Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions, 68218-68261 [05-21627]
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telephone (919) 541–5562.
(Fox.Tyler@epa.gov).
ENVIRONMENTAL PROTECTION
AGENCY
SUPPLEMENTARY INFORMATION:
40 CFR Part 51
[AH–FRL–7990–9]
RIN 2060–AK60
Revision to the Guideline on Air
Quality Models: Adoption of a
Preferred General Purpose (Flat and
Complex Terrain) Dispersion Model
and Other Revisions
Environmental Protection
Agency (EPA).
ACTION: Final rule.
AGENCY:
EPA’s Guideline on Air
Quality Models (‘‘Guideline’’) addresses
the regulatory application of air quality
models for assessing criteria pollutants
under the Clean Air Act. In today’s
action we promulgate several additions
and changes to the Guideline. We
recommend a new dispersion model—
AERMOD—for adoption in appendix A
of the Guideline. AERMOD replaces the
Industrial Source Complex (ISC3)
model, applies to complex terrain, and
incorporates a new downwash
algorithm—PRIME. We remove an
existing model—the Emissions
Dispersion Modeling System (EDMS)—
from appendix A. We also make various
editorial changes to update and
reorganize information.
DATES: This rule is effective December 9,
2005. As proposed, beginning November
9, 2006, the new model—AERMOD—
should be used for appropriate
application as replacement for ISC3.
During the one-year period following
this promulgation, protocols for
modeling analyses based on ISC3 which
are submitted in a timely manner may
be approved at the discretion of the
appropriate Reviewing Authority.
Applicants are therefore encouraged to
consult with the Reviewing Authority as
soon as possible to assure acceptance
during this period.
ADDRESSES: All documents relevant to
this rule have been placed in Docket No.
A–99–05 at the following address: Air
Docket in the EPA Docket Center, (EPA/
DC) EPA West (MC 6102T), 1301
Constitution Ave., NW., Washington,
DC 20004. This docket is available for
public inspection and copying between
8 a.m. and 5:30 p.m., Monday through
Friday, at the address above.
FOR FURTHER INFORMATION CONTACT:
Tyler J. Fox, Air Quality Modeling
Group (MD–D243–01), Office of Air
Quality Planning and Standards, U.S.
Environmental Protection Agency,
Research Triangle Park, NC 27711;
SUMMARY:
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Outline
I. General Information
II. Background
III. Public Hearing on the April 2000
proposal
IV. Discussion of Public Comments and
Issues from our April 21, 2000 Proposal
A. AERMOD and PRIME
B. Appropriate for Proposed Use
C. Implementation Issues/Additional
Guidance
D. AERMOD revision and reanalyses in
2003
1. Performance analysis for AERMOD
(02222)
a. Non-downwash cases: AERMOD (99351)
vs. AERMOD (02222)
b. Downwash cases
2. Analysis of regulatory design
concentrations for AERMOD (02222)
a. Non-downwash cases
b. Downwash cases
c. Complex terrain
E. Emission and Dispersion Modeling
System (EDMS)
V. Discussion of Public Comments and Issues
from our September 8, 2003 Notice of
Data Availability
VI. Final action
VII. Final editorial changes to appendix W
VIII. Statutory and Executive Order Reviews
I. General Information
A. How Can I Get Copies of Related
Information?
EPA established an official public
docket for this action under Docket No.
A–99–05. The official public docket is
the collection of materials that is
available for public viewing at the Air
Docket in the EPA Docket Center, (EPA/
DC) EPA West (MC 6102T), 1301
Constitution Ave., NW., Washington,
DC 20004. The EPA Docket Center
Public Reading Room (B102) is open
from 8:30 a.m. to 4:30 p.m., Monday
through Friday, excluding legal
holidays. The telephone number for the
Reading Room is (202) 566–1744, and
the telephone number for the Air Docket
is (202) 566–1742. An electronic image
of this docket may be accessed via
Internet at www.epa.gov/eDocket, where
Docket No. A–99–05 is indexed as
OAR–2003–0201. Materials related to
our Notice of Data Availability
(published September 8, 2003) and
public comments received pursuant to
the notice were placed in eDocket OAR–
2003–0201.1
Our Air Quality Modeling Group
maintain an Internet website (Support
Center for Regulatory Air Models—
1 https://cascade.epa.gov/RightSite/
dk_public_collection_detail.htm?
ObjectType=dk_docket_collection&cid=OAR-20030201&ShowList=items&Action=view.
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SCRAM) at: www.epa.gov/scram001.
You may find codes and documentation
for models referenced in today’s action
on the SCRAM Web site. We have also
uploaded various support documents
(e.g., evaluation reports).
II. Background
The Guideline is used by EPA, States,
and industry to prepare and review new
source permits and State
Implementation Plan revisions. The
Guideline is intended to ensure
consistent air quality analyses for
activities regulated at 40 CFR 51.112,
51.117, 51.150, 51.160, 51.166, and
52.21. We originally published the
Guideline in April 1978 and it was
incorporated by reference in the
regulations for the Prevention of
Significant Deterioration (PSD) of Air
Quality in June 1978. We revised the
Guideline in 1986, and updated it with
supplement A in 1987, supplement B in
July 1993, and supplement C in August
1995. We published the Guideline as
appendix W to 40 CFR part 51 when we
issued supplement B. We republished
the Guideline in August 1996 (61 FR
41838) to adopt the CFR system for
labeling paragraphs. On April 21, 2000
we issued a Notice of Proposed
Rulemaking (NPR) in the Federal
Register (65 FR 21506), which was the
original proposal for today’s
promulgation.
III. Public Hearing on the April 2000
Proposal
We held the 7th Conference on Air
Quality Modeling (7th conference) in
Washington, DC on June 28–29, 2000.
As required by Section 320 of the Clean
Air Act, these conferences take place
approximately every three years to
standardize modeling procedures, with
special attention given to appropriate
modeling practices for carrying out
programs PSD (42 U.S.C. 7620). This
conference served as the forum for
receiving public comments on the
Guideline revisions proposed in April
2000. The 7th conference featured
presentations in several key modeling
areas that support the revisions
promulgated today. A presentation by
the American Meteorological Society
(AMS)/EPA Regulatory Model
Improvement Committee (AERMIC)
covered the enhanced Gaussian
dispersion model with boundary layer
parameterization: AERMOD.2 Also at
the 7th conference, the Electric Power
Research Institute (EPRI) presented
evaluation results from the recent
research efforts to better define and
characterize dispersion around
2 AMS/EPA
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buildings (downwash effects). These
efforts were part of a program called the
Plume RIse Model Enhancements
(PRIME). At the time, PRIME was
integrated within ISC3ST (ISC–PRIME)
and the results presented were within
the ISC3 context. As discussed in
today’s rule, the PRIME algorithm has
now been fully integrated into
AERMOD.
We proposed an update to the
Emissions and Dispersion Modeling
System (EDMS 3.1), which is used for
assessing air quality impacts from
airports. A representative of the Federal
Aviation Administration (FAA)
presented a further upgrade to EDMS
4.0 that would include AERMOD and
forthcoming performance evaluations
for two airports.
The presentations were followed by a
critical review/discussion of AERMOD
and available performance evaluations,
facilitated jointly by the Air & Waste
Management Association’s AB–3
Committee and the American
Meteorological Society’s Committee of
Meteorological Aspects of Air Pollution.
For the new models and modeling
techniques proposed in April 2000, we
asked the public to address the
following questions:
• Has the scientific merit of the
models presented been established?
• Are the models’ accuracy
sufficiently documented?
• Are the proposed regulatory uses of
individual models for specific
applications appropriate and
reasonable?
• Do significant implementation
issues remain or is additional guidance
needed?
• Are there serious resource
constraints imposed by modeling
systems presented?
• What additional analyses or
information are needed?
We placed a transcript of the 7th
conference proceedings and a copy of
all written comments, many of which
address the above questions, in Docket
No. A–99–05. The comments on
AERMOD were reviewed and nearly
every commenter urged us to integrate
aerodynamic downwash into AERMOD
(i.e., not to require two models for some
analyses). The only comments calling
for further actions were associated with
the need for documentation, evaluation
and review of the suggested downwash
enhancement to AERMOD.
As a result of American
Meteorological Society (AMS)/EPA
Regulatory Model Improvement
Committee’s (AERMIC) efforts to revise
AERMOD, incorporating the PRIME
algorithm and making certain other
incidental modifications and to respond
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to public concerns, we believed that the
revised AERMOD merited another
public examination of performance
results. Also, since the April 2000 NPR,
the Federal Aviation Administration
(FAA) decided to configure EDMS 3.1 to
incorporate the AERMOD dispersion
model. FAA presented this strategy at
the 7th conference and performance
evaluations at two airports were to be
available before final promulgation.
This was in response to public concern
over lack of EDMS evaluation.
On April 15, 2003 we published a
Notice of Final Rulemaking (NFR; 68 FR
18440) that adopted CALPUFF in
appendix A of the Guideline. We also
made various editorial changes to
update and reorganize information, and
removed obsolete models. We
announced that action on AERMOD and
the Emissions and Dispersion Model
(EDMS) for assessing airport impacts
was being deferred, and would be
reconsidered in a separate action when
new information became available for
these models.
This deferred action took the form of
a Notice of Data Availability (NDA),
which was published on September 8,
2003 (68 FR 52934). In this notice, we
made clear that the purpose of the NDA
was to furnish pertinent technical
details related to model changes since
the April 2000 NPR. New performance
data and evaluation of design
concentration using the revised
AERMOD are contained in reports cited
later in this preamble (see section V). In
our April 2003 NFR, we stated that
results of EDMS 4.0 performance (with
AERMOD) had recently become
available. In the NDA we clarified that
these results would not be provided
because of FAA’s decision to withdraw
EDMS from the Guideline’s appendix A,
and we affirmed our support for this
removal. We solicited public comments
on the new data and information related
to AERMOD.
IV. Discussion of Public Comments and
Issues From Our April 21, 2000
Proposal
All comments submitted to Docket
No. A–99–05 are filed in Category IV–
D.3 We summarized these comments,
developed detailed responses, and
documented conclusions on appropriate
actions in a Response-to-Comments
document.4 In this document, we
3 Additional comments received since we
published the final rule on April 15, 2003
(discussed in the previous section) are filed in
category IV–E. This category includes comments
received pursuant to the Notice of Data Availability
we published in September 2003.
4 Summary of Public Comments and EPA
Responses: AERMOD; 7th Conference on Air
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considered and discussed all significant
comments. Whenever the comments
revealed any new information or
suggested any alternative solutions, we
considered this prior to taking final
action.
The remainder of this preamble
section discusses the primary issues
encountered by the Agency during the
public comment period associated with
the April 2000 proposal. This overview
also serves in part to explain the
changes to the Guideline in today’s
action, and the main technical and
policy concerns addressed by the
Agency.
A. AERMOD and PRIME
AERMOD is a best state-of-thepractice Gaussian plume dispersion
model whose formulation is based on
planetary boundary layer principles.
AERMOD provides better
characterization of plume dispersion
than does ISC3. At the 7th conference,
AERMIC members presented
developmental and evaluation results of
AERMOD. Comprehensive comments
were submitted on the AERMOD code
and formulation document and on the
AERMET draft User’s Guide (AERMET
is the meteorological preprocessor for
AERMOD).
As identified in the April 2000
Federal Register proposal, applications
for which AERMOD was suited include
assessment of plume impacts from
stationary sources in simple,
intermediate, and complex terrain, for
other than downwash and deposition
applications. We invited comments on
whether technical concerns had been
reasonably addressed and whether
AERMOD is appropriate for its intended
applications. Since AERMOD lacks a
general (all-terrain) screening tool, we
invited comment on the practicality of
using SCREEN3 as an interim tool for
AERMOD. We also sought comments on
minor changes to the list of acceptable
screening techniques for complex
terrain.
PRIME was designed to incorporate
the latest scientific algorithms for
evaluating building downwash. At the
time of the proposal, the PRIME
algorithm for simulating aerodynamic
downwash was not incorporated into
AERMOD. For testing purposes, PRIME
was implemented within ISC3ST (shortterm average version of the Industrial
Source Complex), which AERMOD was
proposed to replace. This special model,
called ISC–PRIME, was proposed for
Quality Modeling; Washington, DC, June 28–29,
2000 AND Notice of Data Availability—September
8, 2003 (Air Docket A–99–05, Item V–C–2). This
document may also be examined from EPA’s
SCRAM Web site at www.epa.gov/scram001.
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aerodynamic downwash and dry
deposition. We sought comment on the
technical viability of AERMOD and
ISC–PRIME for its intended
applications.
Scientific merit and accuracy.
Regarding the scientific merits of
AERMOD, substantial support was
expressed in public comments that
AERMOD represents sound and
significant advances over ISC3ST. The
scientific merits of this approach have
been documented both through
scientific peer review and performance
evaluations. The formulation of
AERMOD has been subjected to an
extensive, independent peer review.5
Findings of the peer review panel
suggest that AERMOD’s scientific basis
is ‘‘state-of-the-science.’’ Additionally,
the model formulations used in
AERMOD and the performance
evaluations have been accepted for
publication in two refereed journals.6 7
Finally, the adequacy of AERMOD’s
complex terrain approach for regulatory
applications is seen most directly in its
performance. AERMOD’s complex
terrain component has been evaluated
extensively by comparing modelestimated regulatory design values and
concentration frequency distributions
with observations. These comparisons
have demonstrated AERMOD’s
superiority to ISC3ST and CTDMPLUS
(Complex Terrain Dispersion Model
PLUS unstable algorithms) in estimating
those flat and complex terrain impacts
of greatest regulatory importance.8 For
incidental and unique situations
involving a well-defined hill or ridge
and where a detailed dispersion
analysis of the spatial pattern of plume
impacts is of interest, CTDMPLUS in the
Guideline’s appendix A remains
available.
Public comments also supported our
conclusion about the scientific merits of
PRIME. A detailed article in a peerreviewed journal has been published
which contains all the basic equations
with clear definitions of the variables,
5 U.S. Environmental Protection Agency, 2002.
Compendium of Reports from the Peer Review
Process for AERMOD. February 2002. Available at
www.epa.gov/scram001/.
6 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.
7 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.
8 Paine R. J. et al., 1998. Evaluation Results for
AERMOD, Draft Report. Docket No. A–99–05; II–A–
05. Available at www.epa.gov./scram001/.
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and the reasoning and references for the
model assumptions.9
Although some comments asked for
more detailed documentation and
review, there were no comments which
questioned the technical credibility of
the PRIME model. In fact, almost every
commenter asked for PRIME to be
incorporated into AERMOD. As
summarized above, we believe that the
scientific merit of PRIME has been
established via (1) model evaluation and
documentation, (2) peer review within
the submittal process to a technical
journal, and (3) via the public review
process.
Based on the external peer review of
the evaluation report and the public
review comments, we have concluded
that: (1) AERMOD’s accuracy is
adequately documented; (2) AERMOD’s
accuracy is an improvement over
ISC3ST’s ability to predict measured
concentrations; and (3) AERMOD is an
acceptable regulatory air dispersion
model replacement for ISC3ST.
Some commenters have identified
what they perceived to be weaknesses in
the evaluation and performance of ISC–
PRIME,10 and some concerns were
raised about the scope of the PRIME
evaluation. However, as shown by the
overwhelming number of requests for
the incorporation of PRIME into
AERMOD, commenters were convinced
that the accuracy of PRIME, as
implemented within the ISC3ST
framework, was reasonably documented
and found acceptable for regulatory
applications. Although some
commenters requested more
evaluations, practical limitations on the
number of valid, available data sets
prevented the inclusion of every source
type and setting in the evaluation. All
the data bases that were reasonably
available were used in the development
and evaluation of the model, and those
data bases were sufficient to establish
the basis for the evaluation. Based on
our review of the documentation and
the public comments, we conclude that
the accuracy of PRIME is sufficiently
documented and find it acceptable for
use in a dispersion model recommended
in the Guideline.
B. Appropriate for Proposed Use
Responding to a question posed in our
April 2000 proposal, the majority of
commenters questioned the
reasonableness of requiring
9 Schulman, L.L. et al., 2000. Development and
Evaluation of the PRIME Plum Rise and Building
Downwash Model. JAWMA 50: 378–390.
10 Electric Power Research Institute, 1997. Results
of the Independent Evaluation of ISCST3 and ISC–
PRIME. Final Report, TR–2460026, November 1997.
Available at www.epa.gov/scram001/.
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simultaneous use of two models (ISC–
PRIME and AERMOD) for those sources
with potential downwash concerns.
Commenters urged the Agency to
eliminate the need to use two models
for evaluating the same source. In
response to this request, AERMIC
developed a version of AERMOD that
incorporates PRIME: AERMOD (02222)
and initiated an analysis to insure that
concentration estimates by AERMOD
(02222) are equivalent to ISC–PRIME
predictions in areas affected by
downwash before it replaces ISC–
PRIME. Careful thought was given to the
way that PRIME was incorporated into
AERMOD, with the goal of making the
merge seamless. While discontinuities
from the concatenation of these two sets
of algorithms were of concern, we
mitigated this situation wherever
possible (see part D of this preamble,
and the Response to Comments
document 4). With regard to testing the
performance of AERMOD (02222), we
have carefully confirmed that the
AERMOD (02222)’s air quality
concentration predictions in the wake
region reasonably compare to those
predictions from ISC–PRIME. In fact,
the results indicate that AERMOD
(02222)’s performance matches the
performance of ISC–PRIME, and are
presented in an updated evaluation
report 11 and analysis of regulatory
design concentrations.12 We discuss
AERMOD (02222) performance in detail
in part D.
Because the technical basis for the
PRIME algorithms and the AERMOD
formulations have been independently
peer-reviewed, we believe that further
peer review of the new model
(AERMOD 02222) is not necessary. The
scientific formulation of the PRIME
algorithms has not been changed.
However, the coding for the interface
between PRIME and the accompanying
dispersion model had to be modified
somewhat to accommodate the different
ways that ISC3ST and AERMOD
simulate the atmosphere. The main
public concern was the interaction
between the two models and whether
the behavior would be appropriate for
all reasonable source settings. This
concern was addressed through the
extensive testing conducted within the
performance evaluation 11 and analysis
of design concentrations.12 Both sets of
11 Environmental Protection Agency, 2003.
AERMOD: Latest Features and Evaluation Results.
Publication No. EPA–454/R–03–003. Available at
www.epa.gov/scram001/.
12 Environmental Protection Agency, 2003.
Comparison of Regulatory Design Concentrations:
AERMOD versus ISC3ST, CTDMPLUS, and ISC–
PRIME. Final Report. Publication No. EPA–454/R–
03–002. Available at www.epa.gov/scram001/.
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analyses indicate that the new model is
performing acceptably well and the
results are similar to those obtained
from the earlier performance
evaluation 8 10 and analysis of regulatory
design concentrations (i.e., for AERMOD
(99351)).13
While dry deposition is treated in
ISC3ST, time and resources did not
allow its incorporation in AERMOD
(99351). Since no recommendation for
deposition is made for regulatory
applications, we did not consider that
the absence of this capability
compromises the suitability of
AERMOD for its intended purposes.
Nevertheless, a number of commenters
requested that deposition algorithms be
added to AERMOD, and we developed
an update to AERMOD (02222) that
offers dry and wet deposition for both
gases and particles as an option.
The version of AERMOD under
review at the 7th Conference was
AERMOD (99351) and, as mentioned
above, AERMIC has made a number of
changes to AERMOD (99351) following
this conference. These changes were
initiated in response to public
comments and, after the release of a new
draft version of the model, in response
to the recommendations from the beta
testers. Changes made to AERMOD
include the following:
• Adding the PRIME algorithms to the
model (response to public comments);
• Modifying the complex terrain
algorithms to make AERMOD less
sensitive to the selection of the domain
of the study area (response to public
comments);
• Modifying the urban dispersion for
low-level emission sources, such as area
sources, to produce a more realistic
urban dispersion and, as a part of this
change, changing the minimum layer
depth used to calculate the effective
dispersion parameters for all dispersion
settings (scientific formulation
correction which was requested by beta
testers); and
• Upgrading AERMOD to include all
the newest features that exist in the
latest version of ISC3ST such as
Fortran90 compliance and allocatable
arrays, EVENTS processing and the
TOXICS option (response to public
comments).
In the follow-up quality control
checking of the model and the source
code, additional changes were identified
as necessary and the following revisions
were made:
• Adding meander treatment to: (1)
Stable and unstable urban cases, and (2)
13 Peters, W.D. et al., 1999. Comparison of
Regulatory Design Concentrations: AERMOD vs.
ISCST3 and CTDMPLUS, Draft Report. Docket No.
A–99–05; II–A–15.
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the rural unstable dispersion settings
(only the rural, stable dispersion setting
considered meander in AERMOD
(99351)—this change created a
consistent treatment of air dispersion in
all dispersion settings);
• Making some changes to the basic
meander algorithms (improved
scientific formulation); and
• Repairing miscellaneous coding
errors.
As we mentioned earlier, the version
of AERMOD that is being promulgated
today—AERMOD (02222)—has been
subjected to further performance
evaluation 11 and analysis of design
concentrations.12
C. Implementation Issues/Additional
Guidance
Other than miscellaneous suggestions
for certain enhancements for AERMOD
(99351) such as a Fortran90 compilation
of the source code, creation of
allocatable arrays, and development of a
Windows graphical user interface, no
significant implementation obstacles
were identified in public comments.
For AERMET (meteorological
preprocessor for AERMOD), we have
implemented some enhancements that
commenters suggested. For site-specific
applications, several commenters cited
AERMOD’s requirements for NWS cloud
cover data. In response, we revised the
AERMET to incorporate the bulk
Richardson number methodology. This
approach uses temperature differences
near the surface of the earth, which can
be routinely monitored, and eliminates
the need for the cloud cover data at
night. We made a number of other
revisions in response to public
comments, enabling AERMET to: (1)
Use the old and the new Forecasting
Systems Laboratory formats, (2) use the
Hourly U.S. Weather Observations/
Automated Surface Observing Stations
(HUSWO/ASOS) data, (3) use sitespecific solar radiation and temperature
gradient data to eliminate the need for
cloud cover data, (4) appropriately
handle meteorological data from above
the arctic circle, and (5) accept a wider
range of reasonable friction velocities
and reduce the number of warning
messages. As mentioned earlier, we
added a meander component to the
treatment of stable and unstable urban
conditions to consistently treat meander
phenomena for all cases.
AERMAP (the terrain preprocessor for
AERMOD) has been upgraded in
response to public comments calling for
it to: (1) Treat complex terrain receptors
without a dependance on the selected
domain, (2) accommodate the Spatial
Data Transfer Standard (SDTS) data
available from the U.S. Geological
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Survey (USGS), (3) appropriately use
Digital Elevation Model (DEM) data
with 2 different datums (NAD27 and
NAD83); (4) accept all 7 digits of the
North UTM coordinate, and (5) do more
error-checking in the raw data (mostly
checking for missing values, but not for
harsh terrain changes in adjacent
points). All of these recommendations
have been implemented.
In response to comments about the
selection of the domain affecting the
results of the maximum concentrations
in complex terrain and the way
AERMAP estimates the effective hill
height scale (hC), the algorithms within
AERMAP and AERMOD have been
adjusted so that the hill height is less
sensitive to the arbitrary selection of the
domain. This adjustment has been
evaluated against the entire set of
evaluation data. The correction was
found to substantially reduce the effect
of the domain size upon the
computation of controlling hill heights
for each receptor. Application of this
change to the evaluation databases did
not materially affect the evaluation
results.
In general, public comments that
requested additional guidance were
either obviated by revisions to AERMOD
(99351) and its related preprocessors or
deemed unnecessary. In the latter case,
the reasons were explained in the
Response-to-Comments document.4
Some public comments suggested
additional testing of AERMOD (99351).
In fact, after the model revisions that
were described earlier were completed,
AERMOD (02222) was subjected to
additional testing.11 12 These new
analyses will be discussed in part D.
With respect to a screening version of
AERMOD, a tool called AERSCREEN is
being developed with a beta version
expected to be publicly available in Fall
2005. SCREEN3 is the current screening
model in the Guideline, and since
SCREEN3 has been successfully applied
for a number of years, we believe that
SCREEN3 produces an acceptable
degree of conservatism for regulatory
applications and may be used until
AERSCREEN or a similar technique
becomes available and tested for general
application.
D. AERMOD Revision and Reanalyses
Published In 2003
1. Performance Analysis for AERMOD
(02222)
We have tested the performance of
AERMOD (02222) by applying all of the
original data sets used to support the
version proposed in April, 2000:
AERMOD (99351) 8 and ISC–PRIME.10
These data sets include: 5 complex
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terrain data sets, 7 building downwash
data sets, and 5 simple terrain data sets
(see appendix A of the Response-toComments document 4). This
performance analysis, which is a check
of the model’s maximum concentration
predictions against observed data,
includes a comparison of the current
version of the new model (AERMOD
02222) with ISC3ST or ISC–PRIME for
downwash conditions. The results and
conclusions of the performance analyses
are presented in 2 sections: Nondownwash and downwash source
scenarios.
a. Non-Downwash Cases
For the user community to obtain a
full understanding of the impacts of
today’s proposal for the non-downwash
source scenarios (flat and complex
terrain), our performance evaluation of
AERMOD (02222) must be discussed
with respect to the old model, ISC3ST,
and with respect to AERMOD (99351).
Based on the evaluation, we have
concluded that AERMOD (02222)
significantly outperforms ISC3ST and
that AERMOD (02222)’s performance is
even better than that of AERMOD
(99351).
Evaluation of AERMOD (99351)
Comparative performance statistics
were calculated for both ISC3ST and
AERMOD (99351) using data sets in
non-downwash conditions. This
analysis looked at combinations of test
sites (flat and complex terrain),
pollutants, and concentration averaging
times. Comparisons indicated very
significant improvements in
performance when applying AERMOD
(99351). In all but 1 of the total of 20
cases in which AERMOD (99351) could
be compared to ISC3ST, AERMOD
performed as well as (but generally
better than) ISC3ST, that is, AERMOD
predicted maximum concentrations that
were closer to the measured maximum
concentrations. In the most dramatic
case (i.e., Lovett; 24-hr) in which
AERMOD performed better than
ISC3ST, AERMOD’s maximum
concentration predictions were about
the same as the measured
concentrations while the ISC3ST’s
predicted maximum concentrations
were about 9 times higher than the
measured concentrations. In the one
case (i.e., Clifty Creek; 3-hr) where
ISC3ST performed better than AERMOD
(99351), ISC3ST’s concentration
predictions matched the observed data
and the AERMOD concentration
predictions were about 25% higher than
the observed data. These results were
reported in the supporting
documentation for AERMOD (99351).
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Evaluation of AERMOD (02222)
With the changes to AERMOD (99351)
as outlined above, how has the
performance of the AERMOD been
affected? The performance of the current
version of AERMOD is about the same
or slightly better than the April 2000
version when a comparison is made
over all the available data sets. There
were examples of AERMOD (02222)
showing better and poorer performance
when compared to the performance
results of AERMOD (99351). However,
for those cases where AERMOD
(02222)’s performance was degraded,
the degradation was small. On the other
side, there were more examples where
AERMOD (02222) more closely
predicted measured concentrations. The
performance improvements were also
rather small but, in general, were
somewhat larger than the size of the
performance degradations. There also
were a number of cases where the
performance remained unchanged
between the 2 models. Thus, overall,
there was a slight improvement in
AERMOD’s performance and,
consequently, we believe that AERMOD
(02222) significantly outperforms
ISC3ST for non-downwash source
scenarios.
For AERMOD (02222) with the 5 data
bases examined for simple terrain, the
ratios of modeled/observed Robust High
Concentration ranged from 0.77 to 1.11
(1-hr average), 0.98 to 1.24 (3-hr
average), 0.94 to 0.97 (24-hr average)
and 0.30 to 0.97 (annual average). These
ratios reflect better performance than
ISC3ST for all cases.
For AERMOD (02222) with the 5 data
bases examined for complex terrain,
these ratios ranged from 1.03 to 1.12 (3hr average), 0.67 to 1.78 (24-hr average)
and 0.54 to 1.59 (annual average). At
Tracy—the only site for which there are
1-hr data—AERMOD performed
considerably better (ratio = 1.04) than
either ISC3ST or CTDMPLUS. At three
of the other four sites, AERMOD
generally performed much better than
either ISC3ST or (where applicable)
alternative models for the 3-hr and 24hr averaging times; results were
comparable for Clifty Creek (for the 3hr averaging times, AERMOD (02222)
predictions were only about 5% higher
than ISC3ST’s—down from 25% for
AERMOD (99351) as described earlier).
At the two sites where annual peak
comparisons are available, AERMOD
performed much better than either
ISC3ST or alternative models.
b. Downwash Cases
For the downwash data sets, there
were combinations of test sites,
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pollutants, stack heights and averaging
times where the proposed (ISC–PRIME)
model performance could be compared
to the performance of AERMOD (02222)
with PRIME incorporated. There was an
equal number of non-downwash cases
where AERMOD performed better than
ISC–PRIME and where ISC–PRIME
performed better than AERMOD. There
was only one case where there was a
significant difference between the two
models’ performance, and AERMOD
clearly performed better than ISC–
PRIME in this case. In all other cases,
the difference in the performance,
whether an improvement or a
degradation, was small. This
comparison indicated that AERMOD
(02222) performs very similarly, if not
somewhat better, when compared to
ISC–PRIME for downwash cases.
2. Analysis of Regulatory Design
Concentrations for AERMOD (02222)
Although not a performance tool, the
analysis of design concentrations
(‘‘consequence’’ analysis) is designed to
test model stability and continuity, and
to help the user community understand
the differences to be expected between
air dispersion models. The
consequences, or changes in the
regulatory concentrations predicted
when using the new model (AERMOD
02222) versus ISC3ST, cover 96 source
scenarios and at least 3 averaging
periods per source scenario, and are
evaluated and summarized here. The
purpose is to provide the user
community with a sense of potential
changes in their air dispersion analyses
when applying the new model over a
broad range of source types and settings.
The consequence analysis, in which
AERMOD was run for hundreds of
source scenarios, also provides a check
for model stability (abnormal halting of
model executions when using valid
control files and input data) and for
spurious results (unusually high or low
concentration predictions which are
unexplained). The results are placed
into 3 categories: non-downwash source
scenarios in flat, simple terrain;
downwash source scenarios in flat
terrain; and, complex terrain source
settings. The focus of this discussion is
on how design concentrations change
from those predicted by ISC3ST when
applying the latest version of AERMOD
versus applying the earlier version of
AERMOD (99351).
a. Non-Downwash Cases
For the non-downwash situations,
there were 48 cases covering a variety of
source types (point, area, and volume
sources), stack heights, terrain types
(flat and simple), and dispersion
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settings (urban and rural). For each case
in the consequence analysis, we
calculated the ratio between AERMOD’s
regulatory concentration predictions
and ISC3ST’s regulatory concentration
predictions. The average ratio of
AERMOD to ISC3ST-predicted
concentrations changed from 1.14 when
applying AERMOD (99351) to 0.96
when applying AERMOD (02222).14
Thus, in general, AERMOD (02222)
tends to predict concentrations closer to
ISC3ST than does version 99351
proposed in April 2000. Also, the
variation of the differences between
ISC3ST and AERMOD has decreased
with AERMOD (02222). Comparing the
earlier consequence analysis to the
latest study with AERMOD (02222), we
saw a 25% reduction in the number of
cases where the AERMOD-predicted
concentrations differed by over a factor
of two from ISC3ST’s predictions.
b. Downwash Cases
For the downwash analysis, there
were 20 cases covering a range of stack
heights, locations of stacks relative to
the building, dispersion settings, and
building shapes. As before, we
calculated the ratio regulatory
concentration predictions from
AERMOD (02222 with PRIME) and
compared them as ratios to those from
ISC3ST for each case. For additional
information, we also included ratios
with ISC–PRIME that was also proposed
in April 2000.
Calculated over all the 20 cases, and
for all averaging times considered, the
average ISC–PRIME to ISC3ST
concentration ratio is about 0.86,
whereas for AERMOD (PRIME) to
ISC3ST, it is 0.82. The maximum value
of the concentration ratios range from
2.24 for ISC–PRIME/ISC3ST to 3.67 for
AERMOD (PRIME)/ISC3ST. Similarly,
the minimum value of the concentration
ratio range from 0.04 for ISC–PRIME/
ISC3ST to 0.08 for AERMOD (PRIME)/
ISC3ST. (See Table 4–5 in reference 12.)
Although results above for the two
models that use PRIME—AERMOD
(02222) and ISC–PRIME—show
differences, we find that building
downwash is not a significant factor in
determining the maximum
concentrations in some of the cases, i.e.,
the PRIME algorithms do not predict a
building cavity concentration. Of those
cases where downwash was important,
the average concentration ratios of ISC–
PRIME/ISC3ST and AERMOD (02222)/
ISC3ST are about 1. The maximum
value of the concentration ratios range
14 A
ratio of 1.00 indicates that the two models
are predicting the same concentrations. See Table
4.1 in reference 12.
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from 2.24 for ISC–PRIME/ISC3ST to
1.87 for AERMOD (02222)/ISC3ST and
the minimum value of the concentration
ratios range from 0.34 for ISC–PRIME/
ISC3ST to 0.38 for AERMOD (02222)/
ISC3ST. These results show relatively
close agreement between the two PRIME
models. (See Table 4–6 in reference 12.)
ISC3ST does not predict cavity
concentrations but comparisons can be
made between AERMOD and ISC–
PRIME. The average AERMOD (02222)
predicted 1-hour cavity concentration is
about the same (112%) as the average
ISC–PRIME 1-hour cavity concentration.
In the extremes, the AERMOD (02222)predicted cavity concentrations ranged
from about 40% higher to 15% lower
than the corresponding ISC–PRIME
cavity concentration predictions. Thus,
in general, where downwash is a
significant factor, AERMOD (02222) and
ISC–PRIME predict similar maximum
concentrations. (See Table 4–8 in
reference 12.)
Although the same downwash
algorithms are used in both models,
there are differences in the melding of
PRIME with the core model, and
differences in the way that these models
simulate the atmosphere.15 The
downwash algorithm implementation
therefore could not be exactly the same.
c. Complex Terrain
During the testing of AERMOD after
modifications were made to the
complex terrain algorithm (see
discussion of hill height scale (hC) in B.
Appropriate for Proposed Use in this
preamble), a small error was found in
the original complex terrain code while
conducting the consequence analysis.
This error was subsequently repaired.
Final testing indicated that the revised
complex terrain code produced
reasonable results for the consequence
analysis, as described below.
The analysis of predicted design
concentrations included a suite of
complex terrain settings. There were 28
cases covering a variety of stack heights,
stack gas buoyancy values, types of
hills, and distances between source and
terrain. The ratios between the
AERMOD (02222 & 99351)—predicted
maximum concentrations and the
ISC3ST maximum concentrations were
calculated for all cases for a series of
averaging times. When comparing
AERMOD (99351) to ISC3ST and then
AERMOD (02222) to ISC3ST, the
average maximum concentration ratio,
the highest ratios and the lowest ratios
15 AERMOD uses more complex techniques to
estimate temperature profiles which, in turn, affect
the calculation of the plume rise. Plume rise may
affect the cavity and downwash concentrations.
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68223
were almost unchanged. There were no
cases in either consequence analysis
where AERMOD (02222 & 99351)
predicted higher concentrations than
those predicted by ISC3ST. Thus, in
general, the consequences of moving
from ISC3ST to AERMOD (02222) rather
than to AERMOD (99351) in complex
terrain were essentially the same. (See
Table 4–9 in reference 12.)
E. Emission and Dispersion Modeling
System (EDMS)
The Emissions and Dispersion
Modeling System (EDMS) was
developed jointly by the Federal
Aviation Administration (FAA) and the
U.S. Air Force in the late 1970s and first
released in 1985 to assess the air quality
of proposed airport development
projects. EDMS has an emissions
preprocessor and its dispersion module
estimates concentrations for various
averaging times for the following
pollutants: CO, HC, NOX, SOX, and
suspended particles (e.g., PM–10). The
first published application of EDMS was
in December 1986 for Stapleton
International Airport (FAA–EE–11–A/
REV2).
In 1988, version 4a4 revised the
dispersion module to include an
integral dispersion submodel: GIMM
(Graphical Input Microcomputer
Model). This version was proposed for
adoption in the Guideline’s appendix A
in February 1991 (56 FR 5900). This
version was included in appendix A in
July 1993 (58 FR 38816) and
recommended for limited applications
for assessments of localized airport
impacts on air quality. FAA later
updated EDMS to Version 3.0.
In response to the growing needs of
air quality analysts and changes in
regulations (e.g., conformity
requirements from the Clean Air Act
Amendment of 1990), FAA updated
EDMS to version 3.1, which is based on
the CALINE3 16 and PAL2 dispersion
kernels. In our April 2000 NPR we
proposed to adopt the version 3.1
update to EDMS. However, this update
had not been subjected to performance
evaluation and no studies of EDMS’
performance have been cited in
appendix A of the Guideline. Comment
was invited on whether this
compromises the viability of EDMS 3.1
as a recommended or preferred model
and how this deficiency can be
corrected.
Several commenters expressed
concern about EDMS 3.1 as a
recommended model in appendix A.
Indeed, there were concerns that EDMS
16 Currently listed in appendix A of the
Guideline.
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3.1 had not been as well validated as
other models, nor subjected to peer
review, as required by the Guideline’s
subsection 3.1.1. One of these
commenters suggested that EDMS 3.1
should be presented only as one of
several alternative models.
At the 7th Conference, FAA proposed
for appendix A adoption an even newer,
enhanced version of EDMS—version
4.0, which incorporates the AERMOD
dispersion kernel (without alteration).
In this system, the latest version of
AERMOD would be employed as a
standalone component of EDMS. This
dispersion kernel was to replace PAL2
and CALINE3 currently in EDMS 3.1.
There were no public comments specific
to FAA’s proposed AERMOD-based
enhancements to EDMS announced after
our April 2000 NPR.
In response to written comments on
our April 2000 NPR, at the 7th
Conference (transcript) FAA promised a
complete evaluation process that would
include sensitivity testing, intermodel
comparison, and analysis of EDMS
predictions against field observations.
The intermodel comparisons were
proposed for the UK’s Atmospheric
Dispersion Modeling System (ADMS).17
As we explained in our September 8,
2003 Notice of Data Availability, FAA
has decided to withdraw EDMS from
the Guideline’s appendix A. We stated
that no new information was therefore
provided in that notice, and we affirmed
support for EDMS’ removal from
appendix A. This removal, which we
promulgate today, obviates the need for
EDMS’ documentation and evaluation at
this time.
V. Discussion of Public Comments on
Our September 8, 2003 Notice of Data
Availability
As mentioned in section III, after
AERMOD was revised pursuant to
comments received on the April 21,
2000 proposal, a Notice of Data
Availability (NDA) was issued on
September 8, 2003 to explain the
modifications and to reveal AERMOD’s
new evaluation data. Public comments
were solicited for 30 days and posted
electronically in eDocket OAR–2003–
0201.1 (As mentioned in section IV,
additional comments received since we
published the final rule on April 15,
2003 are filed in Docket A–99–05;
category IV–E.) We summarized these
comments and developed detailed
responses; these appear as appendix C
to the Response-to-Comments
document.4 In appendix C, we
considered and discussed all significant
17 Cambridge Environmental Research
Consultants; https://www.cerc.co.uk/.
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comments, developed responses, and
documented conclusions on appropriate
actions for today’s notice. Whenever the
comments revealed any new
information or suggested any alternative
solutions, we considered them in our
final action and made corrections or
enhancements where appropriate.
In the remainder of this preamble
section we highlight the main issues
raised by the commenters who reviewed
the NDA, and summarize our responses.
These comments broadly fall into two
categories: technical/operational, and
administrative.
The technical/operational comments
were varied. One commenter thought
EPA’s sensitivity studies for simulating
area sources were too limited, and noted
that AERMOD, when used to simulate
an area source adjacent to gently sloping
terrain, produced ground-level
concentrations not unlike those from
ISC3ST. In response we explained
qualitatively how AERMOD interprets
this situation and cautioned that
reviewing authorities should be
consulted in such scenarios for
guidance on switch settings. Other
commenters believed that AERMOD
exhibited unrealistic treatment of
complex terrain elements and offered
supporting data. In response, AERMIC
concluded that AERMOD does exhibit
terrain amplification factors on the
windward side of isolated hills, where
impacts are expected to be greatest.
Commenters also presented evidence
that the PRIME algorithm in AERMOD
misbehaves in its treatment of building
wake and wind incidence. Another
model was cited as having better skill in
this regard. In response, we
acknowledged this but established that
AERMOD’s capability was acceptable
for handling the majority of building
geometries encountered (see Responseto-Comments document 4 for more
details).
A number of commenters addressed
administrative or procedural matters.
Some believed that the transition period
for implementation—one year—is too
short. We explained in response that
one year is consistent with past practice
and is adequate for most users and
reviewing authorities given our previous
experience with new models and the
fact that AERMOD has been in the
public domain for several years. Some
were disappointed that the review
period (30 days) for the NDA was too
short. We believe that the period was
adequate to review the two reports that
presented updated information on the
performance and practical consequences
of the model as revised. Regarding the
evaluation/comparison regime used for
AERMOD, others objected to the
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methodology used to evaluate AERMOD
(one that emphasizes Robust High
Concentration), claiming it is ill-suited
to the way dispersion models estimate
ambient concentrations. We
acknowledged that other methods are
available that are designed to reflect the
underlying physics and formulations of
dispersion models, and may be more
robust in their mechanisms to account
for the stochastic nature of the
atmosphere. In fact, we cited several
recent cases from the literature in which
such methods were applied in
evaluations that included AERMOD. We
also explained that the approach taken
by AERMIC was based on existing
guidance in section 9 of Appendix W,
and expressed a commitment to explore
other methods in the future, including
an update to section 9. We believe
however that the evaluation
methodology used was reasonable for its
intended purpose—examining a large
array of concentrations for a wide
variety of source types—and confers a
measure of consistency given its past
use. Other commenters expressed
disappointment that AERMOD wasn’t
compared to state-of-the-science models
as advised in its peer review report. In
response, we cited a substantial list of
studies in which AERMOD has, in fact,
been compared to some of these models,
e.g., HPDM and ADMS (in various
combinations). On the whole, as we
noted in our response, AERMOD
typically performed as well as HPDM
and ADMS, and all of them generally
performed better than ISC3ST. Still
others expressed disappointment that
the evaluation input data weren’t posted
on our Web site until January 22, 2004—
three months after the close of the
comment period. We acknowledge that
the input data were not posted when the
NDA was published. However, the
actual evaluation input data for
AERMOD had not been requested
previously, and we did not believe they
were required as a basis for reviewing
the reports we released. Moreover, since
the posting, we are unaware of any
belated adverse comments from anyone
attempting to access and use the data.
We believe we have carefully
considered and responded to public
comments and concerns regarding
AERMOD. We have also made efforts to
update appendix W to better reflect
current practice in model solicitation,
evaluation and selection. We also have
made other technical revisions so the
guidance conforms with the latest form
of the PM–10 National Ambient Air
Quality Standard.
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VI. Final Action
In this section we explain the changes
to the Guideline in today’s action in
terms of the main technical and policy
concerns addressed by the Agency in its
response to public comments (sections
IV & V). Air quality modeling involves
estimating ambient concentrations using
scientific methodologies selected from a
range of possible methods, and should
utilize the most advanced practical
technology that is available at a
reasonable cost to users, keeping in
mind the intended uses of the modeling
and ensuring transparency to the public.
With these changes, we believe that the
Guideline continues to reflect recent
advances in the field and balance these
important considerations. Today’s
action amends Appendix W of 40 CFR
part 51 as detailed below:
AERMOD
Based on the supporting information
contained in the docket, and reflected in
peer review and public comments, we
find that the AERMOD modeling system
and PRIME are based on sound
scientific principles and provide
significant improvements over the
current regulatory model, ISC3ST.
AERMOD characterizes plume
dispersion better than ISC3ST. The
accuracy of the AERMOD system is
generally well-documented and superior
to that of ISC3ST. We are adopting the
model based on its performance and
other factors.
Public comments on the April 2000
proposal expressed significant concern
about the need to use two models
(AERMOD and ISC–PRIME) to simulate
just one source when downwash posed
a potential impact. In response to this
concern we incorporated PRIME into
AERMOD and documented satisfactory
tests of the algorithm. AERMOD, with
the inclusion of PRIME, is now
appropriate and practical for regulatory
applications.
The state-of-the-science for modeling
atmospheric deposition continues to
evolve, the best techniques are currently
being assessed, and their results are
being compared with observations.
Consequently, as we now say in
Guideline paragraph 4.2.2(c), the
approach taken for any regulatory
purpose should be coordinated with the
appropriate reviewing authority. We
agreed with the public comments
calling for the addition of state-of-thescience deposition algorithms, and
developed a modification to AERMOD
(02222) for beta testing. This model,
AERMOD (04079) was posted on our
Web site https://www.epa.gov/scram001/
tt25.htm#aermoddep on March 19,
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2004. The latest version of AERMOD
may now be used for deposition
analysis in special situations.
Since AERMOD treats dispersion in
complex terrain, we have merged
sections 4 and 5 of appendix W, as
proposed in the April 2000 NPR. And
while AERMOD produces acceptable
regulatory design concentrations in
complex terrain, it does not replace
CTDMPLUS for detailed or receptororiented complex terrain analysis, as we
have made clear in Guideline section
4.2.2. CTDMPLUS remains available for
use in complex terrain.
We have implemented the majority of
suggestions to improve the AERMET,
AERMAP, and AERMOD source code to
reflect all the latest features that have
been available in ISC3ST and that are
available in the latest versions of
Fortran compilers. Also, the latest
formats for meteorological and terrain
input data are now accepted by the new
versions of AERMET and AERMAP. Our
guidance, documentation and users’
guides have been modified in response
to a number of detailed comments.
With respect to AERMOD (02222)’s
performance, we have concluded that:
(1) AERMOD (99351), the version
proposed in April 2000, performs
significantly better than ISC3ST, and
AERMOD (02222) performs slightly
better than AERMOD (99351) in nondownwash settings in both simple and
complex terrain;
(2) The performance evaluation
indicates that AERMOD (02222)
performs slightly better than ISC–PRIME
for downwash cases.
With respect to changes in AERMOD’s
regulatory design concentrations
compared to those for ISC3ST, we have
concluded that:
• For non-downwash settings,
AERMOD (02222), on average, tends to
predict concentrations closer to ISC3ST,
and with somewhat smaller variations,
than the April 2000 proposal of
AERMOD;
• Where downwash is a significant
factor in the air dispersion analysis,
AERMOD (02222) predicts maximum
concentrations that are very similar to
ISC–PRIME’s predictions;
• For those source scenarios where
maximum 1-hour cavity concentrations
are calculated, the average AERMOD
(02222)-predicted cavity concentration
tends to be about the same as the
average ISC–PRIME cavity
concentrations; and
• In complex terrain, the
consequences of using AERMOD
(02222) instead of ISC3ST remained
essentially unchanged in general,
although they varied based on
individual circumstances.
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68225
Since AERMOD (02222) was released,
an updated version was posted on our
Web site on March 22, 2004: AERMOD
(04079). The version we are releasing
pursuant to today’s promulgation,
however, is AERMOD (04300). This
version, consonant with AERMOD
(02222) in its formulations, addresses
the following minor code issues:
• The area source algorithm in simple
and complex terrain required a
correction to the way the dividing
streamline height is calculated.
• In PRIME, incorrect turbulence
parameters were being passed to one of
the numerical plume rise routines, and
this has been corrected.
• A limit has been placed on plume
cooling within PRIME to avoid
supercooling, which had been causing
runtime instability.
• A correction has been made to
avoid AERMOD’s termination under
certain situations with capped stacks
(i.e., where the routine was attempting
to take a square root of a negative
number). Our testing has demonstrated
only very minor impacts from these
corrections on the evaluation results or
the consequence analysis.
AERMOD (04300) has other draft
portions of code that represent options
not required for regulatory applications.
These include:
• Dry and wet deposition for both
gases and particles;
• The ozone limiting method (OLM),
referenced in section 5.2.4 (Models for
Nitrogen Dioxide—Annual Average) of
the Guideline for treating NOX
conversion; and
• The Plume Volume Molar Ratio
Method (PVMRM) for treating NOX
conversion.
• The bulk Richardson number
approach (discussed earlier) for using
near-surface temperature difference has
been corrected in AERMOD (04300).
Based on the technical information
contained in the docket for this rule,
and with consideration of the
performance analysis in combination
with the analysis of design
concentrations, we believe that
AERMOD is appropriate for regulatory
use and we are revising the Guideline to
adopt it as a refined model today.
In implementing the changes to the
Guideline, we recognize that there may
arise occasions in which the application
of a new model can result in the
discovery by a permit applicant of
previously unknown violations of
NAAQS or PSD increments due to
emissions from existing nearby sources.
This potential has been acknowledged
previously and is addressed in existing
EPA guidance (‘‘Air Quality Analysis for
Prevention of Significant Deterioration
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(PSD),’’ Gerald A. Emison, July 5, 1988).
To summarize briefly, the guidance
identifies three possible outcomes of
modeling by a permit applicant and
details actions that should be taken in
response to each:
1. Where dispersion modeling shows
no violation of a NAAQS or PSD
increment in the impact area of the
proposed source, a permit may be
issued and no further action is required.
2. Where dispersion modeling
predicts a violation of a NAAQS or PSD
increment within the impact area but it
is determined that the proposed source
will not have a significant impact (i.e.,
will not be above de minimis levels) at
the point and time of the modeled
violation, then the permit may be issued
immediately, but the State must take
appropriate actions to remedy the
violations within a timely manner.
3. Where dispersion modeling
predicts a violation of a NAAQS or PSD
increment within the impact area and it
is determined that the proposed source
will have a significant impact at the
point and time of the modeled violation,
then the permit may not be issued until
the source owner or operator eliminates
or reduces that impact below
significance levels through additional
controls or emissions offsets. Once it
does so, then the permit may be issued
even if the violation persists after the
source owner or operator eliminates its
contribution, but the State must take
further appropriate actions at nearby
sources to eliminate the violations
within a timely manner.
In previous promulgations, we have
traditionally allowed a one-year
transition (‘‘grandfather’’) period for
new refined techniques. Accordingly,
for appropriate applications, AERMOD
may be substituted for ISC3 during the
one-year period following the
promulgation of today’s notice.
Beginning one year after promulgation
of today’s notice, (1) applications of
ISC3 with approved protocols may be
accepted (see DATES section) and (2)
AERMOD should be used for
appropriate applications as a
replacement for ISC3.
We separately issue guidance for use
of modeling for facility-specific and
community-scale air toxics risk
assessments through the Air Toxics Risk
Assessment Reference Library.18 We
recognize that the tools and approaches
recommended therein will eventually
reflect the improved formulations of the
AERMOD modeling system and we
expect to appropriately incorporate
them as expeditiously as practicable. In
18 https://www.epa.gov/ttn/fera/risk
_atra_main.html.
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the interim, as appropriate, we will
consider the use of either ISC3 or
AERMOD in air toxic risk assessment
applications.
EDMS
FAA has completed development of
the new EDMS4.0 to incorporate
AERMOD. The result is a conforming
enhancement that offers a stronger
scientific basis for air quality modeling.
FAA has made this model available on
its Web site, which we cite in an
updated Guideline paragraph 7.2.4(c).
As described earlier in this preamble,
the summary description for EDMS will
be removed from appendix A.
VII. Final Editorial Changes to
Appendix W
Today’s update of the Guideline takes
the form of many revisions, and some of
the text is unaltered. Therefore, as a
purely practical matter, we have chosen
to publish the new version of the entire
text of appendix W and its appendix A.
Guidance and editorial changes
associated with the resolution of the
issues discussed in the previous section
are adopted in the appropriate sections
of the Guideline, as follows:
Preface
You will note some minor revisions of
appendix W to reflect current EPA
practice.
Section 4
As mentioned earlier, we revised
section 4 to present AERMOD as a
refined regulatory modeling technique
for particular applications.
Section 5
As mentioned above, we merged
pertinent guidance in section 5
(Modeling in Complex Terrain) with
that in section 4. With the anticipated
widespread use of AERMOD for all
terrain types, there is no longer any
utility in the previous differentiation
between simple and complex terrain for
model selection. To further simplify, the
list of acceptable, yet equivalent,
screening techniques for complex
terrain was removed. CTSCREEN and
guidance for its use are retained;
CTSCREEN remains acceptable for all
terrain above stack top. The screening
techniques whose descriptions we
removed, i.e., Valley (as implemented in
SCREEN3), COMPLEX I (as
implemented in ISC3ST), and RTDM
remain available for use in applicable
cases where established/accepted
procedures are used. Consultation with
the appropriate reviewing authority is
still advised for application of these
screening models.
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Section 6
As proposed, we renumbered this to
become section 5. In subsection 5.1, we
reference the Plume Volume Molar
Ratio Method (PVMRM) for point
sources of NOX, and mention that it is
currently being tested to determine
suitability as a refined method.
Section 7
As proposed, we renumbered this to
become section 6. We updated the
reference to the Emissions and
Dispersion Modeling System (EDMS).
Section 8
As proposed, we revised section 8
(renumbered to section 7) to provide
guidance for using AERMET
(AERMOD’s meteorological
preprocessor).
• In subsection 7.2.4, we introduce
the atmospheric stability
characterization for AERMOD.
• In subsection 7.2.5, we describe the
plume rise approaches used by
AERMOD.
Section 9
As proposed, we renumbered section
9 to become section 8. We added
paragraphs 8.3.1.2(e) and 8.3.1.2(f) to
clarify use of site specific
meteorological data for driving
CALMET in the separate circumstances
of long range transport and for complex
terrain applications.
Section 10
As proposed, we revised section 10
(renumbered section 9) to include
AERMOD. In May 1999, the D.C. Court
of Appeals vacated the PM–10 standard
we promulgated in 1997, and this
standard has since been removed from
the CFR (69 FR 45592; July 30, 2004).
Paragraph 10.2.3.2(a) has been corrected
to be consistent with the current
(original) PM–10 standard, which is
based on expected exceedances.
Section 11
As proposed, we renumbered section
11 to become section 10.
Sections 12 & 13
We renumbered section 12 to become
section 11, and section 13 (References)
to become section 12. We revised
renumbered section 12 by adding some
references, deleting obsolete/superseded
ones, and resequencing. You will note
that the peer scientific review for
AERMOD and latest evaluation
references have been included.
Appendix A
We added AERMOD (with the PRIME
downwash algorithm integrated) to
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appendix A. We removed EDMS from
appendix A. We also updated the
description for CALPUFF, and made
minor updates to some of the other
model descriptions.
Availability of Related Information
Our Air Quality Modeling Group
maintains an Internet Web site (Support
Center for Regulatory Air Models—
SCRAM) at: https://www.epa.gov/
scram001. You may find codes and
documentation for models referenced in
today’s action on the SCRAM Web site.
In addition, we have uploaded various
support documents (e.g., evaluation
reports).
VIII. Statutory and Executive Order
Reviews
A. Executive Order 12866: Regulatory
Planning and Review
Under Executive Order 12866 [58 FR
51735 (October 4, 1993)], the Agency
must determine whether the regulatory
action is ‘‘significant’’ and therefore
subject to review by the Office of
Management and Budget (OMB) and the
requirements of the Executive Order.
The Order defines ‘‘significant
regulatory action’’ as one that is likely
to result in a rule that may:
(1) Have an annual effect on the
economy of $100 million or more or
adversely affect in a material way the
economy, a sector of the economy,
productivity, competition, jobs, the
environment, public health or safety, or
State, local, or tribal governments or
communities;
(2) Create a serious inconsistency or
otherwise interfere with an action taken
or planned by another agency;
(3) Materially alter the budgetary
impact of entitlements, grants, user fees,
or loan programs of the rights and
obligations of recipients thereof; or
(4) Raise novel legal or policy issues
arising out of legal mandates, the
President’s priorities, or the principles
set forth in the Executive Order.
It has been determined that this rule
is not a ‘‘significant regulatory action’’
under the terms of Executive Order
12866 and is therefore not subject to EO
12866 review.
B. Paperwork Reduction Act
This final rule does not contain any
information collection requirements
subject to review by OMB under the
Paperwork Reduction Act, 44 U.S.C.
3501 et seq.
Burden means the total time, effort, or
financial resources expended by persons
to generate, maintain, retain, or disclose
or provide information to or for a
Federal agency. This includes the time
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needed to review instructions; develop,
acquire, install, and utilize technology
and systems for the purposes of
collecting, validating, and verifying
information, processing and
maintaining information, and disclosing
and providing information; adjust the
existing ways to comply with any
previously applicable instructions and
requirements; train personnel to be able
to respond to a collection of
information; search data sources;
complete and review the collection of
information; and transmit or otherwise
disclose the information.
An agency may not conduct or
sponsor, and a person is not required to
respond to a collection of information
unless it displays a currently valid OMB
control number. The OMB control
numbers for EPA’s regulations in 40
CFR are listed in 40 CFR part 9.
C. Regulatory Flexibility Act (RFA)
The 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 impact
of today’s rule on small entities, small
entities are defined as: (1) A small
business that meets the RFA default
definitions for small business (based on
Small Business Administration size
standards), as described in 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.
After considering the economic
impacts of today’s final rule on small
entities, I certify that this action will not
have a significant economic impact on
a substantial number of small entities.
As this rule merely updates existing
technical requirements for air quality
modeling analyses mandated by various
CAA programs (e.g., prevention of
significant deterioration, new source
review, State Implementation Plan
revisions) and imposes no new
regulatory burdens, there will be no
additional impact on small entities
regarding reporting, recordkeeping, and
compliance requirements.
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D. Unfunded Mandates Reform Act of
1995
Title II of the Unfunded Mandates
Reform Act of 1995 (UMRA), Public
Law 104–4, establishes requirements for
Federal agencies to assess the effects of
their regulatory actions on State, local,
and tribal governments and the private
sector. Under section 202 of the UMRA,
EPA generally must prepare a written
statement, including a cost-benefit
analysis, for proposed and final rules
with ‘‘Federal mandates’’ that may
result in expenditures to State, local,
and tribal governments, in the aggregate,
or to the private sector, of $100 million
or more in any one year. Before
promulgating an EPA rule for which a
written statement is needed, section 205
of the UMRA generally requires EPA to
identify and consider a reasonable
number of regulatory alternatives and
adopt the least costly, most costeffective or least burdensome alternative
that achieves the objectives of the rule.
The provisions of section 205 do not
apply when they are inconsistent with
applicable law. Moreover, section 205
allows EPA to adopt an alternative other
than the least costly, most cost-effective
or least burdensome alternative if the
Administrator publishes with the final
rule an explanation why that alternative
was not adopted. Before EPA establishes
any regulatory requirements that may
significantly or uniquely affect small
governments, including tribal
governments, it must have developed
under section 203 of the UMRA a small
government agency plan.
The plan must provide for notifying
potentially affected small governments,
enabling officials of affected small
governments to have meaningful and
timely input in the development of EPA
regulatory proposals with significant
Federal intergovernmental mandates,
and informing, educating, and advising
small governments on compliance with
the regulatory requirements.
Today’s rule recommends a new
modeling system, AERMOD, to replace
ISC3ST as an analytical tool for use in
SIP revisions and for calculating PSD
increment consumption. AERMOD has
been used for these purposes on a caseby-case basis (per Guideline subsection
3.2.2) for several years. Since the two
modeling systems are comparable in
scope and purpose, use of AERMOD
itself does not involve any significant
increase in costs. Moreover, modeling
costs (which include those for input
data acquisition) are typically among
the implementation costs that are
considered as part of the programs (i.e.,
PSD) that establish and periodically
revise requirements for compliance.
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Any incremental modeling costs
attributable to today’s rule do not
approach the $100 million threshold
prescribed by UMRA. EPA has
determined that this rule contains no
regulatory requirements that might
significantly or uniquely affect small
governments. This rule therefore
contains no Federal mandates (under
the regulatory provisions of Title II of
the UMRA) for State, local, or tribal
governments or the private sector.
E. Executive Order 13132: Federalism
Executive Order 13132, entitled
‘‘Federalism’’ (64 FR 43255, August 10,
1999), requires EPA to develop an
accountable process to ensure
‘‘meaningful and timely input by State
and local officials in the development of
regulatory policies that have federalism
implications.’’ ‘‘Policies that have
federalism implications’’ is defined in
the Executive Order to include
regulations that 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.’’
This final rule 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. The rule does not
impose any enforceable duties on these
entities (see D. Unfunded Mandates
Reform Act of 1995, above). The rule
would add better, more accurate
techniques for air dispersion modeling
analyses and does not impose any
additional requirements for any of the
affected parties covered under Executive
Order 13132. Thus, Executive Order
13132 does not apply to this rule.
F. Executive Order 13175: Consultation
and Coordination With Indian Tribal
Governments
Executive Order 13175, entitled
‘‘Consultation and Coordination with
Indian Tribal Governments’’ (65 FR
67249, November 9, 2000), requires EPA
to develop an accountable process to
ensure ‘‘meaningful and timely input by
tribal officials in the development of
regulatory policies that have tribal
implications.’’ This final rule does not
have tribal implications, as specified in
Executive Order 13175. As stated above
(see D. Unfunded Mandates Reform Act
of 1995, above), the rule does not
impose any new requirements for
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calculating PSD increment
consumption, and does not impose any
additional requirements for the
regulated community, including Indian
Tribal Governments. Thus, Executive
Order 13175 does not apply to this rule.
Today’s final rule does not
significantly or uniquely affect the
communities of Indian tribal
governments. Accordingly, the
requirements of section 3(b) of
Executive Order 13175 do not apply to
this rule.
test methods, sampling procedures, and
business practices) that are developed or
adopted by voluntary consensus
standards bodies. The NTTAA directs
EPA to provide Congress, through OMB,
explanations when the Agency decides
not to use available and applicable
voluntary consensus standards.
This action does not involve technical
standards. Therefore, EPA did not
consider the use of any voluntary
consensus standards.
G. Executive Order 13045: Protection of
Children From Environmental Health
and Safety Risks
Executive Order 13045 applies to any
rule that EPA determines (1) to be
‘‘economically significant’’ as defined
under Executive Order 12866, and (2)
the environmental health or safety risk
addressed by the rule has a
disproportionate effect on children. If
the regulatory action meets both the
criteria, the Agency must evaluate the
environmental health or safety effects of
the planned rule on children; and
explain why the planned regulation is
preferable to other potentially effective
and reasonably feasible alternatives
considered by the Agency.
This final rule is not subject to
Executive Order 13045, entitled
‘‘Protection of Children from
Environmental Health Risks and Safety
Risks’’ (62 FR 19885, April 23, 1997)
because it does not impose an
economically significant regulatory
action as defined by Executive Order
12866 and the action does not involve
decisions on environmental health or
safety risks that may disproportionately
affect children.
J. Congressional Review Act of 1998
H. Executive Order 13211: Actions That
Significantly Affect Energy Supply,
Distribution, or Use
This rule is not subject to Executive
Order 13211, ‘‘Actions Concerning
Regulations That Significantly Affect
Energy Supply, Distribution, or Use’’ (66
FR 28355 (May 22, 2001)) because it is
not a significant regulatory action under
Executive Order 12866.
I. National Technology Transfer and
Advancement Act of 1995
Section 12(d) of the National
Technology Transfer and Advancement
Act of 1995 (‘‘NTTAA’’), Public Law
104–113, section 12(d) (15 U.S.C. 272
note) directs EPA to use voluntary
consensus standards in its regulatory
activities unless to do so would be
inconsistent with applicable law or
otherwise impractical. Voluntary
consensus standards are technical
standards (e.g., materials specifications,
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The Congressional Review Act, 5
U.S.C. 801 et seq., as added by the Small
Business Regulatory Enforcement
Fairness Act of 1996, generally provides
that before a rule may take effect, the
agency promulgating the rule must
submit a rule report, which includes a
copy of the rule, to each House of the
Congress and to the Comptroller General
of the United States. EPA will submit a
report containing this rule and other
required information to the U.S. Senate,
the U.S. House of Representatives, and
the Comptroller General of the United
States prior to publication of the rule in
the Federal Register. A Major rule
cannot take effect until 60 days after it
is published in the Federal Register.
This action is not a ‘‘major rule’’ as
defined by 5 U.S.C. 804(2), and will be
effective 30 days from the publication
date of this notice.
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: October 21, 2005.
Stephen L. Johnson,
Administrator.
Part 51, chapter I, title 40 of the Code
of Federal Regulations is amended as
follows:
I
PART 51—REQUIREMENTS FOR
PREPARATION, ADOPTION, AND
SUBMITTAL OF IMPLEMENTATION
PLANS
1. The authority citation for part 51
continues to read as follows:
I
Authority: 23 U.S.C. 100; 42 U.S.C. 7401–
7671q.
2. Appendix W to Part 51 revised to
read as follows:
I
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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, 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 emission 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 annual EPA workshops 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 EPA of
privately developed models. After extensive
evaluation and scientific review, these
models, as well as those made available by
EPA, have been considered for recognition in
the Guideline. The third activity is the
extensive on-going research efforts by 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. EPA does not make changes to the
guidance on a predetermined schedule, but
rather on an as-needed basis. 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
2.2 Levels of Sophistication of Models
2.3 Availability of Models
3.0 Recommended Air Quality Models
3.1 Preferred Modeling Techniques
3.1.1 Discussion
3.1.2 Recommendations
3.2 Use of Alternative Models
3.2.1 Discussion
3.2.2 Recommendations
3.3 Availability of Supplementary Modeling
Guidance
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4.0 Stationary-Source Models
4.1 Discussion
4.2 Recommendations
4.2.1 Screening Techniques
4.2.1.1 Simple Terrain
4.2.1.2 Complex Terrain
4.2.2 Refined Analytical Techniques
5.0 Models for Ozone, Particulate Matter,
Carbon Monoxide, Nitrogen Dioxide, and
Lead
5.1 Discussion
5.2 Recommendations
5.2.1 Models for Ozone
5.2.2 Models for Particulate Matter
5.2.2.1 PM–2.5
5.2.2.2 PM–10
5.2.3 Models for Carbon Monoxide
5.2.4 Models for Nitrogen Dioxide
(Annual Average)
5.2.5 Models for Lead
6.0 Other Model Requirements
6.1 Discussion
6.2 Recommendations
6.2.1 Visibility
6.2.2 Good Engineering Practice Stack
Height
6.2.3 Long Range Transport (LRT) (i.e.,
beyond 50 km)
6.2.4 Modeling Guidance for Other
Governmental Programs
7.0 General Modeling Considerations
7.1 Discussion
7.2 Recommendations
7.2.1 Design Concentrations
7.2.2 Critical Receptor Sites
7.2.3 Dispersion Coefficients
7.2.4 Stability Categories
7.2.5 Plume Rise
7.2.6 Chemical Transformation
7.2.7 Gravitational Settling and
Deposition
7.2.8 Complex Winds
7.2.9 Calibration of Models
8.0 Model Input Data
8.1 Source Data
8.1.1 Discussion
8.1.2 Recommendations
8.2 Background Concentrations
8.2.1 Discussion
8.2.2 Recommendations (Isolated Single
Source)
8.2.3 Recommendations (Multi-Source
Areas)
8.3 Meteorological Input Data
8.3.1 Length of Record of Meteorological
Data
8.3.2 National Weather Service Data
8.3.3 Site Specific Data
8.3.4 Treatment of Near-calms and Calms
9.0 Accuracy and Uncertainty of Models
9.1 Discussion
9.1.1 Overview of Model Uncertainty
9.1.2 Studies of Model Accuracy
9.1.3 Use of Uncertainty in DecisionMaking
9.1.4 Evaluation of Models
9.2 Recommendations
10.0 Regulatory Application of Models
10.1 Discussion
10.2 Recommendations
10.2.1 Analysis Requirements
10.2.2 Use of Measured Data in Lieu of
Model Estimates
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10.2.3 Emission Limits
11.0 Bibliography
12.0 References
Appendix A to Appendix W of 40 CFR Part
51—Summaries of Preferred Air Quality
Models
LIST OF TABLES
Table No.
Title
4–1a ............
Neutral/Stable Meteorological
Matrix for CTSCREEN.
Unstable/Convective Meteorological Matrix for
CTSCREEN.
Model Emission Input Data for
Point Sources.
Point Source Model Emission
Input Data for NAAQS Compliance in PSD Demonstrations.
Averaging Times for Site Specific Wind and Turbulence
Measurements.
4–1b ............
8–1 ..............
8–2 ..............
8–3 ..............
1.0 Introduction
a. The Guideline recommends air quality
modeling techniques that should be applied
to State Implementation Plan (SIP) revisions
for existing sources and to new source
reviews (NSR), including prevention of
significant deterioration (PSD).1 2 3
Applicable only to criteria air pollutants, it
is intended for use by EPA Regional Offices
in judging the adequacy of modeling analyses
performed by EPA, State and local agencies
and by industry. The guidance is appropriate
for use by other Federal agencies and by State
agencies with air quality and land
management responsibilities. The Guideline
serves to identify, for all interested parties,
those techniques and data bases 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. Due to limitations in the spatial and
temporal coverage of air quality
measurements, monitoring data normally are
not sufficient as the sole basis for
demonstrating the adequacy of emission
limits for existing sources. Also, the impacts
of new sources that do not yet exist can only
be determined through modeling. Thus,
models, while uniquely filling one program
need, have become a primary analytical tool
in most air quality assessments. Air quality
measurements can be used in a
complementary manner to dispersion
models, with due regard for the strengths and
weaknesses of both analysis techniques.
Measurements are particularly useful in
assessing the accuracy of model estimates.
The use of air quality measurements alone
however could be preferable, as detailed in
a later section of this document, when
models are found to be unacceptable and
monitoring data with sufficient spatial and
temporal coverage are available.
c. It would be advantageous to categorize
the various regulatory programs and to apply
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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 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 States and
EPA Regional Offices, by many industries
and trade associations, and also by the
deliberations of Congress, that consistency in
the selection and application of models and
data bases 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
emission limits. Such consistency is not,
however, promoted at the expense of model
and data base accuracy. The Guideline
provides a consistent basis for selection of
the most accurate models and data bases for
use in air quality assessments.
e. Recommendations are made in the
Guideline concerning air quality models, data
bases, requirements for concentration
estimates, the use of measured data in lieu
of model estimates, and model evaluation
procedures. Models are identified for some
specific applications. The guidance provided
here should be followed in air quality
analyses relative to State Implementation
Plans and in supporting analyses required by
EPA, State and local agency air programs.
EPA may approve the use of another
technique that can be demonstrated to be
more appropriate than those recommended
in this guide. This is discussed at greater
length in Section 3. In all cases, the model
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 this guide
should be carefully documented and fully
supported.
f. From time to time situations arise
requiring clarification of the intent of the
guidance on a specific topic. Periodic
workshops are held with the headquarters,
Regional Office, State, and local agency
modeling representatives to ensure
consistency in modeling guidance and to
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promote the use of more accurate air quality
models and data bases. The workshops serve
to provide further explanations of Guideline
requirements to the Regional Offices and
workshop reports 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 guidance may be
indicated.
g. All changes to the Guideline must follow
rulemaking requirements since the Guideline
is codified in Appendix W of Part 51. EPA
will promulgate proposed and final rules in
the Federal Register to amend this
Appendix. Ample opportunity for public
comment will be provided for each proposed
change and public hearings scheduled if
requested.
h. A wide range of topics on modeling and
data bases are discussed in the Guideline.
Section 2 gives an overview of models and
their appropriate use. Section 3 provides
specific guidance on the use of ‘‘preferred’’
air quality models and on the selection of
alternative techniques. Sections 4 through 7
provide recommendations on modeling
techniques for application to simple-terrain
stationary source problems, complex terrain
problems, and mobile source problems.
Specific modeling requirements for selected
regulatory issues are also addressed. Section
8 discusses issues common to many
modeling analyses, including acceptable
model components. Section 9 makes
recommendations for data inputs to models
including source, meteorological and
background air quality data. Section 10
covers the uncertainty in model estimates
and how that information can be useful to the
regulatory decision-maker. The last chapter
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 itself
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 specific
applications; both EPA models and models
developed by others are included.
inventory, meteorological data, and air
quality data. Appropriate data should be
available before any attempt is made to apply
a model. A model that requires detailed,
precise, input data should not be used when
such data are unavailable. However,
assuming the data are adequate, the greater
the detail with which a model considers the
spatial and temporal variations in emissions
and meteorological conditions, the greater
the ability to evaluate the source impact and
to distinguish the effects of various control
strategies.
b. Air quality models have been applied
with the most accuracy, or the least degree
of uncertainty, to simulations of long term
averages in areas with relatively simple
topography. Areas subject to major
topographic influences experience
meteorological complexities that are
extremely difficult to simulate. Although
models are available for such circumstances,
they are frequently site specific and resource
intensive. In the absence of a model capable
of simulating such complexities, only a
preliminary approximation may be feasible
until such time as better models and data
bases become available.
c. Models are highly specialized tools.
Competent and experienced personnel are an
essential prerequisite to the successful
application of simulation models. The need
for specialists is critical when the more
sophisticated models are used or the area
being investigated has complicated
meteorological or topographic features. A
model applied improperly, or with
inappropriate data, can lead to serious
misjudgements regarding the source impact
or the effectiveness of a control strategy.
d. The resource demands generated by use
of air quality models vary widely depending
on the specific application. The resources
required depend on the nature of the model
and its complexity, the detail of the data
base, the difficulty of the application, and the
amount and level of expertise required. The
costs of manpower and computational
facilities may also be important factors in the
selection and use of a model for a specific
analysis. However, it should be recognized
that under some sets of physical
circumstances and accuracy requirements, no
present model may be appropriate. Thus,
consideration of these factors should lead to
selection of an appropriate model.
2.0 Overview of Model Use
a. 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 use. Such information is provided
in this section.
2.2 Levels of Sophistication of Models
a. There are two levels of sophistication of
models. The first level consists of relatively
simple estimation techniques that generally
use preset, worst-case meteorological
conditions to provide conservative estimates
of the air quality impact of a specific source,
or source category. These are called screening
techniques or screening models. The purpose
of such techniques is to eliminate the need
of more detailed modeling for those sources
that clearly will not cause or contribute to
ambient concentrations in excess of either
the National Ambient Air Quality Standards
(NAAQS) 4 or the allowable prevention of
significant deterioration (PSD) concentration
increments.2 3 If a screening technique
indicates that the concentration contributed
by the source exceeds the PSD increment or
2.1 Suitability of Models
a. The extent to which a specific air quality
model is suitable for the evaluation of source
impact depends upon several factors. These
include: (1) The meteorological and
topographic complexities of the area; (2) the
level of detail and accuracy needed for the
analysis; (3) the technical competence of
those undertaking such simulation modeling;
(4) the resources available; and (5) the detail
and accuracy of the data base, i.e., emissions
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the increment remaining to just meet the
NAAQS, then the second level of more
sophisticated models should be applied.
b. The second level consists of those
analytical techniques that provide more
detailed treatment of physical and chemical
atmospheric processes, require more detailed
and precise input data, and provide more
specialized concentration estimates. As a
result they provide a more refined and, at
least theoretically, a more accurate estimate
of source impact and the effectiveness of
control strategies. These are referred to as
refined models.
c. The use of screening techniques
followed, as appropriate, by a more refined
analysis is always desirable. However there
are situations where the screening techniques
are practically and technically the only
viable option for estimating source impact. In
such cases, an attempt should be made to
acquire or improve the necessary data bases
and to develop appropriate analytical
techniques.
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 available for download from
EPA’s Support Center for Regulatory Air
Modeling (SCRAM) Internet Web site at
https://www.epa.gov/scram001. A list of
alternate models that can be used with caseby-case justification (subsection 3.2) and an
example air quality analysis checklist are
also posted on this Web site. This is a site
with which modelers should become
familiar.
3.0 Recommended Air Quality Models
a. This section recommends the approach
to be taken in determining refined modeling
techniques for use in regulatory air quality
programs. The status of models developed by
EPA, as well as those submitted to EPA for
review and possible inclusion in this
guidance, is discussed. The section also
addresses the selection of models for
individual cases and provides
recommendations for situations where the
preferred models are not applicable. Two
additional sources of modeling guidance are
the Model Clearinghouse 5 and periodic
Regional/State/Local Modelers workshops.
b. In this guidance, when approval is
required for a particular modeling technique
or analytical procedure, we often refer to the
‘‘appropriate reviewing authority’’. In some
EPA regions, authority for NSR and PSD
permitting and related activities has been
delegated to State and even local agencies. In
these cases, such agencies are
‘‘representatives’’ of the respective regions.
Even in these circumstances, the Regional
Office retains the ultimate authority in
decisions and approvals. Therefore, as
discussed above and depending on the
circumstances, the appropriate reviewing
authority may be the Regional Office, Federal
Land Manager(s), State agency(ies), or
perhaps local agency(ies). In cases where
review and approval comes solely from the
Regional Office (sometimes stated as
‘‘Regional Administrator’’), this will be
stipulated. If there is any question as to the
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appropriate reviewing authority, you should
contact the Regional modeling contact
(https://www.epa.gov/scram001/
tt28.htm#regionalmodelingcontacts) in the
appropriate EPA Regional Office, whose
jurisdiction generally includes the physical
location of the source in question and its
expected impacts.
c. In all regulatory analyses, especially if
other-than-preferred models are selected for
use, early discussions among Regional Office
staff, State and local control agencies,
industry representatives, and where
appropriate, the Federal Land Manager, are
invaluable and are encouraged. Agreement
on the data base(s) to be used, modeling
techniques to be applied and the overall
technical approach, prior to the actual
analyses, helps avoid misunderstandings
concerning the final results and may reduce
the later need for additional analyses. The
use of an air quality analysis checklist, such
as is posted on EPA’s Internet SCRAM Web
site (subsection 2.3), and the preparation of
a written protocol help to keep
misunderstandings at a minimum.
d. It should not be construed 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 models is needed to promote
consistency in model selection and
application.
e. The 1980 solicitation of new or different
models from the technical community 6 and
the program whereby these models were
evaluated, established a means by which new
models are identified, reviewed and made
available in the Guideline. There is a pressing
need for the development of models for a
wide range of regulatory applications.
Refined models that more realistically
simulate the physical and chemical process
in the atmosphere and that more reliably
estimate pollutant concentrations are needed.
3.1 Preferred Modeling Techniques
3.1.1 Discussion
a. EPA has developed models suitable for
regulatory application. Other models have
been submitted by private developers for
possible inclusion in the Guideline. Refined
models which are preferred and
recommended by EPA have undergone
evaluation exercises 7 8 9 10 that include
statistical measures of model performance in
comparison with measured air quality data as
suggested by the American Meteorological
Society 11 and, where possible, peer scientific
reviews.12 13 14
b. 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, cost or resource requirements,
and availability. Accordingly, dispersion
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 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 data set 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 pollution control
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 comparison with air quality data (and/or
tracer measurements) or with other wellestablished 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.
c. The evaluation process 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
data base 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.
d. No further evaluation of a preferred
model is required for a particular application
if the EPA recommendations 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 subsection 3.2.
3.1.2 Recommendations
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 should select
a model from that appendix. These models
may be used without a formal demonstration
of applicability as long as they are used as
indicated in each model summary of
Appendix A. Further recommendations for
the application of these models to specific
source problems are found in subsequent
sections of the Guideline.
b. If changes are made to a preferred model
without affecting the concentration estimates,
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 affect only the format
or averaging time of the model results.
However, when any changes are made, the
Regional Administrator should require a test
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case example to demonstrate that the
concentration estimates are not affected.
c. A preferred model should be operated
with the options listed in Appendix A as
‘‘Recommendations for Regulatory Use.’’ 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 as a preferred model.
Use of the model must then be justified on
a case-by-case basis.
3.2 Use of Alternative Models
3.2.1 Discussion
a. Selection of the best 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. An EPA reference 15
provides a statistical technique for evaluating
model performance for predicting peak
concentration values, as might be observed at
individual monitoring locations. This
protocol is available to assist in developing
a consistent approach when justifying the use
of other-than-preferred modeling techniques
recommended in the Guideline. 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. An ASTM reference 16 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.
b. This section discusses the use of
alternate modeling techniques and defines
three situations when alternative models may
be used.
3.2.2 Recommendations
a. Determination of acceptability of a
model is a Regional Office responsibility.
Where the Regional Administrator finds that
an alternative model is more appropriate
than a preferred model, that model may be
used subject to the recommendations 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 analytical procedure is
available and applicable.
b. An alternative model should 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 normally 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
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better for the given application than a
comparable model in Appendix A; or (3) if
the preferred model is less appropriate for
the specific application, or there is no
preferred model. Any one of these three
separate conditions may make use of an
alternative model acceptable. Some known
alternative models that are applicable for
selected situations are listed on EPA’s
SCRAM Internet Web site (subsection 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. Two percent was selected as the basis
for equivalency since it is a rough
approximation of the fraction that PSD Class
I increments are of the NAAQS for SO2, i.e.,
the difference in concentrations that is
judged to be significant. 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 procedures and
techniques 15 16 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 refined
model may be used provided that:
i. The model has received a scientific peer
review;
ii. The model can be demonstrated to be
applicable to the problem on a theoretical
basis;
iii. The data bases which are necessary to
perform the analysis are available and
adequate;
iv. Appropriate performance evaluations of
the model have shown that the model is not
biased toward underestimates; and
v. A protocol on methods and procedures
to be followed has been established.
3.3 Availability of Supplementary Modeling
Guidance
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 and consistency in modeling
decisions is fostered among the various
Regional Offices and the States. To satisfy
that need, EPA established the Model
Clearinghouse 5 and also holds periodic
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workshops with headquarters, Regional
Office, State, and local agency modeling
representatives.
b. The 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 Regional
Office may request assistance from the Model
Clearinghouse after an initial evaluation and
decision has been reached concerning the
application of a model, analytical technique
or data base in a particular regulatory action.
4.0
Traditional Stationary Source Models
4.1 Discussion
a. Guidance in this section applies to
modeling analyses for which the
predominant meteorological conditions that
control the design concentration are steady
state and for which the transport distances
are nominally 50km or less. The models
recommended in this section are generally
used in the air quality impact analysis of
stationary sources for most criteria
pollutants. The averaging time of the
concentration estimates produced by these
models ranges from 1 hour to an annual
average.
b. 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.
c. In the early 1980s, model evaluation
exercises were conducted to determine the
‘‘best, most appropriate point source model’’
for use in simple terrain.12 No one model was
found to be clearly superior and, based on
past use, public familiarity, and availability,
ISC (predecessor to ISC3 17) became the
recommended model for a wide range of
regulatory applications. Other refined models
which also employed the same basic
Gaussian kernel as in ISC, i.e., BLP, CALINE3
and OCD, were developed for specialized
applications (Appendix A). Performance
evaluations were also made for these models,
which are identified below.
d. Encouraged by the development of
pragmatic methods for better characterization
of plume dispersion 18 19 20 21 the AMS/EPA
Regulatory Model Improvement Committee
(AERMIC) developed AERMOD.22 AERMOD
employs best state-of-practice
parameterizations for characterizing the
meteorological influences and dispersion.
The model utilizes a probability density
function (pdf) and the superposition of
several Gaussian plumes to characterize the
distinctly non-Gaussian nature of the vertical
pollutant distribution for elevated plumes
during convective conditions; otherwise the
distribution is Gaussian. Also, nighttime
urban boundary layers (and plumes within
them) have the turbulence enhanced by
AERMOD to simulate the influence of the
urban heat island. AERMOD has been
evaluated using a variety of data sets and has
been found to perform better than ISC3 for
many applications, and as well or better than
CTDMPLUS for several complex terrain data
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sets (Section A.1; subsection n). The current
version of AERMOD has been modified to
include an algorithm for dry and wet
deposition for both gases and particles. Note
that when deposition is invoked, mass in the
plume is depleted. Availability of this
version is described in Section A.1, and is
subject to applicable guidance published in
the Guideline.
e. A new building downwash algorithm 23
was developed and tested within AERMOD.
The PRIME algorithm has been evaluated
using a variety of data sets and has been
found to perform better than the downwash
algorithm that is in ISC3, and has been
shown to perform acceptably in tests within
AERMOD (Section A.1; subsection n).
4.2 Recommendations
4.2.1 Screening Techniques
4.2.1.1 Simple Terrain
a. Where a preliminary or conservative
estimate is desired, point source screening
techniques are an acceptable approach to air
quality analyses. EPA has published
guidance for screening procedures.24 25
b. All screening procedures should be
adjusted to the site and problem at hand.
Close attention should be paid to whether the
area should be classified urban or rural in
accordance with Section 7.2.3. The
climatology of the area should be studied to
help define the worst-case meteorological
conditions. Agreement should be reached
between the model user and the appropriate
reviewing authority on the choice of the
screening model for each analysis, and on the
input data as well as the ultimate use of the
results.
4.2.1.2 Complex Terrain
a. CTSCREEN 26 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 model description and user’s
instructions are contained in the user’s
guide.26 The terrain data must be digitized in
the same manner as for CTDMPLUS and a
terrain processor is available.27 A discussion
of the model’s performance characteristics is
provided in a technical paper.28 CTSCREEN
is designed to execute a fixed matrix of
meteorological values for wind speed (u),
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. Table 4–1 contains the matrix of
meteorological variables that is used for each
CTSCREEN analysis. There are 96
combinations, including exceptions, for each
wind direction for the neutral/stable case,
and 108 combinations for the unstable case.
The specification of wind direction, however,
is handled internally, based on the source
and terrain geometry. Although CTSCREEN
is designed to address a single source
scenario, there are a number of options that
can be selected on a case-by-case basis to
address multi-source situations. However,
the appropriate reviewing authority should
be consulted, and concurrence obtained, on
the protocol for modeling multiple sources
with CTSCREEN to ensure that the worst case
is identified and assessed. 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.
b. 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. In order to avoid excessively
large computer runs due to such a large array
of receptors, it is often desirable to model the
area twice. The first model run would use a
moderate number of receptors carefully
located over the area of interest. The second
model run would use a more dense array of
receptors in areas showing potential for high
concentrations, as indicated by the results of
the first model run.
c. As mentioned above, digitized contour
data must be preprocessed 27 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
68233
the terrain both as a single feature and as
multiple hills to determine design
concentrations.
d. Other screening techniques 17 25 29 may
be acceptable for complex terrain cases
where established procedures are used. The
user is encouraged to confer with the
appropriate reviewing authority if any
unresolvable problems are encountered, e.g.,
applicability, meteorological data, receptor
siting, or terrain contour processing issues.
4.2.2
Refined Analytical Techniques
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 should be selected
when running the program, and output
options.
b. For a wide range of regulatory
applications in all types of terrain, the
recommended model is AERMOD. This
recommendation is based on extensive
developmental and performance evaluation
(Section A.1; subsection n). 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.
c. If aerodynamic building downwash is
important for the modeling analysis, e.g.,
paragraph 6.2.2(b), then the recommended
model is AERMOD. The state-of-the-science
for modeling atmospheric deposition is
evolving and the best techniques are
currently being assessed and their results are
being compared with observations.
Consequently, while deposition treatment is
available in AERMOD, the approach taken for
any purpose should be coordinated with the
appropriate reviewing authority. Line sources
can be simulated with AERMOD if point or
volume sources are appropriately combined.
If buoyant plume rise from line sources is
important for the modeling analysis, the
recommended model is BLP. For other
special modeling applications, CALINE3 (or
CAL3QHCR on a case-by-case basis), OCD,
and EDMS are available as described in
Sections 5 and 6.
d. If the modeling application involves a
well defined hill or ridge and a detailed
dispersion analysis of the spatial pattern of
plume impacts is of interest, CTDMPLUS,
listed in Appendix A, is available.
CDTMPLUS provides greater resolution of
concentrations about the contour of the hill
feature than does AERMOD through a
different plume-terrain interaction algorithm.
TABLE 4–1A.—NEUTRAL/STABLE METEOROLOGICAL MATRIX FOR CTSCREEN
Variable
Specific values
U (m/s) .............................................................................................
sv (m/s) ............................................................................................
sw (m/s) ............................................................................................
Dq/Dz (K/m) ......................................................................................
WD ...................................................................................................
1.0
2.0
3.0
4.0
5.0
0.3
0.75
0.08
0.15
0.30
0.75
0.01
0.02
0.035
(Wind direction is optimized internally for each meteorological combination.)
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Exceptions:
(1) If U ≤ 2 m/s and sv ≤ 0.3 m/s, then include
sw = 0.04 m/s.
(2) If sw = 0.75 m/s and U ≥ 3.0 m/s, then
Dq/Dz is limited to ≤ 0.01 K/m.
(3) If U ≥ 4 m/s, then sw ≥ 0.15 m/s.
(4) sw ≤ sv
TABLE 4–1B.—UNSTABLE/CONVECTIVE METEOROLOGICAL MATRIX FOR CTSCREEN
Variable
Specific values
U (m/s) ...............................................................................................
U* (m/s) ..............................................................................................
L (m) ...................................................................................................
Dq/Dz (K/m) ........................................................................................
Zi (m) ..................................................................................................
5.0 Models for Ozone, Particulate Matter,
Carbon Monoxide, Nitrogen Dioxide, and
Lead
5.1 Discussion
a. This section identifies modeling
approaches or models appropriate for
addressing ozone (O3) a, carbon monoxide
(CO), nitrogen dioxide (NO2), particulates
(PM–2.5 a and PM–10), and lead. These
pollutants are often associated with
emissions from numerous sources. Generally,
mobile sources contribute significantly to
emissions of these pollutants or their
precursors. For cases where it is of interest
to estimate concentrations of CO or NO2 near
a single or small group of stationary sources,
refer to Section 4. (Modeling approaches for
SO2 are discussed in Section 4.)
b. Several of the pollutants mentioned in
the preceding paragraph are closely related to
each other in that they share common
sources of emissions and/or are subject to
chemical transformations of similar
precursors.30 31 For example, strategies
designed to reduce ozone could have an
effect on the secondary component of PM–2.5
and vice versa. Thus, it makes sense to use
models which take into account the chemical
coupling between O3 and PM–2.5, when
feasible. This should promote consistency
among methods used to evaluate strategies
for reducing different pollutants as well as
consistency among the strategies themselves.
Regulatory requirements for the different
pollutants are likely to be due at different
times. Thus, the following paragraphs
identify appropriate modeling approaches for
pollutants individually.
c. The NAAQS for ozone was revised on
July 18, 1997 and is now based on an 8-hour
averaging period. Models for ozone are
needed primarily to guide choice of strategies
to correct an observed ozone problem in an
area not attaining the NAAQS for ozone. Use
of photochemical grid models is the
recommended means for identifying
strategies needed to correct high ozone
concentrations in such areas. Such models
need to consider emissions of volatile organic
compounds (VOC), nitrogen oxides (NOX)
and carbon monoxide (CO), as well as means
for generating meteorological data governing
a Modeling for attainment demonstrations for O
3
and PM–2.5 should be conducted in time to meet
required SIP submission dates as provided for in
the respective implementation rules. Information on
implementation of the 8-hr O3 and PM–2.5
standards is available at: https://www.epa.gov/ttn/
naags/.
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1.0
0.1
¥10
0.030
0.5h
2.0
3.0
4.0
5.0
0.3
0.5
¥50
¥90
(potential temperature gradient above Zi)
1.0h
1.5h
(h = terrain height)
transport and dispersion of ozone and its
precursors. Other approaches, such as
Lagrangian or observational models may be
used to guide choice of appropriate strategies
to consider with a photochemical grid model.
These other approaches may be sufficient to
address ozone in an area where observed
concentrations are near the NAAQS or only
slightly above it. Such a decision needs to be
made on a case-by-case basis in concert with
the Regional Office.
d. A control agency with jurisdiction over
one or more areas with significant ozone
problems should review available ambient air
quality data to assess whether the problem is
likely to be significantly impacted by
regional transport.32 Choice of a modeling
approach depends on the outcome of this
review. In cases where transport is
considered significant, use of a nested
regional model may be the preferred
approach. If the observed problem is believed
to be primarily of local origin, use of a model
with a single horizontal grid resolution and
geographical coverage that is less than that of
a regional model may suffice.
e. The fine particulate matter NAAQS,
promulgated on July 18, 1997, includes
particles with an aerodynamic diameter
nominally less than or equal to 2.5
micrometers (PM–2.5). Models for PM–2.5
are needed to assess adequacy of a proposed
strategy for meeting annual and/or 24-hour
NAAQS for PM–2.5. PM–2.5 is a mixture
consisting of several diverse components.
Because chemical/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 PM–
2.5 is estimated from the sum of the effects
on the components composing PM–2.5.
Model users may refer to guidance 33 for
further details concerning appropriate
modeling approaches.
f. A control agency with jurisdiction over
one or more areas with PM–2.5 problems
should review available ambient air quality
data to assess which components of PM–2.5
are likely to be major contributors to the
problem. If it is determined that regional
transport of secondary particulates, such as
sulfates or nitrates, is likely to contribute
significantly to the problem, use of a regional
model may be the preferred approach.
Otherwise, coverage may be limited to a
domain that is urban scale or less. Special
care should be taken to select appropriate
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geographical coverage for a modeling
application.33
g. The NAAQS for PM–10 was
promulgated in July 1987 (40 CFR 50.6). A
SIP development guide 34 is available to
assist in PM–10 analyses and control strategy
development. EPA promulgated regulations
for PSD increments measured as PM–10 in a
notice published on June 3, 1993 (40 CFR
51.166(c)). As an aid to assessing the impact
on ambient air quality of particulate matter
generated from prescribed burning activities,
a reference 35 is available.
h. Models for assessing the impacts of
particulate matter may involve dispersion
models or receptor models, or a combination
(depending on the circumstances). Receptor
models focus on the behavior of the ambient
environment at the point of impact as
opposed to source-oriented dispersion
models, which focus on the transport,
diffusion, and transformation that begin at
the source and continue to the receptor site.
Receptor models attempt to identify and
apportion sources by relating known sample
compositions at receptors to measured or
inferred compositions of source emissions.
When complete and accurate emission
inventories or meteorological
characterization are unavailable, or unknown
pollutant sources exist, receptor modeling
may be necessary.
i. Models for assessing the impact of CO
emissions are needed for a number of
different purposes. Examples include
evaluating effects of point sources, congested
intersections and highways, as well as the
cumulative effect of numerous sources of CO
in an urban area.
j. Models for assessing the impact of
sources on ambient NO2 concentrations are
primarily needed to meet new source review
requirements, such as addressing the effect of
a proposed source on PSD increments for
annual concentrations of NO2. 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.
There are several approaches for estimating
effects of an individual source on ambient
NO2. One approach is through use of a
plume-in-grid algorithm imbedded within a
photochemical grid model. However, because
of the rigor and complexity involved, and
because this approach may not be capable of
defining sub-grid concentration gradients, the
plume-in-grid approach may be impractical
for estimating effects on an annual PSD
increment. A second approach which does
not have this limitation and accommodates
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distance-dependent conversion ratios—the
Plume Volume Molar Ratio Method
(PVMRM) 36—is currently being tested to
determine suitability as a refined method. A
third (screening) approach is to develop site
specific (domain-wide) conversion factors
based on measurements. If it is not possible
to develop site specific conversion factors
and use of the plume-in-grid algorithm is also
not feasible, other screening procedures may
be considered.
k. In January 1999 (40 CFR Part 58,
Appendix D), EPA gave notice that concern
about ambient lead impacts was being shifted
away from roadways and toward a focus on
stationary point sources. EPA has also issued
guidance on siting ambient monitors in the
vicinity of such sources.37 For lead, the SIP
should contain an air quality analysis to
determine the maximum quarterly lead
concentration resulting from major lead point
sources, such as smelters, gasoline additive
plants, etc. General guidance for lead SIP
development is also available.38
5.2 Recommendations
5.2.1 Models for Ozone
a. Choice of Models for Multi-source
Applications. Simulation of ozone formation
and transport is a highly complex and
resource intensive exercise. Control agencies
with jurisdiction over areas with ozone
problems are encouraged to use
photochemical grid models, such as the
Models-3/Community Multi-scale Air
Quality (CMAQ) modeling system,39 to
evaluate the relationship between precursor
species and ozone. Judgement 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.32
Similar models for the 8-hour NAAQS and
for the 1-hour NAAQS are appropriate.
b. Choice of Models to Complement
Photochemical Grid Models. As previously
noted, observational models, Lagrangian
models, or the refined version of the Ozone
Isopleth Plotting Program (OZIPR) 40 may be
used to help guide choice of strategies to
simulate with a photochemical grid model
and to corroborate results obtained with a
grid model. Receptor models have also been
used to apportion sources of ozone
precursors (e.g., VOC) in urban domains. EPA
has issued guidance 32 in selecting
appropriate techniques.
c. Estimating the Impact of Individual
Sources. Choice of methods used to assess
the impact of an individual source depends
on the nature of the source and its emissions.
Thus, model users should consult with the
Regional Office to determine the most
suitable approach on a case-by-case basis
(subsection 3.2.2).
5.2.2 Models for Particulate Matter
5.2.2.1 PM–2.5
a. Choice of Models for Multi-source
Applications. Simulation of phenomena
resulting in high ambient PM–2.5 can be a
multi-faceted and complex problem resulting
from PM–2.5’s existence as an aerosol
mixture. Treating secondary components of
PM–2.5, such as sulfates and nitrates, can be
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a highly complex and resource-intensive
exercise. Control agencies with jurisdiction
over areas with secondary PM–2.5 problems
are encouraged to use models which integrate
chemical and physical processes important
in the formation, decay and transport of these
species (e.g., Models-3/CMAQ 38 or
REMSAD 41). Primary components can be
simulated using less resource-intensive
techniques. Suitability of a modeling
approach or mix of modeling approaches for
a given application requires technical
judgement,33 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.
b. Choice of Analysis Techniques to
Complement Air Quality Simulation Models.
Receptor models may be used to corroborate
predictions obtained with one or more air
quality simulation models. They may also be
potentially useful in helping to define
specific source categories contributing to
major components of PM–2.5.33
c. Estimating the Impact of Individual
Sources. Choice of methods used to assess
the impact of an individual source depends
on the nature of the source and its emissions.
Thus, model users should consult with the
Regional Office to determine the most
suitable approach on a case-by-case basis
(subsection 3.2.2).
5.2.2.2 PM–10
a. Screening techniques like those
identified in subsection 4.2.1 are applicable
to PM–10. Conservative assumptions which
do not allow removal or transformation are
suggested for screening. Thus, it is
recommended that subjectively determined
values for ‘‘half-life’’ or pollutant decay not
be used as a surrogate for particle removal.
Proportional models (rollback/forward) may
not be applied for screening analysis, unless
such techniques are used in conjunction with
receptor modeling.34
b. Refined models such as those discussed
in subsection 4.2.2 are recommended for
PM–10. However, where possible, particle
size, gas-to-particle formation, and their
effect on ambient concentrations may be
considered. For point sources of small
particles and for source-specific analyses of
complicated sources, use the appropriate
recommended steady-state plume dispersion
model (subsection 4.2.2).
c. Receptor models have proven useful for
helping validate emission inventories and for
corroborating source-specific impacts
estimated by dispersion models. The
Chemical Mass Balance (CMB) model is
useful for apportioning impacts from
localized sources.42 43 44 Other receptor
models, e.g., the Positive Matrix
Factorization (PMF) model 45 and Unmix,46
which don’t share some of CMB’s constraints,
have also been applied. In regulatory
applications, dispersion models have been
used in conjunction with receptor models to
attribute source (or source category)
contributions. Guidance is available for PM–
10 sampling and analysis applicable to
receptor modeling.47
d. Under certain conditions, recommended
dispersion models may not be reliable. In
such circumstances, the modeling approach
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should be approved by the Regional Office on
a case-by-case basis. Analyses involving
model calculations for stagnation conditions
should also be justified on a case-by-case
basis (subsection 7.2.8).
e. 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.
Reentrained dust is that which is put into the
air by reason of vehicles driving over dirt
roads (or dirty roads) and dusty areas. Such
sources can be characterized as line, area or
volume sources. Emission rates may be based
on site specific data or values from the
general literature. 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, it is recommended that the
proposed procedure be cleared by the
Regional Office for each specific situation
before the modeling exercise is begun.
5.2.3
Models for Carbon Monoxide
a. Guidance is available for analyzing CO
impacts at roadway intersections.48 The
recommended screening model for such
analyses is CAL3QHC.49 50 This model
combines CALINE3 (listed in Appendix A)
with a traffic model to calculate delays and
queues that occur at signalized intersections.
The screening approach is described in
reference 48; a refined approach may be
considered on a case-by-case basis with
CAL3QHCR.51 The latest version of the
MOBILE (mobile source emission factor)
model should be used for emissions input to
intersection models.
b. For analyses of highways characterized
by uninterrupted traffic flows, CALINE3 is
recommended, with emissions input from the
latest version of the MOBILE model. A
scientific review article for line source
models is available.52
c. For urban area wide analyses of CO, an
Eulerian grid model should be used.
Information on SIP development and
requirements for using such models can be
found in several references.48 53 54 55
d. Where point sources of CO are of
concern, they should be treated using the
screening and refined techniques described
in Section 4.
5.2.4 Models for Nitrogen Dioxide (Annual
Average)
a. A tiered screening approach is
recommended to obtain annual average
estimates of NO2 from point sources for New
Source Review analysis, including PSD, and
for SIP planning purposes. This multi-tiered
approach is conceptually shown in Figure 5–
1 and described in paragraphs b through d of
this subsection:
Figure 5–1
Multi-tiered screening approach for
Estimating Annual NO2 Concentrations from
Point Sources
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b. For Tier 1 (the initial screen), use an
appropriate model in subsection 4.2.2 to
estimate the maximum annual average
concentration and assume a total conversion
of NO to NO2. If the concentration exceeds
the NAAQS and/or PSD increments for NO2,
proceed to the 2nd level screen.
c. For Tier 2 (2nd level) screening analysis,
multiply the Tier 1 estimate(s) by an
empirically derived NO2/NOX value of 0.75
(annual national default).56 The reviewing
agency may establish an alternative default
NO2/NOX ratio based on ambient annual
average NO2 and annual average NOX data
representative of area wide quasi-equilibrium
conditions. Alternative default NO2/NOX
ratios should be based on data satisfying
quality assurance procedures that ensure data
accuracy for both NO2 and NOX within the
typical range of measured values. In areas
with relatively low NOX concentrations, the
quality assurance procedures used to
determine compliance with the NO2 national
ambient air quality standard may not be
adequate. In addition, default NO2/NOX
ratios, including the 0.75 national default
value, can underestimate long range NO2
impacts and should be used with caution in
long range transport scenarios.
d. For Tier 3 (3rd level) analysis, a detailed
screening method may be selected on a caseby-case basis. For point source modeling,
detailed screening techniques such as the
Ozone Limiting Method 57 may also be
considered. Also, a site specific NO2/NOX
ratio may be used as a detailed screening
method if it meets the same restrictions as
described for alternative default NO2/NOX
ratios. Ambient NOX monitors used to
develop a site specific ratio should be sited
to obtain the NO2 and NOX concentrations
under quasi-equilibrium conditions. Data
obtained from monitors sited at the
maximum NOX impact site, as may be
required in a PSD pre-construction
monitoring program, likely reflect
transitional NOX conditions. Therefore, NOX
data from maximum impact sites may not be
suitable for determining a site specific NO2/
NOX ratio that is applicable for the entire
modeling analysis. A site specific ratio
derived from maximum impact data can only
be used to estimate NO2 impacts at receptors
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located within the same distance of the
source as the source-to-monitor distance.
e. In urban areas (subsection 7.2.3), a
proportional model may be used as a
preliminary assessment to evaluate control
strategies to meet the NAAQS for multiple
minor sources, i.e., minor point, area and
mobile sources of NOX; concentrations
resulting from major point sources should be
estimated separately as discussed above, then
added to the impact of the minor sources. An
acceptable screening technique for urban
complexes is to assume that all NOX is
emitted in the form of NO2 and to use a
model from Appendix A for nonreactive
pollutants to estimate NO2 concentrations. A
more accurate estimate can be obtained by:
(1) Calculating the annual average
concentrations of NOX with an urban model,
and (2) converting these estimates to NO2
concentrations using an empirically derived
annual NO2/NOX ratio. A value of 0.75 is
recommended for this ratio. However, a
spatially averaged alternative default annual
NO2/NOX ratio may be determined from an
existing air quality monitoring network and
used in lieu of the 0.75 value if it is
determined to be representative of prevailing
ratios in the urban area by the reviewing
agency. To ensure use of appropriate locally
derived annual average NO2/NOX ratios,
monitoring data under consideration should
be limited to those collected at monitors
meeting siting criteria defined in 40 CFR Part
58, Appendix D as representative of
‘‘neighborhood’’, ‘‘urban’’, or ‘‘regional’’
scales. Furthermore, the highest annual
spatially averaged NO2/NOX ratio from the
most recent 3 years of complete data should
be used to foster conservatism in estimated
impacts.
f. To demonstrate compliance with NO2
PSD increments in urban areas, emissions
from major and minor sources should be
included in the modeling analysis. Point and
area source emissions should be modeled as
discussed above. If mobile source emissions
do not contribute to localized areas of high
ambient NO2 concentrations, they should be
modeled as area sources. When modeled as
area sources, mobile source emissions should
be assumed uniform over the entire highway
link and allocated to each area source grid
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square based on the portion of highway link
within each grid square. If localized areas of
high concentrations are likely, then mobile
sources should be modeled as line sources
using an appropriate steady-state plume
dispersion model (e.g., CAL3QHCR;
subsection 5.2.3).
g. More refined techniques to handle
special circumstances may be considered on
a case-by-case basis and agreement with the
appropriate reviewing authority (paragraph
3.0(b)) should be obtained. Such techniques
should consider individual quantities of NO
and NO2 emissions, atmospheric transport
and dispersion, and atmospheric
transformation of NO to NO2. Where they are
available, site specific data on the conversion
of NO to NO2 may be used. Photochemical
dispersion models, if used for other
pollutants in the area, may also be applied
to the NOX problem.
5.2.5 Models for Lead
a. For major lead point sources, such as
smelters, which contribute fugitive emissions
and for which deposition is important,
professional judgement should be used, and
there should be coordination with the
appropriate reviewing authority (paragraph
3.0(b)). To model an entire major urban area
or to model areas without significant sources
of lead emissions, as a minimum a
proportional (rollback) model may be used
for air quality analysis. The rollback
philosophy assumes that measured pollutant
concentrations are proportional to emissions.
However, urban or other dispersion models
are encouraged in these circumstances where
the use of such models is feasible.
b. In modeling the effect of traditional line
sources (such as a specific roadway or
highway) on lead air quality, dispersion
models applied for other pollutants can be
used. Dispersion models such as CALINE3
and CAL3QHCR have been used for modeling
carbon monoxide emissions from highways
and intersections (subsection 5.2.3). Where
there is a point source in the middle of a
substantial road network, the lead
concentrations that result from the road
network should be treated as background
(subsection 8.2); the point source and any
nearby major roadways should be modeled
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separately using the appropriate
recommended steady-state plume dispersion
model (subsection 4.2.2).
no apparent bias toward over or under
prediction, so long as the transport distance
was limited to less than 300km.60
6.0
6.2 Recommendations
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 Clean Air Act,
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 PM–2.5 is the most
significant component of visibility
impairment. The key components of PM–2.5
contributing to visibility impairment include
sulfates, nitrates, organic carbon, elemental
carbon, and crustal material.
b. The visibility regulations as promulgated
in December 1980 (40 CFR 51.300–307)
require States to mitigate visibility
impairment, in any of the 156 mandatory
Federal Class I areas, that is found to be
‘‘reasonably attributable’’ to a single source
or a small group of sources. In 1985, EPA
promulgated Federal Implementation Plans
(FIPs) for several States without approved
visibility provisions in their SIPs. The
IMPROVE (Interagency Monitoring for
Protected Visual Environments) monitoring
network, a cooperative effort between EPA,
the States, and Federal land management
agencies, was established to implement the
monitoring requirements in these FIPs. Data
has been collected by the IMPROVE network
since 1988.
c. In 1999, EPA issued revisions to the
1980 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 51.308–309). The state of relevant
scientific knowledge has expanded
significantly since the Clean Air Act
Amendments of 1977. A number of studies
and reports 61 62 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. Section 169A of the Act
requires states to develop SIPs containing
long-term strategies for remedying existing
and preventing future visibility impairment
in 156 mandatory Class I federal areas. In
order to develop long-term strategies to
address regional haze, many States will need
to conduct regional-scale modeling of fine
particulate concentrations and associated
visibility impairment (e.g., light extinction
and deciview metrics).
d. To calculate the potential impact of a
plume of specified emissions for specific
transport and dispersion conditions (‘‘plume
blight’’), a screening model, VISCREEN, and
guidance are available.63 If a more
comprehensive analysis is required, a refined
model should be selected . The model
selection (VISCREEN vs. PLUVUE II or some
other refined model), procedures, and
analyses should be determined in
consultation with the appropriate reviewing
Other Model Requirements
6.1 Discussion
a. This section covers those cases where
specific techniques have been developed for
special regulatory programs. 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. This section will undergo periodic
revision as new programs are added and new
techniques are developed.
b. Other Federal agencies have also
developed specific modeling approaches for
their own regulatory or other requirements.58
Although such regulatory requirements and
manuals may 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 manual or directive.
c. The need to estimate impacts at
distances greater than 50km (the nominal
distance to which EPA considers most
steady-state Gaussian plume models are
applicable) is an important one especially
when considering the effects from secondary
pollutants. Unfortunately, models originally
available to EPA had not undergone
sufficient field evaluation to be
recommended for general use. Data bases
from field studies at mesoscale and long
range transport distances were limited in
detail. This limitation was a result of the
expense to perform the field studies required
to verify and improve mesoscale and long
range transport models. Meteorological data
adequate for generating three-dimensional
wind fields were particularly sparse.
Application of models to complicated terrain
compounds the difficulty of making good
assessments of long range transport impacts.
EPA completed limited evaluation of several
long range transport (LRT) models against
two sets of field data and evaluated results.59
Based on the results, EPA concluded that
long range and mesoscale transport models
were limited for regulatory use to a case-bycase basis. However a more recent series of
comparisons has been completed for a new
model, CALPUFF (Section A.3). Several of
these field studies involved three-to-four
hour releases of tracer gas sampled along arcs
of receptors at distances greater than 50km
downwind. In some cases, short-term
concentration sampling was available, such
that the transport of the tracer puff as it
passed the arc could be monitored.
Differences on the order of 10 to 20 degrees
were found between the location of the
simulated and observed center of mass of the
tracer puff. Most of the simulated centerline
concentration maxima along each arc were
within a factor of two of those observed. It
was concluded from these case studies that
the CALPUFF dispersion model had
performed in a reasonable manner, and had
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authority (paragraph 3.0(b)) and the affected
Federal Land Manager (FLM). FLMs are
responsible for determining whether there is
an adverse effect by a plume on a Class I area.
e. CALPUFF (Section A.3) may be applied
when assessment is needed of reasonably
attributable haze impairment or atmospheric
deposition due to one or a small group of
sources. This situation may involve more
sources and larger modeling domains than
that to which VISCREEN ideally may be
applied. The procedures and analyses should
be determined in consultation with the
appropriate reviewing authority (paragraph
3.0(b)) and the affected FLM(s).
f. Regional scale models are used by EPA
to develop and evaluate national policy and
assist State and local control agencies. Two
such models which can be used to assess
visibility impacts from source emissions are
Models-3/CMAQ 38 and REMSAD.41 Model
users should consult with the appropriate
reviewing authority (paragraph 3.0(b)), which
in this instance would include FLMs.
6.2.2 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 emission limitations by 40
CFR 51.118 and 40 CFR 51.164. The
definitions of 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 64 65 66 67, 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 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 (see
reference 66). Detailed downwash screening
procedures 24 for both the cavity and wake
regions should be followed. If more refined
concentration estimates are required, the
recommended steady-state plume dispersion
model in subsection 4.2.2 contains
algorithms for building wake calculations
and should be used.
6.2.3 Long Range Transport (LRT) (i.e.,
Beyond 50km)
a. Section 165(d) of the Clean Air Act
requires that suspected adverse impacts on
PSD Class I areas be determined. However,
50km is the useful distance to which most
steady-state Gaussian plume models are
considered accurate for setting emission
limits. Since in many cases PSD analyses
show that Class I areas may be threatened at
distances greater than 50km from new
sources, some procedure is needed to (1)
determine if an adverse impact will occur,
and (2) identify the model to be used in
setting an emission limit if the Class I
increments are threatened. In addition to the
situations just described, there are certain
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applications containing a mixture of both
long range and short range source-receptor
relationships in a large modeled domain (e.g.,
several industrialized areas located along a
river or valley). Historically, these
applications have presented considerable
difficulty to an analyst if impacts from
sources having transport distances greater
than 50km significantly contributed to the
design concentrations. To properly analyze
applications of this type, a modeling
approach is needed which has the capability
of combining, in a consistent manner,
impacts involving both short and long range
transport. The CALPUFF modeling system,
listed in Appendix A, has been designed to
accommodate both the Class I area LRT
situation and the large modeling domain
situation. Given the judgement and
refinement involved, conducting a LRT
modeling assessment will require significant
consultation with the appropriate reviewing
authority (paragraph 3.0(b)) and the affected
FLM(s). The FLM has an affirmative
responsibility to protect air quality related
values (AQRVs) that may be affected, and to
provide the appropriate procedures and
analysis techniques. Where there is no
increment violation, the ultimate decision on
whether a Class I area is adversely affected
is the responsibility of the appropriate
reviewing authority (Section 165(d)(2)(C)(ii)
of the Clean Air Act), taking into
consideration any information on the impacts
on AQRVs provided by the FLM. According
to Section 165(d)(2)(C)(iii) of the Clean Air
Act, if there is a Class I increment violation,
the source must demonstrate to the
satisfaction of the FLM that the emissions
from the source will have no adverse impact
on the AQRVs.
b. If LRT is determined to be important,
then refined estimates utilizing the CALPUFF
modeling system should be obtained. A
screening approach 60 68 is also available for
use on a case-by-case basis that generally
provides concentrations that are higher than
those obtained using refined
characterizations of the meteorological
conditions. The meteorological input data
requirements for developing the time and
space varying three-dimensional winds and
dispersion meteorology for refined analyses
are discussed in paragraph 8.3.1.2(d).
Additional information on applying this
model is contained in Appendix A. To
facilitate use of complex air quality and
meteorological modeling systems, a written
protocol approved by the appropriate
reviewing authority (paragraph 3.0(b)) and
the affected FLM(s) may be considered for
developing consensus in the methods and
procedures to be followed.
6.2.4 Modeling Guidance for Other
Governmental Programs
a. When using the models 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. For modeling
associated with PSD permit applications that
involve a Class I area, the appropriate Federal
Land Manager should be consulted on all
modeling questions.
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b. The Offshore and Coastal Dispersion
(OCD) model, described in Appendix A, was
developed by the Minerals Management
Service and is recommended for estimating
air quality impact from offshore sources on
onshore, flat terrain areas. The OCD model is
not recommended for use in air quality
impact assessments for onshore sources.
Sources located on or just inland of a
shoreline where fumigation is expected
should be treated in accordance with
subsection 7.2.8.
c. The latest version of the Emissions and
Dispersion Modeling System (EDMS), was
developed and is supported by the Federal
Aviation Administration (FAA), and is
appropriate for air quality assessment of
primary pollutant impacts at airports or air
bases. EDMS has adopted AERMOD for
treating dispersion. Application of EDMS 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. If changes in
other than aircraft operations are associated
with analyses, a model recommended in
Chapter 4 or 5 should be used. The latest
version of EDMS may be obtained from FAA
at its Web site: https://www.aee.faa.gov/
emissions/edms/edmshome.htm.
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 this
guide. The topics covered here are not
specific to any one program or modeling area
but are common to nearly all modeling
analyses for criteria pollutants.
7.2 Recommendations
7.2.1 Design Concentrations (See Also
Subsection 10.2.3.1)
7.2.1.1 Design Concentrations for SO2, PM–
10, CO, Pb, and NO2
a. An air quality analysis for SO2, PM–10,
CO, Pb, and NO2 is required to determine if
the source will (1) cause a violation of the
NAAQS, or (2) cause or contribute to air
quality deterioration greater than the
specified allowable PSD increment. For the
former, background concentration
(subsection 8.2) should be added to the
estimated impact of the source to determine
the design concentration. For the latter, the
design concentration includes impact from
all increment consuming sources.
b. If the air quality analyses are conducted
using the period of meteorological input data
recommended in subsection 8.3.1.2 (e.g., 5
years of National Weather Service (NWS)
data or at least 1 year of site specific data;
subsection 8.3.3), then the design
concentration based on the highest, secondhighest short term concentration over the
entire receptor network for each year
modeled or the highest long term average
(whichever is controlling) should be used to
determine emission limitations to assess
compliance with the NAAQS and PSD
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increments. For the 24-hour PM–10 NAAQS
(which is a probabilistic standard)—when
multiple years are modeled, they collectively
represent a single period. Thus, if 5 years of
NWS data are modeled, then the highest
sixth highest concentration for the whole
period becomes the design value. And in
general, when n years are modeled, the
(n+1)th highest concentration over the n-year
period is the design value, since this
represents an average or expected exceedance
rate of one per year.
c. When sufficient and representative data
exist for less than a 5-year period from a
nearby NWS site, or when site specific data
have been collected for less than a full
continuous year, or when it has been
determined that the site specific data may not
be temporally representative (subsection
8.3.3), then the highest concentration
estimate should be considered the design
value. This is because the length of the data
record may be too short to assure that the
conditions producing worst-case estimates
have been adequately sampled. The highest
value is then a surrogate for the
concentration that is not to be exceeded more
than once per year (the wording of the
deterministic standards). Also, the highest
concentration should be used whenever
selected worst-case conditions are input to a
screening technique, as described in EPA
guidance.24
d. If the controlling concentration is an
annual average value and multiple years of
data (site specific or NWS) are used, then the
design value is the highest of the annual
averages calculated for the individual years.
If the controlling concentration is a quarterly
average and multiple years are used, then the
highest individual quarterly average should
be considered the design value.
e. As long a period of record as possible
should be used in making estimates to
determine design values and PSD
increments. If more than 1 year of site
specific data is available, it should be used.
7.2.1.2 Design Concentrations for O3 and
PM–2.5
a. Guidance and specific instructions for
the determination of the 1-hr and 8-hr design
concentrations for ozone are provided in
Appendix H and I (respectively) of reference
4. Appendix H explains how to determine
when the expected number of days per
calendar year with maximum hourly
concentrations above the NAAQS is equal to
or less than 1. Appendix I explains the data
handling conventions and computations
necessary for determining whether the 8-hour
primary and secondary NAAQS are met at an
ambient monitoring site. For PM–2.5,
Appendix N of reference 4, and
supplementary guidance,69 explain the data
handling conventions and computations
necessary for determining when the annual
and 24-hour primary and secondary NAAQS
are met. For all SIP revisions the user should
check with the Regional Office to obtain the
most recent guidance documents and policy
memoranda concerning the pollutant in
question. There are currently no PSD
increments for O3 and PM–2.5.
7.2.2 Critical Receptor Sites
a. Receptor sites for refined modeling
should be utilized in sufficient detail to
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estimate the highest concentrations and
possible violations of a NAAQS or a PSD
increment. In designing a receptor network,
the emphasis should be placed on receptor
resolution and location, not total number of
receptors. The selection of receptor sites
should be a case-by-case determination
taking into consideration the topography, the
climatology, monitor sites, and the results of
the initial screening procedure.
7.2.3 Dispersion Coefficients
a. Steady-state Gaussian plume models
used in most applications should employ
dispersion coefficients consistent with those
contained in the preferred models in
Appendix A. Factors such as averaging time,
urban/rural surroundings (see paragraphs
(b)—(f) of this subsection), and type of source
(point vs. line) may dictate the selection of
specific coefficients. Coefficients used in
some Appendix A models are identical to, or
at least based on, Pasquill-Gifford
coefficients 70 in rural areas and McElroyPooler 71 coefficients in urban areas. A key
feature of AERMOD’s formulation is the use
of directly observed variables of the
boundary layer to parameterize dispersion.22
b. The selection of either rural or urban
dispersion coefficients in a specific
application should follow one of the
procedures suggested by Irwin 72 and briefly
described in paragraphs (c)—(f) of this
subsection. These include a land use
classification procedure or a population
based procedure to determine whether the
character of an area is primarily urban or
rural.
c. 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 73; (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.
d. 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.
e. Of the two methods, the land use
procedure is considered more definitive.
Population density should be used with
caution and should not be applied to highly
industrialized areas where the population
density may be low and thus a rural
classification would be indicated, but the
area is sufficiently built-up so that the urban
land use criteria would be satisfied. In this
case, the classification should already be
‘‘urban’’ and urban dispersion parameters
should be used.
f. Sources located in an area defined as
urban should be modeled using urban
dispersion parameters. Sources located in
areas defined as rural should be modeled
using the rural dispersion parameters. 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.
g. Buoyancy-induced dispersion (BID), as
identified by Pasquill 74, is included in the
preferred models and should be used where
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buoyant sources, e.g., those involving fuel
combustion, are involved.
7.2.4 Stability Categories
a. The Pasquill approach to classifying
stability is commonly used in preferred
models (Appendix A). The Pasquill method,
as modified by Turner 75, was developed for
use with commonly observed meteorological
data from the National Weather Service and
is based on cloud cover, insolation and wind
speed.
b. Procedures to determine Pasquill
stability categories from other than NWS data
are found in subsection 8.3. Any other
method to determine Pasquill stability
categories must be justified on a case-by-case
basis.
c. For a given model application where
stability categories are the basis for selecting
dispersion coefficients, both sy and sz should
be determined from the same stability
category. ‘‘Split sigmas’’ in that instance are
not recommended. Sector averaging, which
eliminates the sy term, is commonly
acceptable in complex terrain screening
methods.
d. AERMOD, also a preferred model in
Appendix A, uses a planetary boundary layer
scaling parameter to characterize stability.22
This approach represents a departure from
the discrete, hourly stability categories
estimated under the Pasquill-Gifford-Turner
scheme.
7.2.5 Plume Rise
a. The plume rise methods of Briggs 76 77
are incorporated in many of the preferred
models and are recommended for use in
many modeling applications. In AERMOD,22
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.78 In AERMOD, plume
rise is computed using the methods of Briggs
excepting cases involving building
downwash, in which a numerical solution of
the mass, energy, and momentum
conservation laws is performed.23 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 (a) above. 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 groundlevel 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 77
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68239
is the recommended technique for this
situation and is used in preferred models for
point sources.
7.2.6 Chemical Transformation
a. The chemical transformation of SO2
emitted from point sources or single
industrial plants in rural areas is generally
assumed to be relatively unimportant to the
estimation of maximum concentrations when
travel time is limited to a few hours.
However, in urban areas, where synergistic
effects among pollutants are of considerable
consequence, chemical transformation rates
may be of concern. In urban area
applications, a half-life of 4 hours 75 may be
applied to the analysis of SO2 emissions.
Calculations of transformation coefficients
from site specific studies can be used to
define a ‘‘half-life’’ to be used in a steadystate Gaussian plume model with any travel
time, or in any application, if appropriate
documentation is provided. Such conversion
factors for pollutant half-life should not be
used with screening analyses.
b. Use of models incorporating complex
chemical mechanisms should be considered
only on a case-by-case basis with proper
demonstration of applicability. These are
generally regional models not designed for
the evaluation of individual sources but used
primarily for region-wide evaluations.
Visibility models also incorporate chemical
transformation mechanisms which are an
integral part of the visibility model itself and
should be used in visibility assessments.
7.2.7 Gravitational Settling and Deposition
a. An ‘‘infinite half-life’’ should be used for
estimates of particle concentrations when
steady-state Gaussian plume models
containing only exponential decay terms for
treating settling and deposition are used.
b. 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 judgement should be used, and
there should be coordination with the
appropriate reviewing authority (paragraph
3.0(b)).
7.2.8 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.79 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 the
special cases described, the CALPUFF
modeling system (described in Appendix A)
may be applied on a case-by-case basis for air
quality estimates in such complex non-
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steady-state meteorological conditions. The
purpose of choosing a modeling system like
CALPUFF is to fully treat the time and space
variations of meteorology effects on transport
and dispersion. The setup and application of
the 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 threedimensional winds and dispersion
meteorology for these situations are
discussed in paragraphs 8.3.1.2(d) and
8.3.1.2(f). Examples of inhomogeneous winds
include, but aren’t limited to, situations
described in the following paragraphs (i)—
(iii):
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 24 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. The Shoreline
Dispersion Model (SDM) listed on EPA’s
Internet SCRAM Web site (subsection 2.3)
may be applied on a case-by-case basis when
air quality estimates under shoreline
fumigation conditions are needed.80
Information on the results of EPA’s
evaluation of this model together with other
coastal fumigation models is available.81
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 for CALPUFF 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)).
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7.2.9 Calibration of Models
a. Calibration of models is not common
practice and is subject to much error and
misunderstanding. There have been attempts
by some to compare model estimates and
measurements on an event-by-event basis
and then to calibrate a model with results of
that comparison. This approach is severely
limited by uncertainties in both source and
meteorological data and therefore it is
difficult to precisely estimate the
concentration at an exact location for a
specific increment of time. Such
uncertainties make calibration of models of
questionable benefit. Therefore, model
calibration is unacceptable.
8.0 Model Input Data
a. Data bases and related procedures for
estimating input parameters are an integral
part of the modeling procedure. The most
appropriate data available should always be
selected for use in modeling analyses.
Concentrations can vary widely depending
on the source data or meteorological data
used. Input data are a major source of
uncertainties in any modeling analysis. This
section attempts to minimize the uncertainty
associated with data base selection and use
by identifying requirements for data used in
modeling. A checklist of input data
requirements for modeling analyses is posted
on EPA’s Internet SCRAM Web site
(subsection 2.3). More specific data
requirements and the format required for the
individual models are described in detail in
the users’ guide for each model.
8.1 Source Data
8.1.1 Discussion
a. Sources of pollutants can be classified as
point, line and area/volume sources. Point
sources are defined in terms of size and may
vary between regulatory programs. The line
sources most frequently considered are
roadways and streets along which there are
well-defined movements of motor vehicles,
but they may 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.
b. Emission factors are compiled in an EPA
publication commonly known as AP–42 82;
an indication of the quality and amount of
data on which many of the factors are based
is also provided. 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.
8.1.2 Recommendations
a. For point source applications the load or
operating condition that causes maximum
ground-level concentrations should be
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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 standards or
PSD increments, this load) a 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. 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. For a steam
power plant, the following (b–h) is typical of
the kind of data on source characteristics and
operating conditions that may be needed.
Generally, input data requirements for air
quality models necessitate the use of metric
units; where English units are common for
engineering usage, a conversion to metric is
required.
b. Plant layout. The connection scheme
between boilers and stacks, and the distance
and direction between stacks, building
parameters (length, width, height, location
and orientation relative to stacks) for plant
structures which house boilers, control
equipment, and surrounding buildings
within a distance of approximately five stack
heights.
c. Stack parameters. For all stacks, the
stack height and inside diameter (meters),
and the temperature (K) and volume flow rate
(actual cubic meters per second) or exit gas
velocity (meters per second) for operation at
100 percent, 75 percent and 50 percent load.
d. Boiler size. For all boilers, the associated
megawatts, 106 BTU/hr, and pounds of steam
per hour, and the design and/or actual fuel
consumption rate for 100 percent load for
coal (tons/hour), oil (barrels/hour), and
natural gas (thousand cubic feet/hour).
e. Boiler parameters. For all boilers, the
percent excess air used, the boiler type (e.g.,
wet bottom, cyclone, etc.), and the type of
firing (e.g., pulverized coal, front firing, etc.).
f. Operating conditions. For all boilers, the
type, amount and pollutant contents of fuel,
the total hours of boiler operation and the
boiler capacity factor during the year, and the
percent load for peak conditions.
g. Pollution control equipment parameters.
For each boiler served and each pollutant
affected, the type of emission control
equipment, the year of its installation, its
design efficiency and mass emission rate, the
date of the last test and the tested efficiency,
the number of hours of operation during the
latest year, and the best engineering estimate
of its projected efficiency if used in
conjunction with coal combustion; data for
any anticipated modifications or additions.
h. Data for new boilers or stacks. For all
new boilers and stacks under construction
a 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.
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and for all planned modifications to existing
boilers or stacks, the scheduled date of
completion, and the data or best estimates
available for items (b) through (g) of this
subsection following completion of
construction or modification.
i. In stationary point source applications
for compliance with short term ambient
standards, SIP control strategies should be
tested using the emission input shown on
Table 8–1. When using a refined model,
sources should be modeled sequentially with
these loads for every hour of the year. To
evaluate SIPs for compliance with quarterly
and annual standards, emission input data
shown in Table 8–1 should again be used.
Emissions from area sources should generally
be based on annual average conditions. The
source input information in each model
user’s guide should be carefully consulted
and the checklist (paragraph 8.0(a)) should
also be consulted for other possible emission
data that could be helpful. NAAQS
compliance demonstrations in a PSD analysis
should follow the emission input data shown
in Table 8–2. For purposes of emissions
trading, new source review and
demonstrations, refer to current EPA policy
and guidance to establish input data.
j. Line source modeling of streets and
highways requires data on the width of the
roadway and the median strip, the types and
amounts of pollutant emissions, the number
of lanes, the emissions from each lane and
the height of emissions. The location of the
ends of the straight roadway segments should
be specified by appropriate grid coordinates.
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.
68241
k. The impact of growth on emissions
should be considered in all modeling
analyses covering existing sources. Increases
in emissions due to planned expansion or
planned fuel switches should be identified.
Increases in emissions at individual sources
that may be associated with a general
industrial/commercial/residential expansion
in multi-source urban areas should also be
treated. For new sources the impact of
growth on emissions should generally be
considered for the period prior to the startup date for the source. Such changes in
emissions should treat increased area source
emissions, changes in existing point source
emissions which were not subject to
preconstruction review, and emissions due to
sources with permits to construct that have
not yet started operation.
TABLE 8–1.—MODEL EMISSION INPUT DATA FOR POINT SOURCES 1
Emission limit
(#/MMBtu) 2
Averaging time
×
Operating level
(MMBtu/hr) 2
Operating factor
(e.g., hr/yr, hr/day)
×
Stationary Point Source(s) Subject to SIP Emission Limit(s) Evaluation for Compliance with Ambient Standards (Including Areawide
Demonstrations)
Annual & quarterly .....................
Maximum allowable emission
limit or federally enforceable
permit limit.
Short term ..................................
Maximum allowable emission
limit or federally enforceable
permit limit.
Actual or design capacity
(whichever is greater), or federally enforceable permit condition.
Actual or design capacity
(whichever is greater), or federally enforceable permit condition.4
Actual operating factor averaged over most recent 2
years.3
Continuous operation, i.e., all
hours of each time period
under consideration (for all
hours of the meteorological
data base).5
Nearby Source(s) 6 7
Same input requirements as for stationary point source(s) above.
Other Source(s) 7
If modeled (subsection 8.2.3), input data requirements are defined below.
Annual & quarterly .....................
Short term ..................................
Maximum allowable emission
limit or federally enforceable
permit limit.6
Maximum allowable emission
limit or federally enforceable
permit limit.6
Annual level when actually
erating, averaged over
most recent 2 years.3
Annual level when actually
erating, averaged over
most recent 2 years.3
opthe
opthe
Actual operating factor averaged over the most recent 2
years.3
Continuous operation, i.e., all
hours of each time period
under consideration (for all
hours of the meteorological
data base).5
1 The model input data requirements shown on this table apply to stationary source control strategies for STATE IMPLEMENTATION PLANS.
For purposes of emissions trading, new source review, or prevention of significant deterioration, other model input criteria may apply. Refer to
the policy and guidance for these programs to establish the input data.
2 Terminology applicable to fuel burning sources; analogous terminology (e.g., #/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 time periods.)
6 See paragraph 8.2.3(c).
7 See paragraph 8.2.3(d).
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TABLE 8–2.—POINT SOURCE MODEL EMISSION INPUT DATA FOR NAAQS COMPLIANCE IN PSD DEMONSTRATIONS
Emission limit
(#/MMBtu) 1
Averaging time
Operating level
(MMBtu/hr) 1
×
Operating factor
(e.g., hr/yr, hr/day)
×
Proposed Major New or Modified Source
Annual & quarterly .....................
Maximum allowable emission
limit or federally enforceable
permit limit.
Maximum allowable emission
limit or federally enforceable
permit limit.
Short term (≤ 24 hours) .............
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
data base).2
Nearby Source(s) 4 6
Annual & quarterly .....................
Maximum allowable emission
limit or federally enforceable
permit limit.5
Short term (≤ 24 hours) .............
Maximum allowable emission
limit or federally enforceable
permit limit.5
Actual or design capacity
(whichever is greater), or federally enforceable permit condition.
Actual or design capacity
(whichever is greater), or federally enforceable permit condition.3
Actual operating factor averaged over the most recent 2
years.7 8
Continuous operation, i.e., all
hours of each time period
under consideration (for all
hours of the meteorological
data base).2
Other Source(s) 6 9
Annual & quarterly .....................
Maximum allowable emission
limit or federally enforceable
permit limit.5
Maximum allowable emission
limit or federally enforceable
permit limit.5
Short term (≤ 24 hours) .............
Annual level when actually
erating, averaged over
most recent 2 years.7
Annual level when actually
erating, averaged over
most recent 2 years.7
opthe
opthe
Actual operating factor averaged over the most recent 2
years.7 8
Continuous operation, i.e., all
hours of each time period
under consideration (for all
hours of the meteorological
data base).2
1 Terminology
applicable to fuel burning sources; analogous terminology (e.g., #/throughput) may be used for other types of sources.
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 time periods.
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 paragraph 8.2.3(c).
6 See paragraph 8.2.3(d).
7 Unless it is determined that this period is not representative.
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 Generally, the ambient impacts from non-nearby (background) sources can be represented by air quality data unless adequate data do not
exist.
2 If
8.2
8.2.1
Background Concentrations
Discussion
a. Background concentrations are an
essential part of the total air quality
concentration to be considered in
determining source impacts. Background air
quality includes pollutant concentrations due
to: (1) Natural sources; (2) nearby sources
other than the one(s) currently under
consideration; and (3) unidentified sources.
b. Typically, air quality data should be
used to establish background concentrations
in the vicinity of the source(s) under
consideration. The monitoring network used
for background determinations should
conform to the same quality assurance and
other requirements as those networks
established for PSD purposes.83 An
appropriate data validation procedure should
be applied to the data prior to use.
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c. If the source is not isolated, it may be
necessary to use a multi-source model to
establish the impact of nearby sources. Since
sources don’t typically operate at their
maximum allowable capacity (which may
include the use of ‘‘dirtier’’ fuels), modeling
is necessary to express the potential
contribution of background sources, and this
impact would not be captured via
monitoring. Background concentrations
should be determined for each critical
(concentration) averaging time.
8.2.2 Recommendations (Isolated Single
Source)
a. Two options (paragraph (b) or (c) of this
section) are available to determine the
background concentration near isolated
sources.
b. Use air quality data collected in the
vicinity of the source to determine the
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background concentration for the averaging
times of concern. Determine the mean
background concentration at each monitor by
excluding values when the source in
question is impacting the monitor. The mean
annual background is the average of the
annual concentrations so determined at each
monitor. For shorter averaging periods, the
meteorological conditions accompanying the
concentrations of concern should be
identified. Concentrations for meteorological
conditions of concern, at monitors not
impacted by the source in question, should
be averaged for each separate averaging time
to determine the average background value.
Monitoring sites inside a 90° sector
downwind of the source may be used to
determine the area of impact. One hour
concentrations may be added and averaged to
determine longer averaging periods.
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c. If there are no monitors located in the
vicinity of the source, a ‘‘regional site’’ may
be used to determine background. A
‘‘regional site’’ is one that is located away
from the area of interest but is impacted by
similar natural and distant man-made
sources.
8.2.3 Recommendations (Multi-Source
Areas)
a. In multi-source areas, two components
of background should be determined:
contributions from nearby sources and
contributions from other sources.
b. Nearby Sources: All sources expected to
cause a significant concentration gradient in
the vicinity of the source or sources under
consideration for emission limit(s) should be
explicitly modeled. The number of such
sources is expected to be small except in
unusual situations. 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 this
term. 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 define
which sources are nearby sources.
c. For compliance with the short-term and
annual ambient standards, the nearby sources
as well as the primary source(s) should be
evaluated using an appropriate Appendix A
model with the emission input data shown
in Table 8–1 or 8–2. When modeling a nearby
source that does not have a permit and the
emission limit 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 emission limit’’ for
such a nearby source may be calculated as
the emission 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.
d. 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) being modeled. Where a
primary source believes that a nearby source
does not, by its nature, operate at the same
time as the primary source being modeled,
the burden is on the primary source to
demonstrate to the satisfaction of the
appropriate reviewing authority (paragraph
3.0(b)) that this is, in fact, the case. Whether
or not the primary source has adequately
demonstrated that fact is a matter of
professional judgement left to the discretion
of the appropriate reviewing authority. 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. Some sources are
only used during certain seasons of the year.
Those sources would not be modeled as
nearby sources during times in which they
do not operate. Similarly, emergency backup
generators that never operate simultaneously
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with the sources that they back up would not
be modeled as nearby sources. To reiterate,
in these examples and other appropriate
cases, the burden is on the primary source
being modeled to make the appropriate
demonstration to the satisfaction of the
appropriate reviewing authority.
e. The impact of the nearby sources should
be examined at locations where interactions
between the plume of the point source under
consideration and those of nearby sources
(plus natural background) can occur.
Significant locations 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. These locations
may be identified through trial and error
analyses.
f. Other Sources: That portion of the
background attributable to all other sources
(e.g., natural sources, minor sources and
distant major sources) should be determined
by the procedures found in subsection 89.2.2
or by application of a model using Table 8–
1 or 8–2.
8.3 Meteorological Input Data
a. 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 data is dependent
on: (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 three dimensional meteorological fields,
as described in paragraphs (c) and (d) below.
b. Model input data are normally obtained
either from the National Weather Service or
as part of a site specific measurement
program. Local universities, Federal Aviation
Administration (FAA), military stations,
industry and pollution control agencies may
also be sources of such data. Some
recommendations for the use of each type of
data are included in this subsection.
c. Regulatory application of AERMOD
requires careful consideration of minimum
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 paramount importance is the
requirement that all meteorological data used
as input to AERMOD must be both laterally
and vertically representative of the transport
and dispersion within the analysis domain.
Where surface conditions vary significantly
over the analysis domain, the emphasis in
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68243
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
representativeness of data that were collected
off-site should be judged, in part, by
comparing the surface characteristics in the
vicinity of the meteorological monitoring site
with the surface characteristics that generally
describe the analysis domain. The surface
characteristics input to AERMET should be
based on the topographic conditions in the
vicinity of the meteorological tower.
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 may
need to be collected very near plume height
to be adequately representative, whereas, for
a variable such as temperature, data from a
station several kilometers away from the
source may in some cases be considered to
be adequately representative.
d. For long range transport modeling
assessments (subsection 6.2.3) or for
assessments where the transport winds are
complex and the application involves a nonsteady-state dispersion model (subsection
7.2.8), use of output from prognostic
mesoscale meteorological models is
encouraged.84 85 86 Some diagnostic
meteorological processors are designed to
appropriately blend available NWS
comparable meteorological observations,
local site specific meteorological
observations, and prognostic mesoscale
meteorological data, using empirical
relationships, to diagnostically adjust the
wind field for mesoscale and local-scale
effects. These diagnostic adjustments can
sometimes be improved through the use of
strategically placed site specific
meteorological observations. The placement
of these special meteorological observations
(often more than one location is needed)
involves expert judgement, and is specific to
the terrain and land use of the modeling
domain. Acceptance for use of output from
prognostic mesoscale meteorological models
is contingent on concurrence by the
appropriate reviewing authorities (paragraph
3.0(b)) that the data are of acceptable quality,
which can be demonstrated through
statistical comparisons with observations of
winds aloft and at the surface at several
appropriate locations.
8.3.1 Length of Record of Meteorological
Data
8.3.1.1 Discussion
a. The model user should acquire enough
meteorological data to ensure that worst-case
meteorological conditions are adequately
represented in the model results. The trend
toward statistically based standards suggests
a need for all meteorological conditions to be
adequately represented in the data set
selected for model input. The number of
years of record needed to obtain a stable
distribution of conditions depends on the
variable being measured and has been
estimated by Landsberg and Jacobs 87 for
various parameters. Although that study
indicates in excess of 10 years may be
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required to achieve stability in the frequency
distributions of some meteorological
variables, such long periods are not
reasonable for model input data. This is due
in part to the fact that hourly data in model
input format are frequently not available for
such periods and that hourly calculations of
concentration for long periods may be
prohibitively expensive. Another study 88
compared various periods from a 17-year
data set to determine the minimum number
of years of data needed to approximate the
concentrations modeled with a 17-year
period of meteorological data from one
station. This study indicated that the
variability of model estimates due to the
meteorological data input was adequately
reduced if a 5-year period of record of
meteorological input was used.
8.3.1.2 Recommendations
a. Five years of representative
meteorological data should be used when
estimating concentrations with an air quality
model. Consecutive years from the most
recent, readily available 5-year period are
preferred. The meteorological data should be
adequately representative, and may be site
specific or from a nearby NWS station. Where
professional judgment indicates NWScollected ASOS (automated surface observing
stations) data are inadequate {for cloud cover
observations}, the most recent 5 years of
NWS data that are observer-based may be
considered for use.
b. The use of 5 years of NWS
meteorological data or at least l year of site
specific data is required. If one year or more
(including partial years), up to five 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
subsection 8.3.3.2.
c. For permitted sources whose emission
limitations are based on a specific year of
meteorological data, that year should be
added to any longer period being used (e.g.,
5 years of NWS data) when modeling the
facility at a later time.
d. For LRT situations (subsection 6.2.3)
and for complex wind situations (paragraph
7.2.8(a)), if only NWS or comparable
standard meteorological observations are
employed, five years of meteorological data
(within and near the modeling domain)
should be used. Consecutive years from the
most recent, readily available 5-year period
are preferred. Less than five, but at least
three, years of meteorological data (need not
be consecutive) may be used if mesoscale
meteorological fields are available, as
discussed in paragraph 8.3(d). These
mesoscale meteorological fields should be
used in conjunction with available standard
NWS or comparable meteorological
observations within and near the modeling
domain.
e. For solely LRT applications (subsection
6.2.3), if site specific meteorological data are
available, these data may be helpful when
used in conjunction with available standard
NWS or comparable observations and
mesoscale meteorological fields as described
in paragraph 8.3.1.2(d).
f. For complex wind situations (paragraph
7.2.8(a)) where site specific meteorological
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data are being relied upon as the basis for
characterizing the meteorological conditions,
a data base of at least 1 full-year of
meteorological data is required. If more data
are available, they should be used. Site
specific meteorological data may have to be
collected at multiple locations. Such data
should have been subjected to quality
assurance procedures as described in
paragraph 8.3.3.2(a), and should be reviewed
for spatial and temporal representativeness.
8.3.2 National Weather Service Data
8.3.2.1 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 model 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 Climatic Data Center (NCDC). These
observations are then preprocessed before
they can be used in the models.
8.3.2.2 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 particular
modeling application, they may be used.
NCDC makes available surface 89 90 and upper
air 91 meteorological data in CD–ROM format.
b. Although most NWS measurements are
made at a standard height of 10 meters, the
actual anemometer height should be used as
input to the preferred model. Note that
AERMOD at a minimum requires wind
observations at a height above ground
between seven times the local surface
roughness height and 100 meters.
c. Wind directions observed by the
National Weather Service 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 NWS data to ensure a
lack of bias in wind direction assignments
within the models.
d. Data from universities, FAA, military
stations, industry and pollution control
agencies may be used if such data are
equivalent in accuracy and detail to the NWS
data, and they are judged to be adequately
representative for the particular application.
8.3.3 Site Specific Data
8.3.3.1 Discussion
a. Spatial or geographical
representativeness is best achieved by
collection of all of the needed model input
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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 92
is available. Site specific data should always
be reviewed for representativeness and
consistency by a qualified meteorologist.
8.3.3.2 Recommendations
a. EPA guidance 92 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.83 93 94
Detailed information on quality assurance is
also available.95 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 data sets to be used in
modeling. Care should be taken to ensure
that meteorological instruments are located
to provide 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 site specific data should be reduced
to hourly averages. Table 8–3 lists the wind
related parameters and the averaging time
requirements.
c. Missing Data Substitution. After valid
data retrieval requirements have been met 92,
hours in the record having missing data
should be treated according to an established
data substitution protocol provided that data
from an adequately representative alternative
site are available. Such protocols are usually
part of the approved monitoring program
plan. Data substitution guidance is provided
in Section 5.3 of reference 92. 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
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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.70
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
observations are unavailable, may be found
in Section 6 of reference 92. In the absence
of requisite data to implement the Turner
method, the 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 92, is modified slightly
from that published from earlier work 96 and
has been evaluated with three site specific
data bases.97 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 92 (note applicable tables in
Section 6). For additional information on the
wind fluctuation methods, several references
are available.98 99 100 101
k. Meteorological Data Preprocessors. The
following meteorological preprocessors are
recommended by EPA: AERMET,102
PCRAMMET,103 MPRM,104 METPRO,105 and
CALMET 106 AERMET, which is patterned
after MPRM, should be used to preprocess all
data for use with AERMOD. Except for
applications that employ AERMOD,
PCRAMMET is the recommended
meteorological preprocessor for use in
applications employing hourly NWS data.
MPRM is a general purpose meteorological
data preprocessor which supports regulatory
models requiring PCRAMMET formatted
(NWS) data. MPRM is available for use in
applications employing site specific
meteorological data. The latest version
(MPRM 1.3) has been configured to
implement the SRDT method for estimating
P–G stability categories. METPRO is the
required meteorological data preprocessor for
use with CTDMPLUS. CALMET is available
for use with applications of CALPUFF. All of
the above mentioned data preprocessors are
available for downloading from EPA’s
Internet SCRAM Web site (subsection 2.3).
TABLE 8–3.—AVERAGING TIMES FOR
SITE SPECIFIC WIND AND TURBULENCE MEASUREMENTS
Averaging
time
(hour)
Parameter
Surface wind speed (for use in
stability determinations) ........
Transport direction ....................
Dilution wind speed ..................
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1
1
1
TABLE 8–3.—AVERAGING TIMES FOR
SITE SPECIFIC WIND AND TURBULENCE MEASUREMENTS—Continued
Parameter
Averaging
time
(hour)
Turbulence measurements (sE
and sA) for use in stability
determinations .......................
Turbulence measurements for
direct input to dispersion
models ...................................
11
1
minimize meander effects in sA when
wind conditions are light and/or variable, determine the hourly average s value from four
sequential 15-minute s’s according to the following formula:
1 To
σ1− hr =
8.3.4
2
2
2
2
σ 15 + σ 15 + σ 15 + σ 15
4
Treatment of Near-Calms and Calms
8.3.4.1 Discussion
a. Treatment of calm or light and variable
wind poses a special problem in model
applications since steady-state Gaussian
plume models assume that concentration is
inversely proportional to wind speed.
Furthermore, concentrations may become
unrealistically large when wind speeds less
than 1 m/s are input to the model.
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.
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, 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
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ER09NO05.002</MATH>
with established site specific meteorological
guidance.92 95
e. Temperature Measurements.
Temperature measurements should be made
at standard shelter height (2m) in accordance
with established site specific meteorological
guidance.92
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 92, and such
guidance should be followed when obtaining
vertical temperature gradient data. AERMET
employs the Bulk Richardson scheme which
requires measurements of temperature
difference. To ensure correct application and
acceptance, AERMOD users should consult
with the appropriate Reviewing Authority
before using the Bulk Richardson scheme for
their analysis.
g. Winds Aloft. 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 required. 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. (For specific
requirements for AERMOD and CTDMPLUS,
see Appendix A.) Specifications for wind
measuring instruments and systems are
contained in reference 92.
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, and CALPUFF). For
specific requirements for CTDMPLUS,
AERMOD, and CALPUFF, see Appendix A.
For technical guidance on measurement and
processing of turbulence parameters, see
reference 92. 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 one hour (Table 8–3). There are other
dispersion models (e.g., BLP, and CALINE3)
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 92. 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.
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
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are used in construction of the modeled wind
speed profile in AERMOD.
8.3.4.2 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 models
listed in Appendix A, a post-processor
computer program, CALMPRO 107 has been
prepared, is available on the SCRAM Internet
Web site (subsection 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
subsection 7.2.8).
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. 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.
9.0
Accuracy and Uncertainty of Models
9.1 Discussion
a. Increasing reliance has been placed on
concentration estimates from models as the
primary basis for regulatory decisions
concerning source permits and emission
control requirements. In many situations,
such as review of a proposed source, no
practical alternative exists. Therefore, there is
an obvious need to know how accurate
models really are and how any uncertainty in
the estimates affects regulatory decisions.
During the 1980’s, attempts were made to
encourage development of standardized
evaluation methods.11 108 EPA recognized the
need for incorporating such information and
has sponsored workshops 109 on model
accuracy, the possible ways to quantify
accuracy, and on considerations in the
incorporation of model accuracy and
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uncertainty in the regulatory process. The
Second (EPA) Conference on Air Quality
Modeling, August 1982 110, was devoted to
that subject.
b. To better deduce the statistical
significance of differences seen in model
performance in the face of unaccounted for
uncertainties and variations, investigators
have more recently explored the use of
bootstrap techniques.111 112 Work is
underway to develop a new generation of
evaluation metrics 16 that takes into account
the statistical differences (in error
distributions) between model predictions and
observations.113 Even though the procedures
and measures are still evolving to describe
performance of models that characterize
atmospheric fate, transport and
diffusion 114 115 116, there has been general
acceptance of a need to address the
uncertainties inherent in atmospheric
processes.
9.1.1 Overview of Model Uncertainty
a. Dispersion models generally attempt to
estimate concentrations at specific sites that
really represent an ensemble average of
numerous repetitions of the same event.16
The event is characterized by measured or
‘‘known’’ conditions that are input to the
models, e.g., wind speed, mixed layer height,
surface heat flux, emission characteristics,
etc. However, in addition to the known
conditions, there are unmeasured or
unknown variations in the conditions of this
event, e.g., unresolved details of the
atmospheric flow such as the turbulent
velocity field. These unknown conditions,
may vary among repetitions of the event. As
a result, deviations in observed
concentrations from their ensemble average,
and from the concentrations estimated by the
model, are likely to occur even though the
known conditions are fixed. Even with a
perfect model that predicts the correct
ensemble average, there are likely to be
deviations from the observed concentrations
in individual repetitions of the event, due to
variations in the unknown conditions. The
statistics of these concentration residuals are
termed ‘‘inherent’’ uncertainty. Available
evidence suggests that this source of
uncertainty alone may be responsible for a
typical range of variation in concentrations of
as much as ±50 percent.117
b. Moreover, there is ‘‘reducible’’
uncertainty 108 associated with the model and
its input conditions; neither models nor data
bases are perfect. Reducible uncertainties are
caused by: (1) Uncertainties in the input
values of the known conditions (i.e.,
emission characteristics and meteorological
data); (2) errors in the measured
concentrations which are used to compute
the concentration residuals; and (3)
inadequate model physics and formulation.
The ‘‘reducible’’ uncertainties can be
minimized through better (more accurate and
more representative) measurements and
better model physics.
c. To use the terminology correctly,
reference to model accuracy should be
limited to that portion of reducible
uncertainty which deals with the physics and
the formulation of the model. The accuracy
of the model is normally determined by an
evaluation procedure which involves the
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comparison of model concentration estimates
with measured air quality data.118 The
statement of accuracy is based on statistical
tests or performance measures such as bias,
noise, correlation, etc.11 However,
information that allows a distinction between
contributions of the various elements of
inherent and reducible uncertainty is only
now beginning to emerge.16 As a result most
discussions of the accuracy of models make
no quantitative distinction between (1)
limitations of the model versus (2)
limitations of the data base and of knowledge
concerning atmospheric variability. The
reader should be aware that statements on
model accuracy and uncertainty may imply
the need for improvements in model
performance that even the ‘‘perfect’’ model
could not satisfy.
9.1.2 Studies of Model Accuracy
a. A number of studies 119 120 have been
conducted to examine model accuracy,
particularly with respect to the reliability of
short-term concentrations required for
ambient standard and increment evaluations.
The results of these studies are not
surprising. Basically, they confirm what
expert atmospheric scientists have said for
some time: (1) Models are more reliable for
estimating longer time-averaged
concentrations than for estimating short-term
concentrations at specific locations; and (2)
the models are reasonably reliable in
estimating the magnitude of highest
concentrations occurring sometime,
somewhere within an area. For example,
errors in highest estimated concentrations of
± 10 to 40 percent are found to be
typical 121 122, i.e., certainly well within the
often quoted factor-of-two accuracy that has
long been recognized for these models.
However, estimates of concentrations that
occur at a specific time and site, are poorly
correlated with actually observed
concentrations and are much less reliable.
b. As noted above, poor correlations
between paired concentrations at fixed
stations may be due to ‘‘reducible’’
uncertainties in knowledge of the precise
plume location and to unquantified inherent
uncertainties. For example, Pasquill 123
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. Uncertainty of five to 10
degrees in the measured wind direction,
which transports the plume, 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.
9.1.3 Use of Uncertainty in DecisionMaking
a. The accuracy of model estimates varies
with the model used, the type of application,
and site specific characteristics. Thus, it is
desirable to quantify the accuracy or
uncertainty associated with concentration
estimates used in decision-making.
Communications between modelers and
decision-makers must be fostered and further
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developed. Communications concerning
concentration estimates currently exist in
most cases, but the communications dealing
with the accuracy of models and its meaning
to the decision-maker are limited by the lack
of a technical basis for quantifying and
directly including uncertainty in decisions.
Procedures for quantifying and interpreting
uncertainty in the practical application of
such concepts are only beginning to evolve;
much study is still required.108 109 110 124 125
b. In all applications of models an effort is
encouraged to identify the reliability of the
model estimates for that particular area and
to determine the magnitude and sources of
error associated with the use of the model.
The analyst is responsible for recognizing
and quantifying limitations in the accuracy,
precision and sensitivity of the procedure.
Information that might be useful to the
decision-maker in recognizing the
seriousness of potential air quality violations
includes such model accuracy estimates as
accuracy of peak predictions, bias, noise,
correlation, frequency distribution, spatial
extent of high concentration, etc. Both space/
time pairing of estimates and measurements
and unpaired comparisons are
recommended. Emphasis should be on the
highest concentrations and the averaging
times of the standards or increments of
concern. Where possible, confidence
intervals about the statistical values should
be provided. However, while such
information can be provided by the modeler
to the decision-maker, it is unclear how this
information should be used to make an air
pollution control decision. Given a range of
possible outcomes, it is easiest and tends to
ensure consistency if the decision-maker
confines his judgement to use of the ‘‘best
estimate’’ provided by the modeler (i.e., the
design concentration estimated by a model
recommended in the Guideline or an
alternate model of known accuracy). This is
an indication of the practical limitations
imposed by current abilities of the technical
community.
c. To improve the basis for decisionmaking, EPA has developed and is
continuing to study procedures for
determining the accuracy of models,
quantifying the uncertainty, and expressing
confidence levels in decisions that are made
concerning emissions controls.126 127
However, work in this area involves
‘‘breaking new ground’’ with slow and
sporadic progress likely. As a result, it may
be necessary to continue using the ‘‘best
estimate’’ until sufficient technical progress
has been made to meaningfully implement
such concepts dealing with uncertainty.
9.1.4 Evaluation of Models
a. A number of actions have been taken to
ensure that the best model is used correctly
for each regulatory application and that a
model is not arbitrarily imposed. First, the
Guideline clearly recommends the most
appropriate model be used in each case.
Preferred models, based on a number of
factors, are identified for many uses. General
guidance on using alternatives to the
preferred models is also provided. Second,
the models have been subjected to a
systematic performance evaluation and a
peer scientific review. Statistical
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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 recommended by the AMS
Woods Hole Workshop 11, were generally
followed. Third, more specific information
has been provided for justifying the site
specific use of alternative models in
previously cited EPA guidance 15, and new
models are under consideration and
review.16 Together these documents provide
methods that allow a judgement to be made
as to what models are most appropriate for
a specific application. For the present,
performance and the theoretical evaluation of
models are being used as an indirect means
to quantify one element of uncertainty in air
pollution regulatory decisions.
b. EPA has participated in a series of
conferences entitled, ‘‘Harmonisation within
Atmospheric Dispersion Modelling for
Regulatory Purposes.’’ 128 for the purpose of
promoting the development of improved
methods for the characterization of model
performance. There is a consensus
developing on what should be considered in
the evaluation of air quality models 129,
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 and
uncertainty 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. Performance
evaluations allow us to decide how well the
model simulates the average temporal and
spatial patterns seen in the observations, and
employ large spatial/temporal scale data sets
(e.g., national data sets). Performance
evaluations also allow determination of
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 allow us to decide if
we get the right answer for the right reason.
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 data sets, there are
severe 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.
c. To extend information from diagnostic
and performance evaluations, sensitivity and
uncertainty analyses are encouraged since
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they can provide additional information on
the effect of inaccuracies in the data bases
and on the uncertainty in model estimates.
Sensitivity analyses can aid in determining
the effect of inaccuracies of variations or
uncertainties in the data bases on the range
of likely concentrations. Uncertainty analyses
can aid in determining the range of likely
concentration values, resulting from
uncertainties in the model inputs, the model
formulations, and parameterizations. Such
information may be used to determine source
impact and to evaluate control strategies.
Where possible, information from such
sensitivity analyses should be made available
to the decision-maker with an appropriate
interpretation of the effect on the critical
concentrations.
9.2 Recommendations
a. No specific guidance on the
quantification of model uncertainty for use in
decision-making is being given at this time.
As procedures for considering uncertainty
develop and become implementable, this
guidance will be changed and expanded. For
the present, continued use of the ‘‘best
estimate’’ is acceptable; however, in specific
circumstances for O3, PM–2.5 and regional
haze, additional information and/or
procedures may be appropriate.32 33
10.0
Regulatory Application of Models
10.1 Discussion
a. Procedures with respect to the review
and analysis of air quality modeling and data
analyses in support of SIP revisions, PSD
permitting or other regulatory requirements
need a certain amount of standardization to
ensure consistency in the depth and
comprehensiveness of both the review and
the analysis itself. This section recommends
procedures that permit some degree of
standardization while at the same time
allowing the flexibility needed to assure the
technically best analysis for each regulatory
application.
b. Dispersion model estimates, especially
with the support of measured air quality
data, are the preferred basis for air quality
demonstrations. Nevertheless, there are
instances where the performance of
recommended dispersion modeling
techniques, by comparison with observed air
quality data, may be shown to be less than
acceptable. Also, there may be no
recommended modeling procedure suitable
for the situation. In these instances, emission
limitations may be established solely on the
basis of observed air quality data as would
be applied to a modeling analysis. The same
care should be given to the analyses of the
air quality data as would be applied to a
modeling analysis.
c. The current NAAQS for SO2 and CO are
both stated in terms of a concentration not to
be exceeded more than once a year. There is
only an annual standard for NO2 and a
quarterly standard for Pb. Standards for fine
particulate matter (PM–2.5) are expressed in
terms of both long-term (annual) and shortterm (daily) averages. The long-term standard
is calculated using the three year average of
the annual averages while the short-term
standard is calculated using the three year
average of the 98th percentile of the daily
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average concentration. For PM–10, the
convention is to compare the arithmetic
mean, averaged over 3 consecutive years,
with the concentration specified in the
NAAQS (50 µg/m3). The 24-hour NAAQS
(150 µg/m3) is met if, over a 3-year period,
there is (on average) no more than one
exceedance per year. As noted in subsection
7.2.1.1, the modeled compliance for this
NAAQS is based on the highest 6th highest
concentration over 5 years. For ozone the
short term 1-hour standard is expressed in
terms of an expected exceedance limit while
the short term 8-hour standard is expressed
in terms of a three year average of the annual
fourth highest daily maximum 8-hour value.
The NAAQS are subjected to extensive
review and possible revision every 5 years.
d. This section discusses general
requirements for concentration estimates and
identifies the relationship to emission limits.
The following recommendations apply to: (1)
Revisions of State Implementation Plans and
(2) the review of new sources and the
prevention of significant deterioration (PSD).
10.2 Recommendations
10.2.1 Analysis Requirements
a. Every 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.
An example of requirements for such an
effort is contained in the Air Quality
Analysis Checklist posted on EPA’s Internet
SCRAM Web site (subsection 2.3). This
checklist suggests the level of detail required
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 preapplication meeting. The protocol
should be written and agreed upon by the
parties concerned, although a formal legal
document is not intended. Changes in such
a protocol are often required as the data
collection and analysis progresses. However,
the protocol establishes a common
understanding of the requirements.
b. An air quality analysis should begin
with a screening model to determine the
potential of the proposed source or control
strategy to violate the PSD increment or
NAAQS. For traditional stationary sources,
EPA guidance 24 should be followed.
Guidance is also available for mobile
sources.48
c. If the concentration estimates from
screening techniques indicate a significant
impact or that the PSD increment or NAAQS
may be approached or exceeded, then a more
refined modeling analysis is appropriate and
the model user should select a model
according to recommendations in Sections 4–
8. In some instances, no refined technique
may be specified in this guide for the
situation. The model user is then encouraged
to submit a model developed specifically for
the case at hand. If that is not possible, a
screening technique may supply the needed
results.
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d. Regional Offices should require permit
applicants to incorporate the pollutant
contributions of all sources into their
analysis. Where necessary this may include
emissions associated with growth in the area
of impact of the new or modified source. PSD
air quality assessments should consider the
amount of the allowable air quality
increment that has already been consumed
by other sources. Therefore, the most recent
source applicant should model the existing
or permitted sources in addition to the one
currently under consideration. This would
permit the use of newly acquired data or
improved modeling techniques if such have
become available since the last source was
permitted. When remodeling, the worst case
used in the previous modeling analysis
should be one set of conditions modeled in
the new analysis. All sources should be
modeled for each set of meteorological
conditions selected.
10.2.2 Use of Measured Data in Lieu of
Model Estimates
a. Modeling is the preferred method for
determining emission limitations for both
new and existing sources. When a preferred
model is available, model results alone
(including background) are sufficient.
Monitoring will normally not be accepted as
the sole basis for emission limitation. In
some instances when the modeling technique
available is only a screening technique, the
addition of air quality data to the analysis
may lend credence to model results.
b. There are circumstances where there is
no applicable model, and measured data may
need to be used. However, only in the case
of a NAAQS assessment for an existing
source should monitoring data alone be a
basis for emission limits. In addition, the
following items (i–vi) should be considered
prior to the acceptance of the measured data:
i. Does a monitoring network exist for the
pollutants and averaging times of concern?
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 data set 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 models are not
applicable?
c. The number of monitors required is a
function of the problem being considered.
The source configuration, terrain
configuration, and meteorological variations
all have an impact on number and placement
of monitors. Decisions can only be made on
a case-by-case basis. Guidance is available for
establishing criteria for demonstrating that a
model is not applicable?
d. Sources should obtain approval from the
appropriate reviewing authority (paragraph
3.0(b)) for the monitoring network prior to
the start of monitoring. A monitoring
protocol agreed to by all concerned parties is
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highly desirable. 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.
10.2.3 Emission Limits
10.2.3.1 Design Concentrations
a. Emission limits should be based on
concentration estimates for the averaging
time that results in the most stringent control
requirements. The concentration used in
specifying emission limits is called the
design value or design concentration and is
a sum of the concentration contributed by the
primary source, other applicable sources,
and—for NAAQS assessments—the
background concentration.
b. To determine the averaging time for the
design value, the most restrictive NAAQS or
PSD increment, as applicable, should be
identified. For a NAAQS assessment, the
averaging time for the design value is
determined by calculating, for each averaging
time, the ratio of the difference between the
applicable NAAQS (S) and the background
concentration (B) to the (model) predicted
concentration (P) (i.e., (S–B)/P). For a PSD
increment assessment, the averaging time for
the design value is determined by
calculating, for each averaging time, the ratio
of the applicable PSD increment (I) and the
model-predicted concentration (P) (i.e., I/P).
The averaging time with the lowest ratio
identifies the most restrictive standard or
increment. If the annual average is the most
restrictive, the highest estimated annual
average concentration from one or a number
of years of data is the design value. When
short term standards are most restrictive, it
may be necessary to consider a broader range
of concentrations than the highest value. For
example, for pollutants such as SO2, the
highest, second-highest concentration is the
design value. For pollutants with statistically
based NAAQS, the design value is found by
determining the more restrictive of: (1) The
short-term concentration over the period
specified in the standard, or (2) the long-term
concentration that is not expected to exceed
the long-term NAAQS. Determination of
design values for PM–10 is presented in more
detail in EPA guidance.34
10.2.3.2 NAAQS Analyses for New or
Modified Sources
a. For new or modified sources predicted
to have a significant ambient impact 83 and to
be located in areas designated attainment or
unclassifiable for the SO2, Pb, NO2, or CO
NAAQS, the demonstration as to whether the
source will cause or contribute to an air
quality violation should be based on: (1) The
highest estimated annual average
concentration determined from annual
averages of individual years; or (2) the
highest, second-highest estimated
concentration for averaging times of 24-hours
or less; and (3) the significance of the spatial
and temporal contribution to any modeled
violation. For Pb, the highest estimated
concentration based on an individual
calendar quarter averaging period should be
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used. Background concentrations should be
added to the estimated impact of the source.
The most restrictive standard should be used
in all cases to assess the threat of an air
quality violation. For new or modified
sources predicted to have a significant
ambient impact 83 in areas designated
attainment or unclassifiable for the PM–10
NAAQS, the demonstration of whether or not
the source will cause or contribute to an air
quality violation should be based on
sufficient data to show whether: (1) The
projected 24-hour average concentrations
will exceed the 24-hour NAAQS more than
once per year, on average; (2) the expected
(i.e., average) annual mean concentration will
exceed the annual NAAQS; and (3) the
source contributes significantly, in a
temporal and spatial sense, to any modeled
violation.
10.2.3.3
Impacts
PSD Air Quality Increments and
a. The allowable PSD increments for
criteria pollutants are established by
regulation and cited in 40 CFR 51.166. These
maximum allowable increases in pollutant
concentrations may be exceeded once per
year at each site, except for the annual
increment that 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.
b. Screening techniques defined in
subsection 4.2.1 can sometimes be used to
estimate short term incremental
concentrations for the first new source that
triggers the baseline in a given area.
However, when multiple incrementconsuming sources are involved in the
calculation, the use of a refined model with
at least 1 year of site specific or 5 years of
(off-site) NWS data is normally required
(subsection 8.3.1.2). In such cases, sequential
modeling must demonstrate that the
allowable increments are not exceeded
temporally and spatially, i.e., for all receptors
for each time period throughout the year(s)
(time period means the appropriate PSD
averaging time, e.g., 3-hour, 24-hour, etc.).
c. The PSD regulations require an
estimation of the SO2, particulate matter
(PM–10), and NO2 impact on any Class I area.
Normally, steady-state Gaussian plume
models should not be applied at distances
greater than can be accommodated by the
steady state assumptions inherent in such
models. The maximum distance for refined
steady-state Gaussian plume model
application for regulatory purposes is
generally considered to be 50km. Beyond the
50km range, screening techniques may be
used to determine if more refined modeling
is needed. If refined models are needed, long
range transport models should be considered
in accordance with subsection 6.2.3. As
previously noted in Sections 3 and 7, the
need to involve the Federal Land Manager in
decisions on potential air quality impacts,
particularly in relation to PSD Class I areas,
cannot be overemphasized.
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11.0 Bibliography a
American Meteorological Society.
Symposia on Turbulence, Diffusion, and Air
Pollution (1st–10th); 1971–1992. Symposia
on Boundary Layers & Turb. 11th–12th;
1995–1997. Boston, MA.
American Meteorological Society, 1977–
1998. Joint Conferences on Applications of
Air Pollution Meteorology (1st–10th).
Sponsored by the American Meteorological
Society and the Air & Waste Management
Association. Boston, MA.
American Meteorological Society, 1978.
Accuracy of Dispersion Models. Bulletin of
the American Meteorological Society, 59(8):
1025–1026.
American Meteorological Society, 1981.
Air Quality Modeling and the Clean Air Act:
Recommendations to EPA on Dispersion
Modeling for Regulatory Applications.
Boston, MA.
Briggs, G.A., 1969. Plume Rise. U.S.
Atomic Energy Commission Critical Review
Series, Oak Ridge National Laboratory, Oak
Ridge, TN.
Drake, R.L. and S.M. Barrager, 1979.
Mathematical Models for Atmospheric
Pollutants. EPRI EA–1131. Electric Power
Research Institute, Palo Alto, CA.
Environmental Protection Agency, 1978.
Workbook for Comparison of Air Quality
Models. Publication No. EPA–450/2–78–028a
and b. Office of Air Quality Planning &
Standards, Research Triangle Park, NC.
Erisman J.W., Van Pul A. and Wyers P.
(1994) Parameterization of surface resistance
for the quantification of atmospheric
deposition of acidifying pollutants and
ozone. Atmos. Environ., 28: 2595–2607.
Fox, D.G., and J.E. Fairobent, 1981. NCAQ
Panel Examines Uses and Limitations of Air
Quality Models. Bulletin of the American
Meteorological Society, 62(2): 218–221.
Gifford, F.A., 1976. Turbulent Diffusion
Typing Schemes: A Review. Nuclear Safety,
17(1): 68–86.
Gudiksen, P.H., and M.H. Dickerson, Eds.,
Executive Summary: Atmospheric Studies in
Complex Terrain Technical Progress Report
FY–1979 Through FY–1983. Lawrence
Livermore National Laboratory, Livermore,
CA. (Docket Reference No. II–I–103).
Hanna, S.R., G.A. Briggs, J. Deardorff, B.A.
Egan, G.A. Gifford and F. Pasquill, 1977.
AMS Workshop on Stability Classification
Schemes And Sigma Curves—Summary of
Recommendations. Bulletin of the American
Meteorological Society, 58(12): 1305–1309.
Hanna, S.R., G.A. Briggs and R.P. Hosker,
Jr., 1982. Handbook on Atmospheric
Diffusion. Technical Information Center, U.S.
Department of Energy, Washington, D.C.
Haugen, D.A., Workshop Coordinator,
1975. Lectures on Air Pollution and
Environmental Impact Analyses. Sponsored
by the American Meteorological Society,
Boston, MA.
Hoffnagle, G.F., M.E. Smith, T.V. Crawford
and T.J. Lockhart, 1981. On-site
Meteorological Instrumentation
Requirements to Characterize Diffusion from
Point Sources—A Workshop, 15–17 January
a The documents listed here are major sources of
supplemental information on the theory and
application of mathematical air quality models.
PO 00000
Frm 00033
Fmt 4701
Sfmt 4700
68249
1980, Raleigh, NC. Bulletin of the American
Meteorological Society, 62(2): 255–261.
Hunt, J.C.R., R.G. Holroyd, D.J. Carruthers,
A.G. Robins, D.D. Apsley, F.B. Smith and D.J.
Thompson, 1990. Developments in Modeling
Air Pollution for Regulatory Uses. In
Proceedings of the 18th NATO/CCMS
International Technical Meeting on Air
Pollution Modeling and its Application,
Vancouver, Canada. Also In Air Pollution
Modeling and its Application VIII (1991). H.
van Dop and D.G. Steyn, eds. Plenum Press,
New York, NY. pp. 17–59
Pasquill, F. and F.B. Smith, 1983.
Atmospheric Diffusion, 3rd Edition. Ellis
Horwood Limited, Chichester, West Sussex,
England, 438pp.
Randerson, D., Ed., 1984. Atmospheric
Science and Power Production. DOE/TIC
2760l. Office of Scientific and Technical
Information, U.S. Department of Energy, Oak
Ridge, TN.
Scire, J.S. and L.L. Schulman, 1980:
Modeling plume rise from low-level buoyant
line and point sources. AMS/APCA Second
Joint Conference on Applications of Air
Pollution Meteorology, March 24–27, New
Orleans, LA.
Smith, M.E., Ed., 1973. Recommended
Guide for the Prediction of the Dispersion of
Airborne Effluents. The American Society of
Mechanical Engineers, New York, NY.
Stern, A.C., Ed., 1976. Air Pollution, Third
Edition, Volume I: Air Pollutants, Their
Transformation and Transport. Academic
Press, New York, NY.
Turner, D.B., 1979. Atmospheric
Dispersion Modeling: A Critical Review.
Journal of the Air Pollution Control
Association, 29(5): 502–519.
Venkatram, A. and J.C. Wyngaard, Editors,
1988. Lectures on Air Pollution Modeling.
American Meteorological Society, Boston,
MA. 390pp.
12.0 References
1. Code of Federal Regulations; Title 40
(Protection of Environment). 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. (Available at:
https://www.epa.gov/ttn/nsr/)
3. Code of Federal Regulations; Title 40
(Protection of Environment). Sections 51.166
and 52.21.
4. Code of Federal Regulations (Title 40,
Part 50): Protection of the Environment;
National Primary and Secondary Ambient
Air Quality Standards.
5. 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)
6. Environmental Protection Agency, 1980.
Guidelines on Air Quality Models. Federal
Register, 45(61): 20157–20158.
7. 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
E:\FR\FM\09NOR3.SGM
09NOR3
68250
Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations
Modeling for Complex Sources, St. Louis,
MO.
8. Environmental Protection Agency, 1986.
Evaluation of Mobile Source Air Quality
Simulation Models. Publication No. EPA–
450/4–86–002. Office of Air Quality Planning
& Standards, Research Triangle Park, NC.
(NTIS No. PB 86–167293)
9. Strimaitis, D.G., J.S. Scire and J.C.
Chang. 1998. Evaluation of the CALPUFF
Dispersion Model with Two Power Plant
Data Sets. Tenth Joint Conference on the
Application of Air Pollution Meteorology,
Phoenix, Arizona. American Meteorological
Society, Boston, MA. January 11–16, 1998.
10. Environmental Protection Agency,
2003. AERMOD: Latest Features and
Evaluation Results. Publication No. EPA–
454/R–03–003. U.S. Environmental
Protection Agency, Research Triangle Park,
NC. (Available at https://www.epa.gov/
scram001/)
11. Fox, D.G., 1981. Judging Air Quality
Model Performance. Bulletin of the American
Meteorological Society, 62(5): 599–609.
12. 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)
13. Allwine, K.J., W.F. Dabberdt and L.L.
Simmons. 1998. Peer Review of the
CALMET/CALPUFF Modeling System.
Prepared by the KEVRIC Company, Inc.
under EPA Contract No. 68–D–98–092 for
Environmental Protection Agency, Research
Triangle Park, NC. (Docket No. A–99–05, II–
A–8)
14. 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)
15. 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. (NTIS
No. PB 93–226082)
16. ASTM D6589: Standard Guide for
Statistical Evaluation of Atmospheric
Dispersion Model Performance. (2000)
17. Environmental Protection Agency,
1995. User’s Guide for the Industrial Source
Complex (ISC3) Dispersion Models, Volumes
1 and 2. Publication Nos. EPA–454/B–95–
003a & b. U.S. Environmental Protection
Agency, Research Triangle Park, NC. (NTIS
Nos. PB 95–222741 and PB 95–222758,
respectively)
18. Hanna, S.R. and R.J. Paine, 1989.
Hybrid Plume Dispersion Model (HPDM)
Development and Evaluation. J. Appl.
Meteorol., 28: 206–224.
19. Hanna, S.R. and J.C. Chang, 1992.
Boundary layer parameterizations for applied
dispersion modeling over urban areas.
Bound. Lay. Meteorol., 58, 229–259.
20. Hanna, S.R. and J.C. Chang, 1993.
Hybrid Plume Dispersion Model (HPDM)
Improvements and Testing at Three Field
Sites. Atmos. Environ., 27A: 1491–1508.
21. American Meteorological Society, 1984.
Workshop on Updating Applied Diffusion
Models. 24–27 January 1984. Clearwater,
VerDate Aug<31>2005
16:55 Nov 08, 2005
Jkt 205001
Florida. J. Climate and Appl. Met., 24(11):
1111—1207.
22. Environmental Protection Agency,
2002. AERMOD: Description of Model
Formulation. Research Triangle Park, NC.
EPA Report No. EPA–454/R–02–002d; April
2002; AND 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.
23. 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.
24. Environmental Protection Agency,
1992. Screening Procedures for Estimating
the Air Quality Impact of Stationary Sources,
Revised. Publication No. EPA–454/R–92–
019. U.S. Environmental Protection Agency,
Research Triangle Park, NC. (NTIS No. PB
93–219095)
25. Environmental Protection Agency,
1995. SCREEN3 User’s Guide. Publication
No. EPA–454/B–95–004. U.S. Environmental
Protection Agency, Research Triangle Park,
NC. (NTIS No. PB 95–222766)
26. 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. (NTIS No. PB 91–136564)
27. 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)
28. 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.
29. 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)
30. Meng, Z.D. Dabdub and J.H. Seinfeld,
1997. Chemical Coupling between
Atmospheric Ozone and Particulate Matter.
Science, 277: 116–119.
31. Hidy, G.M, P.M. Roth, J.M. Hales and
R.D. Scheffe, 1998. Fine Particles and
Oxidant Pollution: Developing an Agenda for
Cooperative Research. JAWMA, 50: 613–632.
32. Environmental Protection Agency,
2005. Guidance on the Use of Models and
Other Analyses in Attainment
Demonstrations for the 8-hr Ozone NAAQS
(Draft Final). Office of Air Quality Planning
& Standards, Research Triangle Park, NC.
(Latest version available on SCRAM Web site
as draft-final-O3.pdf; see subsection 2.3)
33. Environmental Protection Agency,
2005. Guidance on the Use of Models and
PO 00000
Frm 00034
Fmt 4701
Sfmt 4700
Other Analyses in Attainment
Demonstrations for the PM–2.5 NAAQS and
Regional Haze Goals. Office of Air Quality
Planning & Standards, Research Triangle
Park, NC. (As of May 2005, this document
has not been finalized; latest version
available on SCRAM Web site as draftpm.pdf; see subsection 2.3)
34. Environmental Protection Agency,
1987. PM–10 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)
35. U.S. Forest Service, 1996. User
Assessment of Smoke-Dispersion Models for
Wildland Biomass Burning. USDA, Pacific
Northwest Research Station, Portland, OR.
General Technical Report PNW–GTR–379.
30pp. (NTIS No. PB 97–163380)
36. Hanrahan, P.L., 1999. The Polar
Volume Polar Ratio Method for Determining
NO2 / NOX Ratios in Modeling—Part I:
Methodology. J. Air & Waste Manage. Assoc.,
49: 1324–1331.
37. 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)
38. 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)
39. Environmental Protection Agency,
1998. EPA Third-Generation Air Quality
Modeling System. Models-3, Volume 9b:
User Manual. Publication No. EPA–600/R–
98/069(b). Office of Research and
Development, Washington, D.C.
40. Gery, M.W. and R.R. Crouse, 1991.
User’s Guide for Executing OZIPR.
Publication No. EPA–600/8–90–069. Office of
Research & Development, Research Triangle
Park, NC. (NTIS No. PB 91–175877)
41. Environmental Protection Agency,
2002. User’s Guide to the Regulatory
Modeling System for Aerosols and
Deposition (REMSAD) Version 7. Prepared
for Environmental Protection Agency under
Contract No. GS–10F–0124J by ICF
Consulting, July 2002. (Available at https://
www.epa.gov/scram001/)
42. Environmental Protection Agency,
2004. EPA–CMB8.2 Users Manual.
Publication No. EPA–452/R–04–011. Office
of Air Quality Planning & Standards,
Research Triangle Park, NC; December 2004.
(Available at https://www.ega.gov/scram001/)
43. Environmental Protection Agency,
2004. Protocol for Applying and Validating
the CMB Model for PM2.5 and VOC.
Publication No. EPA–451/R–04–001. Office
of Air Quality Planning & Standards,
Research Triangle Park, NC; December 2004.
(Available at https://www.ega.gov/scram001/)
44. Environmental Protection Agency,
1988. Chemical Mass Balance Model
Diagnostics. Publication No. EPA–450/4–88–
005. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. (NTIS
No. PB 88–208319)
45. Paatero, P. and U. Tapper, 1994.
Positive Matrix Factorization: A Non-
E:\FR\FM\09NOR3.SGM
09NOR3
Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations
negative Factor Model with Optimal
Utilization of Error Estimates of Data Values.
Environmetrics, 5: 111–126. (Other
documents related to PMF may be accessed
via FTP at ftp://rock.helsinki.fi/pub/misc/
pmf.)
46. Lewis, C.W., G.A. Norris, R.C. Henry
and T.L. Conner, 2003. Source
Apportionment of Phoenix PM–2.5 Aerosol
with the Unmix Receptor Model. Journal of
the Air & Waste Management Association,
53(3): 325–338.
47. Environmental Protection Agency,
1994. Guidelines for PM10 Sampling and
Analysis Applicable to Receptor Modeling.
Publication No. EPA–452/R–94–009. Office
of Air Quality Planning & Standards,
Research Triangle Park, NC. (NTIS No. PB
94–177441)
48. Environmental Protection Agency,
1992. Guideline for Modeling Carbon
Monoxide from Roadway Intersections.
Publication No. EPA–454/R–92–005. Office
of Air Quality Planning & Standards,
Research Triangle Park, NC. (NTIS No. PB
93–210391)
49. Environmental Protection Agency,
1992. User’s Guide for CAL3QHC Version 2:
A Modeling Methodology for Predicting
Pollutant Concentrations near Roadway
Intersections. Publication No. EPA–454/R–
92–006. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. (NTIS
No. PB 93–210250)
50. Environmental Protection Agency,
1992. Evaluation of CO Intersection Modeling
techniques Using a New York City Database.
Publication No. EPA–454/R–92–004. Office
of Air Quality Planning & Standards, RTP,
NC 27711. (NTIS No. PB 93–105559)
51. Environmental Protection Agency,
1995. Addendum to the User’s Guide to
CAL3QHC Version 2.0. Staff Report. Office of
Air Quality Planning & Standards, Research
Triangle Park, NC. (Available at https://
www.epa.gov/scram001/)
52. Shannon, J.D., 1987. Mobile Source
Modeling Review. A report prepared under a
cooperative agreement with the
Environmental Protection Agency. 5pp.
(Docket No. A–88–04, II–J–2)
53. Environmental Protection Agency,
1991. Emission Inventory Requirements for
Carbon Monoxide State Implementation
Plans. Publication No. EPA–450/4–91–011.
Office of Air Quality Planning & Standards,
Research Triangle Park, NC. (NTIS No. PB
92–112150)
54. Environmental Protection Agency,
1992. Guideline for Regulatory Application
of the Urban Airshed Model for Areawide
Carbon Monoxide. Publication No. EPA–450/
4–92–011a and b. Office of Air Quality
Planning & Standards, Research Triangle
Park, NC. (NTIS Nos. PB 92–213222 and PB
92–213230)
55. Environmental Protection Agency,
1992. Technical Support Document to Aid
States with the Development of Carbon
Monoxide State Implementation Plans.
Publication No. EPA–452/R–92–003. Office
of Air Quality Planning & Standards,
Research Triangle Park, NC. (NTIS No. PB
92–233055)
56. Chu, S.H. and E.L. Meyer, 1991. Use of
Ambient Ratios to Estimate Impact of NOX
VerDate Aug<31>2005
16:55 Nov 08, 2005
Jkt 205001
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)
57. 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.
58. U.S. Department of Housing and Urban
Development, 1980. Air Quality
Considerations in Residential Planning. U.S.
Superintendent of Documents, Washington,
DC. (GPO Order Nos. 023–000–00577–8,
023–000–00576–0, 023–000–00575–1)
59. Environmental Protection Agency,
1986. Evaluation of Short-Term Long-Range
Transport Models, Volumes I and II.
Publication Nos. EPA–450/4–86–016a and b.
Office of Air Quality Planning & Standards,
Research Triangle Park, NC. (NTIS Nos. PB
87–142337 and PB 87–142345)
60. Environmental Protection Agency,
1998. Interagency Workgroup on Air Quality
Modeling (IWAQM) Phase 2 Summary Report
and Recommendations for Modeling LongRange Transport Impacts. Publication No.
EPA–454/R–98–019. Office of Air Quality
Planning & Standards, Research Triangle
Park, NC.(NTIS No. PB 99–121089)
61. National Acid Precipitation Assessment
Program (NAPAP), 1991. Acid Deposition:
State of Science and Technology. Volume III
Terrestrial, Materials, Health and Visibility
Effects. Report 24, Visibility: Existing and
Historical Conditions—Causes and Effects
Edited by Patricia M. Irving. Washington, DC
129pp.
62. National Research Council, 1993.
Protecting Visibility in National Parks and
Wilderness Areas. National Academy Press,
Washington, DC 446pp.
63. 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)
64. 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)
65. 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)
66. 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. (NTIS No. PB 85–225241)
67. 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/
PO 00000
Frm 00035
Fmt 4701
Sfmt 4700
68251
3–85–022. Office of Research & Development,
Research Triangle Park, NC. (NTIS No. PB
85–203107)
68. Bennett, M.J, M.E. Yansura, I.G.
Hornyik, J.M. Nall, D.G. Caniparoli and C.G.
Ashmore, 2002. Evaluation of the CALPUFF
Long-range Transport Screening Technique
by Comparison to Refined CALPUFF Results
for Several Power Plants in Both the Eastern
and Western United States. Proceedings of
the Air & Waste Management Association’s
95th Annual Conference, June 23–27, 2002;
Baltimore, MD. Paper #43454.
69. Environmental Protection Agency,
1999. Guideline of Data Handling
Conventions for the PM NAAQS. Publication
No. EPA–454/R–99–008. Office of Air
Quality Planning & Standards, Research
Triangle Park. (NTIS PB 99–149023)
70. 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)
71. 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)
72. 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, IIB–8)
73. Auer, Jr., A.H., 1978. Correlation of
Land Use and Cover with Meteorological
Anomalies. Journal of Applied Meteorology,
17(5): 636–643.
74. 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)
75. Turner, D.B., 1964. A Diffusion Model
for an Urban Area. Journal of Applied
Meteorology, 3(1): 83–91.
76. 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.
77. 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)
78. 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.
79. Stull, R.B., 1988. An Introduction to
Boundary Layer Meteorology. Kluwer
Academic Publishers, Boston, MA. 666pp.
80. Environmental Protection Agency,
1988. User’s Guide to SDM—A Shoreline
Dispersion Model. Publication No. EPA–450/
E:\FR\FM\09NOR3.SGM
09NOR3
68252
Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations
4–88–017. Office of Air Quality Planning &
Standards, Research Triangle Park, NC. (NTIS
No. PB 89–164305)
81. 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)
82. 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 Internet Web site at
https://www.epa.gov/ttn/chief/ap42.html;
Volume II can be downloaded from https://
www.epa.gov/omswww/ap42.htm
83. 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)
84. Stauffer, D.R. and Seaman, N.L., 1990.
Use of four-dimensional data assimilation in
a limited-area mesoscale model. Part I:
Experiments with synoptic-scale data.
Monthly Weather Review, 118: 1250–1277.
85. Stauffer, D.R., N.L. Seaman and F.S.
Binkowski, 1991. Use of four-dimensional
data assimilation in a limited-area mesoscale
model. Part II: Effect of data assimilation
within the planetary boundary layer. Monthly
Weather Review, 119: 734–754.
86. Grell, G.A., J. Dudhia, and D.R.
Stauffer, 1994. A Description of the FifthGeneration Penn State/NCAR Mesoscale
Model (MM5). NCAR Technical Note, NCAR/
TN–398+STR, National Center for
Atmospheric Research, Boulder, CO; 138pp.
https://www.mmm.ucar.edu/mm5/mm5home.html
87. Landsberg, H.E. and W.C. Jacobs, 1951.
Compendium of Meteorology. American
Meteorological Society, Boston, MA; pp.
976–992.
88. Burton, C.S., T.E. Stoeckenius and J.P.
Nordin, 1983. The Temporal
Representativeness of Short-Term
Meteorological Data Sets: Implications for Air
Quality Impact Assessments. Systems
Applications, Inc., San Rafael, CA. (Docket
No. A–80–46, II-G–11)
89. 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 Internet Web site at
https://www.NNDC.NOAA.GOV/.
90. 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 Internet Web site at https://
lwf.ncdc.noaa.gov/oa/ncdc.html
91. Radiosonde Data of North America,
1946–1996; 4-volume CD–ROM. August
1996. Produced jointly by Forecast Systems
laboratory and National Climatic Data Center.
VerDate Aug<31>2005
16:55 Nov 08, 2005
Jkt 205001
Can be ordered from NOAA National Data
Center’s Internet Web site at https://
lwf.ncdc.noaa.gov/oa/ncdc.html
92. 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. (PB 2001–
103606) (Available at https://www.epa.gov/
scram001/)
93. ASTM D5527: Standard Practice for
Measuring Surface Winds and Temperature
by Acoustic Means. (1994)
94. ASTM D5741: Standard Practice for
Characterizing Surface Wind Using Wind
Vane and Rotating Anemometer. (1996)
95. 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., Cincinatti,
OH 45268. Phone (513) 569–7562 or (800)
490–9198 (automated request line)
96. 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)
97. 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)
98. 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)
99. 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)
100. Smedman—Hogstrom, A. and V.
Hogstrom, 1978. A Practical Method for
Determining Wind Frequency Distributions
for the Lowest 200m from Routine
Meteorological Data. J. of Applied
Meteorology, 17(7): 942–954.
101. 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)
102. Environmental Protection Agency,
2004. User’s Guide for the AERMOD
Meteorological Preprocessor (AERMET).
Publication No. EPA–454/B–03–002. U.S.
Environmental Protection Agency, Research
Triangle Park, NC. (Available at https://
www.epa.gov/scram001/)
103. 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)
PO 00000
Frm 00036
Fmt 4701
Sfmt 4700
104. Environmental Protection Agency,
1996. Meteorological Processor for
Regulatory Models (MPRM) User’s Guide.
Publication No. EPA–454/B–96–002. Office
of Air Quality Planning & Standards,
Research Triangle Park, NC. (NTIS No. PB
96–180518)
105. 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)
106. Scire, J.S., F.R. Francoise, M.E. Fernau
and R.J. Yamartino, 1998. A User’s Guide for
the CALMET Meteorological Model (Version
5.0). Earth Tech, Inc., Concord, MA. (https://
www.src.com/calpuff/calpuff1.htm)
107. 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)
108. Fox, D.G., 1984. Uncertainty in air
quality modeling. Bulletin of the American
Meteorological Society, 65(1): 27–36.
109. Burton, C.S., 1981. The Role of
Atmospheric Models in Regulatory DecisionMaking: 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)
110. Environmental Protection Agency,
1981. Proceedings of the Second Conference
on Air Quality Modeling, Washington, DC.
Office of Air Quality Planning & Standards,
Research Triangle Park, NC. (Docket No. A–
80–46,
II–M–16)
111. 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.
112. Cox, W.M. and J.A. Tikvart, 1990. A
statistical procedure for determining the best
performing air quality simulation model.
Atmos. Environ., 24A(9): 2387–2395.
113. Oreskes, N.K., K. Shrader-Frechette
and K. Beliz, 1994. Verification, validation
and confirmation of numerical models in the
earth sciences. Science, 263: 641–646.
114. Dekker, C.M., A. Groenendijk, C.J.
Sliggers and G.K. Verboom, 1990. Quality
Criteria for Models to Calculate Air Pollution.
Lucht (Air) 90, Ministry of Housing, Physical
Planning and Environment, Postbus 450,
2260 MB Leidschendam, The Netherlands;
52pp.
115. Weil, J.C., R.I. Sykes and A.
Venkatram, 1992. Evaluating air-quality
models: review and outlook. Journal of
Applied Meteorology, 31: 1121–1145.
116. Cole, S.T. and P.J. Wicks, Editors
(1995): Model Evaluation Group: Report of
the Second Open Meeting. EUR 15990 EN,
European Commission, Directorate-General
XII, Environmental Research Programme, L–
2920 Luxembourg; 77pp.
117. Hanna, S.R., 1982. Natural Variability
of Observed Hourly SO2 and CO
Concentrations in St. Louis. Atmospheric
Environment, 16(6): 1435–1440.
118. Bowne, N.E., 1981. Validation and
Performance Criteria for Air Quality Models.
E:\FR\FM\09NOR3.SGM
09NOR3
Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 / Rules and Regulations
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)
119. Bowne, N.E. and R.J. Londergan, 1983.
Overview, Results, and Conclusions for the
EPRI Plume Model Validation and
Development Project: Plains Site. EPRI EA–
3074. Electric Power Research Institute, Palo
Alto, CA.
120. Moore, G.E., T.E. Stoeckenius and
D.A. Stewart, 1982. A Survey of Statistical
Measures of Model Performance and
Accuracy for Several Air Quality Models.
Publication No. EPA–450/4–83–001. Office of
Air Quality Planning & Standards, Research
Triangle Park, NC. (NTIS No. PB 83–260810)
121. 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)
122. Hanna, S.R., 1993. Uncertainties in air
quality model predictions. Boundary-Layer
Meteorology, 62: 3–20.
123. Pasquill, F., 1974. Atmospheric
Diffusion, 2nd Edition. John Wiley and Sons,
New York, NY; 479pp.
124. Morgan, M.G. and M. Henrion, 1990.
Uncertainty, A Guide to Dealing With
Uncertainty in Quantitative Risk and Policy
Analysis. Cambridge University Press. New
York, NY; 332pp.
125. Irwin, J.S., K. Steinberg, C.
Hakkarinen and H. Feldman, 2001.
Uncertainty in Air Quality Modeling for Risk
Calculations. (CD–ROM) Proceedings of
Guideline on Air Quality Models: A New
Beginning. April 4–6, 2001, Newport, RI, Air
& Waste Management Association.
Pittsburgh, PA; 17pp.
126. Austin, B.S., T.E. Stoeckenius, M.C.
Dudik and T.S. Stocking, 1988. User’s Guide
to the Expected Exceedances System.
Systems Applications, Inc., San Rafael, CA.
Prepared under Contract No. 68–02–4352
Option I for the U.S. Environmental
Protection Agency, Research Triangle Park,
NC. (Docket No. A–88–04, II–I–3)
127. Thrall, A.D., T.E. Stoeckenius and C.S.
Burton, 1985. A Method for Calculating
Dispersion Modeling Uncertainty Applied to
the Regulation of an Emission Source.
Systems Applications, Inc., San Rafael, CA.
Prepared for the U.S. Environmental
Protection Agency, Research Triangle Park,
NC. (Docket No. A–80–46, IV–G–1)
128. ‘‘Ten years of Harmonisation
activities: Past, present and future’’ at https://
www.dmu.dk/AtmosphericEnvironment/
Harmoni/Conferences/Belgirate/
BelgiratePapers.asp.
129. ‘‘A platform for model evaluation’’ at
https://www.dmu.dk/
AtmosphericEnvironment/Harmoni/
Conferences/Belgirate/BelgiratePapers.asp.
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
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A.2
Buoyant Line and Point Source
Dispersion Model (BLP)
A.3 CALINE3
A.4 CALPUFF
A.5 Complex Terrain Dispersion Model
Plus Algorithms for Unstable Situations
(CTDMPLUS)
A.6 Offshore and Coastal Dispersion Model
(OCD)
A.REF References
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 EPA’s Support Center for Regulatory Air
Models (SCRAM) Web site at https://
www.epa.gov/scram001. Documentation is
also available from the National Technical
Information Service (NTIS), https://
www.ntis.gov or U.S. Department of
Commerce, Springfield, VA 22161; phone:
(800) 553–6847. Where possible, accession
numbers are provided.
A.1 AMS/EPA Regulatory Model—
AERMOD
References
Environmental Protection Agency, 2004.
AERMOD: Description of Model
Formulation. Publication No. EPA–454/R–
03–004. U.S. Environmental Protection
Agency, Research Triangle Park, NC 27711;
September 2004. (Available at https://
www.epa.gov/scram001/)
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–
PO 00000
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454/B–03–001. U.S. Environmental
Protection Agency, Research Triangle Park,
NC 27711; September 2004. (Available at
https://www.epa.gov/scram001/)
Environmental Protection Agency, 2004.
User’s Guide for the AERMOD
Meteorological Preprocessor (AERMET).
Publication No. EPA–454/B–03–002. U.S.
Environmental Protection Agency, Research
Triangle Park, NC 27711; November 2004.
(Available at https://www.epa.gov/scram001/)
Environmental Protection Agency, 2004.
User’s Guide for the AERMOD Terrain
Preprocessor (AERMAP). Publication No.
EPA–454/B–03–003. U.S. Environmental
Protection Agency, Research Triangle Park,
NC 27711; October 2004. (Available at https://
www.epa.gov/scram001/)
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.
Availability
The model codes and associated
documentation are available on EPA’s
Internet SCRAM Web site (Section A.0).
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 one
hour to one year (also multiple years).
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
information for input to AERMOD.
a. Recommendations for Regulatory Use
(1) AERMOD is appropriate for the
following applications:
• Point, volume, and area 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
regulatory default mode, pollutant half life or
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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
(edcwww.cr.usgs.gov/doc/edchome/ndcdb/
ndcdb.html) or equivalent (approx. 30-meter
resolution) should be used in all
applications. In some cases, exceptions of the
terrain data requirement may be made in
consultation with the permit/SIP reviewing
authority.
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.
(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).
Surface characteristics may be varied by
wind sector and by season or month. A
morning sounding (in National Weather
Service format) from a representative upper
air station, latitude, longitude, time zone, and
wind speed threshold are also required in
AERMET (instrument threshold is only
required for site specific data). 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. The
surface file contains observed and calculated
surface variables, one record per hour. The
profile file contains the observations made at
each level of a meteorological tower (or
remote sensor), or the one-level observations
taken from other representative data (e.g.,
National Weather Service surface
observations), one record per level per hour.
(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, 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.
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(ii) For recommendations regarding the
length of meteorological record needed to
perform a regulatory analysis with AERMOD,
see Section 8.3.1.
(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 Digital Elevation Model (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; 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
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
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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,
similar to that in the CTDMPLUS model (see
A.5 in this appendix).
(3) Stack-tip downwash and buoyancy
induced dispersion effects are modeled.
Building wake effects are simulated for stacks
less than good engineering practice height
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 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.
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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 (subsection 5.1)
and the Ozone Limiting Method (subsection
5.2.4) and for point-source NO2 analyses are
available as non-regulatory options.
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,
D.C., 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.
U.S. Environmental Protection Agency,
Research Triangle Park, NC. Available at
https://www.epa.gov/scram001/.
A.2 Buoyant Line and Point Source
Dispersion Model (BLP)
Reference
Schulman, Lloyd 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; also available at https://
www.epa.gov/scram001/)
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Availability
c. Output
The computer code is available on EPA’s
Internet SCRAM Web site and also on
diskette (as PB 2002–500051) from the
National Technical Information Service (see
Section A.0).
(1) Printed output (from a separate postprocessor program) includes:
(2) Total concentration or, optionally,
source contribution analysis; monthly and
annual frequency distributions for 1-, 3-, and
24-hour average concentrations; tables of
1-, 3-, and 24-hour average concentrations at
each receptor; table of the annual (or length
of run) average concentrations at each
receptor;
(3) Five highest 1-, 3-, and 24-hour average
concentrations at each receptor; and
(4) Fifty highest 1-, 3-, and 24-hour
concentrations over the receptor field.
d. Type of Model
BLP is a gaussian plume model.
e. Pollutant Types
BLP may be used to model primary
pollutants. This model does not treat settling
and deposition.
f. Source-Receptor Relationship
(1) BLP treats up to 50 point sources, 10
parallel line sources, and 100 receptors
arbitrarily located.
(2) User-input topographic elevation is
applied for each stack and each receptor.
g. Plume Behavior
(1) BLP uses plume rise formulas of
Schulman and Scire (1980).
(2) Vertical potential temperature gradients
of 0.02 Kelvin per meter for E stability and
0.035 Kelvin per meter are used for stable
plume rise calculations. An option for user
input values is included.
(3) Transitional rise is used for line
sources.
(4) Option to suppress the use of
transitional plume rise for point sources is
included.
(5) The building downwash algorithm of
Schulman and Scire (1980) is used.
h. Horizontal Winds
(1) Constant, uniform (steady-state) wind is
assumed for an hour.
Straight line plume transport is assumed to
all downwind distances.
(2) Wind speeds profile exponents of 0.10,
0.15, 0.20, 0.25, 0.30, and 0.30 are used for
stability classes A through F, respectively.
An option for user-defined values and an
option to suppress the use of the wind speed
profile feature are included.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to
zero.
j. Horizontal Dispersion
(1) Rural dispersion coefficients are from
Turner (1969), with no adjustment made for
variations in surface roughness or averaging
time.
(2) Six stability classes are used.
k. Vertical Dispersion
(1) Rural dispersion coefficients are from
Turner (1969), with no adjustment made for
variations in surface roughness.
(2) Six stability classes are used.
(3) Mixing height is accounted for with
multiple reflections until the vertical plume
standard deviation equals 1.6 times the
Abstract
BLP is a Gaussian plume dispersion model
designed to handle unique modeling
problems associated with aluminum
reduction plants, and other industrial sources
where plume rise and downwash effects from
stationary line sources are important.
a. Recommendations for Regulatory Use
(1) The BLP model is appropriate for the
following applications:
• Aluminum reduction plants which
contain buoyant, elevated line sources;
• Rural areas;
• Transport distances less than 50
kilometers;
• Simple terrain; and
• One hour to one year averaging times.
(2) The following options should be
selected for regulatory applications:
(i) Rural (IRU=1) mixing height option;
(ii) Default (no selection) for plume rise
wind shear (LSHEAR), transitional point
source plume rise (LTRANS), vertical
potential temperature gradient (DTHTA),
vertical wind speed power law profile
exponents (PEXP), maximum variation in
number of stability classes per hour (IDELS),
pollutant decay (DECFAC), the constant in
Briggs’ stable plume rise equation (CONST2),
constant in Briggs’ neutral plume rise
equation (CONST3), convergence criterion
for the line source calculations (CRIT), and
maximum iterations allowed for line source
calculations (MAXIT); and
(iii) Terrain option (TERAN) set equal to
0.0, 0.0, 0.0, 0.0, 0.0, 0.0
(3) For other applications, BLP can be used
if it can be demonstrated to give the same
estimates as a recommended model for the
same application, and will subsequently be
executed in that mode.
(4) BLP can be used on a case-by-case basis
with specific options not available in a
recommended model if it can be
demonstrated, using the criteria in Section
3.2, that the model is more appropriate for a
specific application.
b. Input Requirements
(1) Source data: point sources require stack
location, elevation of stack base, physical
stack height, stack inside diameter, stack gas
exit velocity, stack gas exit temperature, and
pollutant emission rate. 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.
(2) Meteorological data: surface weather
data from a preprocessor such as
PCRAMMET which provides hourly stability
class, wind direction, wind speed,
temperature, and mixing height.
(3) Receptor data: locations and elevations
of receptors, or location and size of receptor
grid or request automatically generated
receptor grid.
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mixing height; uniform mixing is assumed
beyond that point.
(4) Perfect reflection at the ground is
assumed.
l. Chemical Transformation
Chemical transformations are treated using
linear decay. Decay rate is input by the user.
m. Physical Removal
Physical removal is not explicitly treated.
n. Evaluation Studies
Schulman, L.L. and J.S. Scire, 1980.
Buoyant Line and Point Source (BLP)
Dispersion Model User’s Guide, P–7304B.
Environmental Research and Technology,
Inc., Concord, MA.
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.
A.3
CALINE3
Reference
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).
Availability
The CALINE3 model is available on
diskette (as PB 95–502712) from NTIS. The
source code and user’s guide are also
available on EPA’s Internet SCRAM Web site
( Section A.0).
Abstract
CALINE3 can be used to estimate the
concentrations of nonreactive pollutants from
highway traffic. This steady-state Gaussian
model can be applied to determine air
pollution concentrations at receptor locations
downwind of ‘‘at-grade,’’ ‘‘fill,’’ ‘‘bridge,’’
and ‘‘cut section’’ highways located in
relatively uncomplicated terrain. The model
is applicable for any wind direction, highway
orientation, and receptor location. The model
has adjustments for averaging time and
surface roughness, and can handle up to 20
links and 20 receptors. It also contains an
algorithm for deposition and settling velocity
so that particulate concentrations can be
predicted.
a. Recommendations for Regulatory Use
CALINE–3 is appropriate for the following
applications:
• Highway (line) sources;
• Urban or rural areas;
• Simple terrain;
• Transport distances less than 50
kilometers; and
• One-hour to 24-hour averaging times.
b. Input Requirements
(1) Source data: up to 20 highway links
classed as ‘‘at-grade,’’ ‘‘fill,’’ ‘‘bridge,’’ or
‘‘depressed’’; coordinates of link end points;
traffic volume; emission factor; source height;
and mixing zone width.
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(2) Meteorological data: wind speed, wind
angle (measured in degrees clockwise from
the Y axis), stability class, mixing height,
ambient (background to the highway)
concentration of pollutant.
(3) Receptor data: coordinates and height
above ground for each receptor.
c. Output
Printed output includes concentration at
each receptor for the specified meteorological
condition.
d. Type of Model
CALINE–3 is a Gaussian plume model.
e. Pollutant Types
CALINE–3 may be used to model primary
pollutants.
f. Source-Receptor Relationship
(1) Up to 20 highway links are treated.
(2) CALINE–3 applies user input location
and emission rate for each link. User-input
receptor locations are applied.
g. Plume Behavior
Plume rise is not treated.
h. Horizontal Winds
(1) User-input hourly wind speed and
direction are applied.
(2) Constant, uniform (steady-state) wind is
assumed for an hour.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to
zero.
j. Horizontal Dispersion
(1) Six stability classes are used.
(2) Rural dispersion coefficients from
Turner (1969) are used, with adjustment for
roughness length and averaging time.
(3) Initial traffic-induced dispersion is
handled implicitly by plume size parameters.
k. Vertical Dispersion
(1) Six stability classes are used.
(2) Empirical dispersion coefficients from
Benson (1979) are used including an
adjustment for roughness length.
(3) Initial traffic-induced dispersion is
handled implicitly by plume size parameters.
(4) Adjustment for averaging time is
included.
l. Chemical Transformation
Not treated.
m. Physical Removal
Optional deposition calculations are
included.
n. Evaluation Studies
Bemis, G.R. et al., 1977. Air Pollution and
Roadway Location, Design, and Operation—
Project Overview. FHWA–CA–TL–7080–77–
25, Federal Highway Administration,
Washington, DC.
Cadle, S.H. et al., 1976. Results of the
General Motors Sulfate Dispersion
Experiment, GMR–2107. General Motors
Research Laboratories, Warren, MI.
Dabberdt, W.F., 1975. Studies of Air
Quality on and Near Highways, Project 2761.
Stanford Research Institute, Menlo Park, CA.
Environmental Protection Agency, 1986.
Evaluation of Mobile Source Air Quality
Simulation Models. EPA Publication No.
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EPA–450/4–86–002. Office of Air Quality
Planning & Standards, Research Triangle
Park, NC. (NTIS No. PB 86–167293)
A.4
CALPUFF
References
Scire, J.S., D.G. Strimaitis and R.J.
Yamartino, 2000. A User’s Guide for the
CALPUFF Dispersion Model (Version 5.0).
Earth Tech, Inc., Concord, MA.
Scire J.S., F.R. Robe, M.E. Fernau and R.J.
Yamartino, 2000. A User’s Guide for the
CALMET Meteorological Model (Version
5.0). Earth Tech, Inc., Concord, MA.
Availability
The model code and its documentation are
available at no cost for download from the
model developers’ Internet Web site: https://
www.src.com/calpuff/calpuff1.htm. You may
also contact Joseph Scire, Earth Tech, Inc.,
196 Baker Avenue, Concord, MA 01742;
Telephone: (978) 371–4270; Fax: (978) 371–
2468; e-mail: JScire@alum.mit.edu.
Abstract
CALPUFF is a multi-layer, multi-species
non-steady-state puff dispersion modeling
system that simulates the effects of time- and
space-varying meteorological conditions on
pollutant transport, transformation, and
removal. CALPUFF is intended for use on
scales from tens of meters from a source to
hundreds of kilometers. It includes
algorithms for near-field effects such as stack
tip downwash, building downwash,
transitional buoyant and momentum plume
rise, rain cap effects, partial plume
penetration, subgrid scale terrain and coastal
interactions effects, and terrain impingement
as well as longer range effects such as
pollutant removal due to wet scavenging and
dry deposition, chemical transformation,
vertical wind shear effects, overwater
transport, plume fumigation, and visibility
effects of particulate matter concentrations.
a. Recommendations for Regulatory Use
(1) CALPUFF is appropriate for long range
transport (source-receptor distances of 50 to
several hundred kilometers) of emissions
from point, volume, area, and line sources.
The meteorological input data should be
fully characterized with time-and-spacevarying three dimensional wind and
meteorological conditions using CALMET, as
discussed in paragraphs 8.3(d) and 8.3.1.2(d)
of Appendix W.
(2) CALPUFF may also be used on a caseby-case basis if it can be demonstrated using
the criteria in Section 3.2 that the model is
more appropriate for the specific application.
The purpose of choosing a modeling system
like CALPUFF is to fully treat stagnation,
wind reversals, and time and space variations
of meteorological conditions on transport and
dispersion, as discussed in paragraph
7.2.8(a).
(3) For regulatory applications of CALMET
and CALPUFF, the regulatory default option
should be used. Inevitably, some of the
model control options will have to be set
specific for the application using expert
judgment and in consultation with the
appropriate reviewing authorities.
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b. Input Requirements
Source Data:
1. Point sources: Source location, stack
height, diameter, exit velocity, exit
temperature, base elevation, wind direction
specific building dimensions (for building
downwash calculations), and emission rates
for each pollutant. Particle size distributions
may be entered for particulate matter.
Temporal emission factors (diurnal cycle,
monthly cycle, hour/season, wind speed/
stability class, or temperature-dependent
emission factors) may also be entered.
Arbitrarily-varying point source parameters
may be entered from an external file.
2. Area sources: Source location and shape,
release height, base elevation, initial vertical
distribution (sz) and emission rates for each
pollutant. Particle size distributions may be
entered for particulate matter. Temporal
emission factors (diurnal cycle, monthly
cycle, hour/season, wind speed/stability
class, or temperature-dependent emission
factors) may also be entered. Arbitrarilyvarying area source parameters may be
entered from an external file. Area sources
specified in the external file are allowed to
be buoyant and their location, size, shape,
and other source characteristics are allowed
to change in time.
3. Volume sources: Source location, release
height, base elevation, initial horizontal and
vertical distributions (sy, sz) and emission
rates for each pollutant. Particle size
distributions may be entered for particulate
matter. Temporal emission factors (diurnal
cycle, monthly cycle, hour/season, wind
speed/stability class, or temperaturedependent emission factors) may also be
entered. Arbitrarily-varying volume source
parameters may be entered from an external
file. Volume sources with buoyancy can be
simulated by treating the source as a point
source and entering initial plume size
parameters—initial (sy, sz)—to define the
initial size of the volume source.
4. Line sources: Source location, release
height, base elevation, average buoyancy
parameter, and emission rates for each
pollutant. Building data may be entered for
line source emissions experiencing building
downwash effects. Particle size distributions
may be entered for particulate matter.
Temporal emission factors (diurnal cycle,
monthly cycle, hour/season, wind speed/
stability class, or temperature-dependent
emission factors) may also be entered.
Arbitrarily-varying line source parameters
may be entered from an external file.
Meteorological Data (different forms of
meteorological input can be used by
CALPUFF):
1. Time-dependent three-dimensional (3–
D) meteorological fields generated by
CALMET. This is the preferred mode for
running CALPUFF. Data inputs used by
CALMET include surface observations of
wind speed, wind direction, temperature,
cloud cover, ceiling height, relative
humidity, surface pressure, and precipitation
(type and amount), and upper air sounding
data (wind speed, wind direction,
temperature, and height) and air-sea
temperature differences (over water).
Optional 3–D meteorological prognostic
model output (e.g., from models such as
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CALMET as well (paragraph 8.3.1.2(d)).
CALMET contains an option to be run in
‘‘No-observations’’ mode (Robe et al., 2002),
which allows the 3–D CALMET
meteorological fields to be based on
prognostic model output alone, without
observations. This allows CALMET and
CALPUFF to be run in prognostic mode for
forecast applications.
2. Single station surface and upper air
meteorological data in CTDMPLUS data file
formats (SURFACE.DAT and PROFILE.DAT
files) or AERMOD data file formats. These
options allow a vertical variation in the
meteorological parameters but no horizontal
spatial variability.
3. Single station meteorological data in
ISCST3 data file format. This option does not
account for variability of the meteorological
parameters in the horizontal or vertical,
except as provided for by the use of stabilitydependent wind shear exponents and average
temperature lapse rates.
Gridded terrain and land use data are
required as input into CALMET when Option
1 is used. Geophysical processor programs
are provided that interface the modeling
system to standard terrain and land use data
bases available from various sources such as
the U.S. Geological Survey (USGS) and the
National Aeronautics and Space
Administration (NASA).
Receptor Data:
CALPUFF includes options for gridded and
non-gridded (discrete) receptors. Special
subgrid-scale receptors are used with the
subgrid-scale complex terrain option. An
option is provided for discrete receptors to be
placed at ground-level or above the local
ground level (i.e., flagpole receptors).
Gridded and subgrid-scale receptors are
placed at the local ground level only.
Other Input:
CALPUFF accepts hourly observations of
ozone concentrations for use in its chemical
transformation algorithm. Monthly
concentrations of ammonia concentrations
can be specified in the CALPUFF input file,
although higher time-resolution ammonia
variability can be computed using the
POSTUTIL program. Subgrid-scale coastlines
can be specified in its coastal boundary file.
Optional, user-specified deposition velocities
and chemical transformation rates can also be
entered. CALPUFF accepts the CTDMPLUS
terrain and receptor files for use in its
subgrid-scale terrain algorithm. Inflow
boundary conditions of modeled pollutants
can be specified in a boundary condition file.
Liquid water content variables including
cloud water/ice and precipitation water/ice
can be used as input for visibility analyses
and other CALPUFF modules.
c. Output
CALPUFF produces files of hourly
concentrations of ambient concentrations for
each modeled species, wet deposition fluxes,
dry deposition fluxes, and for visibility
applications, extinction coefficients.
Postprocessing programs (PRTMET,
CALPOST, CALSUM, APPEND, and
POSTUTIL) provide options for summing,
scaling, analyzing and displaying the
modeling results. CALPOST contains options
for computing of light extinction (visibility)
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and POSTUTIL allows the re-partitioning of
nitric acid and nitrate to account for the
effects of ammonia limitation (Scire et al.,
2001; Escoffier-Czaja and Scire, 2002).
CALPUFF contains an options to output
liquid water concentrations for use in
computing visible plume lengths and
frequency of icing and fogging from cooling
towers and other water vapor sources. The
CALPRO Graphical User Interface (GUI)
contains options for creating graphics such as
contour plots, vector plots and other displays
when linked to graphics software.
d. Type of Model
(1) CALPUFF is a non-steady-state timeand space-dependent Gaussian puff model.
CALPUFF treats primary pollutants and
simulates secondary pollutant formation
using a parameterized, quasi-linear chemical
conversion mechanism. Pollutants treated
include SO2, SO4=, NOX (i.e., NO + NO2),
HNO3, NO3-, NH3, PM–10, PM–2.5, toxic
pollutants and others pollutant species that
are either inert or subject to quasi-linear
chemical reactions. The model includes a
resistance-based dry deposition model for
both gaseous pollutants and particulate
matter. Wet deposition is treated using a
scavenging coefficient approach. The model
has detailed parameterizations of complex
terrain effects, including terrain
impingement, side-wall scrapping, and steepwalled terrain influences on lateral plume
growth. A subgrid-scale complex terrain
module based on a dividing streamline
concept divides the flow into a lift
component traveling over the obstacle and a
wrap component deflected around the
obstacle.
(2) The meteorological fields used by
CALPUFF are produced by the CALMET
meteorological model. CALMET includes a
diagnostic wind field model containing
parameterized treatments of slope flows,
valley flows, terrain blocking effects, and
kinematic terrain effects, lake and sea breeze
circulations, a divergence minimization
procedure, and objective analysis of
observational data. An energy-balance
scheme is used to compute sensible and
latent heat fluxes and turbulence parameters
over land surfaces. A profile method is used
over water. CALMET contains interfaces to
prognostic meteorological models such as the
Penn State/NCAR Mesoscale Model (e.g.,
MM5; Section 12.0, ref. 86), as well as the
RAMS, Ruc and Eta models.
e. Pollutant Types
CALPUFF may be used to model gaseous
pollutants or particulate matter that are inert
or which undergo quasi-linear chemical
reactions, such as SO2, SO4 =, NOX (i.e., NO
+ NO2), HNO3, NO3-, NH3, PM–10, PM–2.5
and toxic pollutants. For regional haze
analyses, sulfate and nitrate particulate
components are explicitly treated.
f. Source-Receptor Relationships
CALPUFF contains no fundamental
limitations on the number of sources or
receptors. Parameter files are provided that
allow the user to specify the maximum
number of sources, receptors, puffs, species,
grid cells, vertical layers, and other model
parameters. Its algorithms are designed to be
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suitable for source-receptor distances from
tens of meters to hundreds of kilometers.
g. Plume Behavior
Momentum and buoyant plume rise is
treated according to the plume rise equations
of Briggs (1975) for non-downwashing point
sources, Schulman and Scire (1980) for line
sources and point sources subject to building
downwash effects using the Schulman-Scire
downwash algorithm, and Zhang (1993) for
buoyant area sources and point sources
affected by building downwash when using
the PRIME building downwash method.
Stack tip downwash effects and partial
plume penetration into elevated temperature
inversions are included. An algorithm to treat
horizontally-oriented vents and stacks with
rain caps is included.
h. Horizontal Winds
A three-dimensional wind field is
computed by the CALMET meteorological
model. CALMET combines an objective
analysis procedure using wind observations
with parameterized treatments of slope flows,
valley flows, terrain kinematic effects, terrain
blocking effects, and sea/lake breeze
circulations. CALPUFF may optionally use
single station (horizontally-constant) wind
fields in the CTDMPLUS, AERMOD or
ISCST3 data formats.
i. Vertical Wind Speed
Vertical wind speeds are not used
explicitly by CALPUFF. Vertical winds are
used in the development of the horizontal
wind components by CALMET.
j. Horizontal Dispersion
Turbulence-based dispersion coefficients
provide estimates of horizontal plume
dispersion based on measured or computed
values of sv. The effects of building
downwash and buoyancy-induced dispersion
are included. The effects of vertical wind
shear are included through the puff splitting
algorithm. Options are provided to use
Pasquill-Gifford (rural) and McElroy-Pooler
(urban) dispersion coefficients. Initial plume
size from area or volume sources is allowed.
k. Vertical Dispersion
Turbulence-based dispersion coefficients
provide estimates of vertical plume
dispersion based on measured or computed
values of sw. The effects of building
downwash and buoyancy-induced dispersion
are included. Vertical dispersion during
convective conditions is simulated with a
probability density function (pdf) model
based on Weil et al. (1997). Options are
provided to use Pasquill-Gifford (rural) and
McElroy-Pooler (urban) dispersion
coefficients. Initial plume size from area or
volume sources is allowed.
l. Chemical Transformation
Gas phase chemical transformations are
treated using parameterized models of SO2
conversion to SO4= and NO conversion to
NO3-, HNO3, and NO2. Organic aerosol
formation is treated. The POSTUTIL program
contains an option to re-partition HNO3 and
NO3- in order to treat the effects of ammonia
limitation.
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m. Physical Removal
Dry deposition of gaseous pollutants and
particulate matter is parameterized in terms
of a resistance-based deposition model.
Gravitational settling, inertial impaction, and
Brownian motion effects on deposition of
particulate matter is included. CALPUFF
contains an option to evaluate the effects of
plume tilt resulting from gravitational
settling. Wet deposition of gases and
particulate matter is parameterized in terms
of a scavenging coefficient approach.
n. Evaluation Studies
Berman, S., J.Y. Ku, J. Zhang and S.T. Rao,
1977. Uncertainties in estimating the mixing
depth—Comparing three mixing depth
models with profiler measurements,
Atmospheric Environment, 31: 3023–3039.
Chang, J.C., P. Franzese, K. Chayantrakom
and S.R. Hanna, 2001. Evaluations of
CALPUFF, HPAC and VLSTRACK with Two
Mesoscale Field Datasets. Journal of Applied
Meteorology, 42(4): 453–466.
Environmental Protection Agency, 1998.
Interagency Workgroup on Air Quality
Modeling (IWAQM) Phase 2 Summary Report
and Recommendations for Modeling LongRange Transport Impacts. EPA Publication
No. EPA–454/R–98–019. Office of Air
Quality Planning & Standards, Research
Triangle Park, NC.
Irwin, J.S., 1997. A Comparison of
CALPUFF Modeling Results with 1997 INEL
Field Data Results. In Air Pollution Modeling
and its Application, XII. Edited by S.E.
Gyrning and N. Chaumerliac. Plenum Press,
New York, NY.
Irwin, J.S., J.S. Scire and D.G. Strimaitis,
1996. A Comparison of CALPUFF Modeling
Results with CAPTEX Field Data Results. In
Air Pollution Modeling and its Application,
XI. Edited by S.E. Gyrning and F.A.
Schiermeier. Plenum Press, New York, NY.
Morrison, K, Z–X Wu, J.S. Scire, J. Chenier
and T. Jeffs-Schonewille, 2003. CALPUFFBased Predictive and Reactive Emission
Control System. 96th A&WMA Annual
Conference & Exhibition, 22–26 June 2003;
San Diego, CA.
Schulman, L.L., D.G. Strimaitis and J.S.
Scire, 2000. Development and evaluation of
the PRIME Plume Rise and Building
Downwash Model. JAWMA, 50: 378–390.
Scire, J.S., Z–X Wu, D.G. Strimaitis and
G.E. Moore, 2001. The Southwest Wyoming
Regional CALPUFF Air Quality Modeling
Study—Volume I. Prepared for the Wyoming
Dept. of Environmental Quality. Available
from Earth Tech at https://www.src.com.
Strimaitis, D.G., J.S. Scire and J.C. Chang,
1998. Evaluation of the CALPUFF Dispersion
Model with Two Power Plant Data Sets.
Tenth Joint Conference on the Application of
Air Pollution Meteorology, Phoenix, Arizona.
American Meteorological Society, Boston,
MA. January 11–16, 1998.
A.5 Complex Terrain Dispersion Model
Plus Algorithms for Unstable Situations
(CTDMPLUS)
Reference
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
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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. (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
This model code is available on EPA’s
Internet SCRAM Web site and also on
diskette (as PB 90–504119) from the National
Technical Information Service (Section A.0).
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
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
• One 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
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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.
• 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.
• 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 4 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 disk 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.
(3) 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.
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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
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
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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.
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.6 Offshore and Coastal Dispersion Model
(OCD)
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. (NTIS Nos. PB
93–144384 and PB 93–144392; also available
at https://www.epa.gov/scram001/)
Availability
This model code is available on EPA’s
Internet SCRAM Web site and also on
diskette (as PB 91–505230) from the National
Technical Information Service (see Section
A.0). 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.
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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).
(2) 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.
(3) 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 files written to disk or
tape 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.
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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
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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.
1. 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.
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.
A. REFERENCES
Benson, P.E., 1979. CALINE3—A Versatile
Dispersion Model for Predicting Air
Pollution Levels Near Highways and Arterial
Streets. Interim Report, Report Number
FHWA/CA/TL–79/23. Federal Highway
Administration, Washington, DC.
Briggs, G.A., 1975. Plume Rise Predictions.
Lectures on Air Pollution and Environmental
Impact Analyses. American Meteorological
Society, Boston, MA, pp. 59–111.
Briggs, G.A., 1984. Analytical
Parameterizations of Diffusion: The
Convective Boundary Layer. Journal of
Climate and Applied Meteorology, 24(11):
1167–1186.
Environmental Protection Agency, 1980.
Recommendations on Modeling (October
1980 Meetings). Appendix G to: Summary of
Comments and Responses on the October
1980 Proposed Revisions to the Guideline on
Air Quality Models. Meteorology and
Assessment Division, Office of Research and
Development, Research Triangle Park, NC
27711.
Environmental Protection Agency, 1998.
Interagency Workgroup on Air Quality
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Modeling (IWAQM) Phase 2 Summary Report
and Recommendations for Modeling LongRange Transport Impacts. Publication No.
EPA–454/R–98–019. (NTIS No. PB 99–
121089).
Escoffier-Czaja, C. and J.S. Scire, 2002. The
Effects of Ammonia Limitation on Nitrate
Aerosol Formation and Visibility Impacts in
Class I Areas. Twelfth AMS/AWMA
Conference on the Application of Air
Pollution Meteorology, 20–24 May 2002;
Norfolk, VA.
Gifford, F.A., Jr. 1976. Turbulent Diffusion
Typing Schemes—A Review. Nuclear Safety,
17: 68–86.
Horst, T.W., 1983. A Correction to the
Gaussian Source-depletion Model. In
Precipitation Scavenging, Dry Deposition and
Resuspension. H. R. Pruppacher, R.G.
Semonin and W.G.N. Slinn, eds., Elsevier,
NY.
Hsu, S.A., 1981. Models for Estimating
Offshore Winds from Onshore Meteorological
Measurements. Boundary Layer Meteorology,
20: 341–352.
Huber, A.H. and W.H. Snyder, 1976.
Building Wake Effects on Short Stack
Effluents. Third Symposium on Atmospheric
Turbulence, Diffusion and Air Quality,
American Meteorological Society, Boston,
MA.
Irwin, J.S., 1979. A Theoretical Variation of
the Wind Profile Power-Law Exponent as a
Function of Surface Roughness and Stability.
Atmospheric Environment, 13: 191–194.
Liu, M.K. et al., 1976. The Chemistry,
Dispersion, and Transport of Air Pollutants
Emitted from Fossil Fuel Power Plants in
California: Data Analysis and Emission
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Impact Model. Systems Applications, Inc.,
San Rafael, CA.
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 Air Quality
Planning & Standards, Research Triangle
Park, NC 27711.
Petersen, W.B., 1980. User’s Guide for
HIWAY–2 A Highway Air Pollution Model.
Publication No. EPA–600/8–80–018. Office of
Research & Development, Research Triangle
Park, NC 27711. (NTIS PB 80–227556)
Rao, T.R. and M.T. Keenan, 1980.
Suggestions for Improvement of the EPA–
HIWAY Model. Journal of the Air Pollution
Control Association, 30: 247–256 (and
reprinted as Appendix C in Petersen, 1980).
Robe, F.R., Z–X. Wu and J.S. Scire, 2002:
Real-time SO2 Forecasting System with
Combined ETA Analysis and CALPUFF
Modeling. Proceedings of the 8th
International Conference on Harmonisation
within Atmospheric Dispersion Modelling
for Regulatory Purposes, 14–17 October 2002;
Sofia, Bulgaria.
Schulman, L.L. and J.S. Scire, 1980:
Buoyant Line and Point Source (BLP)
dispersion model user’s guide. The
Aluminum Association; Washington, DC.
(See A.2 in this appendix.)
Schulman, L.L. and S.R. Hanna, 1986.
Evaluation of Downwash Modification to the
Industrial Source Complex Model. Journal of
the Air Pollution Control Association, 36:
258–264.
Segal, H.M., 1983. Microcomputer
Graphics in Atmospheric Dispersion
PO 00000
Frm 00045
Fmt 4701
Sfmt 4700
68261
Modeling. Journal of the Air Pollution
Control Association, 23: 598–600.
Snyder, W.H., R.S. Thompson, R.E.
Eskridge, R.E. Lawson, I.P. Castro, J.T. Lee,
J.C.R. Hunt, and Y. Ogawa, 1985. The
structure of the strongly stratified flow over
hills: Dividing streamline concept. Journal of
Fluid Mechanics, 152: 249–288.
Turner, D.B., 1969. Workbook of
Atmospheric Dispersion Estimates. PHS
Publication No. 999–26. U.S. Environmental
Protection Agency, Research Triangle, Park,
NC 27711.
Weil, J.C. and R.P. Brower, 1984. An
Updated Gaussian Plume Model for Tall
Stacks. Journal of the Air Pollution Control
Association, 34: 818–827.
Weil, J.C., 1996. A new dispersion
algorithm for stack sources in building
wakes, Paper 6.6. Ninth Joint Conference on
Applications of Air Pollution Meteorology
with A&WMA, January 28–February 2, 1996.
Atlanta, GA.
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.
Zhang, X., 1993. A computational analysis
of the rise, dispersion, and deposition of
buoyant plumes. Ph.D. Thesis, Massachusetts
Institute of Technology, Cambridge, MA.
Zhang, X. and A.F. Ghoniem, 1993. A
computational model for the rise and
dispersion of wind-blown, buoyancy-driven
plumes—I. Neutrally stratified atmosphere.
Atmospheric Environment, 15: 2295–2311.
[FR Doc. 05–21627 Filed 11–8–05; 8:45 am]
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]]>Agencies
[Federal Register Volume 70, Number 216 (Wednesday, November 9, 2005)]
[Rules and Regulations]
[Pages 68218-68261]
From the Federal Register Online via the Government Printing Office [www.gpo.gov]
[FR Doc No: 05-21627]
[[Page 68217]]
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Part III
Environmental Protection Agency
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40 CFR Part 51
Revision to the Guideline on Air Quality Models: Adoption of a
Preferred General Purpose (Flat and Complex Terrain) Dispersion Model
and Other Revisions; Final Rule
Federal Register / Vol. 70, No. 216 / Wednesday, November 9, 2005 /
Rules and Regulations
[[Page 68218]]
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ENVIRONMENTAL PROTECTION AGENCY
40 CFR Part 51
[AH-FRL-7990-9]
RIN 2060-AK60
Revision to the Guideline on Air Quality Models: Adoption of a
Preferred General Purpose (Flat and Complex Terrain) Dispersion Model
and Other Revisions
AGENCY: Environmental Protection Agency (EPA).
ACTION: Final rule.
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SUMMARY: EPA's Guideline on Air Quality Models (``Guideline'')
addresses the regulatory application of air quality models for
assessing criteria pollutants under the Clean Air Act. In today's
action we promulgate several additions and changes to the Guideline. We
recommend a new dispersion model--AERMOD--for adoption in appendix A of
the Guideline. AERMOD replaces the Industrial Source Complex (ISC3)
model, applies to complex terrain, and incorporates a new downwash
algorithm--PRIME. We remove an existing model--the Emissions Dispersion
Modeling System (EDMS)--from appendix A. We also make various editorial
changes to update and reorganize information.
DATES: This rule is effective December 9, 2005. As proposed, beginning
November 9, 2006, the new model--AERMOD--should be used for appropriate
application as replacement for ISC3. During the one-year period
following this promulgation, protocols for modeling analyses based on
ISC3 which are submitted in a timely manner may be approved at the
discretion of the appropriate Reviewing Authority. Applicants are
therefore encouraged to consult with the Reviewing Authority as soon as
possible to assure acceptance during this period.
ADDRESSES: All documents relevant to this rule have been placed in
Docket No. A-99-05 at the following address: Air Docket in the EPA
Docket Center, (EPA/DC) EPA West (MC 6102T), 1301 Constitution Ave.,
NW., Washington, DC 20004. This docket is available for public
inspection and copying between 8 a.m. and 5:30 p.m., Monday through
Friday, at the address above.
FOR FURTHER INFORMATION CONTACT: Tyler J. Fox, Air Quality Modeling
Group (MD-D243-01), Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711;
telephone (919) 541-5562. (Fox.Tyler@epa.gov).
SUPPLEMENTARY INFORMATION:
Outline
I. General Information
II. Background
III. Public Hearing on the April 2000 proposal
IV. Discussion of Public Comments and Issues from our April 21, 2000
Proposal
A. AERMOD and PRIME
B. Appropriate for Proposed Use
C. Implementation Issues/Additional Guidance
D. AERMOD revision and reanalyses in 2003
1. Performance analysis for AERMOD (02222)
a. Non-downwash cases: AERMOD (99351) vs. AERMOD (02222)
b. Downwash cases
2. Analysis of regulatory design concentrations for AERMOD
(02222)
a. Non-downwash cases
b. Downwash cases
c. Complex terrain
E. Emission and Dispersion Modeling System (EDMS)
V. Discussion of Public Comments and Issues from our September 8,
2003 Notice of Data Availability
VI. Final action
VII. Final editorial changes to appendix W
VIII. Statutory and Executive Order Reviews
I. General Information
A. How Can I Get Copies of Related Information?
EPA established an official public docket for this action under
Docket No. A-99-05. The official public docket is the collection of
materials that is available for public viewing at the Air Docket in the
EPA Docket Center, (EPA/DC) EPA West (MC 6102T), 1301 Constitution
Ave., NW., Washington, DC 20004. The EPA Docket Center Public Reading
Room (B102) is open from 8:30 a.m. to 4:30 p.m., Monday through Friday,
excluding legal holidays. The telephone number for the Reading Room is
(202) 566-1744, and the telephone number for the Air Docket is (202)
566-1742. An electronic image of this docket may be accessed via
Internet at www.epa.gov/eDocket, where Docket No. A-99-05 is indexed as
OAR-2003-0201. Materials related to our Notice of Data Availability
(published September 8, 2003) and public comments received pursuant to
the notice were placed in eDocket OAR-2003-0201.\1\
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\1\ https://cascade.epa.gov/RightSite/dk_public_collection_
detail.htm?ObjectType=dk_docket_collection&cid=OAR-2003-
0201&ShowList=items&Action=view.
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Our Air Quality Modeling Group maintain an Internet website
(Support Center for Regulatory Air Models--SCRAM) at: www.epa.gov/
scram001. You may find codes and documentation for models referenced in
today's action on the SCRAM Web site. We have also uploaded various
support documents (e.g., evaluation reports).
II. Background
The Guideline is used by EPA, States, and industry to prepare and
review new source permits and State Implementation Plan revisions. The
Guideline is intended to ensure consistent air quality analyses for
activities regulated at 40 CFR 51.112, 51.117, 51.150, 51.160, 51.166,
and 52.21. We originally published the Guideline in April 1978 and it
was incorporated by reference in the regulations for the Prevention of
Significant Deterioration (PSD) of Air Quality in June 1978. We revised
the Guideline in 1986, and updated it with supplement A in 1987,
supplement B in July 1993, and supplement C in August 1995. We
published the Guideline as appendix W to 40 CFR part 51 when we issued
supplement B. We republished the Guideline in August 1996 (61 FR 41838)
to adopt the CFR system for labeling paragraphs. On April 21, 2000 we
issued a Notice of Proposed Rulemaking (NPR) in the Federal Register
(65 FR 21506), which was the original proposal for today's
promulgation.
III. Public Hearing on the April 2000 Proposal
We held the 7th Conference on Air Quality Modeling (7th conference)
in Washington, DC on June 28-29, 2000. As required by Section 320 of
the Clean Air Act, these conferences take place approximately every
three years to standardize modeling procedures, with special attention
given to appropriate modeling practices for carrying out programs PSD
(42 U.S.C. 7620). This conference served as the forum for receiving
public comments on the Guideline revisions proposed in April 2000. The
7th conference featured presentations in several key modeling areas
that support the revisions promulgated today. A presentation by the
American Meteorological Society (AMS)/EPA Regulatory Model Improvement
Committee (AERMIC) covered the enhanced Gaussian dispersion model with
boundary layer parameterization: AERMOD.\2\ Also at the 7th conference,
the Electric Power Research Institute (EPRI) presented evaluation
results from the recent research efforts to better define and
characterize dispersion around
[[Page 68219]]
buildings (downwash effects). These efforts were part of a program
called the Plume RIse Model Enhancements (PRIME). At the time, PRIME
was integrated within ISC3ST (ISC-PRIME) and the results presented were
within the ISC3 context. As discussed in today's rule, the PRIME
algorithm has now been fully integrated into AERMOD.
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\2\ AMS/EPA Regulatory MODel.
---------------------------------------------------------------------------
We proposed an update to the Emissions and Dispersion Modeling
System (EDMS 3.1), which is used for assessing air quality impacts from
airports. A representative of the Federal Aviation Administration (FAA)
presented a further upgrade to EDMS 4.0 that would include AERMOD and
forthcoming performance evaluations for two airports.
The presentations were followed by a critical review/discussion of
AERMOD and available performance evaluations, facilitated jointly by
the Air & Waste Management Association's AB-3 Committee and the
American Meteorological Society's Committee of Meteorological Aspects
of Air Pollution.
For the new models and modeling techniques proposed in April 2000,
we asked the public to address the following questions:
Has the scientific merit of the models presented been
established?
Are the models' accuracy sufficiently documented?
Are the proposed regulatory uses of individual models for
specific applications appropriate and reasonable?
Do significant implementation issues remain or is
additional guidance needed?
Are there serious resource constraints imposed by modeling
systems presented?
What additional analyses or information are needed?
We placed a transcript of the 7th conference proceedings and a copy
of all written comments, many of which address the above questions, in
Docket No. A-99-05. The comments on AERMOD were reviewed and nearly
every commenter urged us to integrate aerodynamic downwash into AERMOD
(i.e., not to require two models for some analyses). The only comments
calling for further actions were associated with the need for
documentation, evaluation and review of the suggested downwash
enhancement to AERMOD.
As a result of American Meteorological Society (AMS)/EPA Regulatory
Model Improvement Committee's (AERMIC) efforts to revise AERMOD,
incorporating the PRIME algorithm and making certain other incidental
modifications and to respond to public concerns, we believed that the
revised AERMOD merited another public examination of performance
results. Also, since the April 2000 NPR, the Federal Aviation
Administration (FAA) decided to configure EDMS 3.1 to incorporate the
AERMOD dispersion model. FAA presented this strategy at the 7th
conference and performance evaluations at two airports were to be
available before final promulgation. This was in response to public
concern over lack of EDMS evaluation.
On April 15, 2003 we published a Notice of Final Rulemaking (NFR;
68 FR 18440) that adopted CALPUFF in appendix A of the Guideline. We
also made various editorial changes to update and reorganize
information, and removed obsolete models. We announced that action on
AERMOD and the Emissions and Dispersion Model (EDMS) for assessing
airport impacts was being deferred, and would be reconsidered in a
separate action when new information became available for these models.
This deferred action took the form of a Notice of Data Availability
(NDA), which was published on September 8, 2003 (68 FR 52934). In this
notice, we made clear that the purpose of the NDA was to furnish
pertinent technical details related to model changes since the April
2000 NPR. New performance data and evaluation of design concentration
using the revised AERMOD are contained in reports cited later in this
preamble (see section V). In our April 2003 NFR, we stated that results
of EDMS 4.0 performance (with AERMOD) had recently become available. In
the NDA we clarified that these results would not be provided because
of FAA's decision to withdraw EDMS from the Guideline's appendix A, and
we affirmed our support for this removal. We solicited public comments
on the new data and information related to AERMOD.
IV. Discussion of Public Comments and Issues From Our April 21, 2000
Proposal
All comments submitted to Docket No. A-99-05 are filed in Category
IV-D.\3\ We summarized these comments, developed detailed responses,
and documented conclusions on appropriate actions in a Response-to-
Comments document.\4\ In this document, we considered and discussed all
significant comments. Whenever the comments revealed any new
information or suggested any alternative solutions, we considered this
prior to taking final action.
---------------------------------------------------------------------------
\3\ Additional comments received since we published the final
rule on April 15, 2003 (discussed in the previous section) are filed
in category IV-E. This category includes comments received pursuant
to the Notice of Data Availability we published in September 2003.
\4\ Summary of Public Comments and EPA Responses: AERMOD; 7th
Conference on Air Quality Modeling; Washington, DC, June 28-29, 2000
AND Notice of Data Availability--September 8, 2003 (Air Docket A-99-
05, Item V-C-2). This document may also be examined from EPA's SCRAM
Web site at www.epa.gov/scram001.
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The remainder of this preamble section discusses the primary issues
encountered by the Agency during the public comment period associated
with the April 2000 proposal. This overview also serves in part to
explain the changes to the Guideline in today's action, and the main
technical and policy concerns addressed by the Agency.
A. AERMOD and PRIME
AERMOD is a best state-of-the-practice Gaussian plume dispersion
model whose formulation is based on planetary boundary layer
principles. AERMOD provides better characterization of plume dispersion
than does ISC3. At the 7th conference, AERMIC members presented
developmental and evaluation results of AERMOD. Comprehensive comments
were submitted on the AERMOD code and formulation document and on the
AERMET draft User's Guide (AERMET is the meteorological preprocessor
for AERMOD).
As identified in the April 2000 Federal Register proposal,
applications for which AERMOD was suited include assessment of plume
impacts from stationary sources in simple, intermediate, and complex
terrain, for other than downwash and deposition applications. We
invited comments on whether technical concerns had been reasonably
addressed and whether AERMOD is appropriate for its intended
applications. Since AERMOD lacks a general (all-terrain) screening
tool, we invited comment on the practicality of using SCREEN3 as an
interim tool for AERMOD. We also sought comments on minor changes to
the list of acceptable screening techniques for complex terrain.
PRIME was designed to incorporate the latest scientific algorithms
for evaluating building downwash. At the time of the proposal, the
PRIME algorithm for simulating aerodynamic downwash was not
incorporated into AERMOD. For testing purposes, PRIME was implemented
within ISC3ST (short-term average version of the Industrial Source
Complex), which AERMOD was proposed to replace. This special model,
called ISC-PRIME, was proposed for
[[Page 68220]]
aerodynamic downwash and dry deposition. We sought comment on the
technical viability of AERMOD and ISC-PRIME for its intended
applications.
Scientific merit and accuracy. Regarding the scientific merits of
AERMOD, substantial support was expressed in public comments that
AERMOD represents sound and significant advances over ISC3ST. The
scientific merits of this approach have been documented both through
scientific peer review and performance evaluations. The formulation of
AERMOD has been subjected to an extensive, independent peer review.\5\
Findings of the peer review panel suggest that AERMOD's scientific
basis is ``state-of-the-science.'' Additionally, the model formulations
used in AERMOD and the performance evaluations have been accepted for
publication in two refereed journals.\6\ \7\ Finally, the adequacy of
AERMOD's complex terrain approach for regulatory applications is seen
most directly in its performance. AERMOD's complex terrain component
has been evaluated extensively by comparing model-estimated regulatory
design values and concentration frequency distributions with
observations. These comparisons have demonstrated AERMOD's superiority
to ISC3ST and CTDMPLUS (Complex Terrain Dispersion Model PLUS unstable
algorithms) in estimating those flat and complex terrain impacts of
greatest regulatory importance.\8\ For incidental and unique situations
involving a well-defined hill or ridge and where a detailed dispersion
analysis of the spatial pattern of plume impacts is of interest,
CTDMPLUS in the Guideline's appendix A remains available.
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\5\ U.S. Environmental Protection Agency, 2002. Compendium of
Reports from the Peer Review Process for AERMOD. February 2002.
Available at www.epa.gov/scram001/.
\6\ 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.
\7\ 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.
\8\ Paine R. J. et al., 1998. Evaluation Results for AERMOD,
Draft Report. Docket No. A-99-05; II-A-05. Available at
www.epa.gov./scram001/.
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Public comments also supported our conclusion about the scientific
merits of PRIME. A detailed article in a peer-reviewed journal has been
published which contains all the basic equations with clear definitions
of the variables, and the reasoning and references for the model
assumptions.\9\
---------------------------------------------------------------------------
\9\ Schulman, L.L. et al., 2000. Development and Evaluation of
the PRIME Plum Rise and Building Downwash Model. JAWMA 50: 378-390.
---------------------------------------------------------------------------
Although some comments asked for more detailed documentation and
review, there were no comments which questioned the technical
credibility of the PRIME model. In fact, almost every commenter asked
for PRIME to be incorporated into AERMOD. As summarized above, we
believe that the scientific merit of PRIME has been established via (1)
model evaluation and documentation, (2) peer review within the
submittal process to a technical journal, and (3) via the public review
process.
Based on the external peer review of the evaluation report and the
public review comments, we have concluded that: (1) AERMOD's accuracy
is adequately documented; (2) AERMOD's accuracy is an improvement over
ISC3ST's ability to predict measured concentrations; and (3) AERMOD is
an acceptable regulatory air dispersion model replacement for ISC3ST.
Some commenters have identified what they perceived to be
weaknesses in the evaluation and performance of ISC-PRIME,\10\ and some
concerns were raised about the scope of the PRIME evaluation. However,
as shown by the overwhelming number of requests for the incorporation
of PRIME into AERMOD, commenters were convinced that the accuracy of
PRIME, as implemented within the ISC3ST framework, was reasonably
documented and found acceptable for regulatory applications. Although
some commenters requested more evaluations, practical limitations on
the number of valid, available data sets prevented the inclusion of
every source type and setting in the evaluation. All the data bases
that were reasonably available were used in the development and
evaluation of the model, and those data bases were sufficient to
establish the basis for the evaluation. Based on our review of the
documentation and the public comments, we conclude that the accuracy of
PRIME is sufficiently documented and find it acceptable for use in a
dispersion model recommended in the Guideline.
---------------------------------------------------------------------------
\10\ Electric Power Research Institute, 1997. Results of the
Independent Evaluation of ISCST3 and ISC-PRIME. Final Report, TR-
2460026, November 1997. Available at www.epa.gov/scram001/.
---------------------------------------------------------------------------
B. Appropriate for Proposed Use
Responding to a question posed in our April 2000 proposal, the
majority of commenters questioned the reasonableness of requiring
simultaneous use of two models (ISC-PRIME and AERMOD) for those sources
with potential downwash concerns. Commenters urged the Agency to
eliminate the need to use two models for evaluating the same source. In
response to this request, AERMIC developed a version of AERMOD that
incorporates PRIME: AERMOD (02222) and initiated an analysis to insure
that concentration estimates by AERMOD (02222) are equivalent to ISC-
PRIME predictions in areas affected by downwash before it replaces ISC-
PRIME. Careful thought was given to the way that PRIME was incorporated
into AERMOD, with the goal of making the merge seamless. While
discontinuities from the concatenation of these two sets of algorithms
were of concern, we mitigated this situation wherever possible (see
part D of this preamble, and the Response to Comments document \4\).
With regard to testing the performance of AERMOD (02222), we have
carefully confirmed that the AERMOD (02222)'s air quality concentration
predictions in the wake region reasonably compare to those predictions
from ISC-PRIME. In fact, the results indicate that AERMOD (02222)'s
performance matches the performance of ISC-PRIME, and are presented in
an updated evaluation report \11\ and analysis of regulatory design
concentrations.\12\ We discuss AERMOD (02222) performance in detail in
part D.
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\11\ Environmental Protection Agency, 2003. AERMOD: Latest
Features and Evaluation Results. Publication No. EPA-454/R-03-003.
Available at www.epa.gov/scram001/.
\12\ Environmental Protection Agency, 2003. Comparison of
Regulatory Design Concentrations: AERMOD versus ISC3ST, CTDMPLUS,
and ISC-PRIME. Final Report. Publication No. EPA-454/R-03-002.
Available at www.epa.gov/scram001/.
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Because the technical basis for the PRIME algorithms and the AERMOD
formulations have been independently peer-reviewed, we believe that
further peer review of the new model (AERMOD 02222) is not necessary.
The scientific formulation of the PRIME algorithms has not been
changed. However, the coding for the interface between PRIME and the
accompanying dispersion model had to be modified somewhat to
accommodate the different ways that ISC3ST and AERMOD simulate the
atmosphere. The main public concern was the interaction between the two
models and whether the behavior would be appropriate for all reasonable
source settings. This concern was addressed through the extensive
testing conducted within the performance evaluation \11\ and analysis
of design concentrations.\12\ Both sets of
[[Page 68221]]
analyses indicate that the new model is performing acceptably well and
the results are similar to those obtained from the earlier performance
evaluation \8\ \10\ and analysis of regulatory design concentrations
(i.e., for AERMOD (99351)).\13\
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\13\ Peters, W.D. et al., 1999. Comparison of Regulatory Design
Concentrations: AERMOD vs. ISCST3 and CTDMPLUS, Draft Report. Docket
No. A-99-05; II-A-15.
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While dry deposition is treated in ISC3ST, time and resources did
not allow its incorporation in AERMOD (99351). Since no recommendation
for deposition is made for regulatory applications, we did not consider
that the absence of this capability compromises the suitability of
AERMOD for its intended purposes. Nevertheless, a number of commenters
requested that deposition algorithms be added to AERMOD, and we
developed an update to AERMOD (02222) that offers dry and wet
deposition for both gases and particles as an option.
The version of AERMOD under review at the 7th Conference was AERMOD
(99351) and, as mentioned above, AERMIC has made a number of changes to
AERMOD (99351) following this conference. These changes were initiated
in response to public comments and, after the release of a new draft
version of the model, in response to the recommendations from the beta
testers. Changes made to AERMOD include the following:
Adding the PRIME algorithms to the model (response to
public comments);
Modifying the complex terrain algorithms to make AERMOD
less sensitive to the selection of the domain of the study area
(response to public comments);
Modifying the urban dispersion for low-level emission
sources, such as area sources, to produce a more realistic urban
dispersion and, as a part of this change, changing the minimum layer
depth used to calculate the effective dispersion parameters for all
dispersion settings (scientific formulation correction which was
requested by beta testers); and
Upgrading AERMOD to include all the newest features that
exist in the latest version of ISC3ST such as Fortran90 compliance and
allocatable arrays, EVENTS processing and the TOXICS option (response
to public comments).
In the follow-up quality control checking of the model and the
source code, additional changes were identified as necessary and the
following revisions were made:
Adding meander treatment to: (1) Stable and unstable urban
cases, and (2) the rural unstable dispersion settings (only the rural,
stable dispersion setting considered meander in AERMOD (99351)--this
change created a consistent treatment of air dispersion in all
dispersion settings);
Making some changes to the basic meander algorithms
(improved scientific formulation); and
Repairing miscellaneous coding errors.
As we mentioned earlier, the version of AERMOD that is being
promulgated today--AERMOD (02222)--has been subjected to further
performance evaluation \11\ and analysis of design concentrations.\12\
C. Implementation Issues/Additional Guidance
Other than miscellaneous suggestions for certain enhancements for
AERMOD (99351) such as a Fortran90 compilation of the source code,
creation of allocatable arrays, and development of a Windows[supreg]
graphical user interface, no significant implementation obstacles were
identified in public comments.
For AERMET (meteorological preprocessor for AERMOD), we have
implemented some enhancements that commenters suggested. For site-
specific applications, several commenters cited AERMOD's requirements
for NWS cloud cover data. In response, we revised the AERMET to
incorporate the bulk Richardson number methodology. This approach uses
temperature differences near the surface of the earth, which can be
routinely monitored, and eliminates the need for the cloud cover data
at night. We made a number of other revisions in response to public
comments, enabling AERMET to: (1) Use the old and the new Forecasting
Systems Laboratory formats, (2) use the Hourly U.S. Weather
Observations/Automated Surface Observing Stations (HUSWO/ASOS) data,
(3) use site-specific solar radiation and temperature gradient data to
eliminate the need for cloud cover data, (4) appropriately handle
meteorological data from above the arctic circle, and (5) accept a
wider range of reasonable friction velocities and reduce the number of
warning messages. As mentioned earlier, we added a meander component to
the treatment of stable and unstable urban conditions to consistently
treat meander phenomena for all cases.
AERMAP (the terrain preprocessor for AERMOD) has been upgraded in
response to public comments calling for it to: (1) Treat complex
terrain receptors without a dependance on the selected domain, (2)
accommodate the Spatial Data Transfer Standard (SDTS) data available
from the U.S. Geological Survey (USGS), (3) appropriately use Digital
Elevation Model (DEM) data with 2 different datums (NAD27 and NAD83);
(4) accept all 7 digits of the North UTM coordinate, and (5) do more
error-checking in the raw data (mostly checking for missing values, but
not for harsh terrain changes in adjacent points). All of these
recommendations have been implemented.
In response to comments about the selection of the domain affecting
the results of the maximum concentrations in complex terrain and the
way AERMAP estimates the effective hill height scale (hC),
the algorithms within AERMAP and AERMOD have been adjusted so that the
hill height is less sensitive to the arbitrary selection of the domain.
This adjustment has been evaluated against the entire set of evaluation
data. The correction was found to substantially reduce the effect of
the domain size upon the computation of controlling hill heights for
each receptor. Application of this change to the evaluation databases
did not materially affect the evaluation results.
In general, public comments that requested additional guidance were
either obviated by revisions to AERMOD (99351) and its related
preprocessors or deemed unnecessary. In the latter case, the reasons
were explained in the Response-to-Comments document.\4\
Some public comments suggested additional testing of AERMOD
(99351). In fact, after the model revisions that were described earlier
were completed, AERMOD (02222) was subjected to additional testing.\11\
\12\ These new analyses will be discussed in part D.
With respect to a screening version of AERMOD, a tool called
AERSCREEN is being developed with a beta version expected to be
publicly available in Fall 2005. SCREEN3 is the current screening model
in the Guideline, and since SCREEN3 has been successfully applied for a
number of years, we believe that SCREEN3 produces an acceptable degree
of conservatism for regulatory applications and may be used until
AERSCREEN or a similar technique becomes available and tested for
general application.
D. AERMOD Revision and Reanalyses Published In 2003
1. Performance Analysis for AERMOD (02222)
We have tested the performance of AERMOD (02222) by applying all of
the original data sets used to support the version proposed in April,
2000: AERMOD (99351) \8\ and ISC-PRIME.\10\ These data sets include: 5
complex
[[Page 68222]]
terrain data sets, 7 building downwash data sets, and 5 simple terrain
data sets (see appendix A of the Response-to-Comments document \4\).
This performance analysis, which is a check of the model's maximum
concentration predictions against observed data, includes a comparison
of the current version of the new model (AERMOD 02222) with ISC3ST or
ISC-PRIME for downwash conditions. The results and conclusions of the
performance analyses are presented in 2 sections: Non-downwash and
downwash source scenarios.
a. Non-Downwash Cases
For the user community to obtain a full understanding of the
impacts of today's proposal for the non-downwash source scenarios (flat
and complex terrain), our performance evaluation of AERMOD (02222) must
be discussed with respect to the old model, ISC3ST, and with respect to
AERMOD (99351). Based on the evaluation, we have concluded that AERMOD
(02222) significantly outperforms ISC3ST and that AERMOD (02222)'s
performance is even better than that of AERMOD (99351).
Evaluation of AERMOD (99351)
Comparative performance statistics were calculated for both ISC3ST
and AERMOD (99351) using data sets in non-downwash conditions. This
analysis looked at combinations of test sites (flat and complex
terrain), pollutants, and concentration averaging times. Comparisons
indicated very significant improvements in performance when applying
AERMOD (99351). In all but 1 of the total of 20 cases in which AERMOD
(99351) could be compared to ISC3ST, AERMOD performed as well as (but
generally better than) ISC3ST, that is, AERMOD predicted maximum
concentrations that were closer to the measured maximum concentrations.
In the most dramatic case (i.e., Lovett; 24-hr) in which AERMOD
performed better than ISC3ST, AERMOD's maximum concentration
predictions were about the same as the measured concentrations while
the ISC3ST's predicted maximum concentrations were about 9 times higher
than the measured concentrations. In the one case (i.e., Clifty Creek;
3-hr) where ISC3ST performed better than AERMOD (99351), ISC3ST's
concentration predictions matched the observed data and the AERMOD
concentration predictions were about 25% higher than the observed data.
These results were reported in the supporting documentation for AERMOD
(99351).
Evaluation of AERMOD (02222)
With the changes to AERMOD (99351) as outlined above, how has the
performance of the AERMOD been affected? The performance of the current
version of AERMOD is about the same or slightly better than the April
2000 version when a comparison is made over all the available data
sets. There were examples of AERMOD (02222) showing better and poorer
performance when compared to the performance results of AERMOD (99351).
However, for those cases where AERMOD (02222)'s performance was
degraded, the degradation was small. On the other side, there were more
examples where AERMOD (02222) more closely predicted measured
concentrations. The performance improvements were also rather small
but, in general, were somewhat larger than the size of the performance
degradations. There also were a number of cases where the performance
remained unchanged between the 2 models. Thus, overall, there was a
slight improvement in AERMOD's performance and, consequently, we
believe that AERMOD (02222) significantly outperforms ISC3ST for non-
downwash source scenarios.
For AERMOD (02222) with the 5 data bases examined for simple
terrain, the ratios of modeled/observed Robust High Concentration
ranged from 0.77 to 1.11 (1-hr average), 0.98 to 1.24 (3-hr average),
0.94 to 0.97 (24-hr average) and 0.30 to 0.97 (annual average). These
ratios reflect better performance than ISC3ST for all cases.
For AERMOD (02222) with the 5 data bases examined for complex
terrain, these ratios ranged from 1.03 to 1.12 (3-hr average), 0.67 to
1.78 (24-hr average) and 0.54 to 1.59 (annual average). At Tracy--the
only site for which there are 1-hr data--AERMOD performed considerably
better (ratio = 1.04) than either ISC3ST or CTDMPLUS. At three of the
other four sites, AERMOD generally performed much better than either
ISC3ST or (where applicable) alternative models for the 3-hr and 24-hr
averaging times; results were comparable for Clifty Creek (for the 3-hr
averaging times, AERMOD (02222) predictions were only about 5% higher
than ISC3ST's--down from 25% for AERMOD (99351) as described earlier).
At the two sites where annual peak comparisons are available, AERMOD
performed much better than either ISC3ST or alternative models.
b. Downwash Cases
For the downwash data sets, there were combinations of test sites,
pollutants, stack heights and averaging times where the proposed (ISC-
PRIME) model performance could be compared to the performance of AERMOD
(02222) with PRIME incorporated. There was an equal number of non-
downwash cases where AERMOD performed better than ISC-PRIME and where
ISC-PRIME performed better than AERMOD. There was only one case where
there was a significant difference between the two models' performance,
and AERMOD clearly performed better than ISC-PRIME in this case. In all
other cases, the difference in the performance, whether an improvement
or a degradation, was small. This comparison indicated that AERMOD
(02222) performs very similarly, if not somewhat better, when compared
to ISC-PRIME for downwash cases.
2. Analysis of Regulatory Design Concentrations for AERMOD (02222)
Although not a performance tool, the analysis of design
concentrations (``consequence'' analysis) is designed to test model
stability and continuity, and to help the user community understand the
differences to be expected between air dispersion models. The
consequences, or changes in the regulatory concentrations predicted
when using the new model (AERMOD 02222) versus ISC3ST, cover 96 source
scenarios and at least 3 averaging periods per source scenario, and are
evaluated and summarized here. The purpose is to provide the user
community with a sense of potential changes in their air dispersion
analyses when applying the new model over a broad range of source types
and settings. The consequence analysis, in which AERMOD was run for
hundreds of source scenarios, also provides a check for model stability
(abnormal halting of model executions when using valid control files
and input data) and for spurious results (unusually high or low
concentration predictions which are unexplained). The results are
placed into 3 categories: non-downwash source scenarios in flat, simple
terrain; downwash source scenarios in flat terrain; and, complex
terrain source settings. The focus of this discussion is on how design
concentrations change from those predicted by ISC3ST when applying the
latest version of AERMOD versus applying the earlier version of AERMOD
(99351).
a. Non-Downwash Cases
For the non-downwash situations, there were 48 cases covering a
variety of source types (point, area, and volume sources), stack
heights, terrain types (flat and simple), and dispersion
[[Page 68223]]
settings (urban and rural). For each case in the consequence analysis,
we calculated the ratio between AERMOD's regulatory concentration
predictions and ISC3ST's regulatory concentration predictions. The
average ratio of AERMOD to ISC3ST-predicted concentrations changed from
1.14 when applying AERMOD (99351) to 0.96 when applying AERMOD
(02222).\14\ Thus, in general, AERMOD (02222) tends to predict
concentrations closer to ISC3ST than does version 99351 proposed in
April 2000. Also, the variation of the differences between ISC3ST and
AERMOD has decreased with AERMOD (02222). Comparing the earlier
consequence analysis to the latest study with AERMOD (02222), we saw a
25% reduction in the number of cases where the AERMOD-predicted
concentrations differed by over a factor of two from ISC3ST's
predictions.
---------------------------------------------------------------------------
\14\ A ratio of 1.00 indicates that the two models are
predicting the same concentrations. See Table 4.1 in reference 12.
---------------------------------------------------------------------------
b. Downwash Cases
For the downwash analysis, there were 20 cases covering a range of
stack heights, locations of stacks relative to the building, dispersion
settings, and building shapes. As before, we calculated the ratio
regulatory concentration predictions from AERMOD (02222 with PRIME) and
compared them as ratios to those from ISC3ST for each case. For
additional information, we also included ratios with ISC-PRIME that was
also proposed in April 2000.
Calculated over all the 20 cases, and for all averaging times
considered, the average ISC-PRIME to ISC3ST concentration ratio is
about 0.86, whereas for AERMOD (PRIME) to ISC3ST, it is 0.82. The
maximum value of the concentration ratios range from 2.24 for ISC-
PRIME/ISC3ST to 3.67 for AERMOD (PRIME)/ISC3ST. Similarly, the minimum
value of the concentration ratio range from 0.04 for ISC-PRIME/ISC3ST
to 0.08 for AERMOD (PRIME)/ISC3ST. (See Table 4-5 in reference 12.)
Although results above for the two models that use PRIME--AERMOD
(02222) and ISC-PRIME--show differences, we find that building downwash
is not a significant factor in determining the maximum concentrations
in some of the cases, i.e., the PRIME algorithms do not predict a
building cavity concentration. Of those cases where downwash was
important, the average concentration ratios of ISC-PRIME/ISC3ST and
AERMOD (02222)/ISC3ST are about 1. The maximum value of the
concentration ratios range from 2.24 for ISC-PRIME/ISC3ST to 1.87 for
AERMOD (02222)/ISC3ST and the minimum value of the concentration ratios
range from 0.34 for ISC-PRIME/ISC3ST to 0.38 for AERMOD (02222)/ISC3ST.
These results show relatively close agreement between the two PRIME
models. (See Table 4-6 in reference 12.)
ISC3ST does not predict cavity concentrations but comparisons can
be made between AERMOD and ISC-PRIME. The average AERMOD (02222)
predicted 1-hour cavity concentration is about the same (112%) as the
average ISC-PRIME 1-hour cavity concentration. In the extremes, the
AERMOD (02222)-predicted cavity concentrations ranged from about 40%
higher to 15% lower than the corresponding ISC-PRIME cavity
concentration predictions. Thus, in general, where downwash is a
significant factor, AERMOD (02222) and ISC-PRIME predict similar
maximum concentrations. (See Table 4-8 in reference 12.)
Although the same downwash algorithms are used in both models,
there are differences in the melding of PRIME with the core model, and
differences in the way that these models simulate the atmosphere.\15\
The downwash algorithm implementation therefore could not be exactly
the same.
---------------------------------------------------------------------------
\15\ AERMOD uses more complex techniques to estimate temperature
profiles which, in turn, affect the calculation of the plume rise.
Plume rise may affect the cavity and downwash concentrations.
---------------------------------------------------------------------------
c. Complex Terrain
During the testing of AERMOD after modifications were made to the
complex terrain algorithm (see discussion of hill height scale
(hC) in B. Appropriate for Proposed Use in this preamble), a
small error was found in the original complex terrain code while
conducting the consequence analysis. This error was subsequently
repaired. Final testing indicated that the revised complex terrain code
produced reasonable results for the consequence analysis, as described
below.
The analysis of predicted design concentrations included a suite of
complex terrain settings. There were 28 cases covering a variety of
stack heights, stack gas buoyancy values, types of hills, and distances
between source and terrain. The ratios between the AERMOD (02222 &
99351)--predicted maximum concentrations and the ISC3ST maximum
concentrations were calculated for all cases for a series of averaging
times. When comparing AERMOD (99351) to ISC3ST and then AERMOD (02222)
to ISC3ST, the average maximum concentration ratio, the highest ratios
and the lowest ratios were almost unchanged. There were no cases in
either consequence analysis where AERMOD (02222 & 99351) predicted
higher concentrations than those predicted by ISC3ST. Thus, in general,
the consequences of moving from ISC3ST to AERMOD (02222) rather than to
AERMOD (99351) in complex terrain were essentially the same. (See Table
4-9 in reference 12.)
E. Emission and Dispersion Modeling System (EDMS)
The Emissions and Dispersion Modeling System (EDMS) was developed
jointly by the Federal Aviation Administration (FAA) and the U.S. Air
Force in the late 1970s and first released in 1985 to assess the air
quality of proposed airport development projects. EDMS has an emissions
preprocessor and its dispersion module estimates concentrations for
various averaging times for the following pollutants: CO, HC,
NOX, SOX, and suspended particles (e.g., PM-10).
The first published application of EDMS was in December 1986 for
Stapleton International Airport (FAA-EE-11-A/REV2).
In 1988, version 4a4 revised the dispersion module to include an
integral dispersion submodel: GIMM (Graphical Input Microcomputer
Model). This version was proposed for adoption in the Guideline's
appendix A in February 1991 (56 FR 5900). This version was included in
appendix A in July 1993 (58 FR 38816) and recommended for limited
applications for assessments of localized airport impacts on air
quality. FAA later updated EDMS to Version 3.0.
In response to the growing needs of air quality analysts and
changes in regulations (e.g., conformity requirements from the Clean
Air Act Amendment of 1990), FAA updated EDMS to version 3.1, which is
based on the CALINE3 \16\ and PAL2 dispersion kernels. In our April
2000 NPR we proposed to adopt the version 3.1 update to EDMS. However,
this update had not been subjected to performance evaluation and no
studies of EDMS' performance have been cited in appendix A of the
Guideline. Comment was invited on whether this compromises the
viability of EDMS 3.1 as a recommended or preferred model and how this
deficiency can be corrected.
---------------------------------------------------------------------------
\16\ Currently listed in appendix A of the Guideline.
---------------------------------------------------------------------------
Several commenters expressed concern about EDMS 3.1 as a
recommended model in appendix A. Indeed, there were concerns that EDMS
[[Page 68224]]
3.1 had not been as well validated as other models, nor subjected to
peer review, as required by the Guideline's subsection 3.1.1. One of
these commenters suggested that EDMS 3.1 should be presented only as
one of several alternative models.
At the 7th Conference, FAA proposed for appendix A adoption an even
newer, enhanced version of EDMS--version 4.0, which incorporates the
AERMOD dispersion kernel (without alteration). In this system, the
latest version of AERMOD would be employed as a standalone component of
EDMS. This dispersion kernel was to replace PAL2 and CALINE3 currently
in EDMS 3.1. There were no public comments specific to FAA's proposed
AERMOD-based enhancements to EDMS announced after our April 2000 NPR.
In response to written comments on our April 2000 NPR, at the 7th
Conference (transcript) FAA promised a complete evaluation process that
would include sensitivity testing, intermodel comparison, and analysis
of EDMS predictions against field observations. The intermodel
comparisons were proposed for the UK's Atmospheric Dispersion Modeling
System (ADMS).\17\
---------------------------------------------------------------------------
\17\ Cambridge Environmental Research Consultants; https://
www.cerc.co.uk/.
---------------------------------------------------------------------------
As we explained in our September 8, 2003 Notice of Data
Availability, FAA has decided to withdraw EDMS from the Guideline's
appendix A. We stated that no new information was therefore provided in
that notice, and we affirmed support for EDMS' removal from appendix A.
This removal, which we promulgate today, obviates the need for EDMS'
documentation and evaluation at this time.
V. Discussion of Public Comments on Our September 8, 2003 Notice of
Data Availability
As mentioned in section III, after AERMOD was revised pursuant to
comments received on the April 21, 2000 proposal, a Notice of Data
Availability (NDA) was issued on September 8, 2003 to explain the
modifications and to reveal AERMOD's new evaluation data. Public
comments were solicited for 30 days and posted electronically in
eDocket OAR-2003-0201.\1\ (As mentioned in section IV, additional
comments received since we published the final rule on April 15, 2003
are filed in Docket A-99-05; category IV-E.) We summarized these
comments and developed detailed responses; these appear as appendix C
to the Response-to-Comments document.\4\ In appendix C, we considered
and discussed all significant comments, developed responses, and
documented conclusions on appropriate actions for today's notice.
Whenever the comments revealed any new information or suggested any
alternative solutions, we considered them in our final action and made
corrections or enhancements where appropriate.
In the remainder of this preamble section we highlight the main
issues raised by the commenters who reviewed the NDA, and summarize our
responses. These comments broadly fall into two categories: technical/
operational, and administrative.
The technical/operational comments were varied. One commenter
thought EPA's sensitivity studies for simulating area sources were too
limited, and noted that AERMOD, when used to simulate an area source
adjacent to gently sloping terrain, produced ground-level
concentrations not unlike those from ISC3ST. In response we explained
qualitatively how AERMOD interprets this situation and cautioned that
reviewing authorities should be consulted in such scenarios for
guidance on switch settings. Other commenters believed that AERMOD
exhibited unrealistic treatment of complex terrain elements and offered
supporting data. In response, AERMIC concluded that AERMOD does exhibit
terrain amplification factors on the windward side of isolated hills,
where impacts are expected to be greatest. Commenters also presented
evidence that the PRIME algorithm in AERMOD misbehaves in its treatment
of building wake and wind incidence. Another model was cited as having
better skill in this regard. In response, we acknowledged this but
established that AERMOD's capability was acceptable for handling the
majority of building geometries encountered (see Response-to-Comments
document \4\ for more details).
A number of commenters addressed administrative or procedural
matters. Some believed that the transition period for implementation--
one year--is too short. We explained in response that one year is
consistent with past practice and is adequate for most users and
reviewing authorities given our previous experience with new models and
the fact that AERMOD has been in the public domain for several years.
Some were disappointed that the review period (30 days) for the NDA was
too short. We believe that the period was adequate to review the two
reports that presented updated information on the performance and
practical consequences of the model as revised. Regarding the
evaluation/comparison regime used for AERMOD, others objected to the
methodology used to evaluate AERMOD (one that emphasizes Robust High
Concentration), claiming it is ill-suited to the way dispersion models
estimate ambient concentrations. We acknowledged that other methods are
available that are designed to reflect the underlying physics and
formulations of dispersion models, and may be more robust in their
mechanisms to account for the stochastic nature of the atmosphere. In
fact, we cited several recent cases from the literature in which such
methods were applied in evaluations that included AERMOD. We also
explained that the approach taken by AERMIC was based on existing
guidance in section 9 of Appendix W, and expressed a commitment to
explore other methods in the future, including an update to section 9.
We believe however that the evaluation methodology used was reasonable
for its intended purpose--examining a large array of concentrations for
a wide variety of source types--and confers a measure of consistency
given its past use. Other commenters expressed disappointment that
AERMOD wasn't compared to state-of-the-science models as advised in its
peer review report. In response, we cited a substantial list of studies
in which AERMOD has, in fact, been compared to some of these models,
e.g., HPDM and ADMS (in various combinations). On the whole, as we
noted in our response, AERMOD typically performed as well as HPDM and
ADMS, and all of them generally performed better than ISC3ST. Still
others expressed disappointment that the evaluation input data weren't
posted on our Web site until January 22, 2004--three months after the
close of the comment period. We acknowledge that the input data were
not posted when the NDA was published. However, the actual evaluation
input data for AERMOD had not been requested previously, and we did not
believe they were required as a basis for reviewing the reports we
released. Moreover, since the posting, we are unaware of any belated
adverse comments from anyone attempting to access and use the data.
We believe we have carefully considered and responded to public
comments and concerns regarding AERMOD. We have also made efforts to
update appendix W to better reflect current practice in model
solicitation, evaluation and selection. We also have made other
technical revisions so the guidance conforms with the latest form of
the PM-10 National Ambient Air Quality Standard.
[[Page 68225]]
VI. Final Action
In this section we explain the changes to the Guideline in today's
action in terms of the main technical and policy concerns addressed by
the Agency in its response to public comments (sections IV & V). Air
quality modeling involves estimating ambient concentrations using
scientific methodologies selected from a range of possible methods, and
should utilize the most advanced practical technology that is available
at a reasonable cost to users, keeping in mind the intended uses of the
modeling and ensuring transparency to the public. With these changes,
we believe that the Guideline continues to reflect recent advances in
the field and balance these important considerations. Today's action
amends Appendix W of 40 CFR part 51 as detailed below:
AERMOD
Based on the supporting information contained in the docket, and
reflected in peer review and public comments, we find that the AERMOD
modeling system and PRIME are based on sound scientific principles and
provide significant improvements over the current regulatory model,
ISC3ST. AERMOD characterizes plume dispersion better than ISC3ST. The
accuracy of the AERMOD system is generally well-documented and superior
to that of ISC3ST. We are adopting the model based on its performance
and other factors.
Public comments on the April 2000 proposal expressed significant
concern about the need to use two models (AERMOD and ISC-PRIME) to
simulate just one source when downwash posed a potential impact. In
response to this concern we incorporated PRIME into AERMOD and
documented satisfactory tests of the algorithm. AERMOD, with the
inclusion of PRIME, is now appropriate and practical for regulatory
applications.
The state-of-the-science for modeling atmospheric deposition
continues to evolve, the best techniques are currently being assessed,
and their results are being compared with observations. Consequently,
as we now say in Guideline paragraph 4.2.2(c), the approach taken for
any regulatory purpose should be coordinated with the appropriate
reviewing authority. We agreed with the public comments calling for the
addition of state-of-the-science deposition algorithms, and developed a
modification to AERMOD (02222) for beta testing. This model, AERMOD
(04079) was posted on our Web site https://www.epa.gov/scram001/
tt25.htm#aermoddep on March 19, 2004. The latest version of AERMOD may
now be used for deposition analysis in special situations.
Since AERMOD treats dispersion in complex terrain, we have merged
sections 4 and 5 of appendix W, as proposed in the April 2000 NPR. And
while AERMOD produces acceptable regulatory design concentrations in
complex terrain, it does not replace CTDMPLUS for detailed or receptor-
oriented complex terrain analysis, as we have made clear in Guideline
section 4.2.2. CTDMPLUS remains available for use in complex terrain.
We have implemented the majority of suggestions to improve the
AERMET, AERMAP, and AERMOD source code to reflect all the latest
features that have been available in ISC3ST and that are available in
the latest versions of Fortran compilers. Also, the latest formats for
meteorological and terrain input data are now accepted by the new
versions of AERMET and AERMAP. Our guidance, documentation and users'
guides have been modified in response to a number of detailed comments.
With respect to AERMOD (02222)'s performance, we have concluded
that:
(1) AERMOD (99351), the version proposed in April 2000, performs
significantly better than ISC3ST, and AERMOD (02222) performs slightly
better than AERMOD (99351) in non-downwash settings in both simple and
complex terrain;
(2) The performance evaluation indicates that AERMOD (02222)
performs slightly better than ISC-PRIME for downwash cases.
With respect to changes in AERMOD's regulatory design
concentrations compared to those for ISC3ST, we have concluded that:
For non-downwash settings, AERMOD (02222), on average,
tends to predict concentrations closer to ISC3ST, and with somewhat
smaller variations, than the April 2000 proposal of AERMOD;
Where downwash is a significant factor in the air
dispersion analysis, AERMOD (02222) predicts maximum concentrations
that are very similar to ISC-PRIME's predictions;
For those source scenarios where maximum 1-hour cavity
concentrations are calculated, the average AERMOD (02222)-predicted
cavity concentration tends to be about the same as the average ISC-
PRIME cavity concentrations; and
In complex terrain, the consequences of using AERMOD
(02222) instead of ISC3ST remained essentially unchanged in general,
although they varied based on individual circumstances.
Since AERMOD (02222) was released, an updated version was posted on
our Web site on March 22, 2004: AERMOD (04079). The version we are
releasing pursuant to today's promulgation, however, is AERMOD (04300).
This version, consonant with AERMOD (02222) in its formulations,
addresses the following minor code issues:
The area source algorithm in simple and complex terrain
required a correction to the way the dividing streamline height is
calculated.
In PRIME, incorrect turbulence parameters were being
passed to one of the numerical plume rise routines, and this has been
corrected.
A limit has been placed on plume cooling within PRIME to
avoid supercooling, which had been causing runtime instability.
A correction has been made to avoid AERMOD's termination
under certain situations with capped stacks (i.e., where the routine
was attempting to take a square root of a negative number). Our testing
has demonstrated only very minor impacts from these corrections on the
evaluation results or the consequence analysis.
AERMOD (04300) has other draft portions of code that represent
options not required for regulatory applications. These include:
Dry and wet deposition for both gases and particles;
The ozone limiting method (OLM), referenced in section
5.2.4 (Models for Nitrogen Dioxide--Annual Average) of the Guideline
for treating NOX conversion; and
The Plume Volume Molar Ratio Method (PVMRM) for treating
NOX conversion.
The bulk Richardson number approach (discussed earlier)
for using near-surface temperature difference has been corrected in
AERMOD (04300).
Based on the technical information contained in the docket for this
rule, and with consideration of the performance analysis in combination
with the analysis of design concentrations, we believe that AERMOD is
appropriate for regulatory use and we are revising the Guideline to
adopt it as a refined model today.
In implementing the changes to the Guideline, we recognize that
there may arise occasions in which the application of a new model can
result in the discovery by a permit applicant of previously unknown
violations of NAAQS or PSD increments due to emissions from existing
nearby sources. This potential has been acknowledged previously and is
addressed in existing EPA guidance (``Air Quality Analysis for
Prevention of Significant Deterioration
[[Page 68226]]
(PSD),'' Gerald A. Emison, July 5, 1988). To summarize briefly, the
guidance identifies three possible outcomes of modeling by a permit
applicant and details actions that should be taken in response to each:
1. Where dispersion modeling shows no violation of a NAAQS or PSD
increment in the impact area of the proposed source, a permit may be
issued and no further action is required.
2. Where dispersion modeling predicts a violation of a NAAQS or PSD
increment within the impact area but it is determined that the proposed
source will not have a significant impact (i.e., will not be above de
minimis levels) at the point and time of the modeled violation, then
the permit may be issued immediately, but the State must take
appropriate actions to remedy the violations within a timely manner.
3. Where dispersion modeling predicts a violation of a NAAQS or PSD
increment within the impact area and it is determined that the proposed
source will have a significant impact at the point and time of the
modeled violation, then the permit may not be issued until the source
owner or operator eliminates or reduces that impact below significance
levels through additional controls or emissions offsets. Once it does
so, then the permit may be issued even if the violation persists after
the source owner or operator eliminates its contribution, but the State
must take further appropriate actions at nearby sources to eliminate
the violations within a timely manner.
In previous promulgations, we have traditionally allowed a one-year
transition (``grandfather'') period for new refined techniques.
Accordingly, for appropriate applications, AERMOD may be substituted
for ISC3 during the one-year period following the promulgation of
today's notice. Beginning one year after promulgation of today's
notice, (1) applications of ISC3 with approved protocols may be
accepted (see DATES section) and (2) AERMOD should be used for
appropriate applications as a replacement for ISC3.
We separately issue guidance for use of modeling for facility-
specific and community-scale air toxics risk assessments through the
Air Toxics Risk Assessment Reference Library.\18\ We recognize that the
tools and approaches recommended therein will eventually reflect the
improved formulations of the AERMOD modeling system and we expect to
appropriately incorporate them as expeditiously as practicable. In the
interim, as appropriate, we will consider the use of either ISC3 or
AERMOD in air toxic risk assessment applications.
---------------------------------------------------------------------------
\18\ https://www.epa.gov/ttn/fera/risk_atra_main.html.
---------------------------------------------------------------------------
EDMS
FAA has completed development of the new EDMS4.0 to incorporate
AERMOD. The result is a conforming enhancement that offers a stronger
scientific basis for air quality modeling. FAA has made this model
available on its Web site, which we cite in an updated Guideline
paragraph 7.2.4(c). As described earlier in this preamble, the summary
description for EDMS will be removed from appendix A.
VII. Final Editorial Changes to Appendix W
Today's update of the Guideline takes the form of many revisions,
and some of the text is unaltered. Therefore, as a purely practical
matter, we have chosen to publish the new version of the entire text of
appendix W and its appendix A. Guidance and editorial changes
associated with the resolution of the issues discussed in the previous
section are adopted in the appropriate sections of the Guideline, as
follows:
Preface
You will note some minor revisions of appendix W to reflect current
EPA practice.
Section 4
As mentioned earlier, we revised section 4 to present AERMOD as a
refined regulatory modeling technique for particular applications.
Section 5
As mentioned above, we merged pertinent guidance in section 5
(Modeling in Complex Terrain) with that in section 4. With the
anticipated widespread use of AERMOD for all terrain types, there is no
longer any utility in the previous differentiation between simple and
complex terrain for m
