72826 Federal Register / Vol. 88, No. 203 / Monday, October 23, 2023 / Proposed Rules 
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. 
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: a-and-r-docket@epa.gov. 
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: Bridgers.George@epa.gov. (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: 
Category NAICSa 
code 
Federal/State/territorial/local/Trib-
al government ......................... 924110 
aNorth 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 
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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 (PM
2.5) National Ambient Air 
Quality Standards (NAAQS), modeling for compliance demonstration of the 1- hour nitrogen dioxide (NO
2) and sulfur 
dioxide (SO 2) NAAQS, and new and 
emerging models/techniques for future 
consideration under the Guideline to 
address single-source modeling for ozone and secondary PM
2.5,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 PM
2.5in 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, NO
2modeling techniques, 
plume rise, deposition, and single source ozone and PM
2.5modeling 
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 
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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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1https://www.epa.gov/scram/12th-conference-air- 
quality-modeling. 
2Fairall, 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. 
3Evaluation 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. 
4U.S. EPA, 2011: COARE Bulk Flux Algorithm to 
Generate Hourly Meteorological Data for Use with 
Continued 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,
1and 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 (NO
X) 
emissions to NO 2and (2) a new source 
type for modeling vehicle roadway emissions. 
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)
23algorithms 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.
45In this 
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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. 
5U.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. 
6U.S. EPA, 2012: User’s Manual AERCOARE 
Version 1.0. EPA–910–R–12–008. U.S. EPA, Region 10, Seattle, WA. 
7Please 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’’). 8Podrez, M. 2015. An Update to the Ambient 
Ratio Method for 1-h NO 2Air Quality Standards 
Dispersion Modeling. Atmospheric Environment, 
103: 163–170. 
9Cole, H.S. and J.E. Summerhays, 1979. A Review 
of Techniques Available for Estimation of Short- Term NO
2Concentrations. Journal of the Air 
Pollution Control Association, 29(8): 812–817. 
10Hanrahan, P.L., 1999. The Polar Volume Polar 
Ratio Method for Determining NO 2/NO XRatios in 
Modeling—Part I: Methodology. Journal of the Air & Waste Management Association, 49: 1324–1331. 
11Chu, S.H. and E.L. Meyer, 1991. Use of 
Ambient Ratios to Estimate Impact of NO XSources 
on Annual NO 2Concentrations. 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). 
12David J. Carruthers, Jenny R. Stocker, Andrew 
Ellis, Martin D. Seaton & Stephen E. Smith (2017) Evaluation of an explicit NO Xchemistry method in 
AERMOD, Journal of the Air & Waste Management 
Association, 67:6, 702–712, DOI: 10.1080/ 
10962247.2017.1280096. 
13Jenny Stocker, Martin Seaton, Stephen Smith, 
James O’Neill, Kate Johnson, Rose Jackson, David 
Carruthers (CERC). Evaluation of the Generic Reaction Set Method for NO
2conversion in 
AERMOD. The modification of AERMOD to include ADMS chemistry. August 8, 2023. Cambridge Environmental Research Consultants (CERC) Technical Report. 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
6processor 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 
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 NO
2 
Section 4.2.3.4 of the 2017 Guideline 
details a 3-tiered approach for evaluating the modeled impacts of NO
X 
sources, which was recommended to assess hourly and annual average NO
2 
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 NO
2and the 
subsequent conversion of NO to NO 2in 
the atmosphere. The tiered levels include: 
Tier 1—assuming that all emitted NO 
is converted to NO
2(full conversion). 
Tier 2—using the Ambient Ratio 
Method 2 (ARM2), which applies an assumed equilibrium ratio of NO
2to 
NO X, 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 NO
2to NO Xin-stack 
ratios.891 01 1  
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 NO
2 
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.
12The primary motivation behind the formulation and development of the GRSM NO
2screening option was to 
address photolytic conversion of NO 2to 
NO and to address the time-of-travel necessary for NO
Xplumes to convert the 
NO portion of the plume to NO 2via 
titration and entrainment of ambient ozone. The existing regulatory non- default Tier 3 NO
2screening 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 
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 NO
2 
photolysis reaction during daytime hours. Modeled source inputs for GRSM require NO
2/NO Xin-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 NO
Xconcentration inputs to 
resolve the daytime photolysis of NO 2 
reaction in equilibrium with ozone titration conversion of the NO portion of the NO
Xplume. GRSM hourly NO X 
concentration inputs can also vary by upwind sector concentration, as appropriate. Background NO
2 
concentrations are accounted for in the GRSM daytime equilibrium NO
2 
concentration estimates based on the chemical reaction balance between ozone entrainment and NO titration, photolysis of NO
2to NO, and ambient 
background NO 2participation in 
titration and photolysis reactions. Nighttime GRSM NO
2estimates are 
based on ozone entrainment and titration of available NO in the NO
X 
plume. Note that all hourly ozone and NO
Xambient inputs to GRSM must 
coincide with the hourly meteorological 
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14Environmental 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. 
15Under 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’’. 
16Snyder, 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. 17Incorporation 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. 
18Heist, 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. 
19U.S. EPA, 2021: PM Hot-spot Guidance; 
Transportation Conformity Guidance for Quantitative Hot-spot Analyses in PM
2.5and PM 10 
Nonattainment and Maintenance Areas. EPA–42–B– 
21–037. U.S. EPA, Office of Transportation and Air Quality, Ann Arbor, MI. 20U.S. Environmental Protection Agency, 2023. 
Draft Guidance on Developing Background 
Continued 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 
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
15model 
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. 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 18This 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.
19The 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. 
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 SO
2designations). 
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.
20This draft guidance 
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Concentrations for Use in Modeling 
Demonstrations. Publication No. EPA–454/P–23– 001. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 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. 
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 NO
2modeling 
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 PM
2.5. 
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 
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21Formerly designated as appendix A. 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
21to Appendix W to Part 51 
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: 
ÆMinimum standard deviation of the 
lateral velocity to the average wind direction; 
ÆMinimum mean wind speed; 
ÆMinimum and maximum meander 
factor; 
ÆMinimum standard deviation of the 
vertical wind speed; and 
ÆTime 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 
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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 NO
Xto NO 2. 
The Travel Time Reaction Method 
(TTRM) was implemented in AERMOD version 21112 as a stand-alone NO
X-to- 
NO 2conversion 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. 
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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 
■1. The authority citation for part 51 
continues to read as follows: 
Authority: 23 U.S.C. 101; 42 U.S.C. 7401– 
7671q. 
■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 PM
2.5 
4.2.3.6 Models for PM 10 
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 PM
2.5 
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 
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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 In-
puts for SIP Revisions of Inert 
Pollutants. 
8–2 ......... Point Source Model Emission In-
puts 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),
123conformity analyses,4and 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
5are 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)
6and PSD increments. Ample 
opportunity for public comment will be provided for each proposed change and public hearings scheduled. 
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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 (PM
2.5), 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.
78These 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.
9Eulerian 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
71 01 11 2 or all 
sources.13 14 15 Photochemical grid models 
simulate a more realistic environment for chemical transformation,
71 2but 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
16are 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.
17The 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 
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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.
23 
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 
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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 25and model 
performance evaluation exercises26 27that 
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
28provides 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.
29This 
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 
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aFor 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 PM
2.5SIP attainment 
demonstrations and other applications that use the model results in a relative sense, the model should not be biased toward overestimates. 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 29for 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;
aand 
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 (SO
2), nitrogen dioxide 
(NO 2), and primary particulates (PM 2.5and 
PM 10). 
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’’ 
uncertainties16can be on a similar scale. For 
example, Pasquill31estimates 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 33However, 
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 
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and increases the robustness of such 
comparisons when the number of available measurements are limited.
34 35Because 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 38and 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
39can 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 NO
2for NO 2 
modeling. These screening techniques are part of the EPA’s preferred modeling approach for NO
2and 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 (Z
o), Bowen ratio (B o), and albedo (r). For 
unstable cases, the convective mixing height (Z
ic) is calculated based on w*, and the 
mechanical mixing height (Z im) 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. CTSCREEN39 41can 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 
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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.
42CTSCREEN is 
designed to execute a fixed matrix of meteorological values for wind speed (u), standard deviation of horizontal and vertical wind speeds (sv, sw), vertical potential 
temperature gradient (dq/dz), friction velocity (u*), Monin-Obukhov length (L), mixing height (z
i) 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
42to 
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
43are 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 45The 
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.
27These 
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.
46The 
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.
50For 
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.
51General 
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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 SO
2are needed to meet NSR 
requirements to address compliance with the 
SO 2NAAQS and PSD increments, for SIP 
attainment demonstrations,53and for 
characterizing current air quality via modeling.
54SO 2is 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 SO
2 
on the short-term time scales of interest (i.e., 1-hour) and the sources of SO
2(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 SO
2modeling 
applications. For urban areas, AERMOD automatically invokes a half-life of 4 hours
55 
to SO 2. 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 NO
2concentrations are 
needed to meet NSR requirements to address 
compliance with the NO 2NAAQS and PSD 
increments. Impact of an individual source on ambient NO
2depends, in part, on the 
chemical environment into which the source’s plume is to be emitted. This is due to the fact that NO
2sources co-emit NO along 
with NO 2and any emitted NO may react with 
ambient ozone to convert to additional NO 2 
downwind. Thus, comprehensive modeling of NO
2would need to consider the ratio of 
emitted NO and NO 2, the ambient levels of ozone and subsequent reactions between ozone and NO, and the photolysis of NO
2to 
NO. 
b. Due to the complexity of NO 2modeling, 
a multi-tiered screening approach is required to obtain hourly and annual average estimates of NO
2.56Since 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 NO
2chemistry 
and is described in paragraphs c through e of this subsection. The tiers of NO
2modeling 
include: 
i. A first-tier (most conservative) ‘‘full’’ 
conversion approach; 
ii. A second-tier approach that assumes 
ambient equilibrium between NO and NO 2; 
and 
iii. A third-tier consisting of several 
detailed screening techniques that account for ambient ozone and the relative amount of NO and NO
2emitted from a source. 
c. For Tier 1, use an appropriate refined 
model (section 4.2.2) to estimate nitrogen oxides (NO
X) concentrations and assume a 
total conversion of NO to NO 2. 
d. For Tier 2, multiply the Tier 1 result(s) 
by the Ambient Ratio Method 2 (ARM2), which provides estimates of representative equilibrium ratios of NO
2/NO Xvalue based 
ambient levels of NO 2and NO Xderived from 
national data from the EPA’s Air Quality System (AQS).
57The national default for 
ARM2 includes a minimum ambient NO 2/ 
NO Xratio of 0.5 and a maximum ambient 
ratio of 0.9. The reviewing agency may establish alternative minimum ambient NO
2/ 
NO Xvalues based on the source’s in-stack 
emissions ratios, with alternative minimum 
ambient ratios reflecting the source’s in-stack NO
2/NO Xratios. Preferably, alternative 
minimum ambient NO 2/NO Xratios should be based on source-specific data which satisfies 
all quality assurance procedures that ensure data accuracy for both NO
2and NO Xwithin 
the typical range of measured values. However, alternate information may be used to justify a source’s anticipated NO
2/NO Xin- 
stack ratios, such as manufacturer test data, State or local agency guidance, peer-reviewed literature, and/or the EPA’s NO
2/NO Xratio 
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),
58the Plume Volume Molar 
Ratio Method (PVMRM),59and the Generic 
Set Reaction Method (GRSM)60 61are three 
detailed screening techniques that may be used for most sources. These three techniques use an appropriate section 4.2.2 model to estimate NO
Xconcentrations and 
then estimate the conversion of primary NO emissions to NO
2based on the ambient levels 
of ozone and the plume characteristics. OLM only accounts for NO
2formation 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 NO
2/NO Xin-stack ratios 
and that ambient ozone concentrations be provided on an hourly basis. GRSM requires 
hourly ambient NO
Xconcentrations 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 NO
2emissions, 
atmospheric transport and dispersion, and atmospheric transformation of NO to NO
2. 
Dispersion models that account for more explicit photochemistry may also be considered as an alternative model to estimate ambient impacts of NO
Xsources. 
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Figure 4–1: Multi-Tiered Approach for 
Estimating NO 2Concentrations 
4.2.3.5 Models for PM 2.5 
a. PM 2.5is a mixture consisting of several 
diverse components.62Ambient PM 2.5 
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 PM
2.5are needed to meet NSR 
requirements to address compliance with the PM
2.5NAAQS 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 PM
2.5, while the methods in 
section 5.4 are applicable for addressing the secondary component of PM
2.5. Guidance for 
PSD assessments is available for determining the best approach to handling sources of primary and secondary PM
2.5.63 
c. For SIP attainment demonstrations and 
regional haze reasonable progress goal analyses, effects of a control strategy on PM
2.5 
are estimated from the sum of the effects on the primary and secondary components composing PM
2.5. Model users should refer to 
section 5.4.1 and associated SIP modeling guidance
64for 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 PM
2.5hot-spot modeling 
for mobile sources. Specific guidance is available for analyzing direct PM
2.5impacts 
from highways, terminals, and other transportation projects.
65 
4.2.3.6 Models for PM 10 
a. Models for PM 10are needed to meet NSR 
requirements to address compliance with the PM
10NAAQS 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 PM
10modeling. 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
66is also available to assist in PM 10 
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 67Emission 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 PM
10. 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 PM
10hot-spot modeling 
for mobile sources. Specific guidance is available for analyzing direct PM
10impacts 
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 PM
2.5are 
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 NO
Xand volatile organic compounds 
(VOCs). Ozone formation is a complicated 
nonlinear process that requires favorable meteorological conditions in addition to VOC and NO
Xemissions. Sometimes complex 
terrain features also contribute to the build- up of precursors and subsequent ozone formation or destruction. 
c. PM
2.5can be either primary (i.e., emitted 
directly from sources) or secondary in nature. The fraction of PM
2.5which is primary versus 
secondary varies by location and season. In the United States, PM
2.5is 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. PM
2.5 
sulfate, nitrate, and ammonium ions are predominantly the result of chemical reactions of the oxidized products of SO
2and 
NO Xemissions with direct ammonia 
emissions.68 
d. Control measures reducing ozone and 
PM 2.5precursor emissions may not lead to 
proportional reductions in ozone and PM 2.5. 
Modeled strategies designed to reduce ozone or PM
2.5levels 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 PM
2.5. 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. PM
2.5is a mixture consisting of several 
diverse chemical species or components of 
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EP23OC23.000</GPH>lotter on DSK11XQN23PROD with PROPOSALS2Tier 1: Full 
Conversion 
Tier 2: 
Ambient 
Ratio 
Method 
Tier 3: 
Detailed 
Screening Assume total conversion of NO to NO2 
Multiply Tier 1 results by an appropriate ambient ratio 
l 
Detailed analysis with OLM, PVMRM, GRSM or other 
alternative screening model or technique 72845 Federal Register / Vol. 88, No. 203 / Monday, October 23, 2023 / Proposed Rules 
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 PM
2.5 
is estimated from the sum of the effects on the specific components comprising PM
2.5. 
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.
9Photochemical 
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.
9These 
types of models are appropriate for assessment of near-field and regional scale reactive pollutant impacts from specific sources
71 01 11 2 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 PM
2.5, 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 PM
2.5, model 
estimates of speciated PM 2.5components 
(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.
69There 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 69Because 
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 PM
2.5for 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 64Users 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 NO
Xand 
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.
71 11 2  
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 71should 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 PM
2.5 
a. Models that estimate PM 2.5 
concentrations are needed to guide the choice of strategies for the purposes of a nonattainment area demonstrating future year attainment of the PM
2.5NAAQS. 
Additionally, models that estimate PM 2.5 
concentrations are needed to assess impacts from specific sources or source complexes to satisfy requirements for NSR and other regulatory programs. Other purposes for PM
2.5modeling include estimating the 
impacts of specific events on air quality, 
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visibility, deposition impacts, and planning 
for areas that may be attaining the PM 2.5 
NAAQS. 
5.4.1 Models for NAAQS Attainment 
Demonstrations and Multi-Source Air Quality Assessments 
a. Models for PM
2.5are needed to assess the 
adequacy of a proposed strategy for meeting 
the annual and 24-hour PM 2.5NAAQS. 
Modeling primary and secondary PM 2.5can 
be a multi-faceted and complex problem, especially for secondary components of PM
2.5 
such as sulfates and nitrates. Control agencies with jurisdiction over areas with secondary PM
2.5problems 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 PM
2.5is 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 PM
2.5components 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.
71 0For situations where a project 
source emits both primary PM 2.5and PM 2.5 
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 
PM 2.5impacts 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 PM
2.5impacts 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 PM
2.5is 
available.70This 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 PM
2.5on 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).
72FLAG 
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 PM
2.5is the most 
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significant component of visibility 
impairment. The key components of PM 2.5 
contributing to visibility impairment include sulfates, nitrates, organic carbon, elemental carbon, and crustal material.
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 74have 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.
72The 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 75If 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.
9Photochemical 
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.
9These 
types of models are appropriate for assessment of near-field and regional scale reactive pollutant impacts from specific sources
71 01 11 2 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, PM
2.5,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.
64Model 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 NO
X, sulfur oxides, NH 3, 
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.
72To 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
72and 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 
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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
77in rural areas and 
McElroy-Pooler78in 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.
44Another 
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
79to 
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, A
o, 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 A
o, 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 A oas defined 
above; (2) If pis 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
81when 
evaluating this situation. 
f. Buoyancy-induced dispersion (BID), as 
identified by Pasquill,82is 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
40that 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.
84Selection 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.
85For 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 
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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.
88Since 
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 45for 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.
92In 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.
93No 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
65and Haul Road Workgroup Final 
Report67provide 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.
65Therefore, 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 PM
2.5, 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.
64Users 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 
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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, PM
2.5, 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 
BILLING CODE 6560–50–P 
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EP23OC23.001</GPH>lotter on DSK11XQN23PROD with PROPOSALS2Table 8-1. -Point Source Model Emission Inputs for SIP Revisions of Inert Pollutants1 
Averaging time Emissions limit 
(lb/MMBtul2 X Operating level 
(MMBtu/hrl2 X Operating factor 
(e.g., hr/yr, hr/day) 
Stationary Point Source(s) Subject to SIP Emissions Limit(s) Evaluation for Compliance with Ambient Standards 
(Including Areawide Demonstrations) 
Annual & quarterly .................... . 
Short term(:,; 24 hours) ............ . 
Annual & quarterly .................... . 
Short term(:,; 24 hours) ............ . Maximum allowable emission 
limit or federally enforceable 
permit limit. 
Maximum allowable em1ss1on 
limit or federally enforceable 
permit limit. Actual or design capacity 
(whichever is greater), or federally 
enforceable permit condition.3 
Actual or design capacity 
(whichever is greater), or federally 
enforceable permit condition.3 
Nearby Source(s) 6 
Maximum allowable emission 
limit or federally enforceable 
permit limit.6 
Maximum allowable emission 
limit or federally enforceable 
permit limit.6 Annual level when actually 
operating, averaged over the most 
recent 2 years.4 
Temporally representative level 
when actually operating, 
reflective of the most recent 2 
years.4' 7 
Other Source(s) 6' 9 Actual operating factor averaged 
over the most recent 2 years.4 
Continuous operation, i.e., all 
hours of each time period under 
consideration (for all hours of the 
meteorological database).5 
Actual operating factor averaged 
over the most recent 2 years.4• 8 
Continuous operation, i.e., all 
hours of each time period under 
consideration (for all hours of the 
meteorological database).5 
The ambient impacts from Non-nearby or Other Sources (e.g., natural, minor, distant major, and unidentified sources) can be represented by air 
quality monitoring data unless adequate data do not exist. 
1. For purposes of emissions trading, NSR, or PSD, other model input criteria may apply. See Section 8.2 for more information regarding attainment 
demonstrations of primary PM2.5. 
2. Terminology applicable to fuel burning sources; analogous terminology (e.g., lb/throughput) may be used for other types of sources. 
3. Operating levels such as SO percent and 75 percent of capacity should also be modeled to determine the load causing the highest concentration. 
4. Unless it is determined that this period is not representative. 
5. If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24-hours) and the source operation is constrained by a 
federally enforceable permit condition, an appropriate adjustment to the modeled emission rate may be made (e.g., if operation is only 8 a.m. to 4 
p.m. each day, only these hours will be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating 
time periods.) 
6. See Section 8.3.3. 
7. Temporally representative operating level could be based on Continuous Emissions Monitoring (CEM) data or other information and should be 
determined through consultation with the appropriate reviewing authority (Paragraph 3.0(b)). 
8. For those permitted sources not in operation or that have not established an appropriate factor, continuous operation (i.e., 8760) should be used. 
9. See Section 8.3.2. 72852 Federal Register / Vol. 88, No. 203 / Monday, October 23, 2023 / Proposed Rules 
BILLING CODE 6560–50–C 
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.
96The 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 
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EP23OC23.002</GPH>lotter on DSK11XQN23PROD with PROPOSALS2Table 8-2. -Point Source Model Emission Inputs for NAAQS Compliance in PSD Demonstrations 
Averaging time 
Annual & quarterly .................... . 
Short term(::; 24 hours) ............ . 
Annual & quarterly .................... . 
Short term(::; 24 hours) ............ . Emissions limit 
(lb/MMBtul1 X Operating level 
(MMBtu/hrl1 
Proposed Major New or Modified Source 
Maximum allowable emission 
limit or federally enforceable 
permit limit. 
Maximum allowable em1ss1on 
limit or federally enforceable 
permit limit. Design capacity or federally 
enforceable permit condition.2 
Design capacity or federally 
enforceable permit condition.2 
Nearby Source(s) 4' 5 
Maximum allowable emission 
limit or federally enforceable 
permit limit.5 
Maximum allowable em1ss1on 
limit or federally enforceable 
permit limit.5 Annual level when actually 
operating, averaged over the most 
recent 2 years.6 
Temporally representative level 
when actually operating, 
reflective of the most recent 2 
years.6'7 
Other Source(s) 5' 9 X Operating factor 
(e.g., hr/yr, hr/day) 
Continuous operation (i.e., 8760 
hours).3 
Continuous operation, i.e., all 
hours of each time period under 
consideration (for all hours of the 
meteorological database).3 
Actual operating factor averaged 
over the most recent 2 years.6• 8 
Continuous operation, i.e., all 
hours of each time period under 
consideration (for all hours of the 
meteorological database).3 
The ambient impacts from Non-nearby or Other Sources (e.g., natural, minor, distant major, and unidentified sources) can be represented by air 
quality monitoring data unless adequate data do not exist. 
1. Terminology applicable to fuel burning sources; analogous terminology (e.g., lb/throughput) may be used for other types of sources. 
2. Operating levels such as SO percent and 75 percent of capacity should also be modeled to determine the load causing the highest concentration. 
3. If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24-hours) and the source operation is constrained by a 
federally enforceable permit condition, an appropriate adjustment to the modeled emission rate may be made (e.g., if operation is only 8 a.m. to 4 
p.m. each day, only these hours will be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating 
time periods. 
4. Includes existing facility to which modification is proposed if the emissions from the existing facility will not be affected by the modification. 
Otherwise use the same parameters as for major modification. 
5. See Section 8.3.3. 
6. Unless it is determined that this period is not representative. 
7. Temporally representative operating level could be based on Continuous Emissions Monitoring (CEM) data or other information and should be 
determined through consultation with the appropriate reviewing authority (Paragraph 3.0(b)). 
8. For those permitted sources not in operation or that have not established an appropriate factor, continuous operation (i.e., 8760) should be used. 
9. See Section 8.3.2. 72853 Federal Register / Vol. 88, No. 203 / Monday, October 23, 2023 / Proposed Rules 
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, PM
2.5and 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 PM
2.5. 
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 96and 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° 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 
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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 96This 
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, PM
2.5,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
100shall 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)
109should 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,103and METPRO.104PCRAMMET 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 
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bFormerly the National Climatic Data Center 
(NCDC). 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
102or MPRM,103that 
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).
bThese 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
101was 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. 
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,
110large 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
110is 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
110provides 
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 
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also available.113As 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 
s
Eand sAmethods) 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 work114and 
has been evaluated with three site-specific databases.
115The two methods of stability 
classification that use wind fluctuation statistics, the s
Eand sAmethods, 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,
108to 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).
120Specific 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 
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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., NO
2chemistry). 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
47was 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
100and AERMOD Implementation 
Guide.81For 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.
110The 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
121has 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
100for guidance on 
wind speed thresholds. For prognostic data, see the latest guidance
109for 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 64for 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. 
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•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. 
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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 
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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. 
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1984. Calms Processor (CALMPRO) User’s Guide. Publication No. EPA–901/ 9–84–001. Office of Air Quality Planning and Standards, Region I, Boston, MA. (NTIS No. 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. 
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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 
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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 NO
2analyses 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 
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and terrain information is different from 
other EPA models; considerable detail for both types of input data is required and is supplied by preprocessors specifically designed for CTDMPLUS. CTDMPLUS requires the parameterization of individual hill shapes using the terrain preprocessor and the association of each model receptor with a particular hill. 
a. 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 sy & sz values, both flat terrain 
and hill induced (the difference shows the effect of the hill) 
—Concentration components due to WRAP, 
LIFT and FLAT. (3) If the user selects the TOPN option, a 
summary table of the top four concentrations at each receptor is given. If the ISOR option is selected, a source contribution table for every hour will be printed. 
(4) A separate output file of predicted (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°, 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 (H
c) 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 H
c, and the 
Froude number above H c. 
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 (H
c); see ‘‘Plume 
Behavior.’’ 
j. Horizontal Dispersion 
Horizontal dispersion for stable/neutral 
conditions is related to the turbulence 
velocity scale for lateral fluctuations, sv, for which a minimum value of 0.2 m/s is used. Convective scaling formulations are used to estimate horizontal dispersion for unstable conditions. 
k. Vertical Dispersion 
Direct estimates of vertical dispersion for 
stable/neutral conditions are based on 
observed vertical turbulence intensity, e.g., sw (standard deviation of the vertical velocity fluctuation). In simulating unstable (convective) conditions, CTDMPLUS relies on a skewed, bi-Gaussian probability density function (pdf) description of the vertical velocities to estimate the vertical distribution of pollutant concentration. 
l. Chemical Transformation 
Chemical transformation is not treated by 
CTDMPLUS. m. Physical Removal 
Physical removal is not treated by 
CTDMPLUS (complete reflection at the 
ground/hill surface is assumed). 
n. Evaluation Studies Burns, D.J., L.H. Adams and S.G. Perry, 1990. 
Testing and Evaluation of the 
CTDMPLUS Dispersion Model: Daytime Convective Conditions. 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. 
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Paumier, J.O., S.G. Perry and D.J. Burns, 
1992. CTDMPLUS: A Dispersion Model 
for Sources near Complex Topography. Part II: Performance Characteristics. Journal of Applied Meteorology, 31(7): 646–660. 
A.3 OCD (Offshore and Coastal Dispersion) 
Model 
Reference 
DiCristofaro, 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– 
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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 
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