas of interest, and areas
with the highest concentrations with
sufficient detail to determine where possible
violations of a NAAQS or PSD increment are
most likely to occur. The placement of
receptor sites should be determined on a
case-by-case basis, taking into consideration
the source characteristics, topography,
climatology, and monitor sites. Locations of
particular importance include: (1) The area of
maximum impact of the point source; (2) the
area of maximum impact of nearby sources;
and (3) the area where all sources combine
to cause maximum impact. Depending on the
complexities of the source and the
environment to which the source is located,
a dense array of receptors may be required in
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some cases. In order to avoid unreasonably
large computer runs due to an excessively
large array of receptors, it is often desirable
to model the area twice. The first model run
would use a moderate number of receptors
more resolved nearby the new or modifying
source and over areas of interest. The second
model run would modify the receptor
network from the first model run with a
denser array of receptors in areas showing
potential for high concentrations and
possible violations, as indicated by the
results of the first model run. Accordingly,
the EPA neither anticipates nor encourages
that numerous iterations of modeling runs be
made to continually refine the receptor
network.
9.2.3 NAAQS and PSD Increments
Compliance Demonstrations for New or
Modified Sources
a. As described in this subsection, the
recommended procedure for conducting
either a NAAQS or PSD increment
assessment under PSD permitting is a multistage approach that includes the following
two stages:
i. The first stage is referred to as a singlesource impact analysis, since only the new or
modifying source is considered in the
analysis. There are two possible levels of
detail in conducting a single-source impact
analysis with the model user beginning with
use of a screening model and proceeding to
use of a refined model as necessary.
ii. The second stage is referred to as a
cumulative impact analysis, since it takes
into account all sources affecting the air
quality in an area. In addition to the project
source impact, it includes consideration of
background, which includes contributions
from natural, nearby, and unknown sources.
b. Each stage involves increasing
complexity and details, as required to fully
demonstrate a new or modifying source will
not cause of contribution to a violation of any
NAAQS or PSD increment. As such, starting
with a single-source impact analysis may
alleviate the need for a more time consuming
and comprehensive cumulative modeling
analysis.
c. The single-source impact analysis, or
first stage of an air quality analysis, begins by
determining the potential of a proposed new
or modifying source to cause or contribute to
a NAAQS or PSD increment violation. In
certain circumstances, a screening model or
technique may be used instead of the
preferred model because it will provide
estimated worst-case ambient impacts from
the proposed new or modifying source. If
these worst case ambient concentration
estimates indicate that there will not be a
significant impact, then the analysis is
sufficient for the required demonstration
under PSD. If the ambient concentration
estimates indicate that significant impacts
may occur, then the use of a refined model
to estimate the source’s impact should be
pursued. The refined modeling analysis
should use a model or technique consistent
with the Guideline (either a preferred model
or technique or an alternative model or
technique) and follow the requirements and
recommendations for model inputs outlined
in section 8. If the estimated ambient
concentrations indicate that there will not be
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a significant impact, then the analysis is
generally sufficient to demonstrate that the
source will not cause or contribute to an
exceedance. However, if the concentration
estimates from the refined modeling analysis
indicate that significant impacts may occur,
then a cumulative impact analysis should be
undertaken. The receptors that indicate the
location of significant impacts should be
used to define the modeling domain for use
in the cumulative impact analysis (section
8.2.2).
d. The cumulative impact analysis, or the
second stage of an air quality analysis,
should be conducted with the same refined
model or technique to characterize the
project source and then include the
appropriate background concentrations
(section 8.3). The resulting design
concentrations are used to determine
whether the source will cause or contribute
to a NAAQS or PSD increment violation.
This determination should be based on: (1)
The appropriate design concentration for
each applicable NAAQS (and averaging
period); and (2) the significance of the
source’s contribution, in a temporal and
spatial sense, to any modeled violation, i.e.,
where and when the predicted design
concentration is greater than the NAAQS. For
PSD increment, the cumulative impact
analysis should also consider the amount of
the air quality increment that has already
been consumed by other sources, or,
conversely, whether increment has expanded
relative to the baseline concentration.
Therefore, the applicant should model the
existing or permitted nearby incrementconsuming and increment-expanding
sources, rather than using past modeling
analyses of those sources as part of
background concentration. This would
permit the use of newly acquired data or
improved modeling techniques if such data
and/or techniques have become available
since the last source was permitted.
9.2.3.1 Considerations in Developing
Emissions Limits
a. Emissions limits and resulting control
requirements should be established to
provide for compliance with each applicable
NAAQS (and averaging period) and PSD
increment. It is possible that multiple
emissions limits will be required for a source
to demonstrate compliance with several
criteria pollutants (and averaging periods)
and PSD increments. Case-by-case
determinations must be made as to the
appropriate form of the limits, i.e., whether
the emissions limits restrict the emission
factor (e.g., limiting lb/MMBTU), the
emission rate (e.g., lb/hr), or both. The
appropriate reviewing authority (paragraph
3.0(b)) and appropriate EPA guidance should
be consulted to determine the appropriate
emissions limits on a case-by-case basis.
9.2.4 Use of Measured Data in Lieu of
Model Estimates
a. As described throughout the Guideline,
modeling is the preferred method for
demonstrating compliance with the NAAQS
and PSD increments and for determining the
most appropriate emissions limits for new
and existing sources. When a preferred
model or adequately justified and approved
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alternative model is available, model results,
including the appropriate background, are
sufficient for air quality demonstrations and
establishing emissions limits, if necessary. In
instances when the modeling technique
available is only a screening technique, the
addition of air quality monitoring data to the
analysis may lend credence to the model
results. However, air quality monitoring data
alone will normally not be acceptable as the
sole basis for demonstrating compliance with
the NAAQS and PSD increments or for
determining emissions limits.
b. There may be rare circumstances where
the performance of the preferred air quality
model will be shown to be less than
reasonably acceptable when compared with
air quality monitoring data measured in the
vicinity of an existing source. Additionally,
there may not be an applicable preferred air
quality model, screening technique, or
justifiable alternative model suitable for the
situation. In these unique instances, there
may be the possibility of establishing
emissions limits and demonstrating
compliance with the NAAQS and PSD
increments solely on the basis of analysis of
observed air quality data in lieu of an air
quality modeling analysis. However, only in
the case of a modification to an existing
source should air quality monitoring data
alone be a basis for determining adequate
emissions limits or for demonstration that the
modification will not cause or contribute to
a violation of any NAAQS or PSD increment.
c. The following items should be
considered prior to the acceptance of an
analysis of measured air quality data as the
sole basis for an air quality demonstration or
determining an emissions limit:
i. Does a monitoring network exist for the
pollutants and averaging times of concern in
the vicinity of the existing source?
ii. Has the monitoring network been
designed to locate points of maximum
concentration?
iii. Do the monitoring network and the data
reduction and storage procedures meet EPA
monitoring and quality assurance
requirements?
iv. Do the dataset and the analysis allow
impact of the most important individual
sources to be identified if more than one
source or emission point is involved?
v. Is at least one full year of valid ambient
data available?
vi. Can it be demonstrated through the
comparison of monitored data with model
results that available air quality models and
techniques are not applicable?
c. Comprehensive air quality monitoring in
the area affected by the existing source with
proposed modifications will be necessary in
these cases. Additional meteorological
monitoring may also be necessary. The
appropriate number of air quality and
meteorological monitors from a scientific and
technical standpoint is a function of the
situation being considered. The source
configuration, terrain configuration, and
meteorological variations all have an impact
on number and optimal placement of
monitors. Decisions on the monitoring
network appropriate for this type of analysis
can only be made on a case-by-case basis.
d. Sources should obtain approval from the
appropriate reviewing authority (paragraph
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3.0(b)) and the EPA Regional Office for the
monitoring network prior to the start of
monitoring. A monitoring protocol agreed to
by all parties involved is necessary to assure
that ambient data are collected in a
consistent and appropriate manner. The
design of the network, the number, type, and
location of the monitors, the sampling
period, averaging time as well as the need for
meteorological monitoring or the use of
mobile sampling or plume tracking
techniques, should all be specified in the
protocol and agreed upon prior to start-up of
the network.
e. Given the uniqueness and complexities
of these rare circumstances, the procedures
can only be established on a case-by-case
basis for analyzing the source’s emissions
data and the measured air quality monitoring
data and for projecting with a reasoned basis
the air quality impact of a proposed
modification to an existing source in order to
demonstrate that emissions from the
construction or operation of the modification
will not cause or contribute to a violation of
the applicable NAAQS and PSD increment,
and to determine adequate emissions limits.
The same attention should be given to the
detailed analyses of the air quality data as
would be applied to a comprehensive model
performance evaluation. In some cases, the
monitoring data collected for use in the
performance evaluation of preferred air
quality models, screening technique, or
existing alternative models may help inform
the development of a suitable new alternative
model. Early coordination with the
appropriate reviewing authority (paragraph
3.0(b)) and the EPA Regional Office is
fundamental with respect to any potential
use of measured data in lieu of model
estimates.
10.0 References
1. Code of Federal Regulations; Title 40
(Protection of Environment); Part 51;
Sections 51.112, 51.117, 51.150, 51.160.
2. Environmental Protection Agency, 1990.
New Source Review Workshop Manual:
Prevention of Significant Deterioration
and Nonattainment Area Permitting
(Draft). Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
https://www.epa.gov/nsr.
3. Code of Federal Regulations; Title 40
(Protection of Environment); Part 51;
Sections 51.166 and 52.21.
4. Code of Federal Regulations; Title 40
(Protection of Environment); Part 93;
Sections 93.116, 93.123, and 93.150.
5. Code of Federal Regulations; Title 40
(Protection of Environment); Part 58
(Ambient Air Quality Surveillance).
6. Code of Federal Regulations; Title 40
(Protection of Environment); Part 50
(National Primary and Secondary
Ambient Air Quality Standards).
7. Baker, K.R., Kelly, J.T., 2014. Single source
impacts estimated with photochemical
model source sensitivity and
apportionment approaches. Atmospheric
Environment, 96: 266–274.
8. ENVIRON, 2012. Evaluation of Chemical
Dispersion Models using Atmospheric
Plume Measurements from Field
Experiments. ENVIRON International,
VerDate Sep<11>2014
19:11 Jul 28, 2015
Jkt 235001
Corp., Novato, CA. Prepared under
contract No. EP–D–07–102 for U.S.
Environmental Protection Agency,
Research Triangle Park, NC. https://
www.epa.gov/ttn/scram/reports/Plume_
Eval_Final_Sep_2012v5.pdf.
9. McMurry, P.H., Shepherd, M.F., Vickery,
J.S., 2004. Particulate matter science for
policy makers: A NARSTO assessment.
Cambridge University Press.
10. Baker, K.R., Foley, K.M., 2011. A
nonlinear regression model estimating
single source concentrations of primary
and secondarily formed PM2.5.
Atmospheric Environment, 45: 3758–
3767.
11. Bergin, M.S., Russell, A.G., Odman, M.T.,
Cohan, D.S., Chameldes, W.L., 2008.
Single-Source Impact Analysis Using
Three-Dimensional Air Quality Models.
Journal of the Air & Waste Management
Association, 58: 1351–1359.
12. Zhou, W., Cohan, D.S., Pinder, R.W.,
Neuman, J.A., Holloway, J.S., Peischl, J.,
Ryerson, T.B., Nowak, J.B., Flocke, F.,
Zheng, W.G., 2012. Observation and
modeling of the evolution of Texas
power plant plumes. Atmospheric
Chemistry and Physics, 12: 455–468.
13. Chen, J., Lu, J., Avise, J.C., DaMassa, J.A.,
Kleeman, M.J., Kaduwela, A.P., 2014.
Seasonal modeling of PM 2.5 in
California’s San Joaquin Valley.
Atmospheric Environment, 92: 182–190.
14. Russell, A.G., 2008. EPA Supersites
program-related emissions-based
particulate matter modeling: initial
applications and advances. Journal of the
Air & Waste Management Association,
58: 289–302.
15. Tesche, T., Morris, R., Tonnesen, G.,
McNally, D., Boylan, J., Brewer, P., 2006.
CMAQ/CAMx annual 2002 performance
evaluation over the eastern US.
Atmospheric Environment, 40: 4906–
4919.
16. Fox, D.G., 1984. Uncertainty in air quality
modeling. Bulletin of the American
Meteorological Society, 65(1): 27–36.
17. Bowne, NE., 1981. Validation and
Performance Criteria for Air Quality
Models. Appendix F in Air Quality
Modeling and the Clean Air Act:
Recommendations to EPA on Dispersion
Modeling for Regulatory Applications.
American Meteorological Society,
Boston, MA; pp. 159–171. (Docket No.
A–80–46, II–A–106).
18. Fox, D.G., 1981. Judging Air Quality
Model Performance. Bulletin of the
American Meteorological Society, 62(5):
599–609.
19. Simon, H., Baker, K.R., Phillips, S., 2012.
Compilation and interpretation of
photochemical model performance
statistics published between 2006 and
2012. Atmospheric Environment, 61:
124–139.
20. Burton, C.S., 1981. The Role of
Atmospheric Models in Regulatory
Decision-Making: Summary Report.
Systems Applications, Inc., San Rafael,
CA. Prepared under contract No. 68–01–
5845 for U.S. Environmental Protection
Agency, Research Triangle Park, NC.
(Docket No. A–80–46, II–M–6).
PO 00000
Frm 00041
Fmt 4701
Sfmt 4702
45379
21. ‘‘Ten years of Harmonisation activities:
Past, present and future’’ at https://
www.dmu.dk/AtmosphericEnvironment/
Harmoni/Conferences/Belgirate/
BelgiratePapers.asp.
22. Weil, Sykes, and Venkatram, 1992.
Evaluating Air-Quality Models: Review
and Outlook. Journal of Applied
Meteorology, 31: 1121–1145.
23. Environmental Protection Agency, 1988.
Model Clearinghouse: Operational Plan
(Revised). Staff Report. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. (Docket No. A–88–04,
II–J–1)
24. American Meteorological Society, 1983.
Synthesis of the Rural Model Reviews.
Publication No. EPA–600/3–83–108.
Office of Research & Development,
Research Triangle Park, NC. (NTIS No.
PB 84–121037)
25. Hanna, S., M. Garrison and B. Turner,
1998. AERMOD Peer Review report.
Prepared by SAI, Inc. under EPA
Contract No. 68–D6–0064/1–14 for
Environmental Protection Agency,
Research Triangle Park, NC. 12pp. &
appendices. (Docket No. A–99–05, II–A–
6)
26. Scire, J.S. and L.L. Schulman, 1981.
Evaluation of the BLP and ISC Models
with SF6 Tracer Data and SO2
Measurements at Aluminum Reduction
Plants. APCA Specialty Conference on
Dispersion Modeling for Complex
Sources, St. Louis, MO.
27. Environmental Protection Agency, 2003.
AERMOD: Latest Features and
Evaluation Results. Publication No.
EPA–454/R–03–003. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/
ttn/scram/7thconf/aermod/aermod_
mep.pdf.
28. ASTM D6589: Standard Guide for
Statistical Evaluation of Atmospheric
Dispersion Model Performance. (2010).
29. Environmental Protection Agency, 1992.
Protocol for Determining the Best
Performing Model. Publication No. EPA–
454/R–92–025. Office of Air Quality
Planning & Standards, Research Triangle
Park, NC. https://www.epa.gov/ttn/scram/
guidance/guide/modleval.zip. (NTIS No.
PB 93–226082)
30. Hanna, S.R., 1982. Natural Variability of
Observed Hourly SO2 and CO
Concentrations in St. Louis. Atmospheric
Environment, 16(6): 1435–1440.
31. Pasquill, F., 1974. Atmospheric Diffusion,
2nd Edition. John Wiley and Sons, New
York, NY; 479pp.
32. Rhoads, R.G., 1981. Accuracy of Air
Quality Models. Staff Report. Office of
Air Quality Planning & Standards,
Research Triangle Park, NC. (Docket No.
A–80–46, II–G–6)
33. Hanna, S.R., 1993. Uncertainties in air
quality model predictions. BoundaryLayer Meteorology, 62: 3–20.
34. Hanna, S.R., 1989. Confidence limits for
air quality model evaluations, as
estimated by bootstrap and jackknife
resampling methods. Atmospheric
Environment, 23(6): 1385–1398.
35. Cox, W.M. and J.A. Tikvart, 1990. A
statistical procedure for determining the
E:\FR\FM\29JYP2.SGM
29JYP2
mstockstill on DSK4VPTVN1PROD with PROPOSALS2
45380
Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules
best performing air quality simulation
model. Atmospheric Environment,
24A(9): 2387–2395.
36. Environmental Protection Agency, 2015.
Technical Support Document (TSD) for
AERMOD-Based Assessments of LongRange Transport Impacts for Primary
Pollutants. Publication No. EPA–454/B–
15–003. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
37. Environmental Protection Agency, 2015:
AERSCREEN User’s Guide. Publication
No. EPA–454-/B–15–005. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/
ttn/scram/dispersion_
screening.htm#aerscreen.
38. Environmental Protection Agency, 2011:
AERSCREEN Released as the EPA
Recommended Screening Model. Tyler
Fox Memorandum dated April 11, 2011,
Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
https://www.epa.gov/ttn/scram/
20110411_AERSCREEN_Release_
Memo.pdf.
39. Perry, S.G., D.J. Burns and A.J. Cimorelli,
1990. User’s Guide to CTDMPLUS:
Volume 2. The Screening Mode
(CTSCREEN). Publication No. EPA–600/
8–90–087. U.S. Environmental
Protection Agency, Research Triangle
Park, NC. https://www.epa.gov/ttn/scram/
dispersion_prefrec.htm#ctdmplus. (NTIS
No. PB 91–136564)
40. Environmental Protection Agency, 1992.
Screening Procedures for Estimating the
Air Quality Impact of Stationary Sources,
Revised. Publication No. EPA–454/R–
92–019. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
(NTIS No. PB 93–219095)
41. Burns, D.J., S.G. Perry and A.J. Cimorelli,
1991. An Advanced Screening Model for
Complex Terrain Applications. Paper
presented at the 7th Joint Conference on
Applications of Air Pollution
Meteorology (cosponsored by the
American Meteorological Society and the
Air & Waste Management Association),
January 13–18, 1991, New Orleans, LA.
42. Mills, M.T., R.J. Paine, E.A. Insley and
B.A. Egan, 1987. The Complex Terrain
Dispersion Model Terrain Preprocessor
System—User’s Guide and Program
Description. Publication No. EPA–600/
8–88–003. U.S. Environmental
Protection Agency, Research Triangle
Park, NC. (NTIS No. PB 88–162094)
43. Environmental Research and Technology,
1987. User’s Guide to the Rough Terrain
Diffusion Model (RTDM), Rev. 3.20. ERT
Document No. P–D535–585.
Environmental Research and
Technology, Inc., Concord, MA. (NTIS
No. PB 88–171467)
44. Environmental Protection Agency, 2004.
AERMOD: Description of Model
Formulation. Publication No. EPA–454/
R–03–004. Office of Air Quality Planning
& Standards, Research Triangle Park, NC;
September 2004. https://www.epa.gov/
ttn/scram/7thconf/aermod/aermod_
mfd.pdf.
45. Cimorelli, A. et al., 2005. AERMOD: A
Dispersion Model for Industrial Source
VerDate Sep<11>2014
19:11 Jul 28, 2015
Jkt 235001
Applications. Part I: General Model
Formulation and Boundary Layer
Characterization. Journal of Applied
Meteorology, 44(5): 682–693.
46. L.L. Schulman, D.G. Strimaitis and J.S.
Scire, 2002. Development and evaluation
of the PRIME plume rise and building
downwash model. Journal of the Air &
Waste Management Association, 50:
378–390.
47. Environmental Protection Agency, 2015,
Technical Support Document (TSD) for
Replacement of CALINE3 with AERMOD
for Transportation Related Air Quality
Analyses. Publication No. EPA–454/B–
15–002. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
48. Environmental Protection Agency, 1997.
Guidance for Siting Ambient Air
Monitors around Stationary Lead
Sources. Publication No. EPA–454/R–
92–009R. Office of Air Quality Planning
& Standards, Research Triangle Park, NC.
(NTIS No. PB 97–208094)
49. LEADPOST processor: https://
www.epa.gov/ttn/scram/models/aermod/
leadpost.zip.
50. Environmental Protection Agency, 1993.
Lead Guideline Document. Publication
No. EPA–452/R–93–009. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. (NTIS No. PB 94–
111846)
51. U.S. EPA, 2014: Guidance for 1-Hour SO2
Nonattaiment Area SIP Submissions.
Stephen D. Page Memorandum dated
April 23, 2011, Office of Air Quality
Planning & Standards, Research Triangle
Park, NC. https://www.epa.gov/airquality/
sulfurdioxide/pdfs/
20140423guidance.pdf.
52. U.S. EPA, 2013: SO2 NAAQS
Designations Modeling Technical
Assistance Document. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/
airquality/sulfurdioxide/pdfs/
SO2ModelingTAD.pdf.
53. Turner, D.B., 1964. A Diffusion Model for
an Urban Area. Journal of Applied
Meteorology, 3(1):83–91.
54. Environmental Protection Agency, 2015.
Technical Support Document (TSD) for
NO2-Related AERMOD Options and
Modifications, Publication No. EPA–454/
B–15–004. Office of Air Quality Planning
& Standards, Research Triangle Park, NC.
55. Podrez, M. 2015. An Update to the
Ambient Ratio Method for 1-h NO2 Air
Quality Standards Dispersion Modeling.
Atmospheric Environment, 103: 163–
170.
56. Cole, H.S. and J.E. Summerhays, 1979. A
Review of Techniques Available for
Estimation of Short-Term NO2
Concentrations. Journal of the Air
Pollution Control Association, 29(8):
812–817.
57. Hanrahan, P.L., 1999. The Polar Volume
Polar Ratio Method for Determining
NO2/NOX Ratios in Modeling—Part I:
Methodology. Journal of the Air & Waste
Management Association, 49: 1324–
1331.
58. Environmental Protection Agency, 2004.
The Particle Pollution Report,
PO 00000
Frm 00042
Fmt 4701
Sfmt 4702
Publication No. EPA–454–R–04–002.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
https://www.epa.gov/airtrends/aqtrnd04/
pmreport03/report_2405.pdf#page=1.
59. Environmental Protection Agency, 2014.
Guidance for PM2.5 Modeling. May 20,
2014, Publication No. EPA–454/B–14–
001. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
https://www.epa.gov/ttn/scram/guidance/
guide/Guidance_for_PM25_Permit_
Modeling.pdf.
60. Environmental Protection Agency, 2014.
Modeling Guidance for Demonstrating
Attainment of Air Quality Goals for
Ozone, PM2.5, and Regional Haze. Office
of Air Quality Planning & Standards,
Research Triangle Park, NC. https://
www.epa.gov/ttn/scram/guidance/guide/
Draft_O3–PM–RH_Modeling_Guidance2014.pdf.
61. Environmental Protection Agency, 2013.
Transportation Conformity Guidance for
Quantitative Hot-Spot Analyses in PM2.5
and PM10 Nonattainment and
Maintenance Areas. Publication No.
EPA–420–B–053. Office of
Transportation and Air Quality, Ann
Arbor, MI. https://www.epa.gov/otaq/
stateresources/transconf/policy/
420b13053-sec.pdf.
62. Environmental Protection Agency, 1987.
PM10 SIP Development Guideline.
Publication No. EPA–450/2–86–001.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
(NTIS No. PB 87–206488)
63. Environmental Protection Agency, 2012.
Memorandum: Haul Road Workgroup
Final Report Submission to EPA–
OAQPS. March 2, 2012. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/
ttn/scram/reports/Haul_Road_
Workgroup-Final_Report_Package20120302.pdf.
64. Seinfeld, J.H., Pandis, S.N., 2012.
Atmospheric chemistry and physics:
from air pollution to climate change.
John Wiley & Sons.
65. Simon, H., Baker, K.R., Phillips, S., 2012.
Compilation and interpretation of
photochemical model performance
statistics published between 2006 and
2012. Atmospheric Environment, 61,
124–139.
66. Environmental Protection Agency, 2015.
Guidance on the use of models for
assessing the impacts from single sources
on secondarily formed pollutants ozone
and PM2.5. Publication No. EPA 454/P–
15–001. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
67. U.S. Department of the Interior, 2010.
Federal Land Managers’ Air Quality
Related Values Work Group (FLAG)
Phase I Report—Revised 2010. https://
www.nature.nps.gov/air/pubs/pdf/flag/
FLAG_2010.pdf. Natural Resource Report
NPS/NPRC/NRR–2010/232.
68. National Acid Precipitation Assessment
Program (NAPAP), 1991. Acid
Deposition: State of Science and
Technology. Volume III Terrestrial,
Materials, Health and Visibility Effects.
E:\FR\FM\29JYP2.SGM
29JYP2
mstockstill on DSK4VPTVN1PROD with PROPOSALS2
Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules
Report 24, Visibility: Existing and
Historical Conditions—Causes and
Effects. Edited by Patricia M. Irving.
Washington, DC 129pp.
69. National Research Council, 1993.
Protecting Visibility in National Parks
and Wilderness Areas. National
Academy Press, Washington, DC 446pp.
70. U.S. Environmental Protection Agency,
1992. Workbook for plume visual impact
screening and analysis (revised).
Publication No. EPA–454/R–92–023.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC
(NTIS No. PB 93–223592).
˚
71. Nilsson, J., Grennfelt, P., Ministerrad, N.,
1988. Critical Loads for Sulphur and
Nitrogen: Report from a Workshop Held
at Skokloster, Sweden, 19–24 March,
1988. Nordic Council of Ministers.
72. Turner, D.B., 1969. Workbook of
Atmospheric Dispersion Estimates. PHS
Publication No. 999–AP–26. U.S.
Department of Health, Education and
Welfare, Public Health Service,
Cincinnati, OH. (NTIS No. PB–191482)
73. McElroy, J.L. and F. Pooler, Jr., 1968. St.
Louis Dispersion Study, Volume II—
Analysis. National Air Pollution Control
Administration Publication No. AP–53,
U.S. Department of Health, Education
and Welfare, Public Health Service,
Arlington, VA. (NTIS No. PB–190255)
74. Irwin, J.S., 1978. Proposed Criteria for
Selection of Urban Versus Rural
Dispersion Coefficients. (Draft Staff
Report). Meteorology and Assessment
Division, U.S. Environmental Protection
Agency, Research Triangle Park, NC.
(Docket No. A–80–46, II–B–8)
75. Auer, Jr., A.H., 1978. Correlation of Land
Use and Cover with Meteorological
Anomalies. Journal of Applied
Meteorology, 17(5): 636–643.
76. Environmental Protection Agency, 2015.
AERMOD Implementation Guide. U.S.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
https://www.epa.gov/ttn/scram/
dispersion_prefrec.htm#aermod.
77. Pasquill, F., 1976. Atmospheric
Dispersion Parameters in Gaussian
Plume Modeling, Part II. Possible
Requirements for Change in the Turner
Workbook Values. Publication No. EPA–
600/4–76–030b. Office of Research &
Development, Research Triangle Park,
NC. (NTIS No. PB–258036/3BA)
78. Stull, R.B., 1988. An Introduction to
Boundary Layer Meteorology. Kluwer
Academic Publishers, Boston, MA.
666pp.
79. Environmental Protection Agency, 1987.
Analysis and Evaluation of Statistical
Coastal Fumigation Models. Publication
No. EPA–450/4–87–002. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. (NTIS No. PB 87–
175519)
80. Wesely, M.L, P.V. Doskey, and J.D.
Shannon, 2002: Deposition
Parameterizations for the Industrial
Source Complex (ISC3) Model. Draft
ANL report ANL/ER/TRB01/003, DOE/
xxnnnn, Argonne National Laboratory,
Argonne, Illinois 60439.
VerDate Sep<11>2014
19:11 Jul 28, 2015
Jkt 235001
81. Environmental Protection Agency, 1981.
Guideline for Use of Fluid Modeling to
Determine Good Engineering Practice
(GEP) Stack Height. Publication No.
EPA–450/4–81–003. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. (NTIS No. PB 82–
145327)
82. Lawson, Jr., R.E. and W.H. Snyder, 1983.
Determination of Good Engineering
Practice Stack Height: A Demonstration
Study for a Power Plant. Publication No.
EPA–600/3–83–024. Office of Research &
Development, Research Triangle Park,
NC. (NTIS No. PB 83–207407)
83. Environmental Protection Agency, 1985.
Guideline for Determination of Good
Engineering Practice Stack Height
(Technical Support Document for the
Stack Height Regulations), Revised.
Publication No. EPA–450/4–80–023R.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
https://www.epa.gov/ttn/scram/guidance/
guide/gep.pdf. (NTIS No. PB 85–225241)
84. Snyder, W.H. and R.E. Lawson, Jr., 1985.
Fluid Modeling Demonstration of Good
Engineering-Practice Stack Height in
Complex Terrain. Publication No. EPA–
600/3–85–022. Office of Research &
Development, Research Triangle Park,
NC. (NTIS No. PB 85–203107)
85. Briggs, G.A., 1975. Plume Rise
Predictions. Chapter 3 in Lectures on Air
Pollution and Environmental Impact
Analyses. American Meteorological
Society, Boston, MA; pp. 59–111.
86. Hanna, S.R., G.A. Briggs and R.P. Hosker,
Jr., 1982. Plume Rise. Chapter 2 in
Handbook on Atmospheric Diffusion.
Technical Information Center, U.S.
Department of Energy, Washington, DC;
pp. 11–24. DOE/TIC–11223 (DE
82002045)
87. Weil, J.C., L.A. Corio and R.P. Brower,
1997. A PDF dispersion model for
buoyant plumes in the convective
boundary layer. Journal of Applied
Meteorology, 36: 982–1003.
88. L.L. Schulman, D.G. Strimaitis and J.S.
Scire, 2002. Development and evaluation
of the PRIME plume rise and building
downwash model. Journal of the Air &
Waste Management Association, 50:
378–390.
89. Environmental Protection Agency, 1995.
Compilation of Air Pollutant Emission
Factors, Volume I: Stationary Point and
Area Sources (Fifth Edition, AP–42: GPO
Stock No. 055–000–00500–1), and
Supplements A- D; Volume II: Mobile
Sources (Fifth Edition). Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. Volume I can be
downloaded from EPA’s Web site at
https://www.epa.gov/ttn/chief/ap42.html;
Volume II can be downloaded from
https://www.epa.gov/omswww/ap42.htm.
90. Environmental Protection Agency, 2014.
Draft Emissions Inventory Guidance for
Implementation of Ozone and Particulate
Matter National Ambient Air Quality
Standards (NAAQS) and Regional Haze
Regulations. Office of Air Quality
Planning & Standards, Research Triangle
Park, NC. https://www.epa.gov/ttn/chief/
PO 00000
Frm 00043
Fmt 4701
Sfmt 4702
45381
eidocs/eiguid/
2014revisedeiguidance.pdf.
91. Environmental Protection Agency, 1987.
Ambient Air Monitoring Guidelines for
Prevention of Significant Deterioration
(PSD). Publication No. EPA–450/4–87–
007. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
(NTIS No. PB 90–168030)
92. Environmental Protection Agency, 2011.
Additional Clarification Regarding
Application of Appendix W Modeling
Guidance for the 1-hour NO2 National
Ambient Air Quality Standard. Office of
Air Quality Planning & Standards,
Research Triangle Park, NC. https://
www.epa.gov/ttn/scram/guidance/
clarification/Additional_Clarifications_
AppendixW_Hourly-NO2-NAAQS_
FINAL_03-01-2011.pdf.
93. Environmental Protection Agency, 2014.
Modeling Guidance For Demonstrating
Attainment of Air Quality Goals for
Ozone, PM2.5, and Regional Haze (Draft).
U.S. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
https://www.epa.gov/ttn/scram/guidance/
guide/Draft_O3-PM-RH_Modeling_
Guidance-2014.pdf.
94. Environmental Protection Agency, 2013.
Memorandum: Use of ASOS
meteorological data in AERMOD
dispersion modeling. March 8, 2013.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
https://www.epa.gov/ttn/scram/guidance/
clarification/20130308_Met_Data_
Clarification.pdf.
95. Environmental Protection Agency, 2004.
User’s Guide for the AERMOD
Meteorological Preprocessor (AERMET).
Publication No. EPA–454/B–03–002.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
https://www.epa.gov/ttn/scram/
metobsdata_procaccprogs.htm#aermet.
96. U.S Environmental Protection Agency.
AERMINUTE User’s Guide. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/
ttn/scram/metobsdata_
procaccprogs.htm#aermet.
97. Environmental Protection Agency, 1993.
PCRAMMET User’s Guide. Publication
No. EPA–454/R–96–001. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. (NTIS No. PB 97–
147912)
98. Environmental Protection Agency, 1996.
Meteorological Processor for Regulatory
Models (MPRM). Publication No. EPA–
454/R–96–002. Office of Air Quality
Planning & Standards, Research Triangle
Park, NC. (NTIS No. PB 96–180518)
99. Paine, R.J., 1987. User’s Guide to the
CTDM Meteorological Preprocessor
Program. Publication No. EPA–600/8–
88–004. Office of Research &
Development, Research Triangle Park,
NC. (NTIS No. PB–88–162102)
100. Perry, S.G., D.J. Burns, L.H. Adams, R.J.
Paine, M.G. Dennis, M.T. Mills, D.G.
Strimaitis, R.J. Yamartino and E.M.
Insley, 1989. User’s Guide to the
Complex Terrain Dispersion Model Plus
Algorithms for Unstable Situations
E:\FR\FM\29JYP2.SGM
29JYP2
mstockstill on DSK4VPTVN1PROD with PROPOSALS2
45382
Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules
(CTDMPLUS). Volume 1: Model
Descriptions and User Instructions. EPA
Publication No. EPA–600/8–89–041.
Environmental Protection Agency,
Research Triangle Park, NC. https://
www.epa.gov/ttn/scram/dispersion_
prefrec.htm#ctdmplus. (NTIS No. PB 89–
181424)
101. Environmental Protection Agency, 2008.
AERSURFACE User’s Guide. Publication
No. EPA–454/B–08–001. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/
ttn/scram/dispersion_
related.htm#aersurface.
102. Wesson, K., R. Brode, W. Peters, and C.
Tillerson, 2006. AERMOD Sensitivity to
the Choice of Surface Characteristics.
A&WMA presentation.
103. Environ, 2014. The Mesocale Model
Interface Program (MMIF) Version 3.1
User’s Manual.
104. Environmental Protection Agency, 2015
Guidance on the Use of the Mesoscale
Model Interface Program (MMIF) for
AERMOD Applications. Publication No.
EPA–454/B–15–001. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC.
105. Solar and Meteorological Surface
Observation Network, 1961–1990; 3volume CD–ROM. Version 1.0,
September 1993. Produced jointly by
National Climatic Data Center and
National Renewable Energy Laboratory.
Can be ordered from NOAA National
Data Center’s Web site at https://
www.nndc.noaa.gov.
106. Hourly United States Weather
Observations, 1990–1995 (CD–ROM).
October 1997. Produced jointly by
National Climatic Data Center and
Environmental Protection Agency. Can
be ordered from NOAA National Data
Center’s Web site at https://
www.ncdc.noaa.gov.
107. Radiosonde Data of North America,
1946–1996; 4-volume CD–ROM.
August1996. Produced jointly by
Forecast Systems laboratory and
National Climatic Data Center. Can be
ordered from NOAA National Data
Center’s Web site at https://
lwf.ncdc.noaa.gov/oa/ncdc.html.
108. Environmental Protection Agency, 2000.
Meteorological Monitoring Guidance for
Regulatory Modeling Applications.
Publication No. EPA–454/R–99–005.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
(NTIS No. PB 2001–103606)
109. ASTM D5527: Standard Practice for
Measuring Surface Winds and
Temperature by Acoustic Means. (2011)
110. ASTM D5741: Standard Practice for
Characterizing Surface Wind Using Wind
Vane and Rotating Anemometer. (2011)
111. Environmental Protection Agency, 1995.
Quality Assurance for Air Pollution
Measurement Systems, Volume IV—
Meteorological Measurements.
Publication No. EPA600/R–94/038d.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
Note: For copies of this handbook, you
may make inquiry to ORD Publications,
VerDate Sep<11>2014
19:11 Jul 28, 2015
Jkt 235001
26 West Martin Luther King Dr.,
Cincinnati, OH 45268.
112. Bowen, B.M., J.M. Dewart and A.I. Chen,
1983. Stability Class Determination: A
Comparison for One Site. Proceedings,
Sixth Symposium on Turbulence and
Diffusion. American Meteorological
Society, Boston, MA; pp. 211–214.
(Docket No. A–92–65, II–A–7)
113. Environmental Protection Agency, 1993.
An Evaluation of a Solar Radiation/
Delta-T (SRDT) Method for Estimating
Pasquill-Gifford (P–G) Stability
Categories. Publication No. EPA–454/R–
93–055. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
(NTIS No. PB 94–113958)
114. Irwin, J.S., 1980. Dispersion Estimate
Suggestion #8: Estimation of Pasquill
Stability Categories. Office of Air Quality
Planning & Standards, Research Triangle
Park, NC. (Docket No. A–80–46, II–B–10)
115. Mitchell, Jr., A.E. and K.O. Timbre,
1979. Atmospheric Stability Class from
Horizontal Wind Fluctuation. Presented
at 72nd Annual Meeting of Air Pollution
Control Association, Cincinnati, OH;
June 24–29, 1979. (Docket No. A–80–46,
II–P–9)
116. Smedman—Hogstrom, A. and V.
Hogstrom, 1978. A Practical Method for
Determining Wind Frequency
Distributions for the Lowest 200m from
Routine Meteorological Data. Journal of
Applied Meteorology, 17(7): 942–954.
117. Smith, T.B. and S.M. Howard, 1972.
Methodology for Treating Diffusivity.
MRI 72 FR–1030. Meteorology Research,
Inc., Altadena, CA. (Docket No. A–80–
46, II–P–8)
118. Environmental Protection Agency, 2015.
Evaluation of Prognostic Meteorological
Data in AERMOD Applications.
Publication No. EPA–454/R–15–004.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
119. Environmental Protection Agency, 1984.
Calms Processor (CALMPRO) User’s
Guide. Publication No. EPA–901/9–84–
001. Office Of Air Quality Planning &
Standards, Region I, Boston, MA. (NTIS
No. PB 84–229467)
Appendix A to Appendix W of Part
51—Summaries of Preferred Air
Quality Models
Table of Contents
A.0 Introduction and Availability
A.1 AERMOD (AMS/EPA Regulatory
Model)
A.2 CTDMPLUS (Complex Terrain
Dispersion Model Plus Algorithms for
Unstable Situations)
A.3 OCD (Offshore and Coastal Dispersion
Model)
A.0 Introduction and Availability
(1) This appendix summarizes key features
of refined air quality models preferred for
specific regulatory applications. For each
model, information is provided on
availability, approximate cost (where
applicable), regulatory use, data input,
output format and options, simulation of
atmospheric physics, and accuracy. These
models may be used without a formal
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demonstration of applicability provided they
satisfy the recommendations for regulatory
use; not all options in the models are
necessarily recommended for regulatory use.
(2) Many of these models have been
subjected to a performance evaluation using
comparisons with observed air quality data.
Where possible, several of the models
contained herein have been subjected to
evaluation exercises, including (1) statistical
performance tests recommended by the
American Meteorological Society and (2)
peer scientific reviews. The models in this
appendix have been selected on the basis of
the results of the model evaluations,
experience with previous use, familiarity of
the model to various air quality programs,
and the costs and resource requirements for
use.
(3) Codes and documentation for all
models listed in this appendix are available
from the EPA’s Support Center for Regulatory
Air Models (SCRAM) Web site at https://
www.epa.gov/ttn/scram. Codes and
documentation may also available from the
National Technical Information Service
(NTIS), https://www.ntis.gov, and, when
available, is referenced with the appropriate
NTIS accession number.
A.1 AERMOD (AMS/EPA Regulatory
Model)
References
Environmental Protection Agency, 2004.
AERMOD: Description of Model
Formulation. Publication No. EPA–454/
R–03–004. Office of Air Quality Planning
& Standards, Research Triangle Park, NC;
September 2004. https://www.epa.gov/
ttn/scram/7thconf/aermod/aermod_
mfd.pdf.
Cimorelli, A., et al., 2005. AERMOD: A
Dispersion Model for Industrial Source
Applications. Part I: General Model
Formulation and Boundary Layer
Characterization. Journal of Applied
Meteorology, 44(5): 682–693.
Perry, S. et al., 2005. AERMOD: A Dispersion
Model for Industrial Source
Applications. Part II: Model Performance
against 17 Field Study Databases. Journal
of Applied Meteorology, 44(5): 694–708.
Environmental Protection Agency, 2004.
User’s Guide for the AMS/EPA
Regulatory Model—AERMOD.
Publication No. EPA–454/B–03–001.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC;
September 2004. https://www.epa.gov/
ttn/scram/dispersion_
prefrec.htm#aermod.
Environmental Protection Agency, 2004.
User’s Guide for the AERMOD
Meteorological Preprocessor (AERMET).
Publication No. EPA–454/B–03–002.
Office of Air Quality Planning &
Standards, Research Triangle Park, NC;
November 2004. https://www.epa.gov/ttn/
scram/metobsdata_
procaccprogs.htm#aermet.
User’s Guide for the AERMOD Terrain
Preprocessor (AERMAP). Publication No.
EPA–454/B–03–003. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC; October 2004.
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https://www.epa.gov/ttn/scram/
dispersion_related.htm#aermap.
Schulman, L. L., D.G. Strimaitis and J.S.
Scire, 2000. Development and evaluation
of the PRIME plume rise and building
downwash model. Journal of the Air and
Waste Management Association, 50:
378–390.
Schulman, L. L., and Joseph S. Scire, 1980.
Buoyant Line and Point Source (BLP)
Dispersion Model User’s Guide.
Document P–7304B. Environmental
Research and Technology, Inc., Concord,
MA. (NTIS No. PB 81–164642).
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Availability
The model codes and associated
documentation are available on EPA’s
SCRAM Web site (paragraph A.0(3)).
Abstract
AERMOD is a steady-state plume
dispersion model for assessment of pollutant
concentrations from a variety of sources.
AERMOD simulates transport and dispersion
from multiple point, area, or volume sources
based on an up-to-date characterization of the
atmospheric boundary layer. Sources may be
located in rural or urban areas, and receptors
may be located in simple or complex terrain.
AERMOD accounts for building wake effects
(i.e., plume downwash) based on the PRIME
building downwash algorithms. The model
employs hourly sequential preprocessed
meteorological data to estimate
concentrations for averaging times from 1hour to 1-year (also multiple years).
AERMOD can be used to estimate the
concentrations of nonreactive pollutants from
highway traffic. AERMOD also handles
unique modeling problems associated with
aluminum reduction plants, and other
industrial sources where plume rise and
downwash effects from stationary buoyant
line sources are important. AERMOD is
designed to operate in concert with two preprocessor codes: AERMET processes
meteorological data for input to AERMOD,
and AERMAP processes terrain elevation
data and generates receptor and hill height
information for input to AERMOD.
a. Recommendations for Regulatory Use
(1) AERMOD is appropriate for the
following applications:
• Point, volume, and area sources;
• Buoyant, elevated line sources (e.g.,
aluminum reduction plants);
• Mobile (line) sources;
• Surface, near-surface, and elevated
releases;
• Rural or urban areas;
• Simple and complex terrain;
• Transport distances over which steadystate assumptions are appropriate, up to
50km;
• 1-hour to annual averaging times; and
• Continuous toxic air emissions.
(2) For regulatory applications of
AERMOD, the regulatory default option
should be set, i.e., the parameter DFAULT
should be employed in the MODELOPT
record in the Control Pathway. The DFAULT
option requires the use of terrain elevation
data, stack-tip downwash, sequential date
checking, and does not permit the use of the
model in the SCREEN mode. In the
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regulatory default mode, pollutant half-life or
decay options are not employed, except in
the case of an urban source of sulfur dioxide
where a four-hour half-life is applied. Terrain
elevation data from the U.S. Geological
Survey 7.5-Minute Digital Elevation Model
(DEM) or equivalent (approx. 30-meter
resolution) should be used in all
applications. Starting in 2011, data from the
National Elevation Dataset (NED, https://
ned.usgs.gov) can also be used in AERMOD,
which includes a range of resolutions,
ranging from 1-m to 2 arc seconds and such
high resolution would always be preferred. In
some cases, exceptions of the terrain data
requirement may be made in consultation
with the appropriate reviewing authority
(paragraph 3.0(b)).
b. Input Requirements
(1) Source data: Required input includes
source type, location, emission rate, stack
height, stack inside diameter, stack gas exit
velocity, stack gas temperature, area and
volume source dimensions, and source
elevation. Building dimensions and variable
emission rates are optional. Buoyant line
sources require coordinates of the end points
of the line, release height, emission rate,
average line source width, average building
width, average spacing between buildings,
and average line source buoyancy parameter.
For mobile sources, traffic volume; emission
factor, source height, and mixing zone width
are needed.
(2) Meteorological data: The AERMET
meteorological preprocessor requires input of
surface characteristics, including surface
roughness (zo), Bowen ratio, and albedo, as
well as, hourly observations of wind speed
between 7zo and 100m (reference wind speed
measurement from which a vertical profile
can be developed), wind direction, cloud
cover, and temperature between zo and 100m
(reference temperature measurement from
which a vertical profile can be developed).
Meteorological data can be in the form of
observed data or prognostic modeled data as
discussed in paragraph 8.4.1(d). Surface
characteristics may be varied by wind sector
and by season or month. When using
observed meteorological data, a morning
sounding (in National Weather Service
format) from a representative upper air
station is required. Latitude, longitude, and
time zone of the surface, site-specific (if
applicable) and upper air meteorological
stations are required. The wind speed
starting threshold is also required in
AERMET for applications involving sitespecific data). When using prognostic data,
modeled profiles of temperature and winds
are input into AERMET. These can be hourly
or a time that represents a morning sounding.
Additionally, measured profiles of wind,
temperature, vertical and lateral turbulence
may be required in certain applications (e.g.,
in complex terrain) to adequately represent
the meteorology affecting plume transport
and dispersion. Optionally, measurements of
solar, or net radiation may be input to
AERMET. Two files are produced by the
AERMET meteorological preprocessor for
input to the AERMOD dispersion model.
When using observed data, the surface file
contains observed and calculated surface
variables, one record per hour. For
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applications with multi-level site-specific
meteorological data, the profile contains the
observations made at each level of the
meteorological tower (or remote sensor).
When using prognostic data, the surface file
contains surface variables calculated by the
prognostic model and AERMET. The profile
file contains the observations made at each
level of a meteorological tower (or remote
sensor), the one-level observations taken
from other representative data (e.g., National
Weather Service surface observations), one
record per level per hour, or in the case of
prognostic data, the prognostic modeled
values of temperature and winds at userspecified levels.
(i) Data used as input to AERMET should
possess an adequate degree of
representativeness to insure that the wind,
temperature and turbulence profiles derived
by AERMOD are both laterally and vertically
representative of the source area. The
adequacy of input data should be judged
independently for each variable. The values
for surface roughness, Bowen ratio, and
albedo should reflect the surface
characteristics in the vicinity of the
meteorological tower or representative grid
cell when using prognostic data, and should
be adequately representative of the modeling
domain. Finally, the primary atmospheric
input variables including wind speed and
direction, ambient temperature, cloud cover,
and a morning upper air sounding should
also be adequately representative of the
source area, when using observed data.
(ii) For recommendations regarding the
length of meteorological record needed to
perform a regulatory analysis with AERMOD,
see section 8.4.2.
(3) Receptor data: Receptor coordinates,
elevations, height above ground, and hill
height scales are produced by the AERMAP
terrain preprocessor for input to AERMOD.
Discrete receptors and/or multiple receptor
grids, Cartesian and/or polar, may be
employed in AERMOD. AERMAP requires
input of DEM terrain data produced by the
U.S. Geological Survey (USGS), or other
equivalent data. AERMAP can be used
optionally to estimate source elevations.
c. Output
Printed output options include input
information, high concentration summary
tables by receptor for user-specified
averaging periods, maximum concentration
summary tables, and concurrent values
summarized by receptor for each day
processed. Optional output files can be
generated for: A listing of occurrences of
exceedances of user-specified threshold
value; a listing of concurrent (raw) results at
each receptor for each hour modeled, suitable
for post-processing; a listing of design values
that can be imported into graphics software
for plotting contours; a listing of results
suitable for NAAQS analyses including
NAAQS exceedances and culpability
analyses; an unformatted listing of raw
results above a threshold value with a special
structure for use with the TOXX model
component of TOXST; a listing of
concentrations by rank (e.g., for use in
quantile-quantile plots); and, a listing of
concentrations, including arc-maximum
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normalized concentrations, suitable for
model evaluation studies.
d. Type of Model
AERMOD is a steady-state plume model,
using Gaussian distributions in the vertical
and horizontal for stable conditions, and in
the horizontal for convective conditions. The
vertical concentration distribution for
convective conditions results from an
assumed bi-Gaussian probability density
function of the vertical velocity.
e. Pollutant Types
AERMOD is applicable to primary
pollutants and continuous releases of toxic
and hazardous waste pollutants. Chemical
transformation is treated by simple
exponential decay.
f. Source-Receptor Relationships
AERMOD applies user-specified locations
for sources and receptors. Actual separation
between each source-receptor pair is used.
Source and receptor elevations are user input
or are determined by AERMAP using USGS
DEM terrain data. Receptors may be located
at user-specified heights above ground level.
g. Plume Behavior
(1) In the convective boundary layer (CBL),
the transport and dispersion of a plume is
characterized as the superposition of three
modeled plumes: The direct plume (from the
stack), the indirect plume, and the penetrated
plume, where the indirect plume accounts
for the lofting of a buoyant plume near the
top of the boundary layer, and the penetrated
plume accounts for the portion of a plume
that, due to its buoyancy, penetrates above
the mixed layer, but can disperse downward
and re-enter the mixed layer. In the CBL,
plume rise is superposed on the
displacements by random convective
velocities (Weil et al., 1997).
(2) In the stable boundary layer, plume rise
is estimated using an iterative approach to
account for height-dependent lapse rates,
similar to that in the CTDMPLUS model (see
A.2 in this appendix).
(3) Stack-tip downwash and buoyancy
induced dispersion effects are modeled.
Building wake effects are simulated for stacks
subject to building downwash using the
methods contained in the PRIME downwash
algorithms (Schulman, et al., 2000). For
plume rise affected by the presence of a
building, the PRIME downwash algorithm
uses a numerical solution of the mass, energy
and momentum conservation laws (Zhang
and Ghoniem, 1993). Streamline deflection
and the position of the stack relative to the
building affect plume trajectory and
dispersion. Enhanced dispersion is based on
the approach of Weil (1996). Plume mass
captured by the cavity is well-mixed within
the cavity. The captured plume mass is reemitted to the far wake as a volume source.
(4) For elevated terrain, AERMOD
incorporates the concept of the critical
dividing streamline height, in which flow
below this height remains horizontal, and
flow above this height tends to rise up and
over terrain (Snyder et al., 1985). Plume
concentration estimates are the weighted sum
of these two limiting plume states. However,
consistent with the steady-state assumption
of uniform horizontal wind direction over the
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modeling domain, straight-line plume
trajectories are assumed, with adjustment in
the plume/receptor geometry used to account
for the terrain effects.
h. Horizontal Winds
Vertical profiles of wind are calculated for
each hour based on measurements and
surface-layer similarity (scaling)
relationships. At a given height above
ground, for a given hour, winds are assumed
constant over the modeling domain. The
effect of the vertical variation in horizontal
wind speed on dispersion is accounted for
through simple averaging over the plume
depth.
i. Vertical Wind Speed
In convective conditions, the effects of
random vertical updraft and downdraft
velocities are simulated with a bi-Gaussian
probability density function. In both
convective and stable conditions, the mean
vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Gaussian horizontal dispersion coefficients
are estimated as continuous functions of the
parameterized (or measured) ambient lateral
turbulence and also account for buoyancyinduced and building wake-induced
turbulence. Vertical profiles of lateral
turbulence are developed from measurements
and similarity (scaling) relationships.
Effective turbulence values are determined
from the portion of the vertical profile of
lateral turbulence between the plume height
and the receptor height. The effective lateral
turbulence is then used to estimate
horizontal dispersion.
k. Vertical Dispersion
In the stable boundary layer, Gaussian
vertical dispersion coefficients are estimated
as continuous functions of parameterized
vertical turbulence. In the convective
boundary layer, vertical dispersion is
characterized by a bi-Gaussian probability
density function, and is also estimated as a
continuous function of parameterized
vertical turbulence. Vertical turbulence
profiles are developed from measurements
and similarity (scaling) relationships. These
turbulence profiles account for both
convective and mechanical turbulence.
Effective turbulence values are determined
from the portion of the vertical profile of
vertical turbulence between the plume height
and the receptor height. The effective vertical
turbulence is then used to estimate vertical
dispersion.
l. Chemical Transformation
Chemical transformations are generally not
treated by AERMOD. However, AERMOD
does contain an option to treat chemical
transformation using simple exponential
decay, although this option is typically not
used in regulatory applications, except for
sources of sulfur dioxide in urban areas.
Either a decay coefficient or a half-life is
input by the user. Note also that the Plume
Volume Molar Ratio Method and the Ozone
Limiting Method (section 4.2.3.4) and for
point-source NO2 analyses are available.
m. Physical Removal
AERMOD can be used to treat dry and wet
deposition for both gases and particles.
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n. Evaluation Studies
American Petroleum Institute, 1998.
Evaluation of State of the Science of Air
Quality Dispersion Model, Scientific
Evaluation, prepared by WoodwardClyde Consultants, Lexington,
Massachusetts, for American Petroleum
Institute, Washington, DC, 20005–4070.
Brode, R.W., 2002. Implementation and
Evaluation of PRIME in AERMOD.
Preprints of the 12th Joint Conference on
Applications of Air Pollution
Meteorology, May 20–24, 2002;
American Meteorological Society,
Boston, MA.
Brode, R.W., 2004. Implementation and
Evaluation of Bulk Richardson Number
Scheme in AERMOD. 13th Joint
Conference on Applications of Air
Pollution Meteorology, August 23–26,
2004; American Meteorological Society,
Boston, MA.
Environmental Protection Agency, 2003.
AERMOD: Latest Features and
Evaluation Results. Publication No.
EPA–454/R–03–003. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/
ttn/scram/7thconf/aermod/aermod_
mep.pdf.
Heist, D., et al, 2013. Estimating near-road
pollutant dispersion: A model intercomparison. Transportation Research
Part D: Transport and Environment, 25:
pp 93–105.
A.2 CTDMPLUS (Complex Terrain
Dispersion Model Plus Algorithms for
Unstable Situations)
References
Perry, S.G., D.J. Burns, L.H. Adams, R.J.
Paine, M.G. Dennis, M.T. Mills, D.G.
Strimaitis, R.J. Yamartino and E.M.
Insley, 1989. User’s Guide to the
Complex Terrain Dispersion Model Plus
Algorithms for Unstable Situations
(CTDMPLUS). Volume 1: Model
Descriptions and User Instructions. EPA
Publication No. EPA–600/8–89–041.
Environmental Protection Agency,
Research Triangle Park, NC. https://
www.epa.gov/ttn/scram/dispersion_
prefrec.htm#ctdmplus. (NTIS No. PB 89–
181424)
Perry, S.G., 1992. CTDMPLUS: A Dispersion
Model for Sources near Complex
Topography. Part I: Technical
Formulations. Journal of Applied
Meteorology, 31(7): 633–645.
Availability
The model codes and associated
documentation are available on the EPA’s
SCRAM Web site (paragraph A.0(3)).
Abstract
CTDMPLUS is a refined point source
Gaussian air quality model for use in all
stability conditions for complex terrain
applications. The model contains, in its
entirety, the technology of CTDM for stable
and neutral conditions. However,
CTDMPLUS can also simulate daytime,
unstable conditions, and has a number of
additional capabilities for improved user
friendliness. Its use of meteorological data
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and terrain information is different from
other EPA models; considerable detail for
both types of input data is required and is
supplied by preprocessors specifically
designed for CTDMPLUS. CTDMPLUS
requires the parameterization of individual
hill shapes using the terrain preprocessor and
the association of each model receptor with
a particular hill.
a. Recommendation for Regulatory Use
CTDMPLUS is appropriate for the
following applications:
• Elevated point sources;
• Terrain elevations above stack top;
• Rural or urban areas;
• Transport distances less than 50
kilometers; and
• 1-hour to annual averaging times when
used with a post-processor program such as
CHAVG.
b. Input Requirements
(1) Source data: For each source, user
supplies source location, height, stack
diameter, stack exit velocity, stack exit
temperature, and emission rate; if variable
emissions are appropriate, the user supplies
hourly values for emission rate, stack exit
velocity, and stack exit temperature.
(2) Meteorological data: For applications of
CTDMPLUS, multiple level (typically three
or more) measurements of wind speed and
direction, temperature and turbulence (wind
fluctuation statistics) are required to create
the basic meteorological data file
(‘‘PROFILE’’). Such measurements should be
obtained up to the representative plume
height(s) of interest (i.e., the plume height(s)
under those conditions important to the
determination of the design concentration).
The representative plume height(s) of interest
should be determined using an appropriate
complex terrain screening procedure (e.g.,
CTSCREEN) and should be documented in
the monitoring/modeling protocol. The
necessary meteorological measurements
should be obtained from an appropriately
sited meteorological tower augmented by
SODAR and/or RASS if the representative
plume height(s) of interest is above the levels
represented by the tower measurements.
Meteorological preprocessors then create a
SURFACE data file (hourly values of mixed
layer heights, surface friction velocity,
Monin-Obukhov length and surface
roughness length) and a RAWINsonde data
file (upper air measurements of pressure,
temperature, wind direction, and wind
speed).
(3) Receptor data: Receptor names (up to
400) and coordinates, and hill number (each
receptor must have a hill number assigned).
(4) Terrain data: User inputs digitized
contour information to the terrain
preprocessor which creates the TERRAIN
data file (for up to 25 hills).
c. Output
(1) When CTDMPLUS is run, it produces
a concentration file, in either binary or text
format (user’s choice), and a list file
containing a verification of model inputs, i.e.,
• Input meteorological data from
‘‘SURFACE’’ and ‘‘PROFILE’’,
• Stack data for each source,
• Terrain information,
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• Receptor information, and
• Source-receptor location (line printer
map).
(2) In addition, if the case-study option is
selected, the listing includes:
• Meteorological variables at plume height,
• Geometrical relationships between the
source and the hill, and
• Plume characteristics at each receptor,
i.e.,
—Distance in along-flow and cross flow
direction
—Effective plume-receptor height difference
—Effective sy & sz values, both flat terrain
and hill induced (the difference shows the
effect of the hill)
—Concentration components due to WRAP,
LIFT and FLAT
(3) If the user selects the TOPN option, a
summary table of the top four concentrations
at each receptor is given. If the ISOR option
is selected, a source contribution table for
every hour will be printed.
(4) A separate output file of predicted (1hour only) concentrations (‘‘CONC’’) is
written if the user chooses this option. Three
forms of output are possible:
(i) A binary file of concentrations, one
value for each receptor in the hourly
sequence as run;
(ii) A text file of concentrations, one value
for each receptor in the hourly sequence as
run; or
(iii) A text file as described above, but with
a listing of receptor information (names,
positions, hill number) at the beginning of
the file.
(5) Hourly information provided to these
files besides the concentrations themselves
includes the year, month, day, and hour
information as well as the receptor number
with the highest concentration.
d. Type of Model
CTDMPLUS is a refined steady-state, point
source plume model for use in all stability
conditions for complex terrain applications.
e. Pollutant Types
CTDMPLUS may be used to model nonreactive, primary pollutants.
f. Source-Receptor Relationship
Up to 40 point sources, 400 receptors and
25 hills may be used. Receptors and sources
are allowed at any location. Hill slopes are
assumed not to exceed 15°, so that the
linearized equation of motion for Boussinesq
flow are applicable. Receptors upwind of the
impingement point, or those associated with
any of the hills in the modeling domain,
require separate treatment.
g. Plume Behavior
(1) As in CTDM, the basic plume rise
algorithms are based on Briggs’ (1975)
recommendations.
(2) A central feature of CTDMPLUS for
neutral/stable conditions is its use of a
critical dividing-streamline height (Hc) to
separate the flow in the vicinity of a hill into
two separate layers. The plume component in
the upper layer has sufficient kinetic energy
to pass over the top of the hill while
streamlines in the lower portion are
constrained to flow in a horizontal plane
around the hill. Two separate components of
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CTDMPLUS compute ground-level
concentrations resulting from plume material
in each of these flows.
(3) The model calculates on an hourly (or
appropriate steady averaging period) basis
how the plume trajectory (and, in stable/
neutral conditions, the shape) is deformed by
each hill. Hourly profiles of wind and
temperature measurements are used by
CTDMPLUS to compute plume rise, plume
penetration (a formulation is included to
handle penetration into elevated stable
layers, based on Briggs (1984)), convective
scaling parameters, the value of Hc, and the
Froude number above Hc.
h. Horizontal Winds
CTDMPLUS does not simulate calm
meteorological conditions. Both scalar and
vector wind speed observations can be read
by the model. If vector wind speed is
unavailable, it is calculated from the scalar
wind speed. The assignment of wind speed
(either vector or scalar) at plume height is
done by either:
• Interpolating between observations
above and below the plume height, or
• Extrapolating (within the surface layer)
from the nearest measurement height to the
plume height.
i. Vertical Wind Speed
Vertical flow is treated for the plume
component above the critical dividing
streamline height (Hc); see ‘‘Plume
Behavior.’’
j. Horizontal Dispersion
Horizontal dispersion for stable/neutral
conditions is related to the turbulence
velocity scale for lateral fluctuations, sv, for
which a minimum value of 0.2 m/s is used.
Convective scaling formulations are used to
estimate horizontal dispersion for unstable
conditions.
k. Vertical Dispersion
Direct estimates of vertical dispersion for
stable/neutral conditions are based on
observed vertical turbulence intensity, e.g.,
sw (standard deviation of the vertical
velocity fluctuation). In simulating unstable
(convective) conditions, CTDMPLUS relies
on a skewed, bi-Gaussian probability density
function (pdf) description of the vertical
velocities to estimate the vertical distribution
of pollutant concentration.
l. Chemical Transformation
Chemical transformation is not treated by
CTDMPLUS.
m. Physical Removal
Physical removal is not treated by
CTDMPLUS (complete reflection at the
ground/hill surface is assumed).
n. Evaluation Studies
Burns, D.J., L.H. Adams and S.G. Perry, 1990.
Testing and Evaluation of the
CTDMPLUS Dispersion Model: Daytime
Convective Conditions. Environmental
Protection Agency, Research Triangle
Park, NC.
Paumier, J.O., S.G. Perry and D.J. Burns,
1990. An Analysis of CTDMPLUS Model
Predictions with the Lovett Power Plant
Data Base. Environmental Protection
Agency, Research Triangle Park, NC.
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Paumier, J.O., S.G. Perry and D.J. Burns,
1992. CTDMPLUS: A Dispersion Model
for Sources near Complex Topography.
Part II: Performance Characteristics.
Journal of Applied Meteorology, 31(7):
646–660.
A.3 OCD (Offshore and Coastal Dispersion
Model)
Reference
DiCristofaro, D.C. and S.R. Hanna, 1989.
OCD: The Offshore and Coastal
Dispersion Model, Version 4. Volume I:
User’s Guide, and Volume II:
Appendices. Sigma Research
Corporation, Westford, MA. https://
www.epa.gov/ttn/scram/dispersion_
prefrec.htm#ocd. (NTIS Nos. PB 93–
144384 and PB 93–144392)
mstockstill on DSK4VPTVN1PROD with PROPOSALS2
Availability
The model codes and associated
documentation are available on EPA’s
SCRAM Web site (paragraph A.0(3)). Official
contact at Minerals Management Service: Mr.
Dirk Herkhof, Parkway Atrium Building, 381
Elden Street, Herndon, VA 20170, Phone:
(703) 787–1735.
Abstract
(1) OCD is a straight-line Gaussian model
developed to determine the impact of
offshore emissions from point, area or line
sources on the air quality of coastal regions.
OCD incorporates overwater plume transport
and dispersion as well as changes that occur
as the plume crosses the shoreline. Hourly
meteorological data are needed from both
offshore and onshore locations. These
include water surface temperature, overwater
air temperature, mixing height, and relative
humidity.
(2) Some of the key features include
platform building downwash, partial plume
penetration into elevated inversions, direct
use of turbulence intensities for plume
dispersion, interaction with the overland
internal boundary layer, and continuous
shoreline fumigation.
a. Recommendations for Regulatory Use
OCD has been recommended for use by the
Minerals Management Service for emissions
located on the Outer Continental Shelf (50 FR
12248; 28 March 1985). OCD is applicable for
overwater sources where onshore receptors
are below the lowest source height. Where
onshore receptors are above the lowest
source height, offshore plume transport and
dispersion may be modeled on a case-by-case
basis in consultation with the appropriate
reviewing authority (paragraph 3.0(b)).
b. Input Requirements
(1) Source data: Point, area or line source
location, pollutant emission rate, building
height, stack height, stack gas temperature,
stack inside diameter, stack gas exit velocity,
stack angle from vertical, elevation of stack
base above water surface and gridded
specification of the land/water surfaces. As
an option, emission rate, stack gas exit
velocity and temperature can be varied
hourly.
(2) Meteorological data (over water): Wind
direction, wind speed, mixing height, relative
humidity, air temperature, water surface
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temperature, vertical wind direction shear
(optional), vertical temperature gradient
(optional), turbulence intensities (optional).
(3) Meteorological data:
Over land: Surface weather data from a
preprocessor such as PCRAMMET which
provides hourly stability class, wind
direction, wind speed, ambient temperature,
and mixing height are required.
Over water: Hourly values for mixing
height, relative humidity, air temperature,
and water surface temperature are required;
if wind speed/direction are missing, values
over land will be used (if available); vertical
wind direction shear, vertical temperature
gradient, and turbulence intensities are
optional.
(4) Receptor data: Location, height above
local ground-level, ground-level elevation
above the water surface.
c. Output
(1) All input options, specification of
sources, receptors and land/water map
including locations of sources and receptors.
(2) Summary tables of five highest
concentrations at each receptor for each
averaging period, and average concentration
for entire run period at each receptor.
(3) Optional case study printout with
hourly plume and receptor characteristics.
Optional table of annual impact assessment
from non-permanent activities.
(4) Concentration output files can be used
by ANALYSIS postprocessor to produce the
highest concentrations for each receptor, the
cumulative frequency distributions for each
receptor, the tabulation of all concentrations
exceeding a given threshold, and the
manipulation of hourly concentration files.
d. Type of Model
OCD is a Gaussian plume model
constructed on the framework of the MPTER
model.
e. Pollutant Types
OCD may be used to model primary
pollutants. Settling and deposition are not
treated.
f. Source-Receptor Relationship
(1) Up to 250 point sources, 5 area sources,
or 1 line source and 180 receptors may be
used.
(2) Receptors and sources are allowed at
any location.
(3) The coastal configuration is determined
by a grid of up to 3600 rectangles. Each
element of the grid is designated as either
land or water to identify the coastline.
g. Plume Behavior
(1) As in ISC, the basic plume rise
algorithms are based on Briggs’
recommendations.
(2) Momentum rise includes consideration
of the stack angle from the vertical.
(3) The effect of drilling platforms, ships,
or any overwater obstructions near the source
are used to decrease plume rise using a
revised platform downwash algorithm based
on laboratory experiments.
(4) Partial plume penetration of elevated
inversions is included using the suggestions
of Briggs (1975) and Weil and Brower (1984).
(5) Continuous shoreline fumigation is
parameterized using the Turner method
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where complete vertical mixing through the
thermal internal boundary layer (TIBL)
occurs as soon as the plume intercepts the
TIBL.
h. Horizontal Winds
(1) Constant, uniform wind is assumed for
each hour.
(2) Overwater wind speed can be estimated
from overland wind speed using relationship
of Hsu (1981).
(3) Wind speed profiles are estimated using
similarity theory (Businger, 1973). Surface
layer fluxes for these formulas are calculated
from bulk aerodynamic methods.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to
zero.
j. Horizontal Dispersion
(1) Lateral turbulence intensity is
recommended as a direct estimate of
horizontal dispersion. If lateral turbulence
intensity is not available, it is estimated from
boundary layer theory. For wind speeds less
than 8 m/s, lateral turbulence intensity is
assumed inversely proportional to wind
speed.
(2) Horizontal dispersion may be enhanced
because of obstructions near the source. A
virtual source technique is used to simulate
the initial plume dilution due to downwash.
(3) Formulas recommended by Pasquill
(1976) are used to calculate buoyant plume
enhancement and wind direction shear
enhancement.
(4) At the water/land interface, the change
to overland dispersion rates is modeled using
a virtual source. The overland dispersion
rates can be calculated from either lateral
turbulence intensity or Pasquill-Gifford
curves. The change is implemented where
the plume intercepts the rising internal
boundary layer.
k. Vertical Dispersion
(1) Observed vertical turbulence intensity
is not recommended as a direct estimate of
vertical dispersion. Turbulence intensity
should be estimated from boundary layer
theory as default in the model. For very
stable conditions, vertical dispersion is also
a function of lapse rate.
(2) Vertical dispersion may be enhanced
because of obstructions near the source. A
virtual source technique is used to simulate
the initial plume dilution due to downwash.
(3) Formulas recommended by Pasquill
(1976) are used to calculate buoyant plume
enhancement.
(4) At the water/land interface, the change
to overland dispersion rates is modeled using
a virtual source. The overland dispersion
rates can be calculated from either vertical
turbulence intensity or the Pasquill-Gifford
coefficients. The change is implemented
where the plume intercepts the rising
internal boundary layer.
l. Chemical Transformation
Chemical transformations are treated using
exponential decay. Different rates can be
specified by month and by day or night.
m. Physical Removal
Physical removal is also treated using
exponential decay.
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n. Evaluation Studies
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DiCristofaro, D.C. and S.R. Hanna, 1989.
OCD: The Offshore and Coastal
Dispersion Model. Volume I: User’s
Guide. Sigma Research Corporation,
Westford, MA.
Hanna, S.R., L.L. Schulman, R.J. Paine and
J.E. Pleim, 1984. The Offshore and
Coastal Dispersion (OCD) Model User’s
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Guide, Revised. OCS Study, MMS 84–
0069. Environmental Research &
Technology, Inc., Concord, MA. (NTIS
No. PB 86–159803).
Hanna, S.R., L.L. Schulman, R.J. Paine, J.E.
Pleim and M. Baer, 1985. Development
and Evaluation of the Offshore and
Coastal Dispersion (OCD) Model. Journal
PO 00000
of the Air Pollution Control Association,
35: 1039–1047.
Hanna, S.R. and D.C. DiCristofaro, 1988.
Development and Evaluation of the
OCD/API Model. Final Report, API Pub.
4461, American Petroleum Institute,
Washington, DC.
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[Federal Register Volume 80, Number 145 (Wednesday, July 29, 2015)]
[Proposed Rules]
[Pages 45339-45387]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2015-18075]
[[Page 45339]]
Vol. 80
Wednesday,
No. 145
July 29, 2015
Part III
Environmental Protection Agency
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40 CFR Part 51
Revision to the Guideline on Air Quality Models: Enhancements to the
AERMOD Dispersion Modeling System and Incorporation of Approaches To
Address Ozone and Fine Particulate Matter; Proposed Rule
Federal Register / Vol. 80 , No. 145 / Wednesday, July 29, 2015 /
Proposed Rules
[[Page 45340]]
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ENVIRONMENTAL PROTECTION AGENCY
40 CFR Part 51
[EPA-HQ-OAR-2015-0310; FRL-9930-11-OAR]
RIN 2060-AS54
Revision to the Guideline on Air Quality Models: Enhancements to
the AERMOD Dispersion Modeling System and Incorporation of Approaches
To Address Ozone and Fine Particulate Matter
AGENCY: Environmental Protection Agency (EPA).
ACTION: Proposed rule; notice of conference.
-----------------------------------------------------------------------
SUMMARY: In this action, the Environmental Protection Agency (EPA)
proposes to revise the Guideline on Air Quality Models (``Guideline'').
The Guideline has been incorporated into EPA's regulations, satisfying
a requirement under the Clean Air Act (CAA) section 165(e)(3) for the
EPA to specify, with reasonable particularity models to be used in the
Prevention of Significant Deterioration (PSD) program. It provides EPA-
preferred models and other recommended techniques, as well as guidance
for their use in predicting ambient concentrations of air pollutants.
The proposed revisions to the Guideline include enhancements to the
formulation and application of the EPA's AERMOD near-field dispersion
modeling system and the incorporation of a tiered demonstration
approach to address the secondary chemical formation of ozone and fine
particulate matter (PM2.5) associated with precursor
emissions from single sources. Additionally, the EPA proposes various
editorial changes to update and reorganize information throughout the
Guideline to streamline the compliance assessment process.
Within this action, the EPA is also announcing the Eleventh
Conference on Air Quality Modeling and invites the public to
participate in the conference. The conference will focus on the
proposed revisions to the Guideline and part of the conference will
also serve as the public hearing for these revisions.
DATES: Comments must be received on or before October 27, 2015.
Public hearing and conference: The public hearing for this action
and the Eleventh Conference on Air Quality Modeling will be held August
12-13, 2015, from 8:30 a.m. to 5:00 p.m.
ADDRESSES: Submit your comments, identified by Docket ID No. EPA-HQ-
OAR-2015-0310, by one of the following methods:
Federal eRulemaking Portal: https://www.regulations.gov.
Follow the online instructions for submitting comments.
Email: A-and-R-Docket@epa.gov. Include docket ID No. EPA-
HQ-OAR-2015-0310 in the subject line of the message.
Fax: (202) 566-9744.
Mail: Environmental Protection Agency, Mail code 28221T,
Attention Docket No. EPA-HQ-OAR-2015-0310, 1200 Pennsylvania Ave. NW.,
Washington, DC 20460. Please include a total of two copies.
Hand/Courier Delivery: EPA Docket Center, Room 3334, EPA
WJC West Building, 1301 Constitution Ave. NW., Washington, DC. Such
deliveries are only accepted during the Docket's normal hours of
operation, and special arrangements should be made for deliveries of
boxed information.
Instructions: Direct your comments to Docket ID No. EPA-HQ-OAR-
2015-0310. The EPA's policy is that all comments received will be
included in the public docket without change and may be made available
online at https://www.regulations.gov, including any personal
information provided, unless the comment includes information claimed
to be Confidential Business Information (CBI) or other information
whose disclosure is restricted by statute. Do not submit information
that you consider to be CBI or otherwise protected through https://www.regulations.gov or email. The www.regulations.gov Web site is an
``anonymous access'' system, which means the EPA will not know your
identity or contact information unless you provide it in the body of
your comment. If you send an email comment directly to the EPA without
going through https://www.regulations.gov, your email address will be
automatically captured and included as part of the comment that is
placed in the public docket and made available on the Internet. If you
submit an electronic comment, the EPA recommends that you include your
name and other contact information in the body of your comment and with
any disk or CD ROM you submit. If the EPA cannot read your comment due
to technical difficulties and cannot contact you for clarification, the
EPA may not be able to consider your comment. Electronic files should
avoid the use of special characters, any form of encryption, and be
free of any defects or viruses. For additional information about the
EPA's public docket, visit the EPA Docket Center homepage at https://www.epa.gov/epahome/dockets.htm.
Docket: All documents in the docket are listed in the https://www.regulations.gov index. Although listed in the index, some
information is not publicly available, e.g., CBI or other information
whose disclosure is restricted by statute. Certain other material, such
as copyrighted material, will be publicly available only in hard copy.
Publicly available docket materials are available either electronically
in www.regulations.gov or in hard copy at the Air and Radiation Docket
and Information Center, EPA/DC, Room 3334, WJC West Building, 1301
Constitution Ave. NW., Washington, DC. The Public Reading Room is open
from 8:30 a.m. to 4:30 p.m., Monday through Friday, excluding legal
holidays. The telephone number for the Public Reading Room is (202)
566-1744 and the telephone number for the Air and Radiation Docket and
Information Center is (202) 566-1742.
Public hearing and conference: The public hearing for this action
and the Eleventh Conference on Air Quality Modeling will be held in the
EPA Auditorium, Room C111, 109 T.W. Alexander Drive, Research Triangle
Park, NC 27711.
FOR FURTHER INFORMATION CONTACT: Mr. George M. Bridgers, Air Quality
Assessment Division, Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency, Mail code C439-01, Research Triangle
Park, NC 27711; telephone: (919) 541-5563; fax: (919) 541-0044; email:
Bridgers.George@epa.gov.
SUPPLEMENTARY INFORMATION:
Table of Contents
The following topics are discussed in this preamble:
I. General Information
A. Does this action apply to me?
B. What should I consider as I prepare my comments for the EPA?
C. Where can I get a copy of this document?
II. Background
A. The Guideline on Air Quality Models and EPA Modeling
Conferences
B. The Tenth Conference on Air Quality Modeling
III. Public Participation Regarding Revisions to the Guideline and
Notice of Eleventh Conference on Air Quality Modeling
IV. Proposed Changes to the Guideline
A. Proposed Actions
1. Clarifications To Distinguish Requirements From
Recommendations
2. Updates to EPA's AERMOD Modeling System
3. Status of AERSCREEN
4. Updates to 3-Tiered Demonstration Approach for NO2
5. Status of CALINE3 Models
6. Addressing Single-Source Impacts on Ozone and Secondary
PM2.5
[[Page 45341]]
7. Status of CALPUFF and Assessing Long-Range Transport for PSD
Increment and Regional Haze
8. Role of EPA's Model Clearinghouse
9. Updates to Modeling Procedures for Cumulative Impact Analysis
10. Updates on Use of Meteorological Input Data for Regulatory
Dispersion Modeling
11. Transition Period for Applicability of Revisions to the
Guideline
B. Proposed Editorial Changes
V. Statutory and Executive Order Reviews
A. Executive Order 12866: Regulatory Planning and Review and
Executive Order 13563: Improving Regulation and Regulatory Review
B. Paperwork Reduction Act
C. Regulatory Flexibility Act
D. Unfunded Mandates Reform Act
E. Executive Order 13132: Federalism
F. Executive Order 13175: Consultation and Coordination With
Indian Tribal Governments
G. Executive Order 13045: Protection of Children From
Environmental Health and Safety Risks
H. Executive Order 13211: Actions Concerning Regulations That
Significantly Affect Energy Supply, Distribution, or Use
I. National Technology Transfer and Advancement Act
J. Executive Order 12898: Federal Actions To Address
Environmental Justice in Minority Populations and Low-Income
Populations
I. General Information
A. Does this action apply to me?
This action applies to federal, state, territorial, and local air
quality management programs that conduct air quality modeling as part
of State Implementation Plan (SIP) submittals and revisions, New Source
Review (NSR), including new or modifying industrial sources under
Prevention of Significant Deterioration (PSD), Conformity, and other
air quality assessments required under EPA regulation. Categories and
entities potentially regulated by this action include:
------------------------------------------------------------------------
NAICS \a\
Category Code
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Federal/state/territorial/local/tribal government.......... 924110
------------------------------------------------------------------------
\a\ North American Industry Classification System.
B. What should I consider as I prepare my comments for the EPA?
1. Submitting CBI. Do not submit this information to the EPA
through https://www.regulations.gov or email. Clearly mark any of the
information that you claim to be CBI. For CBI information in a disk or
CD ROM that you mail to the EPA, mark the outside of the disk or CD ROM
as CBI and then identify electronically within the disk or CD ROM the
specific information that is claimed as CBI. In addition to one
complete version of the comment that includes information claimed as
CBI, a copy of the comment that does not contain the information
claimed as CBI must be submitted for inclusion in the public docket.
Information so marked will not be disclosed except in accordance with
procedures set forth in 40 CFR part 2.
2. Tips for preparing your comments. When submitting comments,
remember to:
Follow directions--The agency may ask you to respond to
specific questions or organize comments by referencing a CFR part or
section number.
Explain why you agree or disagree, suggest alternatives,
and substitute language for your requested changes.
Describe any assumptions and provide any technical
information and/or data that you used.
If you estimate potential costs or burdens, explain how
you arrived at your estimate in sufficient detail to allow for it to be
reproduced.
Provide specific examples to illustrate your concerns and
suggest alternatives.
Explain your views as clearly as possible, avoiding the
use of profanity or personal threats.
Make sure to submit your comments by the comment period
deadline identified.
C. Where can I get a copy of this document?
In addition to being available in the docket, an electronic copy of
this proposed rule will also be available on the Worldwide Web (WWW)
through the EPA's Technology Transfer Network (TTN). Following
signature, a copy of this proposed rule will be posted on the TTN's
Support Center for Regulatory Atmospheric Modeling (SCRAM) Web site at
the following address: https://www.epa.gov/ttn/scram. The TTN provides
information and technology exchange in various areas of air pollution
control.
II. Background
A. The Guideline on Air Quality Models and EPA Modeling Conferences
The Guideline is used by the EPA, other federal, state,
territorial, and local air quality agencies, and industry to prepare
and review new source permits, source permit modifications, SIP
submittals and revisions, conformity, and other air quality assessments
required under EPA regulation. The Guideline serves as a means by which
national consistency is maintained in air quality analyses for
regulatory activities under 40 CFR 51.112, 51.117, 51.150, 51.160,
51.165, 51.166, 52.21, 93.116, 93.123, and 93.150.
The EPA originally published the Guideline in April 1978 (EPA-450/
2-78-027), and it was incorporated by reference in the regulations for
the PSD program in June 1978. The EPA revised the Guideline in 1986 (51
FR 32176), and updated it with supplement A in 1987 (53 FR 32081),
supplement B in July 1993 (58 FR 38816), and supplement C in August
1995 (60 FR 40465). The EPA published the Guideline as appendix W to 40
CFR part 51 when the EPA issued supplement B. The EPA republished the
Guideline in August 1996 (61 FR 41838) to adopt the CFR system for
labeling paragraphs. The publication and incorporation of the Guideline
by reference into the EPA's PSD regulations satisfies the requirement
under the CAA section 165(e)(3) for the EPA to promulgate regulations
that specify with reasonable particularity models to be used under
specified sets of conditions for purposes of the PSD program.
To support the process of developing and revising the Guideline
during the period of 1977-1988, we held the First, Second, and Third
Conferences on Air Quality Modeling as required by CAA section 320 to
help standardize modeling procedures. These modeling conferences
provided a forum for comments on the Guideline and associated
revisions, thereby helping us introduce improved modeling techniques
into the regulatory process.
In October 1988, we held the Fourth Conference on Air Quality
Modeling to advise the public on new modeling techniques and to solicit
comments to guide our consideration of any rulemaking needed to further
revise the Guideline. We held the Fifth Conference in March 1991, which
also served as a public hearing for the proposed revisions to the
Guideline. In August 1995, we held the Sixth Conference as a forum to
update our available modeling tools with state-of-the-science
techniques and for the public to offer new ideas.
The Seventh Conference was held in June 2000, and also served as a
public hearing for another round of proposed changes to the recommended
air quality models in the Guideline. These changes included the CALPUFF
modeling system, AERMOD modeling system, and ISC-PRIME model.
Subsequently, the EPA revised the Guideline on April 15, 2003 (68
FR 18440), to adopt CALPUFF as the preferred model for long-range
transport
[[Page 45342]]
of emissions from 50 to several hundred kilometers and to make various
editorial changes to update and reorganize information and remove
obsolete models.
We held the Eighth Conference on Air Quality Modeling in September
2005. This conference provided details on changes to the preferred air
quality models, including available methods for model performance
evaluation and the notice of data availability that the EPA published
in September 2003, related to the incorporation of the PRIME downwash
algorithm in the AERMOD dispersion model (in response to comments
received from the Seventh Conference). Additionally, at the Eighth
Conference, a panel of experts discussed the use of state-of-the-
science prognostic meteorological data for informing the dispersion
models.
The EPA further revised the Guideline on November 9, 2005 (70 FR
68218), to adopt AERMOD as the preferred model for near-field
dispersion of emissions for distances up to 50 kilometers.
The Ninth Conference on Air Quality Modeling was held in October
2008, and emphasized the following topics: Reinstituting the Model
Clearinghouse, review of non-guideline applications of dispersion
models, regulatory status updates of AERMOD and CALPUFF, continued
discussions on the use of prognostic meteorological data for informing
dispersion models, and presentations reviewing the available model
evaluation methods.
B. The Tenth Conference on Air Quality Modeling
The most recent EPA modeling conference was the Tenth Conference on
Air Quality Modeling held in March 2012. This conference covered
multiple topics which have been vital in the development of the
proposed revisions to the Guideline. The conference addressed updates
on the regulatory status and future development of AERMOD and CALPUFF,
review of the Mesoscale Model Interface (MMIF) prognostic
meteorological data processing tool for dispersion models, draft
modeling guidance for compliance demonstrations of the PM2.5
National Ambient Air Quality Standards (NAAQS), modeling for compliance
demonstration of the 1-hour nitrogen dioxide (NO2) and
sulfur dioxide (SO2) NAAQS, and new and emerging models/
techniques for future consideration under the Guideline to address
single-source modeling for ozone and secondary PM2.5, as
well as long-range transport and chemistry. A transcript of the
conference proceedings and a document that summarizes the public
comments received are available at EPA's SCRAM Web site at https://www.epa.gov/ttn/scram/10thmodconf.htm.
The EPA promulgated a new 1-hour NAAQS for NO2 in
January 2010, and a new 1-hour NAAQS for SO2 in June 2010.
Although AERMOD evaluations that formed the basis of its promulgation
as the EPA's preferred dispersion model demonstrated that AERMOD
provides generally unbiased estimates of ambient concentrations, the
increased stringency of these new standards resulted in increased
scrutiny by the modeling community of AERMOD model performance. In
response, the EPA issued several guidance memoranda to clarify the
applicability of the Guideline and address initial issues with use of
current models and procedures under PSD permitting.1 2 3 4
However, the situation also necessitated the EPA and the modeling
community to more closely evaluate the science and model formulation of
AERMOD to better understand the issues being experienced by
stakeholders and to address performance issues in its use for PSD
permitting under these new standards. As part of this effort, the EPA
reconvened the AERMOD Implementation Workgroup (AIWG) with state and
local agency modelers to evaluate AERMOD across a variety of
hypothetical sources and results from this assessment were also
presented at this conference to inform the modeling community of
potential implications and areas for improvement in the model and
guidance on their use.
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\1\ U.S. EPA, 2010. Applicability of Appendix W Modeling
Guidance for the 1-hour NO2 NAAQS. Tyler Fox Memorandum
dated June 28, 2010, Office of Air Quality Planning & Standards,
Research Triangle Park, North Carolina 27711. https://www.epa.gov/ttn/scram/guidance/clarification/ClarificationMemo_AppendixW_Hourly-NO2-NAAQS_FINAL_06-28-2010.pdf.
\2\ U.S. EPA, 2010. Applicability of Appendix W Modeling
Guidance for the 1-hour SO2 NAAQS. Tyler Fox Memorandum
dated August 23, 2010, Office of Air Quality Planning & Standards,
Research Triangle Park, North Carolina 27711. https://www.epa.gov/ttn/scram/guidance/clarification/ClarificationMemo_AppendixW_Hourly-SO2-NAAQS_FINAL_08-23-2010.pdf.
\3\ U.S. EPA, 2010. Guidance Concerning the Implementation of
the 1-hour SO2 NAAQS for the Prevention of Significant
Deterioration Program. Stephen D. Page, Memorandum dated August 23,
2010, Office of Air Quality Planning & Standards, Research Triangle
Park, North Carolina 27711. https://www.epa.gov/region07/air/nsr/nsrmemos/appwso2.pdf.
\4\ U.S. EPA, 2010. Guidance Concerning the Implementation of
the 1-hour NO2 NAAQS for the Prevention of Significant
Deterioration Program. Stephen D. Page, Memorandum dated June 29,
2010, Office of Air Quality Planning & Standards, Research Triangle
Park, North Carolina 27711. https://www.epa.gov/ttn/scram/guidance/clarification/ClarificationMemo_AppendixW_Hourly-SO2-NAAQS_FINAL_08-23-2010.pdf.
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Several presentations at the Tenth Modeling Conference addressed
issues and challenges associated with demonstrating compliance with
these new 1-hour NAAQS for NO2 and SO2. This
included results from a study sponsored by the American Petroleum
Institute (API) that evaluated AERMOD model performance under low wind
speed conditions using additional National Oceanic and Atmospheric
Administration (NOAA) field studies at Oak Ridge, TN, and Idaho Falls,
ID, which were not included in the original 17 databases used to
support AERMOD's promulgation in 2005. The API low wind study \5\
showed significant overprediction of observed concentrations,
especially for the Oak Ridge study where observed wind speeds were
below 0.5 m/s for 10 of the 11 tracer tests, and included wind speeds
as low as 0.15 m/s. The API low wind study also included proposed
modifications to the AERMET meteorological processor and AERMOD model
to address this bias toward overprediction under stable/light wind
conditions.
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\5\ AECOM, 2010. AERMOD low wind speed evaluation study results,
https://mycommittees.api.org/rasa/amp/Modeling%20Documents/AECOM%202009%20Low%20Wind%20Speed%20Evaluation%20Study%20Report.pdf.
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Prior to the promulgation of the 1-hour NO2 NAAQS,
compliance with the previous annual NO2 NAAQS was routinely
demonstrated based on the Tier 1 assumption of full conversion or a
Tier 2 option based on an ambient ratio of 75 percent conversion of
nitrogen oxides (NOX) to NO2, referred to as the
Ambient Ratio Method (ARM). However, compliance with the new 1-hour
NAAQS has typically required a more refined treatment of NOX
conversion to NO2. Therefore, several presentations at the
Tenth Modeling Conference focused on issues associated with
demonstrating compliance with the new 1-hour NO2 NAAQS.
These presentations included an overview of an API funded study to
develop a Tier 2 ambient ratio method for the 1-hour NO2
NAAQS, referred to as ARM2. The ARM2 approach was developed based on an
extensive analysis of ambient ratios of NO2/NOX
that were analyzed by land use (urban vs. rural) and geographical
areas. Based on these analyses of the ambient NO2/
NOX ratios, an empirical relationship between ambient
concentrations of NO2 and NOX was developed. The
EPA subsequently reviewed and evaluated this ARM2 approach and then
[[Page 45343]]
incorporated this screening technique as a non-Default/Beta option in
version 13350 of AERMOD in December 2013. Another issue associated with
NO2 NAAQS compliance presented at this conference focused on
the use of relative (instantaneous) dispersion coefficients to define
the plume volume which determines the amount of ozone available to
convert nitrogen (NO) to NO2 using the Plume Volume Molar
Ratio Method (PVMRM) option in AERMOD. The relative dispersion
coefficients originally incorporated in AERMOD for PVMRM are best
representative of daytime convective conditions and may tend to
overestimate plume volumes during stable conditions. Such
overestimation of the plume volume will tend to result in PVMRM to
overestimate concentrations of NO2.
In addition, modeling of single-source impacts for ozone and
secondarily formed PM2.5 was a topic of discussion at the
Tenth Modeling Conference. On January 4, 2012, the EPA granted a
petition submitted on behalf of the Sierra Club on July 28, 2010 \6\
and committed to engage in rulemaking to evaluate whether updates to
the Guideline are warranted and, as appropriate, incorporate new
analytical techniques or models for ozone and secondarily formed
PM2.5. As a part of satisfying this commitment, there were
presentations of ongoing research at the Tenth Modeling Conference
regarding single-source plume chemistry and photochemical grid modeling
techniques, as well as several public forums. In addition, the EPA
presented an overview along with a panel discussion of its Draft
Guideline for PM2.5 Permit Modeling that addressed the need for
consideration of secondary PM2.5 in demonstrating compliance
with the PM2.5 NAAQS.\7\ Subsequently, written comments
pertaining to such modeling were submitted to the EPA.
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\6\ U.S. EPA, 2012. Gina McCarthy Letter to Robert Ukeiley dated
January 4, 2012, Washington, DC 20460. https://www.epa.gov/ttn/scram/10thmodconf/review_material/Sierra_Club_Petition_OAR-11-002-1093.pdf.
\7\ U.S. EPA, 2014. Guidance for PM2.5 Modeling. May
20, 2014, EPA-454/B-14-001. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. https://www.epa.gov/ttn/scram/guidance/guide/Guidance_for_PM25_Permit_Modeling.pdf.
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As introduced at the Tenth Modeling Conference, the Interagency
Workgroup on Air Quality Modeling (IWAQM) process was formally
reinitiated in June 2013 to inform the EPA's process of updating the
Guideline to address chemically reactive pollutants in near-field and
long-range transport applications. The IWAQM, which consists of
representatives from the EPA, the U.S. Forest Service, the National
Park Service, and the U.S. Fish and Wildlife Service, was initially
formed to support development of technically sound recommendations
regarding assessment of air pollutant source impacts on Federal Class I
parks and wilderness areas. Comments received from stakeholders at the
Tenth Modeling Conference supported reinitiating this interagency
collaborative effort (as ``Phase 3'') to provide additional guidance
for modeling single-source impacts on secondarily formed pollutants
(e.g., ozone and PM2.5) in the near-field and for long-range
transport. Stakeholder comments also support the idea of this
collaborative effort working in parallel with stakeholders to further
model development and evaluation. This renewed \8\ effort included the
establishment of two separate working groups, one focused on long-range
transport of primary and secondary pollutants and the other on near-
field single-source impacts of secondary pollutants. The primary
objectives of this phase of IWAQM include reviewing existing approaches
for estimating single-source secondary pollutant impacts, developing
revisions to the Guideline, and the development of guidance for using
technical methods to estimate downwind secondary pollutant impacts.
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\8\ Phase 1 of the IWAQM effort focused on review of respective
regional modeling programs, development of an organizational
framework, and formulating reasonable objectives and plans that were
presented to EPA management for support and commitment. Phase 2 of
the IWAQM process continued this work and largely concluded in 1998
with the publication of the Interagency Workgroup on Air Quality
Modeling (IWAQM) Phase 2 Summary Report and Recommendations for
Modeling Long Range Transport Impacts (EPA-454/R-98-019) (USEPA,
1998). This report provided a series of recommendations concerning
the application of the CALPUFF model for use in regulatory long-
range transport (LRT) modeling that supported the revisions in 2003
to the Guideline.
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III. Public Participation Regarding Revisions to the Guideline and
Notice of Eleventh Conference on Air Quality Modeling
Interested persons may provide the EPA with their views on the
proposed revisions to the Guideline in several ways. This includes
submitting written comments to the EPA, participating in the Eleventh
Conference on Air Quality Modeling, and speaking at the public hearing
that will be conducted as part of the conference. Additional
information on how to submit written comments on the proposed revisions
to the Guideline is provided in the ADDRESSES section above.
The public hearing for this action and the Eleventh Conference on
Air Quality Modeling will be held August 12-13, 2015, from 8:30 a.m. to
5:00 p.m., in the EPA Auditorium, Room C111, 109 T.W. Alexander Drive,
Research Triangle Park, NC 27711. On August 12, 2015, the first half of
the conference will consist of a structured agenda with presentations.
The second half of the first and all of the second day (August 13,
2015), is reserved for the public hearing on this proposed rule.
Advance requests for reserved time to speak during the public hearing
should be submitted by August 7, 2015, to Mr. George M. Bridgers, Air
Quality Assessment Division, Office of Air Quality Planning and
Standards, U.S. Environmental Protection Agency, Mail code C439-01,
Research Triangle Park, NC 27711; telephone: (919) 541-5563; fax: (919)
541-0044; email: Bridgers.George@epa.gov. The EPA will also provide an
opportunity for oral presentations by individuals that sign up at the
public hearing. Information submitted to the EPA during the conference
will be placed in the docket for this rule proposing revisions to the
Guideline.
Background information: Preregistration details, additional
background information, and a more detailed agenda for the Eleventh
Conference on Air Quality Modeling are electronically available at
https://www.epa.gov/ttn/scram/11thmodconf.htm. Preregistration for the
conference, while not required, is strongly recommended due to
heightened security protocols at the EPA-RTP facility.
REAL ID Act: Because this hearing is being held at a U.S.
government facility, individuals planning to attend the hearing should
be prepared to show valid picture identification to the security staff
in order to gain access to the meeting room. Please note that the REAL
ID Act, passed by Congress in 2005, established new requirements for
entering federal facilities. These requirements took effect July 21,
2014. If your driver's license is issued by Alaska, American Samoa,
Arizona, Kentucky, Louisiana, Maine, Massachusetts, Minnesota, Montana,
New York, Oklahoma, or the state of Washington, you must present an
additional form of identification to enter the federal buildings where
the public hearings will be held. Acceptable alternative forms of
identification include: Federal employee badges, passports, enhanced
driver's licenses and military identification cards. We will list any
additional acceptable forms of identification at: https://www.epa.gov/
[[Page 45344]]
ttn/scram/11thmodconf.htm. In addition, you will need to obtain a
property pass for any personal belongings you bring with you. Upon
leaving the building, you will be required to return this property pass
to the security desk. No large signs will be allowed in the building,
cameras may only be used outside of the building and demonstrations
will not be allowed on federal property for security reasons.
Conference and Public Hearing: The Eleventh Conference on Air
Quality Modeling will be open to the public. No registration fee is
charged. The conference will be conducted informally and chaired by an
EPA official. As required under CAA section 320, a verbatim transcript
of the conference proceedings will be produced and placed in the docket
for this proposed rule.
The Eleventh Conference on Air Quality Modeling will begin with
introductory remarks by the presiding EPA official. The EPA staff will
then provide an overview of the revisions to the Guideline as proposed
in this document and present on the research that supports those
revisions and supports formulation updates to the preferred models. The
following topics will be presented:
I. Overview of the Eleventh Conference on Air Quality Modeling;
II. Review of the proposed revisions to the Guideline; and
III. Review of the proposed revisions to the preferred air quality
models.
At the conclusion of the presentations, the EPA will convene the
public hearing on the proposed revisions to the Guideline. The public
hearing will span the second half of the first day and throughout the
second day of the conference.
Those wishing to reserve time to speak at the public hearing,
whether to volunteer a presentation on a special topic or to offer
general comment on any of the modeling techniques scheduled for
presentation, should contact us at the address given in the FOR FURTHER
INFORMATION CONTACT section (note the cutoff date). Such persons should
identify the organization (if any) on whose behalf they are speaking
and the length of the presentation. If a scheduled presentation is
projected to be longer than 10 minutes, the presenter should also state
why a longer period is needed. Scheduled speakers should bring extra
copies of their presentation for inclusion in the docket and for the
convenience of the recorder. Scheduled speakers will also be permitted
to enter additional written comments into the record.
Any person in attendance wishing to speak at the public hearing who
has not reserved time prior to the conference may provide oral comments
on the proposed revisions to the Guideline during time allotted on the
last day of the conference. These parties will need to sign up to speak
on the second day of the hearing and the EPA may need to limit the
duration of presentations to allow all participants to be heard.
Additional written statements or comments on the proposed revisions
should be sent to the OAR Regulatory Docket (see ADDRESSES section). A
transcript of the conference proceedings and a copy of all written
comments will be maintained in Docket ID No. EPA-HQ-OAR-2015-0310,
which will remain open until October 27, 2015, for the purpose of
receiving additional comments after the conference and the public
hearing on the proposed revisions to the Guideline.
IV. Proposed Changes to the Guideline
In this action, the EPA is proposing two type of revisions to the
Guideline. The first involve substantive changes to address various
topics, including those presented and discussed at the Tenth Modeling
Conference. These proposed revisions to the Guideline include
enhancements to the formulation and application of the EPA's preferred
dispersion modeling system, AERMOD, and the incorporation of a tiered
demonstration approach to address the secondary chemical formation of
ozone and PM2.5 associated with precursor emissions from
single sources. The second type of revision involves editorial changes
to update and reorganize information throughout the Guideline. These
revisions are not intended to meaningfully change the substance of the
Guideline, but rather to make the Guideline easier to use and to
streamline the compliance assessment process.
A. Proposed Actions
This section provides a detailed overview of the substantive
proposed changes to the Guideline that are intended to improve the
science of the models and approaches used in regulatory assessments.
1. Clarifications To Distinguish Requirements From Recommendations
The EPA's PSD permitting regulations specify that ``[a]ll
applications of air quality modeling involved in this subpart shall be
based on the applicable models, data bases, and other requirements
specified in appendix W of this part (Guideline on Air Quality
Models).'' 40 CFR 51.166(l); see also 40 CFR 52.21(l). The applicable
models are the preferred models listed in appendix A to appendix W to
40 CFR part 51. However, there has been some ambiguity in the past with
respect to the ``other requirements'' specified in the Guideline that
must be used in PSD permitting analysis and other regulatory modeling
assessments.
Ambiguity can result because the Guideline generally contains
``recommendations'' and these recommendations are expressed in non-
mandatory language. For instance, the Guideline frequently uses
``should'' and ``may'' rather than ``shall'' and ``must.'' This
approach is generally preferred throughout the Guideline because of the
need to exercise expert judgment in air quality analysis and the
reasons discussed in the Guideline that ``dictate against a strict
modeling `cookbook'.'' (40 CFR part 51, appendix W, section 1.0(c))
Considering the non-mandatory language used throughout the
Guideline, the EPA's Environmental Appeals Board has correctly observed
the following:
``Although appendix W has been promulgated as codified
regulatory text, appendix W provides permit issuers broad latitude
and considerable flexibility in application of air quality modeling.
Appendix W is replete with references to ``recommendations,''
``guidelines,'' and reviewing authority discretion.''
In Re Prairie State Generating Company, 13 E.A.D. 1, 99 (EAB 2005)
(internal citations omitted).
Although this approach is typical throughout the Guideline, there
are instances where the EPA does not believe permit issues should have
broad latitude. Some principles of air quality modeling described in
the Guideline must always be applied to produce an acceptable analysis.
Thus, to promote clarity in the use and interpretation of the revised
Guideline, we have, in these cases used mandatory language, and made
specific reference to ``requirements'' throughout the proposed text
where appropriate to distinguish requirements from recommendations in
the application of models for regulatory purposes. We solicit comment
regarding the appropriateness of these revisions in providing the
necessary clarity on the requirements under the proposed revisions to
the Guideline as distinct from the recommendations in the revised text
while noting the continued flexibilities provided for within the
Guideline including but not limited to use and approval of alternative
models.
[[Page 45345]]
2. Updates to EPA's AERMOD Modeling System
Based on studies presented and discussed at the Tenth Modeling
Conference, and additional relevant research since 2010, the EPA and
other researchers have conducted additional model evaluations and
developed changes to the model formulation of the AERMOD modeling
system to improve model performance in its regulatory applications. We
propose the following updates to the AERMOD modeling system to address
a number of technical concerns expressed by stakeholders:
1. A proposed option incorporated in AERMET to adjust the surface
friction velocity (u*) to address issues with AERMOD model
overprediction under stable, low wind speed conditions. This proposed
option is selected by the user with the METHOD STABLEBL ADJ_U* record
in the AERMET Stage 3 input file.
2. A proposed low wind option in AERMOD to address issues with
model overprediction under low wind speed conditions. The low wind
option will increase the minimum value of the lateral turbulence
intensity (sigma-v) from 0.2 to 0.3 and adjusts the dispersion
coefficient to account for the effects of horizontal plume meander on
the plume centerline concentration. It also eliminates upwind
dispersion which is incongruous with a straight-line, steady-state
plume dispersion model such as AERMOD. The proposed option is selected
by specifying ``LOWWIND3'' on the CO MODELOPT keyword in the AERMOD
input file.
3. Modifications to AERMOD formulation to address issues with
overprediction for applications involving relatively tall stacks
located near relatively small urban areas (no user input is required).
4. Proposed regulatory default options in AERMOD to address plume
rise for horizontal and capped stacks based on the July 9, 1993, Model
Clearinghouse memorandum,\9\ with adjustments to account for the PRIME
algorithm for sources subject to building downwash. These options are
selected by the model user specifying ``POINTCAP'' or ``POINTHOR'' for
source type on the SO LOCATION keyword in the AERMOD input file.
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\9\ U.S. EPA, 1993. ``Proposal for Calculating Plume Rise for
Stacks with Horizontal Releases or Rain Caps for Cookson Pigment,
Newark, New Jersey'', Memorandum from Joseph A. Tikvart, U.S. EPA/
OAQPS, Research Triangle Park, NC. July 9, 1993. https://www.epa.gov/ttn/scram/guidance/mch/new_mch/R1076_TIKVART_9_JUL_93.pdf.
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5. A proposed buoyant line source option, based on the BLP model,
has been incorporated in AERMOD. This proposed option is selected by
the model user with the SOURCE type ``BOUYLINE'' to specify the
individual buoyant line source locations and emissions and the new
``BLAVGVAL'' keyword to specify average parameters for a composite
buoyant line.
6. Proposed updates to the NO2 Tier 2 and Tier 3
screening techniques coded within AERMOD as described more fully later
in this preamble section.
Model performance evaluation and peer scientific review references
for the updated AERMOD modeling system are cited, as appropriate. An
updated user's guide and model formulation documents for version 15181
have been placed in the docket. We have updated the summary description
of the AERMOD modeling system to appendix A of the Guideline to reflect
these proposed updates. The essential codes, preprocessors, and test
cases have been updated and posted to the EPA's SCRAM Web site, https://www.epa.gov/ttn/scram.
We invite comments on whether we have reasonably addressed the
technical concerns expressed by the stakeholder community and are on
sound footing to recommend these updates to the regulatory default
version of the AERMOD modeling system which includes its replacement of
BLP as an appendix A model for the intended regulatory applications.
3. Status of AERSCREEN
In the preamble of the 2005 Guideline, we stated that a screening
version of AERMOD called AERSCREEN was being developed and, in the
meantime, SCREEN3 may be used until AERSCREEN was available. In 2011,
the EPA released AERSCREEN, a program that creates inputs and runs
AERMOD in screening mode. AERSCREEN also interfaces with AERMOD's
terrain processor, AERMAP, the building processor for AERMOD,
BPIPPRIME, and can use AERSURFACE surface characteristics in the
generation of meteorological data for AERMOD via the MAKEMET utility.
In an April 2011 memorandum, the EPA stated that AERSCREEN was the
recommended screening model for simple and complex terrain and replaced
SCREEN3. Since AERSCREEN invokes AERMOD, AERSCREEN represents the state
of the science in screening dispersion models. As part of this proposed
update to AERSCREEN, AERSCREEN now includes inversion break-up and
coastal fumigation, features that were part of SCREEN3. These
fumigation algorithms also take advantage of AERMOD's boundary layer
parameterizations for calculating variables needed by the algorithms.
We invite comment on incorporation of AERSCREEN into the Guideline
as the screening model for AERMOD that may be applicable in
applications in all types of terrain and for applications involving
building downwash.
4. Updates to 3-Tiered Demonstration Approach for NO2
Section 5.2.4 of the 2005 Guideline details a 3-tiered approach for
assessing NOX sources, which was recommended to obtain
annual average estimates of NO2 from point sources for
purposes of NSR analysis, including the PSD program and SIP planning
purposes. This 3-tiered approach addresses the co-emissions of NO and
NO2 and the subsequent conversion of NO to NO2 in
the atmosphere. The tiered levels include: (1) Assuming that all NO is
converted to NO2 (full conversion), (2) using the Ambient
Ratio Method (ARM), which applies an assumed equilibrium ratio of
NO2 to NOX, based on observed ambient conditions,
to the annual results from the Tier 1 full conversion, and (3) detailed
screening options focused on determining site-specific ratios of
NO2 to NOX.
In January 2010, a new 1-hour NO2 standard was
promulgated. Prior to the adoption of the 1-hour NO2
standard, few PSD permit applications required the use of Tier 3
options and guidance available at the time did not fully address the
modeling needs for a 1-hour standard, i.e., tiered approaches for
NO2 in the 2005 Guideline specifically targeted an annual
standard. As a result, several guidance memoranda have been issued by
the EPA to further inform modeling procedures for sources demonstrating
compliance with the new 1-hour standard.1 2 3 4. In response
to the 1-hour NO2 standard, the EPA is proposing several
modifications to the Tier 2 and 3 NO2 screening techniques
incorporated into AERMOD.
For the Tier 2 technique, the EPA is proposing to replace the
existing ARM with a revised Ambient Ratio Method 2 (ARM2). The existing
Tier 2 technique, ARM, was based on a study that focused exclusively on
long-term averages.\10\ A recently published study \11\ presented a new
analysis of national levels of ambient ratios of NO2 to
NOX based on
[[Page 45346]]
hourly data from the EPA's Air Quality System (AQS). Based on this
analysis, a new second tier NO2 screening technique, ARM2,
has been developed and incorporated into AERMOD. Because ARM2 is based
on hourly measurements of the NO2 to NOX ratios
and provides more detailed estimates of this ratio based on the total
NOX present, the EPA is proposing to incorporate a modified
version of ARM2 as the new preferred second tier NOX
modeling approach.
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\10\ Chu, S.H. and E.L. Meyer, 1991. Use of Ambient Ratios to
Estimate Impact of NOX Sources on Annual NO2
Concentrations. Proceedings, 84th Annual Meeting & Exhibition of the
Air & Waste Management Association, Vancouver, B.C.; 16-21 June
1991. (16pp.) (Docket No. A-92-65, II-A-9).
\11\ Podrez, M. 2015. An Update to the Ambient Ratio Method for
1-h NO2 Air Quality Standards Dispersion Modeling. Atmospheric
Environment, 103: 163-170.
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For the Tier 3 technique, the EPA proposes that the existing
detailed screening options of the Ozone Limiting Method (OLM) \12\ and
PVMRM \13\ be formally incorporated into the regulatory version of
AERMOD. Both OLM and PVMRM have been available as non-regulatory, non-
default options in AERMOD for many years, but their usage in a NAAQS
compliance demonstration required approval by the reviewing authority.
Based on the EPA's evaluation and external studies available on their
performance, which show that OLM and PVMRM are capable of modeling 1-
hour NO2 impacts and NO and NO2 speciation with
reasonable accuracy when applied appropriately, both OLM and PVMRM are
being proposed as preferred Tier 3 screening methods for NO2
modeling. In addition, the EPA is proposing to incorporate a revised
version of the PVMRM option, referred to as PVMRM2, that utilizes
relative dispersion coefficients to estimate plume volume during
convective conditions and total dispersion coefficients during stable
conditions. These adjustments to the calculation of plume volume are
intended to mitigate potential overprediction of NO2
conversion in multisource applications, especially during stable
meteorological conditions. The EPA is proposing to replace the existing
PVMRM with the new PVMRM2 with both versions being made available in
the proposed version of AERMOD to facilitate testing and evaluation of
the EPA's proposed replacement of PVMRM option with new PVMRMR2 option.
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\12\ Cole, H.S. and J.E. Summerhays, 1979. A Review of
Techniques Available for Estimation of Short-Term NO2
Concentrations. Journal of the Air Pollution Control Association,
29(8): 812-817.
\13\ Hanrahan, P.L., 1999. The Polar Volume Polar Ratio Method
for Determining NO2/NOX Ratios in Modeling--Part I: Methodology.
Journal of the Air & Waste Management Association, 49: 1324-1331.
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We invite comments on whether we have reasonably addressed
technical concerns regarding the 3-tiered demonstration approach and
specific NO2 screening techniques within AERMOD and whether
we are on sound foundation to recommend the updates described above.
5. Status of CALINE3 Models
The 2005 Guideline identified CALINE3 \14\ and its variants
(CAL3QHC and CAL3QHCR) as the preferred model for mobile source
modeling for carbon monoxide (CO), particulate matter (PM), and lead.
CALINE3 was developed in the late 1970's using P-G stability classes as
the basis for the dispersion algorithms. AERMOD, on the other hand,
uses a planetary boundary layer scaling parameter to characterize
stability and determine dispersion rates, which has been found to be
superior to dispersion parameterizations based on P-G stability
classes.\15\ In addition, the LINE and AREA source options in AERMOD
implement a full numerical integration of emissions across the LINE or
AREA sources, whereas the CALINE3 family of models incorporate a much
less refined approach. Thus, AERMOD provides a more scientifically
credible and accurate characterization of plume dispersion than
CALINE3. Recent model performance studies \16\ have shown that the
CALINE models performed poorly when compared to AERMOD and other modern
dispersion models which also employ state-of-the-science dispersion
parameters. AERMOD is also able to model multiple years in a single
model run, while the CALINE3 variants are limited to either a single
meteorological condition (CALINE3 and CAL3QHC) or a single year of
meteorological data (CAL3QHCR). Additionally, AERMOD is able to utilize
more recent, and more representative, meteorological observations than
are readily available for modeling with CAL3QHCR. Based on the more
scientifically sound basis for AERMOD, improved model performance over
CALINE3, and the availability of more representative meteorological
data, the EPA proposes replacing CALINE3 with AERMOD as the preferred
appendix A model for determining near-field impacts for primary
emissions from mobile sources, including PM2.5,
PM10, and CO hot-spot analyses.\17\
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\14\ Benson, Paul E., 1979. CALINE3--A Versatile Dispersion
Model for Predicting Air Pollutant Levels Near Highways and Arterial
Streets. Interim Report, Report Number FHWA/CA/TL-79/23. Federal
Highway Administration, Washington, DC (NTIS No. PB 80-220841).
\15\ Cimorelli, A. et al., 2005. AERMOD: A Dispersion Model for
Industrial Source Applications. Part I: General Model Formulation
and Boundary Layer Characterization. Journal of Applied Meteorology,
44(5): 682-693.
\16\ Heist, D., V. Isakov, S. Perry, M. Snyder, A. Venkatram, C.
Hood, J. Stocker, D. Carruthers, S. Arunachalam, AND C. Owen.
Estimating near-road pollutant dispersion: a model inter-comparison.
Transportation Research Part D: Transport and Environment. Elsevier
BV, AMSTERDAM, Netherlands, 25:93-105, (2013).
\17\ U.S. EPA, 2013, Transportation Conformity Guidance for
Quantitative Hot-Spot Analyses in PM2.5 and
PM10 Nonattainment and Maintenance Areas. Publication No.
EPA-420-B-13-053, Office of Transportation and Air Quality, Ann
Arbor, MI. https://www.epa.gov/otaq/stateresources/transconf/policy/420b13053-sec.pdf.
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We solicit comments on our proposal to identify AERMOD as a
replacement for CALINE3 as an appendix A model for its intended
regulatory applications.
6. Addressing Single-Source Impacts on Ozone and Secondary
PM2.5
On January 4, 2012, the EPA granted a petition submitted on behalf
of the Sierra Club on July 28, 2010,\18\ that requested the EPA
initiate rulemaking to establish air quality models for ozone and
PM2.5 for use by all major sources applying for a PSD
permit. In granting that petition, the EPA explained that the ``complex
chemistry of ozone and secondary formation of PM2.5 are
well-documented and have historically presented significant challenges
to the designation of particular models for assessing the impacts of
individual stationary sources on the formation of these air
pollutants'' and further explained that ``[b]ecause of these
considerations, the EPA's judgment in the past has been that it was not
technically sound to designate with particularity specific models that
must be used to assess the impacts of a single source on ozone
concentrations,'' but rather the EPA had established a process for
determining on a case-by-case basis the analytical techniques that
should be used for ozone, as well as for secondary formation of
PM2.5.
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\18\ U.S. EPA, 2012. Gina McCarthy Letter to Robert Ukeiley
dated January 4, 2012, Washington, DC 20460. https://www.epa.gov/ttn/scram/10thmodconf/review_material/Sierra_Club_Petition_OAR-11-002-1093.pdf.
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In the petition grant, the EPA committed to engage in rulemaking to
evaluate whether updates to the Guideline are warranted and, as
appropriate, incorporate new analytical techniques or models for ozone
and secondarily formed PM2.5. This rulemaking satisfies the
EPA's commitment in the petition grant. As a part of this commitment
and in compliance with CAA section 320, the EPA conducted the Tenth
Modeling Conference in March 2012, where there were presentations of
ongoing research of single-source plume chemistry and photochemical
grid modeling techniques, as well as several public forums, and the EPA
subsequently received written comments pertaining to such modeling.
[[Page 45347]]
The EPA initiated Phase 3 of the IWAQM process in June 2013 to
inform this process to update the Guideline to address chemically
reactive pollutants for near-field and long-range transport
applications. Comments received from stakeholders at the Tenth Modeling
Conference supported this collaborative effort to provide additional
guidance for modeling single-source impacts of secondarily formed
pollutants in the near-field and for long-range transport. Stakeholder
comments also supported the idea of this collaborative effort occurring
in parallel with stakeholders' efforts to further model development and
evaluation. The EPA's recommended revisions to the Guideline are
largely based on detailed review and assessment of this input.
For this proposed revision to the Guideline, the EPA has determined
that advances in photochemical modeling science indicate it is now
reasonable to provide more specific, generally-applicable guidance that
identifies particular models or analytical techniques that may be used
under specific circumstances for assessing the impacts of an individual
source on ozone and secondary PM2.5.
Quantifying secondary pollutant formation requires simulating
chemical reactions and thermodynamic partitioning in a realistic
chemical and physical environment. Chemical transport models treat
atmospheric chemical and physical processes such as deposition and
transport. There are two types of chemical transport models, which are
differentiated based on a fixed frame of reference (i.e., Eulerian
models, specifically photochemical grid models) or a frame of reference
that moves with parcels of air between the source and receptor point
(i.e., Lagrangian models).\19\
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\19\ McMurry, P.H., Shepherd, M.F., Vickery, J.S., 2004.
Particulate matter science for policy makers: A NARSTO assessment.
Cambridge University Press.
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Comparing these two types of chemical transport models,
photochemical grid models are integrated, three-dimensional grid-based
models that treat chemical and physical processes in each grid cell and
use Eulerian diffusion and transport processes to move chemical species
to other grid cells.\19\ While some Lagrangian models also treat in-
plume gas and particulate chemistry, to do so these models require time
and space varying oxidant concentrations, and in the case of
PM2.5, neutralizing agents such as ammonia, because
important secondary impacts happen when plume edges start to interact
with the surrounding chemical environment.20 21 These
oxidant and neutralizing agents are not routinely measured, but can be
generated with a three-dimensional photochemical transport model and
subsequently input to a Lagrangian modeling system.
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\20\ Baker, K.R., Kelly, J.T., 2014. Single source impacts
estimated with photochemical model source sensitivity and
apportionment approaches. Atmospheric Environment, 96: 266-274.
\21\ ENVIRON, 2012. Evaluation of chemical dispersion models
using atmospheric plume measurements from field experiments, EPA
Contract No: EP-D-07-102. September 2012. 06-20443M6. https://www.epa.gov/ttn/scram/reports/Plume_Eval_Final_Sep_2012v5.pdf.
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In light of these differences between photochemical grid models and
Lagrangian models that address chemistry, the EPA believes
photochemical grid models are generally most appropriate for addressing
ozone and secondary PM2.5 because they provide a spatially
and temporally dynamic realistic chemical and physical environment for
plume growth and chemical transformation.20 22 Publically
available and documented Eulerian photochemical grid models such as the
Comprehensive Air Quality Model with Extensions (CAMx) \23\ and the
Community Multiscale Air Quality (CMAQ) \24\ model treat emissions,
chemical transformation, transport, and deposition using time and space
variant meteorology. These modeling systems include primarily emitted
species and secondarily formed pollutants such as ozone and
PM2.5.25 26 27 28 These models have been used
extensively to support ozone and PM2.5 SIPs and to explore
relationships between inputs and air quality impacts in the United
States and elsewhere.26 29 30
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\22\ Zhou, W., Cohan, D.S., Pinder, R.W., Neuman, J.A.,
Holloway, J.S., Peischl, J., Ryerson, T.B., Nowak, J.B., Flocke, F.,
Zheng, W.G., 2012. Observation and modeling of the evolution of
Texas power plant plumes. Atmospheric Chemistry and Physics, 12:
455-468.
\23\ ENVIRON, 2014. User's Guide Comprehensive Air Quality Model
with Extensions version 6, https://www.camx.com. ENVIRON
International Corporation, Novato.
\24\ Byun, D., Schere, K.L., 2006. Review of the governing
equations, computational algorithms, and other components of the
models-3 Community Multiscale Air Quality (CMAQ) modeling system.
Applied Mechanics Reviews, 59: 51-77.
\25\ Chen, J., Lu, J., Avise, J.C., DaMassa, J.A., Kleeman,
M.J., Kaduwela, A.P., 2014. Seasonal modeling of PM 2.5 in
California's San Joaquin Valley. Atmospheric Environment, 92: 182-
190.
\26\ Civerolo, K., Hogrefe, C., Zalewsky, E., Hao, W., Sistla,
G., Lynn, B., Rosenzweig, C., Kinney, P.L., 2010. Evaluation of an
18-year CMAQ simulation: Seasonal variations and long-term temporal
changes in sulfate and nitrate. Atmospheric Environment, 44: 3745-
3752.
\27\ Russell, A.G., 2008. EPA Supersites program-related
emissions-based particulate matter modeling: initial applications
and advances. Journal of the Air & Waste Management Association, 58:
289-302.
\28\ Tesche, T., Morris, R., Tonnesen, G., McNally, D., Boylan,
J., Brewer, P., 2006. CMAQ/CAMx annual 2002 performance evaluation
over the eastern US. Atmospheric Environment, 40: 4906-4919.
\29\ Cai, C., Kelly, J.T., Avise, J.C., Kaduwela, A.P.,
Stockwell, W.R., 2011. Photochemical modeling in California with two
chemical mechanisms: model intercomparison and response to emission
reductions. Journal of the Air & Waste Management Association, 61:
559-572.
\30\ Hogrefe, C., Hao, W., Zalewsky, E., Ku, J.-Y., Lynn, B.,
Rosenzweig, C., Schultz, M., Rast, S., Newchurch, M., Wang, L.,
2011. An analysis of long-term regional-scale ozone simulations over
the Northeastern United States: variability and trends. Atmospheric
Chemistry and Physics, 11: 567-582.
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For assessing secondary pollutant impacts from single sources, the
degree of complexity required to assess potential impacts varies
depending on the nature of the source, its emissions, and the
background environment. In order to provide the user community
flexibility in estimating single-source secondary pollutant impacts and
given the emphasis on the use of photochemical grid models for these
purposes, the EPA is proposing a two-tiered demonstration approach for
addressing single-source impacts on ozone and secondary
PM2.5. The first tier involves use of technically credible
relationships between precursor emissions and a source's impacts that
may be published in the peer-reviewed literature; developed from
modeling that was previously conducted for an area by a source, a
governmental agency, or some other entity and that is deemed
sufficient; or generated by a peer-reviewed reduced form model. The
second tier involves application of more sophisticated case-specific
chemical transport models (e.g., photochemical grid models) to be
determined in consultation with the EPA Regional Office and conducted
consistent with new EPA single-source modeling guidance.\31\ The
appropriate tier for a given application should be selected in
consultation with the appropriate reviewing authority and be consistent
with EPA guidance.
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\31\ U.S. EPA, 2015. Guidance on the use of models for assessing
the impacts from single sources on secondarily formed pollutants
ozone and PM2.5. Publication No. EPA 454/P-15-001. Office
of Air Quality Planning & Standards, Research Triangle Park, North
Carolina 27711.
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To fully implement these proposed changes to the Guideline related
to addressing ozone and secondary PM2.5 impacts, the EPA
intends to pursue a separate rulemaking to establish a technical basis
and new values for PM2.5 Significant Impact Levels (SILs)
and to introduce a new demonstration tool for ozone and
PM2.5 precursors referred to as Model Emissions Rates for
Precursors (MERP). When completed, this rule
[[Page 45348]]
would differ from the current process recommended in the EPA's Guidance
for PM2.5 Permit Modeling.\7\ A MERP would neither replace the existing
Significant Emissions Rates (SERs) for these pollutants nor serve as
the basis for the applicability of PSD requirements to sources with
emissions above the SER. However, a MERP would represent a level of
emissions of precursors that is not expected to contribute
significantly to concentrations of ozone or secondarily-formed
PM2.5. Our present understanding of the atmospheric science
of ozone and secondary PM2.5 formation indicates that MERP
values will likely be higher than the SERs and more appropriate for
evaluating the impacts of these criteria pollutants as precursors to
ozone and PM2.5 formation. As part of the separate
rulemaking, the EPA intends to demonstrate that a source with precursor
emissions (e.g., NOX and SO2 for
PM2.5) below the MERP level will have ambient impacts that
will be less than the SIL and, thereby, provide a sufficient
demonstration that the source will not cause or contribute to a
violation of the PM2.5 NAAQS or PSD increments. The EPA's
Guidance for PM2.5 Permit Modeling \7\ provides for a three-
tiered approach to address secondary PM2.5 with (1) a
qualitative assessment; (2) a hybrid qualitative/quantitative
assessment utilizing existing technical work; and (3) a full
quantitative modeling exercise. The EPA expects that MERPs as a
demonstration tool will replace the first tier of a qualitative
assessment as sources that currently would provide a qualitative
assessment are expected to have precursor emissions levels below the
MERP. The second and third tier of assessment will then be consistent
with the EPA's proposed two-tiered demonstration approach for
PM2.5 reflected in this proposed revisions to the Guideline.
To specifically assist the public in commenting on this rule within the
overall context of the NSR program, including PSD, the EPA has added
two separate memoranda to the docket of this proposed rule. These
memoranda provide more details on how this future approach to PSD
compliance demonstrations will work for secondary PM2.5 and
also describe our expectations for how such an approach might work for
ozone based on a future, separate action to similarly establish a SIL
and MERPs (for VOC and NOX precursors) for ozone using
approaches similar to those for PM2.5.32 33
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\32\ U.S. EPA, 2015. ``Proposed Approach for Demonstrating
PM2.5 PSD Compliance'', Memorandum to Docket No. EPA-HQ-
OAR-2015-0310 by Tyler J Fox, U.S. EPA/OAQPS, Research Triangle
Park, NC. June 30, 2015.
\33\ U.S. EPA, 2015. ``Proposed Approach for Demonstrating Ozone
PSD Compliance'', Memorandum to Docket No. EPA-HQ-OAR-2015-0310 by
Tyler J Fox, U.S. EPA/OAQPS, Research Triangle Park, NC. June 30,
2015.
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While the development of MERPs for ozone and secondary
PM2.5 precursors is expected to address a number of PSD
permitting situations, the EPA believes that most of the remaining
situations in which a source must demonstrate compliance under the
proposed Guideline will be addressed sufficiently under the proposed
first tier where existing technical information could be used in
combination with other supportive information and analysis for the
purposes of estimating secondary impacts from a particular source. The
existing technical information should provide a credible and
representative estimate of the secondary impacts from the project
source. In these situations, a more refined approach for estimating
secondary pollutant impacts from project sources may not be necessary.
The EPA has been compiling and reviewing screening approaches that are
based on technically credible tools (e.g., photochemical grid models)
that relate source precursor emissions to secondary impacts. In review
of existing approaches detailed in peer reviewed journal articles and
non-peer reviewed forms (e.g., technical reports, conference
presentations), it is not clear that a single approach has been clearly
proposed to and evaluated by the modeling community for estimating
screening level secondary impacts from single sources. Other screening
level alternatives to photochemical grid model application may include
the use of existing credible photochemical model impacts for sources
deemed to be similar in terms of emission rates, release parameters,
and background environment. The EPA will continue to engage with the
modeling community to identify credible alternative approaches for
estimating single-source secondary pollutant impacts which provide
flexibility and are less resource intensive for permit demonstration
purposes.
For those situations for which existing modeling or screening
estimates are not available or appropriate, the second tier proposed by
the EPA would apply and involve use of more sophisticated case-specific
chemical transport models (e.g., photochemical grid models) to be
determined in consultation with the appropriate EPA Regional Office
based upon new EPA single-source modeling guidance.\31\ Based on
several scientific studies, the EPA proposes to determine that
photochemical grid models are appropriate for assessment of near-field
and regional scale reactive pollutant impacts from specific sources
20 22 34 35 or a group of multiple sources impacting an
area.25 27 28 Even though single-source emissions are
injected into a grid volume, photochemical transport models have been
shown to adequately capture single-source impacts when compared with
downwind in-plume measurements.20 22 Where set up
appropriately for the purposes of assessing the contribution of single
sources to primary and secondarily formed pollutants, photochemical
grid models can be used with a variety of approaches to estimate these
impacts. These approaches generally fall into the category of source
sensitivity (how air quality changes due to changes in emissions) and
source apportionment (what air quality impacts are related to certain
emissions). Source apportionment has been used to differentiate the
contribution from single sources on model predicted ozone and
PM2.5 concentrations.20 34 The direct decoupled
method (DDM) has also been used to estimate ozone and PM2.5
impacts from specific sources 20 35 as well as the simpler
brute-force sensitivity approach.20 22 35 Limited comparison
of single-source impacts between models \36\ and approaches to
differentiate single-source impacts 20 36 show generally
similar downwind spatial gradients and impacts.
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\34\ Baker, K.R., Foley, K.M., 2011. A nonlinear regression
model estimating single source concentrations of primary and
secondarily formed PM2.5. Atmospheric Environment, 45:
3758-3767.
\35\ Bergin, M.S., Russell, A.G., Odman, M.T., Cohan, D.S.,
Chameldes, W.L., 2008. Single-Source Impact Analysis Using Three-
Dimensional Air Quality Models. Journal of the Air & Waste
Management Association, 58: 1351-1359.
\36\ Baker, K.R., Kelly, J.T., Fox, T., 2013. Estimating second
pollutant impacts from single sources (control #27). https://aqmodels.awma.org/conference-proceedings.
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Near-source in-plume aircraft based measurement field studies
provide an opportunity for evaluating model estimates of (near-source)
downwind transport and chemical impacts from single stationary point
sources.\21\ Photochemical grid model source apportionment and source
sensitivity simulation of a single source downwind impacts compare well
against field study primary and secondary ambient measurements made in
Tennessee and Texas.20 21 This work indicates photochemical
grid models and source
[[Page 45349]]
apportionment and source sensitivity approaches provide meaningful
estimates of single-source impacts on ozone and secondarily-formed
PM2.5. Additional evaluations for longer time periods and
more diverse environments, both physical and chemical, would be
valuable to generate broader confidence in these approaches for this
purpose.
We invite comments on whether the proposed two-tiered demonstration
approach and related EPA guidance is appropriately based on sound
science and practical application of available models and tools to
address single-source impacts on ozone and secondary PM2.5.
7. Status of CALPUFF and Assessing Long-Range Transport for PSD
Increment and Regional Haze
The 2003 Guideline recommended CALPUFF as the preferred model for
long-range transport (i.e., source-receptor distances of 50 to several
hundred kilometers) of emissions from point, volume, area, and line
sources for primary criteria pollutants (e.g., PM and SO2).
Since that time, as discussed previously in this preamble, the EPA has
received input from stakeholders and has worked through the IWAQM
process on analytical techniques to address chemically reactive
pollutants for near-field and long-range transport applications. As a
result, in order to provide the user community flexibility in
estimating single-source secondary pollutant impacts and given the
availability of more appropriate modeling techniques, such as
photochemical transport models (which address limitations of models
like CALPUFF \37\), the EPA is proposing that the Guideline no longer
contain language that requires the use of CALPUFF or another Lagrangian
puff model for long-range transport assessments. Additionally, the EPA
is proposing to remove the CALPUFF modeling system as an EPA-preferred
model for long-range transport due to concerns about the management and
maintenance of the model code given the frequent change in ownership of
the model code since promulgation in the previous version of the
Guideline.\38\ The EPA recognizes that long-range transport assessments
may be necessary in certain limited situations for PSD increment. For
these situations, the EPA is proposing a screening approach where
CALPUFF along with other appropriate screening tools and methods may be
used to support long-range transport PSD increment assessments.
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\37\ U.S. EPA, 2009. Reassessment of the Interagency Workgroup
on Air Quality Modeling (IWAQM) Phase 2 Summary Report; Revisions to
Phase 2 Recommendations. Draft. Office of Air Quality Planning &
Standards, Research Triangle Park, North Carolina 27711. https://www.epa.gov/ttn/scram/guidance/reports/Draft_IWAQM_Reassessment_052709.pdf.
\38\ U.S. EPA, 2015. ``Summary of CALPUFF Ownership Since 2003
Promulgation'', Memorandum to Docket No. EPA-HQ-OAR-2015-0310 by
Tyler J Fox, U.S. EPA/OAQPS, Research Triangle Park, NC. June 30,
2015.
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To determine if a Class I PSD increment analyses may be necessary
beyond 50 km (i.e., long-range transport assessment), the EPA is
recommending a screening approach to determine if a significant impact
will occur with particular focus on Class I areas that may be
threatened at such distances. The first step relies upon the near-field
application of the appropriate screening and/or preferred model to
determine the significance of ambient impact at or about 50 km from the
new of modifying source. If this initial analysis indicates there may
be significant ambient impacts at that distance, then further analysis
is necessary. For assessment of Class I ambient impacts, under the
proposed Guideline, there will not a preferred model for distances
beyond 50 km. Typically, a Lagrangian model is the type of model
appropriate to use for these screening assessments; however, applicants
should establish approaches (models and modeling parameters) on a case-
by-case basis in consultation with the appropriate reviewing authority,
Regional Office, and the affected Federal Land Manager(s) (FLM(s)). If
a cumulative increment analysis is necessary, for these limited
situations, the selection and use of an alternative model shall occur
in agreement with the appropriate reviewing authority (paragraph
3.0(b)) and approval by the EPA Regional Office based on the
requirements of section 3.2.2(e).
As previously noted, Phase 3 of the IWAQM process was reinitiated
in June 2013 to inform the EPA's commitment to update the Guideline to
address chemically reactive pollutants in near-field and long-range
transport applications. This Phase 3 effort included the establishment
of a working group composed of EPA and FLM technical staff focused on
long-range transport of primary and secondary pollutants with an
emphasis on use of consistent approaches to those being developed and
applied to meet near-field assessment needs for ozone and secondarily-
formed PM2.5. The EPA expects that such approaches will be
focused on state of the science chemical transport models (CTMs) as
detailed in IWAQM reports 39 40 and published literature.
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\39\ U.S. EPA, 2015. Interagency Workgroup on Air Quality
Modeling Phase 3 Summary Report: Near-Field Single Source Secondary
Impacts. Publication No. EPA 454/P-15-002. Office of Air Quality
Planning & Standards, Research Triangle Park, North Carolina 27711.
\40\ U.S. EPA, 2015. Interagency Workgroup on Air Quality
Modeling Phase 3 Summary Report: Long Range Transport and Air
Quality Related Values. Publication No. EPA 454/P-15-003. Office of
Air Quality Planning & Standards, Research Triangle Park, North
Carolina 27711.
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To inform future consideration of visibility modeling in regulatory
applications consistent with proposed changes for addressing chemistry
for single-source impact on ozone and secondary PM2.5, the
final report \40\ of the IWAQM long-range transport subgroup identified
that modern CTMs have evolved sufficiently and provide a credible
platform for estimating potential visibility impacts from a single or
small group of emission sources. Chemical transport models are well
suited for the purpose of estimating long-range impacts of secondary
pollutants, such as PM2.5, that contribute to regional haze
and other secondary pollutants, such as ozone, that contribute to
negative impacts on vegetation through deposition processes. These
multiple needs require a full chemistry photochemical model capable of
representing both gas, particle, and aqueous phase chemistry for
PM2.5, haze, and ozone.
Photochemical transport models are suitable for estimating
visibility and deposition since important physical and chemical
processes related to the formation and transport of PM are
realistically treated. Source sensitivity and apportionment techniques
implemented in photochemical grid models have evolved sufficiently and
provide the opportunity for estimating potential visibility and
deposition impacts from one or a small group of emission sources using
a full science photochemical grid model. Photochemical grid models
using meteorology output from prognostic meteorological models have
demonstrated skill in estimating source-receptor relationships in the
near-field 20 21 and over long distances.\41\
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\41\ ENVIRON, 2012. Documentation of the Evaluation of CALPUFF
and Other Long Range Transport Models using Tracer Field Experiment
Data, EPA Contract No: EP-D-07-102. February 2012. 06-20443M4.
https://www.epa.gov/ttn/scram/reports/EPA-454_R-12-003.pdf.
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It is important that modeling tools used for single-source long-
range transport impacts assessments demonstrate skill in adequately
replicating source-receptor relationships that are not in close
proximity. For
[[Page 45350]]
source-receptor distances greater than 50 km, regional scale
photochemical grid models may be applied for the assessment of
visibility impacts due to one or a small group of sources. Skill in
estimating source-receptor relationships on this scale can be
illustrated by evaluating modeling systems against regional scale inert
tracer release experiments. Historically, several regional tracer
release experiments have been used to demonstrate skill in long-range
transport of inert pollutants: 1980 Great Plains Mesoscale Tracer Field
Experiment, the 1983 Cross-Appalachian Tracer Experiment (CAPTEX), the
1987 Across North American Tracer Experiment (ANATEX), and 1994
European Tracer Experiment (ETEX).41 42 Photochemical grid
models have been shown to demonstrate similar skill to Lagrangian
models for pollutant transport when compared to measurements made from
multiple mesoscale field experiments.\41\ Use of CTMs for Air Quality
Related Values (AQRV) analysis requirements, while not subject to
specific EPA model approval requirements outlined in 40 CFR
51.166(l)(2) and 40 CFR 52.21(l)(2), should be justified for each
application following the general recommendations outlined in section
3.2, and concurrence sought with the affected FLM(s).
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\42\ Hegarty, J., Draxler, R.R., Stein, A.F., Brioude, J.,
Mountain, M., Eluszkiewicz, J., Nehrkorn, T., Ngan, F., Andrews, A.,
2013. Evaluation of Lagrangian particle dispersion models with
measurements from controlled tracer releases. Journal of Applied
Meteorology and Climatology, 52: 2623-2637.
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In 2005, the EPA issued guidelines for implementation of the best
available retrofit technology (BART) requirements under the Regional
Haze Rule. In these BART Guidelines, the EPA addressed the question of
how states could best predict a single source's contribution to
visibility impairment.\43\ At the time, the EPA recognized that CALPUFF
had not yet been fully evaluated for secondary pollutant formation, but
the EPA still considered CALPUFF to be the best application for
assessing a single source's impact on visibility in a Class I area for
purposes of the regional haze program. The EPA took note of the
limitations of CALPUFF for this purpose but concluded that CALPUFF was
the best modeling application for use in evaluating BART, especially
given how the modeling results would be used. Based on this assessment,
the EPA recommended that the states use CALPUFF. The EPA also made
clear, however, that states could use other alternative approaches,
including photochemical grid models, if done in consultation with the
appropriate EPA Regional Office.
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\43\ See 70 FR 39104, 39122-23 (July 6, 2005).
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The current version of the Guideline does not contain any explicit
recommendation regarding the use of CALPUFF in the regional haze
program, but in advising states and in making its own BART
determinations, the EPA has looked to the Guideline to resolve
questions regarding the proper application of the model. In particular,
the EPA has guided states to use the applicable regulatory version of
CALPUFF for such assessments. Following the EPA's recommendations,
states have used the EPA-preferred version of CALPUFF in hundreds of
BART determinations since 2005. Although most assessments of BART are
now complete, a handful of BART determinations remain outstanding. We
expect most of the remaining actions addressing the BART requirements
to be completed within the next two years.
The proposed changes to the Guideline do not affect the EPA's
recommendation in the 2005 BART Guidelines to use CALPUFF in the BART
determination process. Given that the overwhelming majority of BART
determinations have been made using CALPUFF, we consider it appropriate
for states (or the EPA) to continue to use this application for the
remaining assessments under the current Guideline with approved
protocols. This approach assures consistency across and within states
in the regional haze program. In addition, in many instances, the
modeling of visibility impacts has already been completed even though
the BART determination process is not yet done. Allowing states to
continue to rely on CALPUFF avoids additional time and expense in
developing a new assessment of visibility impacts for a SIP initially
due in 2007. We intend to continue to advise states with respect to the
EPA-preferred version of CALPUFF that should be used in specific BART
cases. Consistent with the BART Guidelines, states may also use
alternative modeling approaches, in consultation with the appropriate
EPA Regional Office.
The EPA is seeking comment on its proposed screening approach to
address long-range transport for purposes of assessing PSD increments;
its decision to remove CALPUFF as a preferred model in appendix A for
such long-range transport assessments; and its decision to consider
CALPUFF as a screening technique along with other Lagrangian models to
be used in consultation with the appropriate reviewing authority. It is
important to note that the EPA's proposed action to remove CALPUFF as
an appendix A model in this Guideline does not affect its use under the
FLM's guidance regarding AQRV assessments (FLAG 2010) nor previous use
of this model as part of regulatory modeling applications required
under the CAA. Similarly, this proposed action does not affect EPA's
recommendation that States use CALPUFF to determine the applicability
and level of BART in regional haze implementation plans.\43\
8. Role of EPA's Model Clearinghouse
The EPA's Model Clearinghouse has been a fundamental aspect of
communication between the EPA Region Offices and with the broader
permitting community on technical modeling and compliance demonstration
issues for almost three decades. The Model Clearinghouse serves a
critical role in helping resolve issues that arise from unique
situations that are not specifically addressed in the Guideline or
necessitate the consideration of an alternative model or technique for
a specific application or range of applications. The Model
Clearinghouse ensures that fairness, consistency, and transparency in
modeling decisions are fostered among the Regional Offices and the
state, local, and tribal agencies.
In this action, we are proposing to codify the long-standing
process of the Regional Offices consulting and coordinating with the
Model Clearinghouse on all approvals of alternative models or
techniques. While the Regional Administrators are the delegated
authority to issue such approvals under section 3.2 of the Guideline,
all alternative model approvals will only be issued after consultation
with the EPA's Model Clearinghouse and formal documentation through a
concurrence memorandum which demonstrates that the requirements within
section 3.2 for use of an alternative model have been met.
We invite comment on our proposal to codify existing practice of
requiring consultation and coordination between the EPA Regional
Offices and the EPA's Model Clearinghouse on all approvals under
section 3.2 of alternative models or techniques.
9. Updates to Modeling Procedures for Cumulative Impact Analysis
Based on input from the Tenth Modeling Conference and recent permit
modeling experiences under new short-term NAAQS for SO2 and
NO2, the EPA is proposing to make modifications to section 8
of the Guideline regarding model inputs and background concentrations
to provide much needed
[[Page 45351]]
clarity associated with input and database selection for use in PSD and
SIP modeling. Many of these revisions are based on the EPA
clarification memoranda issued since 2010 that were intended to provide
the necessary clarification regarding applicability of the Guideline to
PSD modeling for these new standards.1 2 44 45 The EPA has
specifically cautioned against the literal and uncritical application
of very prescriptive procedures for conducting NAAQS and PSD modeling
compliance demonstrations as described in chapter C of the draft New
Source Review Workshop Manual.\46\ Our main concern is that following
such procedures in a literal and uncritical manner has led to practices
that are overly conservative and unnecessarily complicate the
permitting process. The proposed changes to section 8 are intended to
modify these practices and provide a more appropriate basis for
selection and use of modeling inputs through the Guideline itself and
supporting guidance.
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\44\ U.S. EPA, 2011. Additional Clarification Regarding
Applicability of Appendix W Modeling Guidance for the 1-hour NO2
NAAQS. Tyler Fox Memorandum dated March 1, 2011, Office of Air
Quality Planning & Standards, Research Triangle Park, North Carolina
27711.https://www.epa.gov/ttn/scram/guidance/clarification/NO2_Clarification_Memo-20140930.pdf.
\45\ U.S. EPA, 2014. Clarification on the Use of AERMOD
Dispersion Modeling for Demonstrating Compliance with the
NO2 National Ambient Air Quality Standard. R. Chris Owen
and Roger Brode Memorandum dated September 30, 2014, Office of Air
Quality Planning & Standards, Research Triangle Park, North Carolina
27711. https://www.epa.gov/ttn/scram/guidance/clarification/NO2_Clarification_Memo-20140930.pdf.
\46\ U.S. EPA, 1990. New Source Review Workshop Manual:
Prevention of Significant Deterioration and Nonattainment Area
Permitting (Draft). Office of Air Quality Planning & Standards,
Research Triangle Park, North Carolina 27711. https://www.epa.gov/nsr.
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We have provided a more definitive definition of the appropriate
modeling domain and how to best characterize the various contributions
to air quality concentrations within that domain. Specifically, we
provide the following recommendations:
Definition and/or factors to consider in determining
appropriate modeling domain for NAAQS and PSD increment assessments and
for SIP attainment demonstrations (see section 8.1).
Revised requirements on how to characterize emissions from
nearby sources to be explicitly modeled for purposes of a cumulative
impact assessment under PSD and new language regarding how to
characterize direct and precursor emissions from modeled sources for
SIP attainment demonstrations for ozone, PM2.5, and regional
haze (see section 8.2).
Revised recommendations on how to determine background
concentrations in constructing the design concentration, or total air
quality concentration, as part of a cumulative impact analysis for
NAAQS and PSD increments. Specific recommendations are proposed for
situations involving isolated single-source(s) and multi-source areas
(see section 8.3) with an emphasis on how to determine which nearby
sources to explicitly model based on the concept of significant
concentration gradients and the use of monitored background to
adequately represent ``other sources'' (i.e., that portion of the
background attributable to natural sources, other unidentified sources
in the vicinity of the project, and regional transport contributions
from more distant sources (domestic and international)). It is
important to note the interconnectedness of these issues as the
question of which nearby sources to include in cumulative modeling is
inextricably linked with the question of what ambient monitoring data
are available and what these data represent for a specific application.
More specific data requirements and the format required for the
individual models are described in detail in the users' guide and/or
associated documentation for each model.
Given the added complexity of the technical issues that arise in
the context of demonstrating compliance with NAAQS through dispersion
modeling, we strongly encourage adherence to the recommendations in
section 9.2.1 of the proposed Guideline regarding development of a
modeling protocol, i.e., that ``[e]very effort should be made by the
Regional Office to meet with all parties involved in either a SIP
revision or a PSD permit application prior to the start of any work on
such a project. During this meeting, a protocol should be established
between the preparing and reviewing parties to define the procedures to
be followed, the data to be collected, the model to be used, and the
analysis of the source and concentration data.'' We expect by providing
more clarity in the Guideline of the factors to be considered in the
cumulative impact assessment, permit applicants and permitting
authorities will be able to find the proper balance of the competing
factors that contribute to these analyses.
We invite comments on whether the updates proposed in section 8 of
the Guideline and associated guidance are appropriate and sufficient to
provide the necessary clarification in selecting and establishing the
model inputs for conducting the regulatory modeling for PSD and SIP
applications.
10. Updates on Use of Meteorological Input Data for Regulatory
Dispersion Modeling
For near-field dispersion modeling applications using National
Weather Service (NWS) Automated Surface Observing Stations (ASOS), the
EPA released a pre-processor to AERMET, called AERMINUTE, in 2011 that
calculates hourly averaged winds from 2-minute winds reported every
minute at NWS ASOS sites. AERMET substitutes these hourly averaged
winds for the standard hourly observations, thus reducing the number of
calms and missing winds for input into AERMOD. The presence of calms
and missing winds were due to the METAR reporting methodology of
surface observations. In March 2013, the EPA released a memorandum
regarding the use of ASOS data in AERMOD as well as the use of
AERMINUTE. When using meteorological data from ASOS sites for input to
AERMOD, hourly averaged winds from AERMINUTE should be used in most
cases.
For a near-field dispersion modeling application where there is no
representative NWS station, and it is prohibitive or not feasible to
collect adequately representative site-specific data, it may be
necessary to use prognostic meteorological data for the application.
The EPA released the MMIF program that converts the prognostic
meteorological data into a format suitable for dispersion modeling
applications. The most recent 3 years of prognostic data are preferred.
Use of the prognostic data is contingent on the concurrence of the
appropriate reviewing authorities and collaborating agencies that the
data are of acceptable quality and representative of the modeling
application.
We solicit comments on our proposed updates regarding use of
meteorological input data for regulatory application of dispersion
models.
11. Transition Period for Applicability of Revisions to the Guideline
In previous rulemakings to revise the Guideline, we have
traditionally communicated that it would be appropriate to provide 1
year to transition to the use of new models, techniques and procedures
in the context of PSD permit applications and other regulatory modeling
applications. We invite comments whether it would be appropriate to
apply a 1-year transition after promulgation of the revised Guideline
(i.e., from its effective
[[Page 45352]]
date) such that applications conducted under the current Guideline with
approved protocols would be acceptable during that period, but new
requirements and recommendations should be used for applications
submitted after that period or protocols approved after that period.
The EPA believes such a transition period is appropriate to avoid
the time and expense of revisiting modeling that is substantially
complete, which would cause undue delays to permit applications that
are pending when the proposed revisions to the Guideline are finalized.
The revisions that the EPA is proposing to the Guideline are intended
as incremental improvements to the Guideline, and such improvements do
not necessarily invalidate past practices under the previous edition of
the Guideline. The requirements and recommendations in the current
(2005) version of the Guideline were previously identified as
acceptable by the EPA, and they will continue to be acceptable for air
quality assessments during the period of transition to the revised
version of the Guideline.
Where a proposed revision to the Guideline does raise questions
about the acceptability of a requirement or recommendation that it
replaces, model users and applicants are encouraged to consult with the
appropriate reviewing authority as soon as possible to assure the
acceptability of modeling used to support permit applications during
this period.
B. Proposed Editorial Changes
The EPA is proposing to make editorial changes to update and
reorganize information throughout the Guideline. These revisions are
not intended to meaningfully change the substance of the Guideline, but
rather to make the Guideline easier to use. One way this is
accomplished is by grouping topics together in a more logical manner to
make related content easier to find. This in turn should streamline the
compliance assessment process.
Editorial changes are described below for each affected section. We
invite comment on any of the changes proposed below for the Guideline
text.
1. Preface
Only a few minor text revisions are proposed to this section for
consistency with the remainder of the Guideline.
2. Section 1
The EPA propose to update the introduction section to reflect the
reorganized nature of the revised Guideline. Minor text revisions are
proposed throughout this section for additional clarity. Additional
information is provided regarding the importance of CAA section 320 to
amendments of the Guideline.
3. Section 2
The EPA proposes to revise section 2 to more appropriately discuss
the process by which models are evaluated and considered for use in
particular applications. We propose to incorporate information from the
previous section 9 pertaining to model accuracy and uncertainty within
this section to clarify how model performance evaluation is critical in
determining the suitability of models for particular application.
We also propose to provide a discussion in section 2.1 (Model
Accuracy and Uncertainty) of the three types of models historically
used for regulatory demonstrations. For each type of model, some
strengths and weaknesses are listed to assist readers in the
understanding of the particular regulatory applications to which they
are most appropriate.
In addition, the EPA proposes revisions to section 2.2 with respect
to the recommended practice of progressing from simplified and
conservative air quality analysis toward more complex and refined
analysis. In this section, the EPA proposes to clarify distinctions
between various types of models that have previously been described as
screening models. In addition, this section clarifies distinctions
between models used for screening purposes and screening techniques and
demonstration tools that may be acceptable in certain applications.
4. Section 3
The EPA proposes minor modifications to section 3 to more
accurately reflect current EPA practices and by moving the discussion
of the EPA's Model Clearinghouse to a revised section 3.3 for ease of
reference and prominence within the Guideline. A change is proposed to
require Regional Office consultation with the Model Clearinghouse on
all alternative model approvals. Previously, section 3 included various
requirements under recommendation subheading that were not clearly
identified as requirements. Accordingly, the EPA is proposing to modify
section 3 with the incorporation of requirement subsections to
eliminate any ambiguity.
5. Section 4
The EPA proposes to significantly revise section 4 to incorporate
the modeling approaches recommended for air quality impact analyses for
the criteria pollutants of CO, lead, SO2, NO2,
and primary PM2.5 and PM10. In many respects, the
proposed revisions to section 4 are a combination of the previous
sections 4 and 5, reflecting inert criteria pollutants only. The EPA
also proposes to modify section 4 to incorporate requirement
subsections to provide clarity of the various requirements where
previously sections 4 and 5 included various requirements under
recommendation subheadings.
As proposed, this section provides an in-depth discussion of
screening and refined models, including the introduction of AERSCREEN
as the recommended screening model for simple and complex terrain for
single sources and options for multi-source screening with AERMOD.\47\
The EPA proposes to include a clear discussion of each appendix A
preferred model in section 4.3 (Refined Models). The EPA also proposes
to modify the discussion for each preferred model (i.e., AERMOD
Modeling System, CTDMPLUS, and OCD) from the previous section 4 with
appropriate edits and some streamlining based on information available
in the respective model formulation documentation and users guides.
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\47\ U.S. EPA, 2015. Technical Support Document (TSD) for
Replacement of CALINE3 with AERMOD for Transportation Related Air
Quality Analyses. Publication No. EPA-454/B-15-002. Office of Air
Quality Planning & Standards, Research Triangle Park, NC.
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The EPA is proposing to add a subsection specifically addressing
the modeling recommendations for SO2 where, previously,
section 4 of the Guideline was generally understood to be applicable
for SO2. Minor updates are proposed with respect to the
modeling recommendations for each of the other inert criteria
pollutants that were previously found in section 5. For NO2,
the ARM2 is proposed to be added as a Tier 2 option, and the Tier 3
options of OLM and PVMRM are proposed to become part of the regulatory
version of AERMOD. For any pollutant that had significant emissions
from mobile sources, our previous recommendation to use the CALINE3
models is proposed to be replaced with AERMOD.
6. Section 5
As already stated, much of the previous section 5 with respect to
the inert criteria pollutants is proposed to be incorporated into the
revised section 4. As proposed, the revised section 5 is now focused
only on the modeling approaches recommended for ozone and secondary
PM2.5.
Both ozone and secondary PM2.5 are formed through
chemical reactions in the atmosphere and are not
[[Page 45353]]
appropriately modeled with traditional steady-state Gaussian plume
models, such as AERMOD. Chemical transport models are necessary to
appropriately assess the single-source air quality impacts of precursor
pollutants on the formation of ozone or secondary PM2.5.
While the proposed revision to section 5 do not specify a
particular EPA-preferred model or technique for use in air quality
assessments, a two-tiered screening approach is proposed for ozone and
secondary PM2.5 with appropriate references to the EPA's new
single-source modeling guidance. The first tier consists of technically
credible and appropriate relationships between emissions and the
impacts developed from existing modeling simulations. If existing
technical information is not available or appropriate, then a second
tier approach would apply, involving use of sophisticated chemical
transport models (e.g., photochemical grid models) as determined in
consultation with the appropriate EPA Regional Office on a case-by-case
basis based upon the EPA's new single-source modeling guidance.
7. Section 6
Revisions to section 6 are proposed to more clearly address the
modeling recommendations of other federal agencies, such as the FLM(s),
that have been developed in response to EPA rules or standards. While
no attempt is made to comprehensively discuss each topic, the EPA
proposes to provide appropriate references to the respective federal
agency guidance documents.
The proposed revision to section 6 focus primarily on AQRVs,
including near-field and long-range transport assessments for
visibility impairment and deposition. The interests of the Bureau of
Ocean Energy and Management for Outer Continental Shelf permitting
situations and of the Federal Aviation Administration for airport and
air base permitting situations are represented in proposed section 6.3
(Modeling Guidance for Other Governmental Programs).
The discussion of Good Engineering Practices (GEP) for stack height
consideration is proposed to be modified and moved to section 7. The
EPA proposed to remove the discussion of long-range transport for PSD
Class I increment and references to the previously preferred long-range
transport model, CALPUFF, in accordance with the more detailed
discussion in the Proposed Actions section of this Preamble.
8. Section 7
We propose to revise section 7 to be more streamlined and
appropriate to the variety of general modeling issues and
considerations that are not already been covered in sections 4, 5, and
6 of the Guideline. The EPA proposes to move the information concerning
design concentrations and receptor sites to section 9. The discussion
of stability categories is proposed to be removed from section 7 since
it is specifically addressed in the model formulation documentation and
guidance for the dispersion models that require stability categories to
be defined. As already stated, the GEP discussion from the previous
section 6 is proposed to be incorporated into this section.
The EPA proposes to expand the recommendations for determining
rural or urban dispersion coefficients to provide more clarity with
respect to appropriate characterization within AERMOD, including a
discussion on the existence of highly industrialized areas where
population density is low that may be best treated with urban rather
than rural dispersion coefficients. References to CALPUFF in the
Complex Winds subsection are proposed to be removed due to technical
issues described in the Proposed Actions section of this preamble. As
proposed, if necessary for special complex wind situations, the setup
and application of an alternative model should now be determined in
consultation with the appropriate reviewing authority. Finally, the EPA
proposes to revise section 7 to include a new discussion of modeling
considerations specific to mobile sources.
9. Section 8
The EPA propose extensive updates and modifications to section 8 to
reflect current EPA practices, requirements, and recommendations for
determining the appropriate modeling domain and model input data from
new or modifying source(s) or sources under consideration for a revised
permit limit, from background concentrations (including air quality
monitoring data and nearby and others sources), and from meteorology.
As with earlier sections, the EPA proposes to modify section 8 to
incorporate requirement subsections where previously section 8
ambiguously included various requirements under recommendation
subheadings.
The Background Concentration subsection is proposed to be
significantly modified from the existing Guideline to include a more
clear and comprehensive discussion of nearby and other sources. This is
intended to eliminate confusion of how to identify nearby sources that
should be explicitly modeled and all other sources that should be
generally represented by air quality monitoring data. In addition to
air quality monitoring data, a brief discussion on the use of
photochemical grid modeling to appropriately characterize background
concentrations has been included in this proposed section. Updates to
Tables 8-1 and 8-2 are proposed per changes in the considerations for
nearby sources, as discussed in the Proposed Actions section of this
Preamble.
The use of prognostic mesoscale meteorological models to provide
meteorological input for regulatory dispersion modeling applications is
proposed to be incorporated throughout the Meteorological Input Data
subsection, including the introduction of the MMIF as a tool to inform
regulatory model applications. Other than additional minor
modifications to the recommendations through this subsection based on
current EPA practices, the most substantive proposed edits relate to
the recommendation to use the AERMINUTE meteorological data processor
to calculate hourly average wind speed and direction when processing
NWS ASOS data for developing AERMET meteorological inputs to the AERMOD
dispersion model.
10. Section 9
The EPA proposes to move all of the information previously in
section 9 related to model accuracy and evaluation into other sections
in the revised Guideline (primarily to the revised section 2 and some
to the revised section 4). This provides for greater clarity in those
topics as applied to selection of models under the Guideline. However,
the EPA proposes to remove subsection on the ``Use of Uncertainty in
Decision Making.''. After removing this content, the EPA proposes to
totally revise section 9 to focus on the regulatory application of
models, which would include the majority of the information found
previously in section 10.
The EPA proposes to revise the discussion portion of section 9 to
more clearly summarize the general concepts presented in earlier
sections of the Guideline and to set the stage for the appropriate
regulatory application of models and/or, in rare circumstances, air
quality monitoring data. The importance of developing and vetting a
modeling protocol is more prominently presented in a separate
subsection.
[[Page 45354]]
The information related to design concentrations is proposed to be
updated and unified from previous language found in sections 7 and 10.
An expanded discussion of receptor sites is proposed based on language
from the previous section 7 and new considerations given past practices
of model users tending to define an excessively large and inappropriate
number of receptors based on vague guidance.
The recommendations for NAAQS and PSD increment compliance
demonstrations are proposed to be overhauled to more clearly and
accurately reflect the long-standing EPA recommendation and practice of
performing a single-source impact analysis as a first stage of the
NAAQS and PSD increment compliance demonstration and, as necessary,
conducting a more comprehensive cumulative impact analysis as the
second stage. The appropriate considerations and applications of
screening and/or refined model are described in each stage.
Finally, the section on Use of Measured Data in Lieu of Model
Estimates subsection is proposed to be revised to provide more details
on the process for determining the rare circumstances in which air
quality monitoring data may be considered for determining the most
appropriate emissions limit for a modification to an existing source.
As with other portions of the revised section 9, the language
throughout this subsection is proposed to be updated to reflect current
EPA practices, as appropriate.
11. Section 10
As discussed, the majority of the information found previously in
section 10 is proposed to be incorporated into the revised section 9.
As proposed, section 10 consists of the references that were in the
previous section 12. We also propose to update each reference, as
appropriate, based on the text revisions throughout the Guideline.
12. Section 11
In a streamlining effort, the EPA proposes to remove this
bibliography section from the Guideline.
13. Section 12
As stated earlier, this references section is now proposed as
section 10 with appropriate updates.
14. Appendix A to the Guideline
The EPA proposes to revise appendix A to the Guideline to remove
the Buoyant Line and Point Source Dispersion Model (BLP), CALINE3, and
CALPUFF as refined air quality models preferred for specific regulator
applications. The rational for the removal of these air quality models
from the preferred status can be found in the Proposed Actions section
of this Preamble.
V. Statutory and Executive Order Reviews
A. Executive Order 12866: Regulatory Planning and Review and Executive
Order 13563: Improving Regulation and Regulatory Review
This proposed action is not a ``significant regulatory action''
under the terms of Executive Order 12866 (58 FR 51735, October 4, 1993)
and is, therefore not subject to OMB review under Executive Orders
12866 and 13563 (76 FR 3821, January 21, 2011).
B. Paperwork Reduction Act
This proposed action does not impose an information collection
burden subject to OMB review under the provisions of the Paperwork
Reduction Act, 44 U.S.C. 3501 et seq.
C. Regulatory Flexibility Act
The Regulatory Flexibility Act (RFA) generally requires an agency
to prepare a regulatory flexibility analysis of any rule subject to
notice and comment rulemaking requirements under the Administrative
Procedure Act or any other statute unless the agency certifies that the
rule will not have a significant economic impact on a substantial
number of small entities. Small entities include small businesses,
small organizations, and small governmental jurisdictions.
For purposes of assessing the impacts of this rule on small
entities, small entity is defined as (1) a small business as defined by
the Small Business Administration's (SBA) regulations at 13 CFR
121.201; (2) a small governmental jurisdiction that is a government of
a city, county, town, school district or special district with a
population of less than 50,000; and (3) a small organization that is
any not-for-profit enterprise which is independently owned and operated
and is not dominant in its field.
The modeling techniques described in this proposed action are
primarily used by air agencies and by industries owning major sources
subject to NSR permitting requirements. To the extent that any small
entities would have to conduct air quality assessments, using the
models and/or techniques described in this proposed action are not
expect to pose any additional burden (compared to the existing models
and/or techniques) on these entities. The proposal features updates to
the existing EPA-preferred model, AERMOD, that serves to increase
efficiency and accuracy by changing only mathematical formulations and
specific data elements. Also, this proposed action will streamline
resources necessary to conduct necessary modeling with AERMOD by
incorporating model algorithms from the BLP model and replacing CALINE3
for mobile source applications. Although this proposed action calls for
new models and/or techniques for use in addressing ozone and secondary
PM2.5, we expect most small entities will generally be able
to rely on existing modeling simulations; so, we expect minimal burden
associated with these assessments. Therefore, we do not believe that
that this proposal poses a significant or unreasonable burden on any
small entities.
After considering the economic impacts of this rule on small
entities, I certify that this action will not have a significant
economic impact on a substantial number of small entities. We continue
to be interested in the potential impacts of the proposed rule on small
entities and welcome comments on issues related to such impacts.
D. Unfunded Mandates Reform Act
This proposed action contains no federal mandates under the
provisions of Title II of the Unfunded Mandates Reform Act of 1995
(UMRA), 2 U.S.C. 1531-1538 for state, local, or tribal governments or
the private sector. This action imposes no enforceable duty on any
state, local or tribal governments or the private sector. Therefore,
this action is not subject to the requirements of sections 202 or 205
of the UMRA. This action is also not subject to the requirements of
section 203 of UMRA because it contains no regulatory requirements that
might significantly or uniquely affect small governments.
E. Executive Order 13132: Federalism
This proposed action does not have federalism implications. It will
not have substantial direct effects on the states, on the relationship
between the national government and the states, or on the distribution
of power and responsibilities among the various levels of government,
as specified in Executive Order 13132. This rule does not create a
mandate on state, local or tribal governments nor does it impose any
enforceable duties on these entities. This action would add better,
more accurate techniques for conducting air quality assessments and
does not add
[[Page 45355]]
any additional requirements for any of the affected parties covered
under Executive Order 13132. Thus, the requirements of section 6 of the
Executive Order do not apply to this proposal. In the spirit of
Executive Order 13132, and consistent with the EPA policy to promote
communications between the EPA and state and local governments, the EPA
specifically solicits comment on this proposed rule from state and
local officials.
F. Executive Order 13175: Consultation and Coordination With Indian
Tribal Governments
This proposed action does not have tribal implications, as
specified in Executive Order 13175 (65 FR 67249, November 9, 2000).
This proposed rule imposes no requirements on tribal governments.
Accordingly, Executive Order 13175 does not apply to this action. In
the spirit of Executive Order 13175, the EPA specifically solicits
additional comment on this proposed action from tribal officials.
G. Executive Order 13045: Protection of Children From Environmental
Health and Safety Risks
The EPA interprets Executive Order 13045 as applying only to those
regulatory actions that concern environmental health or safety risks
that the EPA has reason to believe may disproportionately affect
children, per the definition of ``covered regulatory action'' in
section 2-202 of the Executive Order. This action is not subject to
Executive Order 13045 because it does not concern an environmental
health risk or safety risk.
H. Executive Order 13211: Actions Concerning Regulations That
Significantly Affect Energy Supply, Distribution, or Use
This action is not a ``significant energy action'' as defined in
Executive Order 13211 (66 FR 28355 (May 22, 2001)), because it is not
likely to have a significant adverse effect on the supply,
distribution, or use of energy.
I. National Technology Transfer and Advancement Act
This rulemaking does not involve technical standards.
J. Executive Order 12898: Federal Actions To Address Environmental
Justice in Minority Populations and Low-Income Populations
The EPA has determined that this proposed rule will not have
disproportionately high and adverse human health or environmental
effects on minority or low-income populations because it does not
affect the level of protection provided to human health or the
environment.
List of Subjects in 40 CFR Part 51
Environmental protection, Administrative practice and procedure,
Air pollution control, Carbon monoxide, Intergovernmental relations,
Nitrogen oxides, Ozone, Particulate Matter, Reporting and recordkeeping
requirements, Sulfur oxides.
Dated: July 14, 2015.
Gina McCarthy,
Administrator.
For the reasons stated in the preamble, title 40, chapter I of the
Code of Federal Regulations is proposed to be amended as follows:
PART 51--REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF
IMPLEMENTATION PLANS
0
1. The authority citation for part 51 continues to read as follows:
Authority: 23 U.S.C. 101; 42 U.S.C. 7401-7671q.
0
2. Appendix W to part 51 is revised to read as follows:
APPENDIX W TO PART 51--Guideline on Air Quality Models Preface
a. Industry and control agencies have long expressed a need for
consistency in the application of air quality models for regulatory
purposes. In the 1977 Clean Air Act (CAA), Congress mandated such
consistency and encouraged the standardization of model
applications. The Guideline on Air Quality Models (hereafter,
Guideline) was first published in April 1978 to satisfy these
requirements by specifying models and providing guidance for their
use. The Guideline provides a common basis for estimating the air
quality concentrations of criteria pollutants used in assessing
control strategies and developing emissions limits.
b. The continuing development of new air quality models in
response to regulatory requirements and the expanded requirements
for models to cover even more complex problems have emphasized the
need for periodic review and update of guidance on these techniques.
Historically, three primary activities have provided direct input to
revisions of the Guideline. The first is a series of periodic EPA
workshops and modeling conferences conducted for the purpose of
ensuring consistency and providing clarification in the application
of models. The second activity was the solicitation and review of
new models from the technical and user community. In the March 27,
1980, Federal Register, a procedure was outlined for the submittal
to the EPA of privately developed models. After extensive evaluation
and scientific review, these models, as well as those made available
by the EPA, have been considered for recognition in the Guideline.
The third activity is the extensive on-going research efforts by the
EPA and others in air quality and meteorological modeling.
c. Based primarily on these three activities, new sections and
topics have been included as needed. The EPA does not make changes
to the guidance on a predetermined schedule, but rather on an as-
needed basis. The EPA believes that revisions of the Guideline
should be timely and responsive to user needs and should involve
public participation to the greatest possible extent. All future
changes to the guidance will be proposed and finalized in the
Federal Register. Information on the current status of modeling
guidance can always be obtained from EPA's Regional Offices.
Table of Contents
List of Tables
1.0 Introduction
2.0 Overview of Model Use
2.1 Suitability of Models
2.1.1 Model Accuracy and Uncertainty
2.2 Levels of Sophistication of Air Quality Analyses and Models
2.3 Availability of Models
3.0 Preferred and Alternative Air Quality Models
3.1 Preferred Models
3.1.1 Discussion
3.1.2 Requirements
3.2 Alternative Models
3.2.1 Discussion
3.2.2 Requirements
3.3 EPA's Model Clearinghouse
4.0 Models for Carbon Monoxide, Lead, Sulfur Dioxide, Nitrogen
Dioxide and Primary Particulate Matter
4.1 Discussion
4.2 Requirements
4.2.1 Screening Models and Techniques
4.2.1.1 AERSCREEN
4.2.1.2 CTSCREEN
4.2.1.3 Screening in Complex Terrain
4.2.2 Refined Models
4.2.2.1 AERMOD
4.2.2.2 CTDMPLUS
4.2.2.3 OCD
4.2.3 Pollutant Specific Modeling Requirements
4.2.3.1 Models for Carbon Monoxide
4.2.3.2 Models for Lead
4.2.3.3 Models for Sulfur Dioxide
4.2.3.4 Models for Nitrogen Dioxide
4.2.3.5 Models for PM2.5
4.2.3.6 Models for PM10
5.0 Models for Ozone and Secondarily Formed Particulate Matter
5.1 Discussion
5.2 Recommendations
5.3 Recommended Models and Approaches for Ozone
5.3.1 Models for NAAQS Attainment Demonstrations and Multi-
Source Air Quality Assessments
5.3.2 Models for Single-Source Air Quality Assessments
5.4 Recommended Models and Approaches for Secondarily Formed
PM2.5
5.4.1 Models for NAAQS Attainment Demonstrations and Multi-
Source Air Quality Assessments
5.4.2 Models for Single-Source Air Quality Assessments
[[Page 45356]]
6.0 Modeling for Air Quality Related Values and Other Governmental
Programs
6.1 Discussion
6.2 Air Quality Related Values
6.2.1 Visibility
6.2.1.1 Models for Estimating Near-Field Visibility Impairment
6.2.1.2 Models for Estimating Visibility Impairment for Long-
Range Transport
6.2.2 Models for Estimating Deposition Impacts
6.3 Modeling Guidance for Other Governmental Programs
7.0 General Modeling Considerations
7.1 Discussion
7.2 Recommendations
7.2.1 All sources
7.2.1.1 Dispersion Coefficients
7.2.1.2 Complex Winds
7.2.1.3 Gravitational Settling and Deposition
7.2.2 Stationary Sources
7.2.2.1 Good Engineering Practice Stack Height
7.2.2.2 Plume Rise
7.2.3 Mobile Sources
8.0 Model Input Data
8.1 Modeling Domain
8.1.1 Discussion
8.1.2 Requirements
8.2 Source Data
8.2.1 Discussion
8.2.2 Requirements
8.3 Background Concentrations
8.3.1 Discussion
8.3.2 Recommendations for Isolated Single Source
8.3.3 Recommendations for Multi-Source Areas
8.4 Meteorological Input Data
8.4.1 Discussion
8.4.2 Recommendations and Requirements
8.4.3 National Weather Service Data
8.4.3.1 Discussion
8.4.3.2 Recommendations
8.4.4 Site-specific data
8.4.4.1 Discussion
8.4.4.2 Recommendations
8.4.5 Prognostic meteorological data
8.4.5.1 Discussion
8.4.5.2 Recommendations
8.4.6 Treatment of Near-Calms and Calms
8.4.6.1 Discussion
8.4.6.2 Recommendations
9.0 Regulatory Application of Models
9.1 Discussion
9.2 Recommendations
9.2.1 Modeling Protocol
9.2.2 Design Concentration and Receptor Sites
9.2.3 NAAQS and PSD Increments Compliance Demonstrations for New
or Modified Sources
9.2.3.1 Considerations in Developing Emissions Limits
9.2.4 Use of Measured Data in Lieu of Model Estimates
10.0 References
Appendix A to Appendix W of Part 51--Summaries of Preferred Air
Quality Models
List of Tables
------------------------------------------------------------------------
Table No. Title
------------------------------------------------------------------------
8-1............................... Point Source Model Emission Input
for SIP Revisions of Inert
Pollutants.
8-2............................... Point Source Model Emission Input
for NAAQS Compliance in PSD
Demonstrations.
------------------------------------------------------------------------
1.0 Introduction
a. The Guideline recommends air quality modeling techniques that
should be applied to State Implementation Plan (SIP) submittals and
revisions, to New Source Review (NSR), including new or modifying
sources under Prevention of Significant Deterioration
(PSD),1 2 3 conformity analyses,\4\ and other air quality
assessments required under EPA regulation. Applicable only to
criteria air pollutants, the Guideline is intended for use by the
EPA Regional Offices in judging the adequacy of modeling analyses
performed by the EPA, by state, local, and tribal permitting
authorities, and by industry. It is appropriate for use by other
federal government agencies and by state, local, and tribal agencies
with air quality and land management responsibilities. The Guideline
serves to identify, for all interested parties, those modeling
techniques and databases that the EPA considers acceptable. The
Guideline is not intended to be a compendium of modeling techniques.
Rather, it should serve as a common measure of acceptable technical
analysis when supported by sound scientific judgment.
b. Air quality measurements \5\ are routinely used to
characterize ambient concentrations of criteria pollutants
throughout the nation but are rarely sufficient for characterizing
the ambient impacts of individual sources or demonstrating adequacy
of emissions limits for an existing source due to limitations in
spatial and temporal coverage of ambient monitoring networks. The
impacts of new sources that do not yet exist and modifications to
existing sources that have yet to be implemented can only be
determined through modeling. Thus, models have become a primary
analytical tool in most air quality assessments. Air quality
measurements can be used in a complementary manner to air quality
models, with due regard for the strengths and weaknesses of both
analysis techniques, and are particularly useful in assessing the
accuracy of model estimates.
c. It would be advantageous to categorize the various regulatory
programs and to apply a designated model to each proposed source
needing analysis under a given program. However, the diversity of
the nation's topography and climate, and variations in source
configurations and operating characteristics dictate against a
strict modeling ``cookbook.'' There is no one model capable of
properly addressing all conceivable situations even within a broad
category such as point sources. Meteorological phenomena associated
with threats to air quality standards are rarely amenable to a
single mathematical treatment; thus, case-by-case analysis and
judgment are frequently required. As modeling efforts become more
complex, it is increasingly important that they be directed by
highly competent individuals with a broad range of experience and
knowledge in air quality meteorology. Further, they should be
coordinated closely with specialists in emissions characteristics,
air monitoring and data processing. The judgment of experienced
meteorologists, atmospheric scientists, and analysts is essential.
d. The model that most accurately estimates concentrations in
the area of interest is always sought. However, it is clear from the
needs expressed by the EPA Regional Offices, by state, local, and
tribal agencies, by many industries and trade associations, and also
by the deliberations of Congress that consistency in the selection
and application of models and databases should also be sought, even
in case-by-case analyses. Consistency ensures that air quality
control agencies and the general public have a common basis for
estimating pollutant concentrations, assessing control strategies,
and specifying emissions limits. Such consistency is not, however,
promoted at the expense of model and database accuracy. The
Guideline provides a consistent basis for selection of the most
accurate models and databases for use in air quality assessments.
e. Recommendations are made in the Guideline concerning air
quality models and techniques, model evaluation procedures, and
model input databases and related requirements. The guidance
provided here should be followed in air quality analyses relative to
SIPs, NSR, and in supporting analyses required by the EPA and by
state, local, and tribal permitting authorities. Specific models are
identified for particular applications. The EPA may approve the use
of an alternative model or technique that can be demonstrated to be
more appropriate than those recommended in the Guideline. In all
cases, the model or technique applied to a given situation should be
the one that provides the most accurate representation of
atmospheric transport, dispersion, and chemical transformations in
the area of interest. However, to ensure consistency, deviations
from the Guideline should be carefully documented as part of the
public record and fully supported by the appropriate reviewing
authority, as discussed later.
f. From time to time, situations arise requiring clarification
of the intent of the guidance on a specific topic. Periodic
workshops are held with EPA headquarters, EPA Regional Office, and
state, local, and tribal agency modeling representatives to ensure
consistency in modeling guidance and to promote the use of more
accurate air quality models, techniques, and databases. The
workshops serve to provide further explanations of Guideline
requirements to the EPA Regional Offices and workshop materials are
issued with this clarifying information. In addition, findings from
ongoing research programs, new model development, or results from
model evaluations and applications are continuously evaluated. Based
on this information, changes in the applicable guidance may be
indicated and appropriate revisions to the Guideline may be
considered.
[[Page 45357]]
g. All changes to the Guideline must follow rulemaking
requirements since the Guideline is codified in appendix W to 40
Code of Federal Regulations (CFR) part 51. The EPA will promulgate
proposed and final rules in the Federal Register to amend this
appendix. The EPA utilizes the existing procedures under CAA section
320 that requires EPA to conduct a Conference on Air Quality
Modeling at least every 3 years. These modeling conferences are
intended to develop standardized air quality modeling procedures and
form the basis for associated revisions to this Guideline in support
of the EPA's continuing effort to prescribe with ``reasonable
particularity'' air quality models and meteorological and emission
databases suitable for modeling National Ambient Air Quality
Standards (NAAQS) \6\ and PSD increments (CAA 320, 42 U.S.C. 7620).
Ample opportunity for public comment will be provided for each
proposed change and public hearings scheduled.
h. A wide range of topics on modeling and databases are
discussed in the Guideline. Section 2 gives an overview of models
and their suitability for use in regulatory applications. Section 3
provides specific guidance on the determination of preferred air
quality models and on the selection of alternative models or
techniques. Sections 4 through 6 provide recommendations on modeling
techniques for assessing criteria pollutant impacts from single and
multiple sources with specific modeling requirements for selected
regulatory applications. Section 7 discusses general considerations
common to many modeling analyses for stationary and mobile sources.
Section 8 makes recommendations for data inputs to models including
source, background air quality, and meteorological data. Section 9
summarizes how estimates and measurements of air quality are used in
assessing source impact and in evaluating control strategies.
i. Appendix W to 40 CFR part 51 contains an appendix: Appendix
A. Thus, when reference is made to ``appendix A'' in this document,
it refers to appendix A to appendix W to 40 CFR part 51. Appendix A
contains summaries of refined air quality models that are
``preferred'' for particular applications; both EPA models and
models developed by others are included.
2.0 Overview of Model Use
a. Increasing reliance has been placed on concentration
estimates from air quality models as the primary basis for
regulatory decisions concerning source permits and emission control
requirements. In many situations, such as review of a proposed new
source, no practical alternative exists. Before attempting to
implement the guidance contained in this document, the reader should
be aware of certain general information concerning air quality
models and their evaluation and use. Such information is provided in
this section.
2.1 Suitability of Models
a. The extent to which a specific air quality model is suitable
for the assessment of source impacts depends upon several factors.
These include: (1) The topographic and meteorological complexities
of the area; (2) the detail and accuracy of the input databases,
i.e., emissions inventory, meteorological data, and air quality
data; (3) the manner in which complexities of atmospheric processes
are handled in the model; (4) the technical competence of those
undertaking such simulation modeling; and (5) the resources
available to apply the model. Any of these factors can have a
significant influence on the overall model performance, which must
be thoroughly evaluated to determine the suitability of an air
quality model to a particular application or range of applications.
b. Air quality models are most accurate and reliable in areas
that have gradual transitions of land use and topography.
Meteorological conditions in these areas are spatially uniform such
that observations are broadly representative and air quality model
projections are not further complicated by a heterogeneous
environment. Areas subject to major topographic influences
experience meteorological complexities that are often difficult to
measure and simulate. Models with adequate performance are available
for increasingly complex environments. However, they are resource
intensive and frequently require site-specific observations and
formulations. Such complexities and the related challenges for the
air quality simulation should be considered when selecting the most
appropriate air quality model for an application.
c. Appropriate model input data should be available before an
attempt is made to evaluate or apply an air quality model. Assuming
the data are adequate, the greater the detail with which a model
considers the spatial and temporal variations in meteorological
conditions and permit-enforceable emissions, the greater the ability
to evaluate the source impact and to distinguish the effects of
various control strategies.
d. There are three types of models that have historically been
used in the regulatory demonstrations applicable in the Guideline,
each having strengths and weaknesses that lend themselves to
particular regulatory applications.
i. Gaussian plume models use a ``steady-state'' approximation,
which assumes that over the model time step, the emissions,
meteorology and other model inputs, are constant throughout the
model domain, resulting in a resolved plume with the emissions
distributed throughout the plume according to a Gaussian
distribution. This formulation allows Gaussian models to estimate
near-field impacts of a limited number of sources at a relatively
high resolution, with temporal scales of an hour and spatial scales
of meters. However, this formulation allows for only relatively
inert pollutants, with very limited considerations of transformation
and removal (e.g., deposition), and further limits the domain for
which the model may be used. Thus, Gaussian models may not be
appropriate if model inputs are changing sharply over the model time
step or within the desired model domain or if more advanced
considerations of chemistry are needed.
ii. Lagrangian puff models, on the other hand, are non-steady-
state, and assume that model input conditions are changing over the
model domain and model time step. Lagrangian models can also be used
to determine near and far-field impacts from a limited number of
sources at a high resolution. Traditionally, Lagrangian models have
been used for relatively inert pollutants, with slightly more
complex considerations of removal than Gaussian models. Some
Lagrangian models treat in-plume gas and particulate chemistry.
However, these models require time and space varying concentration
fields of oxidants and, in the case of fine particulate matter
(PM2.5), neutralizing agents, such as ammonia. Reliable
background fields are critical for applications involving secondary
pollutant formation because secondary impacts generally occur when
in-plume precursors mix and react with species in the background
atmosphere.7 8 These oxidant and neutralizing agents are
not routinely measured, but can be generated with a three-
dimensional photochemical grid model.
iii. Photochemical grid models are three-dimensional Eulerian
grid-based models that treat chemical and physical processes in each
grid cell and use diffusion and transport processes to move chemical
species between grid cells.9 Eulerian models assume that
emissions are spread evenly throughout each model grid cell.
Typically, Eulerian models have difficulty with fine scale
resolution of individual plumes. However, these types of models can
be appropriately applied for assessment of near-field and regional
scale reactive pollutant impacts from specific sources
7 10 11 12 or all sources.13 14 15
Photochemical gird models simulate a more realistic environment for
chemical transformation,7 12 but simulations can be more
resource intensive than Lagrangian or Gaussian plume models.
e. Competent and experienced meteorologists, atmospheric
scientists, and analysts are an essential prerequisite to the
successful application of air quality models. The need for such
specialists is critical when the more sophisticated models are used
or the area being investigated has complicated meteorological or
topographic features. It is important to note that a model applied
improperly or with inappropriate data can lead to serious
misjudgments regarding the source impact or the effectiveness of a
control strategy.
f. The resource demands generated by use of air quality models
vary widely depending on the specific application. The resources
required may be important factors in the selection and use of a
model or technique for a specific analysis. These resources depend
on the nature of the model and its complexity, the detail of the
databases, the difficulty of the application, the amount and level
of expertise required, and the costs of manpower and computational
facilities.
2.1.1 Model Accuracy and Uncertainty
a. The formulation and application of air quality models are
accompanied by several sources of uncertainty. ``Irreducible''
uncertainty stems from the ``unknown'' conditions, which may not be
explicitly accounted for in the model (e.g., the turbulent velocity
field). Thus, there are likely to be deviations from the observed
[[Page 45358]]
concentrations in individual events due to variations in the unknown
conditions. ``Reducible'' uncertainties 16 are caused by:
(1) Uncertainties in the ``known'' input conditions (e.g., emission
characteristics and meteorological data); (2) errors in the measured
concentrations; and (3) inadequate model physics and formulation.
b. Evaluations of model accuracy should focus on the reducible
uncertainty associated with physics and the formulation of the
model. The accuracy of the model is normally determined by an
evaluation procedure which involves the comparison of model
concentration estimates with measured air quality data.17
The statement of model accuracy is based on statistical tests or
performance measures such as bias, noise, correlation,
etc.18 19
c. Since the 1980's, the EPA has worked with the modeling
community to encourage development of standardized model evaluation
methods and the development of continually improved methods for the
characterization of model performance.16 18 20 21 22
There is general consensus on what should be considered in the
evaluation of air quality models; namely, quality assurance
planning, documentation and scrutiny should be consistent with the
intended use and should include:
Scientific peer review;
Supportive analyses (diagnostic evaluations, code
verification, sensitivity analyses);
Diagnostic and performance evaluations with data
obtained in trial locations; and
Statistical performance evaluations in the
circumstances of the intended applications.
Performance evaluations and diagnostic evaluations assess different
qualities of how well a model is performing, and both are needed to
establish credibility within the client and scientific community.
d. Performance evaluations allow the EPA and model users to
determine the relative performance of a model in comparison with
alternative modeling systems. Diagnostic evaluations allow
determination of a model capability to simulate individual processes
that affect the results, and usually employ smaller spatial/temporal
scale date sets (e.g., field studies). Diagnostic evaluations enable
the EPA and model users to build confidence that model predictions
are accurate for the right reasons. However, the objective
comparison of modeled concentrations with observed field data
provides only a partial means for assessing model performance. Due
to the limited supply of evaluation datasets, there are practical
limits in assessing model performance. For this reason, the
conclusions reached in the science peer reviews and the supportive
analyses have particular relevance in deciding whether a model will
be useful for its intended purposes.
2.2 Levels of Sophistication of Air Quality Analyses and Models
a. It is desirable to begin an air quality analysis by using
simplified or conservative methods (or both) followed, as
appropriate, by more complex and refined methods. The purpose of
this approach is to streamline the process and sufficiently address
regulatory requirements by eliminating the need of more detailed
modeling when it is not necessary in a specific regulatory
application. For example, in the context of a PSD permit
application, a simplified or conservative analysis may be sufficient
where it shows the proposed construction clearly will not cause or
contribute to ambient concentrations in excess of either the NAAQS
or the PSD increments.2 3
b. There are two general levels of sophistication of air quality
models. The first level consists of screening models that provide
conservative modeled estimates of the air quality impact of a
specific source or source category based on simplified assumptions
of the model inputs (e.g., preset, worst-case meteorological
conditions). In the case of a PSD assessment, if a screening model
indicates that the concentration contributed by the source could
cause or contribute to a violation of any NAAQS or PSD increment,
then the second level of more sophisticated models should be
applied.
c. The second level consists of refined models that provide more
detailed treatment of physical and chemical atmospheric processes,
require more detailed and precise input data, and provide spatially
and temporally resolved concentration estimates. As a result they
provide a more sophisticated and, at least theoretically, a more
accurate estimate of source impact and the effectiveness of control
strategies.
d. There are situations where a screening model or a refined
model is not available such that screening and refined modeling are
not viable options to determine source-specific air quality impacts.
In such situations, a screening technique or reduced-form model may
be viable options for estimating source impacts.
i. Screening techniques are differentiated from a screening
model in that screening techniques are approaches that make
simplified and conservative assumptions about the physical and
chemical atmospheric processes important to determining source
impacts while screening models make assumptions about conservative
inputs to a specific model. The complexity of screening techniques
ranges from simplified assumptions of chemistry applied to refined
or screening model output to sophisticated approximations of the
chemistry applied within a refined model.
ii. Reduced-form models are computationally efficient simulation
tools for characterizing the pollutant response to specific types of
emission reductions for a particular geographic area or background
environmental conditions that reflect underlying atmospheric science
of a refined model but reduce the computational resources of running
a complex, numerical air quality model such as a photochemical grid
model.
In such situations, an attempt should be made to acquire or improve
the necessary databases and to develop appropriate analytical
techniques, but the screening technique or reduced-form model may be
sufficient in conducting regulatory modeling applications when
applied in consultation with the EPA Regional Office.
e. Consistent with the general principle described in paragraph
2.2(a), the EPA may establish a demonstration tool or method as a
sufficient means for a user or applicant to make a demonstration
required by regulation, either by itself or as part of a modeling
demonstration. To be used for such regulatory purposes, such a tool
or method must be reflected in a codified regulation or have a well-
documented technical basis and reasoning that is contained or
incorporated in the record of the regulatory decision in which it is
applied.
2.3 Availability of Models
a. For most of the screening and refined models discussed in the
Guideline, codes, associated documentation and other useful
information are publicly available for download from the EPA's
Support Center for Regulatory Atmospheric Modeling (SCRAM) Web site
at https://www.epa.gov/ttn/scram. This is a Web site with which air
quality modelers should become familiar and regularly visit for
important model updates and additional clarifications and revisions
to modeling guidance documents that are applicable to EPA programs
and regulations. Codes and documentation may also available from the
National Technical Information Service (NTIS), https://www.ntis.gov,
and, when available, is referenced with the appropriate NTIS
accession number.
3.0 Preferred and Alternative Air Quality Models
a. This section specifies the approach to be taken in
determining preferred models for use in regulatory air quality
programs. The status of models developed by the EPA, as well as
those submitted to the EPA for review and possible inclusion in this
Guideline, is discussed in this section. The section also provides
the criteria and process for obtaining EPA approval for use of
alternative models for individual cases in situations where the
preferred models are not applicable or available. Additional sources
of relevant modeling information are the EPA's Model Clearinghouse
\23\ (section 3.3), EPA modeling conferences, periodic Regional,
State, and Local Modelers' Workshops, and the EPA's SCRAM Web site
(section 2.3).
b. When approval is required for a specific modeling technique
or analytical procedure in this Guideline, we refer to the
``appropriate reviewing authority.'' Many states and some local
agencies administer NSR and PSD permitting under programs approved
into SIPs. In some EPA regions, federal authority to administer NSR
and PSD permitting and related activities has been delegated to
state or local agencies. In these cases, such agencies ``stand in
the shoes'' of the respective EPA regions. Therefore, depending on
the circumstances, the appropriate reviewing authority may be an EPA
Regional Office, a state, local, or tribal agency, or perhaps the
Federal Land Manager (FLM). In some cases, the Guideline requires
review and approval of the use of an alternative model by the EPA
Regional Office (sometimes stated as ``Regional Administrator'').
For all approvals of alternative models or techniques, the EPA
Regional Office will coordinate and shall seek concurrence with the
EPA's Model Clearinghouse. If there is any question as to
[[Page 45359]]
the appropriate reviewing authority, you should contact the EPA
Regional Office modeling contact (https://www.epa.gov/ttn/scram/guidance_cont_regions.htm), whose jurisdiction generally includes
the physical location of the source in question and its expected
impacts.
c. In all regulatory analyses, early discussions among the EPA
Regional Office staff, state, local, and tribal agency staff,
industry representatives, and where appropriate, the FLM, are
invaluable and are strongly encouraged. Prior to the actual
analyses, agreement on the databases to be used, modeling techniques
to be applied, and the overall technical approach helps avoid
misunderstandings concerning the final results and may reduce the
later need for additional analyses. The preparation of a written
modeling protocol that is vetted with the appropriate reviewing
authority helps to keep misunderstandings and resource expenditures
at a minimum.
d. The identification of preferred models in this Guideline
should not be construed as a determination that the preferred models
identified here are to be permanently used to the exclusion of all
others or that they are the only models available for relating
emissions to air quality. The model that most accurately estimates
concentrations in the area of interest is always sought. However,
designation of specific preferred models is needed to promote
consistency in model selection and application.
3.1 Preferred Models
3.1.1 Discussion
a. The EPA has developed some models suitable for regulatory
application, while other models have been submitted by private
developers for possible inclusion in the Guideline. Refined models
that are preferred and required by the EPA for particular
applications have undergone the necessary peer scientific reviews
\24\ \25\ and model performance evaluation exercises \26\ \27\ that
include statistical measures of model performance in comparison with
measured air quality data as described in section 2.1.1.
b. An American Society for Testing and Materials (ASTM)
reference 28 provides a general philosophy for developing
and implementing advanced statistical evaluations of atmospheric
dispersion models, and provides an example statistical technique to
illustrate the application of this philosophy. Consistent with this
approach, the EPA has determined and applied a specific evaluation
protocol that provides a statistical technique for evaluating model
performance for predicting peak concentration values, as might be
observed at individual monitoring locations.29
c. When a single model is found to perform better than others,
it is recommended for application as a preferred model and listed in
appendix A. If no one model is found to clearly perform better
through the evaluation exercise, then the preferred model listed in
appendix A may be selected on the basis of other factors such as
past use, public familiarity, resource requirements, and
availability. Accordingly, the models listed in appendix A meet
these conditions:
i. The model must be written in a common programming language,
and the executable(s) must run on a common computer platform.
ii. The model must be documented in a user's guide or model
formulation report which identifies the mathematics of the model,
data requirements and program operating characteristics at a level
of detail comparable to that available for other recommended models
in appendix A.
iii. The model must be accompanied by a complete test dataset
including input parameters and output results. The test data must be
packaged with the model in computer-readable form.
iv. The model must be useful to typical users, e.g., state air
agencies, for specific air quality control problems. Such users
should be able to operate the computer program(s) from available
documentation.
v. The model documentation must include a robust comparison with
air quality data (and/or tracer measurements) or with other well-
established analytical techniques.
vi. The developer must be willing to make the model and source
code available to users at reasonable cost or make them available
for public access through the Internet or National Technical
Information Service. The model and its code cannot be proprietary.
d. The EPA's process of establishing a preferred model includes
a determination of technical merit, in accordance with the above six
items including the practicality of the model for use in ongoing
regulatory programs. Each model will also be subjected to a
performance evaluation for an appropriate database and to a peer
scientific review. Models for wide use (not just an isolated case)
that are found to perform better will be proposed for inclusion as
preferred models in future Guideline revisions.
e. No further evaluation of a preferred model is required for a
particular application if the EPA requirements for regulatory use
specified for the model in the Guideline are followed. Alternative
models to those listed in appendix A should generally be compared
with measured air quality data when they are used for regulatory
applications consistent with recommendations in section 3.2.
3.1.2 Requirements
a. Appendix A identifies refined models that are preferred for
use in regulatory applications. If a model is required for a
particular application, the user must select a model from appendix A
or follow procedures in section 3.2.2 for use of an alternative
model or technique. Preferred models may be used without a formal
demonstration of applicability as long as they are used as indicated
in each model summary in appendix A. Further recommendations for the
application of preferred models to specific source applications are
found in subsequent sections of the Guideline.
b. If changes are made to a preferred model without affecting
the modeled concentrations, the preferred status of the model is
unchanged. Examples of modifications that do not affect
concentrations are those made to enable use of a different computer
platform or those that only affect the format or averaging time of
the model results. The integration of a graphical user interface
(GUI) to facilitate setting up the model inputs and/or analyzing the
model results without otherwise altering the model kernel is another
example of a modification that does not affect concentrations.
However, when any changes are made, the Regional Administrator must
require a test case example to demonstrate that the modeled
concentration are not affected.
c. A preferred model must be operated with the options listed in
appendix A for its intended regulatory application. If other options
are exercised, the model is no longer ``preferred.'' Any other
modification to a preferred model that would result in a change in
the concentration estimates likewise alters its status so that it is
no longer a preferred model. Use of the modified model must then be
justified as an alternative model on a case-by-case basis to the
appropriate reviewing authority and approved by the Regional
Administrator.
d. Where the EPA has not identified a preferred model for a
particular pollutant or situation, the EPA may establish a multi-
tiered approach for making a demonstration required under PSD or
another CAA program. The initial tier or tiers may involve use of
demonstration tools, screening models, screening techniques, or
reduced-form models; while the last tier may involve the use of
demonstration tools, refinded models or techniques, or alternative
models approved under section 3.2.
3.2 Alternative Models
3.2.1 Discussion
a. Selection of the best model or techniques for each individual
air quality analysis is always encouraged, but the selection should
be done in a consistent manner. A simple listing of models in this
Guideline cannot alone achieve that consistency nor can it
necessarily provide the best model for all possible situations. As
discussed in section 3.1.1, the EPA has determined and applied a
specific evaluation protocol that provides a statistical technique
for evaluating model performance for predicting peak concentration
values, as might be observed at individual monitoring locations.\29\
This protocol is available to assist in developing a consistent
approach when justifying the use of other-than-preferred models
recommended in the Guideline (i.e., alternative models). The
procedures in this protocol provide a general framework for
objective decision-making on the acceptability of an alternative
model for a given regulatory application. These objective procedures
may be used for conducting both the technical evaluation of the
model and the field test or performance evaluation.
b. This subsection discusses the use of alternate models and
defines three situations when alternative models may be used. This
subsection also provides a procedure for implementing 40 CFR
51.166(l)(2) in PSD permitting. This provision requires written
approval of the Administrator for any modification or substitution
of an applicable model. An applicable model for purposes of 40 CFR
51.166(l) is a preferred model in appendix A to the Guideline.
Approval to use an alternative model under section 3.2 of the
Guideline qualifies as approval for the modification or substitution
of a model under
[[Page 45360]]
40 CFR 51.166(l)(2). The Regional Administrators are delegated
authority to issue such approvals under section 3.2 of the
Guideline, provided that such approval is issued after consultation
with EPA's Model Clearinghouse and formally documented in a
concurrence memorandum from EPA's Model Clearinghouse which
demonstrates that the requirements within section 3.2 for use of an
alternative model have been met.
3.2.2 Requirements
a. Determination of acceptability of an alternative model is an
EPA Regional Office responsibility in consultation with EPA's Model
Clearinghouse as discussed in paragraphs 3.0(b) and 3.2.1(b). Where
the Regional Administrator finds that an alternative model is more
appropriate than a preferred model, that model may be used subject
to the approval of the EPA Regional Office based on the requirements
of this subsection. This finding will normally result from a
determination that (1) a preferred air quality model is not
appropriate for the particular application; or (2) a more
appropriate model or technique is available and applicable.
b. An alternative model shall be evaluated from both a
theoretical and a performance perspective before it is selected for
use. There are three separate conditions under which such a model
may be approved for use:
1. If a demonstration can be made that the model produces
concentration estimates equivalent to the estimates obtained using a
preferred model;
2. If a statistical performance evaluation has been conducted
using measured air quality data and the results of that evaluation
indicate the alternative model performs better for the given
application than a comparable model in appendix A; or
3. If there is no preferred model.
Any one of these three separate conditions may justify use of an
alternative model. Some known alternative models that are applicable
for selected situations are listed on the EPA's SCRAM Web site
(section 2.3). However, inclusion there does not confer any unique
status relative to other alternative models that are being or will
be developed in the future.
c. Equivalency, condition (1) in paragraph (b) of this
subsection, is established by demonstrating that the maximum or
highest, second highest concentrations are within +/- 2 percent of
the estimates obtained from the preferred model. The option to show
equivalency is intended as a simple demonstration of acceptability
for an alternative model that is so nearly identical (or contains
options that can make it identical) to a preferred model that it can
be treated for practical purposes as the preferred model. However,
notwithstanding this demonstration, models that are not equivalent
may be used when one of the two other conditions described in
paragraphs (d) and (e) of this subsection are satisfied.
d. For condition (2) in paragraph (b) of this subsection,
established statistical performance evaluation procedures and
techniques 28 29 for determining the acceptability of a
model for an individual case based on superior performance should be
followed, as appropriate. Preparation and implementation of an
evaluation protocol which is acceptable to both control agencies and
regulated industry is an important element in such an evaluation.
e. Finally, for condition (3) in paragraph (b) of this
subsection, an alternative model or technique may be approved for
use provided that:
i. The model or technique has received a scientific peer review;
ii. The model or technique can be demonstrated to be applicable
to the problem on a theoretical basis;
iii. The databases which are necessary to perform the analysis
are available and adequate;
iv. Appropriate performance evaluations of the model or
technique have shown that the model or technique is not
inappropriately biased for regulatory application; \a\ and
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\a\ For PSD and other applications that use the model results in
an absolute sense, the model should not be biased toward
underestimates. Alternatively, for ozone and PM2.5 SIP
attainment demonstrations and other applications that use the model
results in a relative sense, the model should note be biased toward
overestimates.
---------------------------------------------------------------------------
v. A protocol on methods and procedures to be followed has been
established.
f. To formally document that the requirements of section 3.2 for
use of an alternative model are satisfied for a particular
application or range of applications, a memorandum will be prepared
by the EPA's Model Clearinghouse through a consultative process with
the Region Office.
3.3 EPA's Model Clearinghouse
a. The Regional Administrator has the authority to select models
that are appropriate for use in a given situation. However, there is
a need for assistance and guidance in the selection process so that
fairness, consistency, and transparency in modeling decisions are
fostered among the EPA Regional Offices and the state, local, and
tribal agencies. To satisfy that need, the EPA established the Model
Clearinghouse \23\ to serve a central role of coordination and
collaboration between EPA headquarters and the EPA Regional Offices.
Additionally, the EPA holds periodic workshops with EPA
headquarters, EPA Regional Office, and state, local, and tribal
agency modeling representatives.
b. The EPA Regional Office should always be consulted for
information and guidance concerning modeling methods and
interpretations of modeling guidance, and to ensure that the air
quality model user has available the latest most up-to-date policy
and procedures. As appropriate, the EPA Regional Office may also
request assistance from the EPA's Model Clearinghouse on other
applications of models, analytical techniques, or databases or to
clarify interpretation of the Guideline or related modeling
guidance.
c. The EPA Regional Office will coordinate with the EPA's Model
Clearinghouse after an initial evaluation and decision has been
developed concerning the application of an alternative model. The
acceptability and formal approval process for an alternative model
is described in section 3.2.
4.0 Models for Carbon Monoxide, Lead, Sulfur Dioxide, Nitrogen Dioxide
and Primary Particulate Matter
4.1 Discussion
a. This section identifies modeling approaches generally used in
the air quality impact analysis of sources that emit the criteria
pollutants carbon monoxide (CO), lead, sulfur dioxide
(SO2), nitrogen dioxide (NO2), and primary
particulates (PM2.5 and PM10).
b. The guidance in this section is specific to the application
of the Gaussian plume models identified in appendix A. Gaussian
plume models assume that emissions and meteorology are in a steady-
state, which is typically based on an hourly time step. This
approach results in a plume that has an hourly-averaged distribution
of emission mass according to a Gaussian curve through the plume.
Though Gaussian steady-state models conserve the mass of the primary
pollutant throughout the plume, they can still take into account a
limited consideration of first-order removal processes (e.g., wet
and dry deposition) and limited chemical conversion (e.g., OH
oxidation).
c. Due to the steady-state assumption, Gaussian plume models are
generally considered applicable to distances less than 50 km, beyond
which, modeled predictions of plume impact are likely conservative.
The locations of these impacts are expected to be unreliable due to
changes in meteorology that are likely to occur during the travel
time.
d. The applicability of Gaussian plume models may vary depending
on the topography of the modeling domain, i.e., simple or complex.
Simple terrain, as used here, is considered to be an area where
terrain features are all lower in elevation than the top of the
stack of the source(s) in question. Complex terrain is defined as
terrain exceeding the height of the stack being modeled.
e. Gaussian models determine source impacts at discrete
locations (receptors) for each meteorological and emission scenario,
and generally attempt to estimate concentrations at specific sites
that represent an ensemble average of numerous repetitions of the
same ``event.'' Uncertainties in model estimates are driven by this
formulation, and as noted in section 2.1.1, evaluations of model
accuracy should focus on the reducible uncertainty associated with
physics and the formulation of the model. The ``irreducible''
uncertainty associated with Gaussian plume models may be responsible
for variation in concentrations of as much as +/- 50 percent.\30\
``Reducible'' uncertainties \16\ can be on a similar scale. For
example, Pasquill \31\ estimates that, apart from data input errors,
maximum ground-level concentrations at a given hour for a point
source in flat terrain could be in error by 50 percent due to these
uncertainties. Errors of 5 to 10 degrees in the measured wind
direction can result in concentration errors of 20 to 70 percent for
a particular time and location, depending on stability and station
location. Such uncertainties do not indicate that an estimated
concentration does not occur, only that the precise time and
locations are in doubt. Composite errors in
[[Page 45361]]
highest estimated concentrations of 10 to 40 percent are found to be
typical.32 33 However, estimates of concentrations paired
in time and space with observed concentrations are less certain.
f. Model evaluations and inter-comparisons should take these
aspects of uncertainty into account. For a regulatory application of
a model, the emphasis of model evaluations is generally placed on
the highest modeled impacts. Thus, the Cox-Tikvart model evaluation
approach, which compares the highest modeled impacts on several
timescales, is recommended for comparisons of models and
measurements and model inter-comparisons. The approach includes
bootstrap techniques to determine the significance of various
modeled predictions and increases the robustness of such comparisons
when the number of available measurements are
limited.34 35 Because of the uncertainty in paired
modeled and observed concentrations, any attempts at calibration of
models based on these comparisons is of questionable benefit and
shall not be done.
4.2 Requirements
a. For NAAQS compliance demonstrations under PSD, use of the
screening and preferred models for the pollutants listed in this
subsection shall be limited to the near-field at a nominal distance
of 50 km or less. Near-field application is consistent with
capabilities of Gaussian plume models and, based on the EPA's
assessment, is sufficient to address whether a source will cause or
contribution to ambient concentrations in excess to a NAAQS. In most
cases, maximum source impacts of inert pollutant are anticipated to
occur within 10 to 20 km from the source. Therefore, the EPA does
not consider a long-range transport assessment beyond 50 km
necessary for these pollutants.\36\
b. For assessment of PSD increments within the near-field
nominal distance of 50 km or less, use of the screening and
preferred models for the pollutants listed in this subsection shall
be limited to the same screening and preferred models approved for
NAAQS compliance demonstrations.
c. To determine if a Class I PSD increment analyses may be
necessary beyond 50 km (i.e., long-range transport assessment), the
following screening approach shall be used to determine if a
significant impact will occur with particular focus on Class I areas
that may be threatened at such distances.
i. Based on application in the near-field of the appropriate
screening and/or preferred model, determine the significance of the
ambient impacts at or about 50 km from the new or modifying source.
If this initial step indicates there may be significant ambient
impacts at that distance or such near-field assessment is not
available, then further assessment is necessary.
ii. For assessment of Class I significance of ambient impacts
and cumulative increment analyses, there is not a preferred model or
screening approach for distances beyond 50 km. Thus, the EPA
Regional Office shall be consulted in determining the appropriate
and agreed upon modeling approach to conduct the second level
assessment. Typically a Lagrangian model may be the type of model
used for this second level assessment, but applicants shall reach
agreed upon approaches (models and modeling parameters) on a case-
by-case basis. When Lagrangian models are used in this manner, they
shall not include plume-depleting reactions, such that model
estimates are considered conservative, as is generally appropriate
for screening assessments.
d. In those limited situations where a cumulative increment
analysis beyond 50 km is necessary, the selection and use of an
alternative model shall occur in agreement with the appropriate
reviewing authority (paragraph 3.0(b)) and approval by the EPA
Regional Office based on the requirements of paragraph 3.2.2(e).
4.2.1 Screening Models and Techniques
a. Where a preliminary or conservative estimate is desired,
point source screening techniques are an acceptable approach to air
quality analyses.
b. As discussed in paragraph 2.2(a), screening models or
techniques are designed to provide a conservative estimate of
concentrations. The screening models used in most applications are
the screening versions of the preferred models for refined
applications. The two screening models, AERSCREEN 37 38
and CTSCREEN, are screening versions of AERMOD (American
Meteorological Society (AMS)/EPA Regulatory Model) and CTDMPLUS
(Complex Terrain Dispersion Model Plus Algorithms for Unstable
Situations), respectively. AERSCREEN is the preferred screening
model for most applications in all types of terrain and for
applications involving building downwash. For those applications in
complex terrain where the application involves a well-defined hill
or ridge, CTSCREEN \39\ can be used.
c. Although AERSCREEN and CTSCREEN are designed to address a
single-source scenario, there are approaches that can be used on a
case-by-case basis to address multi-source situations using
screening meteorology or other conservative model assumptions.
However, the appropriate reviewing authority (paragraph 3.0(b))
shall be consulted, and concurrence obtained, on the protocol for
modeling multiple sources with AERSCREEN or CTSCREEN to ensure that
the worst case is identified and assessed.
d. As discussed in section 4.2.3.4, there are also screening
techniques built into AERMOD that use simplified or limited
chemistry assumptions for determining the partitioning of NO and
NO2 for NO2 modeling. These screening
techniques are part of the EPA's preferred modeling approach for
NO2 and do not need to be approved as an alternative
model. However, as with other screening models and techniques, their
usage shall occur in agreement with the appropriate reviewing
authority (paragraph 3.0(b)).
e. All screening models and techniques shall be configured to
appropriately address the site and problem at hand. Close attention
must be paid to whether the area should be classified urban or rural
in accordance with section 7.2.1.1. The climatology of the area must
be studied to help define the worst-case meteorological conditions.
Agreement shall be reached between the model user and the
appropriate reviewing authority (paragraph 3.0(b)) on the choice of
the screening model or technique for each analysis, on the input
data and model settings, and the appropriate metric for satisfying
regulatory requirements.
4.2.1.1 AERSCREEN
a. Released in 2011, AERSCREEN is the EPA's recommended
screening model for simple and complex terrain for single sources
including point sources, area sources, horizontal stacks, capped
stacks, and flares. AERSCREEN runs AERMOD in a screening mode and
consists of two main components: (1) The MAKEMET program which
generates a site-specific matrix of meteorological conditions for
input into the AERMOD model; and (2) the AERSCREEN command-prompt
interface.
b. The MAKEMET program generates a matrix of meteorological
conditions, in the form of AERMOD-ready surface and profile files,
based on user-specified surface characteristics, ambient
temperatures, minimum wind speed, and anemometer height. The
meteorological matrix is generated based on looping through a range
of wind speeds, cloud covers, ambient temperatures, solar elevation
angles, and convective velocity scales (w*, for convective
conditions only) based on user-specified surface characteristics
(Zo, Bo, r). For unstable cases, the convective mixing
height (Zic) is calculated based on w*, and the
mechanical mixing height (Zim) is calculated for unstable
and stable conditions based on the friction velocity, u*.
c. For applications involving simple or complex terrain,
AERSCREEN interfaces with AERMAP. AERSCREEN also interfaces with
BIPPRM to provide the necessary building parameters for applications
involving building downwash using the PRIME downwash algorithm.
AERSCREEN generates inputs to AERMOD via MAKEMET, AERMAP, and
BPIPPRM and invokes AERMOD in a screening mode. The screening mode
of AERMOD forces the AERMOD model calculations to represent values
for the plume centerline, regardless of the source-receptor-wind
direction orientation. The maximum concentration output from
AERSCREEN represents a worst-case 1-hour concentration. Averaging-
time scaling factors of 0.9 for 3-hour, 0.7 for 8-hour, 0.40 for 24-
hour, and 0.08 for annual concentration averages are applied
internally by AERSCREEN to the highest 1-hour concentration
calculated by the model for non-area type sources. For area type
source concentrations for averaging times greater than one hour, the
concentrations are equal to the 1-hour estimates.37 40
4.2.1.2 CTSCREEN
a. CTSCREEN 39 41 can be used to obtain conservative,
yet realistic, worst-case estimates for receptors located on terrain
above stack height. CTSCREEN accounts for the three-dimensional
nature of plume and terrain interaction and requires detailed
terrain data representative of the modeling domain. The terrain data
must be digitized in the same manner as for CTDMPLUS and a terrain
processor is available.\42\ CTSCREEN is designed to execute a fixed
matrix of meteorological values for wind speed (u),
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standard deviation of horizontal and vertical wind speeds ([sigma]v,
[sigma]w), vertical potential temperature gradient (d[thgr]/dz),
friction velocity (u*), Monin-Obukhov length (L), mixing height
(zi) as a function of terrain height, and wind directions
for both neutral/stable conditions and unstable convective
conditions. The maximum concentration output from CTSCREEN
represents a worst-case 1-hour concentration. Time-scaling factors
of 0.7 for 3-hour, 0.15 for 24-hour and 0.03 for annual
concentration averages are applied internally by CTSCREEN to the
highest 1-hour concentration calculated by the model.
4.2.1.3 Screening in Complex Terrain
a. For applications utilizing AERSCREEN, AERSCREEN automatically
generates a polar-grid receptor network with spacing determined by
the maximum distance to model. If the application warrants a
different receptor network than that generated by AERSCREEN, it may
be necessary to run AERMOD in screening mode with a user-defined
network. For CTSCREEN applications or AERMOD in screening mode
outside of AERSCREEN, placement of receptors requires very careful
attention when modeling in complex terrain. Often the highest
concentrations are predicted to occur under very stable conditions,
when the plume is near, or impinges on, the terrain. The plume under
such conditions may be quite narrow in the vertical, so that even
relatively small changes in a receptor's location may substantially
affect the predicted concentration. Receptors within about a
kilometer of the source may be even more sensitive to location.
Thus, a dense array of receptors may be required in some cases.
b. For applications involving AERSCREEN, AERSCREEN interfaces
with AERMAP to generate the receptor elevations. For applications
involving CTSCREEN, digitized contour data must be preprocessed \42\
to provide hill shape parameters in suitable input format. The user
then supplies receptors either through an interactive program that
is part of the model or directly, by using a text editor; using both
methods to select receptors will generally be necessary to assure
that the maximum concentrations are estimated by either model. In
cases where a terrain feature may ``appear to the plume'' as
smaller, multiple hills, it may be necessary to model the terrain
both as a single feature and as multiple hills to determine design
concentrations.
c. Other screening techniques may be acceptable for complex
terrain cases where established procedures \43\ are used. The user
is encouraged to confer with the appropriate reviewing authority
(paragraph 3.0(b)) if any unresolvable problems are encountered,
e.g., applicability, meteorological data, receptor siting, or
terrain contour processing issues.
4.2.2 Refined Models
a. A brief description of each preferred model for refined
applications is found in appendix A. Also listed in that appendix
are availability, the model input requirements, the standard options
that shall be selected when running the program, and output options.
4.2.2.1 AERMOD
a. For a wide range of regulatory applications in all types of
terrain, and for aerodynamic building downwash, the recommended
model is AERMOD.44 45 The AERMOD regulatory modeling
system consists of the AERMOD dispersion model, the AERMET
meteorological processor, and the AERMAP terrain processor. AERMOD
is a steady-state Gaussian plume model applicable to directly
emitted air pollutants that employs best state-of-practice
parameterizations for characterizing the meteorological influences
and dispersion. Differentiation of simple versus complex terrain is
unnecessary with AERMOD. In complex terrain, AERMOD employs the
well-known dividing-streamline concept in a simplified simulation of
the effects of plume-terrain interactions.
b. The AERMOD modeling system has been extensively evaluated
across a wide range of scenarios based on numerous field studies,
including tall stacks in flat and complex terrain settings, sources
subject to building downwash influences, and low-level non-buoyant
sources.\27\ These evaluations included several long-term field
studies associated with operating plants as well as several
intensive tracer studies. Based on these evaluations, AERMOD has
shown consistently good performance, with ``errors'' in predicted
vs. observed peak concentrations, based on the Robust Highest
Concentration (RHC) metric, consistently within the range of 10 to
40 percent cited in paragraph 4.1(g).
c. AERMOD incorporates the Plume Rise Model Enhancements (PRIME)
algorithm to account for enhanced plume growth and restricted plume
rise for plumes affected by building wake effects.\46\ The PRIME
algorithm accounts for entrainment of plume mass into the cavity
recirculation region, including re-entrainment of plume mass into
the wake region beyond the cavity.
d. AERMOD incorporates the Buoyant Line and Point Source (BLP)
Dispersion model to account for buoyant plume rise from line
sources. The BLP option within AERMOD utilizes the standard
meteorological inputs provided by the AERMET meteorological
processor.
e. The state-of-the-science for modeling atmospheric deposition
is evolving and new modeling techniques are continually being
assessed and their results are being compared with observations.
Consequently, while deposition treatment is available in AERMOD, the
approach taken for any purpose shall be coordinated with the
appropriate reviewing authority (paragraph 3.0(b)).
4.2.2.2 CTDMPLUS
a. If the modeling application involves an elevated point source
with a well-defined hill or ridge and a detailed dispersion analysis
of the spatial pattern of plume impacts is of interest, CTDMPLUS is
available. CTDMPLUS provides greater resolution of concentrations
about the contour of the hill feature than does AERMOD through a
different plume-terrain interaction algorithm.
4.2.2.3 OCD
a. If the modeling application involves determining the impact
of offshore emissions from point, area, or line sources on the air
quality of coastal regions, the recommended model is the OCD
(Offshore and Coastal Dispersion) Model. OCD is a straight-line
Gaussian model that incorporates overwater plume transport and
dispersion as well as changes that occur as the plume crosses the
shoreline. OCD is also applicable for situations that involve
platform building downwash.
4.2.3 Pollutant Specific Modeling Requirements
4.2.3.1 Models for Carbon Monoxide
a. Models for assessing the impact of CO emissions are needed to
meet NSR requirements, including PSD, to address compliance with the
CO NAAQS and to determine localized impacts from transportations
projects. Examples include evaluating effects of point sources,
congested roadway intersections, and highways, as well as the
cumulative effect of numerous sources of CO in an urban area.
b. The general modeling recommendations and requirements for
screening models in section 4.2.1 and refined models in section
4.2.2 shall be applied for CO modeling. Given the relatively low CO
background concentrations, screening techniques are likely to be
adequate in most cases. However, since the screening model specified
in section 4.2.1 (AERSCREEN) can only handle one source at a time, a
section 4.2.2 model may be used with screening meteorology (e.g.,
generated with MAKEMET) to conduct screening assessments of CO
projects involving more than one source (e.g., roadway hotspot
assessments).\47\
4.2.3.2 Models for Lead
a. In January 1999 (40 CFR part 58, appendix D), the EPA gave
notice that concern about ambient lead impacts was being shifted
away from roadways and toward a focus on stationary point sources.
Thus, models for assessing the impact of lead emissions are needed
to meet NSR requirements, including PSD, to address compliance with
the lead NAAQS and for SIP attainment demonstrations. The EPA has
also issued guidance on siting ambient monitors in the vicinity of
stationary point sources.\48\ For lead, the SIP should contain an
air quality analysis to determine the maximum rolling 3-month
average lead concentration resulting from major lead point sources,
such as smelters, gasoline additive plants, etc. The EPA has
developed a post-processor to calculate rolling 3-month average
concentrations from model output.\49\ General guidance for lead SIP
development is also available.\50\
b. For major lead point sources, such as smelters, which
contribute fugitive emissions and for which deposition is important,
professional judgment should be used, and there shall be
coordination with the appropriate reviewing authority (paragraph
3.0(b)). For most applications, the general requirements for
screening and refined models of section 4.2.1 and 4.2.2 are
applicable to lead modeling.
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4.2.3.3 Models for Sulfur Dioxide
a. Models for SO2 are needed to meet NSR
requirements, including PSD, to address compliance with the
SO2 NAAQS and PSD increments, for SIP attainment
demonstrations,\51\ and for characterizing current air quality via
modeling.\52\ SO2 is one of a group of highly reactive
gasses known as ``oxides of sulfur'' with largest emissions sources
being fossil fuel combustion at power plants and other industrial
facilities.
b. Given the relatively inert nature of SO2 on the
short-term time scales of interest (i.e., 1-hour) and the sources of
SO2 (i.e., stationary point sources), the general
modeling requirements for screening models in section 4.2.1 and
refined models in section 4.2.2 are applicable for SO2
modeling applications. For urban areas, AERMOD automatically invokes
a half-life of 4 hours \53\ to SO2. Therefore, care must
be taken when determining whether a source is urban or rural (see
section 7.2.1.1 for urban/rural determination methodology).
4.2.3.4 Models for Nitrogen Dioxide
a. Models for assessing the impact of sources on ambient
NO2 concentrations are needed to meet NSR requirements,
including PSD, to address compliance with the NO2 NAAQS
and PSD increments. Impact of an individual source on ambient
NO2 depends, in part, on the chemical environment into
which the source's plume is to be emitted. This is due to the fact
that NO2 sources co-emit NO along with NO2 and
any emitted NO may react with ambient ozone to convert to additional
NO2 downwind. Thus, comprehensive modeling of
NO2 would need to consider the ratio of emitted NO and
NO2, the ambient levels of ozone and subsequent reactions
between ozone and NO, and the photolysis of NO2 to NO.
b. Due to the complexity of NO2 modeling, a multi-
tiered approach is required to obtain hourly and annual average
estimates of NO2.\54\ Since these methods are considered
screening, their usage shall occur in agreement with the appropriate
reviewing authority (paragraph 3.0(b)). Additionally, since
screening techniques are conservative by their nature, there are
limitations to how these options can be used. Specifically, negative
emissions should not be modeled because decreases in concentrations
would be overestimated. Each tiered approach (see Figure 4-1)
accounts for increasing complex considerations of NO2
chemistry and is described in paragraphs b through d of this
subsection. The tiers of NO2 modeling include:
i. A first-tier (most conservative) ``full'' conversion
approach;
ii. A second-tier approach that assumes ambient equilibrium
between NO and NO2; and
iii. A third-tier consisting of several detailed screening
techniques that account for ambient ozone and the relative amount of
NO and NO2 emitted from a source.
c. For Tier 1, use an appropriate section 4.2.2 refined model to
estimate nitrogen oxides (NOX) concentrations and assume
a total conversion of NO to NO2. If the resulting design
concentrations exceed the NAAQS or PSD increments for
NO2, proceed to Tier 2.
d. For Tier 2, multiply the Tier 1 result(s) by the Ambient
Ratio Method 2 (ARM2), which provides estimates of representative
equilibrium ratios of NO2/NOX value based
ambient levels of NO2 and NOX derived from
national data from the EPA's Air Quality System (AQS).\55\ The
national default for ARM2 will include a minimum NO2/
NOX ratio of 0.5 and a maximum ratio of 0.9. The
reviewing agency may establish alternative default minimum
NO2/NOX values based on the source's in-stack
emissions ratios, with alternative minimum values reflecting the
source's in-stack NO2/NOX ratios. Preferably,
alternative default NO2/NOX values should be
based on source-specific data which satisfies all quality assurance
procedures that ensure data accuracy for both NO2 and
NOX within the typical range of measured values. However,
alternate information may be used to justify a source's anticipated
NO2/NOX in-stack ratios, such as manufacturer
test data, state or local agency guidance, peer-reviewed literature,
the EPA's NO2/NOX ratio database.
e. For Tier 3, a detailed screening technique shall be applied
on a case-by-case basis. Because of the additional input data
requirements and complexities associated with the Tier 3 options,
their usage shall occur in consultation with the EPA Regional Office
in addition to the appropriate reviewing authority. The Ozone
Limiting Method (OLM) \56\ and the Plume Volume Molar Ratio Method
(PVMRM) \57\ are two detailed screening techniques that may be used
for most sources. These two techniques use an appropriate section
4.2.2 model to estimate NOX concentrations and then
estimate the conversion of primary NO emissions to NO2
based on the ambient levels of ozone and the plume characteristics.
OLM only accounts for NO2 formation based on the ambient
levels of ozone while PVMRM also accommodates distance-dependent
conversion ratios based on ambient ozone. Both PVMRM and OLM require
that ambient ozone concentrations be provided on an hourly basis and
explicit specification of the speciation of the NO2/
NOX in-stack ratios. PVMRM works best for relatively
isolated and elevated point source modeling while OLM works best for
large groups of sources, area sources, and near-surface releases,
including road-way sources.
f. Alternative models or techniques may be considered on a case-
by-case basis and their usage shall be approved by the EPA Regional
Office (section 3.2). Such techniques should consider individual
quantities of NO and NO2 emissions, atmospheric transport
and dispersion, and atmospheric transformation of NO to
NO2. Dispersion models that account for more explicit
photochemistry may also be applied to estimate ambient impacts of
NOX sources.
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4.2.3.5 Models for PM2.5
a. The PM2.5 NAAQS, promulgated on July 18, 1997,
includes particles with an aerodynamic diameter nominally less than
or equal to 2.5 micrometers. PM2.5 is a mixture
consisting of several diverse components\58\. Ambient
PM2.5 generally consists of two components, the primary
component, emitted directly from a source, and the secondary
component, which is formed in the atmosphere from other pollutants
emitted from the source. Models for PM2.5 are needed to
meet NSR requirements, including PSD, to address compliance with the
PM2.5 NAAQS and PSD increments and for SIP attainment
demonstrations.
b. For NSR, including PSD, modeling assessments, the refined
methods in section 4.2.2 are required for modeling the primary
component of PM2.5, while the methods in section 5.4 are
recommended for addressing the secondary component of
PM2.5. Guidance for PSD assessments is available for
determining the best approach to handling sources of primary and
secondary PM2.5.\59\
c. For SIP attainment demonstrations and regional haze
reasonable progress goal analyses, effects of a control strategy on
PM2.5 are estimated from the sum of the effects on the
primary and secondary components composing PM2.5. Model
users should refer to section 5.4.1 and associated SIP modeling
guidance \60\ for further details concerning appropriate modeling
approaches.
d. The general modeling requirements for the refined models
discussed in section 4.2.2 should be applied for PM2.5
hot-spot modeling for mobile sources. Specific guidance is available
for analyzing direct PM2.5 impacts from highways,
terminals, and other projects.\61\
4.2.3.6 Models for PM10
a. The NAAQS for PM10 was promulgated on July 1,
1987. The EPA promulgated regulations for PSD increment measured as
PM10 in a document published on June 3, 1993. Models for
PM10 are needed to meet NSR requirements, including PSD,
to address compliance with the PM10 NAAQS and PSD
increments and for SIP attainment demonstrations.
b. For most sources, the general modeling requirements for
screening models in section 4.2.1 and refined models in section
4.2.2 shall be applied for PM10 modeling. In cases where
the particle size and its effect on ambient concentrations need to
be considered, particle deposition may be used in on a case-by-case
basis and their usage shall be approved by the EPA Regional Office
(section 3.2). A SIP development guide \62\ is also available to
assist in PM10 analyses and control strategy development.
c. Fugitive dust usually refers to dust put into the atmosphere
by the wind blowing over plowed fields, dirt roads or desert or
sandy areas with little or no vegetation. Fugitive emissions include
the emissions resulting from the industrial process that are not
captured and vented through a stack but may be released from various
locations within the complex. In some unique cases, a model
developed specifically for the situation may be needed. Due to the
difficult nature of characterizing and modeling fugitive dust and
fugitive emissions, the proposed procedure shall be determined in
consultation with the appropriate reviewing authority (paragraph
3.0(b)) for each specific situation before the modeling exercise is
begun. Re-entrained dust is created by vehicles driving over dirt
roads (e.g., haul roads) and dust-covered roads typically found in
arid areas. Such sources can be characterized as line, area or
volume sources.61 63 Emission rates may be based on site-
specific data or values from the general literature.
d. Under certain conditions, recommended dispersion models may
not be suitable to appropriately address the nature of ambient
PM10. In these circumstances, the alternative modeling
approach shall be approved by the EPA Regional Office (section 3.2).
e. The general modeling requirements for the refined models
discussed in section 4.2.2 should be applied for PM10
hot-spot modeling for mobile sources. Specific guidance is available
for analyzing direct PM10 impacts from highways,
terminals, and other projects.\61\
5.0 Models for Ozone and Secondarily Formed Particulate Matter
5.1 Discussion
a. Air pollutants formed through chemical reactions in the
atmosphere are referred to as secondary pollutants. For example,
ground-level ozone and a portion of particulate matter with
aerodynamic diameter less than 2.5 [mu] m (PM2.5 or fine
PM) are secondary pollutants formed through photochemical reactions.
Ozone and secondarily formed particulate matter are closely related
to each other in that they share common sources of emissions or are
formed in the atmosphere from chemical reactions with similar
precursors.
b. Ozone formation is driven by emissions of NOX and
volatile organic compounds (VOCs). Ozone formation is a complicated
nonlinear process that requires favorable meteorological conditions
in addition to VOC and NOX emissions. Sometimes complex
terrain features also contribute to the build-up of precursors and
subsequent ozone formation or destruction.
c. PM2.5 can be either primary (i.e., emitted
directly from sources) or secondary in nature. The fraction of
PM2.5 which is primary versus secondary varies by
location and season. In the United States, PM2.5 is
dominated by a variety of chemical species or components of
atmospheric particles, such as ammonium sulfate, ammonium nitrate,
organic carbon (OC) mass, elemental carbon (EC), and other soil
compounds and oxidized metals. PM2.5
[[Page 45365]]
sulfate, nitrate, and ammonium ions are predominantly the result of
chemical reactions of the oxidized products of sulfur dioxide
(SO2) and NOX emissions with direct ammonia
(NH3) emissions.\64\
d. Modeled strategies designed to reduce ozone or
PM2.5 levels typically need to consider the chemical
coupling between these pollutants. Control measures reducing ozone
and PM2.5 precursor emissions may not lead to
proportional reductions in ozone and PM2.5. This coupling
is important in understanding processes that control the levels of
both pollutants. Thus, when feasible, it is important to use models
that take into account the chemical coupling between ozone and
PM2.5. In addition, using such a multi-pollutant modeling
system can reduce the resource burden associated with applying and
evaluating separate models for each pollutant and promotes
consistency among the strategies themselves.
e. PM2.5 is a mixture consisting of several diverse
chemical species or components of atmospheric particles. Because
chemical and physical properties and origins of each component
differ, it may be appropriate to use either a single model capable
of addressing several of the important components or to model
primary and secondary components using different models. Effects of
a control strategy on PM2.5 is estimated from the sum of
the effects on the specific components composing PM2.5.
5.2 Recommendations
a. Chemical transformations can play an important role in
defining the concentrations and properties of certain air
pollutants. Models that take into account chemical reactions and
physical processes of various pollutants (including precursors) are
needed for determining the current state of air quality, as well as
predicting and projecting the future evolution of these pollutants.
It is important that a modeling system provide a realistic
representation of chemical and physical processes leading to
secondary pollutant formation and removal from the atmosphere.
b. Chemical transport models treat atmospheric chemical and
physical processes such as deposition and motion. There are two
types of chemical transport models, Eulerian (grid based) and
Lagrangian. These types of models are differentiated from each other
by their frame of reference. Eulerian models are based on a fixed
frame of reference and Lagrangian models use a frame of reference
that moves with parcels of air between the source and receptor
point.\9\ Photochemical grid models are three-dimensional Eulerian
grid-based models that treat chemical and physical processes in each
grid cell and use diffusion and transport processes to move chemical
species between grid cells. These types of models are appropriate
for assessment of near-field and regional scale reactive pollutant
impacts from specific sources7 10 11 12 or all
sources.13 14 15 In some limited cases, the secondary
processes can be treated with a box model, potentially in
combination with a number of other modeling techniques and/or
analyses to treat individual source sectors.
c. Regardless of the modeling system used to estimate secondary
impacts of ozone and/or PM2.5, model results should be
compared to observation data to generate confidence that the
modeling system is representative of the local and regional air
quality. For ozone related projects, model estimates of ozone should
be compared with observations in both time and space. For
PM2.5, model estimates of speciated PM2.5
components (such as sulfate ion, nitrate ion, etc.) should be
compared with observations in both time and space.\65\
d. Model performance metrics comparing observations and
predictions are often used to summarize model performance. These
metrics include mean bias, mean error, fractional bias, fractional
error, and correlation coefficient.\65\ There are no specific levels
of any model performance metric that indicate ``acceptable'' model
performance. The EPA's preferred approach for providing context
about model performance is to compare model performance metrics with
similar contemporary applications.60 65 Because model
application purpose and scope vary, model users should consult with
the appropriate reviewing authority (paragraph 3.0(b)) to determine
what model performance elements should be emphasized and presented
to provide confidence in the regulatory model application.
e. There is no preferred modeling system or technique for
estimating ozone or secondary PM2.5 for specific source
impacts or to assess impacts from multiple sources. For assessing
secondary pollutant impacts from single sources, the degree of
complexity required to assess potential impacts varies depending on
the nature of the source, its emissions, and the background
environment. The EPA recommends a two-tiered approach where the
first tier consists of using existing technically credible and
appropriate relationships between emissions and impacts developed
from previous modeling that is deemed sufficient for evaluating a
source's impacts. The second tier consists of more sophisticated
case-specific modeling analyses. The appropriate tier for a given
application should be selected in consultation with the appropriate
reviewing authority (paragraph 3.0(b)) and be consistent with EPA
guidance.\66\
5.3 Recommended Models and Approaches for Ozone
a. Models that estimate ozone concentrations are needed to guide
the choice of strategies for the purposes of a nonattainment area
demonstrating future year attainment of the ozone NAAQS.
Additionally, models that estimate ozone concentrations are needed
to assess impacts from specific sources or source complexes to
satisfy requirements for NSR, including PSD, and other regulatory
programs. Other purposes for ozone modeling include estimating the
impacts of specific events on air quality, ozone deposition impacts,
and planning for areas that may be attaining the ozone NAAQS.
5.3.1 Models for NAAQS Attainment Demonstrations and Multi-Source Air
Quality Assessments
a. Simulation of ozone formation and transport is a complex
exercise. Control agencies with jurisdiction over areas with ozone
problems should use photochemical grid models to evaluate the
relationship between precursor species and ozone. Use of
photochemical grid models is the recommended means for identifying
control strategies needed to address high ozone concentrations in
such areas. Judgment on the suitability of a model for a given
application should consider factors that include use of the model in
an attainment test, development of emissions and meteorological
inputs to the model, and choice of episodes to model. Guidance on
the use of models and other analyses for demonstrating attainment of
the air quality goals for ozone is available.\60\ Users should
consult with the appropriate reviewing authority (paragraph 3.0(b))
to ensure the most current modeling guidance is applied.
5.3.2 Models for Single-Source Air Quality Assessments
a. Depending on the magnitude of emissions, estimating the
impact of an individual source's emissions of NOX and VOC
on ozone concentrations is necessary for obtaining a permit. The
simulation of ozone formation and transport requires realistic
treatment of atmospheric chemistry and deposition. Models should be
applied which integrate chemical and physical processes important in
the formation, decay, and transport of ozone and important precursor
species (e.g., Lagrangian and photochemical grid models).
Photochemical grid models are primarily designed to characterize
precursor emissions and impacts from a wide variety of sources over
a large geographic area but can also be used to assess the impacts
from specific sources.7 11 12
b. The first tier of assessment for ozone impacts involves those
situations where existing technical information is available (e.g.,
results from existing photochemical grid modeling, published
empirical estimates of source specific impacts, or reduced-form
models) in combination with other supportive information and
analysis for the purposes of estimating secondary impacts from a
particular source. The existing technical information should provide
a credible and representative estimate of the secondary impacts from
the project source. The appropriate reviewing authority (paragraph
3.0(b)) and appropriate EPA guidance \66\ should be consulted to
determine what types of assessments may be appropriate on a case-by-
case basis.
c. The second tier of assessment for ozone impacts involves
those situations where existing technical information is not
available such that chemical transport models (e.g., photochemical
grid models) should be used to address single-source impacts.
Special considerations are needed when using these models to
evaluate the ozone impact from an individual source. Guidance on the
use of models and other analyses for demonstrating the impacts of
single sources for ozone is available.\66\ This document provides a
more detailed discussion of the appropriate approaches to obtaining
estimates of ozone impacts from a single source. Model users should
use the latest version of this guidance in consultation with the
appropriate reviewing authority
[[Page 45366]]
(paragraph 3.0(b)) to determine the most suitable single-source
ozone modeling approach on a case-by-case basis.
5.4 Recommended Models and Approaches for Secondarily Formed PM 2.5
a. Models are needed to guide the choice of strategies to
address an observed PM2.5 problem in an area not
attaining the PM2.5 NAAQS. Additionally, models are
needed to assess PM2.5 impacts from specific sources or
industrial source complexes to satisfy requirements for NSR,
including PSD, and other regulatory programs. Other purposes for
PM2.5 modeling include estimating the impacts of specific
events on air quality, visibility, deposition impacts, and planning
for areas that may be attaining the PM2.5 NAAQS.
5.4.1 Models for NAAQS Attainment Demonstrations and Multi-Source Air
Quality Assessments
a. Models for PM2.5 are needed to assess the adequacy
of a proposed strategy for meeting the annual and/or 24-hour
PM2.5 NAAQS. Modeling primary and secondary
PM2.5 can be a multi-faceted and complex problem,
especially for secondary components of PM2.5 such as
sulfates and nitrates. Control agencies with jurisdiction over areas
with secondary PM2.5 problems should use models which
integrate chemical and physical processes important in the
formation, decay, and transport of these species (e.g.,
photochemical grid models). Suitability of a modeling approach or
mix of modeling approaches for a given application requires
technical judgment as well as professional experience in choice of
models, use of the model(s) in an attainment test, development of
emissions and meteorological inputs to the model, and selection of
days to model. Guidance on the use of models and other analyses for
demonstrating attainment of the air quality goals for
PM2.5 is available.59 60 Users should consult
with the appropriate reviewing authority (paragraph 3.0(b)) to
ensure the most current modeling guidance is applied.
5.4.2 Models for Single-Source Air Quality Assessments
a. Depending on the magnitude of emissions, estimating the
impact of an individual source's emissions on secondary particulate
matter concentrations is necessary for obtaining a permit. Primary
PM2.5 components shall be simulated using AERMOD (see
section 4.2.2). The simulation of secondary particulate matter
formation and transport is a complex exercise requiring realistic
treatment of atmospheric chemistry and deposition. Models should be
applied which integrate chemical and physical processes important in
the formation, decay, and transport of these species (e.g.,
Lagrangian and photochemical grid models). Photochemical grid models
are primarily designed to characterize precursor emissions and
impacts from a wide variety of sources over a large geographic area
and can also be used to assess the impacts from specific
sources.7 10
b. The first tier of assessment for secondary PM2.5
impacts involves those situations where existing technical
information is available (e.g., results from existing photochemical
grid modeling, published empirical estimates of source specific
impacts, or reduced-form models) in combination with other
supportive information and analysis for the purposes of estimating
secondary impacts from a particular source. The existing technical
information should provide a credible and representative estimate of
the secondary impacts from the project source. The appropriate
reviewing authority (paragraph 3.0(b)) and appropriate EPA guidance
\66\ should be consulted to determine what types of assessments may
be appropriate on a case-by-case basis.
c. The second tier of assessment for secondary PM2.5
impacts involves those situations where existing technical
information is not available such that chemical transport models
(e.g., photochemical grid models) should be used for assessments of
single-source impacts. Special considerations are needed when using
these models to evaluate the secondary particulate matter impact
from an individual source. Guidance on the use of models and other
analyses for demonstrating the impacts of single sources for
secondary PM2.5 is available.\66\ This document provides
a more detailed discussion of the appropriate approaches to
obtaining estimates of secondary particulate matter concentrations
from a single source. Model users should use the latest version of
this guidance in consultation with the appropriate reviewing
authority (paragraph 3.0(b)) to determine the most suitable single-
source modeling approach for secondary PM2.5 on a case-
by-case basis.
6.0 Modeling for Air Quality Related Values and Other Governmental
Programs
6.1 Discussion
a. Other federal agencies have also developed specific modeling
approaches for their own regulatory or other requirements. Although
such regulatory requirements and guidance have come about because of
EPA rules or standards, the implementation of such regulations and
the use of the modeling techniques is under the jurisdiction of the
agency issuing the guidance or directive. This section covers such
situations with reference to those guidance documents, when they are
available.
b. When using the model recommended or discussed in the
Guideline in support of programmatic requirements not specifically
covered by EPA regulations, the model user should consult the
appropriate federal or state agency to ensure the proper application
and use of the models and/or techniques. Other federal agencies have
developed specific modeling approaches for their own regulatory or
other requirements. Most of the programs have, or will have when
fully developed, separate guidance documents that cover the program
and a discussion of the tools that are needed. The following
paragraphs reference those guidance documents, when they are
available. No attempt has been made to provide a comprehensive
discussion of each topic since the reference documents were designed
to do that.
6.2 Air Quality Related Values
a. The 1997 CAA Amendments give FLMs an ``affirmative
responsibility'' to protect the natural and cultural resources of
Class I areas from the adverse impacts of air pollution and to
provide the appropriate procedures and analysis techniques. The Act
identifies the FLM as the Secretary of the department, or their
designee, with authority over these lands. Mandatory Federal Class I
areas are defined in the CAA as international parks, national parks
over 6,000 acres and wilderness areas and memorial parks over 5,000
acres, established as of 1977. The FLMs are also concerned with the
protection of resources in federally managed Class II areas because
of other statutory mandates to protect these areas.
b. The FLM agency responsibilities include the review of air
quality permit applications from proposed new or modified major
pollution sources that may affect these Class I areas to determine
if emissions from a proposed or modified source will cause or
contribute to adverse impacts on air quality related values (AQRVs)
of a Class I area and making recommendations to the FLM. AQRVs are
resources identified by the FLM agencies, which have the potential
to be affected by air pollution. These resources may include
visibility, scenic, cultural, physical, or ecological resources for
a particular area. The FLM agencies take into account the particular
resources and AQRVs that would be affected; the frequency and
magnitude of any potential impacts; and the direct, indirect, and
cumulative effects of any potential impacts in making their
recommendations.
c. While the AQRV notification and impact analysis requirements
are outlined in the PSD regulations at 40 CFR 51.166(p) and 40 CFR
52.21(p), determination of appropriate analytical methods and
metrics for AQRV's are determined by the FLM agencies and are
published in guidance external to the general recommendations of
this paragraph.
d. To develop greater consistency in the application of air
quality models to assess potential AQRV impacts in both Class I
areas and protected Class II areas, the FLM agencies have developed
the Federal Land Managers' Air Quality Related Values Work Group
Phase I Report (FLAG) \67\. FLAG focuses upon specific technical and
policy issues associated with visibility impairment, effects of
pollutant deposition on soils and surface waters, and ozone effects
on vegetation. Model users should consult the latest version of the
FLAG report for current modeling guidance and with affected FLM
agency representatives for any application specific guidance which
is beyond the scope of the Guideline.
6.2.1 Visibility
a. Visibility in important natural areas (e.g., Federal Class I
areas) is protected under a number of provisions of the CAA,
including sections 169A and 169B (addressing impacts primarily from
existing sources) and section 165 (new source review). Visibility
impairment is caused by light scattering and light absorption
associated with particles and gases in the atmosphere. In most areas
of the country, light scattering by PM2.5 is the most
[[Page 45367]]
significant component of visibility impairment. The key components
of PM2.5 contributing to visibility impairment include
sulfates, nitrates, organic carbon, elemental carbon, and crustal
material.\67\
b. Visibility regulations (40 CFR 51.300 through 51.309) require
state, local, and tribal agencies to mitigate current and prevent
future visibility impairment in any of the 156 mandatory Federal
Class I areas where visibility is considered an important attribute.
In 1999, the EPA issued revisions to the regulations to address
visibility impairment in the form of regional haze, which is caused
by numerous, diverse sources (e.g., stationary, mobile, and area
sources) located across a broad region (40 CFR 51.308 through
51.309). The state of relevant scientific knowledge has expanded
significantly since the 1997 CAA Amendments. A number of studies and
reports 68 69 have concluded that long-range transport
(e.g., up to hundreds of kilometers) of fine particulate matter
plays a significant role in visibility impairment across the
country. CAA section 169A requires states to develop SIPs containing
long-term strategies for remedying existing and preventing future
visibility impairment in the 156 mandatory Class I Federal areas,
where visibility is considered an important attribute. In order to
develop long-term strategies to address regional haze, many state,
local, and tribal agencies will need to conduct regional-scale
modeling of fine particulate concentrations and associated
visibility impairment.
c. The FLAG visibility modeling recommendations are divided into
two distinct sections to address different requirements for (1) near
field modeling where plumes or layers are compared against a viewing
background and (2) distant/multi-source modeling for plumes and
aggregations of plumes that affect the general appearance of a
scene.\67\ The recommendations separately address visibility
assessments for sources proposing to locate relatively near and at
farther distances from these areas.\67\
6.2.1.1 Models for Estimating Near-Field Visibility Impairment
a. To calculate the potential impact of a plume of specified
emissions for specific transport and dispersion conditions (``plume
blight'') for source-receptor distances less than 50 km, a screening
model and guidance are available.67 70 If a more
comprehensive analysis is necessary, a refined model should be
selected. The model selection, procedures, and analyses should be
determined in consultation with the appropriate reviewing authority
(paragraph 3.0(b)) and the affected FLM(s).
6.2.1.2 Models for Estimating Visibility Impairment for Long-Range
Transport
a. Chemical transformations can play an important role in
defining the concentrations and properties of certain air
pollutants. Models that take into account chemical reactions and
physical processes of various pollutants (including precursors) are
needed for determining the current state of air quality, as well as
predicting and projecting the future evolution of these pollutants.
It is important that a modeling system provide a realistic
representation of chemical and physical processes leading to
secondary pollutant formation and removal from the atmosphere.
b. Chemical transport models treat atmospheric chemical and
physical processes such as deposition and motion. There are two
types of chemical transport models, Eulerian (grid based) and
Lagrangian. These types of models are differentiated from each other
by their frame of reference. Eulerian models are based on a fixed
frame of reference and Lagrangian models use a frame of reference
that moves with parcels of air between the source and receptor
point.9 Photochemical grid models are three-dimensional
Eulerian grid-based models that treat chemical and physical
processes in each grid cell and use diffusion and transport
processes to move chemical species between grid cells.9
These types of models are appropriate for assessment of near-field
and regional scale reactive pollutant impacts from specific sources
7 10 11 12 or all sources.13 14 15
c. Development of the requisite meteorological and emissions
databases necessary for use of photochemical grid models to estimate
AQRVs should conform to recommendations in section 8 and those
outlined in the EPA's Modeling Guidance for Demonstrating Attainment
of Air Quality Goals for Ozone, PM2.5, and Regional
Haze.\60\ Demonstration of the adequacy of prognostic meteorological
fields can be established through appropriate diagnostic and
statistical performance evaluations consistent with recommendations
provided in the appropriate guidance.\60\ Model users should consult
the latest version of this guidance and with the appropriate
reviewing authority (paragraph 3.0(b)) for any application specific
guidance which is beyond the scope of this subsection.
6.2.2 Models for Estimating Deposition Impacts
a. For many Class I areas, AQRVs have been identified that are
sensitive to atmospheric deposition of air pollutants. Emissions of
NOX, sulfur oxides, NH3, mercury, and
secondary pollutants such as ozone and particulate matter affect
components of ecosystems. In sensitive ecosystems, these compounds
can acidify soils and surface waters, add nutrients that change
biodiversity, and affect the ecosystem services provided by forests
and natural areas.67 To address the relationship between
deposition and ecosystem effects the FLM agencies have developed
estimates of critical loads. A critical load is defined as ``A
quantitative estimate of an exposure to one or more pollutants below
which significant harmful effects on specified sensitive elements of
the environment do not occur according to present knowledge.''
71
b. The FLM deposition modeling recommendations are divided into
two distinct sections to address different requirements for (1) near
field modeling, and (2) distant/multi-source modeling for cumulative
effects. The recommendations separately address deposition
assessments for sources proposing to locate relatively near and at
farther distances from these areas.67 Where the source
and receptors are not in close proximity, chemical transport (e.g.,
photochemical grid) models generally should be applied for an
assessment of deposition impacts due to one or a small group of
sources. Over these distances chemical and physical transformations
can change atmospheric residence time due to different propensity
for deposition to the surface of different forms of nitrate and
sulfate. Users should consult the latest version of the FLAG report
67 and relevant FLM representatives for guidance on the
use of models for deposition. Where source and receptors are in
close proximity, users should contact the appropriate FLM for
application specific guidance.
6.3 Modeling Guidance for Other Governmental Programs
a. Dispersion and photochemical grid modeling need to be
conducted to ensure that individual and cumulative offshore oil and
gas exploration, development, and production plans and activities do
not significantly affect the air quality of any state as required
under the Outer Continental Shelf Lands Act (OCSLA). Air quality
modeling requires various input datasets, including emissions
sources, meteorology, and pre-existing pollutant concentrations. For
sources under the reviewing authority of the Department of Interior,
Bureau of Ocean Energy Management (BOEM), guidance for the
development of all necessary Outer Continental Shelf (OCS) air
quality modeling inputs and appropriate model selection and
application is available from the BOEMS's Web site: https://www.boem.gov/Environmental-Stewardship/Environmental-Studies/Gulf-of-Mexico-Region/Approved-Air-Quality-Models-for-the-GOMR.aspx.
b. The Federal Aviation Administration (FAA) is the appropriate
reviewing authority for air quality assessments of primary pollutant
impacts at airports and air bases. Air quality application for this
purpose is intended for estimating the collective impact of changes
in aircraft operations, point source, and mobile source emissions at
airports on pollutant concentrations. The latest version of the
Aviation Environmental Design Tool (AEDT), is developed and is
supported by the FAA, and is appropriate for air quality assessment
of primary pollutant impacts at airports or air bases. AEDT has
adopted AERMOD for treating dispersion. Application of AEDT is
intended for estimating the collective impact of changes in aircraft
operations, point source, and mobile source emissions on pollutant
concentrations. It is not intended for PSD, SIP, or other regulatory
air quality analyses of point or mobile sources at or peripheral to
airport property that are unrelated to airport operations. The
latest version of AEDT may be obtained from FAA at its Web site:
https://aedt.faa.gov.
7.0 General Modeling Considerations
7.1 Discussion
a. This section contains recommendations concerning a number of
different issues not explicitly covered in other sections of the
Guideline. The topics covered here are not specific to any one
program or modeling area but are common to dispersion modeling
analyses for criteria pollutants.
[[Page 45368]]
7.2 Recommendations
7.2.1 All Sources
7.2.1.1 Dispersion Coefficients
a. For any dispersion modeling exercise, the urban or rural
determination of a source is critical in determining the boundary
layer characteristics that affect the model's prediction of downwind
concentrations. Historically, steady-state Gaussian plume models
used in most applications have employed dispersion coefficients
based on Pasquill-Gifford 72 in rural areas and McElroy-
Pooler 73 in urban areas. These coefficients are still
incorporated in the BLP and OCD models. However, the AERMOD model
incorporates a more up-to-date characterization of the atmospheric
boundary layer using continuous functions of parameterized
horizontal and vertical turbulence based on Monin-Obukhov similarity
(scaling) relationships.44 Another key feature of
AERMOD's formulation is the option to use directly observed
variables of the boundary layer to parameterize
dispersion.44 45
b. The selection of rural or urban dispersion coefficients in a
specific application should follow one of the procedures suggested
by Irwin 74 to determine whether the character of an area
is primarily urban or rural:
i. Land Use Procedure: (1) Classify the land use within the
total area, Ao, circumscribed by a 3km radius circle
about the source using the meteorological land use typing scheme
proposed by Auer; 75 (2) if land use types I1, I2, C1,
R2, and R3 account for 50 percent or more of Ao, use
urban dispersion coefficients; otherwise, use appropriate rural
dispersion coefficients.
ii. Population Density Procedure: (1) Compute the average
population density, p per square kilometer with Ao as
defined above; (2) If p is greater than 750 people/km\2\, use urban
dispersion coefficients; otherwise use appropriate rural dispersion
coefficients. (Of the two methods, the land use procedure is
considered more definitive.)
c. Population density should be used with caution and generally
not be applied to highly industrialized areas where the population
density may be low and thus a rural classification would be
indicated. However, the area is likely to be sufficiently built-up
so that the urban land use criteria would be satisfied. Therefore,
in this case, the classification should be ``urban'' and urban
dispersion parameters should be used.
d. For applications of AERMOD in urban areas, under either the
Land Use Procedure or the Population Density Procedure, the user
needs to estimate the population of the urban area affecting the
modeling domain because the urban influence in AERMOD is scaled
based on a user-specified population. For non-population oriented
urban areas, or areas influenced by both population and industrial
activity, the user will need to estimate an equivalent population to
adequately account for the combined effects of industrialized areas
and populated areas within the modeling domain. Selection of the
appropriate population for these applications should be determined
in consultation with the appropriate reviewing authority (paragraph
3.0(b)) and the latest version of the AERMOD Implementation
Guide.\76\
e. It should be noted that AERMOD allows for modeling rural and
urban sources in a single model run. For analyses of whole urban
complexes, the entire area should be modeled as an urban region if
most of the sources are located in areas classified as urban. For
tall stacks located within or adjacent to small or moderate sized
urban areas, the stack height or effective plume height may extend
above the urban boundary layer and, therefore, may be more
appropriately modeled using rural coefficients. Model users should
consult with the appropriate reviewing authority (paragraph 3.0(b))
when evaluating this situation and the latest version of the AERMOD
Implementation Guide.\76\
f. Buoyancy-induced dispersion (BID), as identified by
Pasquill,\77\ is included in the preferred models and should be used
where buoyant sources, e.g., those involving fuel combustion, are
involved.
7.2.1.2 Complex Winds
a. Inhomogeneous local winds. In many parts of the United
States, the ground is neither flat nor is the ground cover (or land
use) uniform. These geographical variations can generate local winds
and circulations, and modify the prevailing ambient winds and
circulations. Geographic effects are most apparent when the ambient
winds are light or calm.\78\ In general these geographically induced
wind circulation effects are named after the source location of the
winds, e.g., lake and sea breezes, and mountain and valley winds. In
very rugged hilly or mountainous terrain, along coastlines, or near
large land use variations, the characterization of the winds is a
balance of various forces, such that the assumptions of steady-state
straight-line transport both in time and space are inappropriate. In
such cases, a model should be chosen to fully treat the time and
space variations of meteorology effects on transport and dispersion.
The setup and application of such a model should be determined in
consultation with the appropriate reviewing authority (paragraph
3.0(b)) consistent with limitations of paragraph 3.2.2(e). The
meteorological input data requirements for developing the time and
space varying three-dimensional winds and dispersion meteorology for
these situations are discussed in paragraph 8.4.1.2(c). Examples of
inhomogeneous winds include, but are not limited to, situations
described in the following paragraphs:
i. Inversion breakup fumigation. Inversion breakup fumigation
occurs when a plume (or multiple plumes) is emitted into a stable
layer of air and that layer is subsequently mixed to the ground
through convective transfer of heat from the surface or because of
advection to less stable surroundings. Fumigation may cause
excessively high concentrations but is usually rather short- lived
at a given receptor. There are no recommended refined techniques to
model this phenomenon. There are, however, screening procedures \40\
that may be used to approximate the concentrations. Considerable
care should be exercised in using the results obtained from the
screening techniques.
ii. Shoreline fumigation. Fumigation can be an important
phenomenon on and near the shoreline of bodies of water. This can
affect both individual plumes and area-wide emissions. When
fumigation conditions are expected to occur from a source or sources
with tall stacks located on or just inland of a shoreline, this
should be addressed in the air quality modeling analysis. EPA has
evaluated several coastal fumigation models, and the evaluation
results of these models are available for their possible application
on a case-by-case basis when air quality estimates under shoreline
fumigation conditions are needed.\79\ Selection of the appropriate
model for applications where shoreline fumigation is of concern
should be determined in consultation with the appropriate reviewing
authority (paragraph 3.0(b)).
iii. Stagnation. Stagnation conditions are characterized by calm
or very low wind speeds, and variable wind directions. These
stagnant meteorological conditions may persist for several hours to
several days. During stagnation conditions, the dispersion of air
pollutants, especially those from low- level emissions sources,
tends to be minimized, potentially leading to relatively high
ground-level concentrations. If point sources are of interest, users
should note the guidance provided in paragraph (a) of this
subsection. Selection of the appropriate model for applications
where stagnation is of concern should be determined in consultation
with the appropriate reviewing authority (paragraph 3.0(b)).
7.2.1.3 Gravitational Settling and Deposition
a. Gravitational settling and deposition may be directly
included in a model if either is a significant factor. When
particulate matter sources can be quantified and settling and dry
deposition are problems, professional judgment should be used, and
there should be coordination with the appropriate reviewing
authority (paragraph 3.0(b)). AERMOD contains algorithms for dry and
wet deposition of gases and particles.\80\ For other Gaussian plume
models, an ``infinite half-life'' may be used for estimates of
particle concentrations when only exponential decay terms are used
for treating settling and deposition. Lagrangian models have varying
degrees of complexity for dealing with settling and deposition and
the selection of a parameterization for such should be included in
the approval process for selecting a Lagrangian model. Eulerian grid
models tend to have explicit parameterizations for gravitational
settling and deposition as well as wet deposition parameters already
included as part of the chemistry scheme.
7.2.2 Stationary Sources
7.2.2.1 Good Engineering Practice Stack Height
a. The use of stack height credit in excess of Good Engineering
Practice (GEP) stack height or credit resulting from any other
dispersion technique is prohibited in the development of emissions
limits by 40 CFR 51.118 and 40 CFR 51.164. The definition of
[[Page 45369]]
GEP stack height and dispersion technique are contained in 40 CFR
51.100. Methods and procedures for making the appropriate stack
height calculations, determining stack height credits and an example
of applying those techniques are found in several
references,81 82 83 84 which provide a great deal of
additional information for evaluating and describing building cavity
and wake effects.
b. If stacks for new or existing major sources are found to be
less than the height defined by the EPA's refined formula for
determining GEP height, then air quality impacts associated with
cavity or wake effects due to the nearby building structures should
be determined. The EPA refined formula height is defined as H +
1.5L.\83\ Since the definition of GEP stack height defines excessive
concentrations as a maximum ground-level concentration due in whole
or in part to downwash of at least 40 percent in excess of the
maximum concentration without downwash, the potential air quality
impacts associated with cavity and wake effects should also be
considered for stacks that equal or exceed the EPA formula height
for GEP. The AERSCREEN model can be used to obtain screening
estimates of potential downwash influences, based on the PRIME
downwash algorithm incorporated in the AERMOD model. If more refined
concentration estimates are required, the recommended steady-state
plume dispersion model in section 4.2.2, AERMOD, should be used.
7.2.2.2 Plume Rise
a. The plume rise methods of Briggs 85 86 are
incorporated in many of the preferred models and are recommended for
use in many modeling applications. In AERMOD,44 45 for
the stable boundary layer, plume rise is estimated using an
iterative approach, similar to that in the CTDMPLUS model. In the
convective boundary layer, plume rise is superposed on the
displacements by random convective velocities.\87\ In AERMOD, plume
rise is computed using the methods of Briggs except cases involving
building downwash, in which a numerical solution of the mass,
energy, and momentum conservation laws is performed.\88\ No explicit
provisions in these models are made for multistack plume rise
enhancement or the handling of such special plumes as flares; these
problems should be considered on a case-by-case basis.
b. Gradual plume rise is generally recommended where its use is
appropriate: (1) In AERMOD; (2) in complex terrain screening
procedures to determine close-in impacts and (3) when calculating
the effects of building wakes. The building wake algorithm in AERMOD
incorporates and exercises the thermodynamically based gradual plume
rise calculations as described in paragraph (a) of this subsection.
If the building wake is calculated to affect the plume for any hour,
gradual plume rise is also used in downwind dispersion calculations
to the distance of final plume rise, after which final plume rise is
used. Plumes captured by the near wake are re-emitted to the far
wake as a ground-level volume source.
c. Stack tip downwash generally occurs with poorly constructed
stacks and when the ratio of the stack exit velocity to wind speed
is small. An algorithm developed by Briggs \86\ is the recommended
technique for this situation and is used in preferred models for
point sources.
7.2.3 Mobile Sources
a. Emissions of primary pollutants from mobile sources can be
modeled with an appropriate model identified in section 4.2.
Screening of mobile sources can be accomplished by using screening
meteorology, such as that generated by the MAKEMET component of
AERSCREEN, which can generate a range of meteorological scenarios
using site-specific characteristics, such as albedo, Bowen ratio,
and surface roughness. Maximum hourly concentrations computed from
screening runs can be converted to longer averaging periods using
the scaling ratios specific in the AERSCREEN User's
Guide.37
b. Mobile sources can be modeled in AERMOD as either line (i.e.,
elongated area) sources or as a series of volume sources. However,
since mobile source modeling usually includes an analysis of very
near-source impacts (e.g., hot-spot modeling, which can include
receptors within 5-10 meters of the roadway), the results can be
highly sensitive to the characterization of the mobile emissions.
When modeling roadway links, such as highway and arterial links, the
EPA recommends that line/area sources instead of volume sources be
used whenever possible, as it is easier to characterize them
correctly. Important characteristics for both line/area and volume
sources include the plume release height, source width, and initial
dispersion characteristics, which should also take into account the
impact of traffic-induced turbulence, which can cause roadway
sources to have larger initial dimensions than might normally be
used for representing line sources.
c. The EPA's quantitative PM hot-spot guidance \61\ and Haul
Road Workgroup Final Report \63\ provide guidance on the appropriate
characterization of mobile sources as a function of the roadway and
vehicle characteristics. The EPA's quantitative PM hot-spot guidance
includes important considerations and should be consulted when
modeling roadway links. Line or area sources are recommended for
mobile sources. However, if volume sources are used, it is
particularly important to insure that roadway emissions are
appropriately spaced when using volume source so that the emissions
field is uniform across the roadway. Additionally, receptor
placement is particularly important for volume sources, which have
``exclusion zones'', where concentrations are not calculated for
receptors located ``within'' the volume sources, i.e., less than
2.15 times the initial lateral dispersion coefficient from the
center of the volume.\61\ Placing receptors in these ``exclusion
zones'' will result in underestimates of roadway impacts.
8.0 Model Input Data
a. Databases and related procedures for estimating input
parameters are an integral part of the modeling process. The most
appropriate input data available should always be selected for use
in modeling analyses. Modeled concentrations can vary widely
depending on the source data or meteorological data used. This
section attempts to minimize the uncertainty associated with
database selection and use by identifying requirements for input
data used in modeling. More specific data requirements and the
format required for the individual models are described in detail in
the users' guide and/or associated documentation for each model.
8.1 Modeling Domain
8.1.1 Discussion
a. The modeling domain is the geographic area for which the
required air quality analyses for the NAAQS and PSD increments are
conducted.
8.1.2 Requirements
a. For a NAAQS or PSD increment assessment, the modeling domain
or project's impact area shall include all locations where the
emissions of a pollutant from the new or modifying source(s) may
cause a significant ambient impact. This impact area is defined as
an area with a radius extending from the new or modifying source to:
(1) The most distant point source where air quality modeling
predicts a significant ambient impact will occur, or (2) the nominal
50 km distance considered applicable for Gaussian dispersion models,
whichever is less. The required air quality analysis shall be
carried out within this geographical area with characterization of
source impacts, nearby source impacts, and background
concentrations, as recommended later in this section.
b. For SIP attainment demonstrations for ozone and
PM2.5, or regional haze reasonable progress goal
analyses, the modeling domain is determined by the nature of the
problem being modeled and the spatial scale of the emissions which
impact the nonattainment or Class I area(s). The modeling domain
shall be designed so that all major upwind source areas that
influence the downwind nonattainment area are included in addition
to all monitor locations that are currently or recently violating
the NAAQS or close to violating the NAAQS in the nonattainment area.
Similarly, all Class I areas to be evaluated in a regional haze
modeling application shall be included and sufficiently distant from
the edge of the modeling domain. Guidance on the determination of
the appropriate modeling domain for photochemical grid models in
demonstrating attainment of these air quality goals is
available.\60\ Users should consult the latest version of this
guidance for the most current modeling guidance and with the
appropriate reviewing authority (paragraph 3.0(b)) for any
application specific guidance which is beyond the scope of this
section.
8.2 Source Data
8.2.1 Discussion
a. Sources of pollutants can be classified as point, line, area,
and volume sources. Point sources are defined in terms of size and
may vary between regulatory programs. The line sources most
frequently considered are
[[Page 45370]]
roadways and streets along which there are well-defined movements of
motor vehicles. They may also be lines of roof vents or stacks, such
as in aluminum refineries. Area and volume sources are often
collections of a multitude of minor sources with individually small
emissions that are impractical to consider as separate point or line
sources. Large area sources are typically treated as a grid network
of square areas, with pollutant emissions distributed uniformly
within each grid square. Generally, input data requirements for air
quality models necessitate the use of metric units. As necessary,
any English units common to engineering applications should be
appropriately converted to metric.
b. For point sources, there are many source characteristics and
operating conditions that may be needed to appropriately model the
facility. For example, the plant layout (e.g., location of stacks
and buildings), stack parameters (e.g., height and diameter), boiler
size and type, potential operating conditions, and pollution control
equipment parameters. Such details are required inputs to air
quality models and are needed to determine maximum potential
impacts.
c. Modeling mobile emissions from streets and highways requires
data on the road layout, including the width of each traveled lane,
the number of lanes, and the width of the median strip.
Additionally, traffic patterns should be taken into account (e.g.,
daily cycles of rush hour, differences in weekday and weekend
traffic volumes, and changes in the distribution of heavy-duty
trucks and light-duty passenger vehicles), as these patterns will
affect the types and amounts of pollutant emissions allocated to
each lane, and the height of emissions.
d. Emission factors can be determined through source specific
testing and measurements (e.g., stack test data) from existing
sources or provided from a manufacturing association or vendor.
Additionally, emissions factors for a variety of source types are
compiled in an EPA publication commonly known as AP-42.\89\ AP-42
also provides an indication of the quality and amount of data on
which many of the factors are based. Other information concerning
emissions is available in EPA publications relating to specific
source categories. The appropriate reviewing authority (paragraph
3.0(b)) should be consulted to determine appropriate source
definitions and for guidance concerning the determination of
emissions from and techniques for modeling the various source types.
8.2.2 Requirements
a. For SIP attainment demonstrations for the purpose of
projecting future year NAAQS attainment for ozone, PM2.5,
and regional haze reasonable progress goal analyses, emissions which
reflect actual emissions during the base modeling year time period
should be input to models for base year modeling. Emissions
projections to future years should account for key variables such as
growth due to increased or decreased activity, expected emissions
controls due to regulations, settlement agreements or consent
decrees, fuel switches, and any other relevant information. Guidance
on emissions estimation techniques (including future year
projections) for SIP attainment demonstrations is
available.60 90
b. For the purpose of SIP revisions for stationary point
sources, the regulatory modeling of inert pollutants shall use the
emissions input data shown in Table 8-1 for short-term and long-term
NAAQS. To demonstrate compliance and/or establish the appropriate
SIP emissions limits, Table 8-1 generally provides for the use of
``allowable'' emissions in the regulatory dispersion modeling of the
stationary point source(s) of interest. In such modeling, these
source(s) should be modeled sequentially with these loads for every
hour of the year. As part of a cumulative impact analysis, Table 8-1
allows for the model user to account for actual operations in
developing the emissions inputs for dispersion modeling of nearby
sources, while other sources are best represented by air quality
monitoring data. Consultation with the appropriate reviewing
authority (paragraph 3.0(b)) is advisable on the establishment of
the appropriate emissions inputs for regulatory modeling
applications with respect to SIP revisions for stationary point
sources.
c. For the purposes of demonstrating NAAQS compliance in a PSD
assessment, the regulatory modeling of inert pollutants shall use
the emissions input data shown in Table 8-2 for short and long-term
NAAQS. The new or modifying stationary point source shall be modeled
with ``allowable'' emission in the regulatory dispersion modeling.
As part of a cumulative impact analysis, Table 8-2 allows for the
model user to account for actual operations in developing the
emissions inputs for dispersion modeling of nearby sources, while
other sources are best represented by air quality monitoring data.
For purposes of situations involving emissions trading refer to
current EPA policy and guidance to establish input data.
Consultation with the appropriate reviewing authority (paragraph
3.0(b)) is advisable on the establishment of the appropriate
emissions inputs for regulatory modeling applications with respect
to PSD assessments for a proposed new or modifying source.
d. For stationary source applications, changes in operating
conditions that affect the physical emission parameters (e.g.,
release height, initial plume volume, and exit velocity) shall be
considered to ensure that maximum potential impacts are
appropriately determined in the assessment. For example, the load or
operating condition for point sources that causes maximum ground-
level concentrations shall be established. As a minimum, the source
should be modeled using the design capacity (100 percent load). If a
source operates at greater than design capacity for periods that
could result in violations of the NAAQS or PSD increment, this load
should be modeled. Where the source operates at substantially less
than design capacity, and the changes in the stack parameters
associated with the operating conditions could lead to higher ground
level concentrations, loads such as 50 percent and 75 percent of
capacity should also be modeled. Malfunctions which may result in
excess emissions are not considered to be a normal operating
condition. They generally should not be considered in determining
allowable emissions. However, if the excess emissions are the result
of poor maintenance, careless operation, or other preventable
conditions, it may be necessary to consider them in determining
source impact. A range of operating conditions should be considered
in screening analyses; the load causing the highest concentration,
in addition to the design load, should be included in refined
modeling.
e. Emissions from mobile sources also have physical and temporal
characteristics that should be appropriately accounted for. For
example, an appropriate emissions model shall be used to determine
emissions profiles. Such emissions should include speciation
specific for the vehicle types used on the roadway (e.g., light duty
and heavy duty trucks) and subsequent parameterizations of the
physical emissions characteristics (e.g., release height) should
reflect those emissions sources. For long-term standards, annual
average emissions may be appropriate, but for short-term standards,
discrete temporal representation of emissions should be used (e.g.,
variations in weekday and weekend traffic or the diurnal rush-hour
profile typical of many cities). Detailed information and data
requirements for modeling mobile sources of pollution are provided
in the user's manuals for each of the models applicable to mobile
sources.\61\ \63\
Table 8-1--Point Source Model Emission Input for SIP Revisions of Inert Pollutants \1\
----------------------------------------------------------------------------------------------------------------
Operating factor
Averaging time Emissions limit (lb/ x Operating level (lb/ x (e.g., hr/yr. hr/
MMBtu) \2\ MMBtu) \2\ day)
----------------------------------------------------------------------------------------------------------------
Stationary Point Source(s) Subject to SIP Emissions Limit(s) Evaluation for Compliance With Ambient Standards
(Including Areawide Demonstrations)
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable Actual or design Actual operating
emission limit or capacity (whichever factor averaged
federal enforceable is greater), or over the most
permit limit. federally permit recent 2 years.\3\
enforceable permit
condition.
[[Page 45371]]
Short term (<=24 hours)........... Maximum allowable Actual or design Continuous
emission limit or capacity (whichever operation, i.e.,
federally is greater), or all hours of each
enforceable permit federally time period under
limit. enforceable permit consideration (for
condition.\4\ all hours of the
meteorological
database).\5\
----------------------------------------------------------------------------------------------------------------
Nearby Source(s).\6\
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable Annual level when Actual operating
emission limit or actually operating, factor averaged
federal enforceable averaged over the over the most
permit limit.\5\ most recent 2 recent 2 years.\3\
years.\3\ \8\
Short term (<=24 hours)........... Maximum allowable Temporally Continuous
emission limit or representative level operation, i.e.,
federal enforceable when actually all hours of each
permit limit.\6\ operating, time period under
reflective of the consideration (for
most recent 2 all hours of the
years.\3\ \7\ meteorological
database).\5\
----------------------------------------------------------------------------------------------------------------
Other Source(s) \8\ \9\
----------------------------------------------------------------------------------------------------------------
The ambient impacts from Non-nearby or Other Sources (e.g., natural sources, minor sources and, distant major
source and unidentified sources) can be represented by air quality monitoring data unless adequate data do not
exist.
----------------------------------------------------------------------------------------------------------------
\1\ For purposes of emissions trading, NSR, or PSD, other model input criteria may apply. See Section 8.2 for
more information regarding attainment demonstrations of primary PM2.5.
\2\ Terminology applicable to fuel burning sources; analogous terminology (e.g., lb/throughput) may be used for
other types of sources.
\3\ Unless it is determined that this period is not representative.
\4\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
causing the highest concentration.
\5\ If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24-hours) and the
source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the
modeled emission rate may be made (e.g., if operation is only 8 a.m. to 4 p.m. each day, only these hours will
be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating
\6\ See Section 8.3.3.
\7\ Temporally representative operating level could be based on Continuous Emissions Monitoring (CEM) data or
other information and should be determined through consultation with the appropriate reviewing authority
(Paragraph 3.0(b)).
\8\ For those permitted sources not in operation or that have not established an appropriate factor, continuous
operation (i.e., 8760) should be used.
\9\ See Section 8.3.2.
Table 8-2--Point Source Model Emission Input for NAAQS Compliance in PSD Demonstrations
----------------------------------------------------------------------------------------------------------------
Operating factor
Averaging time Emissions limit (lb/ x Operating level (lb/ x (e.g., hr/yr. hr/
MMBtu) \1\ MMBtu) \2\ day)
----------------------------------------------------------------------------------------------------------------
Proposed Major New or Modified Source
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable Design capacity or Continuous operation
emission limit or federally (i.e., 8760
federal enforceable enforceable permit hours).\2\
permit limit. condition.
Short term (<=24 hours)........... Maximum allowable Design capacity or Continuous
emission limit or federally operation, i.e.,
federal enforceable enforceable permit all hours of each
permit limit. condition.\3\ time period under
consideration (for
all hours of the
meteorological
database).\2\
----------------------------------------------------------------------------------------------------------------
Nearby Source(s) \4\ \5\
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable Annual level when Actual operating
emission limit or actually operating, factor averaged
federal enforceable averaged over the over the most
permit limit.\5\ most recent 2 recent 2 years.\6\
years.\6\ \8\
Short term (<=24 hours)........... Maximum allowable Annual level when Continuous
emission limit or actually operating, operation, i.e.,
federal enforceable averaged over the all hours of each
permit limit.\5\ most recent 2 time period under
years.\6\ \7\ consideration (for
all hours of the
meteorological
database).\2\
----------------------------------------------------------------------------------------------------------------
Other Source(s) \5\ \9\
----------------------------------------------------------------------------------------------------------------
The ambient impacts from Non-nearby or Other Sources (e.g., natural sources, minor sources and,distant major
sources, and unidentified sources) can be represented by air quality monitoring data unless adequate data do not
exist.
----------------------------------------------------------------------------------------------------------------
\1\ Terminology applicable to fuel burning sources; analogous terminology (e.g., lb/throughput) may be used for
other types of sources.
[[Page 45372]]
\2\ If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24-hours) and the
source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the
modeled emission rate may be made (e.g., if operation is only 8 a.m. to 4 p.m. each day, only these hours will
be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating
\3\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
causing the highest concentration.
\4\ Includes existing facility to which modification is proposed if the emissions from the existing facility
will not be affected by the modification. Otherwise use the same parameters as for major modification.
\5\ See Section 8.3.3.
\6\ Unless it is determined that this period is not representative.
\7\ Temporally representative operating level could be based on Continuous Emissions Monitoring (CEM) data or
other information and should be determined through consultation with the appropriate reviewing authority
(Paragraph 3.0(b)).
\8\ For those permitted sources not in operation or that have not established an appropriate factor, continuous
operation (i.e., 8760) should be used.
\9\ See Section 8.3.2.
8.3 Background Concentrations
8.3.1 Discussion
a. Background concentrations are essential in constructing the
design concentration, or total air quality concentration, as part of
a cumulative impact analysis for NAAQS and PSD increments (section
9.2.4). Background air quality should not include the ambient
impacts of the project source under consideration. Instead, it
should include:
i. Nearby sources: These are individual sources in the vicinity
of the source(s) under consideration for emissions limits that are
not adequately represented by ambient monitoring data. Typically,
sources that cause a significant concentration gradient in the
vicinity of the source(s) under consideration for emissions limits
are not adequately represented by background ambient monitoring. The
ambient contributions from these nearby sources are thereby
accounted for by explicitly modeling their emissions (section 8.2).
ii. Other sources: That portion of the background attributable
to natural sources, other unidentified sources in the vicinity of
the project, and regional transport contributions from more distant
sources (domestic and international). The ambient contributions from
these sources are typically accounted for through use of ambient
monitoring data or, in some cases, regional-scale photochemical grid
modeling results.
b. The monitoring network used for developing background
concentrations is expected to conform to the same quality assurance
and other requirements as those networks established for PSD
purposes.\91\ Accordingly, the air quality monitoring data should be
of sufficient completeness and follow appropriate data validation
procedures. These data should be adequately representative of the
area to inform calculation of the design concentration for
comparison to the applicable NAAQS (section 9.2.2)
c. For photochemical grid modeling conducted in SIP attainment
demonstrations for ozone, PM2.5 and regional haze, the
emissions from nearby and other sources are included as model inputs
and fully accounted for in the modeling application and predicted
concentrations. The concept of adding individual components to
develop a design concentration, therefore, do not apply in these SIP
applications. However, such modeling results may then be appropriate
for consideration in characterizing background concentrations for
other regulatory applications. Also, as noted in section 5, this
modeling approach does provide for an appropriate atmospheric
environment to assess single-sources impacts for ozone and secondary
PM2.5.
d. For PSD assessments in general and SIP attainment
demonstrations for inert pollutants, the development of the
appropriate background concentration for a cumulative impact
analysis involves proper accounting of each contribution to the
design concentration and will depend upon whether the project area's
situation consists of either an isolated single source(s) or a
multitude of sources.
8.3.2 Recommendations for Isolated Single Source
a. In areas with an isolated source(s), determining the
appropriate background concentration should focus on
characterization of contributions from all other sources through
adequately representative ambient monitoring data.
b. The EPA recommends use of the most recent quality assured air
quality monitoring data collected in the vicinity of the source to
determine the background concentration for the averaging times of
concern. In most cases, the EPA recommends using data from the
monitor closest to and upwind of the project area. If several
monitors are available, preference should be given to the monitor
with the most similar characteristics as the project area. If there
are no monitors located in the vicinity of the new or modify source,
a ``regional site'' may be used to determine background
concentrations. A regional site is one that is located away from the
area of interest but is impacted by similar or adequately
representative sources.
c. Many of the challenges related to cumulative impact analyses
arise in the context of defining the appropriate metric to
characterize background concentrations from ambient monitoring data
and determining the appropriate method for combining this monitor-
based background contribution to the modeled impact of the project
and other nearby sources. For many cases, the best starting point
would be use of the current design value for the applicable NAAQS as
a uniform monitored background contribution across the project area.
However, there are cases in which the current design value may not
be appropriate. Such cases include but are not limited to:
i. For situations involving a modifying source where the
existing facility is determined to impact the ambient monitor, the
background concentration at each monitor can be determined by
excluding values when the source in question is impacting the
monitor. In such cases, monitoring sites inside a 90[deg] sector
downwind of the source may be used to determine the area of impact.
ii. There may be other circumstances which would necessitate
modifications to the ambient data record. Such cases could include
removal of data from specific days or hours when a monitor is being
impacted activities that are not typical or expected to occur again
in the future (e.g., construction, roadway repairs, forest fires, or
unusual agricultural activities). There may also be cases where
scaling (multiplying the monitored concentrations with a scaling
factor) or adjusting (adding or subtracting a constant value the
monitored concentrations) of data from specific days or hours. Such
adjustments would make the monitored background concentrations more
temporally and/or spatially representative of area around the new or
modifying source for the purposes of the regulator assessment.
iii. For short-term standards, the diurnal or seasonal patterns
of the air quality monitoring data may differ significantly from the
patterns associated with the modeled concentrations. When this
occurs, it may be appropriate to pair the air quality monitoring
data in a temporal manner that reflects these patterns (e.g.,
pairing by season and/or hour of day).\92\
iv. For situations where monitored air quality concentrations
vary across the modeling domain, it may be appropriate to consider
air quality monitoring data from multiple monitors within the
project area.
d. Determination of the appropriate background concentrations
should be consistent with appropriate EPA modeling guidance
59 92 and justified in the modeling protocol that is
vetted with the appropriate reviewing authority (paragraph 3.0(b)).
e. Considering the spatial and temporal variability throughout a
typical modeling domain on an hourly basis and the complexities and
limitations of hourly observations from the ambient monitoring
network, the EPA does not recommend hourly or daily pairing of
monitored background and modeled concentrations except in rare cases
of relatively isolated sources where the available monitor can be
shown to be representative of the ambient concentration levels in
the areas of maximum impact from the proposed new source. The
implicit assumption underlying hourly pairing is that the background
monitored levels for each hour are spatially uniform and that the
monitored values are fully
[[Page 45373]]
representative of background levels at each receptor for each hour.
Such an assumption clearly ignores the many factors that contribute
to the temporal and spatial variability of ambient concentrations
across a typical modeling domain on an hourly basis. In most cases,
the seasonal (or quarterly) pairing of monitored and modeled
concentrations should sufficiently address situations to which the
impacts from modeled emissions are not temporally correlated with
background monitored levels.
f. In those cases where adequately representative monitoring
data to characterize background concentrations are not available, it
may be appropriate to use results from a regional-scale
photochemical grid model or other representative model application
as background concentrations consistent with the considerations
discussed above and in consultation with the appropriate reviewing
authority (paragraph 3.0(b)).
8.3.3 Recommendations for Multi-Source Areas
a. In multi-source areas, determining the appropriate background
concentration involves: (1) identification and characterization of
contributions from nearby sources through explicit modeling, and (2)
characterization of contributions from other sources through
adequately representative ambient monitoring data. A key point here
is the interconnectedness of each component in that the question of
which nearby sources to include in the cumulative modeling is
inextricably linked to the question of what the ambient monitoring
data represents within the project area.
b. Nearby sources: All sources in the vicinity of the source(s)
under consideration for emissions limits that are not adequately
represented by ambient monitoring data should be explicitly modeled.
Since an ambient monitor is limited to characterizing air quality at
a fixed location, sources that causes a significant concentration
gradient in the vicinity of the source(s) under consideration for
emissions limits are not likely to be adequately characterized by
the monitored data due to the high degree of variability of the
source's impact.
i. The pattern of concentration gradients can vary significantly
based on the averaging period being assessed. In general,
concentration gradients will be smaller and more spatially uniform
for annual averages than for short-term averages, especially for
hourly averages. The spatial distribution of annual impacts around a
source will often have a single peak downwind of the source based on
the prevailing wind direction, except in cases where terrain or
other geographic effects are important. By contrast, the spatial
distribution of peak short-term impacts will typically show several
localized concentration peaks with more significant gradient.
ii. Concentration gradients associated with a particular source
will generally be largest between that source's location and the
distance to the maximum ground-level concentrations from that
source. Beyond the maximum impact distance, concentration gradients
will generally be much smaller and more spatially uniform. Thus, the
magnitude of a concentration gradient will be greatest in the
proximity of the source and will generally not be significant at
distances greater than 10 times the height of the stack(s) at that
source without consideration of terrain influences.
iii. The number of nearby sources to be explicitly modeled in
the air quality analysis is expected to be few except in unusual
situations. In most cases, the few nearby sources will be located
within 10 to 20 km from the source(s) under consideration. Owing to
both the uniqueness of each modeling situation and the large number
of variables involved in identifying nearby sources, no attempt is
made here to comprehensively define a ``significant concentration
gradient.'' Rather, identification of nearby sources calls for the
exercise of professional judgement by the appropriate reviewing
authority (paragraph 3.0(b)). This guidance is not intended to alter
the exercise of that judgement or to comprehensively prescribe which
sources should be included as nearby sources.
c. For cumulative impact analyses of short-term and annual
ambient standards, the nearby sources as well as the project
source(s) must be evaluated using an appropriate appendix A model or
approved alternative model with the emission input data shown in
Table 8-1 or 8-2.
i. When modeling a nearby source that does not have a permit and
the emissions limits contained in the SIP for a particular source
category is greater than the emissions possible given the source's
maximum physical capacity to emit, the ``maximum allowable emissions
limit'' for such a nearby source may be calculated as the emissions
rate representative of the nearby source's maximum physical capacity
to emit, considering its design specifications and allowable fuels
and process materials. However, the burden is on the permit
applicant to sufficiently document what the maximum physical
capacity to emit is for such a nearby source.
ii. It is appropriate to model nearby sources only during those
times when they, by their nature, operate at the same time as the
primary source(s). Accordingly, it is not necessary to model impacts
of a nearby source that does not, by its nature, operate at the same
time as the primary source, regardless of an identified significant
concentration gradient from the nearby source. The burden is on the
permit applicant to adequately justify the exclusion of nearby
sources to the satisfaction of the appropriate reviewing authority
(paragraph 3.0(b)). The following examples illustrate two cases in
which a nearby source may be shown not to operate at the same time
as the primary source(s) being modeled: (1) Seasonal sources (only
used during certain seasons of the year). Such sources would not be
modeled as nearby sources during times in which they do not operate;
and (2) Emergency backup generators, to the extent that they do not
operate simultaneously with the sources that they back up. Such
emergency equipment would not be modeled as nearby sources.
d. Other sources. That portion of the background attributable to
all other sources (e.g., natural sources, minor and distance major
sources) should be accounted for through use of ambient monitoring
data and determined by the procedures found in section 8.3.2 in
keeping with eliminating or reducing the source-oriented impacts
from nearby sources to avoid potential double-counting of modeled
and monitored contributions.
8.4 Meteorological Input Data
8.4.1 Discussion
a. This subsection covers meteorological input data for use in
dispersion modeling for regulatory applications and is separate from
recommendations made for photochemical grid modeling.
Recommendations for meteorological data for photochemical grid
modeling applications are outlined in the latest version of EPA's
Guidance on the Use of Models and Other Analyses for Demonstrating
Attainment of Air Quality Goals for Ozone, PM2.5, and
Regional Haze \93\. In cases where Lagrangian models are applied for
regulatory purposes, appropriate meteorological inputs should be
determined in consultation with the appropriate reviewing authority
(paragraph 3.0(b)).
b. The meteorological data used as input to a dispersion model
should be selected on the basis of spatial and climatological
(temporal) representativeness as well as the ability of the
individual parameters selected to characterize the transport and
dispersion conditions in the area of concern. The representativeness
of the measured data is dependent on numerous factors including but
not limited to: (1) The proximity of the meteorological monitoring
site to the area under consideration; (2) The complexity of the
terrain; (3) The exposure of the meteorological monitoring site; and
(4) The period of time during which data are collected. The spatial
representativeness of the data can be adversely affected by large
distances between the source and receptors of interest and the
complex topographic characteristics of the area. Temporal
representativeness is a function of the year-to-year variations in
weather conditions. Where appropriate, data representativeness
should be viewed in terms of the appropriateness of the data for
constructing realistic boundary layer profiles and, where
applicable, three-dimensional meteorological fields, as described in
paragraphs (c) and (d) of this subsection.
c. The meteorological data should be adequately representative
and may be site-specific data, data from a nearby National Weather
Service (NWS) or comparable station, or prognostic meteorological
data. The implementation of ASOS (automated surface observing
stations) in recent years should not preclude the use of NWS-ASOS
data if such a station is determined to be representative of the
modeled area.\94\
d. Model input data are normally obtained either from the NWS or
as part of a site-specific measurement program. State climatology
offices, local universities, FAA, military stations, industry and
pollution control agencies may also be sources of such data. In
specific cases, prognostic meteorological data may be appropriate
for use and obtained from similar sources. Some
[[Page 45374]]
recommendations and requirements for the use of each type of data
are included in this subsection.
8.4.2 Recommendations and Requirements
a. AERMET \95\ shall be used to preprocess all meteorological
data, be it observed or prognostic, for use with AERMOD in
regulatory applications. The AERMINUTE \96\ processor, in most
cases, should be used to process 1-minute ASOS wind data for input
into AERMET when processing NWS ASOS sites in AERMET. When
processing prognostic meteorological data for AERMOD, the Mesoscale
Model Interface Program (MMIF) \93\ should be used to process data
for input into AERMET. Other methods of processing prognostic
meteorological data for input into AERMET should be approved by the
appropriate reviewing authority. Additionally, the following
meteorological preprocessors are recommended by the EPA: PCRAMMET
\97\, MPRM \98\, and METPRO \99\. PCRAMMET is the recommended
meteorological data preprocessor for use in applications of OCD
employing hourly NWS data. MPRM is the recommended meteorological
data preprocessor for applications of OCD employing site-specific
meteorological data. METPRO is the recommended meteorological data
preprocessor for use with CTDMPLUS.\100\
b. Regulatory application of AERMOD necessitates careful
consideration of the meteorological data for input to AERMET. Data
representativeness, in the case of AERMOD, means utilizing data of
an appropriate type for constructing realistic boundary layer
profiles. Of particular importance is the requirement that all
meteorological data used as input to AERMOD should be adequately
representative of the transport and dispersion within the analysis
domain. Where surface conditions vary significantly over the
analysis domain, the emphasis in assessing representativeness should
be given to adequate characterization of transport and dispersion
between the source(s) of concern and areas where maximum design
concentrations are anticipated to occur. The EPA recommends that the
surface characteristics input to AERMET should be representative of
the land cover in the vicinity of the meteorological data, i.e., the
location of the meteorological tower for measured data or the
representative grid cell for prognostic data. Therefore, the model
user should apply the latest version AERSURFACE 101 102,
where applicable, for determining surface characteristics when
processing measured meteorological data through AERMET. In areas
where it is not possible to use AERSURFACE output, surface
characteristics can determined using techniques that apply the same
analysis as AERSURFACE. In the case of prognostic meteorological
data, the surface characteristics associated with the prognostic
meteorological model output for the representative grid cell should
be used.103 104 Furthermore, since the spatial scope of
each variable could be different, representativeness should be
judged for each variable separately. For example, for a variable
such as wind direction, the data should ideally be collected near
plume height to be adequately representative, especially for sources
located in complex terrain. Whereas, for a variable such as
temperature, data from a station several kilometers away from the
source may be considered to be adequately representative. More
information about meteorological data, representativeness, and
surface characteristics can be found in the AERMOD Implementation
Guide \76\.
c. Regulatory application of CTDMPLUS requires the input of
multi-level measurements of wind speed, direction, temperature, and
turbulence from an appropriately sited meteorological tower. The
measurements should be obtained up to the representative plume
height(s) of interest. Plume heights of interest can be determined
by use of screening procedures such as CTSCREEN.
d. Regulatory application of OCD requires meteorological data
over land and over water. The over land or surface data processed
through PCRAMMET \97\ which provides hourly stability class, wind
direction and speed, ambient temperature, and mixing height are
required. Data over water requires hourly mixing height, relative
humidity, air temperature, and water surface temperature. Missing
winds are substituted with the surface winds. Vertical wind
direction shear, vertical temperature gradient, and turbulence
intensities are optional.
e. The model user should acquire enough meteorological data to
ensure that worst-case meteorological conditions are adequately
represented in the model results. The use of 5 years of adequately
representative NWS meteorological data, at least 1 year of site-
specific, or at least 3 years of prognostic meteorological data are
required. If 1 year or more, up to 5 years, of site-specific data is
available, these data are preferred for use in air quality analyses.
Such data should have been subjected to quality assurance procedures
as described in section 8.4.4.2.
f. Objective analysis in meteorological modeling is to improve
meteorological analyses (the ``first guess field'') used as initial
conditions for prognostic meteorological models by incorporating
information from meteorological observations. Direct and indirect
(using remote sensing techniques) observations of temperature,
humidity, and wind from surface and radiosonde reports are commonly
employed to improve these analysis fields. For LRT applications, it
is recommended that objective analysis procedures using direct and
indirect meteorological observations be employed in preparing input
fields to produce prognostic meteorological datasets. The length of
record of observations should conform to recommendations outlined in
paragraph 8.4.2(e) for prognostic meteorological model datasets.
8.4.3 National Weather Service Data
8.4.3.1 Discussion
a. The NWS meteorological data are routinely available and
familiar to most model users. Although the NWS does not provide
direct measurements of all the needed dispersion model input
variables, methods have been developed and successfully used to
translate the basic NWS data to the needed model input. Site-
specific measurements of model input parameters have been made for
many modeling studies, and those methods and techniques are becoming
more widely applied, especially in situations such as complex
terrain applications, where available NWS data are not adequately
representative. However, there are many modeling applications where
NWS data are adequately representative, and the applications still
rely heavily on the NWS data.
b. Many models use the standard hourly weather observations
available from the National Centers for Environmental Information
(NCEI) \b\. These observations are then preprocessed before they can
be used in the models. Prior to the advent of ASOS in the early
1990's, the ``hourly'' weather observation was a human observer-
based observation reflecting a single 2-minute average generally
taken about 10 minutes before the hour. However, beginning with
January 2000 for first-order stations and March 2005 for all
stations, NCEI has archived the rolling 2-minute average winds at
every minute for ASOS sites. The AERMINUTE processor \96\ was
developed to reduce calm and missing hours by taking advantage of
the availability of the 1-minute ASOS wind data to calculate full
hourly average winds to replace standard hourly observations and
reduce the number of calm and missing winds in AERMET processing.
---------------------------------------------------------------------------
\b\ Formerly the National Climatic Data Center (NCDC).
---------------------------------------------------------------------------
8.4.3.2 Recommendations
a. The preferred models listed in appendix A all accept as input
the NWS meteorological data preprocessed into model compatible form.
If NWS data are judged to be adequately representative for a
specific modeling application, they may be used. NEIS makes
available surface 105 106 and upper air \107\
meteorological data online and in CD-ROM format. Upper air data are
also available at the Earth System Research Laboratory Global
Systems Divisions Web site (https://esrl.noaa.gov/gsd).
b. Although most NWS wind measurements are made at a standard
height of 10 meters, the actual anemometer height should be used as
input to the preferred meteorological processor and model.
c. Standard hourly NWS wind directions are reported to the
nearest 10 degrees. A specific set of randomly generated numbers has
been developed for use with the preferred EPA models and should be
used with standard NWS data to ensure a lack of bias in wind
direction assignments within the models.
d. Beginning with year 2000, NCDC began archiving 2-minute
winds, reported every minute for NWS ASOS sites. The AERMINUTE
processor was developed to read those winds and calculate hourly
average winds for input into AERMET. When such data are available
for the NWS ASOS site being processed, the AERMINUTE processor
should be used in most cases to calculate hourly average wind speed
and direction when processing NWS ASOS data for input to AERMOD.\94\
[[Page 45375]]
e. Data from universities, FAA, military stations, industry and
pollution control agencies may be used if such data are equivalent
in accuracy and detail (e.g., siting criteria, frequency of
observations, data completeness, etc.) to the NWS data, they are
judged to be adequately representative for the particular
application and have undergone quality assurance checks.
f. After valid data retrieval requirements have been met,\108\
large number of hours in the record having missing data should be
treated according to an established data substitution protocol
provided that adequately representative alternative data are
available. Data substitution guidance is provided in section 5.3 of
reference 108. If no representative alternative data are available
for substitution, the absent data should be coded as missing using
missing data codes appropriate to the applicable meteorological pre-
processor. Appropriate model options for treating missing data, if
available in the model, should be employed.
8.4.4 Site-Specific data
8.4.4.1 Discussion
a. Spatial or geographical representativeness is best achieved
by collection of all of the needed model input data in close
proximity to the actual site of the source(s). Site-specific
measured data are therefore preferred as model input, provided that
appropriate instrumentation and quality assurance procedures are
followed and that the data collected are adequately representative
(free from inappropriate local or microscale influences) and
compatible with the input requirements of the model to be used. It
should be noted that, while site-specific measurements are
frequently made ``on-property'' (i.e., on the source's premises),
acquisition of adequately representative site-specific data does not
preclude collection of data from a location off property.
Conversely, collection of meteorological data on a source's property
does not of itself guarantee adequate representativeness. For help
in determining representativeness of site-specific measurements,
technical guidance \108\ is available. Site-specific data should
always be reviewed for representativeness and adequacy by an
experienced meteorologist, atmospheric scientist, or other qualified
scientist.
8.4.4.2 Recommendations
a. The EPA guidance \108\ provides recommendations on the
collection and use of site-specific meteorological data.
Recommendations on characteristics, siting, and exposure of
meteorological instruments and on data recording, processing,
completeness requirements, reporting, and archiving are also
included. This publication should be used as a supplement to other
limited guidance on these subjects.5 91 109 110 Detailed
information on quality assurance is also available.\111\ As a
minimum, site-specific measurements of ambient air temperature,
transport wind speed and direction, and the variables necessary to
estimate atmospheric dispersion should be available in
meteorological datasets to be used in modeling. Care should be taken
to ensure that meteorological instruments are located to provide an
adequately representative characterization of pollutant transport
between sources and receptors of interest. The appropriate reviewing
authority (paragraph 3.0(b)) is available to help determine the
appropriateness of the measurement locations.
b. All processed site-specific data should be in the form of
hourly averages for input into the dispersion model. These data
include surface wind speed, transport direction, dilution wind
speed, and turbulence measurements [sigma]A and
[sigma]E (for use in stability determinations and direct
input into the dispersion model). The hourly average turbulence
measurements should be the square root of the arithmetic average of
the 15-minute average variances (square of [sigma]A or
[sigma]E).
c. Missing data substitution. After valid data retrieval
requirements have been met,\108\ hours in the record having missing
data should be treated according to an established data substitution
protocol provided that adequately representative alternative data
are available. Such protocols are usually part of the approved
monitoring program plan. Data substitution guidance is provided in
section 5.3 of reference 108. If no representative alternative data
are available for substitution, the absent data should be coded as
missing using missing data codes appropriate to the applicable
meteorological pre-processor. Appropriate model options for treating
missing data, if available in the model, should be employed.
d. Solar radiation measurements. Total solar radiation or net
radiation should be measured with a reliable pyranometer or net
radiometer, sited and operated in accordance with established site-
specific meteorological guidance.108 111
e. Temperature measurements. Temperature measurements should be
made at standard shelter height (2m) in accordance with established
site-specific meteorological guidance.\108\
f. Temperature difference measurements. Temperature difference
(DT) measurements should be obtained using matched thermometers or a
reliable thermocouple system to achieve adequate accuracy. Siting,
probe placement, and operation of DT systems should be based on
guidance found in Chapter 3 of reference 108 and such guidance
should be followed when obtaining vertical temperature gradient
data. AERMET may employ the Bulk Richardson scheme, which requires
measurements of temperature difference, in lieu of cloud cover or
insolation data. To ensure correct application and acceptance,
AERMOD users should consult with the appropriate reviewing authority
(paragraph 3.0(b)) before using the Bulk Richardson scheme for their
analysis.
g. Wind measurements. For simulation of plume rise and
dispersion of a plume emitted from a stack, characterization of the
wind profile up through the layer in which the plume disperses is
desirable. This is especially important in complex terrain and/or
complex wind situations where wind measurements at heights up to
hundreds of meters above stack base may be required in some
circumstances. For tall stacks when site-specific data are needed,
these winds have been obtained traditionally using meteorological
sensors mounted on tall towers. A feasible alternative to tall
towers is the use of meteorological remote sensing instruments
(e.g., acoustic sounders or radar wind profilers) to provide winds
aloft, coupled with 10-meter towers to provide the near-surface
winds. Note that when site-specific wind measurements are used,
AERMOD, at a minimum, requires wind observations at a height above
ground between seven times the local surface roughness height and
100 meters. (For additional requirements for AERMOD and CTDMPLUS,
see appendix A.) Specifications for wind measuring instruments and
systems are contained in reference 108.
h. Turbulence. There are several dispersion models that are
capable of using direct measurements of turbulence (wind
fluctuations) in the characterization of the vertical and lateral
dispersion (e.g., CTDMPLUS, AERMOD). For specific requirements for
CTDMPLUS, AERMOD, see appendix A. For technical guidance on
measurement and processing of turbulence parameters, see reference
108. When turbulence data are used in this manner to directly
characterize the vertical and lateral dispersion, the averaging time
for the turbulence measurements should be 1 hour. However, since
AERMOD incorporates an algorithm to account for horizontal plume
meander under low wind conditions, the methodology outlined in
paragraph 8.4.4.2(b) should be used to calculate hourly averages of
[sigma][thetas], based on four 15-minuite values, to
minimize ``double counting'' of plume spread associated with
meander. The calculation of hourly [sigma][thetas]
discussed above is automatically applied within AERMET when sub-
hourly data are processed. There are other dispersion models that
employ P-G stability categories for the characterization of the
vertical and lateral dispersion. Methods for using site-specific
turbulence data for the characterization of P-G stability categories
are discussed in reference 108. When turbulence data are used in
this manner to determine the P-G stability category, the averaging
time for the turbulence measurements should be 15 minutes, with
hourly averaged values based on methodology in paragraph 8.4.4.2(b).
i. Stability categories. For dispersion models that employ P-G
stability categories for the characterization of the vertical and
lateral dispersion, the P-G stability categories, as originally
defined, couple near-surface measurements of wind speed with
subjectively determined insolation assessments based on hourly cloud
cover and ceiling height observations. The wind speed measurements
are made at or near 10m. The insolation rate is typically assessed
using observations of cloud cover and ceiling height based on
criteria outlined by Turner.\72\ It is recommended that the P-G
stability category be estimated using the Turner method with site-
specific wind speed measured at or near 10m and representative cloud
cover and ceiling height. Implementation of the Turner method, as
well as considerations in determining representativeness of cloud
cover and ceiling height in cases for which site-specific cloud
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observations are unavailable, may be found in section 6 of reference
108. In the absence of requisite data to implement the Turner
method, the solar radiation/delta-T (SRDT) method or wind
fluctuation statistics (i.e., the [sigma]E and
[sigma]A methods) may be used.
j. The SRDT method, described in section 6.4.4.2 of reference
108, is modified slightly from that published from earlier work
\112\ and has been evaluated with three site-specific
databases.\113\ The two methods of stability classification which
use wind fluctuation statistics, the [sigma]E and
[sigma]A methods, are also described in detail in section
6.4.4 of reference 108 (note applicable tables in section 6). For
additional information on the wind fluctuation methods, several
references are available.114 115 116 117
8.4.5 Prognostic Meteorological Data
8.4.5.1 Discussion
a. For some modeling applications, there may not be a
representative NWS or comparable meteorological station available
(e.g., complex terrain), and it may be cost prohibitive or
infeasible to collect adequately representative site-specific data.
For these cases, it may be necessary to use prognostic
meteorological data in a regulatory modeling application.
b. The EPA has developed a processor, the MMIF (Mesoscale Model
Interface Program) to process MM5 (Mesoscale Model 5) or WRF
(Weather Research and Forecasting) model data for input into various
models including AERMOD. MMIF can process data for input into AERMET
or AERMOD for a single grid cell or multiple grid cells. MMIF output
has been found to compare favorably against observed data (site-
specific or NWS).\118\ Specific guidance on processing MMIF for
AERMOD can be found in reference 104. When using MMIF to process
prognostic data for regulatory applications, the data should be
processed to generate AERMET inputs and the data subsequently
processed through AERMET for input into AERMOD. If an alternative
method of processing data for input into AERMET is used, it must be
approved by the appropriate reviewing authority (paragraph 3.0(b)).
8.4.5.2 Recommendations
a. Prognostic model evaluation. Appropriate effort should be
devoted to the process of evaluating the prognostic meteorological
data. The modeling data should be compared to NWS observational data
in an effort to show that the data are accurately replicating the
observed meteorological conditions of the time periods modeled. An
operational evaluation of the modeling data for all model years
(i.e., statistical, graphical) should be completed.\93\ The use of
output from prognostic mesoscale meteorological models is contingent
upon the concurrence with the appropriate reviewing authority
(paragraph 3.0(b)) that the data are of acceptable quality, which
can be demonstrated through statistical comparisons with
meteorological observations aloft and at the surface at several
appropriate locations.\93\
b. Representativeness. When processing MMIF data for use with
AERMOD, the grid cell used for the dispersion modeling should be
adequately spatially representative of the analysis domain. In most
cases, this may be the grid cell containing the emission source of
interest. Since the dispersion modeling may involve multiple sources
and the domain may cover several grid cells, depending on grid
resolution of the prognostic model, professional judgement may be
needed to select the appropriate grid cell to use. In such cases,
the selected grid cell should be adequately representative of the
entire domain.
c. Grid resolution. The grid resolution of the prognostic
meteorological data should be considered and evaluated
appropriately, particularly for projects involving complex terrain.
The operational evaluation of the modeling data should consider
whether a finer grid resolution is needed to ensure that the data
are representative. The use of output from prognostic mesoscale
meteorological models is contingent upon the concurrence with the
appropriate reviewing authority (paragraph 3.0(b)) that the data are
of acceptable quality.
8.4.6 Treatment of Near-Calms and Calms
8.4.6.1 Discussion
a. Treatment of calm or light and variable wind poses a special
problem in modeling applications since steady-state Gaussian plume
models assume that concentration is inversely proportional to wind
speed, depending on model formulations. Procedures have been
developed to prevent the occurrence of overly conservative
concentration estimates during periods of calms. These procedures
acknowledge that a steady-state Gaussian plume model does not apply
during calm conditions, and that our knowledge of wind patterns and
plume behavior during these conditions does not, at present, permit
the development of a better technique. Therefore, the procedures
disregard hours which are identified as calm. The hour is treated as
missing and a convention for handling missing hours is recommended.
With the advent of the AERMINUTE processor, when processing NWS ASOS
data, the inclusion of hourly averaged winds from AERMINUTE will, in
some instances, dramatically reduce the number of calm and missing
hours, especially when the ASOS wind are derived from a sonic
anemometer. To alleviate concerns about low winds, especially those
introduced with AERMINUTE, the EPA implemented a wind speed
threshold in AERMET for use with ASOS derived winds.\96\ Winds below
the threshold will be treated as calms.
b. AERMOD, while fundamentally a steady-state Gaussian plume
model, contains algorithms for dealing with low wind speed (near
calm) conditions. As a result, AERMOD can produce model estimates
for conditions when the wind speed may be less than 1 m/s, but still
greater than the instrument threshold. Required input to AERMET for
site-specific data, the meteorological processor for AERMOD,
includes a threshold wind speed and a reference wind speed. The
threshold wind speed is typically the threshold of the instrument
used to collect the wind speed data. The reference wind speed is
selected by the model as the lowest level of non-missing wind speed
and direction data where the speed is greater than the wind speed
threshold, and the height of the measurement is between seven times
the local surface roughness and 100 meters. If the only valid
observation of the reference wind speed between these heights is
less than the threshold, the hour is considered calm, and no
concentration is calculated. None of the observed wind speeds in a
measured wind profile that are less than the threshold speed are
used in construction of the modeled wind speed profile in AERMOD.
8.4.6.2 Recommendations
a. Hourly concentrations calculated with steady-state Gaussian
plume models using calms should not be considered valid; the wind
and concentration estimates for these hours should be disregarded
and considered to be missing. Critical concentrations for 3-, 8-,
and 24-hour averages should be calculated by dividing the sum of the
hourly concentrations for the period by the number of valid or non-
missing hours. If the total number of valid hours is less than 18
for 24-hour averages, less than 6 for 8-hour averages or less than 3
for 3-hour averages, the total concentration should be divided by 18
for the 24-hour average, 6 for the 8-hour average and 3 for the 3-
hour average. For annual averages, the sum of all valid hourly
concentrations is divided by the number of non-calm hours during the
year. AERMOD has been coded to implement these instructions. For
hours that are calm or missing, the AERMOD hourly concentrations
will be zero. For other models listed in appendix A, a post-
processor computer program, CALMPRO \119\ has been prepared, is
available on the EPA's SCRAM Web site (section 2.3), and should be
used.
b. Stagnant conditions that include extended periods of calms
often produce high concentrations over wide areas for relatively
long averaging periods. The standard steady-state Gaussian plume
models are often not applicable to such situations. When stagnation
conditions are of concern, other modeling techniques should be
considered on a case-by-case basis (see also section 7.2.1.2).
c. When used in steady-state Gaussian plume models, measured
site-specific wind speeds of less than 1 m/s but higher than the
response threshold of the instrument should be input as 1 m/s; the
corresponding wind direction should also be input. Wind observations
below the response threshold of the instrument should be set to
zero, with the input file in ASCII format. For input to AERMOD, no
adjustment should be made to the site-specific wind data. For NWS
ASOS data, especially data using the 1-minute ASOS winds, a wind
speed threshold option is allowed with a recommended speed of 0.5 m/
s. \94\ When using prognostic data processed by MMIF, a 0.5 m/s
threshold is also invoked by MMIF for input into AERMET.
Observations with wind speeds less than the threshold are considered
calm, and no concentration is calculated. In all cases involving
steady-state Gaussian plume models, calm hours should be treated as
missing, and concentrations should be calculated as in paragraph (a)
of this subsection.
[[Page 45377]]
9.0 Regulatory Application of Models
9.1 Discussion
a. Standardized procedures are valuable in the review of air
quality modeling and data analyses conducted to support SIP
submittals and revisions, NSR, including PSD, or other EPA
requirements to ensure consistency in their regulatory application.
This section recommends procedures specific to NSR, including PSD,
that facilitate some degree of standardization while at the same
time allowing the flexibility needed to assure the technically best
analysis for each regulatory application. For SIP attainment
demonstrations, refer to the appropriate EPA guidance
51 60 for the recommended procedures.
b. Air quality model estimates, especially with the support of
measured air quality data, are the preferred basis for air quality
demonstrations. A number of actions have been taken to ensure that
the best air quality model is used correctly for each regulatory
application and that it is not arbitrarily imposed.
First, the Guideline clearly recommends that the most
appropriate model be used in each case. Preferred models are
identified, based on a number of factors, for many uses.
Second, the preferred models have been subjected to a
systematic performance evaluation and a peer scientific review.
Statistical performance measures, including measures of difference
(or residuals) such as bias, variance of difference and gross
variability of the difference, and measures of correlation such as
time, space, and time and space combined as described in section
2.1.1, were generally followed.
Third, more specific information has been provided for
considering the incorporation of new models into the Guideline
(section 3.1) and the Guideline contains procedures for justifying
the case-by-case use of alternative models and obtaining EPA
approval (section 3.2).
The Guideline, therefore, provides objective methods that allow
a determination to be made as to what air quality model or technique
is most appropriate for a particular application.
c. Air quality modeling is the preferred basis for air quality
demonstrations. Nevertheless, there are rare circumstances where the
performance of the preferred air quality model may be shown to be
less than reasonably acceptable or where no preferred air quality
model, screening model or technique, or alternative model are
suitable for the situation. In these unique instances, there is the
possibility of assuring compliance and establishing emissions limits
for an existing source solely on the basis of observed air quality
data in lieu of an air quality modeling analysis. Comprehensive air
quality monitoring in the vicinity of the existing source with
proposed modifications will be necessary in these cases. The same
attention should be given to the detailed analyses of the air
quality data as would be applied to a model performance evaluation.
d. The current levels and forms of the NAAQS for the six
criteria pollutants can be found on the EPA's NAAQS Web site at
https://www.epa.gov/air/criteria.html. Under the CAA, the NAAQS are
subjected to extensive review every 5 years and the standards,
including the level and the form, may be revised as part of that
review. The criteria pollutants have either long-term (annual or
quarterly) and/or short-term (24-hour or less) forms that are not to
be exceeded more than a certain frequency over a period of time
(e.g., no exceedance on a rolling 3-month average, no more than once
per year, or no more than once per year averaged over 3 years), are
averaged over a period of time (e.g., an annual mean or an annual
mean averaged over 3 years), or are some percentile that is averaged
over a period of time (e.g., annual 99th or 98th percentile averaged
over 3 years). The 3-year period for ambient monitoring design
values does not dictate the length of the data periods recommended
for modeling (i.e., 5 years of NWS meteorological data, at least 1
year of site-specific, or at least 3 years of prognostic
meteorological data).
e. This section discusses general recommendations on the
regulatory application of models for the purposes of NSR, including
PSD permitting, and particularly for estimating design
concentration(s), appropriately comparing these estimates to NAAQS
and PSD increment, and developing emissions limits. Lastly, this
section provides the criteria necessary for considering use of
analysis based on measured ambient data in lieu of modeling as the
sole basis for demonstrating compliance with NAAQS and PSD
increments.
9.2 Recommendations
9.2.1 Modeling Protocol
a. Every effort should be made by the appropriate reviewing
authority (paragraph 3.0(b)) to meet with all parties involved in
either a SIP submission or revision or a PSD permit application
prior to the start of any work on such a project. During this
meeting, a protocol should be established between the preparing and
reviewing parties to define the procedures to be followed, the data
to be collected, the model to be used, and the analysis of the
source and concentration data to be performed. An example of the
content for such an effort is contained in the Air Quality Analysis
Checklist posted on the EPA's SCRAM Web site (section 2.3). This
checklist suggests the appropriate level of detail to assess the air
quality resulting from the proposed action. Special cases may
require additional data collection or analysis and this should be
determined and agreed upon at this pre-application meeting. The
protocol should be written and agreed upon by the parties concerned,
although it is not intended that this protocol be a binding, formal
legal document. Changes in such a protocol or deviations from the
protocol are often necessary as the data collection and analysis
progresses. However, the protocol establishes a common understanding
of how the demonstration required to meet regulatory requirements
will be made.
9.2.2 Design Concentration and Receptor Sites
a. Under the PSD permitting program, an air quality analysis for
criteria pollutants is required to demonstrate that emissions from
the construction or operation of a proposed new source or
modification will not cause or contribute to a violation of the
NAAQS or PSD increments.
i. For a NAAQS assessment, the design concentration is the
combination of the appropriate background concentration (section
8.3) with the estimated modeled impact of the source. The NAAQS
design concentration is then compared to the applicable NAAQS.
ii. For a PSD increment assessment, the design concentration
includes impacts after the appropriate baseline date from all
increment consuming and increment expanding sources. The PSD
increment design concentration is then compared to the applicable
PSD increment.
b. The specific form of the NAAQS for the pollutant(s) of
concern will also influence how the background and modeled data
should be combined for appropriate comparison with the respective
NAAQS in such a modeling demonstration. Given the potential for
revision of the form of the NAAQS and the complexities of combining
background and modeled data, specific details on this process can be
found in applicable modeling guidance available on the EPA's SCRAM
Web site (section 2.3). Modeled concentrations should not be rounded
before comparing the resulting design concentration to the NAAQS or
PSD increments. Ambient monitoring and dispersion modeling address
different issues and needs relative to each aspect of the overall
air quality assessment.
c. The PSD increments for criteria pollutants are listed in 40
CFR 52.21(c) and 40 CFR 51.166(c). For short-term increments, these
maximum allowable increases in pollutant concentrations may be
exceeded once per year at each site, while the annual increment may
not be exceeded. The highest, second-highest increase in estimated
concentrations for the short-term averages as determined by a model
should be less than or equal to the permitted increment. The modeled
annual averages should not exceed the increment.
d. Receptor sites for refined dispersion modeling should be
located within the modeling domain (section 8.1). In designing a
receptor network, the emphasis should be placed on receptor density
and location, not total number of receptors. Typically, the density
of receptor sites should be progressively more resolved near the new
or modifying source, areas of interest, and areas with the highest
concentrations with sufficient detail to determine where possible
violations of a NAAQS or PSD increment are most likely to occur. The
placement of receptor sites should be determined on a case-by-case
basis, taking into consideration the source characteristics,
topography, climatology, and monitor sites. Locations of particular
importance include: (1) The area of maximum impact of the point
source; (2) the area of maximum impact of nearby sources; and (3)
the area where all sources combine to cause maximum impact.
Depending on the complexities of the source and the environment to
which the source is located, a dense array of receptors may be
required in
[[Page 45378]]
some cases. In order to avoid unreasonably large computer runs due
to an excessively large array of receptors, it is often desirable to
model the area twice. The first model run would use a moderate
number of receptors more resolved nearby the new or modifying source
and over areas of interest. The second model run would modify the
receptor network from the first model run with a denser array of
receptors in areas showing potential for high concentrations and
possible violations, as indicated by the results of the first model
run. Accordingly, the EPA neither anticipates nor encourages that
numerous iterations of modeling runs be made to continually refine
the receptor network.
9.2.3 NAAQS and PSD Increments Compliance Demonstrations for New or
Modified Sources
a. As described in this subsection, the recommended procedure
for conducting either a NAAQS or PSD increment assessment under PSD
permitting is a multi-stage approach that includes the following two
stages:
i. The first stage is referred to as a single-source impact
analysis, since only the new or modifying source is considered in
the analysis. There are two possible levels of detail in conducting
a single-source impact analysis with the model user beginning with
use of a screening model and proceeding to use of a refined model as
necessary.
ii. The second stage is referred to as a cumulative impact
analysis, since it takes into account all sources affecting the air
quality in an area. In addition to the project source impact, it
includes consideration of background, which includes contributions
from natural, nearby, and unknown sources.
b. Each stage involves increasing complexity and details, as
required to fully demonstrate a new or modifying source will not
cause of contribution to a violation of any NAAQS or PSD increment.
As such, starting with a single-source impact analysis may alleviate
the need for a more time consuming and comprehensive cumulative
modeling analysis.
c. The single-source impact analysis, or first stage of an air
quality analysis, begins by determining the potential of a proposed
new or modifying source to cause or contribute to a NAAQS or PSD
increment violation. In certain circumstances, a screening model or
technique may be used instead of the preferred model because it will
provide estimated worst-case ambient impacts from the proposed new
or modifying source. If these worst case ambient concentration
estimates indicate that there will not be a significant impact, then
the analysis is sufficient for the required demonstration under PSD.
If the ambient concentration estimates indicate that significant
impacts may occur, then the use of a refined model to estimate the
source's impact should be pursued. The refined modeling analysis
should use a model or technique consistent with the Guideline
(either a preferred model or technique or an alternative model or
technique) and follow the requirements and recommendations for model
inputs outlined in section 8. If the estimated ambient
concentrations indicate that there will not be a significant impact,
then the analysis is generally sufficient to demonstrate that the
source will not cause or contribute to an exceedance. However, if
the concentration estimates from the refined modeling analysis
indicate that significant impacts may occur, then a cumulative
impact analysis should be undertaken. The receptors that indicate
the location of significant impacts should be used to define the
modeling domain for use in the cumulative impact analysis (section
8.2.2).
d. The cumulative impact analysis, or the second stage of an air
quality analysis, should be conducted with the same refined model or
technique to characterize the project source and then include the
appropriate background concentrations (section 8.3). The resulting
design concentrations are used to determine whether the source will
cause or contribute to a NAAQS or PSD increment violation. This
determination should be based on: (1) The appropriate design
concentration for each applicable NAAQS (and averaging period); and
(2) the significance of the source's contribution, in a temporal and
spatial sense, to any modeled violation, i.e., where and when the
predicted design concentration is greater than the NAAQS. For PSD
increment, the cumulative impact analysis should also consider the
amount of the air quality increment that has already been consumed
by other sources, or, conversely, whether increment has expanded
relative to the baseline concentration. Therefore, the applicant
should model the existing or permitted nearby increment-consuming
and increment-expanding sources, rather than using past modeling
analyses of those sources as part of background concentration. This
would permit the use of newly acquired data or improved modeling
techniques if such data and/or techniques have become available
since the last source was permitted.
9.2.3.1 Considerations in Developing Emissions Limits
a. Emissions limits and resulting control requirements should be
established to provide for compliance with each applicable NAAQS
(and averaging period) and PSD increment. It is possible that
multiple emissions limits will be required for a source to
demonstrate compliance with several criteria pollutants (and
averaging periods) and PSD increments. Case-by-case determinations
must be made as to the appropriate form of the limits, i.e., whether
the emissions limits restrict the emission factor (e.g., limiting
lb/MMBTU), the emission rate (e.g., lb/hr), or both. The appropriate
reviewing authority (paragraph 3.0(b)) and appropriate EPA guidance
should be consulted to determine the appropriate emissions limits on
a case-by-case basis.
9.2.4 Use of Measured Data in Lieu of Model Estimates
a. As described throughout the Guideline, modeling is the
preferred method for demonstrating compliance with the NAAQS and PSD
increments and for determining the most appropriate emissions limits
for new and existing sources. When a preferred model or adequately
justified and approved alternative model is available, model
results, including the appropriate background, are sufficient for
air quality demonstrations and establishing emissions limits, if
necessary. In instances when the modeling technique available is
only a screening technique, the addition of air quality monitoring
data to the analysis may lend credence to the model results.
However, air quality monitoring data alone will normally not be
acceptable as the sole basis for demonstrating compliance with the
NAAQS and PSD increments or for determining emissions limits.
b. There may be rare circumstances where the performance of the
preferred air quality model will be shown to be less than reasonably
acceptable when compared with air quality monitoring data measured
in the vicinity of an existing source. Additionally, there may not
be an applicable preferred air quality model, screening technique,
or justifiable alternative model suitable for the situation. In
these unique instances, there may be the possibility of establishing
emissions limits and demonstrating compliance with the NAAQS and PSD
increments solely on the basis of analysis of observed air quality
data in lieu of an air quality modeling analysis. However, only in
the case of a modification to an existing source should air quality
monitoring data alone be a basis for determining adequate emissions
limits or for demonstration that the modification will not cause or
contribute to a violation of any NAAQS or PSD increment.
c. The following items should be considered prior to the
acceptance of an analysis of measured air quality data as the sole
basis for an air quality demonstration or determining an emissions
limit:
i. Does a monitoring network exist for the pollutants and
averaging times of concern in the vicinity of the existing source?
ii. Has the monitoring network been designed to locate points of
maximum concentration?
iii. Do the monitoring network and the data reduction and
storage procedures meet EPA monitoring and quality assurance
requirements?
iv. Do the dataset and the analysis allow impact of the most
important individual sources to be identified if more than one
source or emission point is involved?
v. Is at least one full year of valid ambient data available?
vi. Can it be demonstrated through the comparison of monitored
data with model results that available air quality models and
techniques are not applicable?
c. Comprehensive air quality monitoring in the area affected by
the existing source with proposed modifications will be necessary in
these cases. Additional meteorological monitoring may also be
necessary. The appropriate number of air quality and meteorological
monitors from a scientific and technical standpoint is a function of
the situation being considered. The source configuration, terrain
configuration, and meteorological variations all have an impact on
number and optimal placement of monitors. Decisions on the
monitoring network appropriate for this type of analysis can only be
made on a case-by-case basis.
d. Sources should obtain approval from the appropriate reviewing
authority (paragraph
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3.0(b)) and the EPA Regional Office for the monitoring network prior
to the start of monitoring. A monitoring protocol agreed to by all
parties involved is necessary to assure that ambient data are
collected in a consistent and appropriate manner. The design of the
network, the number, type, and location of the monitors, the
sampling period, averaging time as well as the need for
meteorological monitoring or the use of mobile sampling or plume
tracking techniques, should all be specified in the protocol and
agreed upon prior to start-up of the network.
e. Given the uniqueness and complexities of these rare
circumstances, the procedures can only be established on a case-by-
case basis for analyzing the source's emissions data and the
measured air quality monitoring data and for projecting with a
reasoned basis the air quality impact of a proposed modification to
an existing source in order to demonstrate that emissions from the
construction or operation of the modification will not cause or
contribute to a violation of the applicable NAAQS and PSD increment,
and to determine adequate emissions limits. The same attention
should be given to the detailed analyses of the air quality data as
would be applied to a comprehensive model performance evaluation. In
some cases, the monitoring data collected for use in the performance
evaluation of preferred air quality models, screening technique, or
existing alternative models may help inform the development of a
suitable new alternative model. Early coordination with the
appropriate reviewing authority (paragraph 3.0(b)) and the EPA
Regional Office is fundamental with respect to any potential use of
measured data in lieu of model estimates.
10.0 References
1. Code of Federal Regulations; Title 40 (Protection of
Environment); Part 51; Sections 51.112, 51.117, 51.150, 51.160.
2. Environmental Protection Agency, 1990. New Source Review Workshop
Manual: Prevention of Significant Deterioration and Nonattainment
Area Permitting (Draft). Office of Air Quality Planning & Standards,
Research Triangle Park, NC. https://www.epa.gov/nsr.
3. Code of Federal Regulations; Title 40 (Protection of
Environment); Part 51; Sections 51.166 and 52.21.
4. Code of Federal Regulations; Title 40 (Protection of
Environment); Part 93; Sections 93.116, 93.123, and 93.150.
5. Code of Federal Regulations; Title 40 (Protection of
Environment); Part 58 (Ambient Air Quality Surveillance).
6. Code of Federal Regulations; Title 40 (Protection of
Environment); Part 50 (National Primary and Secondary Ambient Air
Quality Standards).
7. Baker, K.R., Kelly, J.T., 2014. Single source impacts estimated
with photochemical model source sensitivity and apportionment
approaches. Atmospheric Environment, 96: 266-274.
8. ENVIRON, 2012. Evaluation of Chemical Dispersion Models using
Atmospheric Plume Measurements from Field Experiments. ENVIRON
International, Corp., Novato, CA. Prepared under contract No. EP-D-
07-102 for U.S. Environmental Protection Agency, Research Triangle
Park, NC. https://www.epa.gov/ttn/scram/reports/Plume_Eval_Final_Sep_2012v5.pdf.
9. McMurry, P.H., Shepherd, M.F., Vickery, J.S., 2004. Particulate
matter science for policy makers: A NARSTO assessment. Cambridge
University Press.
10. Baker, K.R., Foley, K.M., 2011. A nonlinear regression model
estimating single source concentrations of primary and secondarily
formed PM2.5. Atmospheric Environment, 45: 3758-3767.
11. Bergin, M.S., Russell, A.G., Odman, M.T., Cohan, D.S.,
Chameldes, W.L., 2008. Single-Source Impact Analysis Using Three-
Dimensional Air Quality Models. Journal of the Air & Waste
Management Association, 58: 1351-1359.
12. Zhou, W., Cohan, D.S., Pinder, R.W., Neuman, J.A., Holloway,
J.S., Peischl, J., Ryerson, T.B., Nowak, J.B., Flocke, F., Zheng,
W.G., 2012. Observation and modeling of the evolution of Texas power
plant plumes. Atmospheric Chemistry and Physics, 12: 455-468.
13. Chen, J., Lu, J., Avise, J.C., DaMassa, J.A., Kleeman, M.J.,
Kaduwela, A.P., 2014. Seasonal modeling of PM 2.5 in California's
San Joaquin Valley. Atmospheric Environment, 92: 182-190.
14. Russell, A.G., 2008. EPA Supersites program-related emissions-
based particulate matter modeling: initial applications and
advances. Journal of the Air & Waste Management Association, 58:
289-302.
15. Tesche, T., Morris, R., Tonnesen, G., McNally, D., Boylan, J.,
Brewer, P., 2006. CMAQ/CAMx annual 2002 performance evaluation over
the eastern US. Atmospheric Environment, 40: 4906-4919.
16. Fox, D.G., 1984. Uncertainty in air quality modeling. Bulletin
of the American Meteorological Society, 65(1): 27-36.
17. Bowne, NE., 1981. Validation and Performance Criteria for Air
Quality Models. Appendix F in Air Quality Modeling and the Clean Air
Act: Recommendations to EPA on Dispersion Modeling for Regulatory
Applications. American Meteorological Society, Boston, MA; pp. 159-
171. (Docket No. A-80-46, II-A-106).
18. Fox, D.G., 1981. Judging Air Quality Model Performance. Bulletin
of the American Meteorological Society, 62(5): 599-609.
19. Simon, H., Baker, K.R., Phillips, S., 2012. Compilation and
interpretation of photochemical model performance statistics
published between 2006 and 2012. Atmospheric Environment, 61: 124-
139.
20. Burton, C.S., 1981. The Role of Atmospheric Models in Regulatory
Decision-Making: Summary Report. Systems Applications, Inc., San
Rafael, CA. Prepared under contract No. 68-01-5845 for U.S.
Environmental Protection Agency, Research Triangle Park, NC. (Docket
No. A-80-46, II-M-6).
21. ``Ten years of Harmonisation activities: Past, present and
future'' at https://www.dmu.dk/AtmosphericEnvironment/Harmoni/Conferences/Belgirate/BelgiratePapers.asp.
22. Weil, Sykes, and Venkatram, 1992. Evaluating Air-Quality Models:
Review and Outlook. Journal of Applied Meteorology, 31: 1121-1145.
23. Environmental Protection Agency, 1988. Model Clearinghouse:
Operational Plan (Revised). Staff Report. Office of Air Quality
Planning & Standards, Research Triangle Park, NC. (Docket No. A-88-
04, II-J-1)
24. American Meteorological Society, 1983. Synthesis of the Rural
Model Reviews. Publication No. EPA-600/3-83-108. Office of Research
& Development, Research Triangle Park, NC. (NTIS No. PB 84-121037)
25. Hanna, S., M. Garrison and B. Turner, 1998. AERMOD Peer Review
report. Prepared by SAI, Inc. under EPA Contract No. 68-D6-0064/1-14
for Environmental Protection Agency, Research Triangle Park, NC.
12pp. & appendices. (Docket No. A-99-05, II-A-6)
26. Scire, J.S. and L.L. Schulman, 1981. Evaluation of the BLP and
ISC Models with SF6 Tracer Data and SO2 Measurements at
Aluminum Reduction Plants. APCA Specialty Conference on Dispersion
Modeling for Complex Sources, St. Louis, MO.
27. Environmental Protection Agency, 2003. AERMOD: Latest Features
and Evaluation Results. Publication No. EPA-454/R-03-003. Office of
Air Quality Planning & Standards, Research Triangle Park, NC. https://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mep.pdf.
28. ASTM D6589: Standard Guide for Statistical Evaluation of
Atmospheric Dispersion Model Performance. (2010).
29. Environmental Protection Agency, 1992. Protocol for Determining
the Best Performing Model. Publication No. EPA-454/R-92-025. Office
of Air Quality Planning & Standards, Research Triangle Park, NC.
https://www.epa.gov/ttn/scram/guidance/guide/modleval.zip. (NTIS No.
PB 93-226082)
30. Hanna, S.R., 1982. Natural Variability of Observed Hourly
SO2 and CO Concentrations in St. Louis. Atmospheric
Environment, 16(6): 1435-1440.
31. Pasquill, F., 1974. Atmospheric Diffusion, 2nd Edition. John
Wiley and Sons, New York, NY; 479pp.
32. Rhoads, R.G., 1981. Accuracy of Air Quality Models. Staff
Report. Office of Air Quality Planning & Standards, Research
Triangle Park, NC. (Docket No. A-80-46, II-G-6)
33. Hanna, S.R., 1993. Uncertainties in air quality model
predictions. Boundary-Layer Meteorology, 62: 3-20.
34. Hanna, S.R., 1989. Confidence limits for air quality model
evaluations, as estimated by bootstrap and jackknife resampling
methods. Atmospheric Environment, 23(6): 1385-1398.
35. Cox, W.M. and J.A. Tikvart, 1990. A statistical procedure for
determining the
[[Page 45380]]
best performing air quality simulation model. Atmospheric
Environment, 24A(9): 2387-2395.
36. Environmental Protection Agency, 2015. Technical Support
Document (TSD) for AERMOD-Based Assessments of Long-Range Transport
Impacts for Primary Pollutants. Publication No. EPA-454/B-15-003.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC.
37. Environmental Protection Agency, 2015: AERSCREEN User's Guide.
Publication No. EPA-454-/B-15-005. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. https://www.epa.gov/ttn/scram/dispersion_screening.htm#aerscreen.
38. Environmental Protection Agency, 2011: AERSCREEN Released as the
EPA Recommended Screening Model. Tyler Fox Memorandum dated April
11, 2011, Office of Air Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/ttn/scram/20110411_AERSCREEN_Release_Memo.pdf.
39. Perry, S.G., D.J. Burns and A.J. Cimorelli, 1990. User's Guide
to CTDMPLUS: Volume 2. The Screening Mode (CTSCREEN). Publication
No. EPA-600/8-90-087. U.S. Environmental Protection Agency, Research
Triangle Park, NC. https://www.epa.gov/ttn/scram/dispersion_prefrec.htm#ctdmplus. (NTIS No. PB 91-136564)
40. Environmental Protection Agency, 1992. Screening Procedures for
Estimating the Air Quality Impact of Stationary Sources, Revised.
Publication No. EPA-454/R-92-019. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. (NTIS No. PB 93-219095)
41. Burns, D.J., S.G. Perry and A.J. Cimorelli, 1991. An Advanced
Screening Model for Complex Terrain Applications. Paper presented at
the 7th Joint Conference on Applications of Air Pollution
Meteorology (cosponsored by the American Meteorological Society and
the Air & Waste Management Association), January 13-18, 1991, New
Orleans, LA.
42. Mills, M.T., R.J. Paine, E.A. Insley and B.A. Egan, 1987. The
Complex Terrain Dispersion Model Terrain Preprocessor System--User's
Guide and Program Description. Publication No. EPA-600/8-88-003.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
(NTIS No. PB 88-162094)
43. Environmental Research and Technology, 1987. User's Guide to the
Rough Terrain Diffusion Model (RTDM), Rev. 3.20. ERT Document No. P-
D535-585. Environmental Research and Technology, Inc., Concord, MA.
(NTIS No. PB 88-171467)
44. Environmental Protection Agency, 2004. AERMOD: Description of
Model Formulation. Publication No. EPA-454/R-03-004. Office of Air
Quality Planning & Standards, Research Triangle Park, NC; September
2004. https://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdf.
45. Cimorelli, A. et al., 2005. AERMOD: A Dispersion Model for
Industrial Source Applications. Part I: General Model Formulation
and Boundary Layer Characterization. Journal of Applied Meteorology,
44(5): 682-693.
46. L.L. Schulman, D.G. Strimaitis and J.S. Scire, 2002. Development
and evaluation of the PRIME plume rise and building downwash model.
Journal of the Air & Waste Management Association, 50: 378-390.
47. Environmental Protection Agency, 2015, Technical Support
Document (TSD) for Replacement of CALINE3 with AERMOD for
Transportation Related Air Quality Analyses. Publication No. EPA-
454/B-15-002. Office of Air Quality Planning & Standards, Research
Triangle Park, NC.
48. Environmental Protection Agency, 1997. Guidance for Siting
Ambient Air Monitors around Stationary Lead Sources. Publication No.
EPA-454/R-92-009R. Office of Air Quality Planning & Standards,
Research Triangle Park, NC. (NTIS No. PB 97-208094)
49. LEADPOST processor: https://www.epa.gov/ttn/scram/models/aermod/leadpost.zip.
50. Environmental Protection Agency, 1993. Lead Guideline Document.
Publication No. EPA-452/R-93-009. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. (NTIS No. PB 94-111846)
51. U.S. EPA, 2014: Guidance for 1-Hour SO2 Nonattaiment
Area SIP Submissions. Stephen D. Page Memorandum dated April 23,
2011, Office of Air Quality Planning & Standards, Research Triangle
Park, NC. https://www.epa.gov/airquality/sulfurdioxide/pdfs/20140423guidance.pdf.
52. U.S. EPA, 2013: SO2 NAAQS Designations Modeling
Technical Assistance Document. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. https://www.epa.gov/airquality/sulfurdioxide/pdfs/SO2ModelingTAD.pdf.
53. Turner, D.B., 1964. A Diffusion Model for an Urban Area. Journal
of Applied Meteorology, 3(1):83-91.
54. Environmental Protection Agency, 2015. Technical Support
Document (TSD) for NO2-Related AERMOD Options and
Modifications, Publication No. EPA-454/B-15-004. Office of Air
Quality Planning & Standards, Research Triangle Park, NC.
55. Podrez, M. 2015. An Update to the Ambient Ratio Method for 1-h
NO2 Air Quality Standards Dispersion Modeling.
Atmospheric Environment, 103: 163-170.
56. Cole, H.S. and J.E. Summerhays, 1979. A Review of Techniques
Available for Estimation of Short-Term NO2
Concentrations. Journal of the Air Pollution Control Association,
29(8): 812-817.
57. Hanrahan, P.L., 1999. The Polar Volume Polar Ratio Method for
Determining NO2/NOX Ratios in Modeling--Part
I: Methodology. Journal of the Air & Waste Management Association,
49: 1324-1331.
58. Environmental Protection Agency, 2004. The Particle Pollution
Report, Publication No. EPA-454-R-04-002. Office of Air Quality
Planning & Standards, Research Triangle Park, NC. https://www.epa.gov/airtrends/aqtrnd04/pmreport03/report_2405.pdf#page=1.
59. Environmental Protection Agency, 2014. Guidance for
PM2.5 Modeling. May 20, 2014, Publication No. EPA-454/B-
14-001. Office of Air Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/ttn/scram/guidance/guide/Guidance_for_PM25_Permit_Modeling.pdf.
60. Environmental Protection Agency, 2014. Modeling Guidance for
Demonstrating Attainment of Air Quality Goals for Ozone,
PM2.5, and Regional Haze. Office of Air Quality Planning
& Standards, Research Triangle Park, NC. https://www.epa.gov/ttn/scram/guidance/guide/Draft_O3-PM-RH_Modeling_Guidance-2014.pdf.
61. Environmental Protection Agency, 2013. Transportation Conformity
Guidance for Quantitative Hot-Spot Analyses in PM2.5 and
PM10 Nonattainment and Maintenance Areas. Publication No.
EPA-420-B-053. Office of Transportation and Air Quality, Ann Arbor,
MI. https://www.epa.gov/otaq/stateresources/transconf/policy/420b13053-sec.pdf.
62. Environmental Protection Agency, 1987. PM10 SIP
Development Guideline. Publication No. EPA-450/2-86-001. Office of
Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS
No. PB 87-206488)
63. Environmental Protection Agency, 2012. Memorandum: Haul Road
Workgroup Final Report Submission to EPA-OAQPS. March 2, 2012.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC. https://www.epa.gov/ttn/scram/reports/Haul_Road_Workgroup-Final_Report_Package-20120302.pdf.
64. Seinfeld, J.H., Pandis, S.N., 2012. Atmospheric chemistry and
physics: from air pollution to climate change. John Wiley & Sons.
65. Simon, H., Baker, K.R., Phillips, S., 2012. Compilation and
interpretation of photochemical model performance statistics
published between 2006 and 2012. Atmospheric Environment, 61, 124-
139.
66. Environmental Protection Agency, 2015. Guidance on the use of
models for assessing the impacts from single sources on secondarily
formed pollutants ozone and PM2.5. Publication No. EPA
454/P-15-001. Office of Air Quality Planning & Standards, Research
Triangle Park, NC.
67. U.S. Department of the Interior, 2010. Federal Land Managers'
Air Quality Related Values Work Group (FLAG) Phase I Report--Revised
2010. https://www.nature.nps.gov/air/pubs/pdf/flag/FLAG_2010.pdf.
Natural Resource Report NPS/NPRC/NRR-2010/232.
68. National Acid Precipitation Assessment Program (NAPAP), 1991.
Acid Deposition: State of Science and Technology. Volume III
Terrestrial, Materials, Health and Visibility Effects.
[[Page 45381]]
Report 24, Visibility: Existing and Historical Conditions--Causes
and Effects. Edited by Patricia M. Irving. Washington, DC 129pp.
69. National Research Council, 1993. Protecting Visibility in
National Parks and Wilderness Areas. National Academy Press,
Washington, DC 446pp.
70. U.S. Environmental Protection Agency, 1992. Workbook for plume
visual impact screening and analysis (revised). Publication No. EPA-
454/R-92-023. Office of Air Quality Planning & Standards, Research
Triangle Park, NC (NTIS No. PB 93-223592).
71. Nilsson, J., Grennfelt, P., Ministerr[aring]d, N., 1988.
Critical Loads for Sulphur and Nitrogen: Report from a Workshop Held
at Skokloster, Sweden, 19-24 March, 1988. Nordic Council of
Ministers.
72. Turner, D.B., 1969. Workbook of Atmospheric Dispersion
Estimates. PHS Publication No. 999-AP-26. U.S. Department of Health,
Education and Welfare, Public Health Service, Cincinnati, OH. (NTIS
No. PB-191482)
73. McElroy, J.L. and F. Pooler, Jr., 1968. St. Louis Dispersion
Study, Volume II-- Analysis. National Air Pollution Control
Administration Publication No. AP-53, U.S. Department of Health,
Education and Welfare, Public Health Service, Arlington, VA. (NTIS
No. PB-190255)
74. Irwin, J.S., 1978. Proposed Criteria for Selection of Urban
Versus Rural Dispersion Coefficients. (Draft Staff Report).
Meteorology and Assessment Division, U.S. Environmental Protection
Agency, Research Triangle Park, NC. (Docket No. A-80-46, II-B-8)
75. Auer, Jr., A.H., 1978. Correlation of Land Use and Cover with
Meteorological Anomalies. Journal of Applied Meteorology, 17(5):
636-643.
76. Environmental Protection Agency, 2015. AERMOD Implementation
Guide. U.S. Office of Air Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/ttn/scram/dispersion_prefrec.htm#aermod.
77. Pasquill, F., 1976. Atmospheric Dispersion Parameters in
Gaussian Plume Modeling, Part II. Possible Requirements for Change
in the Turner Workbook Values. Publication No. EPA-600/4-76-030b.
Office of Research & Development, Research Triangle Park, NC. (NTIS
No. PB-258036/3BA)
78. Stull, R.B., 1988. An Introduction to Boundary Layer
Meteorology. Kluwer Academic Publishers, Boston, MA. 666pp.
79. Environmental Protection Agency, 1987. Analysis and Evaluation
of Statistical Coastal Fumigation Models. Publication No. EPA-450/4-
87-002. Office of Air Quality Planning & Standards, Research
Triangle Park, NC. (NTIS No. PB 87-175519)
80. Wesely, M.L, P.V. Doskey, and J.D. Shannon, 2002: Deposition
Parameterizations for the Industrial Source Complex (ISC3) Model.
Draft ANL report ANL/ER/TRB01/003, DOE/xxnnnn, Argonne National
Laboratory, Argonne, Illinois 60439.
81. Environmental Protection Agency, 1981. Guideline for Use of
Fluid Modeling to Determine Good Engineering Practice (GEP) Stack
Height. Publication No. EPA-450/4-81-003. Office of Air Quality
Planning & Standards, Research Triangle Park, NC. (NTIS No. PB 82-
145327)
82. Lawson, Jr., R.E. and W.H. Snyder, 1983. Determination of Good
Engineering Practice Stack Height: A Demonstration Study for a Power
Plant. Publication No. EPA-600/3-83-024. Office of Research &
Development, Research Triangle Park, NC. (NTIS No. PB 83-207407)
83. Environmental Protection Agency, 1985. Guideline for
Determination of Good Engineering Practice Stack Height (Technical
Support Document for the Stack Height Regulations), Revised.
Publication No. EPA-450/4-80-023R. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. https://www.epa.gov/ttn/scram/guidance/guide/gep.pdf. (NTIS No. PB 85-225241)
84. Snyder, W.H. and R.E. Lawson, Jr., 1985. Fluid Modeling
Demonstration of Good Engineering-Practice Stack Height in Complex
Terrain. Publication No. EPA-600/3-85-022. Office of Research &
Development, Research Triangle Park, NC. (NTIS No. PB 85-203107)
85. Briggs, G.A., 1975. Plume Rise Predictions. Chapter 3 in
Lectures on Air Pollution and Environmental Impact Analyses.
American Meteorological Society, Boston, MA; pp. 59-111.
86. Hanna, S.R., G.A. Briggs and R.P. Hosker, Jr., 1982. Plume Rise.
Chapter 2 in Handbook on Atmospheric Diffusion. Technical
Information Center, U.S. Department of Energy, Washington, DC; pp.
11-24. DOE/TIC-11223 (DE 82002045)
87. Weil, J.C., L.A. Corio and R.P. Brower, 1997. A PDF dispersion
model for buoyant plumes in the convective boundary layer. Journal
of Applied Meteorology, 36: 982-1003.
88. L.L. Schulman, D.G. Strimaitis and J.S. Scire, 2002. Development
and evaluation of the PRIME plume rise and building downwash model.
Journal of the Air & Waste Management Association, 50: 378-390.
89. Environmental Protection Agency, 1995. Compilation of Air
Pollutant Emission Factors, Volume I: Stationary Point and Area
Sources (Fifth Edition, AP-42: GPO Stock No. 055-000-00500-1), and
Supplements A- D; Volume II: Mobile Sources (Fifth Edition). Office
of Air Quality Planning & Standards, Research Triangle Park, NC.
Volume I can be downloaded from EPA's Web site at https://www.epa.gov/ttn/chief/ap42.html; Volume II can be downloaded from
https://www.epa.gov/omswww/ap42.htm.
90. Environmental Protection Agency, 2014. Draft Emissions Inventory
Guidance for Implementation of Ozone and Particulate Matter National
Ambient Air Quality Standards (NAAQS) and Regional Haze Regulations.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC. https://www.epa.gov/ttn/chief/eidocs/eiguid/2014revisedeiguidance.pdf.
91. Environmental Protection Agency, 1987. Ambient Air Monitoring
Guidelines for Prevention of Significant Deterioration (PSD).
Publication No. EPA-450/4-87-007. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. (NTIS No. PB 90-168030)
92. Environmental Protection Agency, 2011. Additional Clarification
Regarding Application of Appendix W Modeling Guidance for the 1-hour
NO2 National Ambient Air Quality Standard. Office of Air
Quality Planning & Standards, Research Triangle Park, NC. https://www.epa.gov/ttn/scram/guidance/clarification/Additional_Clarifications_AppendixW_Hourly-NO2-NAAQS_FINAL_03-01-2011.pdf.
93. Environmental Protection Agency, 2014. Modeling Guidance For
Demonstrating Attainment of Air Quality Goals for Ozone,
PM2.5, and Regional Haze (Draft). U.S. Office of Air
Quality Planning & Standards, Research Triangle Park, NC. https://www.epa.gov/ttn/scram/guidance/guide/Draft_O3-PM-RH_Modeling_Guidance-2014.pdf.
94. Environmental Protection Agency, 2013. Memorandum: Use of ASOS
meteorological data in AERMOD dispersion modeling. March 8, 2013.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC. https://www.epa.gov/ttn/scram/guidance/clarification/20130308_Met_Data_Clarification.pdf.
95. Environmental Protection Agency, 2004. User's Guide for the
AERMOD Meteorological Preprocessor (AERMET). Publication No. EPA-
454/B-03-002. Office of Air Quality Planning & Standards, Research
Triangle Park, NC. https://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet.
96. U.S Environmental Protection Agency. AERMINUTE User's Guide.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC. https://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet.
97. Environmental Protection Agency, 1993. PCRAMMET User's Guide.
Publication No. EPA-454/R-96-001. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. (NTIS No. PB 97-147912)
98. Environmental Protection Agency, 1996. Meteorological Processor
for Regulatory Models (MPRM). Publication No. EPA-454/R-96-002.
Office of Air Quality Planning & Standards, Research Triangle Park,
NC. (NTIS No. PB 96-180518)
99. Paine, R.J., 1987. User's Guide to the CTDM Meteorological
Preprocessor Program. Publication No. EPA-600/8-88-004. Office of
Research & Development, Research Triangle Park, NC. (NTIS No. PB-88-
162102)
100. Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis,
M.T. Mills, D.G. Strimaitis, R.J. Yamartino and E.M. Insley, 1989.
User's Guide to the Complex Terrain Dispersion Model Plus Algorithms
for Unstable Situations
[[Page 45382]]
(CTDMPLUS). Volume 1: Model Descriptions and User Instructions. EPA
Publication No. EPA-600/8-89-041. Environmental Protection Agency,
Research Triangle Park, NC. https://www.epa.gov/ttn/scram/dispersion_prefrec.htm#ctdmplus. (NTIS No. PB 89-181424)
101. Environmental Protection Agency, 2008. AERSURFACE User's Guide.
Publication No. EPA-454/B-08-001. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. https://www.epa.gov/ttn/scram/dispersion_related.htm#aersurface.
102. Wesson, K., R. Brode, W. Peters, and C. Tillerson, 2006. AERMOD
Sensitivity to the Choice of Surface Characteristics. A&WMA
presentation.
103. Environ, 2014. The Mesocale Model Interface Program (MMIF)
Version 3.1 User's Manual.
104. Environmental Protection Agency, 2015 Guidance on the Use of
the Mesoscale Model Interface Program (MMIF) for AERMOD
Applications. Publication No. EPA-454/B-15-001. Office of Air
Quality Planning & Standards, Research Triangle Park, NC.
105. Solar and Meteorological Surface Observation Network, 1961-
1990; 3-volume CD-ROM. Version 1.0, September 1993. Produced jointly
by National Climatic Data Center and National Renewable Energy
Laboratory. Can be ordered from NOAA National Data Center's Web site
at https://www.nndc.noaa.gov.
106. Hourly United States Weather Observations, 1990-1995 (CD-ROM).
October 1997. Produced jointly by National Climatic Data Center and
Environmental Protection Agency. Can be ordered from NOAA National
Data Center's Web site at https://www.ncdc.noaa.gov.
107. Radiosonde Data of North America, 1946-1996; 4-volume CD-ROM.
August1996. Produced jointly by Forecast Systems laboratory and
National Climatic Data Center. Can be ordered from NOAA National
Data Center's Web site at https://lwf.ncdc.noaa.gov/oa/ncdc.html.
108. Environmental Protection Agency, 2000. Meteorological
Monitoring Guidance for Regulatory Modeling Applications.
Publication No. EPA-454/R-99-005. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. (NTIS No. PB 2001-103606)
109. ASTM D5527: Standard Practice for Measuring Surface Winds and
Temperature by Acoustic Means. (2011)
110. ASTM D5741: Standard Practice for Characterizing Surface Wind
Using Wind Vane and Rotating Anemometer. (2011)
111. Environmental Protection Agency, 1995. Quality Assurance for
Air Pollution Measurement Systems, Volume IV-- Meteorological
Measurements. Publication No. EPA600/R-94/038d. Office of Air
Quality Planning & Standards, Research Triangle Park, NC. Note: For
copies of this handbook, you may make inquiry to ORD Publications,
26 West Martin Luther King Dr., Cincinnati, OH 45268.
112. Bowen, B.M., J.M. Dewart and A.I. Chen, 1983. Stability Class
Determination: A Comparison for One Site. Proceedings, Sixth
Symposium on Turbulence and Diffusion. American Meteorological
Society, Boston, MA; pp. 211-214. (Docket No. A-92-65, II-A-7)
113. Environmental Protection Agency, 1993. An Evaluation of a Solar
Radiation/Delta-T (SRDT) Method for Estimating Pasquill-Gifford (P-
G) Stability Categories. Publication No. EPA-454/R-93-055. Office of
Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS
No. PB 94-113958)
114. Irwin, J.S., 1980. Dispersion Estimate Suggestion #8:
Estimation of Pasquill Stability Categories. Office of Air Quality
Planning & Standards, Research Triangle Park, NC. (Docket No. A-80-
46, II-B-10)
115. Mitchell, Jr., A.E. and K.O. Timbre, 1979. Atmospheric
Stability Class from Horizontal Wind Fluctuation. Presented at 72nd
Annual Meeting of Air Pollution Control Association, Cincinnati, OH;
June 24-29, 1979. (Docket No. A-80-46, II-P-9)
116. Smedman--Hogstrom, A. and V. Hogstrom, 1978. A Practical Method
for Determining Wind Frequency Distributions for the Lowest 200m
from Routine Meteorological Data. Journal of Applied Meteorology,
17(7): 942-954.
117. Smith, T.B. and S.M. Howard, 1972. Methodology for Treating
Diffusivity. MRI 72 FR-1030. Meteorology Research, Inc., Altadena,
CA. (Docket No. A-80-46, II-P-8)
118. Environmental Protection Agency, 2015. Evaluation of Prognostic
Meteorological Data in AERMOD Applications. Publication No. EPA-454/
R-15-004. Office of Air Quality Planning & Standards, Research
Triangle Park, NC.
119. Environmental Protection Agency, 1984. Calms Processor
(CALMPRO) User's Guide. Publication No. EPA-901/9-84-001. Office Of
Air Quality Planning & Standards, Region I, Boston, MA. (NTIS No. PB
84-229467)
Appendix A to Appendix W of Part 51--Summaries of Preferred Air Quality
Models
Table of Contents
A.0 Introduction and Availability
A.1 AERMOD (AMS/EPA Regulatory Model)
A.2 CTDMPLUS (Complex Terrain Dispersion Model Plus Algorithms for
Unstable Situations)
A.3 OCD (Offshore and Coastal Dispersion Model)
A.0 Introduction and Availability
(1) This appendix summarizes key features of refined air quality
models preferred for specific regulatory applications. For each
model, information is provided on availability, approximate cost
(where applicable), regulatory use, data input, output format and
options, simulation of atmospheric physics, and accuracy. These
models may be used without a formal demonstration of applicability
provided they satisfy the recommendations for regulatory use; not
all options in the models are necessarily recommended for regulatory
use.
(2) Many of these models have been subjected to a performance
evaluation using comparisons with observed air quality data. Where
possible, several of the models contained herein have been subjected
to evaluation exercises, including (1) statistical performance tests
recommended by the American Meteorological Society and (2) peer
scientific reviews. The models in this appendix have been selected
on the basis of the results of the model evaluations, experience
with previous use, familiarity of the model to various air quality
programs, and the costs and resource requirements for use.
(3) Codes and documentation for all models listed in this
appendix are available from the EPA's Support Center for Regulatory
Air Models (SCRAM) Web site at https://www.epa.gov/ttn/scram. Codes
and documentation may also available from the National Technical
Information Service (NTIS), https://www.ntis.gov, and, when
available, is referenced with the appropriate NTIS accession number.
A.1 AERMOD (AMS/EPA Regulatory Model)
References
Environmental Protection Agency, 2004. AERMOD: Description of Model
Formulation. Publication No. EPA-454/R-03-004. Office of Air Quality
Planning & Standards, Research Triangle Park, NC; September 2004.
https://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdf.
Cimorelli, A., et al., 2005. AERMOD: A Dispersion Model for
Industrial Source Applications. Part I: General Model Formulation
and Boundary Layer Characterization. Journal of Applied Meteorology,
44(5): 682-693.
Perry, S. et al., 2005. AERMOD: A Dispersion Model for Industrial
Source Applications. Part II: Model Performance against 17 Field
Study Databases. Journal of Applied Meteorology, 44(5): 694-708.
Environmental Protection Agency, 2004. User's Guide for the AMS/EPA
Regulatory Model--AERMOD. Publication No. EPA-454/B-03-001. Office
of Air Quality Planning & Standards, Research Triangle Park, NC;
September 2004. https://www.epa.gov/ttn/scram/dispersion_prefrec.htm#aermod.
Environmental Protection Agency, 2004. User's Guide for the AERMOD
Meteorological Preprocessor (AERMET). Publication No. EPA-454/B-03-
002. Office of Air Quality Planning & Standards, Research Triangle
Park, NC; November 2004. https://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet.
User's Guide for the AERMOD Terrain Preprocessor (AERMAP).
Publication No. EPA-454/B-03-003. Office of Air Quality Planning &
Standards, Research Triangle Park, NC; October 2004.
[[Page 45383]]
https://www.epa.gov/ttn/scram/dispersion_related.htm#aermap.
Schulman, L. L., D.G. Strimaitis and J.S. Scire, 2000. Development
and evaluation of the PRIME plume rise and building downwash model.
Journal of the Air and Waste Management Association, 50: 378-390.
Schulman, L. L., and Joseph S. Scire, 1980. Buoyant Line and Point
Source (BLP) Dispersion Model User's Guide. Document P-7304B.
Environmental Research and Technology, Inc., Concord, MA. (NTIS No.
PB 81-164642).
Availability
The model codes and associated documentation are available on
EPA's SCRAM Web site (paragraph A.0(3)).
Abstract
AERMOD is a steady-state plume dispersion model for assessment
of pollutant concentrations from a variety of sources. AERMOD
simulates transport and dispersion from multiple point, area, or
volume sources based on an up-to-date characterization of the
atmospheric boundary layer. Sources may be located in rural or urban
areas, and receptors may be located in simple or complex terrain.
AERMOD accounts for building wake effects (i.e., plume downwash)
based on the PRIME building downwash algorithms. The model employs
hourly sequential preprocessed meteorological data to estimate
concentrations for averaging times from 1-hour to 1-year (also
multiple years). AERMOD can be used to estimate the concentrations
of nonreactive pollutants from highway traffic. AERMOD also handles
unique modeling problems associated with aluminum reduction plants,
and other industrial sources where plume rise and downwash effects
from stationary buoyant line sources are important. AERMOD is
designed to operate in concert with two pre-processor codes: AERMET
processes meteorological data for input to AERMOD, and AERMAP
processes terrain elevation data and generates receptor and hill
height information for input to AERMOD.
a. Recommendations for Regulatory Use
(1) AERMOD is appropriate for the following applications:
Point, volume, and area sources;
Buoyant, elevated line sources (e.g., aluminum
reduction plants);
Mobile (line) sources;
Surface, near-surface, and elevated releases;
Rural or urban areas;
Simple and complex terrain;
Transport distances over which steady- state
assumptions are appropriate, up to 50km;
1-hour to annual averaging times; and
Continuous toxic air emissions.
(2) For regulatory applications of AERMOD, the regulatory
default option should be set, i.e., the parameter DFAULT should be
employed in the MODELOPT record in the Control Pathway. The DFAULT
option requires the use of terrain elevation data, stack-tip
downwash, sequential date checking, and does not permit the use of
the model in the SCREEN mode. In the regulatory default mode,
pollutant half-life or decay options are not employed, except in the
case of an urban source of sulfur dioxide where a four-hour half-
life is applied. Terrain elevation data from the U.S. Geological
Survey 7.5-Minute Digital Elevation Model (DEM) or equivalent
(approx. 30-meter resolution) should be used in all applications.
Starting in 2011, data from the National Elevation Dataset (NED,
https://ned.usgs.gov) can also be used in AERMOD, which includes a
range of resolutions, ranging from 1-m to 2 arc seconds and such
high resolution would always be preferred. In some cases, exceptions
of the terrain data requirement may be made in consultation with the
appropriate reviewing authority (paragraph 3.0(b)).
b. Input Requirements
(1) Source data: Required input includes source type, location,
emission rate, stack height, stack inside diameter, stack gas exit
velocity, stack gas temperature, area and volume source dimensions,
and source elevation. Building dimensions and variable emission
rates are optional. Buoyant line sources require coordinates of the
end points of the line, release height, emission rate, average line
source width, average building width, average spacing between
buildings, and average line source buoyancy parameter. For mobile
sources, traffic volume; emission factor, source height, and mixing
zone width are needed.
(2) Meteorological data: The AERMET meteorological preprocessor
requires input of surface characteristics, including surface
roughness (zo), Bowen ratio, and albedo, as well as, hourly
observations of wind speed between 7zo and 100m (reference wind
speed measurement from which a vertical profile can be developed),
wind direction, cloud cover, and temperature between zo and 100m
(reference temperature measurement from which a vertical profile can
be developed). Meteorological data can be in the form of observed
data or prognostic modeled data as discussed in paragraph 8.4.1(d).
Surface characteristics may be varied by wind sector and by season
or month. When using observed meteorological data, a morning
sounding (in National Weather Service format) from a representative
upper air station is required. Latitude, longitude, and time zone of
the surface, site-specific (if applicable) and upper air
meteorological stations are required. The wind speed starting
threshold is also required in AERMET for applications involving
site-specific data). When using prognostic data, modeled profiles of
temperature and winds are input into AERMET. These can be hourly or
a time that represents a morning sounding. Additionally, measured
profiles of wind, temperature, vertical and lateral turbulence may
be required in certain applications (e.g., in complex terrain) to
adequately represent the meteorology affecting plume transport and
dispersion. Optionally, measurements of solar, or net radiation may
be input to AERMET. Two files are produced by the AERMET
meteorological preprocessor for input to the AERMOD dispersion
model. When using observed data, the surface file contains observed
and calculated surface variables, one record per hour. For
applications with multi-level site-specific meteorological data, the
profile contains the observations made at each level of the
meteorological tower (or remote sensor). When using prognostic data,
the surface file contains surface variables calculated by the
prognostic model and AERMET. The profile file contains the
observations made at each level of a meteorological tower (or remote
sensor), the one-level observations taken from other representative
data (e.g., National Weather Service surface observations), one
record per level per hour, or in the case of prognostic data, the
prognostic modeled values of temperature and winds at user-specified
levels.
(i) Data used as input to AERMET should possess an adequate
degree of representativeness to insure that the wind, temperature
and turbulence profiles derived by AERMOD are both laterally and
vertically representative of the source area. The adequacy of input
data should be judged independently for each variable. The values
for surface roughness, Bowen ratio, and albedo should reflect the
surface characteristics in the vicinity of the meteorological tower
or representative grid cell when using prognostic data, and should
be adequately representative of the modeling domain. Finally, the
primary atmospheric input variables including wind speed and
direction, ambient temperature, cloud cover, and a morning upper air
sounding should also be adequately representative of the source
area, when using observed data.
(ii) For recommendations regarding the length of meteorological
record needed to perform a regulatory analysis with AERMOD, see
section 8.4.2.
(3) Receptor data: Receptor coordinates, elevations, height
above ground, and hill height scales are produced by the AERMAP
terrain preprocessor for input to AERMOD. Discrete receptors and/or
multiple receptor grids, Cartesian and/or polar, may be employed in
AERMOD. AERMAP requires input of DEM terrain data produced by the
U.S. Geological Survey (USGS), or other equivalent data. AERMAP can
be used optionally to estimate source elevations.
c. Output
Printed output options include input information, high
concentration summary tables by receptor for user-specified
averaging periods, maximum concentration summary tables, and
concurrent values summarized by receptor for each day processed.
Optional output files can be generated for: A listing of occurrences
of exceedances of user-specified threshold value; a listing of
concurrent (raw) results at each receptor for each hour modeled,
suitable for post-processing; a listing of design values that can be
imported into graphics software for plotting contours; a listing of
results suitable for NAAQS analyses including NAAQS exceedances and
culpability analyses; an unformatted listing of raw results above a
threshold value with a special structure for use with the TOXX model
component of TOXST; a listing of concentrations by rank (e.g., for
use in quantile-quantile plots); and, a listing of concentrations,
including arc-maximum
[[Page 45384]]
normalized concentrations, suitable for model evaluation studies.
d. Type of Model
AERMOD is a steady-state plume model, using Gaussian
distributions in the vertical and horizontal for stable conditions,
and in the horizontal for convective conditions. The vertical
concentration distribution for convective conditions results from an
assumed bi-Gaussian probability density function of the vertical
velocity.
e. Pollutant Types
AERMOD is applicable to primary pollutants and continuous
releases of toxic and hazardous waste pollutants. Chemical
transformation is treated by simple exponential decay.
f. Source-Receptor Relationships
AERMOD applies user-specified locations for sources and
receptors. Actual separation between each source-receptor pair is
used. Source and receptor elevations are user input or are
determined by AERMAP using USGS DEM terrain data. Receptors may be
located at user-specified heights above ground level.
g. Plume Behavior
(1) In the convective boundary layer (CBL), the transport and
dispersion of a plume is characterized as the superposition of three
modeled plumes: The direct plume (from the stack), the indirect
plume, and the penetrated plume, where the indirect plume accounts
for the lofting of a buoyant plume near the top of the boundary
layer, and the penetrated plume accounts for the portion of a plume
that, due to its buoyancy, penetrates above the mixed layer, but can
disperse downward and re-enter the mixed layer. In the CBL, plume
rise is superposed on the displacements by random convective
velocities (Weil et al., 1997).
(2) In the stable boundary layer, plume rise is estimated using
an iterative approach to account for height-dependent lapse rates,
similar to that in the CTDMPLUS model (see A.2 in this appendix).
(3) Stack-tip downwash and buoyancy induced dispersion effects
are modeled. Building wake effects are simulated for stacks subject
to building downwash using the methods contained in the PRIME
downwash algorithms (Schulman, et al., 2000). For plume rise
affected by the presence of a building, the PRIME downwash algorithm
uses a numerical solution of the mass, energy and momentum
conservation laws (Zhang and Ghoniem, 1993). Streamline deflection
and the position of the stack relative to the building affect plume
trajectory and dispersion. Enhanced dispersion is based on the
approach of Weil (1996). Plume mass captured by the cavity is well-
mixed within the cavity. The captured plume mass is re-emitted to
the far wake as a volume source.
(4) For elevated terrain, AERMOD incorporates the concept of the
critical dividing streamline height, in which flow below this height
remains horizontal, and flow above this height tends to rise up and
over terrain (Snyder et al., 1985). Plume concentration estimates
are the weighted sum of these two limiting plume states. However,
consistent with the steady-state assumption of uniform horizontal
wind direction over the modeling domain, straight-line plume
trajectories are assumed, with adjustment in the plume/receptor
geometry used to account for the terrain effects.
h. Horizontal Winds
Vertical profiles of wind are calculated for each hour based on
measurements and surface-layer similarity (scaling) relationships.
At a given height above ground, for a given hour, winds are assumed
constant over the modeling domain. The effect of the vertical
variation in horizontal wind speed on dispersion is accounted for
through simple averaging over the plume depth.
i. Vertical Wind Speed
In convective conditions, the effects of random vertical updraft
and downdraft velocities are simulated with a bi-Gaussian
probability density function. In both convective and stable
conditions, the mean vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Gaussian horizontal dispersion coefficients are estimated as
continuous functions of the parameterized (or measured) ambient
lateral turbulence and also account for buoyancy- induced and
building wake-induced turbulence. Vertical profiles of lateral
turbulence are developed from measurements and similarity (scaling)
relationships. Effective turbulence values are determined from the
portion of the vertical profile of lateral turbulence between the
plume height and the receptor height. The effective lateral
turbulence is then used to estimate horizontal dispersion.
k. Vertical Dispersion
In the stable boundary layer, Gaussian vertical dispersion
coefficients are estimated as continuous functions of parameterized
vertical turbulence. In the convective boundary layer, vertical
dispersion is characterized by a bi-Gaussian probability density
function, and is also estimated as a continuous function of
parameterized vertical turbulence. Vertical turbulence profiles are
developed from measurements and similarity (scaling) relationships.
These turbulence profiles account for both convective and mechanical
turbulence. Effective turbulence values are determined from the
portion of the vertical profile of vertical turbulence between the
plume height and the receptor height. The effective vertical
turbulence is then used to estimate vertical dispersion.
l. Chemical Transformation
Chemical transformations are generally not treated by AERMOD.
However, AERMOD does contain an option to treat chemical
transformation using simple exponential decay, although this option
is typically not used in regulatory applications, except for sources
of sulfur dioxide in urban areas. Either a decay coefficient or a
half-life is input by the user. Note also that the Plume Volume
Molar Ratio Method and the Ozone Limiting Method (section 4.2.3.4)
and for point-source NO2 analyses are available.
m. Physical Removal
AERMOD can be used to treat dry and wet deposition for both
gases and particles.
n. Evaluation Studies
American Petroleum Institute, 1998. Evaluation of State of the
Science of Air Quality Dispersion Model, Scientific Evaluation,
prepared by Woodward-Clyde Consultants, Lexington, Massachusetts,
for American Petroleum Institute, Washington, DC, 20005-4070.
Brode, R.W., 2002. Implementation and Evaluation of PRIME in AERMOD.
Preprints of the 12th Joint Conference on Applications of Air
Pollution Meteorology, May 20-24, 2002; American Meteorological
Society, Boston, MA.
Brode, R.W., 2004. Implementation and Evaluation of Bulk Richardson
Number Scheme in AERMOD. 13th Joint Conference on Applications of
Air Pollution Meteorology, August 23-26, 2004; American
Meteorological Society, Boston, MA.
Environmental Protection Agency, 2003. AERMOD: Latest Features and
Evaluation Results. Publication No. EPA-454/R-03-003. Office of Air
Quality Planning & Standards, Research Triangle Park, NC. https://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mep.pdf.
Heist, D., et al, 2013. Estimating near-road pollutant dispersion: A
model inter-comparison. Transportation Research Part D: Transport
and Environment, 25: pp 93-105.
A.2 CTDMPLUS (Complex Terrain Dispersion Model Plus Algorithms for
Unstable Situations)
References
Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T.
Mills, D.G. Strimaitis, R.J. Yamartino and E.M. Insley, 1989. User's
Guide to the Complex Terrain Dispersion Model Plus Algorithms for
Unstable Situations (CTDMPLUS). Volume 1: Model Descriptions and
User Instructions. EPA Publication No. EPA-600/8-89-041.
Environmental Protection Agency, Research Triangle Park, NC. https://www.epa.gov/ttn/scram/dispersion_prefrec.htm#ctdmplus. (NTIS No. PB
89-181424)
Perry, S.G., 1992. CTDMPLUS: A Dispersion Model for Sources near
Complex Topography. Part I: Technical Formulations. Journal of
Applied Meteorology, 31(7): 633-645.
Availability
The model codes and associated documentation are available on
the EPA's SCRAM Web site (paragraph A.0(3)).
Abstract
CTDMPLUS is a refined point source Gaussian air quality model
for use in all stability conditions for complex terrain
applications. The model contains, in its entirety, the technology of
CTDM for stable and neutral conditions. However, CTDMPLUS can also
simulate daytime, unstable conditions, and has a number of
additional capabilities for improved user friendliness. Its use of
meteorological data
[[Page 45385]]
and terrain information is different from other EPA models;
considerable detail for both types of input data is required and is
supplied by preprocessors specifically designed for CTDMPLUS.
CTDMPLUS requires the parameterization of individual hill shapes
using the terrain preprocessor and the association of each model
receptor with a particular hill.
a. Recommendation for Regulatory Use
CTDMPLUS is appropriate for the following applications:
Elevated point sources;
Terrain elevations above stack top;
Rural or urban areas;
Transport distances less than 50 kilometers; and
1-hour to annual averaging times when used with a post-
processor program such as CHAVG.
b. Input Requirements
(1) Source data: For each source, user supplies source location,
height, stack diameter, stack exit velocity, stack exit temperature,
and emission rate; if variable emissions are appropriate, the user
supplies hourly values for emission rate, stack exit velocity, and
stack exit temperature.
(2) Meteorological data: For applications of CTDMPLUS, multiple
level (typically three or more) measurements of wind speed and
direction, temperature and turbulence (wind fluctuation statistics)
are required to create the basic meteorological data file
(``PROFILE''). Such measurements should be obtained up to the
representative plume height(s) of interest (i.e., the plume
height(s) under those conditions important to the determination of
the design concentration). The representative plume height(s) of
interest should be determined using an appropriate complex terrain
screening procedure (e.g., CTSCREEN) and should be documented in the
monitoring/modeling protocol. The necessary meteorological
measurements should be obtained from an appropriately sited
meteorological tower augmented by SODAR and/or RASS if the
representative plume height(s) of interest is above the levels
represented by the tower measurements. Meteorological preprocessors
then create a SURFACE data file (hourly values of mixed layer
heights, surface friction velocity, Monin-Obukhov length and surface
roughness length) and a RAWINsonde data file (upper air measurements
of pressure, temperature, wind direction, and wind speed).
(3) Receptor data: Receptor names (up to 400) and coordinates,
and hill number (each receptor must have a hill number assigned).
(4) Terrain data: User inputs digitized contour information to
the terrain preprocessor which creates the TERRAIN data file (for up
to 25 hills).
c. Output
(1) When CTDMPLUS is run, it produces a concentration file, in
either binary or text format (user's choice), and a list file
containing a verification of model inputs, i.e.,
Input meteorological data from ``SURFACE'' and
``PROFILE'',
Stack data for each source,
Terrain information,
Receptor information, and
Source-receptor location (line printer map).
(2) In addition, if the case-study option is selected, the
listing includes:
Meteorological variables at plume height,
Geometrical relationships between the source and the
hill, and
Plume characteristics at each receptor, i.e.,
--Distance in along-flow and cross flow direction
--Effective plume-receptor height difference
--Effective [sigma]y & [sigma]z values, both flat terrain and hill
induced (the difference shows the effect of the hill)
--Concentration components due to WRAP, LIFT and FLAT
(3) If the user selects the TOPN option, a summary table of the
top four concentrations at each receptor is given. If the ISOR
option is selected, a source contribution table for every hour will
be printed.
(4) A separate output file of predicted (1-hour only)
concentrations (``CONC'') is written if the user chooses this
option. Three forms of output are possible:
(i) A binary file of concentrations, one value for each receptor
in the hourly sequence as run;
(ii) A text file of concentrations, one value for each receptor
in the hourly sequence as run; or
(iii) A text file as described above, but with a listing of
receptor information (names, positions, hill number) at the
beginning of the file.
(5) Hourly information provided to these files besides the
concentrations themselves includes the year, month, day, and hour
information as well as the receptor number with the highest
concentration.
d. Type of Model
CTDMPLUS is a refined steady-state, point source plume model for
use in all stability conditions for complex terrain applications.
e. Pollutant Types
CTDMPLUS may be used to model non- reactive, primary pollutants.
f. Source-Receptor Relationship
Up to 40 point sources, 400 receptors and 25 hills may be used.
Receptors and sources are allowed at any location. Hill slopes are
assumed not to exceed 15[deg], so that the linearized equation of
motion for Boussinesq flow are applicable. Receptors upwind of the
impingement point, or those associated with any of the hills in the
modeling domain, require separate treatment.
g. Plume Behavior
(1) As in CTDM, the basic plume rise algorithms are based on
Briggs' (1975) recommendations.
(2) A central feature of CTDMPLUS for neutral/stable conditions
is its use of a critical dividing-streamline height (Hc)
to separate the flow in the vicinity of a hill into two separate
layers. The plume component in the upper layer has sufficient
kinetic energy to pass over the top of the hill while streamlines in
the lower portion are constrained to flow in a horizontal plane
around the hill. Two separate components of CTDMPLUS compute ground-
level concentrations resulting from plume material in each of these
flows.
(3) The model calculates on an hourly (or appropriate steady
averaging period) basis how the plume trajectory (and, in stable/
neutral conditions, the shape) is deformed by each hill. Hourly
profiles of wind and temperature measurements are used by CTDMPLUS
to compute plume rise, plume penetration (a formulation is included
to handle penetration into elevated stable layers, based on Briggs
(1984)), convective scaling parameters, the value of Hc,
and the Froude number above Hc.
h. Horizontal Winds
CTDMPLUS does not simulate calm meteorological conditions. Both
scalar and vector wind speed observations can be read by the model.
If vector wind speed is unavailable, it is calculated from the
scalar wind speed. The assignment of wind speed (either vector or
scalar) at plume height is done by either:
Interpolating between observations above and below the
plume height, or
Extrapolating (within the surface layer) from the
nearest measurement height to the plume height.
i. Vertical Wind Speed
Vertical flow is treated for the plume component above the
critical dividing streamline height (Hc); see ``Plume
Behavior.''
j. Horizontal Dispersion
Horizontal dispersion for stable/neutral conditions is related
to the turbulence velocity scale for lateral fluctuations, [sigma]v,
for which a minimum value of 0.2 m/s is used. Convective scaling
formulations are used to estimate horizontal dispersion for unstable
conditions.
k. Vertical Dispersion
Direct estimates of vertical dispersion for stable/neutral
conditions are based on observed vertical turbulence intensity,
e.g., [sigma]w (standard deviation of the vertical velocity
fluctuation). In simulating unstable (convective) conditions,
CTDMPLUS relies on a skewed, bi-Gaussian probability density
function (pdf) description of the vertical velocities to estimate
the vertical distribution of pollutant concentration.
l. Chemical Transformation
Chemical transformation is not treated by CTDMPLUS.
m. Physical Removal
Physical removal is not treated by CTDMPLUS (complete reflection
at the ground/hill surface is assumed).
n. Evaluation Studies
Burns, D.J., L.H. Adams and S.G. Perry, 1990. Testing and Evaluation
of the CTDMPLUS Dispersion Model: Daytime Convective Conditions.
Environmental Protection Agency, Research Triangle Park, NC.
Paumier, J.O., S.G. Perry and D.J. Burns, 1990. An Analysis of
CTDMPLUS Model Predictions with the Lovett Power Plant Data Base.
Environmental Protection Agency, Research Triangle Park, NC.
[[Page 45386]]
Paumier, J.O., S.G. Perry and D.J. Burns, 1992. CTDMPLUS: A
Dispersion Model for Sources near Complex Topography. Part II:
Performance Characteristics. Journal of Applied Meteorology, 31(7):
646-660.
A.3 OCD (Offshore and Coastal Dispersion Model)
Reference
DiCristofaro, D.C. and S.R. Hanna, 1989. OCD: The Offshore and
Coastal Dispersion Model, Version 4. Volume I: User's Guide, and
Volume II: Appendices. Sigma Research Corporation, Westford, MA.
https://www.epa.gov/ttn/scram/dispersion_prefrec.htm#ocd. (NTIS Nos.
PB 93-144384 and PB 93-144392)
Availability
The model codes and associated documentation are available on
EPA's SCRAM Web site (paragraph A.0(3)). Official contact at
Minerals Management Service: Mr. Dirk Herkhof, Parkway Atrium
Building, 381 Elden Street, Herndon, VA 20170, Phone: (703) 787-
1735.
Abstract
(1) OCD is a straight-line Gaussian model developed to determine
the impact of offshore emissions from point, area or line sources on
the air quality of coastal regions. OCD incorporates overwater plume
transport and dispersion as well as changes that occur as the plume
crosses the shoreline. Hourly meteorological data are needed from
both offshore and onshore locations. These include water surface
temperature, overwater air temperature, mixing height, and relative
humidity.
(2) Some of the key features include platform building downwash,
partial plume penetration into elevated inversions, direct use of
turbulence intensities for plume dispersion, interaction with the
overland internal boundary layer, and continuous shoreline
fumigation.
a. Recommendations for Regulatory Use
OCD has been recommended for use by the Minerals Management
Service for emissions located on the Outer Continental Shelf (50 FR
12248; 28 March 1985). OCD is applicable for overwater sources where
onshore receptors are below the lowest source height. Where onshore
receptors are above the lowest source height, offshore plume
transport and dispersion may be modeled on a case-by-case basis in
consultation with the appropriate reviewing authority (paragraph
3.0(b)).
b. Input Requirements
(1) Source data: Point, area or line source location, pollutant
emission rate, building height, stack height, stack gas temperature,
stack inside diameter, stack gas exit velocity, stack angle from
vertical, elevation of stack base above water surface and gridded
specification of the land/water surfaces. As an option, emission
rate, stack gas exit velocity and temperature can be varied hourly.
(2) Meteorological data (over water): Wind direction, wind
speed, mixing height, relative humidity, air temperature, water
surface temperature, vertical wind direction shear (optional),
vertical temperature gradient (optional), turbulence intensities
(optional).
(3) Meteorological data:
Over land: Surface weather data from a preprocessor such as
PCRAMMET which provides hourly stability class, wind direction, wind
speed, ambient temperature, and mixing height are required.
Over water: Hourly values for mixing height, relative humidity,
air temperature, and water surface temperature are required; if wind
speed/direction are missing, values over land will be used (if
available); vertical wind direction shear, vertical temperature
gradient, and turbulence intensities are optional.
(4) Receptor data: Location, height above local ground-level,
ground-level elevation above the water surface.
c. Output
(1) All input options, specification of sources, receptors and
land/water map including locations of sources and receptors.
(2) Summary tables of five highest concentrations at each
receptor for each averaging period, and average concentration for
entire run period at each receptor.
(3) Optional case study printout with hourly plume and receptor
characteristics. Optional table of annual impact assessment from
non-permanent activities.
(4) Concentration output files can be used by ANALYSIS
postprocessor to produce the highest concentrations for each
receptor, the cumulative frequency distributions for each receptor,
the tabulation of all concentrations exceeding a given threshold,
and the manipulation of hourly concentration files.
d. Type of Model
OCD is a Gaussian plume model constructed on the framework of
the MPTER model.
e. Pollutant Types
OCD may be used to model primary pollutants. Settling and
deposition are not treated.
f. Source-Receptor Relationship
(1) Up to 250 point sources, 5 area sources, or 1 line source
and 180 receptors may be used.
(2) Receptors and sources are allowed at any location.
(3) The coastal configuration is determined by a grid of up to
3600 rectangles. Each element of the grid is designated as either
land or water to identify the coastline.
g. Plume Behavior
(1) As in ISC, the basic plume rise algorithms are based on
Briggs' recommendations.
(2) Momentum rise includes consideration of the stack angle from
the vertical.
(3) The effect of drilling platforms, ships, or any overwater
obstructions near the source are used to decrease plume rise using a
revised platform downwash algorithm based on laboratory experiments.
(4) Partial plume penetration of elevated inversions is included
using the suggestions of Briggs (1975) and Weil and Brower (1984).
(5) Continuous shoreline fumigation is parameterized using the
Turner method where complete vertical mixing through the thermal
internal boundary layer (TIBL) occurs as soon as the plume
intercepts the TIBL.
h. Horizontal Winds
(1) Constant, uniform wind is assumed for each hour.
(2) Overwater wind speed can be estimated from overland wind
speed using relationship of Hsu (1981).
(3) Wind speed profiles are estimated using similarity theory
(Businger, 1973). Surface layer fluxes for these formulas are
calculated from bulk aerodynamic methods.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
(1) Lateral turbulence intensity is recommended as a direct
estimate of horizontal dispersion. If lateral turbulence intensity
is not available, it is estimated from boundary layer theory. For
wind speeds less than 8 m/s, lateral turbulence intensity is assumed
inversely proportional to wind speed.
(2) Horizontal dispersion may be enhanced because of
obstructions near the source. A virtual source technique is used to
simulate the initial plume dilution due to downwash.
(3) Formulas recommended by Pasquill (1976) are used to
calculate buoyant plume enhancement and wind direction shear
enhancement.
(4) At the water/land interface, the change to overland
dispersion rates is modeled using a virtual source. The overland
dispersion rates can be calculated from either lateral turbulence
intensity or Pasquill-Gifford curves. The change is implemented
where the plume intercepts the rising internal boundary layer.
k. Vertical Dispersion
(1) Observed vertical turbulence intensity is not recommended as
a direct estimate of vertical dispersion. Turbulence intensity
should be estimated from boundary layer theory as default in the
model. For very stable conditions, vertical dispersion is also a
function of lapse rate.
(2) Vertical dispersion may be enhanced because of obstructions
near the source. A virtual source technique is used to simulate the
initial plume dilution due to downwash.
(3) Formulas recommended by Pasquill (1976) are used to
calculate buoyant plume enhancement.
(4) At the water/land interface, the change to overland
dispersion rates is modeled using a virtual source. The overland
dispersion rates can be calculated from either vertical turbulence
intensity or the Pasquill-Gifford coefficients. The change is
implemented where the plume intercepts the rising internal boundary
layer.
l. Chemical Transformation
Chemical transformations are treated using exponential decay.
Different rates can be specified by month and by day or night.
m. Physical Removal
Physical removal is also treated using exponential decay.
[[Page 45387]]
n. Evaluation Studies
DiCristofaro, D.C. and S.R. Hanna, 1989. OCD: The Offshore and
Coastal Dispersion Model. Volume I: User's Guide. Sigma Research
Corporation, Westford, MA.
Hanna, S.R., L.L. Schulman, R.J. Paine and J.E. Pleim, 1984. The
Offshore and Coastal Dispersion (OCD) Model User's Guide, Revised.
OCS Study, MMS 84-0069. Environmental Research & Technology, Inc.,
Concord, MA. (NTIS No. PB 86-159803).
Hanna, S.R., L.L. Schulman, R.J. Paine, J.E. Pleim and M. Baer,
1985. Development and Evaluation of the Offshore and Coastal
Dispersion (OCD) Model. Journal of the Air Pollution Control
Association, 35: 1039-1047.
Hanna, S.R. and D.C. DiCristofaro, 1988. Development and Evaluation
of the OCD/API Model. Final Report, API Pub. 4461, American
Petroleum Institute, Washington, DC.
[FR Doc. 2015-18075 Filed 7-28-15; 8:45 am]
BILLING CODE 6560-50-P
