
[Federal Register Volume 88, Number 203 (Monday, October 23, 2023)]
[Proposed Rules]
[Pages 72826-72868]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2023-22876]



[[Page 72825]]

Vol. 88

Monday,

No. 203

October 23, 2023

Part II





Environmental Protection Agency





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40 CFR Part 51





Guideline on Air Quality Models; Enhancements to the AERMOD Dispersion 
Modeling System; Proposed Rule

  Federal Register / Vol. 88, No. 203 / Monday, October 23, 2023 / 
Proposed Rules  

[[Page 72826]]


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ENVIRONMENTAL PROTECTION AGENCY

40 CFR Part 51

[EPA-HQ-OAR-2022-0872; FRL-10391-01-OAR]
RIN 2060-AV92


Guideline on Air Quality Models; Enhancements to the AERMOD 
Dispersion Modeling System

AGENCY: Environmental Protection Agency (EPA).

ACTION: Proposed rule; notification of public hearing and conference.

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SUMMARY: In this action, the Environmental Protection Agency (EPA) 
proposes to revise the Guideline on Air Quality Models (``Guideline''). 
The Guideline has been incorporated into EPA's regulations, satisfying 
a requirement under the Clean Air Act (CAA) section 165(e)(3)(D) for 
the EPA to specify, with reasonable particularity, models to be used in 
the Prevention of Significant Deterioration (PSD) program. It provides 
EPA-preferred models and other recommended techniques, as well as 
guidance for their use in predicting ambient concentrations of air 
pollutants. In this action, the EPA is proposing revisions to the 
Guideline, including enhancements to the formulation and application of 
the EPA's near-field dispersion modeling system, AERMOD, and updates to 
the recommendations for the development of appropriate background 
concentration for cumulative impact analyses. Within this action, the 
EPA is also announcing the Thirteenth Conference on Air Quality 
Modeling and invites the public to participate in the conference. The 
conference will focus on the proposed revisions to the Guideline, and 
part of the conference will also serve as the public hearing for these 
revisions.

DATES: Comments must be received on or before December 22, 2023.
    Public hearing and conference: The public hearing for this action 
and the Thirteenth Conference on Air Quality Modeling will be held 
November 14-15, 2023, from 8:30 a.m. to 5:00 p.m. Eastern Standard Time 
(EST).

ADDRESSES: Submit your comments, identified by Docket ID No. EPA-HQ-
OAR-2022-0872, by one of the following methods:
     Federal eRulemaking Portal: https://www.regulations.gov. 
Follow the online instructions for submitting comments.
     Email: [email protected]. Include Docket ID No. EPA-
HQ-OAR-2022-0872 in the subject line of the message.
     Fax: (202) 566-9744.
     Mail: Environmental Protection Agency, EPA Docket Center, 
Office of Air and Radiation Docket, Mail code 28221T, Attention Docket 
No. EPA-HQ-OAR-2022-0872, 1200 Pennsylvania Ave. NW, Washington, DC 
20460.
     Hand/Courier Delivery: EPA Docket Center, Room 3334, EPA 
WJC West Building, 1301 Constitution Ave. NW, Washington, DC. The 
Docket Center's hours of operations are 8:30 a.m.-4:30 p.m., Monday-
Friday (except Federal Holidays).
    Instructions: All submissions received must include the Docket ID 
No. for this rulemaking. Comments received may be posted without change 
to https://www.regulations.gov, including any personal information 
provided. For detailed instructions on sending comments and additional 
information on the rulemaking process, see the ``Public Participation'' 
heading of the SUPPLEMENTARY INFORMATION section of this document.
    The public hearing will be held at 109 T.W. Alexander Drive, 
Research Triangle Park, North Carolina 27711. The hearing will convene 
at 8:30 a.m. (local time) and will conclude at 5:00 p.m. (local time). 
Refer to the SUPPLEMENTARY INFORMATION section below for additional 
information.

FOR FURTHER INFORMATION CONTACT: Mr. George M. Bridgers, Office of Air 
Quality Planning and Standards, Air Quality Assessment Division, Air 
Quality Modeling Group, U.S. Environmental Protection Agency, Mail code 
C439-01, Research Triangle Park, NC 27711; telephone: (919) 541-5563; 
email: [email protected]. (and include ``2023 Revisions to the 
Guideline on Air Quality Models'' in the subject line of the message).

SUPPLEMENTARY INFORMATION: 
    The information in this preamble is organized as follows:

Table of Contents

I. General Information
    A. Does this action apply to me?
    B. Where can I get a copy of this document?
II. Background
    A. The Guideline on Air Quality Models and EPA Modeling 
Conferences
    B. The Twelfth Conference on Air Quality Modeling
    C. Alpha and Beta Categorization of Non-Regulatory Options
III. Public Participation
    A. Written Comments
    B. Notice of Public Hearing and the Thirteenth Conference on Air 
Quality Models
IV. Proposed Revisions to the Guideline
    A. Proposed Revisions
V. Ongoing Model Development
VI. Statutory and Executive Order Reviews
    A. Executive Order 12866: Regulatory Planning and Review and 
Executive Order 14094: Modernizing Regulatory Review
    B. Paperwork Reduction Act (PRA)
    C. Regulatory Flexibility Act (RFA)
    D. Unfunded Mandates Reform Act (UMRA)
    E. Executive Order 13132: Federalism
    F. Executive Order 13175: Consultation and Coordination With 
Indian Tribal Governments
    G. Executive Order 13045: Protection of Children From 
Environmental Health Risks and Safety Risks
    H. Executive Order 13211: Actions Concerning Regulations That 
Significantly Affect Energy Supply, Distribution, or Use
    I. National Technology Transfer and Advancement Act
    J. Executive Order 12898: Federal Actions To Address 
Environmental Justice in Minority Populations and Low-Income 
Populations

I. General Information

A. Does this action apply to me?

    This action applies to Federal, State, territorial, and local air 
quality management programs that conduct air quality modeling as part 
of State Implementation Plan (SIP) submittals and revisions, New Source 
Review (NSR), including new or modifying industrial sources under 
Prevention of Significant Deterioration (PSD), Conformity, and other 
air quality assessments required under EPA regulation. Categories and 
entities potentially regulated by this action include:

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                                                               NAICS \a\
                          Category                               code
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Federal/State/territorial/local/Tribal government...........      924110
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\a\ North American Industry Classification System.

B. Where can I get a copy of this document?

    In addition to being available in the docket, an electronic copy of 
this proposed rule and relative supporting documentation will also be 
available on EPA's Support Center for Regulatory Atmospheric Modeling 
(SCRAM) website. Following signature, these materials will be posted on 
SCRAM at the following address: https://www.epa.gov/scram/13th-conference-air-quality-modeling.

II. Background

A. The Guideline on Air Quality Models and EPA Modeling Conferences

    The Guideline is used by the EPA, other Federal, State, 
territorial, and local

[[Page 72827]]

air quality agencies, and industry to prepare and review 
preconstruction permit applications for new sources and modifications, 
SIP submittals and revisions, determinations that actions by Federal 
agencies are in conformity with SIPs, and other air quality assessments 
required under EPA regulation. The Guideline serves as a means by which 
national consistency is maintained in air quality analyses for 
regulatory activities under CAA regulations, including 40 CFR 51.112, 
51.117, 51.150, 51.160, 51.165, 51.166, 52.21, 93.116, 93.123, and 
93.150.
    The EPA originally published the Guideline in April 1978 (EPA-450/
2-78-027), and it was incorporated by reference in the regulations for 
the PSD program in June 1978. The EPA revised the Guideline in 1986 (51 
FR 32176) and updated it with supplement A in 1987 (53 FR 32081), 
supplement B in July 1993 (58 FR 38816), and supplement C in August 
1995 (60 FR 40465). The EPA published the Guideline as Appendix W to 40 
CFR part 51 when the EPA issued supplement B. The EPA republished the 
Guideline in August 1996 (61 FR 41838) to adopt the CFR system for 
labeling paragraphs. The publication and incorporation of the Guideline 
by reference into the EPA's PSD regulations satisfies the requirement 
under the CAA section 165(e)(3)(D) for the EPA to promulgate 
regulations that specify with reasonable particularity models to be 
used under specified sets of conditions for purposes of the PSD 
program.
    To support the process of developing and revising the Guideline 
during the period of 1977 to 1988, we held the First, Second, and Third 
Conferences on Air Quality Modeling as required by CAA section 320 to 
help standardize modeling procedures. These modeling conferences 
provided a forum for comments on the Guideline and associated 
revisions, thereby helping us introduce improved modeling techniques 
into the regulatory process. Between 1988 and 1995, we conducted the 
Fourth, Fifth, and Sixth Conferences on Air Quality Modeling to solicit 
comments from the stakeholder community to guide our consideration of 
further revisions to the Guideline, update the available modeling tools 
based on the current state-of-the-science, and advise the public on new 
modeling techniques.
    The Seventh Conference was held in June 2000 and also served as a 
public hearing for the proposed revisions to the recommended air 
quality models in the Guideline (65 FR 21506). These changes included 
the CALPUFF modeling system, AERMOD Modeling System, and ISC-PRIME 
model. Subsequently, the EPA revised the Guideline on April 15, 2003 
(68 FR 18440), to adopt CALPUFF as the preferred model for long-range 
transport of emissions from 50 to several hundred kilometers and to 
make various editorial changes to update and reorganize information and 
remove obsolete models.
    We held the Eighth Conference on Air Quality Modeling in September 
2005. This conference provided details on changes to the preferred air 
quality models, including available methods for model performance 
evaluation and the notice of data availability that the EPA published 
in September 2003, related to the incorporation of the PRIME downwash 
algorithm in the AERMOD dispersion model (in response to comments 
received from the Seventh Conference). Additionally, at the Eighth 
Conference, a panel of experts discussed the use of state-of-the-
science prognostic meteorological data for informing the dispersion 
models. The EPA further revised the Guideline on November 9, 2005 (70 
FR 68218), to adopt AERMOD as the preferred model for near-field 
dispersion of emissions for distances up to 50 kilometers.
    The Ninth Conference on Air Quality Modeling was held in October 
2008 and emphasized the following topics: reinstituting the Model 
Clearinghouse, review of non-guideline applications of dispersion 
models, regulatory status updates of AERMOD and CALPUFF, continued 
discussions on the use of prognostic meteorological data for informing 
dispersion models, and presentations reviewing the available model 
evaluation methods. To further inform the development of additional 
revisions to the Guideline, we held the Tenth Conference on Air Quality 
Modeling in March 2012. The conference addressed updates on: the 
regulatory status and future development of AERMOD and CALPUFF, review 
of the Mesoscale Model Interface (MMIF) prognostic meteorological data 
processing tool for dispersion models, draft modeling guidance for 
compliance demonstrations of the fine particulate matter 
(PM2.5) National Ambient Air Quality Standards (NAAQS), 
modeling for compliance demonstration of the 1-hour nitrogen dioxide 
(NO2) and sulfur dioxide (SO2) NAAQS, and new and 
emerging models/techniques for future consideration under the Guideline 
to address single-source modeling for ozone and secondary 
PM2.5, as well as long-range transport and chemistry.
    The Eleventh Conference on Air Quality Modeling was held August 12-
13, 2015, and included the public hearing for the most recently 
proposed version of the Guideline. The conference included 
presentations summarizing the proposed updates to the AERMOD Modeling 
System, replacement of CALINE3 with AERMOD for modeling of mobile 
sources, incorporation of prognostic meteorological data for use in 
dispersion modeling, the proposed screening approach for long-range 
transport for NAAQS and PSD increments assessments with use of CALPUFF 
as a screening technique rather than an EPA-preferred model, the 
proposed 2-tiered screening approach to address ozone and 
PM2.5 in PSD compliance demonstrations, the status and role 
of the Model Clearinghouse, and updates to procedures for single-source 
and cumulative modeling analyses (e.g., modeling domain, source input 
data, background data, and compliance demonstration procedures).
    Additionally, the 2015 proposed action included a reorganization of 
the Guideline to make it easier to use and to streamline the compliance 
assessment process (80 FR 45340), and also included additional clarity 
in distinguishing requirements from recommendations while noting the 
continued flexibilities provided within the Guideline, including but 
not limited to use and approval of alternative models (82 FR at 45344). 
These proposed revisions were adopted and reflected in the latest 
version of the Guideline, promulgated on January 17, 2017 (82 FR 5182).

B. The Twelfth Conference on Air Quality Modeling

    The most recent EPA modeling conference was the Twelfth Conference 
on Air Quality Modeling, which was held in August 2019 in continuing 
compliance with CAA section 320. While not associated with a regulatory 
action, the Twelfth Conference was held with the intent to inform the 
ongoing development of EPA's preferred air quality models and potential 
revisions to the Guideline. The conference included expert panel 
discussions and invited presentations covering the following model/
technique enhancements: treatment of low wind conditions, overwater 
modeling, mobile source modeling, building downwash, prognostic 
meteorological data, near-field and long-range model evaluation 
criteria, NO2 modeling techniques, plume rise, deposition, 
and single source ozone and PM2.5 modeling techniques. At 
the conclusion of the expert panels and invited presentations, there 
were several presentations given by the public, including industrial 
trade groups, on recommended areas for additional model development and

[[Page 72828]]

future revision in the Guideline. The proposed regulatory updates to 
the AERMOD Modeling System in this action address topics on which there 
was focused discussion and engagement with the stakeholder community 
through these expert panels and invited and public presentations during 
the Twelfth Conference.
    All the presentations, along with the transcript of the conference 
proceedings, are available in the docket for the Twelfth Conference on 
Air Quality Models (Docket ID No. EPA-HQ-OAR-2019-0454). Additionally, 
all the materials associated with the Twelfth Conference and the public 
hearing are available on the EPA's SCRAM website at https://www.epa.gov/scram/12th-conference-air-quality-modeling.

C. Alpha and Beta Categorization of Non-Regulatory Options

    With the release of AERMOD version 18181 in 2018, the EPA adopted a 
new paradigm for engagement with the scientific community to facilitate 
the continued development of the AERMOD Modeling System. Previously, 
updates to the scientific formulation of the model were not made 
available to the public for review, testing, evaluation, and comment 
prior to the proposal stage of the formal rulemaking process when an 
update was made to the Guideline. This limited the public's engagement 
and feedback to a short, predefined comment period, typically only one 
to two months. The new approach enables the EPA to release potential 
formulation updates as non-regulatory ``alpha'' and ``beta'' options as 
they are being developed. As non-regulatory options, they can be made 
available during any release cycle, thereby enabling feedback as they 
are being developed. This approach allows for more robust testing and 
evaluation during development, benefitting from the experience of a 
broad expert community. In addition, the EPA developed a protocol to 
enable the external community to submit model updates to the EPA for 
review and consideration for inclusion as new alpha or beta options. A 
pathway such as this that facilitates more frequent and active 
engagement with the external community allows for a more informed and 
timely regulatory update process when the EPA has determined an update 
has met the criteria required for consideration as a science 
formulation update to the regulatory version of the model.
    In this alpha/beta construct, alpha options are updates to the 
scientific formulation that are thought to have merit but are 
considered experimental, still in the research and development stage. 
Alpha options have not yet been fully tested, evaluated, or vetted 
through peer review and should not be considered for use as an 
alternative model for regulatory applications of the model.
    Beta options, on the other hand, have been demonstrated to be 
applicable on a theoretical basis, have undergone scientific peer 
review, and are supported with performance evaluations using available 
and adequate databases that demonstrate unbiased, improved model 
performance. In general, beta options have met the necessary criteria 
to be formally proposed and adopted as updates to the regulatory 
version of the model but have not yet been proposed through the 
required rulemaking process, which includes a public hearing and formal 
comment period. Beta options are mature enough in the development 
process to be considered for use as an alternative model, provided an 
appropriate site-specific modeling demonstration is completed to show 
the alternative model is appropriate for the site and conditions where 
it will be applied and the requirements of the Guideline, section 3.2, 
are fully satisfied, including formal concurrence by the EPA's Model 
Clearinghouse.

III. Public Participation

    Interested persons may provide the EPA with their views on the 
proposed revisions to the Guideline in several ways. This includes 
submitting written comments to the EPA, participating in the Thirteenth 
Conference on Air Quality Modeling, and speaking at the public hearing 
that will be conducted as part of the conference. Additional 
information on where to submit written comments on the proposed 
revisions to the Guideline is provided in the ADDRESSES section above.

A. Written Comments

    Submit your comments, identified by Docket ID No. EPA-HQ-OAR-2022-
0872, at https://www.regulations.gov (our preferred method), or the 
other methods identified in the ADDRESSES section. Once submitted, 
comments cannot be edited or removed from the docket. The EPA may 
publish any comment received to its public docket. Do not submit to 
EPA's docket at https://www.regulations.gov any information you 
consider to be Confidential Business Information (CBI), Proprietary 
Business Information (PBI), or other information whose disclosure is 
restricted by statute. Multimedia submissions (audio, video, etc.) must 
be accompanied by a written comment. The written comment is considered 
the official comment and should include discussion of all points you 
wish to make. The EPA will generally not consider comments or comment 
contents located outside of the primary submission (i.e., on the web, 
cloud, or other file sharing system). Please visit https://www.epa.gov/dockets/commenting-epa-dockets for additional submission methods; the 
full EPA public comment policy; information about CBI, PBI, or 
multimedia submissions; and general guidance on making effective 
comments.

B. Notice of Public Hearing and the Thirteenth Conference on Air 
Quality Models

    The public hearing for this action and the Thirteenth Conference on 
Air Quality Modeling will be held on November 14-15, 2023, in the EPA 
Nantahala Auditorium, Room C111, 109 T.W. Alexander Drive, Research 
Triangle Park, NC 27711. The hearing and conference will convene each 
day at 8:30 a.m. EST and will conclude at 5:00 p.m. EST.
    The Thirteenth Conference on Air Quality Modeling will be open to 
the public. No registration fee is charged. The conference will be 
formally conducted and chaired by an EPA official. As required under 
CAA section 320, a verbatim transcript of the conference proceedings 
will be produced and placed in the docket for this proposed action. The 
conference will begin with introductory remarks by the presiding EPA 
official. The EPA staff and EPA invited speakers will then provide a 
structured overview of the revisions to the Guideline as proposed in 
this document and present on the research that supports those revisions 
and supports formulation updates to the preferred models. The following 
topics will be presented:

    I. Overview of the Thirteenth Conference on Air Quality 
Modeling;
    II. Review of the proposed revisions to the preferred air 
quality models; and
    III. Review of the proposed revisions to the Guideline.

    At the conclusion of these presentations, the EPA will convene the 
public hearing on the proposed revisions to the Guideline. The public 
hearing will span a portion of the afternoon of the first day and 
throughout the second day of the conference. The EPA will make every 
effort to follow the schedule as closely as possible on the days of the 
conference; however, please plan for the public hearing to run either 
ahead of schedule or behind schedule. The EPA may close the hearing 15 
minutes after

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the last pre-registered speaker has testified on November 15, if there 
are no additional speakers.
    Those wishing to reserve time to speak at the public hearing, 
whether to offer specific comments on the proposed rule, volunteer a 
presentation on a special topic, or to offer recommendations on any 
regulatory modeling techniques, should contact us at the address given 
in the FOR FURTHER INFORMATION CONTACT section by no later than 
November 10, 2023. Such persons should identify the organization (if 
any) on whose behalf they are speaking and the length of the 
presentation. If a scheduled presentation is projected to be longer 
than 10 minutes, the presenter should also state why a longer period is 
needed. Scheduled speakers should bring extra copies of their 
presentation for inclusion in the docket and for the convenience of the 
recorder. Scheduled speakers will also be permitted to enter additional 
written comments into the record.
    Any person in attendance wishing to speak at the public hearing who 
has not reserved time in advance may provide oral comments on the 
proposed revisions to the Guideline during time allotted on the last 
day. These parties will need to sign up to speak on the second day of 
the hearing, and the EPA may need to limit the duration of 
presentations to allow all participants to be heard.
    The EPA may ask clarifying questions during the oral presentations 
but will not respond to the presentations at that time. Information 
submitted to the EPA during the public hearing will be placed in the 
docket for this proposed action. Written statements and supporting 
information submitted during the comment period will be considered with 
the same weight as oral testimony and supporting information presented 
at the public hearing.
    Conference background information. Preregistration details, 
additional background information, and a more detailed agenda for the 
Thirteenth Conference on Air Quality Modeling are electronically 
available at https://www.epa.gov/scram/13th-conference-air-quality-modeling. Preregistration for the conference, while not required, is 
strongly recommended due to heightened security protocols at the EPA-
RTP facility.
    Access to U.S. government facility. Because this hearing is being 
held at a U.S. government facility, individuals planning to attend the 
conference and/or public hearing should be prepared to show valid 
picture identification to the security staff in order to gain access to 
the meeting room. Please note that the REAL ID Act, passed by Congress 
in 2005, established new requirements for entering Federal facilities. 
For purposes of the REAL ID Act, EPA will accept government-issued IDs, 
including drivers' licenses, from the District of Columbia and all 
States and territories except from American Samoa. If your 
identification is issued by American Samoa, you must present an 
additional form of identification to enter the Federal building where 
the public hearing will be held. Acceptable alternative forms of 
identification include Federal employee badges, passports, enhanced 
driver's licenses, and military identification cards. For additional 
information for the status of your State regarding REAL ID, go to: 
https://www.dhs.gov/real-id-enforcement-brieffrequently-asked-questions. Any objects brought into the building need to fit through 
the security screening system, such as a purse, laptop bag, or small 
backpack. Demonstrations will not be allowed on Federal property for 
security reasons. Attendees are encouraged to arrive at least 15 
minutes prior to the start of the meeting to allow enough time for 
security screening.

IV. Proposed Revisions to the Guideline

    In this action, the EPA is proposing updates to the Guideline 
corresponding to updates to the scientific formulation of the AERMOD 
Modeling System and updates to the recommendations for the development 
of appropriate background concentration for cumulative impact analyses. 
When and where appropriate, the EPA has engaged with our Federal 
partners, including the Bureau of Ocean Energy Management (BOEM) and 
the Federal Highway Administration (FHWA), to collaborate on these 
proposed updates to the Guideline. There are additional editorial 
changes proposed to the Guideline to correct minor typographical errors 
found in the 2017 Guideline and update website links.

A. Proposed Revisions

    This section provides a detailed overview of the substantive 
proposed changes to the Guideline that are intended to improve the 
science of the models and approaches used in regulatory assessments.
1. Proposed Updates to EPA's AERMOD Modeling System
    Based on studies presented and discussed at the Twelfth Conference 
on Air Quality Models held on October 2-3, 2019,\1\ and additional 
relevant research since 2017, the EPA and other researchers have 
conducted additional model evaluations and developed changes to the 
model formulation of the AERMOD Modeling System to improve model 
performance in its regulatory applications. One update is to the AERMET 
meteorological preprocessor for AERMOD. This update provides the 
capability to process measured and prognostic marine-based meteorology 
for offshore applications. Separate updates are related to the AERMOD 
dispersion model and include (1) a new Tier 3 screening method for the 
conversion of nitrogen oxides (NOX) emissions to 
NO2 and (2) a new source type for modeling vehicle roadway 
emissions.
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    \1\ https://www.epa.gov/scram/12th-conference-air-quality-modeling.
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    Each of the proposed formulation updates to the AERMOD Modeling 
System is provided as a non-regulatory beta option in the release of 
the relevant modeling system components that is occurring concurrent 
with this proposed rule. If EPA adopts these formulation updates in a 
subsequent final rule, the beta categorization would be removed and the 
respective model option(s) could be considered regulatory model 
options.
    The EPA proposes the following updates to the AERMOD Modeling 
System to address several technical concerns expressed by stakeholders:
a. Incorporation of COARE Algorithms Into AERMET for Use in Overwater 
Marine Boundary Layer Environments
    As the number of overwater applications has increased in recent 
years, the EPA is proposing to add the Coupled Ocean-Atmosphere 
Response Experiment (COARE) 2 3 algorithms to AERMET for 
meteorological data processing in applications using either observed or 
prognostic meteorological data in overwater marine boundary layer 
environments. One of the first notable uses of AERMOD for an overwater 
application was an alternative model application--AERMOD-COARE was used 
in 2011 in an ice-free arctic environment of Alaska.4 5 In 
this

[[Page 72830]]

application, the incorporation of the COARE bulk flux algorithm was 
used as an alternative to the AERMET meteorological processor to 
AERMOD. This led to the development of the AERCOARE \6\ processor that 
can be used with either measured or prognostic data for overwater 
applications in lieu of AERMET. AERCOARE has been approved as an 
alternative model for several overwater applications since 2011.\7\
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    \2\ Fairall, C.W., E.F. Bradley, J.E. Hare, A.A. Grachev, and 
J.B. Edson, 2003: ``Bulk Parameterization of Air-Sea Fluxes: Updates 
and Verification for the COARE Algorithm.'' Journal of Climate, 16, 
571-591.
    \3\ Evaluation of the Implementation of the Coupled Ocean-
Atmosphere Response Experiment (COARE) algorithms into AERMET for 
Boundary Layer Environments. EPA-2023/R-23-008, Office of Air 
Quality Planning and Standards, RTP, NC.
    \4\ U.S. EPA, 2011: COARE Bulk Flux Algorithm to Generate Hourly 
Meteorological Data for Use with the AERMOD dispersion program; 
Section 3.2.2.e Alternative Refined Model Demonstration Herman Wong 
Memorandum dated April 1, 2011, Office of Environmental Assessment, 
Region 10, Seattle, Washington 98101.
    \5\ U.S. EPA, 2011: Model Clearinghouse Review AERMOD-COARE as 
an Alternative Model in an Arctic Ice Free Environment. George 
Bridgers Memorandum dated May 6, 2011, Office of Air Quality 
Planning and Standards, Research Triangle Park, North Carolina 
27711.
    \6\ U.S. EPA, 2012: User's Manual AERCOARE Version 1.0. EPA-910-
R-12-008. U.S. EPA, Region 10, Seattle, WA.
    \7\ Please reference the EPA Model Clearinghouse Information 
Storage and Retrieval System (MCHISRS) database for more information 
regarding AERCOARE alternative model approvals (https://cfpub.epa.gov/oarweb/MCHISRS, text Search term ``AERCOARE'').
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    For overwater applications, the algorithms in COARE are better 
suited for overwater boundary layer calculations than the existing 
algorithms in AERMET that are better suited for land-based data. These 
calculations include calculation of surface roughness, stability 
classification, effects of moisture on Monin-Obukhov length, and the 
use of Bowen ratio by AERMET for heat flux calculations.\5\ The EPA 
proposes to add COARE to AERMET in order to ensure that the COARE 
algorithms are updated regularly as part of routine AERMET updates, to 
provide consistent data handling among land based and overwater based 
meteorological data (e.g., treatment of missing data and treatment of 
calms), and to have all meteorological processing for AERMOD 
applications in one program.
    The addition of the COARE algorithms to AERMET would replace the 
standalone AERCOARE program and the AERCOARE output option in MMIF for 
prognostic data overwater. This proposed option is selected by the user 
with the METHOD COARE RUN-COARE record in the AERMET Stage 2 input 
file. For prognostic applications processed through the MMIF, the user 
can run MMIF for AERMET input for overwater applications.
    The addition of COARE to AERMET would eliminate the previous 
alternative model demonstration requirements for use of AERMOD in 
marine environments, and this elimination is contingent upon 
consultation with the EPA Regional Office and appropriate reviewing 
authority. This consultation will ensure that platform downwash and 
shoreline fumigation are adequately considered in the modeling 
demonstration.
b. Proposed Addition of a New Tier 3 Detailed Screening Technique for 
NO2
    Section 4.2.3.4 of the 2017 Guideline details a 3-tiered approach 
for evaluating the modeled impacts of NOX sources, which was 
recommended to assess hourly and annual average NO2 impacts 
from point, volume, and area sources for the purposes of the PSD 
program, SIP planning, and transportation general conformity. This 3-
tiered approach addresses the co-emissions of NO and NO2 and 
the subsequent conversion of NO to NO2 in the atmosphere. 
The tiered levels include:
    Tier 1--assuming that all emitted NO is converted to NO2 
(full conversion).
    Tier 2--using the Ambient Ratio Method 2 (ARM2), which applies an 
assumed equilibrium ratio of NO2 to NOX, based on 
analysis of and correlation with nationwide hourly observed ambient 
conditions.
    Tier 3--applying the Ozone Limiting Method (OLM) and Plume Volume 
Molar Ratio (PVMRM) screening options based on site-specific hourly 
ozone data and source-specific NO2 to NOX in-
stack ratios.8 9 10 11
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    \8\ Podrez, M. 2015. An Update to the Ambient Ratio Method for 
1-h NO2 Air Quality Standards Dispersion Modeling. 
Atmospheric Environment, 103: 163-170.
    \9\ Cole, H.S. and J.E. Summerhays, 1979. A Review of Techniques 
Available for Estimation of Short-Term NO2 
Concentrations. Journal of the Air Pollution Control Association, 
29(8): 812-817.
    \10\ Hanrahan, P.L., 1999. The Polar Volume Polar Ratio Method 
for Determining NO2/NOX Ratios in Modeling--
Part I: Methodology. Journal of the Air & Waste Management 
Association, 49: 1324-1331.
    \11\ Chu, S.H. and E.L. Meyer, 1991. Use of Ambient Ratios to 
Estimate Impact of NOX Sources on Annual NO2 
Concentrations. Proceedings, 84th Annual Meeting & Exhibition of the 
Air & Waste Management Association, Vancouver, B.C.; 16-21 June 
1991. (16pp.) (Docket No. A-92-65, II-A-9).
---------------------------------------------------------------------------

    As further discussed in section 4.2.3.4(e) of the Guideline, 
regulatory application of Tier 3 screening options shall occur in 
consultation with the EPA Regional Office and appropriate reviewing 
authority.
    The EPA proposes to include the Generic Reaction Set Method (GRSM) 
as a regulatory non-default Tier 3 NO2 screening option. 
Following a peer-reviewed publication in 2017, GRSM was added to AERMOD 
as an alpha option in version 21112 and updated as a beta option in 
version 22112.\12\ The primary motivation behind the formulation and 
development of the GRSM NO2 screening option was to address 
photolytic conversion of NO2 to NO and to address the time-
of-travel necessary for NOX plumes to convert the NO portion 
of the plume to NO2 via titration and entrainment of ambient 
ozone. The existing regulatory non-default Tier 3 NO2 
screening options, PVMRM and OLM, do not address or provide for 
treatment of these mechanisms, and have been shown to over-predict for 
some source characterizations and model configurations at project 
source ambient air boundaries and within the first 1-3 km.\13\
---------------------------------------------------------------------------

    \12\ David J. Carruthers, Jenny R. Stocker, Andrew Ellis, Martin 
D. Seaton & Stephen E. Smith (2017) Evaluation of an explicit 
NOX chemistry method in AERMOD, Journal of the Air & 
Waste Management Association, 67:6, 702-712, DOI: 10.1080/
10962247.2017.1280096.
    \13\ Jenny Stocker, Martin Seaton, Stephen Smith, James O'Neill, 
Kate Johnson, Rose Jackson, David Carruthers (CERC). Evaluation of 
the Generic Reaction Set Method for NO2 conversion in 
AERMOD. The modification of AERMOD to include ADMS chemistry. August 
8, 2023. Cambridge Environmental Research Consultants (CERC) 
Technical Report.
---------------------------------------------------------------------------

    The functionality of the GRSM implementation in AERMOD is similar 
to that of the PVMRM and OLM schemes, with exception to some additional 
input requirements necessary for treatment of the reverse 
NO2 photolysis reaction during daytime hours. Modeled source 
inputs for GRSM require NO2/NOX in-stack ratios, 
with similar assumptions as applied to PVMRM and OLM according to 
section 4.2.3.4 of the Guideline. Ambient inputs for GRSM require 
hourly ozone concentrations taken from an appropriately representative 
monitoring station or selection of monitoring stations for varying 
upwind sector concentrations. GRSM also requires hourly NOX 
concentration inputs to resolve the daytime photolysis of 
NO2 reaction in equilibrium with ozone titration conversion 
of the NO portion of the NOX plume. GRSM hourly 
NOX concentration inputs can also vary by upwind sector 
concentration, as appropriate. Background NO2 concentrations 
are accounted for in the GRSM daytime equilibrium NO2 
concentration estimates based on the chemical reaction balance between 
ozone entrainment and NO titration, photolysis of NO2 to NO, 
and ambient background NO2 participation in titration and 
photolysis reactions. Nighttime GRSM NO2 estimates are based 
on ozone entrainment and titration of available NO in the 
NOX plume. Note that all hourly ozone and NOX 
ambient inputs to GRSM must coincide with the hourly meteorological

[[Page 72831]]

data records for the period of the modeling analysis (i.e., minimum of 
1 year for on-site data, 3 years of prognostic data, and 5 years of 
airport data).
    Updates to the GRSM formulation in AERMOD version 22112 were 
developed in late 2022 to address more realistic building effects on 
instantaneous plume spread, accounting of multiple plume effects on 
entrainment of ozone, and the tendency of GRSM to over-predict in the 
far-field (e.g., beyond approximately 3 km for typical point source 
releases). Sensitivity testing and model performance evaluations of 
these updates to GRSM in AERMOD version 23132 have shown consistent or 
improved model behavior and performance.\14\
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    \14\ Environmental Protection Agency, 2023. Technical Support 
Document (TSD) for Adoption of the Generic Reaction Set Method 
(GRSM) as a Regulatory Non-Default Tier-3 NO2 Screening Option, 
Publication No. EPA-454/R-23-009. Office of Air Quality Planning & 
Standards, Research Triangle Park, NC.
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c. Proposed Addition of RLINE as Mobile Source Type
    As a culmination of an Interagency Agreement between EPA and FHWA, 
the EPA proposes to add the RLINE source type as a new source type 
applicable for regulatory modeling of mobile sources. This is in 
addition to the AREA, LINE, and VOLUME source types already available 
for mobile source modeling. The proposed addition of RLINE as a mobile 
source type is an extension of the 2017 update to the Guideline in 
which AERMOD replaced CALINE3 as the addendum A \15\ model for mobile 
source modeling. At that time, AERMOD's AREA, LINE, and VOLUME sources 
were available for mobile source modeling. The basis of the RLINE 
source type is the EPA's Office of Research and Development (ORD) 
Research LINE (RLINE) model \16\ released in 2013. The RLINE model was 
designed for near-surface releases to simulate mobile source dispersion 
with an emphasis on the near-road environment. The RLINE model was 
first incorporated into AERMOD as a beta source type in AERMOD version 
19191 in 2019.
---------------------------------------------------------------------------

    \15\ Under the codification requirements of the Administrative 
Committee of the Federal Register (ACFR), only subparts, parts, 
subchapters, and chapters may have appendices. Therefore, we have 
changed the naming convention from ``appendix A'' to ``addendum A''.
    \16\ Snyder, M.G., Venkatram, A., Heist, D.K., Perry, S.G., 
Petersen, W.B. and Isakov, V., 2013. RLINE: A line source dispersion 
model for near-surface releases. Atmospheric environment, 77, 
pp.748-756.
---------------------------------------------------------------------------

    The RLINE source type for this proposed action has undergone 
significant evaluation by the EPA and the FHWA as part of the 
Interagency Agreement and has shown improved 
performance.17 18 This proposed option is selected by the 
model user with the SOURCE type ``RLINE''. In addition to proposing 
RLINE as a new source type, the EPA is also proposing the use of the 
AERMOD urban option (accounting for urban heat island effect in stable 
conditions) and terrain with the RLINE source type. However, the 
inclusion of terrain with RLINE does not supersede the EPA's PM Hot-
spot guidance where FLAT terrain is recommended for modeling 
applications.\19\ The EPA also emphasizes that the inclusion of RLINE 
as a source type for mobile source modeling does not preclude the use 
of the existing AREA, LINE, and VOLUME source types thereby extending 
the flexibility of users in best characterizing mobile source for 
regulatory modeling.
---------------------------------------------------------------------------

    \17\ Incorporation and Evaluation of the RLINE source type in 
AERMOD for Mobile Source Applications. EPA-2023/R-23-011, Office of 
Air Quality Planning and Standards, RTP, NC.
    \18\ Heist, D., et al., 2023. Integration of RLINE dispersion 
model into EPA's AERMOD: updated formulation and evaluations. 
Journal of the Air & Waste Management Association, Manuscript 
submitted for publication.
    \19\ U.S. EPA, 2021: PM Hot-spot Guidance; Transportation 
Conformity Guidance for Quantitative Hot-spot Analyses in 
PM2.5 and PM10 Nonattainment and Maintenance 
Areas. EPA-42-B-21-037. U.S. EPA, Office of Transportation and Air 
Quality, Ann Arbor, MI.
---------------------------------------------------------------------------

d. Support Information, Documentation, and Model Code
    Model performance evaluation and peer-reviewed scientific 
references for each of these three proposed updates to the AERMOD 
Modeling System are cited and placed in the docket, as appropriate. An 
updated user's guide and model formulation documents for version 23132 
have also been placed in the docket. We have updated the summary 
description of the AERMOD Modeling System to addendum A of the 
Guideline to reflect these proposed updates. The essential codes, 
preprocessors, and test cases have been updated and posted to the EPA's 
SCRAM website, https://www.epa.gov/scram.
2. Proposed Updates to Recommendations on the Development of Background 
Concentration
    Based on permit modeling experiences since the 2017 revisions to 
the Guideline, the EPA proposes revisions to section 8 of the Guideline 
to refine the recommendations regarding the determination of 
appropriate model input data, specifically background concentration, 
for use in NAAQS implementation modeling demonstrations (e.g., PSD 
compliance demonstrations, SIP demonstrations for inert pollutants, and 
SO2 designations). The Guideline recommends that a 
representative background concentration should include contributions 
from all sources, including both nearby and other sources. When 
identifying nearby sources that may not be adequately represented by 
ambient monitoring data, the Guideline recommends selecting sources 
``that cause a significant concentration gradient in the vicinity of 
the source(s) under consideration.'' The EPA recognizes that the 
recommended method for identifying nearby sources lacks specificity, is 
used and referenced inconsistently, and may lead to overly conservative 
modeling exercises. The proposed revisions to section 8 are intended to 
provide a more robust framework for characterizing background 
concentrations for cumulative modeling with particular attention to 
identifying and modeling nearby sources in multi-source areas.
    The EPA proposes to revise recommendations for the determination of 
background concentrations in constructing the design concentration, or 
total air quality concentration in multi-source areas (see section 
8.3), as part of a cumulative impact analysis for NAAQS implementation 
modeling demonstrations. The EPA's proposed framework includes a 
stepwise set of considerations to replace the narrow recommendation of 
modeling nearby sources that cause a significant concentration 
gradient. This framework focuses the inherent discretion in defining 
representative background concentrations through qualitative and semi-
quantitative considerations within a transparent process using the 
variety of emissions and air quality data available to the permit 
applicant. To construct a background concentration for model input 
under the framework, permit applicants should consider the 
representativeness of relevant emissions, air quality monitoring, and 
pre-exiting air quality modeling to appropriately represent background 
concentrations for the cumulative impact analysis.
    In conjunction with the proposed revisions to section 8 of the 
Guideline, the EPA developed the Draft Guidance on Developing 
Background Concentrations for Use in Modeling Demonstrations.\20\ This 
draft guidance

[[Page 72832]]

document details the EPA-recommended framework with illustrative 
examples to assist permit applicants in characterizing a credible and 
appropriately representative background concentration for cumulative 
impact analyses including the contributions from nearby sources in 
multi-source areas.
---------------------------------------------------------------------------

    \20\ U.S. Environmental Protection Agency, 2023. Draft Guidance 
on Developing Background Concentrations for Use in Modeling 
Demonstrations. Publication No. EPA-454/P-23-001. Office of Air 
Quality Planning and Standards, Research Triangle Park, NC.
---------------------------------------------------------------------------

3. Transition Period for Applicability of Revisions to the Guideline
    In previous rulemakings to revise the Guideline, we have 
traditionally communicated that it would be appropriate to provide 1-
year to transition to the use of new models, techniques and procedures 
in the context of PSD permit applications and other regulatory modeling 
applications. We invite comments whether it would be appropriate to 
apply a 1-year transition after promulgation of the revised Guideline 
(i.e., from its effective date) such that applications conducted under 
the existing Guideline with approved protocols would be acceptable 
during that period, but new requirements and recommendations should be 
used for applications submitted after that period or protocols approved 
after that period.
    Such a transition period is consistent with previous revisions to 
the Guideline and appropriate to avoid the time and expense of 
revisiting modeling that is substantially complete, which would cause 
undue delays to permit applications that are pending when the proposed 
revisions to the Guideline are finalized. The proposed revisions to the 
Guideline are intended as incremental improvements to the Guideline, 
and such improvements do not necessarily invalidate past practices 
under the previous editions of the Guideline. The requirements and 
recommendations in the existing (2017) version of the Guideline were 
previously identified as acceptable by the EPA, and they will continue 
to be acceptable for air quality assessments during the period of 
transition to the revised version of the Guideline, if finalized.
    Where a proposed revision to the Guideline does raise questions 
about the acceptability of a requirement or recommendation that it 
replaces, model users and applicants are encouraged to consult with the 
appropriate reviewing authority as soon as possible to assure the 
acceptability of modeling used to support permit applications during 
this period.
4. Proposed Revisions by Section
a. Section 1.0--Introduction
    The EPA proposes to correct paragraph (i) by combining the 
inadvertently created paragraph (A), which is actually part of the 
phrase ``addendum A'' in the first sentence.
b. Section 3.0--Preferred and Alternative Air Quality Models
    The EPA proposes to revise an outdated website link in section 
3.0(b).
    In sections 3.1.1(c) and 3.1.2(a), the EPA proposes to correct the 
sections by combining the inadvertently created paragraph (A), which is 
actually part of the phrase ``addendum A'' in the first sentence.
c. Section 4.0--Models for Carbon Monoxide, Lead, Sulfur Dioxide, 
Nitrogen Dioxide and Primary Particulate Matter
    The EPA proposes to update reference numbers where necessary due to 
added references.
    In sections 4.1(b) and 4.2.2(a), the EPA proposes to correct the 
sections by combining the inadvertently created paragraph (A), which is 
actually part of the phrase ``addendum A'' in the first sentence.
    In section 4.2.2.1, the EPA proposes to add a new paragraph (f) 
regarding the use of AERMOD in certain overwater situations. A 
typographical correction is proposed in section 4.2.2.1(b).
    The EPA proposes amendments to section 4.2.2.3 to account for 
circumstances where OCD is available to evaluate situations where 
shoreline fumigation and/or platform downwash are important.
    In section 4.2.3.4, the EPA proposes to revise paragraph (e) to 
adopt the Generic Reaction Set Method (GRSM) as a regulatory Tier 3 
detailed screening technique for NO2 modeling 
demonstrations. Sentences in this section would be updated to 
incorporate GRSM with the existing regulatory Tier 3 screening 
techniques OLM and PVMRM. An additional statement is proposed 
indicating GRSM model performance may be better than OLM and PVMRM 
under certain source characterization situations. The EPA also proposes 
to add two references to the section including one for the peer-
reviewed paper on development and evaluation of GRSM, and a second 
reference to the EPA Technical Support Document (TSD) on GRSM.
    The EPA proposes to revise Table 4-1 in section 4.2.3.4(f) to 
include GRSM as a Tier 3 detailed screening option.
d. Section 5.0--Models for Ozone and Secondarily Formed Particulate 
Matter
    The EPA proposes to update reference numbers where necessary due to 
added references. In section 5.2, the EPA proposes to revise paragraph 
(c) to include a reference for guidance on the use of models to assess 
the impacts of emissions from single sources on secondarily formed 
ozone and PM2.5.
e. Section 6.0--Modeling for Air Quality Related Values and Other 
Governmental Programs
    The EPA proposes to update reference numbers where necessary due to 
added references and revise an outdated website link in section 6.3(a).
f. Section 7.0--General Modeling Considerations
    The EPA proposes to update reference numbers where necessary due to 
added references.
    In section 7.2.3, the EPA proposes to revise paragraph (b) to 
include the addition of RLINE as a source type for use in regulatory 
applications of AERMOD and remove references to specific distances that 
receptors can be placed from the roadway.
    Also in section 7.2.3, the EPA proposes to revise paragraph (c) to 
include RLINE as a source type that can be used to model mobile sources 
and clarify that an area source can be categorized in AERMOD using the 
AREA, LINE, or RLINE source type.
g. Section 8.0--Model Input Data
    The EPA proposes to update reference numbers where necessary due to 
added references.
    The EPA proposes to revise Table 8-1 and Table 8-2 to correct 
typographical errors and update the footnotes in each of the tables.
    The EPA proposes to revise section 8.3.1 to address current EPA 
practices and recommendations for determining the appropriate 
background concentration as model input data for a new or modifying 
source(s) or sources under consideration for a revised permit limit. 
This revision would provide a stepwise framework for modeling isolated 
single sources and multi-source areas as part of a cumulative impact 
analysis. The EPA also proposes to remove the term ``significant 
concentration gradient'' and its related content in section 8.3.1(a)(i) 
due to the ambiguity and lack of definition of this term in the context 
of modeling multi-source areas.
    The EPA proposes to remove paragraph (d) in section 8.3.2 and 
renumber paragraphs (e) and (f) to (d) and (e), respectively. The 
content of

[[Page 72833]]

paragraph (d) is proposed to be included in the proposed revision of 
paragraph (a) in section 8.3.2.
    In section 8.3.3, the EPA proposes revisions to the content in 
section 8.3.3(b) on the recommendations for determining nearby sources 
to explicitly model as part of a cumulative impact analysis. The EPA 
proposes to remove the content related to the term ``significant 
concentration gradient'' in section 8.3.3(b)(i), section 8.3.3(b)(ii), 
and section 8.3.3(b)(iii) due to the lack of definition of this term in 
the context of modeling multi-source areas. The EPA also proposes to 
revise the example given in section 8.3.3(d) to be consistent with the 
discussion of other sources in section 8.3.1(a)(ii) and the proposed 
revisions to Tables 8-1 and 8-2.
    In section 8.4.1, the EPA proposes to include buoy data as an 
example of site-specific data as a result of the inclusion of the 
Coupled-Ocean Atmosphere Response Experiment (COARE) algorithms to 
AERMET for marine boundary layer processing. The EPA proposes to revise 
paragraph (a) of section 8.4.2 to note that MMIF should be used to 
process prognostic meteorological data for both land-based and 
overwater applications, and to revise paragraph (b) to clarify that 
AERSURFACE should be used to calculate surface characteristics for 
land-based data and AERMET calculates surface characteristics for 
overwater applications. Also, the EPA proposes to revise paragraph (e) 
of this section to clarify that at least 1-year of site-specific data 
applies to both land-based and overwater-based data.
    The EPA proposes to revise paragraph (a) of section 8.4.3.2 to 
remove references to specific weblinks and to state that users should 
refer to the latest guidance documents for weblinks.
    The EPA proposes to add a new section 8.4.6 to discuss the 
implementation of COARE for marine boundary layer processing and to 
renumber the existing section 8.4.6 (in the 2017 Guideline) to a new 
section 8.4.7. References to specific wind speed thresholds are 
proposed to be replaced with guidance to consult the appropriate 
guidance documents for the latest thresholds.
h. Section 9.0--Regulatory Application of Models
    The EPA proposes to update reference numbers where necessary due to 
added references.
    In section 9.2.3, the EPA proposes to revise the example given in 
section 9.2.3(a)(ii) to be consistent with the discussion of other 
sources in section 8.3.1(a)(ii) and the proposed revisions to Tables 8-
1 and 8-2.
i. Section 10.0--References
    The EPA proposes updates to references in section 10.0 to remove 
outdated website links and reflect current versions of guidance 
documents, user's guides, and other supporting documentation where 
applicable. The EPA also proposes to add references to support proposed 
updates to the AERMOD Modeling System described in this proposed update 
to the Guideline.
5. Proposed Revisions to Addendum A \21\ to Appendix W to Part 51
---------------------------------------------------------------------------

    \21\ Formerly designated as appendix A.
---------------------------------------------------------------------------

a. Section A.0
    The EPA proposes to revise section A.0 to remove references that 
indicate there are ``many'' preferred models while the number is 
currently only three.
b. Section A.1
    The EPA proposes to revise the References section to include 
additional references that support our proposed updates to the AERMOD 
Modeling System.
    In the Abstract section, the EPA proposes to add line type sources 
as one of the source types AERMOD can simulate.
    The EPA proposes to revise section A.1(a) to include overwater 
applications for regulatory modeling where shoreline fumigation and/or 
platform downwash are not important to facilitate the use of AERMOD 
with COARE processing. This revision would remove the need to request 
an alternative model demonstration for such applications. The EPA also 
proposes to clarify elevation data that can be used in AERMOD, 
specifically the change in the name of the U.S. Geological Survey 
(USGS) National Dataset (NED) to 3D Elevation Program (3DEP). For 
consistency, references to NED would be updated to 3DEP throughout 
section A.1.
    The EPA proposes to revise section A.1(b) to include prognostic 
data as meteorological input to the AERMOD Modeling System, as 
applicable.
    The EPA proposes to revise section A.1(l) to include the proposed 
Generic Reaction Set Method in the discussion on chemical 
transformation in AERMOD. We also propose to clarify the status of the 
different deposition options in A.1(l).
    The EPA proposes to revise section A.1(n) to include references to 
additional evaluation studies to support our proposed updates to the 
AERMOD Modeling System.
c. Section A.3
    In section A.3, the EPA proposes to remove the reference to the 
Bureau of Ocean Energy Management's (BOEM) outdated guidance.

V. Ongoing Model Development

    In addition to the proposed beta options above, AERMOD version 
23132 also includes alpha options that are thought to have scientific 
merit and that are still being developed or evaluated and peer 
reviewed. These alpha options are not being proposed as updates to the 
regulatory formulation of the AERMOD Modeling System, and the EPA is 
not taking comment on the alpha options during this rulemaking. A list 
of alpha options on which the EPA has placed a high priority for 
continued research and development for model improvement is included 
below. Refer to the AERMOD User's Guide for details and usage of each 
option.
    The AERMOD Modeling System, version 23131, includes but is not 
limited to the following alpha options:
     Low Wind Default Overrides (LOW_WIND).
    LOW_WIND was first implemented as a collection of non-regulatory 
beta test options in AERMOD version 12345 (LOWWIND1 and LOWWIND2) and 
expanded in version 15481(LOWWIND3). Each of these options altered the 
default model values for minimum sigma-v, minimum wind speed, and the 
minimum meander factor with different combinations of hardcoded values. 
Though the original LOW_WIND beta test options are no longer 
implemented in AERMOD, the LOW_WIND option was recategorized as an 
alpha option in AERMOD version 18181. The LOW_WIND option in version 
23132 enables the user to override AERMOD default values with user-
defined values for one or more of the following parameters:
    [cir] Minimum standard deviation of the lateral velocity to the 
average wind direction;
    [cir] Minimum mean wind speed;
    [cir] Minimum and maximum meander factor;
    [cir] Minimum standard deviation of the vertical wind speed; and
    [cir] Time scale for random dispersion.
     Modifications to PRIME Building Downwash (AWMADWNW and 
ORD_DWNW).
    Beginning with AERMOD version 19191, two distinct sets of alpha 
options were added that modify the building downwash algorithm, PRIME. 
The two sets of options were developed

[[Page 72834]]

independently by EPA's ORD (ORD_DWNW) and the Air & Waste Management 
Association (A&WMA) (AWMADWNW). With a couple of exceptions, the 
options within each set can be employed individually or combined with 
other options from each set.
     Downwash from Offshore Drilling Platforms (PLATFORM).
    To enhance AERMOD's offshore modeling capabilities, the platform 
downwash algorithm, adapted from the Offshore Coastal Dispersion (OCD) 
dispersion model, was incorporated in AERMOD version 22112 and requires 
further development, testing, and evaluation. The PLATFORM option 
simulates the building downwash effect from platforms commonly used for 
offshore drilling, made up of both porous and solid structures and 
which are elevated with airflow beneath them.
     Extended RLINE Source Type Including Barriers and 
Depressed Roadways (RLINEXT).
    The RLINEXT source type was implemented in AERMOD version 18181 and 
is an extended version of the RLINE source type that allows for a more 
refined characterization of a road segment. It accepts separate inputs 
for the elevations of each end of the road segment and extended options 
for modeling with roadway barriers (RBARRIER) and depressed roadways 
(RDEPRESS).
     TTRM and TTRM2 for Conversion of NOX to 
NO2.
    The Travel Time Reaction Method (TTRM) was implemented in AERMOD 
version 21112 as a stand-alone NOX-to-NO2 
conversion option that accounts for plume travel time, applicable only 
in the near field. TTRM was further integrated in AERMOD version 22112 
as TTRM2 which can be paired with any one of the Ambient Ration Method 
(ARM2), OLM, or PVMRM. When paired with one of these, TTRM is applied 
in the near field and the other specified option is applied in the far 
field where travel time is not as relevant.
     Highly Buoyant Plume (HBP).
    A Highly Buoyant Plume (HBP) option was implemented as an alpha 
option that can be applied to POINT source types beginning with AERMOD 
version 23132 to further explore AERMOD's treatment of the penetrated 
plume. A penetrated plume occurs when a plume released into the mixed 
layer, and a portion of the plume eventually penetrates the top of the 
mixed layer during convective hours as it continues to rise due to 
either buoyancy or momentum.
     Aircraft Plume Rise (AREA/VOLUME Source Types).
    Beginning with AERMOD version 23132, the characterization of AREA 
and VOLUME sources was extended to account for the buoyancy and 
horizontal momentum of aircraft emissions. The aircraft plume rise 
formulation and code for AREA and VOLUME sources was independently 
developed and provided by the Federal Aviation Administration (FAA). 
EPA continues to collaborate with FAA on model evaluation and peer 
review of the aircraft plume rise formulations.

VI. Statutory and Executive Order Reviews

    Additional information about these statutes and Executive Orders 
can be found at https://www.epa.gov/laws-regulations/laws-and-executive-orders.

A. Executive Order 12866: Regulatory Planning and Review and Executive 
Order 14094: Modernizing Regulatory Review

    This action is not a significant regulatory action as defined in 
Executive Order 12866, as amended by Executive Order 14094, and was 
therefore not subject to a requirement for Executive Order 12866 
review.

B. Paperwork Reduction Act (PRA)

    This action does not impose an information collection burden under 
the PRA. This action does not contain any information collection 
activities, nor does it add any information collection requirements 
beyond those imposed by existing New Source Review requirements.

C. Regulatory Flexibility Act (RFA)

    I certify that this action will not have a significant economic 
impact on a substantial number of small entities under the RFA. This 
action will not impose any requirements on small entities. This action 
proposes revisions to the Guideline, including enhancements to the 
formulation and application of the EPA's near-field dispersion modeling 
system, AERMOD, and updates to the recommendations for the development 
of appropriate background concentration for cumulative impact analyses. 
Use of the models and/or techniques described in this action is not 
expected to pose any additional burden on small entities.

D. Unfunded Mandates Reform Act (UMRA)

    This action does not contain any unfunded mandate as described in 
UMRA, 2 U.S.C. 1531-1538, and does not significantly or uniquely affect 
small governments. This action imposes no enforceable duty on any 
State, local or Tribal governments or the private sector.

E. Executive Order 13132: Federalism

    This action does not have federalism implications. It will not have 
substantial direct effects on the States, on the relationship between 
the national government and the States, or on the distribution of power 
and responsibilities among the various levels of government.

F. Executive Order 13175: Consultation and Coordination With Indian 
Tribal Governments

    This action does not have Tribal implications, as specified in 
Executive Order 13175. This action provides proposed revisions to the 
Guideline which is used by the EPA, other Federal, State, territorial, 
local, and Tribal air quality agencies, and industry to prepare and 
review preconstruction permit applications, SIP submittals and 
revisions, determinations of conformity, and other air quality 
assessments required under EPA regulation. Separate from this action, 
the Tribal Air Rule implements the provisions of section 301(d) of the 
CAA authorizing eligible Tribes to implement their own Tribal air 
program. Thus, Executive Order 13175 does not apply to this action.
    The EPA provided information regarding this action to the Tribes 
during a monthly National Tribal Air Association (NTAA) call and will 
continue to provide any new or subsequent updates to EPA modeling 
guidance and other regulatory compliance demonstration related topics 
upon request of the NTAA. Additionally, the EPA specifically solicits 
any comments on this proposed action from Tribal officials.

G. Executive Order 13045: Protection of Children From Environmental 
Health Risks and Safety Risks

    EPA interprets Executive Order 13045 as applying only to those 
regulatory actions that concern environmental health or safety risks 
that EPA has reason to believe may disproportionately affect children, 
per the definition of ``covered regulatory action'' in section 2-202 of 
the Executive Order.
    Therefore, this action is not subject to Executive Order 13045 
because it does not concern an environmental health risk or safety 
risk. Since this action does not concern human health, EPA's Policy on 
Children's Health also does not apply.

[[Page 72835]]

H. Executive Order 13211: Actions Concerning Regulations That 
Significantly Affect Energy Supply, Distribution, or Use

    This action is not subject to Executive Order 13211, because it is 
not a significant regulatory action under Executive Order 12866.

I. National Technology Transfer and Advancement Act

    This rulemaking does not involve technical standards.

J. Executive Order 12898: Federal Actions To Address Environmental 
Justice in Minority Populations and Low-Income Populations and 
Executive Order 14096: Revitalizing Our Nation's Commitment to 
Environmental Justice for All

    The EPA believes that this action does not have disproportionate 
and adverse human health or environmental effects on communities with 
environmental justice concerns because it does not establish an 
environmental health or safety standard. This action proposes revisions 
to the Guideline, including enhancements to the formulations and 
application of EPA's near-field dispersion modeling system, AERMOD, 
that would assist and expand assessment options in Environmental 
Justice determinations. While the EPA does not expect this action to 
directly impact air quality, the proposed revisions are important 
because the Guideline is used by air permitting authorities and 
industry to prepare and review NSR permits and serves as a benchmark of 
consistency across the nation. This consistency has value to all 
communities including communities with environmental justice concerns.

List of Subjects in 40 CFR Part 51

    Environmental protection, Administrative practice and procedure, 
Air pollution control, Carbon monoxide, Criteria pollutants, 
Intergovernmental relations, Lead, Mobile sources, Nitrogen oxides, 
Ozone, Particulate matter, Reporting and recordkeeping requirements, 
Stationary sources, Sulfur oxides.

Michael S. Regan,
Administrator.

    For the reasons stated in the preamble, the Environmental 
Protection Agency is amending title 40, chapter I of the Code of 
Federal Regulations as follows:

PART 51--REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF 
IMPLEMENTATION PLANS

0
1. The authority citation for part 51 continues to read as follows:

    Authority: 23 U.S.C. 101; 42 U.S.C. 7401-7671q.

0
2. Appendix W to part 51 is revised to read as follows:

Appendix W to Part 51--Guideline on Air Quality Models Preface

    a. Industry and control agencies have long expressed a need for 
consistency in the application of air quality models for regulatory 
purposes. In the 1977 Clean Air Act (CAA), Congress mandated such 
consistency and encouraged the standardization of model 
applications. The Guideline on Air Quality Models (hereafter, 
Guideline) was first published in April 1978 to satisfy these 
requirements by specifying models and providing guidance for their 
use. The Guideline provides a common basis for estimating the air 
quality concentrations of criteria pollutants used in assessing 
control strategies and developing emissions limits.
    b. The continuing development of new air quality models in 
response to regulatory requirements and the expanded requirements 
for models to cover even more complex problems have emphasized the 
need for periodic review and update of guidance on these techniques. 
Historically, three primary activities have provided direct input to 
revisions of the Guideline. The first is a series of periodic EPA 
workshops and modeling conferences conducted for the purpose of 
ensuring consistency and providing clarification in the application 
of models. The second activity was the solicitation and review of 
new models from the technical and user community. In the March 27, 
1980, Federal Register, a procedure was outlined for the submittal 
to the EPA of privately developed models. After extensive evaluation 
and scientific review, these models, as well as those made available 
by the EPA, have been considered for recognition in the Guideline. 
The third activity is the extensive on-going research efforts by the 
EPA and others in air quality and meteorological modeling.
    c. Based primarily on these three activities, new sections and 
topics have been included as needed. The EPA does not make changes 
to the guidance on a predetermined schedule, but rather on an as-
needed basis. The EPA believes that revisions of the Guideline 
should be timely and responsive to user needs and should involve 
public participation to the greatest possible extent. All future 
changes to the guidance will be proposed and finalized in the 
Federal Register. Information on the current status of modeling 
guidance can always be obtained from the EPA's Regional Offices.

Table of Contents

List of Tables

1.0 Introduction
2.0 Overview of Model Use
2.1 Suitability of Models
    2.1.1 Model Accuracy and Uncertainty
2.2 Levels of Sophistication of Air Quality Analyses and Models
2.3 Availability of Models
3.0 Preferred and Alternative Air Quality Models
3.1 Preferred Models
    3.1.1 Discussion
    3.1.2 Requirements
3.2 Alternative Models
    3.2.1 Discussion
    3.2.2 Requirements
3.3 EPA's Model Clearinghouse
4.0 Models for Carbon Monoxide, Lead, Sulfur Dioxide, Nitrogen 
Dioxide and Primary Particulate Matter
4.1 Discussion
4.2 Requirements
    4.2.1 Screening Models and Techniques
    4.2.1.1 AERSCREEN
    4.2.1.2 CTSCREEN
    4.2.1.3 Screening in Complex Terrain
    4.2.2 Refined Models
    4.2.2.1 AERMOD
    4.2.2.2 CTDMPLUS
    4.2.2.3 OCD
    4.2.3 Pollutant Specific Modeling Requirements
    4.2.3.1 Models for Carbon Monoxide
    4.2.3.2 Models for Lead
    4.2.3.3 Models for Sulfur Dioxide
    4.2.3.4 Models for Nitrogen Dioxide
    4.2.3.5 Models for PM2.5
    4.2.3.6 Models for PM10
5.0 Models for Ozone and Secondarily Formed Particulate Matter
5.1 Discussion
5.2 Recommendations
5.3 Recommended Models and Approaches for Ozone
    5.3.1 Models for NAAQS Attainment Demonstrations and Multi-
Source Air Quality Assessments
    5.3.2 Models for Single-Source Air Quality Assessments
5.4 Recommended Models and Approaches for Secondarily Formed 
PM2.5
    5.4.1 Models for NAAQS Attainment Demonstrations and Multi-
Source Air Quality Assessments
    5.4.2 Models for Single-Source Air Quality Assessments
6.0 Modeling for Air Quality Related Values and Other Governmental 
Programs
6.1 Discussion
6.2 Air Quality Related Values
    6.2.1 Visibility
    6.2.1.1 Models for Estimating Near-Field Visibility Impairment
    6.2.1.2 Models for Estimating Visibility Impairment for Long-
Range Transport
    6.2.2 Models for Estimating Deposition Impacts
6.3 Modeling Guidance for Other Governmental Programs
7.0 General Modeling Considerations
7.1 Discussion
7.2 Recommendations
    7.2.1 All sources
    7.2.1.1 Dispersion Coefficients
    7.2.1.2 Complex Winds
    7.2.1.3 Gravitational Settling and Deposition
    7.2.2 Stationary Sources
    7.2.2.1 Good Engineering Practice Stack Height

[[Page 72836]]

    7.2.2.2 Plume Rise
    7.2.3 Mobile Sources
8.0 Model Input Data
8.1 Modeling Domain
    8.1.1 Discussion
    8.1.2 Requirements
8.2 Source Data
    8.2.1 Discussion
    8.2.2 Requirements
8.3 Background Concentrations
    8.3.1 Discussion
    8.3.2 Recommendations for Isolated Single Sources
    8.3.3 Recommendations for Multi-Source Areas
8.4 Meteorological Input Data
    8.4.1 Discussion
    8.4.2 Recommendations and Requirements
    8.4.3 National Weather Service Data
    8.4.3.1 Discussion
    8.4.3.2 Recommendations
    8.4.4 Site-specific data
    8.4.4.1 Discussion
    8.4.4.2 Recommendations
    8.4.5 Prognostic meteorological data
    8.4.5.1 Discussion
    8.4.5.2 Recommendations
    8.4.6 Marine Boundary Layer Environments
    8.4.6.1 Discussion
    8.4.6.2 Recommendations
    8.4.7 Treatment of Near-Calms and Calms
    8.4.7.1 Discussion
    8.4.7.2 Recommendations
9.0 Regulatory Application of Models
9.1 Discussion
9.2 Recommendations
    9.2.1 Modeling Protocol
    9.2.2 Design Concentration and Receptor Sites
    9.2.3 NAAQS and PSD Increments Compliance Demonstrations for New 
or Modified Sources
    9.2.3.1 Considerations in Developing Emissions Limits
    9.2.4 Use of Measured Data in Lieu of Model Estimates
10.0 References

Addendum A to Appendix W of Part 51--Summaries of Preferred Air Quality 
Models

List of Tables

------------------------------------------------------------------------
             Table No.                              Title
------------------------------------------------------------------------
8-1...............................  Point Source Model Emission Inputs
                                     for SIP Revisions of Inert
                                     Pollutants.
8-2...............................  Point Source Model Emission Inputs
                                     for NAAQS Compliance in PSD
                                     Demonstrations.
------------------------------------------------------------------------

1.0 Introduction

    a. The Guideline provides air quality modeling techniques that 
should be applied to State Implementation Plan (SIP) submittals and 
revisions, to New Source Review (NSR), including new or modifying 
sources under Prevention of Significant Deterioration 
(PSD),1 2 3 conformity analyses,\4\ and other air quality 
assessments required under EPA regulation. Applicable only to 
criteria air pollutants, the Guideline is intended for use by the 
EPA Regional Offices in judging the adequacy of modeling analyses 
performed by the EPA, by State, local, and Tribal permitting 
authorities, and by industry. It is appropriate for use by other 
Federal government agencies and by State, local, and Tribal agencies 
with air quality and land management responsibilities. The Guideline 
serves to identify, for all interested parties, those modeling 
techniques and databases that the EPA considers acceptable. The 
Guideline is not intended to be a compendium of modeling techniques. 
Rather, it should serve as a common measure of acceptable technical 
analysis when supported by sound scientific judgment.
    b. Air quality measurements \5\ are routinely used to 
characterize ambient concentrations of criteria pollutants 
throughout the nation but are rarely sufficient for characterizing 
the ambient impacts of individual sources or demonstrating adequacy 
of emissions limits for an existing source due to limitations in 
spatial and temporal coverage of ambient monitoring networks. The 
impacts of new sources that do not yet exist, and modifications to 
existing sources that have yet to be implemented, can only be 
determined through modeling. Thus, models have become a primary 
analytical tool in most air quality assessments. Air quality 
measurements can be used in a complementary manner to air quality 
models, with due regard for the strengths and weaknesses of both 
analysis techniques, and are particularly useful in assessing the 
accuracy of model estimates.
    c. It would be advantageous to categorize the various regulatory 
programs and to apply a designated model to each proposed source 
needing analysis under a given program. However, the diversity of 
the nation's topography and climate, and variations in source 
configurations and operating characteristics dictate against a 
strict modeling ``cookbook.'' There is no one model capable of 
properly addressing all conceivable situations even within a broad 
category such as point sources. Meteorological phenomena associated 
with threats to air quality standards are rarely amenable to a 
single mathematical treatment; thus, case-by-case analysis and 
judgment are frequently required. As modeling efforts become more 
complex, it is increasingly important that they be directed by 
highly competent individuals with a broad range of experience and 
knowledge in air quality meteorology. Further, they should be 
coordinated closely with specialists in emissions characteristics, 
air monitoring and data processing. The judgment of experienced 
meteorologists, atmospheric scientists, and analysts is essential.
    d. The model that most accurately estimates concentrations in 
the area of interest is always sought. However, it is clear from the 
needs expressed by the EPA Regional Offices, by State, local, and 
Tribal agencies, by many industries and trade associations, and also 
by the deliberations of Congress, that consistency in the selection 
and application of models and databases should also be sought, even 
in case-by-case analyses. Consistency ensures that air quality 
control agencies and the general public have a common basis for 
estimating pollutant concentrations, assessing control strategies, 
and specifying emissions limits. Such consistency is not, however, 
promoted at the expense of model and database accuracy. The 
Guideline provides a consistent basis for selection of the most 
accurate models and databases for use in air quality assessments.
    e. Recommendations are made in the Guideline concerning air 
quality models and techniques, model evaluation procedures, and 
model input databases and related requirements. The guidance 
provided here should be followed in air quality analyses relative to 
SIPs, NSR, and in supporting analyses required by the EPA and by 
State, local, and Tribal permitting authorities. Specific models are 
identified for particular applications. The EPA may approve the use 
of an alternative model or technique that can be demonstrated to be 
more appropriate than those recommended in the Guideline. In all 
cases, the model or technique applied to a given situation should be 
the one that provides the most accurate representation of 
atmospheric transport, dispersion, and chemical transformations in 
the area of interest. However, to ensure consistency, deviations 
from the Guideline should be carefully documented as part of the 
public record and fully supported by the appropriate reviewing 
authority, as discussed later.
    f. From time to time, situations arise requiring clarification 
of the intent of the guidance on a specific topic. Periodic 
workshops are held with EPA headquarters, EPA Regional Offices, and 
State, local, and Tribal agency modeling representatives to ensure 
consistency in modeling guidance and to promote the use of more 
accurate air quality models, techniques, and databases. The 
workshops serve to provide further explanations of Guideline 
requirements to the EPA Regional Offices and workshop materials are 
issued with this clarifying information. In addition, findings from 
ongoing research programs, new model development, or results from 
model evaluations and applications are continuously evaluated. Based 
on this information, changes in the applicable guidance may be 
indicated and appropriate revisions to the Guideline may be 
considered.
    g. All changes to the Guideline must follow rulemaking 
requirements since the Guideline is codified in Appendix W to 40 
Code of Federal Regulations (CFR) part 51. The EPA will promulgate 
proposed and final rules in the Federal Register to amend this 
appendix. The EPA utilizes the existing procedures under CAA section 
320 that requires the EPA to conduct a Conference on Air Quality 
Modeling at least every 3 years (CAA 320, 42 U.S.C. 7620). These 
modeling conferences are intended to develop standardized air 
quality modeling procedures and form the basis for associated 
revisions to this Guideline in support of the EPA's continuing 
effort to prescribe with ``reasonable particularity'' air quality 
models and meteorological and emission databases suitable for 
modeling National Ambient Air Quality Standards (NAAQS) \6\ and PSD 
increments. Ample opportunity for public comment will be provided 
for each proposed change and public hearings scheduled.

[[Page 72837]]

    h. A wide range of topics on modeling and databases are 
discussed in the Guideline. Section 2 gives an overview of models 
and their suitability for use in regulatory applications. Section 3 
provides specific guidance on the determination of preferred air 
quality models and on the selection of alternative models or 
techniques. Sections 4 through 6 provide recommendations on modeling 
techniques for assessing criteria pollutant impacts from single and 
multiple sources with specific modeling requirements for selected 
regulatory applications. Section 7 discusses general considerations 
common to many modeling analyses for stationary and mobile sources. 
Section 8 makes recommendations for data inputs to models including 
source, background air quality, and meteorological data. Section 9 
summarizes how estimates and measurements of air quality are used in 
assessing source impact and in evaluating control strategies.
    i. Appendix W to 40 CFR part 51 contains an addendum: addendum 
A. Thus, when reference is made to ``addendum A'' in this document, 
it refers to addendum A to appendix W to 40 CFR part 51. Addendum A 
contains summaries of refined air quality models that are 
``preferred'' for particular applications; both EPA models and 
models developed by others are included.

2.0 Overview of Model Use

    a. Increasing reliance has been placed on concentration 
estimates from air quality models as the primary basis for 
regulatory decisions concerning source permits and emission control 
requirements. In many situations, such as review of a proposed new 
source, no practical alternative exists. Before attempting to 
implement the guidance contained in this document, the reader should 
be aware of certain general information concerning air quality 
models and their evaluation and use. Such information is provided in 
this section.

2.1 Suitability of Models

    a. The extent to which a specific air quality model is suitable 
for the assessment of source impacts depends upon several factors. 
These include: (1) the topographic and meteorological complexities 
of the area; (2) the detail and accuracy of the input databases, 
i.e., emissions inventory, meteorological data, and air quality 
data; (3) the manner in which complexities of atmospheric processes 
are handled in the model; (4) the technical competence of those 
undertaking such simulation modeling; and (5) the resources 
available to apply the model. Any of these factors can have a 
significant influence on the overall model performance, which must 
be thoroughly evaluated to determine the suitability of an air 
quality model to a particular application or range of applications.
    b. Air quality models are most accurate and reliable in areas 
that have gradual transitions of land use and topography. 
Meteorological conditions in these areas are spatially uniform such 
that observations are broadly representative and air quality model 
projections are not further complicated by a heterogeneous 
environment. Areas subject to major topographic influences 
experience meteorological complexities that are often difficult to 
measure and simulate. Models with adequate performance are available 
for increasingly complex environments. However, they are resource 
intensive and frequently require site-specific observations and 
formulations. Such complexities and the related challenges for the 
air quality simulation should be considered when selecting the most 
appropriate air quality model for an application.
    c. Appropriate model input data should be available before an 
attempt is made to evaluate or apply an air quality model. Assuming 
the data are adequate, the greater the detail with which a model 
considers the spatial and temporal variations in meteorological 
conditions and permit-enforceable emissions, the greater the ability 
to evaluate the source impact and to distinguish the effects of 
various control strategies.
    d. There are three types of models that have historically been 
used in the regulatory demonstrations applicable in the Guideline, 
each having strengths and weaknesses that lend themselves to 
particular regulatory applications.
    i. Gaussian plume models use a ``steady-state'' approximation, 
which assumes that over the model time step, the emissions, 
meteorology and other model inputs, are constant throughout the 
model domain, resulting in a resolved plume with the emissions 
distributed throughout the plume according to a Gaussian 
distribution. This formulation allows Gaussian models to estimate 
near-field impacts of a limited number of sources at a relatively 
high resolution, with temporal scales of an hour and spatial scales 
of meters. However, this formulation allows for only relatively 
inert pollutants, with very limited considerations of transformation 
and removal (e.g., deposition), and further limits the domain for 
which the model may be used. Thus, Gaussian models may not be 
appropriate if model inputs are changing sharply over the model time 
step or within the desired model domain, or if more advanced 
considerations of chemistry are needed.
    ii. Lagrangian puff models, on the other hand, are non-steady-
state, and assume that model input conditions are changing over the 
model domain and model time step. Lagrangian models can also be used 
to determine near- and far-field impacts from a limited number of 
sources. Traditionally, Lagrangian models have been used for 
relatively inert pollutants, with slightly more complex 
considerations of removal than Gaussian models. Some Lagrangian 
models treat in-plume gas and particulate chemistry. However, these 
models require time and space varying concentration fields of 
oxidants and, in the case of fine particulate matter 
(PM2.5), neutralizing agents, such as ammonia. Reliable 
background fields are critical for applications involving secondary 
pollutant formation because secondary impacts generally occur when 
in-plume precursors mix and react with species in the background 
atmosphere.7 8 These oxidant and neutralizing agents are 
not routinely measured, but can be generated with a three-
dimensional photochemical grid model.
    iii. Photochemical grid models are three-dimensional Eulerian 
grid-based models that treat chemical and physical processes in each 
grid cell and use diffusion and transport processes to move chemical 
species between grid cells.\9\ Eulerian models assume that emissions 
are spread evenly throughout each model grid cell. At coarse grid 
resolutions, Eulerian models have difficulty with fine scale 
resolution of individual plumes. However, these types of models can 
be appropriately applied for assessment of near-field and regional 
scale reactive pollutant impacts from specific sources 
7 10 11 12 or all sources.13 14 15 
Photochemical grid models simulate a more realistic environment for 
chemical transformation,7 12 but simulations can be more 
resource intensive than Lagrangian or Gaussian plume models.
    e. Competent and experienced meteorologists, atmospheric 
scientists, and analysts are an essential prerequisite to the 
successful application of air quality models. The need for such 
specialists is critical when sophisticated models are used or the 
area has complicated meteorological or topographic features. It is 
important to note that a model applied improperly or with 
inappropriate data can lead to serious misjudgments regarding the 
source impact or the effectiveness of a control strategy.
    f. The resource demands generated by use of air quality models 
vary widely depending on the specific application. The resources 
required may be important factors in the selection and use of a 
model or technique for a specific analysis. These resources depend 
on the nature of the model and its complexity, the detail of the 
databases, the difficulty of the application, the amount and level 
of expertise required, and the costs of manpower and computational 
facilities.

2.1.1 Model Accuracy and Uncertainty

    a. The formulation and application of air quality models are 
accompanied by several sources of uncertainty. ``Irreducible'' 
uncertainty stems from the ``unknown'' conditions, which may not be 
explicitly accounted for in the model (e.g., the turbulent velocity 
field). Thus, there are likely to be deviations from the observed 
concentrations in individual events due to variations in the unknown 
conditions. ``Reducible'' uncertainties \16\ are caused by: (1) 
uncertainties in the ``known'' input conditions (e.g., emission 
characteristics and meteorological data); (2) errors in the measured 
concentrations; and (3) inadequate model physics and formulation.
    b. Evaluations of model accuracy should focus on the reducible 
uncertainty associated with physics and the formulation of the 
model. The accuracy of the model is normally determined by an 
evaluation procedure which involves the comparison of model 
concentration estimates with measured air quality data.\17\ The 
statement of model accuracy is based on statistical tests or 
performance measures such as bias, error, correlation, 
etc.18 19
    c. Since the 1980's, the EPA has worked with the modeling 
community to encourage development of standardized model evaluation 
methods and the development of continually improved methods for the 
characterization of model

[[Page 72838]]

performance.16 18 20 21 22 There is general consensus on 
what should be considered in the evaluation of air quality models; 
namely, quality assurance planning, documentation and scrutiny 
should be consistent with the intended use and should include:
     Scientific peer review;
     Supportive analyses (diagnostic evaluations, code 
verification, sensitivity analyses);
     Diagnostic and performance evaluations with data 
obtained in trial locations; and
     Statistical performance evaluations in the 
circumstances of the intended applications.
    Performance evaluations and diagnostic evaluations assess 
different qualities of how well a model is performing, and both are 
needed to establish credibility within the client and scientific 
community.
    d. Performance evaluations allow the EPA and model users to 
determine the relative performance of a model in comparison with 
alternative modeling systems. Diagnostic evaluations allow 
determination of a model capability to simulate individual processes 
that affect the results, and usually employ smaller spatial/temporal 
scale data sets (e.g., field studies). Diagnostic evaluations enable 
the EPA and model users to build confidence that model predictions 
are accurate for the right reasons. However, the objective 
comparison of modeled concentrations with observed field data 
provides only a partial means for assessing model performance. Due 
to the limited supply of evaluation datasets, there are practical 
limits in assessing model performance. For this reason, the 
conclusions reached in the science peer reviews and the supportive 
analyses have particular relevance in deciding whether a model will 
be useful for its intended purposes.

2.2 Levels of Sophistication of Air Quality Analyses and Models

    a. It is desirable to begin an air quality analysis by using 
simplified and conservative methods followed, as appropriate, by 
more complex and refined methods. The purpose of this approach is to 
streamline the process and sufficiently address regulatory 
requirements by eliminating the need of more detailed modeling when 
it is not necessary in a specific regulatory application. For 
example, in the context of a PSD permit application, a simplified 
and conservative analysis may be sufficient where it shows the 
proposed construction clearly will not cause or contribute to 
ambient concentrations in excess of either the NAAQS or the PSD 
increments.2 3
    b. There are two general levels of sophistication of air quality 
models. The first level consists of screening models that provide 
conservative modeled estimates of the air quality impact of a 
specific source or source category based on simplified assumptions 
of the model inputs (e.g., preset, worst-case meteorological 
conditions). In the case of a PSD assessment, if a screening model 
indicates that the increase in concentration attributable to the 
source could cause or contribute to a violation of any NAAQS or PSD 
increment, then the second level of more sophisticated models should 
be applied unless appropriate controls or operational restrictions 
are implemented based on the screening modeling.
    c. The second level consists of refined models that provide more 
detailed treatment of physical and chemical atmospheric processes, 
require more detailed and precise input data, and provide spatially 
and temporally resolved concentration estimates. As a result, they 
provide a more sophisticated and, at least theoretically, a more 
accurate estimate of source impact and the effectiveness of control 
strategies.
    d. There are situations where a screening model or a refined 
model is not available such that screening and refined modeling are 
not viable options to determine source-specific air quality impacts. 
In such situations, a screening technique or reduced-form model may 
be viable options for estimating source impacts.
    i. Screening techniques are differentiated from a screening 
model in that screening techniques are approaches that make 
simplified and conservative assumptions about the physical and 
chemical atmospheric processes important to determining source 
impacts, while screening models make assumptions about conservative 
inputs to a specific model. The complexity of screening techniques 
ranges from simplified assumptions of chemistry applied to refined 
or screening model output to sophisticated approximations of the 
chemistry applied within a refined model.
    ii. Reduced-form models are computationally efficient simulation 
tools for characterizing the pollutant response to specific types of 
emission reductions for a particular geographic area or background 
environmental conditions that reflect underlying atmospheric science 
of a refined model but reduce the computational resources of running 
a complex, numerical air quality model such as a photochemical grid 
model.
    In such situations, an attempt should be made to acquire or 
improve the necessary databases and to develop appropriate 
analytical techniques, but the screening technique or reduced-form 
model may be sufficient in conducting regulatory modeling 
applications when applied in consultation with the EPA Regional 
Office.
    e. Consistent with the general principle described in paragraph 
2.2(a), the EPA may establish a demonstration tool or method as a 
sufficient means for a user or applicant to make a demonstration 
required by regulation, either by itself or as part of a modeling 
demonstration. To be used for such regulatory purposes, such a tool 
or method must be reflected in a codified regulation or have a well-
documented technical basis and reasoning that is contained or 
incorporated in the record of the regulatory decision in which it is 
applied.

2.3 Availability of Models

    a. For most of the screening and refined models discussed in the 
Guideline, codes, associated documentation and other useful 
information are publicly available for download from the EPA's 
Support Center for Regulatory Atmospheric Modeling (SCRAM) website 
at https://www.epa.gov/scram. This is a website with which air 
quality modelers should become familiar and regularly visit for 
important model updates and additional clarifications and revisions 
to modeling guidance documents that are applicable to EPA programs 
and regulations. Codes and documentation may also be available from 
the National Technical Information Service (NTIS), https://www.ntis.gov, and, when available, is referenced with the 
appropriate NTIS accession number.

3.0 Preferred and Alternative Air Quality Models

    a. This section specifies the approach to be taken in 
determining preferred models for use in regulatory air quality 
programs. The status of models developed by the EPA, as well as 
those submitted to the EPA for review and possible inclusion in this 
Guideline, is discussed in this section. The section also provides 
the criteria and process for obtaining EPA approval for use of 
alternative models for individual cases in situations where the 
preferred models are not applicable or available. Additional sources 
of relevant modeling information are: the EPA's Model Clearinghouse 
\23\ (section 3.3); EPA modeling conferences; periodic Regional, 
State, and Local Modelers' Workshops; and the EPA's SCRAM website 
(section 2.3).
    b. When approval is required for a specific modeling technique 
or analytical procedure in this Guideline, we refer to the 
``appropriate reviewing authority.'' Many States and some local 
agencies administer NSR permitting under programs approved into 
SIPs. In some EPA regions, Federal authority to administer NSR 
permitting and related activities has been delegated to State or 
local agencies. In these cases, such agencies ``stand in the shoes'' 
of the respective EPA Region. Therefore, depending on the 
circumstances, the appropriate reviewing authority may be an EPA 
Regional Office, a State, local, or Tribal agency, or perhaps the 
Federal Land Manager (FLM). In some cases, the Guideline requires 
review and approval of the use of an alternative model by the EPA 
Regional Office (sometimes stated as ``Regional Administrator''). 
For all approvals of alternative models or techniques, the EPA 
Regional Office will coordinate and shall seek concurrence with the 
EPA's Model Clearinghouse. If there is any question as to the 
appropriate reviewing authority, you should contact the EPA Regional 
Office modeling contact (https://www.epa.gov/scram/air-modeling-regional-contacts), whose jurisdiction generally includes the 
physical location of the source in question and its expected 
impacts.
    c. In all regulatory analyses, early discussions among the EPA 
Regional Office staff, State, local, and Tribal agency staff, 
industry representatives, and where appropriate, the FLM, are 
invaluable and are strongly encouraged. Prior to the actual 
analyses, agreement on the databases to be used, modeling techniques 
to be applied, and the overall technical approach helps avoid 
misunderstandings concerning the final results and may reduce the 
later need for additional analyses. The preparation of a written 
modeling protocol that is vetted with the appropriate reviewing 
authority helps to

[[Page 72839]]

keep misunderstandings and resource expenditures at a minimum.
    d. The identification of preferred models in this Guideline 
should not be construed as a determination that the preferred models 
identified here are to be permanently used to the exclusion of all 
others or that they are the only models available for relating 
emissions to air quality. The model that most accurately estimates 
concentrations in the area of interest is always sought. However, 
designation of specific preferred models is needed to promote 
consistency in model selection and application.

3.1 Preferred Models

3.1.1 Discussion

    a. The EPA has developed some models suitable for regulatory 
application, while other models have been submitted by private 
developers for possible inclusion in the Guideline. Refined models 
that are preferred and required by the EPA for particular 
applications have undergone the necessary peer scientific reviews 
24 25 and model performance evaluation exercises 
26 27 that include statistical measures of model 
performance in comparison with measured air quality data as 
described in section 2.1.1.
    b. An American Society for Testing and Materials (ASTM) 
reference \28\ provides a general philosophy for developing and 
implementing advanced statistical evaluations of atmospheric 
dispersion models, and provides an example statistical technique to 
illustrate the application of this philosophy. Consistent with this 
approach, the EPA has determined and applied a specific evaluation 
protocol that provides a statistical technique for evaluating model 
performance for predicting peak concentration values, as might be 
observed at individual monitoring locations.\29\
    c. When a single model is found to perform better than others, 
it is recommended for application as a preferred model and listed in 
addendum A. If no one model is found to clearly perform better 
through the evaluation exercise, then the preferred model listed in 
addendum A may be selected on the basis of other factors such as 
past use, public familiarity, resource requirements, and 
availability. Accordingly, the models listed in addendum A meet 
these conditions:
    i. The model must be written in a common programming language, 
and the executable(s) must run on a common computer platform.
    ii. The model must be documented in a user's guide or model 
formulation report which identifies the mathematics of the model, 
data requirements and program operating characteristics at a level 
of detail comparable to that available for other recommended models 
in addendum A.
    iii. The model must be accompanied by a complete test dataset 
including input parameters and output results. The test data must be 
packaged with the model in computer-readable form.
    iv. The model must be useful to typical users, e.g., State air 
agencies, for specific air quality control problems. Such users 
should be able to operate the computer program(s) from available 
documentation.
    v. The model documentation must include a robust comparison with 
air quality data (and/or tracer measurements) or with other well-
established analytical techniques.
    vi. The developer must be willing to make the model and source 
code available to users at reasonable cost or make them available 
for public access through the internet or National Technical 
Information Service. The model and its code cannot be proprietary.
    d. The EPA's process of establishing a preferred model includes 
a determination of technical merit, in accordance with the above six 
items, including the practicality of the model for use in ongoing 
regulatory programs. Each model will also be subjected to a 
performance evaluation for an appropriate database and to a peer 
scientific review. Models for wide use (not just an isolated case) 
that are found to perform better will be proposed for inclusion as 
preferred models in future Guideline revisions.
    e. No further evaluation of a preferred model is required for a 
particular application if the EPA requirements for regulatory use 
specified for the model in the Guideline are followed. Alternative 
models to those listed in addendum A should generally be compared 
with measured air quality data when they are used for regulatory 
applications consistent with recommendations in section 3.2.

3.1.2 Requirements

    a. Addendum A identifies refined models that are preferred for 
use in regulatory applications. If a model is required for a 
particular application, the user must select a model from addendum A 
or follow procedures in section 3.2.2 for use of an alternative 
model or technique. Preferred models may be used without a formal 
demonstration of applicability as long as they are used as indicated 
in each model summary in addendum A. Further recommendations for the 
application of preferred models to specific source applications are 
found in subsequent sections of the Guideline.
    b. If changes are made to a preferred model without affecting 
the modeled concentrations, the preferred status of the model is 
unchanged. Examples of modifications that do not affect 
concentrations are those made to enable use of a different computer 
platform or those that only affect the format or averaging time of 
the model results. The integration of a graphical user interface 
(GUI) to facilitate setting up the model inputs and/or analyzing the 
model results without otherwise altering the preferred model code is 
another example of a modification that does not affect 
concentrations. However, when any changes are made, the Regional 
Administrator must require a test case example to demonstrate that 
the modeled concentrations are not affected.
    c. A preferred model must be operated with the options listed in 
addendum A for its intended regulatory application. If the 
regulatory options are not applied, the model is no longer 
``preferred.'' Any other modification to a preferred model that 
would result in a change in the concentration estimates likewise 
alters its status so that it is no longer a preferred model. Use of 
the modified model must then be justified as an alternative model on 
a case-by-case basis to the appropriate reviewing authority and 
approved by the Regional Administrator.
    d. Where the EPA has not identified a preferred model for a 
particular pollutant or situation, the EPA may establish a multi-
tiered approach for making a demonstration required under PSD or 
another CAA program. The initial tier or tiers may involve use of 
demonstration tools, screening models, screening techniques, or 
reduced-form models; while the last tier may involve the use of 
demonstration tools, refined models or techniques, or alternative 
models approved under section 3.2.

3.2 Alternative Models

3.2.1 Discussion

    a. Selection of the best model or techniques for each individual 
air quality analysis is always encouraged, but the selection should 
be done in a consistent manner. A simple listing of models in this 
Guideline cannot alone achieve that consistency nor can it 
necessarily provide the best model for all possible situations. As 
discussed in section 3.1.1, the EPA has determined and applied a 
specific evaluation protocol that provides a statistical technique 
for evaluating model performance for predicting peak concentration 
values, as might be observed at individual monitoring locations.\29\ 
This protocol is available to assist in developing a consistent 
approach when justifying the use of other-than-preferred models 
recommended in the Guideline (i.e., alternative models). The 
procedures in this protocol provide a general framework for 
objective decision-making on the acceptability of an alternative 
model for a given regulatory application. These objective procedures 
may be used for conducting both the technical evaluation of the 
model and the field test or performance evaluation.
    b. This subsection discusses the use of alternate models and 
defines three situations when alternative models may be used. This 
subsection also provides a procedure for implementing 40 CFR 
51.166(l)(2) in PSD permitting. This provision requires written 
approval of the Administrator for any modification or substitution 
of an applicable model. An applicable model for purposes of 40 CFR 
51.166(l) is a preferred model in addendum A to the Guideline. 
Approval to use an alternative model under section 3.2 of the 
Guideline qualifies as approval for the modification or substitution 
of a model under 40 CFR 51.166(l)(2). The Regional Administrators 
have delegated authority to issue such approvals under section 3.2 
of the Guideline, provided that such approval is issued after 
consultation with the EPA's Model Clearinghouse and formally 
documented in a concurrence memorandum from the EPA's Model 
Clearinghouse which demonstrates that the requirements within 
section 3.2 for use of an alternative model have been met.

3.2.2 Requirements

    a. Determination of acceptability of an alternative model is an 
EPA Regional Office responsibility in consultation with the EPA's 
Model Clearinghouse as discussed in paragraphs 3.0(b) and 3.2.1(b). 
Where the Regional Administrator finds that an

[[Page 72840]]

alternative model is more appropriate than a preferred model, that 
model may be used subject to the approval of the EPA Regional Office 
based on the requirements of this subsection. This finding will 
normally result from a determination that: (1) a preferred air 
quality model is not appropriate for the particular application; or 
(2) a more appropriate model or technique is available and 
applicable.
    b. An alternative model shall be evaluated from both a 
theoretical and a performance perspective before it is selected for 
use. There are three separate conditions under which such a model 
may be approved for use:
    1. If a demonstration can be made that the model produces 
concentration estimates equivalent to the estimates obtained using a 
preferred model;
    2. If a statistical performance evaluation has been conducted 
using measured air quality data and the results of that evaluation 
indicate the alternative model performs better for the given 
application than a comparable model in addendum A; or
    3. If there is no preferred model.
    Any one of these three separate conditions may justify use of an 
alternative model. Some known alternative models that are applicable 
for selected situations are listed on the EPA's SCRAM website 
(section 2.3). However, inclusion there does not confer any unique 
status relative to other alternative models that are being or will 
be developed in the future.
    c. Equivalency, condition (1) in paragraph (b) of this 
subsection, is established by demonstrating that the appropriate 
regulatory metric(s) 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 nearly identical (or contains options that 
can make it identical) to a preferred model that it can be treated 
for practical purposes as the preferred model. However, 
notwithstanding this demonstration, models that are not equivalent 
may be used when one of the two other conditions described in 
paragraphs (d) and (e) of this subsection are satisfied.
    d. For condition (2) in paragraph (b) of this subsection, 
established statistical performance evaluation procedures and 
techniques 28 29 for determining the acceptability of a 
model for an individual case based on superior performance should be 
followed, as appropriate. Preparation and implementation of an 
evaluation protocol that is acceptable to both control agencies and 
regulated industry is an important element in such an evaluation.
    e. Finally, for condition (3) in paragraph (b) of this 
subsection, an alternative model or technique may be approved for 
use provided that:
    i. The model or technique has received a scientific peer review;
    ii. The model or technique can be demonstrated to be applicable 
to the problem on a theoretical basis;
    iii. The databases which are necessary to perform the analysis 
are available and adequate;
    iv. Appropriate performance evaluations of the model or 
technique have shown that the model or technique is not 
inappropriately biased for regulatory application; \a\ and
---------------------------------------------------------------------------

    \a\ For PSD and other applications that use the model results in 
an absolute sense, the model should not be biased toward 
underestimates. Alternatively, for ozone and PM2.5 SIP 
attainment demonstrations and other applications that use the model 
results in a relative sense, the model should not be biased toward 
overestimates.
---------------------------------------------------------------------------

    v. A protocol on methods and procedures to be followed has been 
established.
    f. To formally document that the requirements of section 3.2 for 
use of an alternative model are satisfied for a particular 
application or range of applications, a memorandum will be prepared 
by the EPA's Model Clearinghouse through a consultative process with 
the EPA Regional Office.

3.3 EPA's Model Clearinghouse

    a. The Regional Administrator has the authority to select models 
that are appropriate for use in a given situation. However, there is 
a need for assistance and guidance in the selection process so that 
fairness, consistency, and transparency in modeling decisions are 
fostered among the EPA Regional Offices and the State, local, and 
Tribal agencies. To satisfy that need, the EPA established the Model 
Clearinghouse \23\ to serve a central role of coordination and 
collaboration between EPA headquarters and the EPA Regional Offices. 
Additionally, the EPA holds periodic workshops with EPA 
Headquarters, EPA Regional Offices, and State, local, and Tribal 
agency modeling representatives.
    b. The appropriate EPA Regional Office should always be 
consulted for information and guidance concerning modeling methods 
and interpretations of modeling guidance, and to ensure that the air 
quality model user has available the latest most up-to-date policy 
and procedures. As appropriate, the EPA Regional Office may also 
request assistance from the EPA's Model Clearinghouse on other 
applications of models, analytical techniques, or databases or to 
clarify interpretation of the Guideline or related modeling 
guidance.
    c. The EPA Regional Office will coordinate with the EPA's Model 
Clearinghouse after an initial evaluation and decision has been 
developed concerning the application of an alternative model. The 
acceptability and formal approval process for an alternative model 
is described in section 3.2.

4.0 Models for Carbon Monoxide, Lead, Sulfur Dioxide, Nitrogen Dioxide 
and Primary Particulate Matter

4.1 Discussion

    a. This section identifies modeling approaches generally used in 
the air quality impact analysis of sources that emit the criteria 
pollutants carbon monoxide (CO), lead, sulfur dioxide 
(SO2), nitrogen dioxide (NO2), and primary 
particulates (PM2.5 and PM10).
    b. The guidance in this section is specific to the application 
of the Gaussian plume models identified in addendum A. Gaussian 
plume models assume that emissions and meteorology are in a steady-
state, which is typically based on an hourly time step. This 
approach results in a plume that has an hourly-averaged distribution 
of emission mass according to a Gaussian curve through the plume. 
Though Gaussian steady-state models conserve the mass of the primary 
pollutant throughout the plume, they can still take into account a 
limited consideration of first-order removal processes (e.g., wet 
and dry deposition) and limited chemical conversion (e.g., OH 
oxidation).
    c. Due to the steady-state assumption, Gaussian plume models are 
generally considered applicable to distances less than 50 km, beyond 
which, modeled predictions of plume impact are likely conservative. 
The locations of these impacts are expected to be unreliable due to 
changes in meteorology that are likely to occur during the travel 
time.
    d. The applicability of Gaussian plume models may vary depending 
on the topography of the modeling domain, i.e., simple or complex. 
Simple terrain is considered to be an area where terrain features 
are all lower in elevation than the top of the stack(s) of the 
source(s) in question. Complex terrain is defined as terrain 
exceeding the height of the stack(s) being modeled.
    e. Gaussian models determine source impacts at discrete 
locations (receptors) for each meteorological and emission scenario, 
and generally attempt to estimate concentrations at specific sites 
that represent an ensemble average of numerous repetitions of the 
same ``event.'' Uncertainties in model estimates are driven by this 
formulation, and as noted in section 2.1.1, evaluations of model 
accuracy should focus on the reducible uncertainty associated with 
physics and the formulation of the model. The ``irreducible'' 
uncertainty associated with Gaussian plume models may be responsible 
for variation in concentrations of as much as +/-50 percent.\30\ 
``Reducible'' uncertainties \16\ can be on a similar scale. For 
example, Pasquill \31\ estimates that, apart from data input errors, 
maximum ground-level concentrations at a given hour for a point 
source in flat terrain could be in error by 50 percent due to these 
uncertainties. Errors of 5 to 10 degrees in the measured wind 
direction can result in concentration errors of 20 to 70 percent for 
a particular time and location, depending on stability and station 
location. Such uncertainties do not indicate that an estimated 
concentration does not occur, only that the precise time and 
locations are in doubt. Composite errors in highest estimated 
concentrations of 10 to 40 percent are found to be 
typical.32 33 However, estimates of concentrations paired 
in time and space with observed concentrations are less certain.
    f. Model evaluations and inter-comparisons should take these 
aspects of uncertainty into account. For a regulatory application of 
a model, the emphasis of model evaluations is generally placed on 
the highest modeled impacts. Thus, the Cox-Tikvart model evaluation 
approach, which compares the highest modeled impacts on several 
timescales, is recommended for comparisons of models and 
measurements and model inter-comparisons. The approach includes 
bootstrap techniques to determine the significance of various 
modeled predictions

[[Page 72841]]

and increases the robustness of such comparisons when the number of 
available measurements are limited.34 35 Because of the 
uncertainty in paired modeled and observed concentrations, any 
attempts at calibration of models based on these comparisons is of 
questionable benefit and shall not be done.

4.2 Requirements

    a. For NAAQS compliance demonstrations under PSD, use of the 
screening and preferred models for the pollutants listed in this 
subsection shall be limited to the near-field at a nominal distance 
of 50 km or less. Near-field application is consistent with 
capabilities of Gaussian plume models and, based on the EPA's 
assessment, is sufficient to address whether a source will cause or 
contribute to ambient concentrations in excess of a NAAQS. In most 
cases, maximum source impacts of inert pollutants will occur within 
the first 10 to 20 km from the source. Therefore, the EPA does not 
consider a long-range transport assessment beyond 50 km necessary 
for these pollutants if a near-field NAAQS compliance demonstration 
is required.\36\
    b. For assessment of PSD increments within the near-field 
distance of 50 km or less, use of the screening and preferred models 
for the pollutants listed in this subsection shall be limited to the 
same screening and preferred models approved for NAAQS compliance 
demonstrations.
    c. To determine if a compliance demonstration for NAAQS and/or 
PSD increments may be necessary beyond 50 km (i.e., long-range 
transport assessment), the following screening approach shall be 
used to determine if a significant ambient impact will occur with 
particular focus on Class I areas and/or the applicable receptors 
that may be threatened at such distances.
    i. Based on application in the near-field of the appropriate 
screening and/or preferred model, determine the significance of the 
ambient impacts at or about 50 km from the new or modifying source. 
If a near-field assessment is not available or this initial analysis 
indicates there may be significant ambient impacts at that distance, 
then further assessment is necessary.
    ii. For assessment of the significance of ambient impacts for 
NAAQS and/or PSD increments, there is not a preferred model or 
screening approach for distances beyond 50 km. Thus, the appropriate 
reviewing authority (paragraph 3.0(b)) and the EPA Regional Office 
shall be consulted in determining the appropriate and agreed upon 
screening technique to conduct the second level assessment. 
Typically, a Lagrangian model is most appropriate to use for these 
second level assessments, but applicants shall reach agreement on 
the specific model and modeling parameters on a case-by-case basis 
in consultation with the appropriate reviewing authority (paragraph 
3.0(b)) and EPA Regional Office. When Lagrangian models are used in 
this manner, they shall not include plume-depleting processes, such 
that model estimates are considered conservative, as is generally 
appropriate for screening assessments.
    d. In those situations where a cumulative impact analysis for 
NAAQS and/or PSD increments analysis beyond 50 km is necessary, the 
selection and use of an alternative model shall occur in agreement 
with the appropriate reviewing authority (paragraph 3.0(b)) and 
approval by the EPA Regional Office based on the requirements of 
paragraph 3.2.2(e).

4.2.1 Screening Models and Techniques

    a. Where a preliminary or conservative estimate is desired, 
point source screening techniques are an acceptable approach to air 
quality analyses.
    b. As discussed in paragraph 2.2(a), screening models or 
techniques are designed to provide a conservative estimate of 
concentrations. The screening models used in most applications are 
the screening versions of the preferred models for refined 
applications. The two screening models, AERSCREEN 37 38 
and CTSCREEN, are screening versions of AERMOD (American 
Meteorological Society (AMS)/EPA Regulatory Model) and CTDMPLUS 
(Complex Terrain Dispersion Model Plus Algorithms for Unstable 
Situations), respectively. AERSCREEN is the recommended screening 
model for most applications in all types of terrain and for 
applications involving building downwash. For those applications in 
complex terrain where the application involves a well-defined hill 
or ridge, CTSCREEN \39\ can be used.
    c. Although AERSCREEN and CTSCREEN are designed to address a 
single-source scenario, there are approaches that can be used on a 
case-by-case basis to address multi-source situations using 
screening meteorology or other conservative model assumptions. 
However, the appropriate reviewing authority (paragraph 3.0(b)) 
shall be consulted, and concurrence obtained, on the protocol for 
modeling multiple sources with AERSCREEN or CTSCREEN to ensure that 
the worst case is identified and assessed.
    d. As discussed in section 4.2.3.4, there are also screening 
techniques built into AERMOD that use simplified or limited 
chemistry assumptions for determining the partitioning of NO and 
NO2 for NO2 modeling. These screening 
techniques are part of the EPA's preferred modeling approach for 
NO2 and do not need to be approved as an alternative 
model. However, as with other screening models and techniques, their 
usage shall occur in agreement with the appropriate reviewing 
authority (paragraph 3.0(b)).
    e. As discussed in section 4.2(c)(ii), there are screening 
techniques needed for long-range transport assessments that will 
typically involve the use of a Lagrangian model. Based on the long-
standing practice and documented capabilities of these models for 
long-range transport assessments, the use of a Lagrangian model as a 
screening technique for this purpose does not need to be approved as 
an alternative model. However, their usage shall occur in 
consultation with the appropriate reviewing authority (paragraph 
3.0(b)) and EPA Regional Office.
    f. All screening models and techniques shall be configured to 
appropriately address the site and problem at hand. Close attention 
must be paid to whether the area should be classified urban or rural 
in accordance with section 7.2.1.1. The climatology of the area must 
be studied to help define the worst-case meteorological conditions. 
Agreement shall be reached between the model user and the 
appropriate reviewing authority (paragraph 3.0(b)) on the choice of 
the screening model or technique for each analysis, on the input 
data and model settings, and the appropriate metric for satisfying 
regulatory requirements.

4.2.1.1 AERSCREEN

    a. Released in 2011, AERSCREEN is the EPA's recommended 
screening model for simple and complex terrain for single sources 
including point sources, area sources, horizontal stacks, capped 
stacks, and flares. AERSCREEN runs AERMOD in a screening mode and 
consists of two main components: (1) the MAKEMET program which 
generates a site-specific matrix of meteorological conditions for 
input to the AERMOD model; and (2) the AERSCREEN command-prompt 
interface.
    b. The MAKEMET program generates a matrix of meteorological 
conditions, in the form of AERMOD-ready surface and profile files, 
based on user-specified surface characteristics, ambient 
temperatures, minimum wind speed, and anemometer height. The 
meteorological matrix is generated based on looping through a range 
of wind speeds, cloud covers, ambient temperatures, solar elevation 
angles, and convective velocity scales (w*, for convective 
conditions only) based on user-specified surface characteristics for 
surface roughness (Zo), Bowen ratio (Bo), and 
albedo (r). For unstable cases, the convective mixing height 
(Zic) is calculated based on w*, and the mechanical 
mixing height (Zim) is calculated for unstable and stable 
conditions based on the friction velocity, u*.
    c. For applications involving simple or complex terrain, 
AERSCREEN interfaces with AERMAP. AERSCREEN also interfaces with 
BPIPPRM to provide the necessary building parameters for 
applications involving building downwash using the Plume Rise Model 
Enhancements (PRIME) downwash algorithm. AERSCREEN generates inputs 
to AERMOD via MAKEMET, AERMAP, and BPIPPRM and invokes AERMOD in a 
screening mode. The screening mode of AERMOD forces the AERMOD model 
calculations to represent values for the plume centerline, 
regardless of the source-receptor-wind direction orientation. The 
maximum concentration output from AERSCREEN represents a worst-case 
1-hour concentration. Averaging-time scaling factors of 1.0 for 3-
hour, 0.9 for 8-hour, 0.60 for 24-hour, and 0.10 for annual 
concentration averages are applied internally by AERSCREEN to the 
highest 1-hour concentration calculated by the model for non-area 
type sources. For area type source concentrations for averaging 
times greater than one hour, the concentrations are equal to the 1-
hour estimates.37 40

4.2.1.2 CTSCREEN

    a. CTSCREEN 39 41 can be used to obtain conservative, 
yet realistic, worst-case estimates for receptors located on terrain 
above stack height. CTSCREEN accounts for the three-dimensional 
nature of plume and

[[Page 72842]]

terrain interaction and requires detailed terrain data 
representative of the modeling domain. The terrain data must be 
digitized in the same manner as for CTDMPLUS and a terrain processor 
is available.\42\ CTSCREEN is designed to execute a fixed matrix of 
meteorological values for wind speed (u), standard deviation of 
horizontal and vertical wind speeds ([sigma]v, [sigma]w), vertical 
potential temperature gradient (d[theta]/dz), friction velocity 
(u*), Monin-Obukhov length (L), mixing height (zi) as a 
function of terrain height, and wind directions for both neutral/
stable conditions and unstable convective conditions. The maximum 
concentration output from CTSCREEN represents a worst-case 1-hour 
concentration. Time-scaling factors of 0.7 for 3-hour, 0.15 for 24-
hour and 0.03 for annual concentration averages are applied 
internally by CTSCREEN to the highest 1-hour concentration 
calculated by the model.

4.2.1.3 Screening in Complex Terrain

    a. For applications utilizing AERSCREEN, AERSCREEN automatically 
generates a polar-grid receptor network with spacing determined by 
the maximum distance to model. If the application warrants a 
different receptor network than that generated by AERSCREEN, it may 
be necessary to run AERMOD in screening mode with a user-defined 
network. For CTSCREEN applications or AERMOD in screening mode 
outside of AERSCREEN, placement of receptors requires very careful 
attention when modeling in complex terrain. Often the highest 
concentrations are predicted to occur under very stable conditions, 
when the plume is near or impinges on the terrain. Under such 
conditions, the plume may be quite narrow in the vertical, so that 
even relatively small changes in a receptor's location may 
substantially affect the predicted concentration. Receptors within 
about a kilometer of the source may be even more sensitive to 
location. Thus, a dense array of receptors may be required in some 
cases.
    b. For applications involving AERSCREEN, AERSCREEN interfaces 
with AERMAP to generate the receptor elevations. For applications 
involving CTSCREEN, digitized contour data must be preprocessed \42\ 
to provide hill shape parameters in suitable input format. The user 
then supplies receptor locations either through an interactive 
program that is part of the model or directly, by using a text 
editor; using both methods to select receptor locations will 
generally be necessary to assure that the maximum concentrations are 
estimated by either model. In cases where a terrain feature may 
``appear to the plume'' as smaller, multiple hills, it may be 
necessary to model the terrain both as a single feature and as 
multiple hills to determine design concentrations.
    c. Other screening techniques may be acceptable for complex 
terrain cases where established procedures \43\ are used. The user 
is encouraged to confer with the appropriate reviewing authority 
(paragraph 3.0(b)) if any unforeseen problems are encountered, e.g., 
applicability, meteorological data, receptor siting, or terrain 
contour processing issues.

4.2.2 Refined Models

    a. A brief description of each preferred model for refined 
applications is found in addendum A. Also listed in that addendum 
Are availability, the model input requirements, the standard options 
that shall be selected when running the program, and output options.

4.2.2.1 AERMOD

    a. For a wide range of regulatory applications in all types of 
terrain, and for aerodynamic building downwash, the required model 
is AERMOD.44 45 The AERMOD regulatory modeling system 
consists of the AERMOD dispersion model, the AERMET meteorological 
processor, and the AERMAP terrain processor. AERMOD is a steady-
state Gaussian plume model applicable to directly emitted air 
pollutants that employs best state-of-practice parameterizations for 
characterizing the meteorological influences and dispersion. 
Differentiation of simple versus complex terrain is unnecessary with 
AERMOD. In complex terrain, AERMOD employs the well-known dividing-
streamline concept in a simplified simulation of the effects of 
plume-terrain interactions.
    b. The AERMOD Modeling System has been extensively evaluated 
across a wide range of scenarios based on numerous field studies, 
including tall stacks in flat and complex terrain settings, sources 
subject to building downwash influences, and low-level non-buoyant 
sources.\27\ These evaluations included several long-term field 
studies associated with operating plants as well as several 
intensive tracer studies. Based on these evaluations, AERMOD has 
shown consistently good performance, with ``errors'' in predicted 
versus observed peak concentrations, based on the Robust Highest 
Concentration (RHC) metric, consistently within the range of 10 to 
40 percent (cited in paragraph 4.1(e)).
    c. AERMOD incorporates the PRIME algorithm to account for 
enhanced plume growth and restricted plume rise for plumes affected 
by building wake effects.\46\ The PRIME algorithm accounts for 
entrainment of plume mass into the cavity recirculation region, 
including re-entrainment of plume mass into the wake region beyond 
the cavity.
    d. AERMOD incorporates the Buoyant Line and Point Source (BLP) 
Dispersion model to account for buoyant plume rise from line 
sources. The BLP option utilizes the standard meteorological inputs 
provided by the AERMET meteorological processor.
    e. The state-of-the-science for modeling atmospheric deposition 
is evolving, new modeling techniques are continually being assessed, 
and their results are being compared with observations. 
Consequently, while deposition treatment is available in AERMOD, the 
approach taken for any purpose shall be coordinated with the 
appropriate reviewing authority (paragraph 3.0(b)).
    f. The AERMET meteorological processor incorporates the COARE 
algorithms to derive marine boundary layer parameters for overwater 
applications of AERMOD.47 48 AERMOD is applicable for 
some overwater applications when platform downwash and shoreline 
fumigation are adequately considered in consultation with the 
Regional Office and appropriate reviewing authority. Where the 
effects of shoreline fumigation and platform downwash need to be 
assessed, the Offshore and Coastal Dispersion (OCD) model is the 
applicable model (paragraph 4.2.2.3).

4.2.2.2 CTDMPLUS

    a. If the modeling application involves an elevated point source 
with a well-defined hill or ridge and a detailed dispersion analysis 
of the spatial pattern of plume impacts is of interest, CTDMPLUS is 
available. CTDMPLUS provides greater resolution of concentrations 
about the contour of the hill feature than does AERMOD through a 
different plume-terrain interaction algorithm.

4.2.2.3 OCD

    a. The OCD (Offshore and Coastal Dispersion) model is a 
straight-line Gaussian model that incorporates overwater plume 
transport and dispersion as well as changes that occur as the plume 
crosses the shoreline. OCD can determine the impact of offshore 
emissions from point, area, or line sources on the air quality of 
coastal regions. OCD is also applicable for situations that involve 
platform building downwash.

4.2.3 Pollutant Specific Modeling Requirements

4.2.3.1 Models for Carbon Monoxide

    a. Models for assessing the impact of CO emissions are needed to 
meet NSR requirements to address compliance with the CO NAAQS and to 
determine localized impacts from transportations projects. Examples 
include evaluating effects of point sources, congested roadway 
intersections and highways, as well as the cumulative effect of 
numerous sources of CO in an urban area.
    b. The general modeling recommendations and requirements for 
screening models in section 4.2.1 and refined models in section 
4.2.2 shall be applied for CO modeling. Given the relatively low CO 
background concentrations, screening techniques are likely to be 
adequate in most cases. In applying these recommendations and 
requirements, the existing 1992 EPA guidance for screening CO 
impacts from highways may be consulted.\49\

4.2.3.2 Models for Lead

    a. In January 1999 (40 CFR part 58, appendix D), the EPA gave 
notice that concern about ambient lead impacts was being shifted 
away from roadways and toward a focus on stationary point sources. 
Thus, models for assessing the impact of lead emissions are needed 
to meet NSR requirements to address compliance with the lead NAAQS 
and for SIP attainment demonstrations. The EPA has also issued 
guidance on siting ambient monitors in the vicinity of stationary 
point sources.\50\ For lead, the SIP should contain an air quality 
analysis to determine the maximum rolling 3-month average lead 
concentration resulting from major lead point sources, such as 
smelters, gasoline additive plants, etc. The EPA has developed a 
post-processor to calculate rolling 3-month average concentrations 
from model output.\51\ General

[[Page 72843]]

guidance for lead SIP development is also available.\52\
    b. For major lead point sources, such as smelters, which 
contribute fugitive emissions and for which deposition is important, 
professional judgment should be used, and there shall be 
coordination with the appropriate reviewing authority (paragraph 
3.0(b)). For most applications, the general requirements for 
screening and refined models of section 4.2.1 and 4.2.2 are 
applicable to lead modeling.

4.2.3.3 Models for Sulfur Dioxide

    a. Models for SO2 are needed to meet NSR requirements 
to address compliance with the SO2 NAAQS and PSD 
increments, for SIP attainment demonstrations,\53\ and for 
characterizing current air quality via modeling.\54\ SO2 
is one of a group of highly reactive gases known as ``oxides of 
sulfur'' with largest emissions sources being fossil fuel combustion 
at power plants and other industrial facilities.
    b. Given the relatively inert nature of SO2 on the 
short-term time scales of interest (i.e., 1-hour) and the sources of 
SO2 (i.e., stationary point sources), the general 
modeling requirements for screening models in section 4.2.1 and 
refined models in section 4.2.2 are applicable for SO2 
modeling applications. For urban areas, AERMOD automatically invokes 
a half-life of 4 hours \55\ to SO2. Therefore, care must 
be taken when determining whether a source is urban or rural (see 
section 7.2.1.1 for urban/rural determination methodology).

4.2.3.4 Models for Nitrogen Dioxide

    a. Models for assessing the impact of sources on ambient 
NO2 concentrations are needed to meet NSR requirements to 
address compliance with the NO2 NAAQS and PSD increments. 
Impact of an individual source on ambient NO2 depends, in 
part, on the chemical environment into which the source's plume is 
to be emitted. This is due to the fact that NO2 sources 
co-emit NO along with NO2 and any emitted NO may react 
with ambient ozone to convert to additional NO2 downwind. 
Thus, comprehensive modeling of NO2 would need to 
consider the ratio of emitted NO and NO2, the ambient 
levels of ozone and subsequent reactions between ozone and NO, and 
the photolysis of NO2 to NO.
    b. Due to the complexity of NO2 modeling, a multi-
tiered screening approach is required to obtain hourly and annual 
average estimates of NO2.\56\ Since these methods are 
considered screening techniques, their usage shall occur in 
agreement with the appropriate reviewing authority (paragraph 
3.0(b)). Additionally, since screening techniques are conservative 
by their nature, there are limitations to how these options can be 
used. Specifically, modeling of negative emissions rates should only 
be done after consultation with the EPA Regional Office to ensure 
that decreases in concentrations would not be overestimated. Each 
tiered approach (see Figure 4-1) accounts for increasingly complex 
considerations of NO2 chemistry and is described in 
paragraphs c through e of this subsection. The tiers of 
NO2 modeling include:
    i. A first-tier (most conservative) ``full'' conversion 
approach;
    ii. A second-tier approach that assumes ambient equilibrium 
between NO and NO2; and
    iii. A third-tier consisting of several detailed screening 
techniques that account for ambient ozone and the relative amount of 
NO and NO2 emitted from a source.
    c. For Tier 1, use an appropriate refined model (section 4.2.2) 
to estimate nitrogen oxides (NOX) concentrations and 
assume a total conversion of NO to NO2.
    d. For Tier 2, multiply the Tier 1 result(s) by the Ambient 
Ratio Method 2 (ARM2), which provides estimates of representative 
equilibrium ratios of NO2/NOX value based 
ambient levels of NO2 and NOX derived from 
national data from the EPA's Air Quality System (AQS).\57\ The 
national default for ARM2 includes a minimum ambient NO2/
NOX ratio of 0.5 and a maximum ambient ratio of 0.9. The 
reviewing agency may establish alternative minimum ambient 
NO2/NOX values based on the source's in-stack 
emissions ratios, with alternative minimum ambient ratios reflecting 
the source's in-stack NO2/NOX ratios. 
Preferably, alternative minimum ambient NO2/
NOX ratios should be based on source-specific data which 
satisfies all quality assurance procedures that ensure data accuracy 
for both NO2 and NOX within the typical range 
of measured values. However, alternate information may be used to 
justify a source's anticipated NO2/NOX in-
stack ratios, such as manufacturer test data, State or local agency 
guidance, peer-reviewed literature, and/or the EPA's NO2/
NOX ratio database.
    e. For Tier 3, a detailed screening technique shall be applied 
on a case-by-case basis. Because of the additional input data 
requirements and complexities associated with the Tier 3 options, 
their usage shall occur in consultation with the EPA Regional Office 
in addition to the appropriate reviewing authority. The Ozone 
Limiting Method (OLM),\58\ the Plume Volume Molar Ratio Method 
(PVMRM),\59\ and the Generic Set Reaction Method (GRSM) 
60 61 are three detailed screening techniques that may be 
used for most sources. These three techniques use an appropriate 
section 4.2.2 model to estimate NOX concentrations and 
then estimate the conversion of primary NO emissions to 
NO2 based on the ambient levels of ozone and the plume 
characteristics. OLM only accounts for NO2 formation 
based on the ambient levels of ozone while PVMRM and GRSM also 
accommodate distance-dependent conversion ratios based on ambient 
ozone. GRSM, PVMRM and OLM require explicit specification of the 
NO2/NOX in-stack ratios and that ambient ozone 
concentrations be provided on an hourly basis. GRSM requires hourly 
ambient NOX concentrations in addition to hourly ozone.
    f. Alternative models or techniques may be considered on a case-
by-case basis and their usage shall be approved by the EPA Regional 
Office (section 3.2). Such models or techniques should consider 
individual quantities of NO and NO2 emissions, 
atmospheric transport and dispersion, and atmospheric transformation 
of NO to NO2. Dispersion models that account for more 
explicit photochemistry may also be considered as an alternative 
model to estimate ambient impacts of NOX sources.

[[Page 72844]]

[GRAPHIC] [TIFF OMITTED] TP23OC23.000

Figure 4-1: Multi-Tiered Approach for Estimating NO2 Concentrations

4.2.3.5 Models for PM2.5

    a. PM2.5 is a mixture consisting of several diverse 
components.\62\ Ambient PM2.5 generally consists of two 
components: (1) the primary component, emitted directly from a 
source; and (2) the secondary component, formed in the atmosphere 
from other pollutants emitted from the source. Models for 
PM2.5 are needed to meet NSR requirements to address 
compliance with the PM2.5 NAAQS and PSD increments and 
for SIP attainment demonstrations.
    b. For NSR modeling assessments, the general modeling 
requirements for screening models in section 4.2.1 and refined 
models in section 4.2.2 are applicable for the primary component of 
PM2.5, while the methods in section 5.4 are applicable 
for addressing the secondary component of PM2.5. Guidance 
for PSD assessments is available for determining the best approach 
to handling sources of primary and secondary PM2.5.\63\
    c. For SIP attainment demonstrations and regional haze 
reasonable progress goal analyses, effects of a control strategy on 
PM2.5 are estimated from the sum of the effects on the 
primary and secondary components composing PM2.5. Model 
users should refer to section 5.4.1 and associated SIP modeling 
guidance \64\ for further details concerning appropriate modeling 
approaches.
    d. The general modeling requirements for the refined models 
discussed in section 4.2.2 shall be applied for PM2.5 
hot-spot modeling for mobile sources. Specific guidance is available 
for analyzing direct PM2.5 impacts from highways, 
terminals, and other transportation projects.\65\

4.2.3.6 Models for PM10

    a. Models for PM10 are needed to meet NSR 
requirements to address compliance with the PM10 NAAQS 
and PSD increments and for SIP attainment demonstrations.
    b. For most sources, the general modeling requirements for 
screening models in section 4.2.1 and refined models in section 
4.2.2 shall be applied for PM10 modeling. In cases where 
the particle size and its effect on ambient concentrations need to 
be considered, particle deposition may be used on a case-by-case 
basis and their usage shall be coordinated with the appropriate 
reviewing authority. A SIP development guide \66\ is also available 
to assist in PM10 analyses and control strategy 
development.
    c. Fugitive dust usually refers to dust put into the atmosphere 
by the wind blowing over plowed fields, dirt roads, or desert or 
sandy areas with little or no vegetation. Fugitive emissions include 
the emissions resulting from the industrial process that are not 
captured and vented through a stack, but may be released from 
various locations within the complex. In some unique cases, a model 
developed specifically for the situation may be needed. Due to the 
difficult nature of characterizing and modeling fugitive dust and 
fugitive emissions, the proposed procedure shall be determined in 
consultation with the appropriate reviewing authority (paragraph 
3.0(b)) for each specific situation before the modeling exercise is 
begun. Re-entrained dust is created by vehicles driving over dirt 
roads (e.g., haul roads) and dust-covered roads typically found in 
arid areas. Such sources can be characterized as line, area or 
volume sources.65 67 Emission rates may be based on site-
specific data or values from the general literature.
    d. Under certain conditions, recommended dispersion models may 
not be suitable to appropriately address the nature of ambient 
PM10. In these circumstances, the alternative modeling 
approach shall be approved by the EPA Regional Office (section 3.2).
    e. The general modeling requirements for the refined models 
discussed in section 4.2.2 shall be applied for PM10 hot-
spot modeling for mobile sources. Specific guidance is available for 
analyzing direct PM10 impacts from highways, terminals, 
and other transportation projects.\65\

5.0 Models for Ozone and Secondarily Formed Particulate Matter

5.1 Discussion

    a. Air pollutants formed through chemical reactions in the 
atmosphere are referred to as secondary pollutants. For example, 
ground-level ozone and a portion of PM2.5 are secondary 
pollutants formed through photochemical reactions. Ozone and 
secondarily formed particulate matter are closely related to each 
other in that they share common sources of emissions and are formed 
in the atmosphere from chemical reactions with similar precursors.
    b. Ozone formation is driven by emissions of NOX and 
volatile organic compounds (VOCs). Ozone formation is a complicated 
nonlinear process that requires favorable meteorological conditions 
in addition to VOC and NOX emissions. Sometimes complex 
terrain features also contribute to the build-up of precursors and 
subsequent ozone formation or destruction.
    c. PM2.5 can be either primary (i.e., emitted 
directly from sources) or secondary in nature. The fraction of 
PM2.5 which is primary versus secondary varies by 
location and season. In the United States, PM2.5 is 
dominated by a variety of chemical species or components of 
atmospheric particles, such as ammonium sulfate, ammonium nitrate, 
organic carbon mass, elemental carbon, and other soil compounds and 
oxidized metals. PM2.5 sulfate, nitrate, and ammonium 
ions are predominantly the result of chemical reactions of the 
oxidized products of SO2 and NOX emissions 
with direct ammonia emissions.\68\
    d. Control measures reducing ozone and PM2.5 
precursor emissions may not lead to proportional reductions in ozone 
and PM2.5. Modeled strategies designed to reduce ozone or 
PM2.5 levels typically need to consider the chemical 
coupling between these pollutants. This coupling is important in 
understanding processes that control the levels of both pollutants. 
Thus, when feasible, it is important to use models that take into 
account the chemical coupling between ozone and PM2.5. In 
addition, using such a multi-pollutant modeling system can reduce 
the resource burden associated with applying and evaluating separate 
models for each pollutant and promotes consistency among the 
strategies themselves.
    e. PM2.5 is a mixture consisting of several diverse 
chemical species or components of

[[Page 72845]]

atmospheric particles. Because chemical and physical properties and 
origins of each component differ, it may be appropriate to use 
either a single model capable of addressing several of the important 
components or to model primary and secondary components using 
different models. Effects of a control strategy on PM2.5 
is estimated from the sum of the effects on the specific components 
comprising PM2.5.

5.2 Recommendations

    a. Chemical transformations can play an important role in 
defining the concentrations and properties of certain air 
pollutants. Models that take into account chemical reactions and 
physical processes of various pollutants (including precursors) are 
needed for determining the current state of air quality, as well as 
predicting and projecting the future evolution of these pollutants. 
It is important that a modeling system provide a realistic 
representation of chemical and physical processes leading to 
secondary pollutant formation and removal from the atmosphere.
    b. Chemical transport models treat atmospheric chemical and 
physical processes such as deposition and motion. There are two 
types of chemical transport models, Eulerian (grid based) and 
Lagrangian. These types of models are differentiated from each other 
by their frame of reference. Eulerian models are based on a fixed 
frame of reference and Lagrangian models use a frame of reference 
that moves with parcels of air between the source and receptor 
point.\9\ Photochemical grid models are three-dimensional Eulerian 
grid-based models that treat chemical and physical processes in each 
grid cell and use diffusion and transport processes to move chemical 
species between grid cells.\9\ These types of models are appropriate 
for assessment of near-field and regional scale reactive pollutant 
impacts from specific sources 7 10 11 12 or all 
sources.13 14 15 In some limited cases, the secondary 
processes can be treated with a box model, ideally in combination 
with a number of other modeling techniques and/or analyses to treat 
individual source sectors.
    c. Regardless of the modeling system used to estimate secondary 
impacts of ozone and/or PM2.5, model results should be 
compared to observation data to generate confidence that the 
modeling system is representative of the local and regional air 
quality. For ozone related projects, model estimates of ozone should 
be compared with observations in both time and space. For 
PM2.5, model estimates of speciated PM2.5 
components (such as sulfate ion, nitrate ion, etc.) should be 
compared with observations in both time and space.\69\
    d. Model performance metrics comparing observations and 
predictions are often used to summarize model performance. These 
metrics include mean bias, mean error, fractional bias, fractional 
error, and correlation coefficient.\69\ There are no specific levels 
of any model performance metric that indicate ``acceptable'' model 
performance. The EPA's preferred approach for providing context 
about model performance is to compare model performance metrics with 
similar contemporary applications.64 69 Because model 
application purpose and scope vary, model users should consult with 
the appropriate reviewing authority (paragraph 3.0(b)) to determine 
what model performance elements should be emphasized and presented 
to provide confidence in the regulatory model application.
    e. There is no preferred modeling system or technique for 
estimating ozone or secondary PM2.5 for specific source 
impacts or to assess impacts from multiple sources. For assessing 
secondary pollutant impacts from single sources, the degree of 
complexity required to assess potential impacts varies depending on 
the nature of the source, its emissions, and the background 
environment. The EPA recommends a two-tiered approach where the 
first tier consists of using existing technically credible and 
appropriate relationships between emissions and impacts developed 
from previous modeling that is deemed sufficient for evaluating a 
source's impacts. The second tier consists of more sophisticated 
case-specific modeling analyses. The appropriate tier for a given 
application should be selected in consultation with the appropriate 
reviewing authority (paragraph 3.0(b)) and be consistent with EPA 
guidance.\70\

5.3 Recommended Models and Approaches for Ozone

    a. Models that estimate ozone concentrations are needed to guide 
the choice of strategies for the purposes of a nonattainment area 
demonstrating future year attainment of the ozone NAAQS. 
Additionally, models that estimate ozone concentrations are needed 
to assess impacts from specific sources or source complexes to 
satisfy requirements for NSR and other regulatory programs. Other 
purposes for ozone modeling include estimating the impacts of 
specific events on air quality, ozone deposition impacts, and 
planning for areas that may be attaining the ozone NAAQS.

5.3.1 Models for NAAQS Attainment Demonstrations and Multi-Source Air 
Quality Assessments

    a. Simulation of ozone formation and transport is a complex 
exercise. Control agencies with jurisdiction over areas with ozone 
problems should use photochemical grid models to evaluate the 
relationship between precursor species and ozone. Use of 
photochemical grid models is the recommended means for identifying 
control strategies needed to address high ozone concentrations in 
such areas. Judgment on the suitability of a model for a given 
application should consider factors that include use of the model in 
an attainment test, development of emissions and meteorological 
inputs to the model, and choice of episodes to model. Guidance on 
the use of models and other analyses for demonstrating attainment of 
the air quality goals for ozone is available.63 64 Users 
should consult with the appropriate reviewing authority (paragraph 
3.0(b)) to ensure the most current modeling guidance is applied.

5.3.2 Models for Single-Source Air Quality Assessments

    a. Depending on the magnitude of emissions, estimating the 
impact of an individual source's emissions of NOX and VOC 
on ambient ozone is necessary for obtaining a permit. The simulation 
of ozone formation and transport requires realistic treatment of 
atmospheric chemistry and deposition. Models (e.g., Lagrangian and 
photochemical grid models) that integrate chemical and physical 
processes important in the formation, decay, and transport of ozone 
and important precursor species should be applied. Photochemical 
grid models are primarily designed to characterize precursor 
emissions and impacts from a wide variety of sources over a large 
geographic area but can also be used to assess the impacts from 
specific sources.7 11 12
    b. The first tier of assessment for ozone impacts involves those 
situations where existing technical information is available (e.g., 
results from existing photochemical grid modeling, published 
empirical estimates of source specific impacts, or reduced-form 
models) in combination with other supportive information and 
analysis for the purposes of estimating secondary impacts from a 
particular source. The existing technical information should provide 
a credible and representative estimate of the secondary impacts from 
the project source. The appropriate reviewing authority (paragraph 
3.0(b)) and appropriate EPA guidance 70 71 should be 
consulted to determine what types of assessments may be appropriate 
on a case-by-case basis.
    c. The second tier of assessment for ozone impacts involves 
those situations where existing technical information is not 
available or a first tier demonstration indicates a more refined 
assessment is needed. For these situations, chemical transport 
models should be used to address single-source impacts. Special 
considerations are needed when using these models to evaluate the 
ozone impact from an individual source. Guidance on the use of 
models and other analyses for demonstrating the impacts of single 
sources for ozone is available.\70\ This guidance document provides 
a more detailed discussion of the appropriate approaches to 
obtaining estimates of ozone impacts from a single source. Model 
users should use the latest version of the guidance in consultation 
with the appropriate reviewing authority (paragraph 3.0(b)) to 
determine the most suitable refined approach for single-source ozone 
modeling on a case-by-case basis.

5.4 Recommended Models and Approaches for Secondarily Formed PM2.5

    a. Models that estimate PM2.5 concentrations are 
needed to guide the choice of strategies for the purposes of a 
nonattainment area demonstrating future year attainment of the 
PM2.5 NAAQS. Additionally, models that estimate 
PM2.5 concentrations are needed to assess impacts from 
specific sources or source complexes to satisfy requirements for NSR 
and other regulatory programs. Other purposes for PM2.5 
modeling include estimating the impacts of specific events on air 
quality,

[[Page 72846]]

visibility, deposition impacts, and planning for areas that may be 
attaining the PM2.5 NAAQS.

5.4.1 Models for NAAQS Attainment Demonstrations and Multi-Source Air 
Quality Assessments

    a. Models for PM2.5 are needed to assess the adequacy 
of a proposed strategy for meeting the annual and 24-hour 
PM2.5 NAAQS. Modeling primary and secondary 
PM2.5 can be a multi-faceted and complex problem, 
especially for secondary components of PM2.5 such as 
sulfates and nitrates. Control agencies with jurisdiction over areas 
with secondary PM2.5 problems should use models that 
integrate chemical and physical processes important in the 
formation, decay, and transport of these species (e.g., 
photochemical grid models). Suitability of a modeling approach or 
mix of modeling approaches for a given application requires 
technical judgment as well as professional experience in choice of 
models, use of the model(s) in an attainment test, development of 
emissions and meteorological inputs to the model, and selection of 
days to model. Guidance on the use of models and other analyses for 
demonstrating attainment of the air quality goals for 
PM2.5 is available.63 64 Users should consult 
with the appropriate reviewing authority (paragraph 3.0(b)) to 
ensure the most current modeling guidance is applied.

5.4.2 Models for Single-Source Air Quality Assessments

    a. Depending on the magnitude of emissions, estimating the 
impact of an individual source's emissions on secondary particulate 
matter concentrations may be necessary for obtaining a permit. 
Primary PM2.5 components shall be simulated using the 
general modeling requirements in section 4.2.3.5. The simulation of 
secondary particulate matter formation and transport is a complex 
exercise requiring realistic treatment of atmospheric chemistry and 
deposition. Models should be applied that integrate chemical and 
physical processes important in the formation, decay, and transport 
of these species (e.g., Lagrangian and photochemical grid models). 
Photochemical grid models are primarily designed to characterize 
precursor emissions and impacts from a wide variety of sources over 
a large geographic area and can also be used to assess the impacts 
from specific sources.7 10 For situations where a project 
source emits both primary PM2.5 and PM2.5 
precursors, the contribution from both should be combined for use in 
determining the source's ambient impact. Approaches for combining 
primary and secondary impacts are provided in appropriate guidance 
for single source permit related demonstrations.\70\
    b. The first tier of assessment for secondary PM2.5 
impacts involves those situations where existing technical 
information is available (e.g., results from existing photochemical 
grid modeling, published empirical estimates of source specific 
impacts, or reduced-form models) in combination with other 
supportive information and analysis for the purposes of estimating 
secondary impacts from a particular source. The existing technical 
information should provide a credible and representative estimate of 
the secondary impacts from the project source. The appropriate 
reviewing authority (paragraph 3.0(b)) and appropriate EPA guidance 
70 71 should be consulted to determine what types of 
assessments may be appropriate on a case-by-case basis.
    c. The second tier of assessment for secondary PM2.5 
impacts involves those situations where existing technical 
information is not available or a first tier demonstration indicates 
a more refined assessment is needed. For these situations, chemical 
transport models should be used for assessments of single-source 
impacts. Special considerations are needed when using these models 
to evaluate the secondary particulate matter impact from an 
individual source. Guidance on the use of models and other analyses 
for demonstrating the impacts of single sources for secondary 
PM2.5 is available.\70\ This guidance document provides a 
more detailed discussion of the appropriate approaches to obtaining 
estimates of secondary particulate matter concentrations from a 
single source. Model users should use the latest version of this 
guidance in consultation with the appropriate reviewing authority 
(paragraph 3.0(b)) to determine the most suitable single-source 
modeling approach for secondary PM2.5 on a case-by-case 
basis.

6.0 Modeling for Air Quality Related Values and Other Governmental 
Programs

6.1 Discussion

    a. Other Federal government agencies and State, local, and 
Tribal agencies with air quality and land management 
responsibilities have also developed specific modeling approaches 
for their own regulatory or other requirements. Although such 
regulatory requirements and guidance have come about because of EPA 
rules or standards, the implementation of such regulations and the 
use of the modeling techniques is under the jurisdiction of the 
agency issuing the guidance or directive. This section covers such 
situations with reference to those guidance documents, when they are 
available.
    b. When using the model recommended or discussed in the 
Guideline in support of programmatic requirements not specifically 
covered by EPA regulations, the model user should consult the 
appropriate Federal, State, local, or Tribal agency to ensure the 
proper application and use of the models and/or techniques. These 
agencies have developed specific modeling approaches for their own 
regulatory or other requirements. Most of the programs have, or will 
have when fully developed, separate guidance documents that cover 
the program and a discussion of the tools that are needed. The 
following paragraphs reference those guidance documents, when they 
are available.

6.2 Air Quality Related Values

    a. The 1990 CAA Amendments give FLMs an ``affirmative 
responsibility'' to protect the natural and cultural resources of 
Class I areas from the adverse impacts of air pollution and to 
provide the appropriate procedures and analysis techniques. The CAA 
identifies the FLM as the Secretary of the department, or their 
designee, with authority over these lands. Mandatory Federal Class I 
areas are defined in the CAA as international parks, national parks 
over 6,000 acres, and wilderness areas and memorial parks over 5,000 
acres, established as of 1977. The FLMs are also concerned with the 
protection of resources in federally managed Class II areas because 
of other statutory mandates to protect these areas. Where State or 
Tribal agencies have successfully petitioned the EPA and lands have 
been redesignated to Class I status, these agencies may have 
equivalent responsibilities to that of the FLMs for these non-
Federal Class I areas as described throughout the remainder of 
section 6.2.
    b. The FLM agency responsibilities include the review of air 
quality permit applications from proposed new or modified major 
pollution sources that may affect these Class I areas to determine 
if emissions from a proposed or modified source will cause or 
contribute to adverse impacts on air quality related values (AQRVs) 
of a Class I area and making recommendations to the FLM. AQRVs are 
resources, identified by the FLM agencies, that have the potential 
to be affected by air pollution. These resources may include 
visibility, scenic, cultural, physical, or ecological resources for 
a particular area. The FLM agencies take into account the particular 
resources and AQRVs that would be affected; the frequency and 
magnitude of any potential impacts; and the direct, indirect, and 
cumulative effects of any potential impacts in making their 
recommendations.
    c. While the AQRV notification and impact analysis requirements 
are outlined in the PSD regulations at 40 CFR 51.166(p) and 40 CFR 
52.21(p), determination of appropriate analytical methods and 
metrics for AQRV's are determined by the FLM agencies and are 
published in guidance external to the general recommendations of 
this paragraph.
    d. To develop greater consistency in the application of air 
quality models to assess potential AQRV impacts in both Class I 
areas and protected Class II areas, the FLM agencies have developed 
the Federal Land Managers' Air Quality Related Values Work Group 
Phase I Report (FLAG).\72\ FLAG focuses upon specific technical and 
policy issues associated with visibility impairment, effects of 
pollutant deposition on soils and surface waters, and ozone effects 
on vegetation. Model users should consult the latest version of the 
FLAG report for current modeling guidance and with affected FLM 
agency representatives for any application specific guidance which 
is beyond the scope of the Guideline.

6.2.1 Visibility

    a. Visibility in important natural areas (e.g., Federal Class I 
areas) is protected under a number of provisions of the CAA, 
including sections 169A and 169B (addressing impacts primarily from 
existing sources) and section 165 (new source review). Visibility 
impairment is caused by light scattering and light absorption 
associated with particles and gases in the atmosphere. In most areas 
of the country, light scattering by PM2.5 is the most

[[Page 72847]]

significant component of visibility impairment. The key components 
of PM2.5 contributing to visibility impairment include 
sulfates, nitrates, organic carbon, elemental carbon, and crustal 
material.\72\
    b. Visibility regulations (40 CFR 51.300 through 51.309) require 
State, local, and Tribal agencies to mitigate current and prevent 
future visibility impairment in any of the 156 mandatory Federal 
Class I areas where visibility is considered an important attribute. 
In 1999, the EPA issued revisions to the regulations to address 
visibility impairment in the form of regional haze, which is caused 
by numerous, diverse sources (e.g., stationary, mobile, and area 
sources) located across a broad region (40 CFR 51.308 through 
51.309). The state of relevant scientific knowledge has expanded 
significantly since that time. A number of studies and reports 
73 74 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 CAA requires States to develop SIPs containing 
long-term strategies for remedying existing and preventing future 
visibility impairment in the 156 mandatory Class I Federal areas, 
where visibility is considered an important attribute. In order to 
develop long-term strategies to address regional haze, many State, 
local, and Tribal agencies will need to conduct regional-scale 
modeling of fine particulate concentrations and associated 
visibility impairment.
    c. The FLAG visibility modeling recommendations are divided into 
two distinct sections to address different requirements for: (1) 
near field modeling where plumes or layers are compared against a 
viewing background, and (2) distant/multi-source modeling for plumes 
and aggregations of plumes that affect the general appearance of a 
scene.\72\ The recommendations separately address visibility 
assessments for sources proposing to locate relatively near and at 
farther distances from these areas.\72\

6.2.1.1 Models for Estimating Near-Field Visibility Impairment

    a. To calculate the potential impact of a plume of specified 
emissions for specific transport and dispersion conditions (``plume 
blight'') for source-receptor distances less than 50 km, a screening 
model and guidance are available.72 75 If a more 
comprehensive analysis is necessary, a refined model should be 
selected. The model selection, procedures, and analyses should be 
determined in consultation with the appropriate reviewing authority 
(paragraph 3.0(b)) and the affected FLM(s).

6.2.1.2 Models for Estimating Visibility Impairment for Long-Range 
Transport

    a. Chemical transformations can play an important role in 
defining the concentrations and properties of certain air 
pollutants. Models that take into account chemical reactions and 
physical processes of various pollutants (including precursors) are 
needed for determining the current state of air quality, as well as 
predicting and projecting the future evolution of these pollutants. 
It is important that a modeling system provide a realistic 
representation of chemical and physical processes leading to 
secondary pollutant formation and removal from the atmosphere.
    b. Chemical transport models treat atmospheric chemical and 
physical processes such as deposition and motion. There are two 
types of chemical transport models, Eulerian (grid based) and 
Lagrangian. These types of models are differentiated from each other 
by their frame of reference. Eulerian models are based on a fixed 
frame of reference and Lagrangian models use a frame of reference 
that moves with parcels of air between the source and receptor 
point.\9\ Photochemical grid models are three-dimensional Eulerian 
grid-based models that treat chemical and physical processes in each 
grid cell and use diffusion and transport processes to move chemical 
species between grid cells.\9\ These types of models are appropriate 
for assessment of near-field and regional scale reactive pollutant 
impacts from specific sources 7 10 11 12 or all 
sources.13 14 15
    c. Development of the requisite meteorological and emissions 
databases necessary for use of photochemical grid models to estimate 
AQRVs should conform to recommendations in section 8 and those 
outlined in the EPA's Modeling Guidance for Demonstrating Attainment 
of Air Quality Goals for Ozone, PM2.5, and Regional 
Haze.64 Demonstration of the adequacy of prognostic 
meteorological fields can be established through appropriate 
diagnostic and statistical performance evaluations consistent with 
recommendations provided in the appropriate guidance.\64\ Model 
users should consult the latest version of this guidance and with 
the appropriate reviewing authority (paragraph 3.0(b)) for any 
application-specific guidance that is beyond the scope of this 
subsection.

6.2.2 Models for Estimating Deposition Impacts

    a. For many Class I areas, AQRVs have been identified that are 
sensitive to atmospheric deposition of air pollutants. Emissions of 
NOX, sulfur oxides, NH3, mercury, and 
secondary pollutants such as ozone and particulate matter affect 
components of ecosystems. In sensitive ecosystems, these compounds 
can acidify soils and surface waters, add nutrients that change 
biodiversity, and affect the ecosystem services provided by forests 
and natural areas.\72\ To address the relationship between 
deposition and ecosystem effects, the FLM agencies have developed 
estimates of critical loads. A critical load is defined as, ``A 
quantitative estimate of an exposure to one or more pollutants below 
which significant harmful effects on specified sensitive elements of 
the environment do not occur according to present knowledge.'' \76\
    b. The FLM deposition modeling recommendations are divided into 
two distinct sections to address different requirements for: (1) 
near field modeling, and (2) distant/multi-source modeling for 
cumulative effects. The recommendations separately address 
deposition assessments for sources proposing to locate relatively 
near and at farther distances from these areas.\72\ Where the source 
and receptors are not in close proximity, chemical transport (e.g., 
photochemical grid) models generally should be applied for an 
assessment of deposition impacts due to one or a small group of 
sources. Over these distances, chemical and physical transformations 
can change atmospheric residence time due to different propensity 
for deposition to the surface of different forms of nitrate and 
sulfate. Users should consult the latest version of the FLAG report 
\72\ and relevant FLM representatives for guidance on the use of 
models for deposition. Where source and receptors are in close 
proximity, users should contact the appropriate FLM for application-
specific guidance.

6.3 Modeling Guidance for Other Governmental Programs

    a. Dispersion and photochemical grid modeling may need to be 
conducted to ensure that individual and cumulative offshore oil and 
gas exploration, development, and production plans and activities do 
not significantly affect the air quality of any State as required 
under the Outer Continental Shelf Lands Act (OCSLA). Air quality 
modeling requires various input datasets, including emissions 
sources, meteorology, and pre-existing pollutant concentrations. For 
sources under the reviewing authority of the Department of Interior, 
Bureau of Ocean Energy Management (BOEM), guidance for the 
development of all necessary Outer Continental Shelf (OCS) air 
quality modeling inputs and appropriate model selection and 
application is available from the BOEM's website: https://www.boem.gov/about-boem/regulations-guidance/guidance-portal.
    b. The Federal Aviation Administration (FAA) is the appropriate 
reviewing authority for air quality assessments of primary pollutant 
impacts at airports and air bases. The Aviation Environmental Design 
Tool (AEDT) is developed and supported by the FAA, and is 
appropriate for air quality assessment of primary pollutant impacts 
at airports or air bases. AEDT has adopted AERMOD for treating 
dispersion. Application of AEDT is intended for estimating the 
change in emissions for aircraft operations, point source, and 
mobile source emissions on airport property and quantify the 
associated pollutant level- concentrations. AEDT is not intended for 
PSD, SIP, or other regulatory air quality analyses of point or 
mobile sources at or peripheral to airport property that are 
unrelated to airport operations. The latest version of AEDT may be 
obtained from the FAA at: https://aedt.faa.gov.

7.0 General Modeling Considerations

7.1 Discussion

    a. This section contains recommendations concerning a number of 
different issues not explicitly covered in other sections of the 
Guideline. The topics covered here are not specific to any one 
program or modeling area, but are common to dispersion modeling 
analyses for criteria pollutants.

7.2 Recommendations

7.2.1 All sources

7.2.1.1 Dispersion Coefficients

    a. For any dispersion modeling exercise, the urban or rural 
determination of a source

[[Page 72848]]

is critical in determining the boundary layer characteristics that 
affect the model's prediction of downwind concentrations. 
Historically, steady-state Gaussian plume models used in most 
applications have employed dispersion coefficients based on 
Pasquill-Gifford \77\ in rural areas and McElroy-Pooler \78\ in 
urban areas. These coefficients are still incorporated in the BLP 
and OCD models. However, the AERMOD model incorporates a more up-to-
date characterization of the atmospheric boundary layer using 
continuous functions of parameterized horizontal and vertical 
turbulence based on Monin-Obukhov similarity (scaling) 
relationships.\44\ Another key feature of AERMOD's formulation is 
the option to use directly observed variables of the boundary layer 
to parameterize dispersion.44 45
    b. The selection of rural or urban dispersion coefficients in a 
specific application should follow one of the procedures suggested 
by Irwin \79\ to determine whether the character of an area is 
primarily urban or rural (of the two methods, the land use procedure 
is considered more definitive.):
    i. Land Use Procedure: (1) Classify the land use within the 
total area, Ao, circumscribed by a 3 km radius circle 
about the source using the meteorological land use typing scheme 
proposed by Auer; \80\ (2) if land use types I1, I2, C1, R2, and R3 
account for 50 percent or more of Ao, use urban 
dispersion coefficients; otherwise, use appropriate rural dispersion 
coefficients.
    ii. Population Density Procedure: (1) Compute the average 
population density, p per square kilometer with Ao as 
defined above; (2) If p is greater than 750 people per square 
kilometer, use urban dispersion coefficients; otherwise use 
appropriate rural dispersion coefficients.
    c. Population density should be used with caution and generally 
not be applied to highly industrialized areas where the population 
density may be low and, thus, a rural classification would be 
indicated. However, the area is likely to be sufficiently built-up 
so that the urban land use criteria would be satisfied. Therefore, 
in this case, the classification should be ``urban'' and urban 
dispersion parameters should be used.
    d. For applications of AERMOD in urban areas, under either the 
Land Use Procedure or the Population Density Procedure, the user 
needs to estimate the population of the urban area affecting the 
modeling domain because the urban influence in AERMOD is scaled 
based on a user-specified population. For non-population oriented 
urban areas, or areas influenced by both population and industrial 
activity, the user will need to estimate an equivalent population to 
adequately account for the combined effects of industrialized areas 
and populated areas within the modeling domain. Selection of the 
appropriate population for these applications should be determined 
in consultation with the appropriate reviewing authority (paragraph 
3.0(b)) and the latest version of the AERMOD Implementation 
Guide.\81\
    e. It should be noted that AERMOD allows for modeling rural and 
urban sources in a single model run. For analyses of whole urban 
complexes, the entire area should be modeled as an urban region if 
most of the sources are located in areas classified as urban. For 
tall stacks located within or adjacent to small or moderate sized 
urban areas, the stack height or effective plume height may extend 
above the urban boundary layer and, therefore, may be more 
appropriately modeled using rural coefficients. Model users should 
consult with the appropriate reviewing authority (paragraph 3.0(b)) 
and the latest version of the AERMOD Implementation Guide \81\ when 
evaluating this situation.
    f. Buoyancy-induced dispersion (BID), as identified by 
Pasquill,\82\ is included in the preferred models and should be used 
where buoyant sources (e.g., those involving fuel combustion) are 
involved.

7.2.1.2 Complex Winds

    a. Inhomogeneous local winds. In many parts of the United 
States, the ground is neither flat nor is the ground cover (or land 
use) uniform. These geographical variations can generate local winds 
and circulations, and modify the prevailing ambient winds and 
circulations. Typically, geographic effects are more apparent when 
the ambient winds are light or calm, as stronger synoptic or 
mesoscale winds can modify, or even eliminate the weak geographic 
circulations.\83\ 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 characteristics of the winds are a 
balance of various forces, such that the assumptions of steady-state 
straight-line transport both in time and space are inappropriate. In 
such cases, a model should be chosen to fully treat the time and 
space variations of meteorology effects on transport and dispersion. 
The setup and application of such a model should be determined in 
consultation with the appropriate reviewing authority (paragraph 
3.0(b)) consistent with limitations of paragraph 3.2.2(e). The 
meteorological input data requirements for developing the time and 
space varying three-dimensional winds and dispersion meteorology for 
these situations are discussed in paragraph 8.4.1.2(c). Examples of 
inhomogeneous winds include, but are not limited to, situations 
described in the following paragraphs:
    i. Inversion breakup fumigation. Inversion breakup fumigation 
occurs when a plume (or multiple plumes) is emitted into a stable 
layer of air and that layer is subsequently mixed to the ground 
through convective transfer of heat from the surface or because of 
advection to less stable surroundings. Fumigation may cause 
excessively high concentrations, but is usually rather short-lived 
at a given receptor. There are no recommended refined techniques to 
model this phenomenon. There are, however, screening procedures \40\ 
that may be used to approximate the concentrations. Considerable 
care should be exercised in using the results obtained from the 
screening techniques.
    ii. Shoreline fumigation. Fumigation can be an important 
phenomenon on and near the shoreline of bodies of water. This can 
affect both individual plumes and area-wide emissions. When 
fumigation conditions are expected to occur from a source or sources 
with tall stacks located on or just inland of a shoreline, this 
should be addressed in the air quality modeling analysis. The EPA 
has evaluated several coastal fumigation models, and the evaluation 
results of these models are available for their possible application 
on a case-by-case basis when air quality estimates under shoreline 
fumigation conditions are needed.\84\ Selection of the appropriate 
model for applications where shoreline fumigation is of concern 
should be determined in consultation with the appropriate reviewing 
authority (paragraph 3.0(b)).
    iii. Stagnation. Stagnation conditions are characterized by calm 
or very low wind speeds, and variable wind directions. These 
stagnant meteorological conditions may persist for several hours to 
several days. During stagnation conditions, the dispersion of air 
pollutants, especially those from low-level emissions sources, tends 
to be minimized, potentially leading to relatively high ground-level 
concentrations. If point sources are of interest, users should note 
the guidance provided in paragraph (a) of this subsection. Selection 
of the appropriate model for applications where stagnation is of 
concern should be determined in consultation with the appropriate 
reviewing authority (paragraph 3.0(b)).

7.2.1.3 Gravitational Settling and Deposition

    a. Gravitational settling and deposition may be directly 
included in a model if either is a significant factor. When 
particulate matter sources can be quantified and settling and dry 
deposition are problems, use professional judgment along with 
coordination with the appropriate reviewing authority (paragraph 
3.0(b)). AERMOD contains algorithms for dry and wet deposition of 
gases and particles.\85\ For other Gaussian plume models, an 
``infinite half-life'' may be used for estimates of particle 
concentrations when only exponential decay terms are used for 
treating settling and deposition. Lagrangian models have varying 
degrees of complexity for dealing with settling and deposition and 
the selection of a parameterization for such should be included in 
the approval process for selecting a Lagrangian model. Eulerian grid 
models tend to have explicit parameterizations for gravitational 
settling and deposition as well as wet deposition parameters already 
included as part of the chemistry scheme.

7.2.2 Stationary Sources

7.2.2.1 Good Engineering Practice Stack Height

    a. The use of stack height credit in excess of Good Engineering 
Practice (GEP) stack height or credit resulting from any other 
dispersion technique is prohibited in the development of emissions 
limits by 40 CFR 51.118 and 40 CFR 51.164. The definition of 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

[[Page 72849]]

techniques are found in several references,86 87 88 89 
that provide a great deal of additional information for evaluating 
and describing building cavity and wake effects.
    b. If stacks for new or existing major sources are found to be 
less than the height defined by the EPA's refined formula for 
determining GEP height, then air quality impacts associated with 
cavity or wake effects due to the nearby building structures should 
be determined. The EPA refined formula height is defined as H + 
1.5L.\88\ Since the definition of GEP stack height defines excessive 
concentrations as a maximum ground-level concentration due in whole 
or in part to downwash of at least 40 percent in excess of the 
maximum concentration without downwash, the potential air quality 
impacts associated with cavity and wake effects should also be 
considered for stacks that equal or exceed the EPA formula height 
for GEP. The AERSCREEN model can be used to obtain screening 
estimates of potential downwash influences, based on the PRIME 
downwash algorithm incorporated in the AERMOD model. If more refined 
concentration estimates are required, AERMOD should be used (section 
4.2.2).

7.2.2.2 Plume Rise

    a. The plume rise methods of Briggs 90 91 are 
incorporated in many of the preferred models and are recommended for 
use in many modeling applications. In AERMOD,44 45 for 
the stable boundary layer, plume rise is estimated using an 
iterative approach, similar to that in the CTDMPLUS model. In the 
convective boundary layer, plume rise is superposed on the 
displacements by random convective velocities.\92\ In AERMOD, plume 
rise is computed using the methods of Briggs, except in cases 
involving building downwash, in which a numerical solution of the 
mass, energy, and momentum conservation laws is performed.\93\ No 
explicit provisions in these models are made for multistack plume 
rise enhancement or the handling of such special plumes as flares.
    b. Gradual plume rise is generally recommended where its use is 
appropriate: (1) in AERMOD; (2) in complex terrain screening 
procedures to determine close-in impacts; and (3) when calculating 
the effects of building wakes. The building wake algorithm in AERMOD 
incorporates and exercises the thermodynamically based gradual plume 
rise calculations as described in paragraph (a) of this subsection. 
If the building wake is calculated to affect the plume for any hour, 
gradual plume rise is also used in downwind dispersion calculations 
to the distance of final plume rise, after which final plume rise is 
used. Plumes captured by the near wake are re-emitted to the far 
wake as a ground-level volume source.
    c. Stack tip downwash generally occurs with poorly constructed 
stacks and when the ratio of the stack exit velocity to wind speed 
is small. An algorithm developed by Briggs \91\ is the recommended 
technique for this situation and is used in preferred models for 
point sources.
    d. On a case-by-case basis, refinements to the preferred model 
may be considered for plume rise and downwash effects and shall 
occur in agreement with the appropriate reviewing authority 
(paragraph 3.0(b)) and approval by the EPA Regional Office based on 
the requirements of section 3.2.2.

7.2.3 Mobile Sources

    a. Emissions of primary pollutants from mobile sources can be 
modeled with an appropriate model identified in section 4.2. 
Screening of mobile sources can be accomplished by using screening 
meteorology, e.g., worst-case meteorological conditions. Maximum 
hourly concentrations computed from screening modeling can be 
converted to longer averaging periods using the scaling ratios 
specified in the AERSCREEN User's Guide.\37\
    b. Mobile sources can be modeled in AERMOD as either line (i.e., 
elongated area) sources or as a series of volume sources. Line 
sources can be represented in AERMOD with the following source 
types: LINE, AREA, VOLUME or RLINE. However, since mobile source 
modeling usually includes an analysis of very near-source impacts, 
the results can be highly sensitive to the characterization of the 
mobile emissions. Important characteristics for both line/area and 
volume sources include the plume release height, source width, and 
initial dispersion characteristics, and should also take into 
account the impact of traffic-induced turbulence that can cause 
roadway sources to have larger initial dimensions than might 
normally be used for representing line sources.
    c. The EPA's quantitative PM hot-spot guidance \65\ and Haul 
Road Workgroup Final Report \67\ provide guidance on the appropriate 
characterization of mobile sources as a function of the roadway and 
vehicle characteristics. The EPA's quantitative PM hot-spot guidance 
includes important considerations and should be consulted when 
modeling roadway links. Area and line sources, which can be 
characterized as AREA, LINE, and RLINE source types in AERMOD, or 
volume sources, may be used for modeling mobile sources. However, 
experience in the field has shown that area sources (characterized 
as AREA, LINE, or RLINE source types) may be easier to characterize 
correctly compared to volume sources. If volume sources are used, it 
is particularly important to ensure that roadway emissions are 
appropriately spaced when using volume source so that the emissions 
field is uniform across the roadway. Additionally, receptor 
placement is particularly important for volume sources that have 
``exclusion zones'' where concentrations are not calculated for 
receptors located ``within'' the volume sources, i.e., less than 
2.15 times the initial lateral dispersion coefficient from the 
center of the volume.\65\ Therefore, placing receptors in these 
``exclusion zones'' will result in underestimates of roadway 
impacts.

8.0 Model Input Data

    a. Databases and related procedures for estimating input 
parameters are an integral part of the modeling process. The most 
appropriate input data available should always be selected for use 
in modeling analyses. Modeled concentrations can vary widely 
depending on the source data or meteorological data used. This 
section attempts to minimize the uncertainty associated with 
database selection and use by identifying requirements for input 
data used in modeling. More specific data requirements and the 
format required for the individual models are described in detail in 
the user's guide and/or associated documentation for each model.

8.1 Modeling Domain

8.1.1 Discussion

    a. The modeling domain is the geographic area for which the 
required air quality analyses for the NAAQS and PSD increments are 
conducted.

8.1.2 Requirements

    a. For a NAAQS or PSD increments assessment, the modeling domain 
or project's impact area shall include all locations where the 
emissions of a pollutant from the new or modifying source(s) may 
cause a significant ambient impact. This impact area is defined as 
an area with a radius extending from the new or modifying source to: 
(1) the most distant location where air quality modeling predicts a 
significant ambient impact will occur, or (2) the nominal 50 km 
distance considered applicable for Gaussian dispersion models, 
whichever is less. The required air quality analysis shall be 
carried out within this geographical area with characterization of 
source impacts, nearby source impacts, and background 
concentrations, as recommended later in this section.
    b. For SIP attainment demonstrations for ozone and 
PM2.5, or regional haze reasonable progress goal 
analyses, the modeling domain is determined by the nature of the 
problem being modeled and the spatial scale of the emissions that 
impact the nonattainment or Class I area(s). The modeling domain 
shall be designed so that all major upwind source areas that 
influence the downwind nonattainment area are included in addition 
to all monitor locations that are currently or recently violating 
the NAAQS or close to violating the NAAQS in the nonattainment area. 
Similarly, all Class I areas to be evaluated in a regional haze 
modeling application shall be included and sufficiently distant from 
the edge of the modeling domain. Guidance on the determination of 
the appropriate modeling domain for photochemical grid models in 
demonstrating attainment of these air quality goals is 
available.\64\ Users should consult the latest version of this 
guidance for the most current modeling guidance and the appropriate 
reviewing authority (paragraph 3.0(b)) for any application specific 
guidance that is beyond the scope of this section.

8.2 Source Data

8.2.1 Discussion

    a. Sources of pollutants can be classified as point, line, area, 
and volume sources. Point sources are defined in terms of size and 
may vary between regulatory programs. The line sources most 
frequently considered are roadways and streets along which there are 
well-defined movements of motor vehicles. They may also be lines of 
roof vents or stacks, such as in aluminum refineries. Area

[[Page 72850]]

and volume sources are often collections of a multitude of minor 
sources with individually small emissions that are impractical to 
consider as separate point or line sources. Large area sources are 
typically treated as a grid network of square areas, with pollutant 
emissions distributed uniformly within each grid square. Generally, 
input data requirements for air quality models necessitate the use 
of metric units. As necessary, any English units common to 
engineering applications should be appropriately converted to 
metric.
    b. For point sources, there are many source characteristics and 
operating conditions that may be needed to appropriately model the 
facility. For example, the plant layout (e.g., location of stacks 
and buildings), stack parameters (e.g., height and diameter), boiler 
size and type, potential operating conditions, and pollution control 
equipment parameters. Such details are required inputs to air 
quality models and are needed to determine maximum potential 
impacts.
    c. Modeling mobile emissions from streets and highways requires 
data on the road layout, including the width of each traveled lane, 
the number of lanes, and the width of the median strip. 
Additionally, traffic patterns should be taken into account (e.g., 
daily cycles of rush hour, differences in weekday and weekend 
traffic volumes, and changes in the distribution of heavy-duty 
trucks and light-duty passenger vehicles), as these patterns will 
affect the types and amounts of pollutant emissions allocated to 
each lane and the height of emissions.
    d. Emission factors can be determined through source-specific 
testing and measurements (e.g., stack test data) from existing 
sources or provided from a manufacturing association or vendor. 
Additionally, emissions factors for a variety of source types are 
compiled in an EPA publication commonly known as AP-42.\94\ AP-42 
also provides an indication of the quality and amount of data on 
which many of the factors are based. Other information concerning 
emissions is available in EPA publications relating to specific 
source categories. The appropriate reviewing authority (paragraph 
3.0(b)) should be consulted to determine appropriate source 
definitions and for guidance concerning the determination of 
emissions from and techniques for modeling the various source types.

8.2.2 Requirements

    a. For SIP attainment demonstrations for the purpose of 
projecting future year NAAQS attainment for ozone, PM2.5, 
and regional haze reasonable progress goal analyses, emissions which 
reflect actual emissions during the base modeling year time period 
should be input to models for base year modeling. Emissions 
projections to future years should account for key variables such as 
growth due to increased or decreased activity, expected emissions 
controls due to regulations, settlement agreements or consent 
decrees, fuel switches, and any other relevant information. Guidance 
on emissions estimation techniques (including future year 
projections) for SIP attainment demonstrations is 
available.64 95
    b. For the purpose of SIP revisions for stationary point 
sources, the regulatory modeling of inert pollutants shall use the 
emissions input data shown in Table 8-1 for short-term and long-term 
NAAQS. To demonstrate compliance and/or establish the appropriate 
SIP emissions limits, Table 8-1 generally provides for the use of 
``allowable'' emissions in the regulatory dispersion modeling of the 
stationary point source(s) of interest. In such modeling, these 
source(s) should be modeled sequentially with these loads for every 
hour of the year. As part of a cumulative impact analysis, Table 8-1 
allows for the model user to account for actual operations in 
developing the emissions inputs for dispersion modeling of nearby 
sources, while other sources are best represented by air quality 
monitoring data. Consultation with the appropriate reviewing 
authority (paragraph 3.0(b)) is advisable on the establishment of 
the appropriate emissions inputs for regulatory modeling 
applications with respect to SIP revisions for stationary point 
sources.
    c. For the purposes of demonstrating NAAQS compliance in a PSD 
assessment, the regulatory modeling of inert pollutants shall use 
the emissions input data shown in Table 8-2 for short and long-term 
NAAQS. The new or modifying stationary point source shall be modeled 
with ``allowable'' emissions in the regulatory dispersion modeling. 
As part of a cumulative impact analysis, Table 8-2 allows for the 
model user to account for actual operations in developing the 
emissions inputs for dispersion modeling of nearby sources, while 
other sources are best represented by air quality monitoring data. 
For purposes of situations involving emissions trading, refer to 
current EPA policy and guidance to establish input data. 
Consultation with the appropriate reviewing authority (paragraph 
3.0(b)) is advisable on the establishment of the appropriate 
emissions inputs for regulatory modeling applications with respect 
to PSD assessments for a proposed new or modifying source.
    d. For stationary source applications, changes in operating 
conditions that affect the physical emission parameters (e.g., 
release height, initial plume volume, and exit velocity) shall be 
considered to ensure that maximum potential impacts are 
appropriately determined in the assessment. For example, the load or 
operating condition for point sources that causes maximum ground-
level concentrations shall be established. As a minimum, the source 
should be modeled using the design capacity (100 percent load). If a 
source operates at greater than design capacity for periods that 
could result in violations of the NAAQS or PSD increments, this load 
should be modeled. Where the source operates at substantially less 
than design capacity, and the changes in the stack parameters 
associated with the operating conditions could lead to higher ground 
level concentrations, loads such as 50 percent and 75 percent of 
capacity should also be modeled. Malfunctions which may result in 
excess emissions are not considered to be a normal operating 
condition. They generally should not be considered in determining 
allowable emissions. However, if the excess emissions are the result 
of poor maintenance, careless operation, or other preventable 
conditions, it may be necessary to consider them in determining 
source impact. A range of operating conditions should be considered 
in screening analyses. The load causing the highest concentration, 
in addition to the design load, should be included in refined 
modeling.
    e. Emissions from mobile sources also have physical and temporal 
characteristics that should be appropriately accounted. For example, 
an appropriate emissions model shall be used to determine emissions 
profiles. Such emissions should include speciation specific for the 
vehicle types used on the roadway (e.g., light duty and heavy duty 
trucks), and subsequent parameterizations of the physical emissions 
characteristics (e.g., release height) should reflect those 
emissions sources. For long-term standards, annual average emissions 
may be appropriate, but for short-term standards, discrete temporal 
representation of emissions should be used (e.g., variations in 
weekday and weekend traffic or the diurnal rush-hour profile typical 
of many cities). Detailed information and data requirements for 
modeling mobile sources of pollution are provided in the user's 
manuals for each of the models applicable to mobile 
sources.65 67
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8.3 Background Concentrations

8.3.1 Discussion

    a. Background concentrations are essential in constructing the 
design concentration, or total air quality concentration, as part of 
a cumulative impact analysis for NAAQS and PSD increments (section 
9.2.3). To assist applicants and reviewing authorities with 
appropriately characterizing background concentrations, EPA has 
developed the Draft Guidance on Developing Background Concentrations 
for Use in Modeling Demonstrations.\96\ The guidance provides a 
recommended framework composed of steps that should be used in 
parallel with the recommendations made in this section. Generally, 
background air quality should not include the ambient impacts of the 
project source under consideration. Instead, it should include:
    i. Nearby sources: These are individual sources located in the 
vicinity of the source(s) under consideration for emissions limits 
that are not adequately represented by ambient monitoring data. The 
ambient contributions from these nearby sources are thereby 
accounted for by explicitly modeling their emissions (section 8.2).
    ii. Other sources: That portion of the background attributable 
to natural sources, other unidentified sources in the vicinity of 
the project, and regional transport contributions from more distant 
sources (domestic and international). The ambient contributions from 
these sources are typically accounted for through use of ambient 
monitoring data or, in some cases, regional-scale photochemical grid 
modeling results.
    b. The monitoring network used for developing background 
concentrations is expected to conform to the same quality assurance 
and other requirements as those networks established for PSD 
purposes.\97\ Accordingly, the air quality monitoring data should be 
of sufficient completeness and follow appropriate data validation

[[Page 72853]]

procedures. These data should be adequately representative of the 
area to inform calculation of the design concentration for 
comparison to the applicable NAAQS (section 9.2.2).
    c. For photochemical grid modeling conducted in SIP attainment 
demonstrations for ozone, PM2.5 and regional haze, the 
emissions from nearby and other sources are included as model inputs 
and fully accounted for in the modeling application and predicted 
concentrations. The concept of adding individual components to 
develop a design concentration, therefore, do not apply in these SIP 
applications. However, such modeling results may then be appropriate 
for consideration in characterizing background concentrations for 
other regulatory applications. Also, as noted in section 5, this 
modeling approach does provide for an appropriate atmospheric 
environment to assess single-source impacts for ozone and secondary 
PM2.5.
    d. For NAAQS assessments and SIP attainment demonstrations for 
inert pollutants, the development of the appropriate background 
concentration for a cumulative impact analysis involves proper 
accounting of each contribution to the design concentration and will 
depend upon whether the project area's situation consists of either 
an isolated single source(s) or a multitude of sources. For PSD 
increment assessments, all impacts after the appropriate baseline 
dates (i.e., trigger date, major source baseline date, and minor 
source baseline date) from all increment-consuming and increment-
expanding sources should be considered in the design concentration 
(section 9.2.2).

8.3.2 Recommendations for Isolated Single Sources

    a. In areas with an isolated source(s), determining the 
appropriate background concentration should focus on 
characterization of contributions from all other sources through 
adequately representative ambient monitoring data. The application 
of EPA's recommended framework for determining an appropriate 
background concentration should be consistent with appropriate EPA 
modeling guidance 63 96 and justified in the modeling 
protocol that is vetted with the appropriate reviewing authority 
(paragraph 3.0(b)).
    b. The EPA recommends use of the most recent quality assured air 
quality monitoring data collected in the vicinity of the source to 
determine the background concentration for the averaging times of 
concern. In most cases, the EPA recommends using data from the 
monitor closest to and upwind of the project area. If several 
monitors are available, preference should be given to the monitor 
with characteristics that are most similar to the project area. If 
there are no monitors located in the vicinity of the new or 
modifying source, a ``regional site'' may be used to determine 
background concentrations. A regional site is one that is located 
away from the area of interest but is impacted by similar or 
adequately representative sources.
    c. Many of the challenges related to cumulative impact analyses 
arise in the context of defining the appropriate metric to 
characterize background concentrations from ambient monitoring data 
and determining the appropriate method for combining this monitor-
based background contribution to the modeled impact of the project 
and other nearby sources. For many cases, the best starting point 
would be use of the current design value for the applicable NAAQS as 
a uniform monitored background contribution across the project area. 
However, there are cases in which the current design value may not 
be appropriate. Such cases include but are not limited to:
    i. For situations involving a modifying source where the 
existing facility is determined to impact the ambient monitor, the 
background concentration at each monitor can be determined by 
excluding values when the source in question is impacting the 
monitor. In such cases, monitoring sites inside a 90[deg] sector 
downwind of the source may be used to determine the area of impact.
    ii. There may be other circumstances which would necessitate 
modifications to the ambient data record. Such cases could include 
removal of data from specific days or hours when a monitor is being 
impacted by activities that are not typical or not expected to occur 
again in the future (e.g., construction, roadway repairs, forest 
fires, or unusual agricultural activities). There may also be cases 
where it may be appropriate to scale (multiplying the monitored 
concentrations with a scaling factor) or adjust (adding or 
subtracting a constant value the monitored concentrations) data from 
specific days or hours. Such adjustments would make the monitored 
background concentrations more temporally and/or spatially 
representative of the area around the new or modifying source for 
the purposes of the regulatory assessment.
    iii. For short-term standards, the diurnal or seasonal patterns 
of the air quality monitoring data may differ significantly from the 
patterns associated with the modeled concentrations. When this 
occurs, it may be appropriate to pair the air quality monitoring 
data in a temporal manner that reflects these patterns (e.g., 
pairing by season and/or hour of day).\98\
    iv. For situations where monitored air quality concentrations 
vary across the modeling domain, it may be appropriate to consider 
air quality monitoring data from multiple monitors within the 
project area.
    d. Considering the spatial and temporal variability throughout a 
typical modeling domain on an hourly basis and the complexities and 
limitations of hourly observations from the ambient monitoring 
network, the EPA does not recommend hourly or daily pairing of 
monitored background and modeled concentrations except in rare cases 
of relatively isolated sources where the available monitor can be 
shown to be representative of the ambient concentration levels in 
the areas of maximum impact from the proposed new source. The 
implicit assumption underlying hourly pairing is that the background 
monitored levels for each hour are spatially uniform and that the 
monitored values are fully representative of background levels at 
each receptor for each hour. Such an assumption clearly ignores the 
many factors that contribute to the temporal and spatial variability 
of ambient concentrations across a typical modeling domain on an 
hourly basis. In most cases, the seasonal (or quarterly) pairing of 
monitored and modeled concentrations should sufficiently address 
situations to which the impacts from modeled emissions are not 
temporally correlated with background monitored levels.
    e. In those cases where adequately representative monitoring 
data to characterize background concentrations are not available, it 
may be appropriate to use results from a regional-scale 
photochemical grid model, or other representative model application, 
as background concentrations consistent with the considerations 
discussed above and in consultation with the appropriate reviewing 
authority (paragraph 3.0(b)).

8.3.3 Recommendations for Multi-Source Areas

    a. In multi-source areas, determining the appropriate background 
concentration involves: (1) characterization of contributions from 
other sources through adequately representative ambient monitoring 
data, and (2) identification and characterization of contributions 
from nearby sources through explicit modeling. A key point here is 
the interconnectedness of each component in that the question of 
which nearby sources to include in the cumulative modeling is 
inextricably linked to the question of what the ambient monitoring 
data represents within the project area.
    b. Nearby sources: All sources in the vicinity of the source(s) 
under consideration for emissions limits that are not adequately 
represented by ambient monitoring data should be explicitly modeled. 
EPA's recommended framework for determining an appropriate 
background concentration \96\ should be applied to identify such 
sources and accurately account for their ambient impacts through 
explicit modeling.
    i. The determination of nearby sources relies on the selection 
of adequately representative ambient monitoring data (section 
8.3.2). The EPA recommends determining the representativeness of the 
monitoring data through a visual assessment of the modeling domain 
considering any relevant nearby sources and their respective air 
quality data. The visual assessment should consider any relevant air 
quality data such as the proximity of nearby sources to the project 
source and the ambient monitor, the nearby source's level of 
emissions with respect to the ambient data, and the dispersion 
environment (i.e., meteorological patterns, terrain, etc.) of the 
modeling domain.
    ii. Nearby sources not adequately represented by the ambient 
monitor through visual assessment should undergo further qualitative 
and quantitative analysis before being explicitly modeled. The EPA 
recommends evaluating any modeling, monitoring, or emissions data 
that may be available for the identified nearby sources with respect 
to possible exceedances of the appropriate SIL or violations to the 
NAAQS.
    iii. The number of nearby sources to be explicitly modeled in 
the air quality analysis is expected to be few except in unusual

[[Page 72854]]

situations. The determination of nearby sources through the 
application of EPA's recommended framework calls for the exercise of 
professional judgment by the appropriate reviewing authority 
(paragraph 3.0(b)) and should be consistent with appropriate EPA 
modeling guidance.63 96 This guidance is not intended to 
alter the exercise of that judgment or to comprehensively prescribe 
which sources should be included as nearby sources.
    c. For cumulative impact analyses of short-term and annual 
ambient standards, the nearby sources as well as the project 
source(s) must be evaluated using an appropriate addendum A model or 
approved alternative model with the emission input data shown in 
Table 8-1 or 8-2.
    i. When modeling a nearby source that does not have a permit and 
the emissions limits contained in the SIP for a particular source 
category is greater than the emissions possible given the source's 
maximum physical capacity to emit, the ``maximum allowable emissions 
limit'' for such a nearby source may be calculated as the emissions 
rate representative of the nearby source's maximum physical capacity 
to emit, considering its design specifications and allowable fuels 
and process materials. However, the burden is on the permit 
applicant to sufficiently document what the maximum physical 
capacity to emit is for such a nearby source.
    ii. It is appropriate to model nearby sources only during those 
times when they, by their nature, operate at the same time as the 
primary source(s) or could have impact on the averaging period of 
concern. Accordingly, it is not necessary to model impacts of a 
nearby source that does not, by its nature, operate at the same time 
as the primary source or could have impact on the averaging period 
of concern, regardless of an identified significant concentration 
gradient from the nearby source. The burden is on the permit 
applicant to adequately justify the exclusion of nearby sources to 
the satisfaction of the appropriate reviewing authority (paragraph 
3.0(b)). The following examples illustrate two cases in which a 
nearby source may be shown not to operate at the same time as the 
primary source(s) being modeled: (1) Seasonal sources (only used 
during certain seasons of the year). Such sources would not be 
modeled as nearby sources during times in which they do not operate; 
and (2) Emergency backup generators, to the extent that they do not 
operate simultaneously with the sources that they back up. Such 
emergency equipment would not be modeled as nearby sources.
    d. Other sources. That portion of the background attributable to 
all other sources (e.g., natural, minor, distant major, and 
unidentified sources) should be accounted for through use of ambient 
monitoring data and determined by the procedures found in section 
8.3.2 in keeping with eliminating or reducing the source-oriented 
impacts from nearby sources to avoid potential double-counting of 
modeled and monitored contributions.

8.4 Meteorological Input Data

8.4.1 Discussion

    a. This subsection covers meteorological input data for use in 
dispersion modeling for regulatory applications and is separate from 
recommendations made for photochemical grid modeling. 
Recommendations for meteorological data for photochemical grid 
modeling applications are outlined in the latest version of EPA's 
Modeling Guidance for Demonstrating Attainment of Air Quality Goals 
for Ozone, PM2.5, and Regional Haze.\64\ In cases where Lagrangian 
models are applied for regulatory purposes, appropriate 
meteorological inputs should be determined in consultation with the 
appropriate reviewing authority (paragraph 3.0(b)).
    b. The meteorological data used as input to a dispersion model 
should be selected on the basis of spatial and climatological 
(temporal) representativeness as well as the ability of the 
individual parameters selected to characterize the transport and 
dispersion conditions in the area of concern. The representativeness 
of the measured data is dependent on numerous factors including, but 
not limited to: (1) the proximity of the meteorological monitoring 
site to the area under consideration; (2) the complexity of the 
terrain; (3) the exposure of the meteorological monitoring site; and 
(4) the period of time during which data are collected. The spatial 
representativeness of the data can be adversely affected by large 
distances between the source and receptors of interest and the 
complex topographic characteristics of the area. Temporal 
representativeness is a function of the year-to-year variations in 
weather conditions. Where appropriate, data representativeness 
should be viewed in terms of the appropriateness of the data for 
constructing realistic boundary layer profiles and, where 
applicable, three-dimensional meteorological fields, as described in 
paragraphs (c) and (d) of this subsection.
    c. The meteorological data should be adequately representative 
and may be site-specific data (land-based or buoy data for overwater 
applications), data from a nearby National Weather Service (NWS) or 
comparable station, or prognostic meteorological data. The 
implementation of NWS Automated Surface Observing Stations (ASOS) in 
the early 1990's should not preclude the use of NWS ASOS data if 
such a station is determined to be representative of the modeled 
area.\99\
    D. Model input data are normally obtained either from the NWS or 
as part of a site-specific measurement program. State climatology 
offices, local universities, FAA, military stations, industry, and 
pollution control agencies may also be sources of such data. In 
specific cases, prognostic meteorological data may be appropriate 
for use and obtained from similar sources. Some recommendations and 
requirements for the use of each type of data are included in this 
subsection.

8.4.2 Recommendations and Requirements

    a. AERMET \100\ shall be used to preprocess all meteorological 
data, be it observed or prognostic, for use with AERMOD in 
regulatory applications. The AERMINUTE \101\ processor, in most 
cases, should be used to process 1-minute ASOS wind data for input 
to AERMET when processing NWS ASOS sites in AERMET. When processing 
prognostic meteorological data for AERMOD, the Mesoscale Model 
Interface Program (MMIF) \109\ should be used to process data for 
input to AERMET, both for land-based applications and overwater 
applications. Other methods of processing prognostic meteorological 
data for input to AERMET should be approved by the appropriate 
reviewing authority. Additionally, the following meteorological 
preprocessors are recommended by the EPA: PCRAMMET,\102\ MPRM,\103\ 
and METPRO.\104\ PCRAMMET is the recommended meteorological data 
preprocessor for use in applications of OCD employing hourly NWS 
data. MPRM is the recommended meteorological data preprocessor for 
applications of OCD employing site-specific meteorological data. 
METPRO is the recommended meteorological data preprocessor for use 
with CTDMPLUS.\105\
    b. Regulatory application of AERMOD necessitates careful 
consideration of the meteorological data for input to AERMET. Data 
representativeness, in the case of AERMOD, means utilizing data of 
an appropriate type for constructing realistic boundary layer 
profiles. Of particular importance is the requirement that all 
meteorological data used as input to AERMOD should be adequately 
representative of the transport and dispersion within the analysis 
domain. Where surface conditions vary significantly over the 
analysis domain, the emphasis in assessing representativeness should 
be given to adequate characterization of transport and dispersion 
between the source(s) of concern and areas where maximum design 
concentrations are anticipated to occur. The EPA recommends that the 
surface characteristics input to AERMET should be representative of 
the land cover in the vicinity of the meteorological data, i.e., the 
location of the meteorological tower for measured data or the 
representative grid cell for prognostic data. Therefore, the model 
user should apply the latest version AERSURFACE,106 107 
where applicable, for determining surface characteristics when 
processing measured land-based meteorological data through AERMET. 
In areas where it is not possible to use AERSURFACE output, surface 
characteristics can be determined using techniques that apply the 
same analysis as AERSURFACE. In the case of measured meteorological 
data for overwater applications, AERMET calculates the surface 
characteristics and AERSURFACE outputs are not needed. In the case 
of prognostic meteorological data, the surface characteristics 
associated with the prognostic meteorological model output for the 
representative grid cell should be used.108 109 
Furthermore, since the spatial scope of each variable could be 
different, representativeness should be judged for each variable 
separately. For example, for a variable such as wind direction, the 
data should ideally be collected near plume height to be adequately 
representative, especially for sources located in complex

[[Page 72855]]

terrain. Whereas, for a variable such as temperature, data from a 
station several kilometers away from the source may be considered to 
be adequately representative. More information about meteorological 
data, representativeness, and surface characteristics can be found 
in the AERMOD Implementation Guide.\81\
    c. Regulatory application of CTDMPLUS requires the input of 
multi-level measurements of wind speed, direction, temperature, and 
turbulence from an appropriately sited meteorological tower. The 
measurements should be obtained up to the representative plume 
height(s) of interest. Plume heights of interest can be determined 
by use of screening procedures such as CTSCREEN.
    d. Regulatory application of OCD requires meteorological data 
over land and over water. The over land or surface data, processed 
through PCRAMMET \102\ or MPRM,\103\ that provides hourly stability 
class, wind direction and speed, ambient temperature, and mixing 
height, are required. Data over water requires hourly mixing height, 
relative humidity, air temperature, and water surface temperature. 
Missing winds are substituted with the surface winds. Vertical wind 
direction shear, vertical temperature gradient, and turbulence 
intensities are optional.
    e. The model user should acquire enough meteorological data to 
ensure that worst-case meteorological conditions are adequately 
represented in the model results. The use of 5 years of adequately 
representative NWS or comparable meteorological data, at least 1 
year of site-specific (either land-based or overwater based), or at 
least 3 years of prognostic meteorological data, are required. If 1 
year or more, up to 5 years, of site-specific data are available, 
these data are preferred for use in air quality analyses. Depending 
on completeness of the data record, consecutive years of NWS, site-
specific, or prognostic data are preferred. Such data must be 
subjected to quality assurance procedures as described in section 
8.4.4.2.
    f. Objective analysis in meteorological modeling is to improve 
meteorological analyses (the ``first guess field'') used as initial 
conditions for prognostic meteorological models by incorporating 
information from meteorological observations. Direct and indirect 
(using remote sensing techniques) observations of temperature, 
humidity, and wind from surface and radiosonde reports are commonly 
employed to improve these analysis fields. For long-range transport 
applications, it is recommended that objective analysis procedures, 
using direct and indirect meteorological observations, be employed 
in preparing input fields to produce prognostic meteorological 
datasets. The length of record of observations should conform to 
recommendations outlined in paragraph 8.4.2(e) for prognostic 
meteorological model datasets.

8.4.3 National Weather Service Data

8.4.3.1 Discussion

    a. The NWS meteorological data are routinely available and 
familiar to most model users. Although the NWS does not provide 
direct measurements of all the needed dispersion model input 
variables, methods have been developed and successfully used to 
translate the basic NWS data to the needed model input. Site-
specific measurements of model input parameters have been made for 
many modeling studies, and those methods and techniques are becoming 
more widely applied, especially in situations such as complex 
terrain applications, where available NWS data are not adequately 
representative. However, there are many modeling applications where 
NWS data are adequately representative and the applications still 
rely heavily on the NWS data.
    b. Many models use the standard hourly weather observations 
available from the National Centers for Environmental Information 
(NCEI).\b\ These observations are then preprocessed before they can 
be used in the models. Prior to the advent of ASOS in the early 
1990's, the standard ``hourly'' weather observation was a human-
based observation reflecting a single 2-minute average generally 
taken about 10 minutes before the hour. However, beginning in 
January 2000 for first-order stations and in March 2005 for all 
stations, the NCEI has archived the 1-minute ASOS wind data (i.e., 
the rolling 2-minute average winds) for the NWS ASOS sites. The 
AERMINUTE processor \101\ was developed to reduce the number of calm 
and missing hours in AERMET processing by substituting standard 
hourly observations with full hourly average winds calculated from 
1-minute ASOS wind data.
---------------------------------------------------------------------------

    \b\ Formerly the National Climatic Data Center (NCDC).
---------------------------------------------------------------------------

8.4.3.2 Recommendations

    a. The preferred models listed in addendum A all accept, as 
input, the NWS meteorological data preprocessed into model 
compatible form. If NWS data are judged to be adequately 
representative for a specific modeling application, they may be 
used. The NCEI makes available surface and upper air meteorological 
data online and in CD-ROM format. Upper air data are also available 
at the Earth System Research Laboratory Global Systems Divisions 
website and from NCEI. For the latest websites of available surface 
and upper air data see reference 100.
    b. Although most NWS wind measurements are made at a standard 
height of 10 m, the actual anemometer height should be used as input 
to the preferred meteorological processor and model.
    c. Standard hourly NWS wind directions are reported to the 
nearest 10 degrees. Due to the coarse resolution of these data, a 
specific set of randomly generated numbers has been developed by the 
EPA and should be used when processing standard hourly NWS data for 
use in the preferred EPA models to ensure a lack of bias in wind 
direction assignments within the models.
    d. Beginning with year 2000, NCEI began archiving 2-minute 
winds, reported every minute to the nearest degree for NWS ASOS 
sites. The AERMINUTE processor was developed to read those winds and 
calculate hourly average winds for input to AERMET. When such data 
are available for the NWS ASOS site being processed, the AERMINUTE 
processor should be used, in most cases, to calculate hourly average 
wind speed and direction when processing NWS ASOS data for input to 
AERMOD.\99\
    e. Data from universities, FAA, military stations, industry and 
pollution control agencies may be used if such data are equivalent 
in accuracy and detail (e.g., siting criteria, frequency of 
observations, data completeness, etc.) to the NWS data, they are 
judged to be adequately representative for the particular 
application, and have undergone quality assurance checks.
    f. After valid data retrieval requirements have been met,\110\ 
large number of hours in the record having missing data should be 
treated according to an established data substitution protocol 
provided that adequately representative alternative data are 
available. Data substitution guidance is provided in section 5.3 of 
reference 110.\110\ If no representative alternative data are 
available for substitution, the absent data should be coded as 
missing using missing data codes appropriate to the applicable 
meteorological pre-processor. Appropriate model options for treating 
missing data, if available in the model, should be employed.

8.4.4 Site-Specific Data

8.4.4.1 Discussion

    a. Spatial or geographical representativeness is best achieved 
by collection of all of the needed model input data in close 
proximity to the actual site of the source(s). Site-specific 
measured data are, therefore, preferred as model input, provided 
that appropriate instrumentation and quality assurance procedures 
are followed, and that the data collected are adequately 
representative (free from inappropriate local or microscale 
influences) and compatible with the input requirements of the model 
to be used. It should be noted that, while site-specific 
measurements are frequently made ``on-property'' (i.e., on the 
source's premises), acquisition of adequately representative site-
specific data does not preclude collection of data from a location 
off property. Conversely, collection of meteorological data on a 
source's property does not of itself guarantee adequate 
representativeness. For help in determining representativeness of 
site-specific measurements, technical guidance \110\ is available. 
Site-specific data should always be reviewed for representativeness 
and adequacy by an experienced meteorologist, atmospheric scientist, 
or other qualified scientist in consultation with the appropriate 
reviewing authority (paragraph 3.0(b)).

8.4.4.2 Recommendations

    a. The EPA guidance \110\ provides recommendations on the 
collection and use of site-specific meteorological data. 
Recommendations on characteristics, siting, and exposure of 
meteorological instruments and on data recording, processing, 
completeness requirements, reporting, and archiving are also 
included. This publication should be used as a supplement to other 
limited guidance on these subjects.5 97 111 112 Detailed 
information on quality assurance is

[[Page 72856]]

also available.\113\ As a minimum, site-specific measurements of 
ambient air temperature, transport wind speed and direction, and the 
variables necessary to estimate atmospheric dispersion should be 
available in meteorological datasets to be used in modeling. Care 
should be taken to ensure that meteorological instruments are 
located to provide an adequately representative characterization of 
pollutant transport between sources and receptors of interest. The 
appropriate reviewing authority (paragraph 3.0(b)) is available to 
help determine the appropriateness of the measurement locations.
    i. Solar radiation measurements. Total solar radiation or net 
radiation should be measured with a reliable pyranometer or net 
radiometer sited and operated in accordance with established site-
specific meteorological guidance.110 113
    ii. Temperature measurements. Temperature measurements should be 
made at standard shelter height (2m) in accordance with established 
site-specific meteorological guidance.\110\
    iii. Temperature difference measurements. Temperature difference 
(DT) measurements should be obtained using matched thermometers or a 
reliable thermocouple system to achieve adequate accuracy. Siting, 
probe placement, and operation of DT systems should be based on 
guidance found in Chapter 3 of reference 110 and such guidance 
should be followed when obtaining vertical temperature gradient 
data. AERMET may employ the Bulk Richardson scheme, which requires 
measurements of temperature difference, in lieu of cloud cover or 
insolation data. To ensure correct application and acceptance, 
AERMOD users should consult with the appropriate reviewing authority 
(paragraph 3.0(b)) before using the Bulk Richardson scheme for their 
analysis.
    iv. Wind measurements. For simulation of plume rise and 
dispersion of a plume emitted from a stack, characterization of the 
wind profile up through the layer in which the plume disperses is 
desirable. This is especially important in complex terrain and/or 
complex wind situations where wind measurements at heights up to 
hundreds of meters above stack base may be required in some 
circumstances. For tall stacks when site-specific data are needed, 
these winds have been obtained traditionally using meteorological 
sensors mounted on tall towers. A feasible alternative to tall 
towers is the use of meteorological remote sensing instruments 
(e.g., acoustic sounders or radar wind profilers) to provide winds 
aloft, coupled with 10-meter towers to provide the near-surface 
winds. Note that when site-specific wind measurements are used, 
AERMOD, at a minimum, requires wind observations at a height above 
ground between seven times the local surface roughness height and 
100 m. (For additional requirements for AERMOD and CTDMPLUS, see 
addendum A.) Specifications for wind measuring instruments and 
systems are contained in reference 110.
    b. All processed site-specific data should be in the form of 
hourly averages for input to the dispersion model.
    i. Turbulence data. 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 or AERMOD). When turbulence data are used 
to directly characterize the vertical and lateral dispersion, the 
averaging time for the turbulence measurements should be 1-hour. For 
technical guidance on processing of turbulence parameters for use in 
dispersion modeling, refer to the user's guide to the meteorological 
processor for each model (see section 8.4.2(a)).
    ii. 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 10 m. The insolation rate is typically assessed 
using observations of cloud cover and ceiling height based on 
criteria outlined by Turner.\77\ It is recommended that the P-G 
stability category be estimated using the Turner method with site-
specific wind speed measured at or near 10 m 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 
110. In the absence of requisite data to implement the Turner 
method, the solar radiation/delta-T (SRDT) method or wind 
fluctuation statistics (i.e., the [sigma]E and 
[sigma]A methods) may be used.
    iii. The SRDT method, described in section 6.4.4.2 of reference 
110, is modified slightly from that published from earlier work 
\114\ and has been evaluated with three site-specific 
databases.\115\ The two methods of stability classification that use 
wind fluctuation statistics, the [sigma]E and 
[sigma]A methods, are also described in detail in section 
6.4.4 of reference 110 (note applicable tables in section 6). For 
additional information on the wind fluctuation methods, several 
references are available.116 117 118 119
    c. Missing data substitution. After valid data retrieval 
requirements have been met,\110\ hours in the record having missing 
data should be treated according to an established data substitution 
protocol provided that adequately representative alternative data 
are available. Such protocols are usually part of the approved 
monitoring program plan. Data substitution guidance is provided in 
section 5.3 of reference 110. If no representative alternative data 
are available for substitution, the absent data should be coded as 
missing, using missing data codes appropriate to the applicable 
meteorological pre-processor. Appropriate model options for treating 
missing data, if available in the model, should be employed.

8.4.5 Prognostic Meteorological Data

8.4.5.1 Discussion

    a. For some modeling applications, there may not be a 
representative NWS or comparable meteorological station available 
(e.g., complex terrain), and it may be cost prohibitive or 
infeasible to collect adequately representative site-specific data. 
For these cases, it may be appropriate to use prognostic 
meteorological data, if deemed adequately representative, in a 
regulatory modeling application. However, if prognostic 
meteorological data are not representative of transport and 
dispersion conditions in the area of concern, the collection of 
site-specific data is necessary.
    b. The EPA has developed a processor, the MMIF,\108\ to process 
MM5 (Mesoscale Model 5) or WRF (Weather Research and Forecasting) 
model data for input to various models including AERMOD. MMIF can 
process data for input to AERMET or AERMOD for a single grid cell or 
multiple grid cells. MMIF output has been found to compare favorably 
against observed data (site-specific or NWS).\120\ Specific guidance 
on processing MMIF for AERMOD can be found in reference 109109. When 
using MMIF to process prognostic data for regulatory applications, 
the data should be processed to generate AERMET inputs and the data 
subsequently processed through AERMET for input to AERMOD. If an 
alternative method of processing data for input to AERMET is used, 
it must be approved by the appropriate reviewing authority 
(paragraph 3.0(b)).

8.4.5.2 Recommendations

    a. Prognostic model evaluation. Appropriate effort by the 
applicant should be devoted to the process of evaluating the 
prognostic meteorological data. The modeling data should be compared 
to NWS observational data or other comparable data in an effort to 
show that the data are adequately replicating the observed 
meteorological conditions of the time periods modeled. An 
operational evaluation of the modeling data for all model years 
(i.e., statistical, graphical) should be completed.\64\ The use of 
output from prognostic mesoscale meteorological models is contingent 
upon the concurrence with the appropriate reviewing authority 
(paragraph 3.0(b)) that the data are of acceptable quality, which 
can be demonstrated through statistical comparisons with 
meteorological observations aloft and at the surface at several 
appropriate locations.\64\
    b. Representativeness. When processing MMIF data for use with 
AERMOD, the grid cell used for the dispersion modeling should be 
adequately spatially representative of the analysis domain. In most 
cases, this may be the grid cell containing the emission source of 
interest. Since the dispersion modeling may involve multiple sources 
and the domain may cover several grid cells, depending on grid 
resolution of the prognostic model, professional judgment may be 
needed to select the appropriate grid cell to use. In such cases, 
the selected grid cells should be adequately representative of the 
entire domain.
    c. Grid resolution. The grid resolution of the prognostic 
meteorological data should be considered and evaluated 
appropriately, particularly for projects involving complex terrain. 
The operational evaluation of the modeling data should consider 
whether a finer grid resolution is needed to ensure that the data 
are representative. The use of output from prognostic mesoscale 
meteorological

[[Page 72857]]

models is contingent upon the concurrence with the appropriate 
reviewing authority (paragraph 3.0(b)) that the data are of 
acceptable quality.

8.4.6 Marine Boundary Layer Environments

8.4.6.1 Discussion

    a. Calculations of boundary layer parameters for the marine 
boundary layer present special challenges as the marine boundary 
layer can be very different from the boundary layer over land. For 
example, convective conditions can occur in the overnight hours in 
the marine boundary layer while typically over land, stable 
conditions occur at night. Also, surface roughness in the marine 
environment is a function of wave height and wind speed and less 
static with time than surface roughness over land.
    b. While the Offshore and Coastal Dispersion Model (OCD) is the 
preferred model for overwater applications, there are applications 
where the use of AERMOD is applicable. These include applications 
that utilize features of AERMOD not included in OCD (e.g., 
NO2 chemistry). Such use of AERMOD would require 
consultation with the Regional Office and appropriate reviewing 
authority to ensure that platform downwash and shoreline fumigation 
are adequately considered in the modeling demonstration.
    c. For the reasons stated above, a standalone pre-processor to 
AERMOD, called AERCOARE \47\ was developed to use the Coupled Ocean 
Atmosphere Response Experiment (COARE) bulk-flux algorithms \48\ to 
bypass AERMET and calculate the boundary layer parameters for input 
to AERMOD for the marine boundary layer. AERCOARE can process either 
measurements from water-based sites such as buoys or prognostic 
data. To better facilitate the use of the COARE algorithms for 
AERMOD, EPA has included the COARE algorithms into AERMET thus 
eliminating the need for a standalone pre-processor and ensuring the 
algorithms are updated as part of routine AERMET updates.

8.4.6.2 Recommendations

    a. Measured data. For applications in the marine environment 
that require the use of AERMOD, measured surface data, such as from 
a buoy or other offshore platform, should be processed in AERMET 
with the COARE processing option following recommendations in the 
AERMET User's Guide \100\ and AERMOD Implementation Guide.\81\ For 
applications in the marine environment that require the use of OCD, 
users should use the recommended meteorological pre-processor MPRM.
    b. Prognostic data. For applications in the marine environment 
that require the use of AERMOD and prognostic data, the prognostic 
data should be processed via MMIF for input to AERMET following 
recommendations in paragraph 8.4.5.1(b) and the guidance found in 
reference 109.

8.4.7 Treatment of Near-Calms and Calms

8.4.7.1 Discussion

    a. Treatment of calm or light and variable wind poses a special 
problem in modeling applications since steady-state Gaussian plume 
models assume that concentration is inversely proportional to wind 
speed, depending on model formulations. Procedures have been 
developed to prevent the occurrence of overly conservative 
concentration estimates during periods of calms. These procedures 
acknowledge that a steady-state Gaussian plume model does not apply 
during calm conditions, and that our knowledge of wind patterns and 
plume behavior during these conditions does not, at present, permit 
the development of a better technique. Therefore, the procedures 
disregard hours that are identified as calm. The hour is treated as 
missing and a convention for handling missing hours is recommended. 
With the advent of the AERMINUTE processor, when processing NWS ASOS 
data, the inclusion of hourly averaged winds from AERMINUTE will, in 
some instances, dramatically reduce the number of calm and missing 
hours, especially when the ASOS wind are derived from a sonic 
anemometer. To alleviate concerns about these issues, especially 
those introduced with AERMINUTE, the EPA implemented a wind speed 
threshold in AERMET for use with ASOS derived 
winds.99 100 Winds below the threshold will be treated as 
calms.
    b. AERMOD, while fundamentally a steady-state Gaussian plume 
model, contains algorithms for dealing with low wind speed (near 
calm) conditions. As a result, AERMOD can produce model estimates 
for conditions when the wind speed may be less than 1 m/s, but still 
greater than the instrument threshold. Required input to AERMET for 
site-specific data, the meteorological processor for AERMOD, 
includes a threshold wind speed and a reference wind speed. The 
threshold wind speed is the greater of the threshold of the 
instrument used to collect the wind speed data or wind direction 
sensor.\110\ 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 length and 100 m. If the only valid observation of the 
reference wind speed between these heights is less than the 
threshold, the hour is considered calm, and no concentration is 
calculated. None of the observed wind speeds in a measured wind 
profile that are less than the threshold speed are used in 
construction of the modeled wind speed profile in AERMOD.

8.4.7.2 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. Model predicted concentrations for 3-, 
8-, and 24-hour averages should be calculated by dividing the sum of 
the hourly concentrations for the period by the number of valid or 
non-missing hours. If the total number of valid hours is less than 
18 for 24-hour averages, less than 6 for 8-hour averages, or less 
than 3 for 3-hour averages, the total concentration should be 
divided by 18 for the 24-hour average, 6 for the 8-hour average, and 
3 for the 3-hour average. For annual averages, the sum of all valid 
hourly concentrations is divided by the number of non-calm hours 
during the year. AERMOD has been coded to implement these 
instructions. For hours that are calm or missing, the AERMOD hourly 
concentrations will be zero. For other models listed in addendum A, 
a post-processor computer program, CALMPRO \121\ has been prepared, 
is available on the EPA's SCRAM website (section 2.3), and should be 
used.
    b. Stagnant conditions that include extended periods of calms 
often produce high concentrations over wide areas for relatively 
long averaging periods. The standard steady-state Gaussian plume 
models are often not applicable to such situations. When stagnation 
conditions are of concern, other modeling techniques should be 
considered on a case-by-case basis (see also section 7.2.1.2).
    c. When used in steady-state Gaussian plume models other than 
AERMOD, 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 such adjustment should be made to the site-
specific wind data, as AERMOD has algorithms to account for light or 
variable winds as discussed in section 8.4.6.1(a). For NWS ASOS 
data, see the AERMET User's Guide \100\ for guidance on wind speed 
thresholds. For prognostic data, see the latest guidance \109\ for 
thresholds. Observations with wind speeds less than the threshold 
are considered calm, and no concentration is calculated. In all 
cases involving steady-state Gaussian plume models, calm hours 
should be treated as missing, and concentrations should be 
calculated as in paragraph (a) of this subsection.

9.0 Regulatory Application of Models

9.1 Discussion

    a. Standardized procedures are valuable in the review of air 
quality modeling and data analyses conducted to support SIP 
submittals and revisions, NSR, or other EPA requirements to ensure 
consistency in their regulatory application. This section recommends 
procedures specific to NSR that facilitate some degree of 
standardization while at the same time allowing the flexibility 
needed to assure the technically best analysis for each regulatory 
application. For SIP attainment demonstrations, refer to the 
appropriate EPA guidance 53 64 for the recommended 
procedures.
    b. Air quality model estimates, especially with the support of 
measured air quality data, are the preferred basis for air quality 
demonstrations. A number of actions have been taken to ensure that 
the best air quality model is used correctly for each regulatory 
application and that it is not arbitrarily imposed.
     First, the Guideline clearly recommends that the most 
appropriate model be used in each case. Preferred models are 
identified, based on a number of factors, for many uses.

[[Page 72858]]

     Second, the preferred models have been subjected to a 
systematic performance evaluation and a scientific peer review. 
Statistical performance measures, including measures of difference 
(or residuals) such as bias, variance of difference and gross 
variability of the difference, and measures of correlation such as 
time, space, and time and space combined, as described in section 
2.1.1, were generally followed.
     Third, more specific information has been provided for 
considering the incorporation of new models into the Guideline 
(section 3.1), and the Guideline contains procedures for justifying 
the case-by-case use of alternative models and obtaining EPA 
approval (section 3.2).
    c. Air quality modeling is the preferred basis for air quality 
demonstrations. Nevertheless, there are rare circumstances where the 
performance of the preferred air quality model may be shown to be 
less than reasonably acceptable or where no preferred air quality 
model, screening model or technique, or alternative model are 
suitable for the situation. In these unique instances, there is the 
possibility of assuring compliance and establishing emissions limits 
for an existing source solely on the basis of observed air quality 
data in lieu of an air quality modeling analysis. Comprehensive air 
quality monitoring in the vicinity of the existing source with 
proposed modifications will be necessary in these cases. The same 
attention should be given to the detailed analyses of the air 
quality data as would be applied to a model performance evaluation.
    d. The current levels and forms of the NAAQS for the six 
criteria pollutants can be found on the EPA's NAAQS website at 
https://www.epa.gov/criteria-air-pollutants. As required by the CAA, 
the NAAQS are subjected to extensive review every 5 years and the 
standards, including the level and the form, may be revised as part 
of that review. The criteria pollutants have either long-term 
(annual or quarterly) and/or short-term (24-hour or less) forms that 
are not to be exceeded more than a certain frequency over a period 
of time (e.g., no exceedance on a rolling 3-month average, no more 
than once per year, or no more than once per year averaged over 3 
years), are averaged over a period of time (e.g., an annual mean or 
an annual mean averaged over 3 years), or are some percentile that 
is averaged over a period of time (e.g., annual 99th or 98th 
percentile averaged over 3 years). The 3-year period for ambient 
monitoring design values does not dictate the length of the data 
periods recommended for modeling (i.e., 5 years of NWS 
meteorological data, at least 1 year of site-specific, or at least 3 
years of prognostic meteorological data).
    e. This section discusses general recommendations on the 
regulatory application of models for the purposes of NSR, including 
PSD permitting, and particularly for estimating design 
concentration(s), appropriately comparing these estimates to NAAQS 
and PSD increments, and developing emissions limits. This section 
also provides the criteria necessary for considering use of an 
analysis based on measured ambient data in lieu of modeling as the 
sole basis for demonstrating compliance with NAAQS and PSD 
increments.

9.2 Recommendations

9.2.1 Modeling Protocol

    a. Every effort should be made by the appropriate reviewing 
authority (paragraph 3.0(b)) to meet with all parties involved in 
either a SIP submission or revision or a PSD permit application 
prior to the start of any work on such a project. During this 
meeting, a protocol should be established between the preparing and 
reviewing parties to define the procedures to be followed, the data 
to be collected, the model to be used, and the analysis of the 
source and concentration data to be performed. An example of the 
content for such an effort is contained in the Air Quality Analysis 
Checklist posted on the EPA's SCRAM website (section 2.3). This 
checklist suggests the appropriate level of detail to assess the air 
quality resulting from the proposed action. Special cases may 
require additional data collection or analysis and this should be 
determined and agreed upon at the pre-application meeting. The 
protocol should be written and agreed upon by the parties concerned, 
although it is not intended that this protocol be a binding, formal 
legal document. Changes in such a protocol or deviations from the 
protocol are often necessary as the data collection and analysis 
progresses. However, the protocol establishes a common understanding 
of how the demonstration required to meet regulatory requirements 
will be made.

9.2.2 Design Concentration and Receptor Sites

    a. Under the PSD permitting program, an air quality analysis for 
criteria pollutants is required to demonstrate that emissions from 
the construction or operation of a proposed new source or 
modification will not cause or contribute to a violation of the 
NAAQS or PSD increments.
    i. For a NAAQS assessment, the design concentration is the 
combination of the appropriate background concentration (section 
8.3) with the estimated modeled impact of the proposed source. The 
NAAQS design concentration is then compared to the applicable NAAQS.
    ii. For a PSD increment assessment, the design concentration 
includes impacts occurring after the appropriate baseline date from 
all increment-consuming and increment-expanding sources. The PSD 
increment design concentration is then compared to the applicable 
PSD increment.
    b. The specific form of the NAAQS for the pollutant(s) of 
concern will also influence how the background and modeled data 
should be combined for appropriate comparison with the respective 
NAAQS in such a modeling demonstration. Given the potential for 
revision of the form of the NAAQS and the complexities of combining 
background and modeled data, specific details on this process can be 
found in the applicable modeling guidance available on the EPA's 
SCRAM website (section 2.3). Modeled concentrations should not be 
rounded before comparing the resulting design concentration to the 
NAAQS or PSD increments. Ambient monitoring and dispersion modeling 
address different issues and needs relative to each aspect of the 
overall air quality assessment.
    c. The PSD increments for criteria pollutants are listed in 40 
CFR 52.21(c) and 40 CFR 51.166(c). For short-term increments, these 
maximum allowable increases in pollutant concentrations may be 
exceeded once per year at each site, while the annual increment may 
not be exceeded. The highest, second-highest increase in estimated 
concentrations for the short-term averages, as determined by a 
model, must be less than or equal to the permitted increment. The 
modeled annual averages must not exceed the increment.
    d. Receptor sites for refined dispersion modeling should be 
located within the modeling domain (section 8.1). In designing a 
receptor network, the emphasis should be placed on receptor density 
and location, not total number of receptors. Typically, the density 
of receptor sites should be progressively more resolved near the new 
or modifying source, areas of interest, and areas with the highest 
concentrations with sufficient detail to determine where possible 
violations of a NAAQS or PSD increments are most likely to occur. 
The placement of receptor sites should be determined on a case-by-
case basis, taking into consideration the source characteristics, 
topography, climatology, and monitor sites. Locations of particular 
importance include: (1) the area of maximum impact of the point 
source; (2) the area of maximum impact of nearby sources; and (3) 
the area where all sources combine to cause maximum impact. 
Depending on the complexities of the source and the environment to 
which the source is located, a dense array of receptors may be 
required in some cases. In order to avoid unreasonably large 
computer runs due to an excessively large array of receptors, it is 
often desirable to model the area twice. The first model run would 
use a moderate number of receptors more resolved near the new or 
modifying source and over areas of interest. The second model run 
would modify the receptor network from the first model run with a 
denser array of receptors in areas showing potential for high 
concentrations and possible violations, as indicated by the results 
of the first model run. Accordingly, the EPA neither anticipates nor 
encourages that numerous iterations of modeling runs be made to 
continually refine the receptor network.

9.2.3 NAAQS and PSD Increments Compliance Demonstrations for New or 
Modifying Sources

    a. As described in this subsection, the recommended procedure 
for conducting either a NAAQS or PSD increments assessment under PSD 
permitting is a multi-stage approach that includes the following two 
stages:
    i. The EPA describes the first stage as a single-source impact 
analysis, since this stage involves considering only the impact of 
the new or modifying source. There are two possible levels of detail 
in conducting a single-source impact analysis with the model user 
beginning with use of a screening model and proceeding to use of a 
refined model as necessary.

[[Page 72859]]

    ii. The EPA describes the second stage as a cumulative impact 
analysis, since it takes into account all sources affecting the air 
quality in an area. In addition to the project source impact, this 
stage includes consideration of background, which includes 
contributions from nearby sources and other sources (e.g., natural, 
minor, distant major, and unidentified sources).
    b. Each stage should involve increasing complexity and details, 
as required, to fully demonstrate that a new or modifying source 
will not cause or contribute to a violation of any NAAQS or PSD 
increment. As such, starting with a single-source impact analysis is 
recommended because, where the analysis at this stage is sufficient 
to demonstrate that a source will not cause or contribute to any 
potential violation, this may alleviate the need for a more time-
consuming and comprehensive cumulative modeling analysis.
    c. The single-source impact analysis, or first stage of an air 
quality analysis, should begin by determining the potential of a 
proposed new or modifying source to cause or contribute to a NAAQS 
or PSD increment violation. In certain circumstances, a screening 
model or technique may be used instead of the preferred model 
because it will provide estimated worst-case ambient impacts from 
the proposed new or modifying source. If these worst case ambient 
concentration estimates indicate that the source will not cause or 
contribute to any potential violation of a NAAQS or PSD increment, 
then the screening analysis should generally be sufficient for the 
required demonstration under PSD. If the ambient concentration 
estimates indicate that the source's emissions have the potential to 
cause or contribute to a violation, then the use of a refined model 
to estimate the source's impact should be pursued. The refined 
modeling analysis should use a model or technique consistent with 
the Guideline (either a preferred model or technique or an 
alternative model or technique) and follow the requirements and 
recommendations for model inputs outlined in section 8. If the 
ambient concentration increase predicted with refined modeling 
indicates that the source will not cause or contribute to any 
potential violation of a NAAQS or PSD increment, then the refined 
analysis should generally be sufficient for the required 
demonstration under PSD. However, if the ambient concentration 
estimates from the refined modeling analysis indicate that the 
source's emissions have the potential to cause or contribute to a 
violation, then a cumulative impact analysis should be undertaken. 
The receptors that indicate the location of significant ambient 
impacts should be used to define the modeling domain for use in the 
cumulative impact analysis (section 8.2.2).
    d. The cumulative impact analysis, or the second stage of an air 
quality analysis, should be conducted with the same refined model or 
technique to characterize the project source and then include the 
appropriate background concentrations (section 8.3). The resulting 
design concentrations should be used to determine whether the source 
will cause or contribute to a NAAQS or PSD increment violation. This 
determination should be based on: (1) The appropriate design 
concentration for each applicable NAAQS (and averaging period); and 
(2) whether the source's emissions cause or contribute to a 
violation at the time and location of any modeled violation (i.e., 
when and where the predicted design concentration is greater than 
the NAAQS). For PSD increments, the cumulative impact analysis 
should also consider the amount of the air quality increment that 
has already been consumed by other sources, or, conversely, whether 
increment has expanded relative to the baseline concentration. 
Therefore, the applicant should model the existing or permitted 
nearby increment-consuming and increment-expanding sources, rather 
than using past modeling analyses of those sources as part of 
background concentration. This would permit the use of newly 
acquired data or improved modeling techniques if such data and/or 
techniques have become available since the last source was 
permitted.

9.2.3.1 Considerations in Developing Emissions Limits

    a. Emissions limits and resulting control requirements should be 
established to provide for compliance with each applicable NAAQS 
(and averaging period) and PSD increment. It is possible that 
multiple emissions limits will be required for a source to 
demonstrate compliance with several criteria pollutants (and 
averaging periods) and PSD increments. Case-by-case determinations 
must be made as to the appropriate form of the limits, i.e., whether 
the emissions limits restrict the emission factor (e.g., limiting 
lb/MMBTU), the emission rate (e.g., lb/hr), or both. The appropriate 
reviewing authority (paragraph 3.0(b)) and appropriate EPA guidance 
should be consulted to determine the appropriate emissions limits on 
a case-by-case basis.

9.2.4 Use of Measured Data in Lieu of Model Estimates

    a. As described throughout the Guideline, modeling is the 
preferred method for demonstrating compliance with the NAAQS and PSD 
increments and for determining the most appropriate emissions limits 
for new and existing sources. When a preferred model or adequately 
justified and approved alternative model is available, model 
results, including the appropriate background, are sufficient for 
air quality demonstrations and establishing emissions limits, if 
necessary. In instances when the modeling technique available is 
only a screening technique, the addition of air quality monitoring 
data to the analysis may lend credence to the model results. 
However, air quality monitoring data alone will normally not be 
acceptable as the sole basis for demonstrating compliance with the 
NAAQS and PSD increments or for determining emissions limits.
    b. There may be rare circumstances where the performance of the 
preferred air quality model will be shown to be less than reasonably 
acceptable when compared with air quality monitoring data measured 
in the vicinity of an existing source. Additionally, there may not 
be an applicable preferred air quality model, screening technique, 
or justifiable alternative model suitable for the situation. In 
these unique instances, there may be the possibility of establishing 
emissions limits and demonstrating compliance with the NAAQS and PSD 
increments solely on the basis of analysis of observed air quality 
data in lieu of an air quality modeling analysis. However, only in 
the case of a modification to an existing source should air quality 
monitoring data alone be a basis for determining adequate emissions 
limits or for demonstration that the modification will not cause or 
contribute to a violation of any NAAQS or PSD increment.
    c. The following items should be considered prior to the 
acceptance of an analysis of measured air quality data as the sole 
basis for an air quality demonstration or determining an emissions 
limit:
    i. Does a monitoring network exist for the pollutants and 
averaging times of concern in the vicinity of the existing source?
    ii. Has the monitoring network been designed to locate points of 
maximum concentration?
    iii. Do the monitoring network and the data reduction and 
storage procedures meet EPA monitoring and quality assurance 
requirements?
    iv. Do the dataset and the analysis allow impact of the most 
important individual sources to be identified if more than one 
source or emission point is involved?
    v. Is at least one full year of valid ambient data available?
    vi. Can it be demonstrated through the comparison of monitored 
data with model results that available air quality models and 
techniques are not applicable?
    d. Comprehensive air quality monitoring in the area affected by 
the existing source with proposed modifications will be necessary in 
these cases. Additional meteorological monitoring may also be 
necessary. The appropriate number of air quality and meteorological 
monitors from a scientific and technical standpoint is a function of 
the situation being considered. The source configuration, terrain 
configuration, and meteorological variations all have an impact on 
number and optimal placement of monitors. Decisions on the 
monitoring network appropriate for this type of analysis can only be 
made on a case-by-case basis.
    e. Sources should obtain approval from the appropriate reviewing 
authority (paragraph 3.0(b)) and the EPA Regional Office for the 
monitoring network prior to the start of monitoring. A monitoring 
protocol agreed to by all parties involved is necessary to assure 
that ambient data are collected in a consistent and appropriate 
manner. The design of the network, the number, type, and location of 
the monitors, the sampling period, averaging time, as well as the 
need for meteorological monitoring or the use of mobile sampling or 
plume tracking techniques, should all be specified in the protocol 
and agreed upon prior to start-up of the network.
    f. Given the uniqueness and complexities of these rare 
circumstances, the procedures can only be established on a case-by-
case basis for analyzing the source's emissions data and the 
measured air quality monitoring data, and for projecting with a 
reasoned basis

[[Page 72860]]

the air quality impact of a proposed modification to an existing 
source in order to demonstrate that emissions from the construction 
or operation of the modification will not cause or contribute to a 
violation of the applicable NAAQS and PSD increment, and to 
determine adequate emissions limits. The same attention should be 
given to the detailed analyses of the air quality data as would be 
applied to a comprehensive model performance evaluation. In some 
cases, the monitoring data collected for use in the performance 
evaluation of preferred air quality models, screening technique, or 
existing alternative models may help inform the development of a 
suitable new alternative model. Early coordination with the 
appropriate reviewing authority (paragraph 3.0(b)) and the EPA 
Regional Office is fundamental with respect to any potential use of 
measured data in lieu of model estimates.

10.0 References

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SO2 and CO Concentrations in St. Louis. Atmospheric 
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predictions. Boundary-Layer Meteorology, 62: 3-20.
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evaluations, as estimated by bootstrap and jackknife resampling 
methods. Atmospheric Environment, 23(6): 1385-1398.
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determining the best performing air quality simulation model. 
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Impacts for Primary Pollutants. Publication No. EPA-454/B-16-007. 
Office of Air Quality Planning and Standards, Research Triangle 
Park, NC.
37. U.S. Environmental Protection Agency, 2021. AERSCREEN User's 
Guide. Publication No. EPA-454/B-21-005. Office of Air Quality 
Planning and Standards, Research Triangle Park, NC.
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as the EPA Recommended Screening Model. Memorandum dated April 11, 
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Triangle Park, NC.
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to CTDMPLUS:

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88. U.S. Environmental Protection Agency, 1985. Guideline for 
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89. Snyder, W.H. and R.E. Lawson, Jr., 1985. Fluid Modeling 
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95. U.S. Environmental Protection Agency, 2017. Emissions Inventory 
Guidance for Implementation of Ozone and Particulate Matter National 
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103. U.S. Environmental Protection Agency, 1996. Meteorological 
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User's Guide to the Complex Terrain Dispersion Model Plus Algorithms 
for Unstable Situations (CTDMPLUS). Volume 1: Model Descriptions and 
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Version 4.1 User's Manual.
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110. U.S. Environmental Protection Agency, 2000. Meteorological 
Monitoring Guidance for Regulatory Modeling Applications. 
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114. Bowen, B.M., J.M. Dewart and A.I. Chen, 1983. Stability Class 
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116. Irwin, J.S., 1980. Dispersion Estimate Suggestion #8: 
Estimation of Pasquill Stability Categories. U.S. Environmental 
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117. Mitchell, Jr., A.E. and K.O. Timbre, 1979. Atmospheric 
Stability Class from Horizontal Wind Fluctuation. Presented at 72nd 
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118. Smedman-Hogstrom, A. and V. Hogstrom, 1978. A Practical Method 
for Determining Wind Frequency Distributions for the Lowest 200 m 
from Routine Meteorological Data. Journal of Applied Meteorology, 
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Diffusivity. MRI 72 FR-1030. Meteorology Research, Inc., Altadena, 
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120. U.S. Environmental Protection Agency, 2018. Evaluation of 
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121. U.S. Environmental Protection Agency, 1984. Calms Processor 
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PB 84-229467).

Addendum A to Appendix W of Part 51--Summaries of Preferred Air Quality 
Models

Table of Contents

A.0 Introduction and Availability
A.1 AERMOD (AMS/EPA Regulatory Model)
A.2 CTDMPLUS (Complex Terrain Dispersion Model Plus Algorithms for 
Unstable Situations)
A.3 OCD (Offshore and Coastal Dispersion Model)

A.0 Introduction and Availability

    (1) This appendix summarizes key features of refined air quality 
models preferred for specific regulatory applications. For each 
model, information is provided on availability, approximate cost 
(where applicable), regulatory use, data input, output format and 
options, simulation of atmospheric physics, and accuracy. These 
models may be used without a formal demonstration of applicability 
provided they satisfy the recommendations for regulatory use; not 
all options in the models are necessarily recommended for regulatory 
use.
    (2) These models have been subjected to a performance evaluation 
using comparisons with observed air quality data. Where possible, 
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 
addendum are available from the EPA's Support Center for Regulatory 
Air Models (SCRAM) website at https://www.epa.gov/scram. Codes and 
documentation may also be available from the National Technical 
Information Service (NTIS), https://www.ntis.gov, and, when 
available, are referenced with the appropriate NTIS accession 
number.

A.1 AERMOD (AMS/EPA Regulatory Model)

References

U.S. Environmental Protection Agency, 2023. AERMOD Model 
Formulation. Publication No. EPA-454/B-23-010. Office of Air Quality 
Planning and Standards, Research Triangle Park, NC.
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.
Heist, D., et al., 2013. Estimating near-road pollutant dispersion: 
A model inter-comparison. Transportation Research Part D: Transport 
and Environment, 25: pp 93-105.
Heist, D., et al., 2023. Integration of RLINE dispersion model into 
EPA's AERMOD: updated formulation and evaluations. Journal of the 
Air & Waste Management Association, Manuscript submitted for 
publication.
U.S. Environmental Protection Agency, 2023. User's Guide for the 
AMS/EPA Regulatory Model (AERMOD). Publication No. EPA-454/B-23-008. 
Office of Air Quality Planning and Standards, Research Triangle 
Park, NC.
U.S. Environmental Protection Agency, 2023. User's Guide for the 
AERMOD Meteorological Preprocessor (AERMET). Publication No. EPA-
454/B-23-005. Office of Air Quality Planning and Standards, Research 
Triangle Park, NC.
U.S. Environmental Protection Agency, 2018. User's Guide for the 
AERMOD Terrain Preprocessor (AERMAP). Publication No. EPA-454/B-18-
004. U.S. Environmental Protection Agency, Office of Air Quality 
Planning and Standards, Research Triangle Park, NC.
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 & Waste Management Association, 50: 378-390.
Schulman, L.L., and Joseph S. Scire, 1980. Buoyant Line and Point 
Source (BLP) Dispersion Model User's Guide. Document P-7304B. 
Environmental Research and Technology, Inc., Concord, MA. (NTIS No. 
PB 81-164642).

Availability

    The model codes and associated documentation are available on 
EPA's SCRAM website (paragraph A.0(3)).

Abstract

    AERMOD is a steady-state plume dispersion model for assessment 
of pollutant concentrations from a variety of sources. AERMOD 
simulates transport and dispersion from multiple point, area, 
volume, and line sources based on an up-to-date characterization of 
the atmospheric boundary layer. Sources may be located in rural or 
urban areas, and receptors may be located in simple or complex 
terrain. AERMOD accounts for building wake effects (i.e., plume 
downwash) based on the PRIME building downwash algorithms. The model 
employs hourly sequential preprocessed meteorological data to 
estimate concentrations for averaging times from 1-hour to 1-year 
(also multiple years). AERMOD can be used to estimate the 
concentrations of nonreactive pollutants from highway traffic. 
AERMOD also handles unique modeling problems associated with 
aluminum reduction plants, and other industrial sources where plume 
rise and downwash effects from stationary buoyant line sources are 
important. AERMOD is designed to operate in concert with two pre-
processor codes: AERMET processes meteorological data for input to 
AERMOD, and AERMAP processes terrain elevation data and generates 
receptor and hill height information for input to AERMOD.

[[Page 72864]]

a. Regulatory Use

    (1) AERMOD is appropriate for the following applications:
     Point, volume, and area sources;
     Buoyant, elevated line sources (e.g., aluminum 
reduction plants);
     Mobile 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 50 km;
     1-hour to annual averaging times,
     Continuous toxic air emissions; and,
     Applications in the marine boundary layer environment 
where the effects of shoreline fumigation and/or platform downwash 
are adequately assessed or are not applicable.
    (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 meteorological data processed with the 
regulatory options in AERMET, the use of terrain elevation data 
processed through the AERMAP terrain processor, stack-tip downwash, 
sequential date checking, and does not permit the use of the model 
in the SCREEN mode. In the regulatory default mode, pollutant half-
life or decay options are not employed, except in the case of an 
urban source of sulfur dioxide where a 4-hour half-life is applied. 
Terrain elevation data from the U.S. Geological Survey (USGS) 7.5-
Minute Digital Elevation Model (DEM), or equivalent (approx. 30-
meter resolution and finer), (processed through AERMAP) should be 
used in all applications. Starting in 2011, data from the 3D 
Elevation Program (3DEP, https://apps.nationalmap.gov/downloader), 
formerly the National Elevation Dataset (NED), can also be used in 
AERMOD, which includes a range of resolutions, from 1-m to 2 arc 
seconds and such high resolution would always be preferred. In some 
cases, exceptions from the terrain data requirement may be made in 
consultation with the appropriate reviewing authority (paragraph 
3.0(b)).

b. Input Requirements

    (1) Source data: Required inputs include source type, location, 
emission rate, stack height, stack inside diameter, stack gas exit 
velocity, stack gas exit temperature, area and volume source 
dimensions, and source base elevation. For point sources subject to 
the influence of building downwash, direction-specific building 
dimensions (processed through the BPIPPRM building processor) should 
be input. Variable emission rates are optional. Buoyant line sources 
require coordinates of the end points of the line, release height, 
emission rate, average line source width, average building width, 
average spacing between buildings, and average line source buoyancy 
parameter. For mobile sources, traffic volume; emission factor, 
source height, and mixing zone width are needed to determine 
appropriate model inputs.
    (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 100 m (reference wind 
speed measurement from which a vertical profile can be developed), 
wind direction, cloud cover, and temperature between zo and 100 m 
(reference temperature measurement from which a vertical profile can 
be developed). Meteorological data can be in the form of observed 
data or prognostic modeled data as discussed in paragraph 8.4.1(d). 
Surface characteristics may be varied by wind sector and by season 
or month. When using observed meteorological data, a morning 
sounding (in National Weather Service format) from a representative 
upper air station is required. Latitude, longitude, and time zone of 
the surface, site-specific or prognostic data (if applicable) and 
upper air meteorological stations are required. The wind speed 
starting threshold is also required in AERMET for applications 
involving site-specific data. When using prognostic data, modeled 
profiles of temperature and winds are input to AERMET. These can be 
hourly or a time that represents a morning sounding. Additionally, 
measured profiles of wind, temperature, vertical and lateral 
turbulence may be required in certain applications (e.g., in complex 
terrain) to adequately represent the meteorology affecting plume 
transport and dispersion. Optionally, measurements of solar and/or 
net radiation may be input to AERMET. Two files are produced by the 
AERMET meteorological preprocessor for input to the AERMOD 
dispersion model. When using observed data, the surface file 
contains observed and calculated surface variables, one record per 
hour. For applications with multi-level site-specific meteorological 
data, the profile contains the observations made at each level of 
the meteorological tower (or remote sensor). When using prognostic 
data, the surface file contains surface variables calculated by the 
prognostic model and AERMET. The profile file contains the 
observations made at each level of a meteorological tower (or remote 
sensor), the one-level observations taken from other representative 
data (e.g., National Weather Service surface observations), one 
record per level per hour, or in the case of prognostic data, the 
prognostic modeled values of temperature and winds at user-specified 
levels.
    (i) Data used as input to AERMET should possess an adequate 
degree of representativeness to ensure that the wind, temperature 
and turbulence profiles derived by AERMOD are both laterally and 
vertically representative of the source impact area. The adequacy of 
input data should be judged independently for each variable. The 
values for surface roughness, Bowen ratio, and albedo should reflect 
the surface characteristics in the vicinity of the meteorological 
tower or representative grid cell when using prognostic data, and 
should be adequately representative of the modeling domain. Finally, 
the primary atmospheric input variables, including wind speed and 
direction, ambient temperature, cloud cover, and a morning upper air 
sounding, should also be adequately representative of the source 
area when using observed data.
    (ii) For applications involving the use of site-specific 
meteorological data that includes turbulences parameters (i.e., 
sigma-theta and/or sigma-w), the application of the ADJ_U* option in 
AERMET would require approval as an alternative model application 
under section 3.2.
    (iii) For recommendations regarding the length of meteorological 
record needed to perform a regulatory analysis with AERMOD, see 
section 8.4.2.
    (3) Receptor data: Receptor coordinates, elevations, height 
above ground, and hill height scales are produced by the AERMAP 
terrain preprocessor for input to AERMOD. Discrete receptors and/or 
multiple receptor grids, Cartesian and/or polar, may be employed in 
AERMOD. AERMAP requires input of DEM or 3DEP terrain data produced 
by the USGS, or other equivalent data. AERMAP can be used optionally 
to estimate source elevations.

c. Output

    Printed output options include input information, high 
concentration summary tables by receptor for user-specified 
averaging periods, maximum concentration summary tables, and 
concurrent values summarized by receptor for each day processed. 
Optional output files can be generated for: a listing of occurrences 
of exceedances of user-specified threshold value; a listing of 
concurrent (raw) results at each receptor for each hour modeled, 
suitable for post-processing; a listing of design values that can be 
imported into graphics software for plotting contours; a listing of 
results suitable for NAAQS analyses including NAAQS exceedances and 
culpability analyses; an unformatted listing of raw results above a 
threshold value with a special structure for use with the TOXX model 
component of TOXST; a listing of concentrations by rank (e.g., for 
use in quantile-quantile plots); and a listing of concentrations, 
including arc-maximum 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 or 3DEP terrain data. Receptors 
may be

[[Page 72865]]

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: (1) the direct plume (from the stack); (2) the 
indirect plume; and (3) the penetrated plume, where the indirect 
plume accounts for the lofting of a buoyant plume near the top of 
the boundary layer, and the penetrated plume accounts for the 
portion of a plume that, due to its buoyancy, penetrates above the 
mixed layer, but can disperse downward and re-enter the mixed layer. 
In the CBL, plume rise is superposed on the displacements by random 
convective velocities (Weil, et al., 1997).
    (2) In the stable boundary layer, plume rise is estimated using 
an iterative approach to account for height-dependent lapse rates, 
similar to that in the CTDMPLUS model (see A.2 in this appendix).
    (3) Stack-tip downwash and buoyancy induced dispersion effects 
are modeled. Building wake effects are simulated for stacks subject 
to building downwash using the methods contained in the PRIME 
downwash algorithms (Schulman, et al., 2000). For plume rise 
affected by the presence of a building, the PRIME downwash algorithm 
uses a numerical solution of the mass, energy and momentum 
conservation laws (Zhang and Ghoniem, 1993). Streamline deflection 
and the position of the stack relative to the building affect plume 
trajectory and dispersion. Enhanced dispersion is based on the 
approach of Weil (1996). Plume mass captured by the cavity is well-
mixed within the cavity. The captured plume mass is re-emitted to 
the far wake as a volume source.
    (4) For elevated terrain, AERMOD incorporates the concept of the 
critical dividing streamline height, in which flow below this height 
remains horizontal, and flow above this height tends to rise up and 
over terrain (Snyder, et al., 1985). Plume concentration estimates 
are the weighted sum of these two limiting plume states. However, 
consistent with the steady-state assumption of uniform horizontal 
wind direction over the modeling domain, straight-line plume 
trajectories are assumed, with adjustment in the plume/receptor 
geometry used to account for the terrain effects.

h. Horizontal Winds

    Vertical profiles of wind are calculated for each hour based on 
measurements and surface-layer similarity (scaling) relationships. 
At a given height above ground, for a given hour, winds are assumed 
constant over the modeling domain. The effect of the vertical 
variation in horizontal wind speed on dispersion is accounted for 
through simple averaging over the plume depth.

i. Vertical Wind Speed

    In convective conditions, the effects of random vertical updraft 
and downdraft velocities are simulated with a bi-Gaussian 
probability density function. In both convective and stable 
conditions, the mean vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

    Gaussian horizontal dispersion coefficients are estimated as 
continuous functions of the parameterized (or measured) ambient 
lateral turbulence and also account for buoyancy-induced and 
building wake-induced turbulence. Vertical profiles of lateral 
turbulence are developed from measurements and similarity (scaling) 
relationships. Effective turbulence values are determined from the 
portion of the vertical profile of lateral turbulence between the 
plume height and the receptor height. The effective lateral 
turbulence is then used to estimate horizontal dispersion.

k. Vertical Dispersion

    In the stable boundary layer, Gaussian vertical dispersion 
coefficients are estimated as continuous functions of parameterized 
vertical turbulence. In the convective boundary layer, vertical 
dispersion is characterized by a bi-Gaussian probability density 
function and is also estimated as a continuous function of 
parameterized vertical turbulence. Vertical turbulence profiles are 
developed from measurements and similarity (scaling) relationships. 
These turbulence profiles account for both convective and mechanical 
turbulence. Effective turbulence values are determined from the 
portion of the vertical profile of vertical turbulence between the 
plume height and the receptor height. The effective vertical 
turbulence is then used to estimate vertical dispersion.

l. Chemical Transformation

    Chemical transformations are generally not treated by AERMOD. 
However, AERMOD does contain an option to treat chemical 
transformation using simple exponential decay, although this option 
is typically not used in regulatory applications except for sources 
of sulfur dioxide in urban areas. Either a decay coefficient or a 
half-life is input by the user. Note also that the Generic Reaction 
Set Method, Plume Volume Molar Ratio Method and the Ozone Limiting 
Method (section 4.2.3.4) for NO2 analyses are available.

m. Physical Removal

    AERMOD can be used to treat dry and wet deposition for both 
gases and particles. Currently, Method 1 particle deposition is 
available for regulatory applications. Method 2 particle deposition 
and gas deposition are currently alpha options and not available for 
regulatory applications

n. Evaluation Studies

American Petroleum Institute, 1998. Evaluation of State of the 
Science of Air Quality Dispersion Model, Scientific Evaluation, 
prepared by Woodward-Clyde Consultants, Lexington, Massachusetts, 
for American Petroleum Institute, Washington, DC 20005-4070.
Brode, R.W., 2002. Implementation and Evaluation of PRIME in AERMOD. 
Preprints of the 12th Joint Conference on Applications of Air 
Pollution Meteorology, May 20-24, 2002; American Meteorological 
Society, Boston, MA.
Brode, R.W., 2004. Implementation and Evaluation of Bulk Richardson 
Number Scheme in AERMOD. 13th Joint Conference on Applications of 
Air Pollution Meteorology, August 23-26, 2004; American 
Meteorological Society, Boston, MA.
U.S. Environmental Protection Agency, 2003. AERMOD: Latest Features 
and Evaluation Results. Publication No. EPA-454/R-03-003. Office of 
Air Quality Planning and Standards, Research Triangle Park, NC.
Heist, D., et al., 2013. Estimating near-road pollutant dispersion: 
A model inter-comparison. Transportation Research Part D: Transport 
and Environment, 25: pp 93-105.
Heist, D., et al., 2023. Integration of RLINE dispersion model into 
EPA's AERMOD: updated formulation and evaluations. Journal of the 
Air & Waste Management Association, Manuscript submitted for 
publication.
Carruthers, D.J.; Stocker, J.R.; Ellis, A.; Seaton, M.D.; Smith, SE 
Evaluation of an explicit NOx chemistry method in AERMOD; Journal of 
the Air & Waste Management Association. 2017, 67 (6), 702-712; 
DOI:10.1080/10962247.2017.1280096.
Environmental Protection Agency, 2023. Technical Support Document 
(TSD) for Adoption of the Generic Reaction Set Method (GRSM) as a 
Regulatory Non-Default Tier-3 NO2 Screening Option. Publication No. 
EPA-454/R-23-009. Office of Air Quality Planning & Standards, 
Research Triangle Park, NC.

A.2 CTDMPLUS (Complex Terrain Dispersion Model Plus Algorithms for 
Unstable Situations)

References

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. 
Mills, D.G. Strimaitis, R.J. Yamartino and E.M. Insley, 1989. User's 
Guide to the Complex Terrain Dispersion Model Plus Algorithms for 
Unstable Situations (CTDMPLUS). Volume 1: Model Descriptions and 
User Instructions. EPA Publication No. EPA-600/8-89-041. U.S. 
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

    The model codes and associated documentation are available on 
the EPA's SCRAM website (paragraph A.0(3)).

Abstract

    CTDMPLUS is a refined point source Gaussian air quality model 
for use in all stability conditions for complex terrain 
applications. The model contains, in its entirety, the technology of 
CTDM for stable and neutral conditions. However, CTDMPLUS can also 
simulate daytime, unstable conditions, and has a number of 
additional capabilities for improved user friendliness. Its use of 
meteorological data

[[Page 72866]]

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. Regulatory Use

    CTDMPLUS is appropriate for the following applications:
     Elevated point sources;
     Terrain elevations above stack top;
     Rural or urban areas;
     Transport distances less than 50 kilometers; and
     1-hour to annual averaging times when used with a post-
processor program such as CHAVG.

b. Input Requirements

    (1) Source data: For each source, user supplies source location, 
height, stack diameter, stack exit velocity, stack exit temperature, 
and emission rate; if variable emissions are appropriate, the user 
supplies hourly values for emission rate, stack exit velocity, and 
stack exit temperature.
    (2) Meteorological data: For applications of CTDMPLUS, multiple 
level (typically three or more) measurements of wind speed and 
direction, temperature and turbulence (wind fluctuation statistics) 
are required to create the basic meteorological data file 
(``PROFILE''). Such measurements should be obtained up to the 
representative plume height(s) of interest (i.e., the plume 
height(s) under those conditions important to the determination of 
the design concentration). The representative plume height(s) of 
interest should be determined using an appropriate complex terrain 
screening procedure (e.g., CTSCREEN) and should be documented in the 
monitoring/modeling protocol. The necessary meteorological 
measurements should be obtained from an appropriately sited 
meteorological tower augmented by SODAR and/or RASS if the 
representative plume height(s) of interest is above the levels 
represented by the tower measurements. Meteorological preprocessors 
then create a SURFACE data file (hourly values of mixed layer 
heights, surface friction velocity, Monin-Obukhov length and surface 
roughness length) and a RAWINsonde data file (upper air measurements 
of pressure, temperature, wind direction, and wind speed).
    (3) Receptor data: receptor names (up to 400) and coordinates, 
and hill number (each receptor must have a hill number assigned).
    (4) Terrain data: user inputs digitized contour information to 
the terrain preprocessor which creates the TERRAIN data file (for up 
to 25 hills).

c. Output

    (1) When CTDMPLUS is run, it produces a concentration file, in 
either binary or text format (user's choice), and a list file 
containing a verification of model inputs, i.e.,
     Input meteorological data from ``SURFACE'' and 
``PROFILE,''
     Stack data for each source,
     Terrain information,
     Receptor information, and
     Source-receptor location (line printer map).
    (2) In addition, if the case-study option is selected, the 
listing includes:
     Meteorological variables at plume height,
     Geometrical relationships between the source and the 
hill, and
     Plume characteristics at each receptor, i.e.,
--Distance in along-flow and cross flow direction
--Effective plume-receptor height difference
--Effective [sigma]y & [sigma]z values, both flat terrain and hill 
induced (the difference shows the effect of the hill)
--Concentration components due to WRAP, LIFT and FLAT.
    (3) If the user selects the TOPN option, a summary table of the 
top four concentrations at each receptor is given. If the ISOR 
option is selected, a source contribution table for every hour will 
be printed.
    (4) A separate output file of predicted (1-hour only) 
concentrations (``CONC'') is written if the user chooses this 
option. Three forms of output are possible:
    (i) A binary file of concentrations, one value for each receptor 
in the hourly sequence as run;
    (ii) A text file of concentrations, one value for each receptor 
in the hourly sequence as run; or
    (iii) A text file as described above, but with a listing of 
receptor information (names, positions, hill number) at the 
beginning of the file.
    (5) Hourly information provided to these files besides the 
concentrations themselves includes the year, month, day, and hour 
information as well as the receptor number with the highest 
concentration.

d. Type of Model

    CTDMPLUS is a refined steady-state, point source plume model for 
use in all stability conditions for complex terrain applications.

e. Pollutant Types

    CTDMPLUS may be used to model non- reactive, primary pollutants.

f. Source-Receptor Relationship

    Up to 40 point sources, 400 receptors and 25 hills may be used. 
Receptors and sources are allowed at any location. Hill slopes are 
assumed not to exceed 15[deg], so that the linearized equation of 
motion for Boussinesq flow are applicable. Receptors upwind of the 
impingement point, or those associated with any of the hills in the 
modeling domain, require separate treatment.

g. Plume Behavior

    (1) As in CTDM, the basic plume rise algorithms are based on 
Briggs' (1975) recommendations.
    (2) A central feature of CTDMPLUS for neutral/stable conditions 
is its use of a critical dividing-streamline height (Hc) 
to separate the flow in the vicinity of a hill into two separate 
layers. The plume component in the upper layer has sufficient 
kinetic energy to pass over the top of the hill while streamlines in 
the lower portion are constrained to flow in a horizontal plane 
around the hill. Two separate components of CTDMPLUS compute ground-
level concentrations resulting from plume material in each of these 
flows.
    (3) The model calculates on an hourly (or appropriate steady 
averaging period) basis how the plume trajectory (and, in stable/
neutral conditions, the shape) is deformed by each hill. Hourly 
profiles of wind and temperature measurements are used by CTDMPLUS 
to compute plume rise, plume penetration (a formulation is included 
to handle penetration into elevated stable layers, based on Briggs 
(1984)), convective scaling parameters, the value of Hc, 
and the Froude number above Hc.

h. Horizontal Winds

    CTDMPLUS does not simulate calm meteorological conditions. Both 
scalar and vector wind speed observations can be read by the model. 
If vector wind speed is unavailable, it is calculated from the 
scalar wind speed. The assignment of wind speed (either vector or 
scalar) at plume height is done by either:
     Interpolating between observations above and below the 
plume height, or
     Extrapolating (within the surface layer) from the 
nearest measurement height to the plume height.

i. Vertical Wind Speed

    Vertical flow is treated for the plume component above the 
critical dividing streamline height (Hc); see ``Plume 
Behavior.''

j. Horizontal Dispersion

    Horizontal dispersion for stable/neutral conditions is related 
to the turbulence velocity scale for lateral fluctuations, [sigma]v, 
for which a minimum value of 0.2 m/s is used. Convective scaling 
formulations are used to estimate horizontal dispersion for unstable 
conditions.

k. Vertical Dispersion

    Direct estimates of vertical dispersion for stable/neutral 
conditions are based on observed vertical turbulence intensity, 
e.g., [sigma]w (standard deviation of the vertical velocity 
fluctuation). In simulating unstable (convective) conditions, 
CTDMPLUS relies on a skewed, bi-Gaussian probability density 
function (pdf) description of the vertical velocities to estimate 
the vertical distribution of pollutant concentration.

l. Chemical Transformation

    Chemical transformation is not treated by CTDMPLUS.

m. Physical Removal

    Physical removal is not treated by CTDMPLUS (complete reflection 
at the ground/hill surface is assumed).

n. Evaluation Studies

Burns, D.J., L.H. Adams and S.G. Perry, 1990. Testing and Evaluation 
of the CTDMPLUS Dispersion Model: Daytime Convective Conditions. 
U.S. 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. 
U.S. Environmental Protection Agency, Research Triangle Park, NC.

[[Page 72867]]

Paumier, J.O., S.G. Perry and D.J. Burns, 1992. CTDMPLUS: A 
Dispersion Model for Sources near Complex Topography. Part II: 
Performance Characteristics. Journal of Applied Meteorology, 31(7): 
646-660.

A.3 OCD (Offshore and Coastal Dispersion) Model

Reference

DiCristofaro, DC 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).

Availability

    The model codes and associated documentation are available on 
EPA's SCRAM website (paragraph A.0(3)).

Abstract

    (1) OCD is a straight-line Gaussian model developed to determine 
the impact of offshore emissions from point, area or line sources on 
the air quality of coastal regions. OCD incorporates overwater plume 
transport and dispersion as well as changes that occur as the plume 
crosses the shoreline. Hourly meteorological data are needed from 
both offshore and onshore locations. These include water surface 
temperature, overwater air temperature, mixing height, and relative 
humidity.
    (2) Some of the key features include platform building downwash, 
partial plume penetration into elevated inversions, direct use of 
turbulence intensities for plume dispersion, interaction with the 
overland internal boundary layer, and continuous shoreline 
fumigation.

a. Regulatory Use

    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: PCRAMMET is the recommended 
meteorological data preprocessor for use in applications of OCD 
employing hourly NWS data. MPRM is the recommended meteorological 
data preprocessor for applications of OCD employing site-specific 
meteorological data
    (i) Over land: Surface weather data including hourly stability 
class, wind direction, wind speed, ambient temperature, and mixing 
height are required.
    (ii) 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 output files can be used by ANALYSIS 
postprocessor to produce the highest concentrations for each 
receptor, the cumulative frequency distributions for each receptor, 
the tabulation of all concentrations exceeding a given threshold, 
and the manipulation of hourly concentration files.

d. Type of Model

    OCD is a Gaussian plume model constructed on the framework of 
the MPTER model.

e. Pollutant Types

    OCD may be used to model primary pollutants. Settling and 
deposition are not treated.

f. Source-Receptor Relationship

    (1) Up to 250 point sources, 5 area sources, or 1 line source 
and 180 receptors may be used.
    (2) Receptors and sources are allowed at any location.
    (3) The coastal configuration is determined by a grid of up to 
3600 rectangles. Each element of the grid is designated as either 
land or water to identify the coastline.

g. Plume Behavior

    (1) The basic plume rise algorithms are based on Briggs' 
recommendations.
    (2) Momentum rise includes consideration of the stack angle from 
the vertical.
    (3) The effect of drilling platforms, ships, or any overwater 
obstructions near the source are used to decrease plume rise using a 
revised platform downwash algorithm based on laboratory experiments.
    (4) Partial plume penetration of elevated inversions is included 
using the suggestions of Briggs (1975) and Weil and Brower (1984).
    (5) Continuous shoreline fumigation is parameterized using the 
Turner method where complete vertical mixing through the thermal 
internal boundary layer (TIBL) occurs as soon as the plume 
intercepts the TIBL.

h. Horizontal Winds

    (1) Constant, uniform wind is assumed for each hour.
    (2) Overwater wind speed can be estimated from overland wind 
speed using relationship of Hsu (1981).
    (3) Wind speed profiles are estimated using similarity theory 
(Businger, 1973). Surface layer fluxes for these formulas are 
calculated from bulk aerodynamic methods.

i. Vertical Wind Speed

    Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

    (1) Lateral turbulence intensity is recommended as a direct 
estimate of horizontal dispersion. If lateral turbulence intensity 
is not available, it is estimated from boundary layer theory. For 
wind speeds less than 8 m/s, lateral turbulence intensity is assumed 
inversely proportional to wind speed.
    (2) Horizontal dispersion may be enhanced because of 
obstructions near the source. A virtual source technique is used to 
simulate the initial plume dilution due to downwash.
    (3) Formulas recommended by Pasquill (1976) are used to 
calculate buoyant plume enhancement and wind direction shear 
enhancement.
    (4) At the water/land interface, the change to overland 
dispersion rates is modeled using a virtual source. The overland 
dispersion rates can be calculated from either lateral turbulence 
intensity or Pasquill-Gifford curves. The change is implemented 
where the plume intercepts the rising internal boundary layer.

k. Vertical Dispersion

    (1) Observed vertical turbulence intensity is not recommended as 
a direct estimate of vertical dispersion. Turbulence intensity 
should be estimated from boundary layer theory as default in the 
model. For very stable conditions, vertical dispersion is also a 
function of lapse rate.
    (2) Vertical dispersion may be enhanced because of obstructions 
near the source. A virtual source technique is used to simulate the 
initial plume dilution due to downwash.
    (3) Formulas recommended by Pasquill (1976) are used to 
calculate buoyant plume enhancement.
    (4) At the water/land interface, the change to overland 
dispersion rates is modeled using a virtual source. The overland 
dispersion rates can be calculated from either vertical turbulence 
intensity or the Pasquill-Gifford coefficients. The change is 
implemented where the plume intercepts the rising internal boundary 
layer.

l. Chemical Transformation

    Chemical transformations are treated using exponential decay. 
Different rates can be specified by month and by day or night.

m. Physical Removal

    Physical removal is also treated using exponential decay.

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-

[[Page 72868]]

0069. Environmental Research & Technology, Inc., Concord, MA. (NTIS 
No. PB 86-159803).
Hanna, S.R., L.L. Schulman, R.J. Paine, J.E. Pleim and M. Baer, 
1985. Development and Evaluation of the Offshore and Coastal 
Dispersion (OCD) Model. Journal of the Air Pollution Control 
Association, 35: 1039-1047.
Hanna, S.R. and D.C. DiCristofaro, 1988. Development and Evaluation 
of the OCD/API Model. Final Report, API Pub. 4461, American 
Petroleum Institute, Washington, DC.

[FR Doc. 2023-22876 Filed 10-20-23; 8:45 am]
BILLING CODE 6560-50-P


