[Federal Register Volume 70, Number 216 (Wednesday, November 9, 2005)]
[Rules and Regulations]
[Pages 68218-68261]
From the Federal Register Online via the Government Publishing 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. ([email protected]).
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\ http://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
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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.
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\2\ AMS/EPA Regulatory MODel.
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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.
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\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\
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\9\ Schulman, L.L. et al., 2000. Development and Evaluation of
the PRIME Plum Rise and Building Downwash Model. JAWMA 50: 378-390.
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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.
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\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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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
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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\
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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
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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; http://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 http://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\ http://www.epa.gov/ttn/fera/risk_atra_main.html.
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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.
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
[[Page 68227]]
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: http://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
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.
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 cost-effective 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 case-by-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.
[[Page 68228]]
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 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.
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.
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, 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.
J. Congressional Review Act of 1998
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.
0
Part 51, chapter I, title 40 of the Code of Federal Regulations is
amended as follows:
PART 51--REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF
IMPLEMENTATION PLANS
0
1. The authority citation for part 51 continues to read as follows:
Authority: 23 U.S.C. 100; 42 U.S.C. 7401-7671q.
0
2. Appendix W to Part 51 revised to read as follows:
[[Page 68229]]
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
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 Decision-Making
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
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.
4-1b................................ Unstable/Convective Meteorological
Matrix for CTSCREEN.
8-1................................. Model Emission Input Data for
Point Sources.
8-2................................. Point Source Model Emission Input
Data for NAAQS Compliance in PSD
Demonstrations.
8-3................................. Averaging Times for Site Specific
Wind and Turbulence Measurements.
------------------------------------------------------------------------
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
[[Page 68230]]
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 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.
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.
2.1 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 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.
2.2 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
[[Page 68231]]
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 http://www.epa.gov/scram001. A list of alternate models that can be used
with case-by-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 appropriate reviewing authority, you should contact the
Regional modeling contact (http://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:
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 well-
established analytical techniques.
vi. The developer must be willing to make the model and source
code available to users at reasonable cost or make them available
for public access through the Internet or National Technical
Information Service: The model and its code cannot be proprietary.
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
[[Page 68232]]
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 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 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
[[Page 68233]]
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 ([sigma]v,
[sigma]w), vertical potential temperature gradient
(d[thetas]/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
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).................................... 1.0 2.0 3.0 4.0 5.0
[sigma]v (m/s)............................. 0.3 0.75
[sigma]w (m/s)............................. 0.08 0.15 0.30 0.75
[Delta][thetas]/[Delta]z (K/m)............. 0.01 0.02 0.035
WD......................................... (Wind direction is optimized internally for each meteorological
combination.)
----------------------------------------------------------------------------------------------------------------
[[Page 68234]]
Exceptions:
(1) If U <= 2 m/s and [sigma]v <= 0.3 m/s, then include
[sigma]w = 0.04 m/s.
(2) If [sigma]w = 0.75 m/s and U >= 3.0 m/s, then
[Delta][thetas]/[Delta]z is limited to <= 0.01 K/m.
(3) If U >= 4 m/s, then [sigma]w >= 0.15 m/s.
(4) [sigma]w <= [sigma]v
Table 4-1b.--Unstable/Convective Meteorological Matrix for CTSCREEN
----------------------------------------------------------------------------------------------------------------
Variable Specific values
---------------------------------------------
U (m/s)..................................... 1.0 2.0 3.0 4.0 5.0
U* (m/s).................................... 0.1 0.3 0.5
L (m)....................................... -10 -50 -90
[Delta][thetas]/[Delta]z (K/m).............. 0.030 (potential temperature gradient above Zi)
Zi (m)...................................... 0.5h 1.0h 1.5h (h = terrain height)
----------------------------------------------------------------------------------------------------------------
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.)
---------------------------------------------------------------------------
\a\ Modeling for attainment demonstrations for O3 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: http://www.epa.gov/ttn/naags/.
---------------------------------------------------------------------------
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 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 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
[[Page 68235]]
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 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 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
[[Page 68236]]
[GRAPHIC] [TIFF OMITTED] TR09NO05.001
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 case-by-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 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
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
[[Page 68237]]
separately using the appropriate recommended steady-state plume
dispersion model (subsection 4.2.2).
6.0 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-by-case 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 no apparent bias toward over or under prediction, so
long as the transport distance was limited to less than 300km.\60\
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
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
[[Page 68238]]
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.
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: http://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, second-highest 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 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
[[Page 68239]]
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 McElroy-Pooler \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
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
[sigma]y and [sigma]z should be determined
from the same stability category. ``Split sigmas'' in that instance
are not recommended. Sector averaging, which eliminates the
[sigma]y 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
ground-level volume source.
c. Stack tip downwash generally occurs with poorly constructed
stacks and when the ratio of the stack exit velocity to wind speed
is small. An algorithm developed by Briggs \77\ 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
steady-state 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-
[[Page 68240]]
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 three-dimensional 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 short-lived 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 low-level emissions sources, tends
to be minimized, potentially leading to relatively high ground-level
concentrations. If point sources are of interest, users should note
the guidance provided 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)).
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
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.
---------------------------------------------------------------------------
\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.
---------------------------------------------------------------------------
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
[[Page 68241]]
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.
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 start-up 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 Operating level Operating factor
Averaging time (/MMBtu) \2\ x (MMBtu/hr) \2\ x (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 .. Actual or design .. Actual operating
emission limit or capacity (whichever factor averaged over
federally enforceable is greater), or most recent 2
permit limit. federally years.\3\
enforceable permit
condition.
Short term........................ Maximum allowable .. Actual or design .. Continuous operation,
emission limit or capacity (whichever i.e., all hours of
federally enforceable is greater), or each time period
permit limit. federally under consideration
enforceable permit (for all hours of
condition.\4\ 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................ Maximum allowable .. Annual level when .. Actual operating
emission limit or actually operating, factor averaged over
federally enforceable averaged over the the most recent 2
permit limit.\6\ most recent 2 years.\3\
years.\3\
Short term........................ Maximum allowable .. Annual level when .. Continuous operation,
emission limit or actually operating, i.e., all hours of
federally enforceable averaged over the each time period
permit limit.\6\ most recent 2 under consideration
years.\3\ (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).
[[Page 68242]]
TABLE 8-2.--Point Source Model Emission Input Data for NAAQS Compliance in PSD Demonstrations
----------------------------------------------------------------------------------------------------------------
Emission limit Operating level Operating factor
Averaging time (/MMBtu) \1\ x (MMBtu/hr) \1\ x (e.g., hr/yr, hr/day)
----------------------------------------------------------------------------------------------------------------
Proposed Major New or Modified Source
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable .. Design capacity or .. Continuous operation
emission limit or federally (i.e., 8760
federally enforceable enforceable permit hours).\2\
permit limit. condition.
Short term (<= 24 hours).......... Maximum allowable .. Design capacity or .. Continuous
emission limit or federally operation, i.e., all
federally enforceable enforceable permit hours of each time
permit limit. condition.\3\ period under
consideration (for
all hours of the
meteorological data
base).\2\
-----------------------------------
Nearby Source(s) \4\ \6\
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable .. Actual or design .. Actual operating
emission limit or capacity (whichever factor averaged over
federally enforceable is greater), or the most recent 2
permit limit.\5\ federally years.7 8
enforceable permit
condition.
Short term (<= 24 hours).......... Maximum allowable .. Actual or design .. Continuous operation,
emission limit or capacity (whichever i.e., all hours of
federally enforceable is greater), or each time period
permit limit.\5\ federally under consideration
enforceable permit (for all hours of
condition.\3\ the meteorological
data base).\2\
-----------------------------------
Other Source(s) \6\ \9\
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................ Maximum allowable .. Annual level when .. Actual operating
emission limit or actually operating, factor averaged over
federally enforceable averaged over the the most recent 2
permit limit.\5\ most recent 2 years.7 8
years.\7\
Short term (<= 24 hours).......... Maximum allowable .. Annual level when .. Continuous operation,
emission limit or actually operating, i.e., all hours of
federally enforceable averaged over the each time period
permit limit.\5\ most recent 2 under consideration
years.\7\ (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.
\2\ If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24 hours) and the
source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the
modeled emission rate may be made (e.g., if operation is only 8 a.m. to 4 p.m. each day, only these hours will
be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating 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.
8.2 Background Concentrations
8.2.1 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.
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 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[deg] 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.
[[Page 68243]]
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 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 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 non-steady-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
[[Page 68244]]
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 NWS-collected ASOS
(automated surface observing stations) data are inadequate {for
cloud cover observations{time} , 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 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 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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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
([Delta]T) measurements should be obtained using matched
thermometers or a reliable thermocouple system to achieve adequate
accuracy. Siting, probe placement, and operation of [Delta]T 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 categories, as originally
defined, couple near-surface measurements of wind speed with
subjectively determined insolation assessments based on hourly cloud
cover and ceiling height observations. The wind speed measurements
are made at or near 10m. The insolation rate is typically assessed
using observations of cloud cover and ceiling height based on
criteria outlined by Turner.\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
[sigma]E and [sigma]A 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 [sigma]E and [sigma]A 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
Parameter time
(hour)
------------------------------------------------------------------------
Surface wind speed (for use in stability determinations)... 1
Transport direction........................................ 1
Dilution wind speed........................................ 1
Turbulence measurements ([sigma]E and [sigma]A) for use in 1\1\
stability determinations..................................
Turbulence measurements for direct input to dispersion 1
models....................................................
------------------------------------------------------------------------
\1\ To minimize meander effects in [sigma]A when wind conditions are
light and/or variable, determine the hourly average [sigma] value from
four sequential 15-minute [sigma]'s according to the following
formula:
[GRAPHIC] [TIFF OMITTED] TR09NO05.002
8.3.4 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
[[Page 68246]]
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 24-hour averages, less than 6 for 8-hour averages or less than 3
for 3-hour averages, the total concentration should be divided by 18
for the 24-hour average, 6 for the 8-hour average and 3 for the 3-
hour average. For annual averages, the sum of all valid hourly
concentrations is divided by the number of non-calm hours during the
year. AERMOD has been coded to implement these instructions. For
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
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 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 Decision-Making
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
[[Page 68247]]
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 decision-making, 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 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 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
short-term (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
[[Page 68248]]
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 [mu]g/m\3\). The 24-hour
NAAQS (150 [mu]g/m\3\) 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.
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 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
[[Page 68249]]
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 PSD Air Quality Increments and Impacts
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 increment-consuming 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.
11.0 Bibliography \a\
---------------------------------------------------------------------------
\a\ The documents listed here are major sources of supplemental
information on the theory and application of mathematical air
quality models.
---------------------------------------------------------------------------
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
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.
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3. Code of Federal Regulations; Title 40 (Protection of
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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
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 http://www.epa.gov/scram001.
Documentation is also available from the National Technical
Information Service (NTIS), http://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 http://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-454/B-03-001. U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711;
September 2004. (Available at http://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 http://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 http://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 steady-state assumptions
are appropriate, up to 50km;
1-hour to annual averaging times; and
Continuous toxic air emissions.
(2) For regulatory applications of AERMOD, the regulatory
default option should be set, i.e., the parameter DFAULT should be
employed in the MODELOPT record in the COntrol Pathway. The DFAULT
option requires the use of terrain elevation data, stack-tip
downwash, sequential date checking, and does not permit the use of
the model in the SCREEN mode. In the regulatory default mode,
pollutant half life or
[[Page 68254]]
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.
(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 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 buoyancy-induced and
building wake-induced turbulence. Vertical profiles of lateral
turbulence are developed from measurements and similarity (scaling)
relationships. Effective turbulence values are determined from the
portion of the vertical profile of lateral turbulence between the
plume height and the receptor height. The effective lateral
turbulence is then used to estimate horizontal dispersion.
[[Page 68255]]
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 http://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 http://www.epa.gov/scram001/)
Availability
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).
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.
c. Output
(1) Printed output (from a separate post-processor 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
[[Page 68256]]
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.
(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. 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: http://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: [email protected].
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-space-varying 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 case-by-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.
[[Page 68257]]
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 ([sigma]z) 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 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
([sigma]y, [sigma]z) 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 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 ([sigma]y,
[sigma]z)--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 MM5, RUC, Eta and RAMS) can be used by 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 stability-dependent 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) 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 time- and 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 steep-walled
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
[[Page 68258]]
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
[sigma]v. 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
[sigma]w. 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.
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 Long-Range 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. CALPUFF-Based 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 http://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 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
[[Page 68259]]
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 [sigma]y & [sigma]z values, both
flat terrain and hill induced (the difference shows the effect of
the hill)
--Concentration components due to WRAP, LIFT and FLAT.
(3) If the user selects the TOPN option, a summary table of the
top 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.
e. Pollutant Types
CTDMPLUS may be used to model non-reactive, primary pollutants.
f. Source-Receptor Relationship
Up to 40 point sources, 400 receptors and 25 hills may be used.
Receptors and sources are allowed at any location. Hill slopes are
assumed not to exceed 15[deg], so that the linearized equation of
motion for Boussinesq flow are applicable. Receptors upwind of the
impingement point, or those associated with any of the hills in the
modeling domain, require separate treatment.
g. Plume Behavior
(1) As in CTDM, the basic plume rise algorithms are based on
Briggs' (1975) recommendations.
(2) A central feature of CTDMPLUS for neutral/stable conditions
is its use of a critical dividing-streamline height (Hc)
to separate the flow in the vicinity of a hill into two separate
layers. The plume component in the upper layer has sufficient
kinetic energy to pass over the top of the hill while streamlines in
the lower portion are constrained to flow in a horizontal plane
around the hill. Two separate components of CTDMPLUS compute ground-
level concentrations resulting from plume material in each of these
flows.
(3) The model calculates on an hourly (or appropriate steady
averaging period) basis how the plume trajectory (and, in stable/
neutral conditions, the shape) is deformed by each hill. Hourly
profiles of wind and temperature measurements are used by CTDMPLUS
to compute plume rise, plume penetration (a formulation is included
to handle penetration into elevated stable layers, based on Briggs
(1984)), convective scaling parameters, the value of Hc,
and the Froude number above Hc.
h. Horizontal Winds
CTDMPLUS does not simulate calm meteorological conditions. Both
scalar and vector wind speed observations can be read by the model.
If vector wind speed is unavailable, it is calculated from the
scalar wind speed. The assignment of wind speed (either vector or
scalar) at plume height is done by either:
Interpolating between observations above and below the
plume height, or
Extrapolating (within the surface layer) from the
nearest measurement height to the plume height.
i. Vertical Wind Speed
Vertical flow is treated for the plume component above the
critical dividing streamline height (Hc); see ``Plume
Behavior''.
j. Horizontal Dispersion
Horizontal dispersion for stable/neutral conditions is related
to the turbulence velocity scale for lateral fluctuations,
[sigma]v, for which a minimum value of 0.2 m/s is used.
Convective scaling formulations are used to estimate horizontal
dispersion for unstable conditions.
k. Vertical Dispersion
Direct estimates of vertical dispersion for stable/neutral
conditions are based on observed vertical turbulence intensity,
e.g., [sigma]w (standard deviation of the vertical
velocity fluctuation). In simulating unstable (convective)
conditions, CTDMPLUS relies on a skewed, bi-Gaussian probability
density function (pdf) description of the vertical velocities to
estimate the vertical distribution of pollutant concentration.
l. Chemical Transformation
Chemical transformation is not treated by CTDMPLUS.
m. Physical Removal
Physical removal is not treated by CTDMPLUS (complete reflection
at the ground/hill surface is assumed).
n. Evaluation Studies
Burns, D.J., L.H. Adams and S.G. Perry, 1990. Testing and
Evaluation of the CTDMPLUS Dispersion Model: Daytime Convective
Conditions. Environmental Protection Agency, Research Triangle Park,
NC.
Paumier, J.O., S.G. Perry and D.J. Burns, 1990. An Analysis of
CTDMPLUS Model Predictions with the Lovett Power Plant Data Base.
Environmental Protection Agency, Research Triangle Park, NC.
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 http://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.
[[Page 68260]]
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.
f. Source-Receptor Relationship
(1) Up to 250 point sources, 5 area sources, or 1 line source
and 180 receptors may be used.
(2) Receptors and sources are allowed at any location.
(3) The coastal configuration is determined by a grid of up to
3600 rectangles. Each element of the grid is designated as either
land or water to identify the coastline.
g. Plume Behavior
(1) As in ISC, the basic plume rise algorithms are based on
Briggs' recommendations.
(2) Momentum rise includes consideration of the stack angle from
the vertical.
(3) The effect of drilling platforms, ships, or any overwater
obstructions near the source are used to decrease plume rise using a
revised platform downwash algorithm based on laboratory experiments.
(4) Partial plume penetration of elevated inversions is included
using the suggestions of Briggs (1975) and Weil and Brower (1984).
(5) Continuous shoreline fumigation is parameterized using the
Turner method where complete vertical mixing through the thermal
internal boundary layer (TIBL) occurs as soon as the plume
intercepts the TIBL.
h. Horizontal Winds
(1) Constant, uniform wind is assumed for each hour.
(2) Overwater wind speed can be estimated from overland wind
speed using relationship of Hsu (1981).
(3) Wind speed profiles are estimated using similarity theory
(Businger, 1973). Surface layer fluxes for these formulas are
calculated from bulk aerodynamic methods.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
(1) Lateral turbulence intensity is recommended as a direct
estimate of horizontal dispersion. If lateral turbulence intensity
is not available, it is estimated from boundary layer theory. For
wind speeds less than 8 m/s, lateral turbulence intensity is assumed
inversely proportional to wind speed.
(2) Horizontal dispersion may be enhanced because of
obstructions near the source. A virtual source technique is used to
simulate the initial plume dilution due to downwash.
(3) Formulas recommended by Pasquill (1976) are used to
calculate buoyant plume enhancement and wind direction shear
enhancement.
(4) At the water/land interface, the change to overland
dispersion rates is modeled using a virtual source. The overland
dispersion rates can be calculated from either lateral turbulence
intensity or Pasquill-Gifford curves. The change is implemented
where the plume intercepts the rising internal boundary layer.
k. Vertical Dispersion
(1) Observed vertical turbulence intensity is not recommended as
a direct estimate of vertical dispersion. Turbulence intensity
should be estimated from boundary layer theory as default in the
model. For very stable conditions, vertical dispersion is also a
function of lapse rate.
(2) Vertical dispersion may be enhanced because of obstructions
near the source. A virtual source technique is used to simulate the
initial plume dilution due to downwash.
(3) Formulas recommended by Pasquill (1976) are used to
calculate buoyant plume enhancement.
(4) At the water/land interface, the change to overland
dispersion rates is modeled using a virtual source. The overland
dispersion rates can be calculated from either vertical turbulence
intensity or the Pasquill-Gifford coefficients. The change is
implemented where the plume intercepts the rising internal boundary
layer.
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
[[Page 68261]]
Modeling (IWAQM) Phase 2 Summary Report and Recommendations for
Modeling Long-Range Transport Impacts. Publication No. EPA-454/R-98-
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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 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 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]
BILLING CODE 6560-50-P