[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]



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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

[[Page 68222]]

terrain data sets, 7 building downwash data sets, and 5 simple terrain 
data sets (see appendix A of the Response-to-Comments document \4\). 
This performance analysis, which is a check of the model's maximum 
concentration predictions against observed data, includes a comparison 
of the current version of the new model (AERMOD 02222) with ISC3ST or 
ISC-PRIME for downwash conditions. The results and conclusions of the 
performance analyses are presented in 2 sections: Non-downwash and 
downwash source scenarios.
a. Non-Downwash Cases
    For the user community to obtain a full understanding of the 
impacts of today's proposal for the non-downwash source scenarios (flat 
and complex terrain), our performance evaluation of AERMOD (02222) must 
be discussed with respect to the old model, ISC3ST, and with respect to 
AERMOD (99351). Based on the evaluation, we have concluded that AERMOD 
(02222) significantly outperforms ISC3ST and that AERMOD (02222)'s 
performance is even better than that of AERMOD (99351).
Evaluation of AERMOD (99351)
    Comparative performance statistics were calculated for both ISC3ST 
and AERMOD (99351) using data sets in non-downwash conditions. This 
analysis looked at combinations of test sites (flat and complex 
terrain), pollutants, and concentration averaging times. Comparisons 
indicated very significant improvements in performance when applying 
AERMOD (99351). In all but 1 of the total of 20 cases in which AERMOD 
(99351) could be compared to ISC3ST, AERMOD performed as well as (but 
generally better than) ISC3ST, that is, AERMOD predicted maximum 
concentrations that were closer to the measured maximum concentrations. 
In the most dramatic case (i.e., Lovett; 24-hr) in which AERMOD 
performed better than ISC3ST, AERMOD's maximum concentration 
predictions were about the same as the measured concentrations while 
the ISC3ST's predicted maximum concentrations were about 9 times higher 
than the measured concentrations. In the one case (i.e., Clifty Creek; 
3-hr) where ISC3ST performed better than AERMOD (99351), ISC3ST's 
concentration predictions matched the observed data and the AERMOD 
concentration predictions were about 25% higher than the observed data. 
These results were reported in the supporting documentation for AERMOD 
(99351).
Evaluation of AERMOD (02222)
    With the changes to AERMOD (99351) as outlined above, how has the 
performance of the AERMOD been affected? The performance of the current 
version of AERMOD is about the same or slightly better than the April 
2000 version when a comparison is made over all the available data 
sets. There were examples of AERMOD (02222) showing better and poorer 
performance when compared to the performance results of AERMOD (99351). 
However, for those cases where AERMOD (02222)'s performance was 
degraded, the degradation was small. On the other side, there were more 
examples where AERMOD (02222) more closely predicted measured 
concentrations. The performance improvements were also rather small 
but, in general, were somewhat larger than the size of the performance 
degradations. There also were a number of cases where the performance 
remained unchanged between the 2 models. Thus, overall, there was a 
slight improvement in AERMOD's performance and, consequently, we 
believe that AERMOD (02222) significantly outperforms ISC3ST for non-
downwash source scenarios.
    For AERMOD (02222) with the 5 data bases examined for simple 
terrain, the ratios of modeled/observed Robust High Concentration 
ranged from 0.77 to 1.11 (1-hr average), 0.98 to 1.24 (3-hr average), 
0.94 to 0.97 (24-hr average) and 0.30 to 0.97 (annual average). These 
ratios reflect better performance than ISC3ST for all cases.
    For AERMOD (02222) with the 5 data bases examined for complex 
terrain, these ratios ranged from 1.03 to 1.12 (3-hr average), 0.67 to 
1.78 (24-hr average) and 0.54 to 1.59 (annual average). At Tracy--the 
only site for which there are 1-hr data--AERMOD performed considerably 
better (ratio = 1.04) than either ISC3ST or CTDMPLUS. At three of the 
other four sites, AERMOD generally performed much better than either 
ISC3ST or (where applicable) alternative models for the 3-hr and 24-hr 
averaging times; results were comparable for Clifty Creek (for the 3-hr 
averaging times, AERMOD (02222) predictions were only about 5% higher 
than ISC3ST's--down from 25% for AERMOD (99351) as described earlier). 
At the two sites where annual peak comparisons are available, AERMOD 
performed much better than either ISC3ST or alternative models.
b. Downwash Cases
    For the downwash data sets, there were combinations of test sites, 
pollutants, stack heights and averaging times where the proposed (ISC-
PRIME) model performance could be compared to the performance of AERMOD 
(02222) with PRIME incorporated. There was an equal number of non-
downwash cases where AERMOD performed better than ISC-PRIME and where 
ISC-PRIME performed better than AERMOD. There was only one case where 
there was a significant difference between the two models' performance, 
and AERMOD clearly performed better than ISC-PRIME in this case. In all 
other cases, the difference in the performance, whether an improvement 
or a degradation, was small. This comparison indicated that AERMOD 
(02222) performs very similarly, if not somewhat better, when compared 
to ISC-PRIME for downwash cases.
2. Analysis of Regulatory Design Concentrations for AERMOD (02222)
    Although not a performance tool, the analysis of design 
concentrations (``consequence'' analysis) is designed to test model 
stability and continuity, and to help the user community understand the 
differences to be expected between air dispersion models. The 
consequences, or changes in the regulatory concentrations predicted 
when using the new model (AERMOD 02222) versus ISC3ST, cover 96 source 
scenarios and at least 3 averaging periods per source scenario, and are 
evaluated and summarized here. The purpose is to provide the user 
community with a sense of potential changes in their air dispersion 
analyses when applying the new model over a broad range of source types 
and settings. The consequence analysis, in which AERMOD was run for 
hundreds of source scenarios, also provides a check for model stability 
(abnormal halting of model executions when using valid control files 
and input data) and for spurious results (unusually high or low 
concentration predictions which are unexplained). The results are 
placed into 3 categories: non-downwash source scenarios in flat, simple 
terrain; downwash source scenarios in flat terrain; and, complex 
terrain source settings. The focus of this discussion is on how design 
concentrations change from those predicted by ISC3ST when applying the 
latest version of AERMOD versus applying the earlier version of AERMOD 
(99351).
a. Non-Downwash Cases
    For the non-downwash situations, there were 48 cases covering a 
variety of source types (point, area, and volume sources), stack 
heights, terrain types (flat and simple), and dispersion

[[Page 68223]]

settings (urban and rural). For each case in the consequence analysis, 
we calculated the ratio between AERMOD's regulatory concentration 
predictions and ISC3ST's regulatory concentration predictions. The 
average ratio of AERMOD to ISC3ST-predicted concentrations changed from 
1.14 when applying AERMOD (99351) to 0.96 when applying AERMOD 
(02222).\14\ Thus, in general, AERMOD (02222) tends to predict 
concentrations closer to ISC3ST than does version 99351 proposed in 
April 2000. Also, the variation of the differences between ISC3ST and 
AERMOD has decreased with AERMOD (02222). Comparing the earlier 
consequence analysis to the latest study with AERMOD (02222), we saw a 
25% reduction in the number of cases where the AERMOD-predicted 
concentrations differed by over a factor of two from ISC3ST's 
predictions.
---------------------------------------------------------------------------

    \14\ A ratio of 1.00 indicates that the two models are 
predicting the same concentrations. See Table 4.1 in reference 12.
---------------------------------------------------------------------------

b. Downwash Cases
    For the downwash analysis, there were 20 cases covering a range of 
stack heights, locations of stacks relative to the building, dispersion 
settings, and building shapes. As before, we calculated the ratio 
regulatory concentration predictions from AERMOD (02222 with PRIME) and 
compared them as ratios to those from ISC3ST for each case. For 
additional information, we also included ratios with ISC-PRIME that was 
also proposed in April 2000.
    Calculated over all the 20 cases, and for all averaging times 
considered, the average ISC-PRIME to ISC3ST concentration ratio is 
about 0.86, whereas for AERMOD (PRIME) to ISC3ST, it is 0.82. The 
maximum value of the concentration ratios range from 2.24 for ISC-
PRIME/ISC3ST to 3.67 for AERMOD (PRIME)/ISC3ST. Similarly, the minimum 
value of the concentration ratio range from 0.04 for ISC-PRIME/ISC3ST 
to 0.08 for AERMOD (PRIME)/ISC3ST. (See Table 4-5 in reference 12.)
    Although results above for the two models that use PRIME--AERMOD 
(02222) and ISC-PRIME--show differences, we find that building downwash 
is not a significant factor in determining the maximum concentrations 
in some of the cases, i.e., the PRIME algorithms do not predict a 
building cavity concentration. Of those cases where downwash was 
important, the average concentration ratios of ISC-PRIME/ISC3ST and 
AERMOD (02222)/ISC3ST are about 1. The maximum value of the 
concentration ratios range from 2.24 for ISC-PRIME/ISC3ST to 1.87 for 
AERMOD (02222)/ISC3ST and the minimum value of the concentration ratios 
range from 0.34 for ISC-PRIME/ISC3ST to 0.38 for AERMOD (02222)/ISC3ST. 
These results show relatively close agreement between the two PRIME 
models. (See Table 4-6 in reference 12.)
    ISC3ST does not predict cavity concentrations but comparisons can 
be made between AERMOD and ISC-PRIME. The average AERMOD (02222) 
predicted 1-hour cavity concentration is about the same (112%) as the 
average ISC-PRIME 1-hour cavity concentration. In the extremes, the 
AERMOD (02222)-predicted cavity concentrations ranged from about 40% 
higher to 15% lower than the corresponding ISC-PRIME cavity 
concentration predictions. Thus, in general, where downwash is a 
significant factor, AERMOD (02222) and ISC-PRIME predict similar 
maximum concentrations. (See Table 4-8 in reference 12.)
    Although the same downwash algorithms are used in both models, 
there are differences in the melding of PRIME with the core model, and 
differences in the way that these models simulate the atmosphere.\15\ 
The downwash algorithm implementation therefore could not be exactly 
the same.
---------------------------------------------------------------------------

    \15\ AERMOD uses more complex techniques to estimate temperature 
profiles which, in turn, affect the calculation of the plume rise. 
Plume rise may affect the cavity and downwash concentrations.
---------------------------------------------------------------------------

c. Complex Terrain
    During the testing of AERMOD after modifications were made to the 
complex terrain algorithm (see discussion of hill height scale 
(hC) in B. Appropriate for Proposed Use in this preamble), a 
small error was found in the original complex terrain code while 
conducting the consequence analysis. This error was subsequently 
repaired. Final testing indicated that the revised complex terrain code 
produced reasonable results for the consequence analysis, as described 
below.
    The analysis of predicted design concentrations included a suite of 
complex terrain settings. There were 28 cases covering a variety of 
stack heights, stack gas buoyancy values, types of hills, and distances 
between source and terrain. The ratios between the AERMOD (02222 & 
99351)--predicted maximum concentrations and the ISC3ST maximum 
concentrations were calculated for all cases for a series of averaging 
times. When comparing AERMOD (99351) to ISC3ST and then AERMOD (02222) 
to ISC3ST, the average maximum concentration ratio, the highest ratios 
and the lowest ratios were almost unchanged. There were no cases in 
either consequence analysis where AERMOD (02222 & 99351) predicted 
higher concentrations than those predicted by ISC3ST. Thus, in general, 
the consequences of moving from ISC3ST to AERMOD (02222) rather than to 
AERMOD (99351) in complex terrain were essentially the same. (See Table 
4-9 in reference 12.)

E. Emission and Dispersion Modeling System (EDMS)

    The Emissions and Dispersion Modeling System (EDMS) was developed 
jointly by the Federal Aviation Administration (FAA) and the U.S. Air 
Force in the late 1970s and first released in 1985 to assess the air 
quality of proposed airport development projects. EDMS has an emissions 
preprocessor and its dispersion module estimates concentrations for 
various averaging times for the following pollutants: CO, HC, 
NOX, SOX, and suspended particles (e.g., PM-10). 
The first published application of EDMS was in December 1986 for 
Stapleton International Airport (FAA-EE-11-A/REV2).
    In 1988, version 4a4 revised the dispersion module to include an 
integral dispersion submodel: GIMM (Graphical Input Microcomputer 
Model). This version was proposed for adoption in the Guideline's 
appendix A in February 1991 (56 FR 5900). This version was included in 
appendix A in July 1993 (58 FR 38816) and recommended for limited 
applications for assessments of localized airport impacts on air 
quality. FAA later updated EDMS to Version 3.0.
    In response to the growing needs of air quality analysts and 
changes in regulations (e.g., conformity requirements from the Clean 
Air Act Amendment of 1990), FAA updated EDMS to version 3.1, which is 
based on the CALINE3 \16\ and PAL2 dispersion kernels. In our April 
2000 NPR we proposed to adopt the version 3.1 update to EDMS. However, 
this update had not been subjected to performance evaluation and no 
studies of EDMS' performance have been cited in appendix A of the 
Guideline. Comment was invited on whether this compromises the 
viability of EDMS 3.1 as a recommended or preferred model and how this 
deficiency can be corrected.
---------------------------------------------------------------------------

    \16\ Currently listed in appendix A of the Guideline.
---------------------------------------------------------------------------

    Several commenters expressed concern about EDMS 3.1 as a 
recommended model in appendix A. Indeed, there were concerns that EDMS

[[Page 68224]]

3.1 had not been as well validated as other models, nor subjected to 
peer review, as required by the Guideline's subsection 3.1.1. One of 
these commenters suggested that EDMS 3.1 should be presented only as 
one of several alternative models.
    At the 7th Conference, FAA proposed for appendix A adoption an even 
newer, enhanced version of EDMS--version 4.0, which incorporates the 
AERMOD dispersion kernel (without alteration). In this system, the 
latest version of AERMOD would be employed as a standalone component of 
EDMS. This dispersion kernel was to replace PAL2 and CALINE3 currently 
in EDMS 3.1. There were no public comments specific to FAA's proposed 
AERMOD-based enhancements to EDMS announced after our April 2000 NPR.
    In response to written comments on our April 2000 NPR, at the 7th 
Conference (transcript) FAA promised a complete evaluation process that 
would include sensitivity testing, intermodel comparison, and analysis 
of EDMS predictions against field observations. The intermodel 
comparisons were proposed for the UK's Atmospheric Dispersion Modeling 
System (ADMS).\17\
---------------------------------------------------------------------------

    \17\ Cambridge Environmental Research Consultants; 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.
---------------------------------------------------------------------------

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

[[Page 68245]]

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.

12.0 References

    1. Code of Federal Regulations; Title 40 (Protection of 
Environment). Sections 51.112, 51.117, 51.150, 51.160.
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Workshop Manual: Prevention of Significant Deterioration and 
Nonattainment Area Permitting (Draft). Office of Air Quality 
Planning & Standards, Research Triangle Park, NC. (Available at: 
http://www.epa.gov/ttn/nsr/)
    3. Code of Federal Regulations; Title 40 (Protection of 
Environment). Sections 51.166 and 52.21.
    4. Code of Federal Regulations (Title 40, Part 50): Protection 
of the Environment; National Primary and Secondary Ambient Air 
Quality Standards.
    5. Environmental Protection Agency, 1988. Model Clearinghouse: 
Operational Plan (Revised). Staff Report. Office of Air Quality 
Planning & Standards, Research Triangle Park, NC. (Docket No. A-88-
04, II-J-1)
    6. Environmental Protection Agency, 1980. Guidelines on Air 
Quality Models. Federal Register, 45(61): 20157-20158.
    7. Scire, J.S. and L.L. Schulman, 1981. Evaluation of the BLP 
and ISC Models with SF6 Tracer Data and SO2 Measurements 
at Aluminum Reduction Plants. APCA Specialty Conference on 
Dispersion

[[Page 68250]]

Modeling for Complex Sources, St. Louis, MO.
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Source Air Quality Simulation Models. Publication No. EPA-450/4-86-
002. Office of Air Quality Planning & Standards, Research Triangle 
Park, NC. (NTIS No. PB 86-167293)
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of the CALPUFF Dispersion Model with Two Power Plant Data Sets. 
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Boston, MA. January 11-16, 1998.
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Features and Evaluation Results. Publication No. EPA-454/R-03-003. 
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Bulletin of the American Meteorological Society, 62(5): 599-609.
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Determining the Best Performing Model. Publication No. EPA-454/R-92-
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Atmospheric Dispersion Model Performance. (2000)
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Industrial Source Complex (ISC3) Dispersion Models, Volumes 1 and 2. 
Publication Nos. EPA-454/B-95-003a & b. U.S. Environmental 
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Cooperative Research. JAWMA, 50: 613-632.
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of Models and Other Analyses in Attainment Demonstrations for the 8-
hr Ozone NAAQS (Draft Final). Office of Air Quality Planning & 
Standards, Research Triangle Park, NC. (Latest version available on 
SCRAM Web site as draft-final-O3.pdf; see subsection 2.3)
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of Models and Other Analyses in Attainment Demonstrations for the 
PM-2.5 NAAQS and Regional Haze Goals. Office of Air Quality Planning 
& Standards, Research Triangle Park, NC. (As of May 2005, this 
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Web site as draft-pm.pdf; see subsection 2.3)
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Planning & Standards, Research Triangle Park, NC. (NTIS No. PB 87-
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Dispersion Models for Wildland Biomass Burning. USDA, Pacific 
Northwest Research Station, Portland, OR. General Technical Report 
PNW-GTR-379. 30pp. (NTIS No. PB 97-163380)
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for Determining NO2 / NOX Ratios in Modeling--
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Ambient Air Monitors around Stationary Lead Sources. Publication No. 
EPA-454/R-92-009R. Office of Air Quality Planning & Standards, 
Research Triangle Park, NC. (NTIS No. PB 97-208094)
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Document. Publication No. EPA-452/R-93-009. Office of Air Quality 
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Air Quality Modeling System. Models-3, Volume 9b: User Manual. 
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Factorization: A Non-

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negative Factor Model with Optimal Utilization of Error Estimates of 
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Sampling and Analysis Applicable to Receptor Modeling. Publication 
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Monitoring Guidance for Regulatory Modeling Applications. 
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Air Pollution Measurement Systems, Volume IV--Meteorological 
Measurements. Publication No. EPA600/R-94/038d. Office of Air 
Quality Planning & Standards, Research Triangle Park, NC. Note: for 
copies of this handbook, you may make inquiry to ORD Publications, 
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569-7562 or (800) 490-9198 (automated request line)
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8: Estimation of Pasquill Stability Categories. Office of 
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No. A-80-46, II-B-10)
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Stability Class from Horizontal Wind Fluctuation. Presented at 72nd 
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Method for Determining Wind Frequency Distributions for the Lowest 
200m from Routine Meteorological Data. J. of Applied Meteorology, 
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Diffusivity. MRI 72 FR-1030. Meteorology Research, Inc., Altadena, 
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Planning & Standards, Research Triangle Park, NC. (NTIS No. PB 97-
147912)
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Processor for Regulatory Models (MPRM) User's Guide. Publication No. 
EPA-454/B-96-002. Office of Air Quality Planning & Standards, 
Research Triangle Park, NC. (NTIS No. PB 96-180518)
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Preprocessor Program. Publication No. EPA-600/8-88-004. Office of 
Research & Development, Research Triangle Park, NC. (NTIS No. PB 88-
162102)
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Yamartino, 1998. A User's Guide for the CALMET Meteorological Model 
(Version 5.0). Earth Tech, Inc., Concord, MA. (http://www.src.com/calpuff/calpuff1.htm)
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(CALMPRO) User's Guide. Publication No. EPA-901/9-84-001. Office of 
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84-229467)
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Bulletin of the American Meteorological Society, 65(1): 27-36.
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Regulatory Decision-Making: Summary Report. Systems Applications, 
Inc., San Rafael, CA. Prepared under contract No. 68-01-5845 for 
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evaluations, as estimated by bootstrap and jackknife resampling 
methods. Atmospheric Environment, 23(6): 1385-1398.
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for determining the best performing air quality simulation model. 
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52pp.
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Group: Report of the Second Open Meeting. EUR 15990 EN, European 
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SO2 and CO Concentrations in St. Louis. Atmospheric 
Environment, 16(6): 1435-1440.
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Air Quality Models.

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Appendix F in Air Quality Modeling and the Clean Air Act: 
Recommendations to EPA on Dispersion Modeling for Regulatory 
Applications. American Meteorological Society, Boston, MA; pp. 159-
171. (Docket No. A-80-46, II-A-106)
    119. Bowne, N.E. and R.J. Londergan, 1983. Overview, Results, 
and Conclusions for the EPRI Plume Model Validation and Development 
Project: Plains Site. EPRI EA-3074. Electric Power Research 
Institute, Palo Alto, CA.
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Survey of Statistical Measures of Model Performance and Accuracy for 
Several Air Quality Models. Publication No. EPA-450/4-83-001. Office 
of Air Quality Planning & Standards, Research Triangle Park, NC. 
(NTIS No. PB 83-260810)
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Report. Office of Air Quality Planning & Standards, Research 
Triangle Park, NC. (Docket No. A-80-46, II-G-6)
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predictions. Boundary-Layer Meteorology, 62: 3-20.
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John Wiley and Sons, New York, NY; 479pp.
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Dealing With Uncertainty in Quantitative Risk and Policy Analysis. 
Cambridge University Press. New York, NY; 332pp.
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2001. Uncertainty in Air Quality Modeling for Risk Calculations. 
(CD-ROM) Proceedings of Guideline on Air Quality Models: A New 
Beginning. April 4-6, 2001, Newport, RI, Air & Waste Management 
Association. Pittsburgh, PA; 17pp.
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Stocking, 1988. User's Guide to the Expected Exceedances System. 
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Agency, Research Triangle Park, NC. (Docket No. A-88-04, II-I-3)
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Method for Calculating Dispersion Modeling Uncertainty Applied to 
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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-
019. (NTIS No. PB 99-121089).
    Escoffier-Czaja, C. and J.S. Scire, 2002. The Effects of Ammonia 
Limitation on Nitrate Aerosol Formation and Visibility Impacts in 
Class I Areas. Twelfth AMS/AWMA Conference on the Application of Air 
Pollution Meteorology, 20-24 May 2002; Norfolk, VA.
    Gifford, F.A., Jr. 1976. Turbulent Diffusion Typing Schemes--A 
Review. Nuclear Safety, 17: 68-86.
    Horst, T.W., 1983. A Correction to the Gaussian Source-depletion 
Model. In Precipitation Scavenging, Dry Deposition and Resuspension. 
H. R. Pruppacher, R.G. Semonin and W.G.N. Slinn, eds., Elsevier, NY.
    Hsu, S.A., 1981. Models for Estimating Offshore Winds from 
Onshore Meteorological Measurements. Boundary Layer Meteorology, 20: 
341-352.
    Huber, A.H. and W.H. Snyder, 1976. Building Wake Effects on 
Short Stack Effluents. Third Symposium on Atmospheric Turbulence, 
Diffusion and Air Quality, American Meteorological Society, Boston, 
MA.
    Irwin, J.S., 1979. A Theoretical Variation of the Wind Profile 
Power-Law Exponent as a Function of Surface Roughness and Stability. 
Atmospheric Environment, 13: 191-194.
    Liu, M.K. et al., 1976. The Chemistry, Dispersion, and Transport 
of Air Pollutants Emitted from Fossil Fuel Power Plants in 
California: Data Analysis and Emission 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