95034 Federal Register / Vol. 89, No. 230 / Friday, November 29, 2024 / Rules and Regulations 

ENVIRONMENTAL PROTECTION 

AGENCY 

40 CFR Part 51 

[EPA–HQ–OAR–2022–0872; FRL–10391–02– 

OAR] 

RIN 2060–AV92 

Guideline on Air Quality Models; 

Enhancements to the AERMOD 

Dispersion Modeling System 

AGENCY: Environmental Protection 

Agency (EPA). 

ACTION: Final rule. 

SUMMARY: In this action, the 

Environmental Protection Agency (EPA) 

promulgates revisions to the Guideline 

on Air Quality Models (‘‘Guideline’’). 

The Guideline has been incorporated 

into the EPA’s regulations, satisfying a 

requirement under the Clean Air Act 

(CAA), for the EPA to specify, with 

reasonable particularity, models to be 

used in the Prevention of Significant 

Deterioration (PSD) program. The 

Guideline provides EPA-preferred 

models and other recommended 

techniques, as well as guidance for their 

use in predicting ambient 

concentrations of air pollutants. The 

EPA is revising 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. 

DATES: This rule is effective January 28, 

2025. 

ADDRESSES: The EPA has established a 

docket for this action under Docket ID 

No. EPA–HQ–OAR–2022–0872. All 

documents in the docket are listed on 

the https://www.regulations.gov 

website. Although listed in the index, 

some information is not publicly 

available, e.g., Confidential Business 

Information (CBI) or other information 

whose disclosure is restricted by statute. 

Certain other material, such as 

copyrighted material, is not placed on 

the internet and will be publicly 

available only in hard copy form. 

Publicly available docket materials are 

available electronically through https:// 

www.regulations.gov. 

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 ‘‘2024 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? 

C. Judicial Review 

D. List of Acronyms 

II. Background 

A. The Guideline on Air Quality Models 

and EPA Modeling Conferences 

B. The Twelfth and Thirteenth Conferences 

on Air Quality Modeling 

C. Alpha and Beta Categorization of NonRegulatory Options 

III. Discussion of Final Action on the 

Revisions to the Guideline 

A. Final Action 

IV. Ongoing Model Development 

V. 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 and Executive Order 14096: 

Revitalizing Our Nation’s Commitment 

to Environmental Justice for All 

K. Congressional Review Act (CRA) 

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 or 

review 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 programs 

in which air quality assessments are 

required under EPA regulation. 

Categories and entities potentially 

regulated by this action include: 

Category NAICSa code 

Federal/State/territorial/ 

local/Tribal government 924110 

a North American Industry Classification 

System. 

B. Where can I get a copy of this 

document? 

In addition to being available in the 

docket, an electronic copy of this final 

rule and relative supporting 

documentation will also be available on 

the 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/2024-appendix-wfinal-rule. 

C. Judicial Review 

Under section 307(b)(1) of the Clean 

Air Act (CAA), this final rule is 

‘‘nationally applicable’’ because it 

revises the Guideline on Air Quality 

Models, 40 CFR part 51, Appendix W. 

Therefore, petitions for judicial review 

of this final action must be filed in the 

U.S. Court of Appeals for the District of 

Columbia Circuit by January 28, 2025. 

Filing a petition for reconsideration by 

the Administrator of this final action 

does not affect the finality of the action 

for the purposes of judicial review, nor 

does it extend the time within which a 

petition for judicial review must be 

filed, and shall not postpone the 

effectiveness of such action. 42 U.S.C. 

7607(b)(1). This rule is also subject to 

section 307(d) of the CAA because it 

revises a regulation addressing a 

requirement under section 165(e)(3)(D) 

of the CAA, which is included in part 

C of title I of the CAA (relating to 

prevention of significant deterioration of 

air quality and protection of visibility). 

42 U.S.C. 7607(d)(1)(J). 

D. List of Acronyms 

AEDT Aviation Environmental Design Tool 

AERMET Meteorological data preprocessor 

for AERMOD 

AERMINUTE Pre-processor to AERMET to 

read 1-minute ASOS data to calculate 

hourly average winds for input into 

AERMET 

AERMOD American Meteorological Society 

(AMS)/EPA Regulatory Model 

AERSCREEN Program to run AERMOD in 

screening mode 

AERSURFACE Land cover data tool in 

AERMET 

AQRV Air Quality Related Value 

AQS Air Quality System 

ARM2 Ambient Ratio Method 2 

ASOS Automated Surface Observing 

Stations 

ASTM American Society for Testing and 

Materials 

Bo Bowen ratio 


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BID Buoyancy-induced dispersion 

BLP Buoyant Line and Point Source model 

BOEM Bureau of Ocean Energy 

Management 

BPIPPRM Building Profile Input Program 

for PRIME 

CAA Clean Air Act 

CAL3QHC Screening version of the 

CALINE3 model 

CAL3QHCR Refined version of the 

CALINE3 model 

CALINE3 CAlifornia LINE Source 

Dispersion Model 

CALMPRO Calms Processor 

CALPUFF California Puff model 

CAMx Comprehensive Air Quality Model 

with Extensions 

COARE Coupled Ocean-Atmosphere 

Response Experiment 

CFR Code of Federal Regulations 

CMAQ Community Multiscale Air Quality 

CO Carbon monoxide 

CTDMPLUS Complex Terrain Dispersion 

Model Plus Algorithms for Unstable 

Situations 

CTSCREEN Screening version of 

CTDMPLUS 

CTM Chemical transport model 

dq/dz Vertical potential temperature 

gradient 

DT Temperature difference 

EPA Environmental Protection Agency 

FAA Federal Aviation Administration 

FHWA Federal Highway Administration 

FLAG Federal Land Managers’ Air Quality 

Related Values Work Group Phase I Report 

FLM Federal Land Manager 

GEP Good engineering practice 

GRSM Generic Reaction Set Method 

GUI Graphical user interface 

IBL Inhomogeneous boundary layer 

ISC Industrial Source Complex model 

IWAQM Interagency Workgroup on Air 

Quality Modeling 

km kilometer 

L Monin-Obukhov length 

m meter 

m/s meter per second 

MAKEMET Program that generates a sitespecific matrix of meteorological 

conditions for input to AERMOD 

MCH Model Clearinghouse 

MCHISRS Model Clearinghouse 

Information Storage and Retrieval System 

MERPs Model Emissions Rates for 

Precursors 

METPRO Meteorological Processor for 

dispersion models 

MM5 Mesoscale Model 5 

MMIF Mesoscale Model Interface program 

MODELOPT Model option keyword 

MPRM Meteorological Processor for 

Regulatory Models 

NAAQS National Ambient Air Quality 

Standards 

NCEI National Centers for Environmental 

Information 

NH3 Ammonia 

NO Nitric oxide 

NOX Nitrogen oxides 

NO2 Nitrogen dioxide 

NSR New Source Review 

NWS National Weather Service 

OCD Offshore and Coastal Dispersion 

Model 

OCS Outer Continental Shelf 

OLM Ozone Limiting Method 

PCRAMMET Meteorological Processor for 

dispersion models 

P–G stability Pasquill-Gifford stability 

PM2.5 Particles less than or equal to 2.5 

micrometers in diameter 

PM10 Particles less than or equal to 10 

micrometers in diameter 

PRIME Plume Rise Model Enhancements 

algorithm 

PSD Prevention of Significant Deterioration 

PVMRM Plume Volume Molar Ratio 

Method 

r Albedo 

RHC Robust Highest Concentration 

RLINE Research LINE source model for 

near-surface releases 

RLINEXT Research LINE source model 

extended 

SCICHEM Second-order Closure Integrated 

Puff Model 

SCRAM Support Center for Regulatory 

Atmospheric Modeling 

SCREEN3 A single source Gaussian plume 

model which provides maximum groundlevel concentrations for point, area, flare, 

and volume sources 

SDM Shoreline Dispersion Model 

SIP State Implementation Plan 

SO2 Sulfur dioxide 

SRDT Solar radiation/delta-T method 

TSD Technical support document 

u Values for wind speed 

u* Surface friction velocity 

VOC Volatile organic compound 

w* Convective velocity scale 

WRF Weather Research and Forecasting 

model 

zi Mixing height 

Zo Surface roughness length 

Zic Convective mixing height 

Zim Mechanical mixing height 

sv, sw Horizontal and vertical wind speeds 

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 

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 Code of Federal Regulations 

(CFR) system for designating 

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-thescience, 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 


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95036 Federal Register / Vol. 89, No. 230 / Friday, November 29, 2024 / Rules and Regulations 

1U.S. Environmental Protection Agency, 2023. 

Draft Guidance on Developing Background 

Concentrations for Use in Modeling 

Demonstrations. Publication No. EPA–454/P–23– 

001. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. 

meteorological data for informing the 

dispersion models. The EPA further 

revised the Guideline on November 9, 

2005 (70 FR 68218), to adopt AERMOD 

as the preferred model for near-field 

dispersion of emissions for distances up 

to 50 kilometers. 

The Ninth Conference on Air Quality 

Modeling was held in October 2008 and 

emphasized the following topics: 

reinstituting the Model Clearinghouse, 

review of non-guideline applications of 

dispersion models, regulatory status 

updates of AERMOD and CALPUFF, 

continued discussions on the use of 

prognostic meteorological data for 

informing dispersion models, and 

presentations reviewing the available 

model evaluation methods. To further 

inform the development of additional 

revisions to the Guideline, we held the 

Tenth Conference on Air Quality 

Modeling in March 2012. The 

conference addressed updates on: the 

regulatory status and future 

development of AERMOD and 

CALPUFF, review of the Mesoscale 

Model Interface (MMIF) prognostic 

meteorological data processing tool for 

dispersion models, draft modeling 

guidance for compliance 

demonstrations of the fine particulate 

matter (PM2.5) national ambient air 

quality standards (NAAQS), modeling 

for compliance demonstration of the 1- 

hour nitrogen dioxide (NO2) and sulfur 

dioxide (SO2) NAAQS, and new and 

emerging models/techniques for future 

consideration under the Guideline to 

address single-source modeling for 

ozone and secondary PM2.5, as well as 

long-range transport and chemistry. 

The Eleventh Conference on Air 

Quality Modeling was held in August 

2015 and included the public hearing 

for a 2015 proposed revision of the 

Guideline. The conference included 

presentations summarizing the 

proposed updates to the AERMOD 

Modeling System, replacement of 

CALINE3 with AERMOD for modeling 

of mobile sources, incorporation of 

prognostic meteorological data for use 

in dispersion modeling, the proposed 

screening approach for long-range 

transport for NAAQS and PSD 

increments assessments with use of 

CALPUFF as a screening technique 

rather than an EPA-preferred model, the 

proposed 2-tiered screening approach to 

address ozone and PM2.5 in PSD 

compliance demonstrations, the status 

and role of the Model Clearinghouse, 

and updates to procedures for singlesource 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 45344). These proposed 

revisions were adopted and reflected in 

the most recent version of the Guideline, 

promulgated on January 17, 2017 (82 FR 

5182). 

B. The Twelfth and Thirteenth 

Conferences on Air Quality Modeling 

Following the 2017 revision of the 

Guideline, the Twelfth Conference on 

Air Quality Modeling 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 the 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, nearfield and long-range model evaluation 

criteria, NO2 modeling techniques, 

plume rise, deposition, and single 

source ozone and PM2.5 modeling 

techniques. At the conclusion of the 

expert panels and invited presentations, 

there were several presentations given 

by the public, including industrial trade 

groups, on recommended areas for 

additional model development and 

future revision in the Guideline. 

Based on the engagement and 

presentations from the Twelfth 

Conference and continuing model 

formulation research and development 

activities in the years since 2019, the 

EPA proposed new revisions to the 

Guideline on October 12, 2023, 

including enhancements to the 

formulation and application of the 

EPA’s near-field dispersion modeling 

system, AERMOD, updates to the 

recommendations for the development 

of appropriate background 

concentration for cumulative impact 

analyses, and various typographical 

updates to the existing regulation (88 FR 

72826). The Thirteenth Conference on 

Air Quality Modeling, held on 

November 14–15, 2023, provided a 

formal venue for EPA presentations to 

the public on the October 2023 

proposed revisions to the Guideline and 

AERMOD. The Thirteenth Modeling 

Conference also served as the public 

hearing for the October 2023 proposed 

rule. 

Specific to the AERMOD Modeling 

System, the October 2023 Guideline 

proposed rule included an update to the 

AERMET meteorological preprocessor 

for AERMOD that would add the 

capability to process measured and 

prognostic marine-based meteorology 

for offshore applications. Additionally, 

the proposed rule had separate 

AERMOD updates that would 

incorporate a new Tier 3 screening 

method for the conversion of nitrogen 

oxides (NOX) emissions to NO2 and 

would add a new source type for 

modeling vehicle roadway emissions. 

Finally, the proposed rule suggested 

minor revisions to the recommendations 

regarding the determination of 

appropriate model input data, 

specifically background concentration, 

for use in NAAQS implementation 

modeling demonstrations in section 8.3 

of the Guideline. In conjunction with 

the October 2023 Guideline proposed 

rule, the EPA developed the Draft 

Guidance on Developing Background 

Concentrations for Use in Modeling 

Demonstrations.1 This draft guidance 

document detailed the EPArecommended framework with stepwise 

considerations to assist permit 

applicants in characterizing a credible 

and appropriately representative 

background concentration for 

cumulative impact analyses through 

qualitative and semi-quantitative 

considerations within a transparent 

process using the variety of emissions 

and air quality data including the 

contributions from nearby sources in 

multi-source areas. 

All of the presentations, along with 

the transcript of the conference and 

public hearing proceedings, are 

available in the docket for the 

Thirteenth Conference on Air Quality 

Models (Docket ID No. EPA–HQ–OAR– 

2022–0872). Additionally, all the 

materials associated with the Thirteenth 

Conference and the public hearing are 

available on the EPA’s SCRAM website 

at https://www.epa.gov/scram/13thconference-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 


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2https://www.epa.gov/scram/12th-conference-airquality-modeling. 

3Fairall, 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. 

4Evaluation 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. 

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. A pathway 

such as this that facilitates more 

frequent and active engagement with the 

external modeling 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 require further 

testing, performance evaluation, and/or 

vetting through peer review and, thus, 

are not intended for regulatory 

applications of the model. 

Beta options, on the other hand, have 

been demonstrated to be suitable and 

applicable to the modeling problem at 

hand on a theoretical basis, have 

undergone scientific peer review, and 

are supported with performance 

evaluations using available and 

adequate databases that demonstrate 

improved model performance and no 

inappropriate model biases. 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 sitespecific 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. With the release of 

AERMOD version 24142, each of the 

beta options that existed in version 

23132 are being promulgated as 

regulatory updates to the formulation of 

AERMOD. All previous alpha options in 

version 23132 are being retained as 

alpha options in version 24142. No 

options are being added as beta options 

and no alpha options are being updated 

to beta status. 

III. Discussion of Final Action on the 

Revisions to the Guideline 

In this action, the EPA is 

promulgating revisions 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 updates 

to the Guideline. There are additional 

editorial changes being made to the 

Guideline to correct minor 

typographical errors found in the 2017 

Guideline and to update website links. 

A. Final Action 

This section provides a detailed 

overview of the substantive changes 

being finalized in the Guideline to 

improve the science of the models and 

approaches used in regulatory 

assessments. 

1. 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, 2019,2 and additional relevant 

research since 2017, the EPA and other 

researchers have conducted additional 

model evaluations and developed 

changes to the model formulation of the 

AERMOD Modeling System to improve 

model performance in its regulatory 

applications. One update is to the 

AERMET meteorological preprocessor 

for AERMOD. This update provides the 

capability to process measured and 

prognostic marine-based meteorology 

for offshore applications. Separate 

updates are related to the AERMOD 

dispersion model and include (1) a new 

Tier 3 screening method for the 

conversion of nitrogen oxides (NOX) 

emissions to NO2 and (2) a new source 

type for modeling vehicle roadway 

emissions. 

Each of these formulation updates to 

the AERMOD Modeling System was 

provided as a non-regulatory beta option 

in the version 23132 release of the 

relevant AERMOD Modeling System 

components. With the release of the 

AERMOD Modeling System version 

24142, the EPA has removed the nonregulatory beta restriction and is 

finalizing 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 

The EPA received a few specific 

comments in support of adding the 

Coupled Ocean-Atmosphere Response 

Experiment (COARE) into AERMET. 

Therefore, the EPA is finalizing the 

integration of the COARE 3 4 algorithms 

to AERMET for meteorological data 

processing in applications using either 

observed or prognostic meteorological 

data in overwater marine boundary 

layer environments. 

As discussed in the preamble to the 

proposed rule, 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. The addition 

of the COARE algorithms to AERMET 

replaces the need of the standalone 

AERCOARE program used for overwater 

applications and ensures that the 

COARE algorithms are updated 

regularly as part of routine AERMET 

updates. For prognostic applications 

processed through the Mesoscale Model 

Interface (MMIF), the addition of 

COARE algorithms to AERMET replaces 

the need to run MMIF for AERCOARE 

input, and the user can run MMIF for 

AERMET input for overwater 

applications. The COARE option is 

selected in AERMET by the user with 

the METHOD COARE RUN–COARE* 

record in the AERMET Stage 2 input 

file. 

We are including the COARE 

algorithms into AERMET as a nondefault regulatory option. This 

eliminates the previous alternative 


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5Environmental Protection Agency, 2024. 

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–24–005. Office 

of Air Quality Planning & Standards, Research 

Triangle Park, NC. 

6 Incorporation and Evaluation of the RLINE 

source type in AERMOD for Mobile Source 

Applications. EPA–2023/R–23–011, Office of Air 

Quality Planning and Standards, RTP, NC. 

7Owen, R., et al., 2024. Incorporation of RLINE 

into AERMOD: An update and evaluation for 

mobile source applications. Journal of the Air & 

Waste Management Association, Manuscript 

submitted for publication. 

8U.S. EPA, 1992: Guideline for modeling carbon 

monoxide from roadway intersections. EPA–454/R– 

92–005. U.S. EPA, Office of Air Quality Planning 

& Standards, RTP, NC. 

9U.S. EPA, 2021: PM Hot-spot Guidance; 

Transportation Conformity Guidance for 

Quantitative Hot-spot Analyses in PM2.5 and PM10

Nonattainment and Maintenance Areas. EPA–42–B– 

21–037. U.S. EPA, Office of Transportation and Air 

Quality, Ann Arbor, MI. 

model demonstration requirements for 

use of AERMOD in marine 

environments, and its use is contingent 

upon consultation with the EPA 

Regional Office and appropriate 

reviewing authority to ensure that 

platform downwash and shoreline 

fumigation are adequately considered in 

the modeling demonstration. Also note 

that since COARE is a non-default 

regulatory option, the user no longer 

must include the BETA option with the 

MODELOPT keyword in the AERMOD 

input file to use AERMET data 

generated using the COARE algorithms. 

b. Addition of a New Tier 3 Detailed 

Screening Technique for NO2

As supported by the discussions in 

the October 2023 proposed revisions to 

the Guideline, and based on the public 

comments received, the EPA is 

finalizing adoption of the Generic 

Reaction Set Method (GRSM) as a 

regulatory non-default, detailed Tier 3 

NO2 screening option in AERMOD 

version 24142. 

As discussed in the preamble to the 

October 2023 proposed revisions to the 

Guideline, the functionality of the 

GRSM implementation in AERMOD is 

similar to that of the existing PVMRM 

and OLM Tier 3 NO2 schemes, with 

exception to some additional input 

requirements necessary (i.e., hourly 

NOX inputs) for treatment of the reverse 

NO2 photolysis reaction during daytime 

hours. Background NO2 concentrations 

are accounted for in the GRSM daytime 

equilibrium NO2 concentration 

estimates based on the chemical 

reaction balance between ozone 

entrainment and NO titration, 

photolysis of NO2 to NO, and ambient 

background NO2 participation in 

titration and photolysis reactions. 

Similar to PVMRM and OLM, nighttime 

GRSM NO2 estimates are based on 

ozone entrainment and titration of 

available NO in the NOX plume. 

The EPA received several comments 

in support of the proposed adoption of 

GRSM as a Tier 3 NO2 screening option 

in AERMOD. Several commenters 

requested further clarification and 

guidance from the EPA on the 

suitability and regulatory modeling 

application of GRSM, as well as the 

selection of GRSM instead of PVMRM 

and OLM for detailed Tier 3 NO2

screening modeling demonstrations. 

The EPA plans to draft NO2 modeling 

guidance in the future to respond to 

these comments. 

One commenter notes that the GRSM 

supporting documentation is unclear on 

what assessment or evaluation was 

conducted that supports the assertion 

that updates to the GRSM code in 

AERMOD version 23132 address NO2

model overpredictions farther 

downwind, thereby improving model 

performance. As discussed in the 

preamble of the October 2023 proposed 

revisions to the Guideline, 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 0.5 to 3 km 

for typical point source releases). In 

response to this comment, the GRSM 

Technical Support Document (TSD) has 

been updated with clarifying 

information in an appendix.5

c. Addition of RLINE as Mobile Source 

Type 

The EPA is finalizing RLINE as a new 

regulatory source type in AERMOD for 

mobile source modeling. The inclusion 

of the RLINE source type is in addition 

to the AREA, LINE, and VOLUME 

source types already available for 

mobile source modeling, giving 

additional flexibility to users in 

characterizing transportation projects 

when modeling them with AERMOD. 

As stated in the preamble to the 

proposed rule, the addition of RLINE as 

a regulatory source type is an extension 

of the 2017 update to the Guideline in 

which AERMOD replaced CALINE3 as 

the Addendum A model for mobile 

source modeling. The RLINE source 

type has undergone significant 

evaluation by the EPA and FHWA as 

part of the Interagency Agreement 

between the EPA and FHWA and, as 

noted in the preamble to the proposed 

rule, has shown improved performance 

since its introduction into AERMOD in 

2019.6 7

The EPA received several comments 

supporting the inclusion of RLINE as a 

regulatory option into AERMOD. 

Several commenters also mentioned the 

need to update the EPA’s guidance. The 

EPA agrees that practitioners will need 

guidance for using RLINE, and we plan 

to update the relevant guidance. 

The EPA also received a comment 

supporting the retention of the 

RLINEXT source type as an ALPHA 

option. As described below, the EPA has 

retained the RLINEXT as an ALPHA 

option for further model development 

and evaluation. 

Commenters also asked whether the 

CAL3QHC model could continue to be 

used for carbon monoxide (CO) hot-spot 

analyses. The EPA confirms that the 

1992 CO Guidance that employs 

CAL3QHC for CO screening analyses is 

still an available screening approach for 

CO hot-spot analyses of transportation 

projects.8 In the EPA’s January 17, 2017 

final rule, section 4.2.3.1(b) of the 

Guideline was modified, and the 1992 

technical guidance (with CAL3QHC) 

remains in place as the recommended 

approach for CO screening analyses (82 

FR 5192). 

The RLINE source type includes the 

ability to include terrain in AERMOD 

modeling as well as the urban source 

algorithms in AERMOD. However, as 

stated in the preamble to the proposed 

rule, the inclusion of RLINE with terrain 

use does not change the EPA’s 

recommendation in the PM Hot-spot 

Guidance 9 to model transportation 

projects with FLAT terrain. Since RLINE 

is now a regulatory source type, the user 

no longer has to include the BETA flag 

with the MODELOPT keyword in the 

AERMOD input file to use the RLINE 

source, including the use of RLINE with 

the AERMOD urban option or RLINE 

with terrain. 

The RLINEXT source type is based on 

the same algorithm as the RLINE source 

type but includes additional parameters 

to allow modeling of other features of 

the source, such as solid barriers and 

the source below grade. As these are not 

yet fully developed, the RLINEXT 

source type continues to be an ALPHA 

option. Therefore, the ALPHA flag must 

be included with MODELOPT keyword 

when using an RLINEXT source. 

d. Support Information, Documentation, 

and Model Code 

Model performance evaluation and 

peer-reviewed scientific references for 

each of these three updates to the 

AERMOD Modeling System are cited 

and placed in the docket for this action. 

An updated user’s guide and model 

formulation documents for version 


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Federal Register / Vol. 89, No. 230 / Friday, November 29, 2024 / Rules and Regulations 95039 

10U.S. Environmental Protection Agency, 2024. 

Guidance on Developing Background 

Concentrations for Use in Modeling 

Demonstrations. Publication No. EPA–454/R–24– 

003. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. 

24142 have also been placed in the 

docket for this action. We have updated 

the summary description of the 

AERMOD Modeling System to 

Addendum A of the Guideline to reflect 

these 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. Updates to Recommendations on the 

Development of Background 

Concentration 

Based on comments received on the 

2023 proposed revisions to the 

Guideline, the EPA is finalizing 

revisions to section 8 of the Guideline 

to refine the recommendations regarding 

the determination of appropriate model 

input data, specifically background 

concentration, for use in NAAQS 

implementation modeling 

demonstrations (e.g., PSD compliance 

demonstrations, SIP demonstrations for 

inert pollutants, and SO2 designations). 

These revisions include the removal of 

the term ‘‘significant concentration 

gradient’’ and the associated 

recommendations which are replaced 

with a more robust framework for 

characterizing background 

concentrations for cumulative modeling 

with particular attention to identifying 

and modeling nearby sources in multisource areas. 

The EPA has revised the 

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 is finalizing 

the proposed framework, which 

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-existing air quality modeling to 

appropriately represent background 

concentrations for the cumulative 

impact analysis. 

The EPA received numerous 

comments on the proposed revisions to 

section 8 of the Guideline. Multiple 

commenters expressed their support of 

the revisions to section 8.3 and the 

removal of the recommendation of 

identifying sources which cause a 

significant concentration gradient from 

the Guideline. Based on this support, 

the EPA is removing the 

recommendations which highlight the 

use of significant concentration 

gradients and finalizing the framework 

of stepwise considerations. 

Several commenters expressed their 

perspective on the contents of the 

framework of stepwise considerations 

for developing background 

concentrations and its future 

implementation. Some commenters 

expressed their concern that the 

framework would limit the flexibility 

that has been afforded to permitting 

authorities, while other commenters 

stated that the framework documents 

steps that have been unofficially used 

by air agencies and modelers for many 

years. Additionally, some commenters 

feel that the steps detailed in the 

framework do not remove the ambiguity 

in the process of developing a 

representative background 

concentration. The EPA recognizes that 

preferred methods for developing 

background concentrations vary at both 

the State and permit-specific level, 

which explains the variety of stances on 

the framework of stepwise 

considerations. With this action, the 

EPA is finalizing the proposed revisions 

to section 8 of the Guideline. These 

revisions strike an appropriate balance 

of the interests raised by comments by 

more clearly documenting the general 

steps recommended for determining 

background concentrations while 

leaving discretion for and 

recommending the exercise of 

professional judgement by the reviewing 

authority to ensure that the background 

concentration is appropriately 

represented in each cumulative impact 

analysis. In conjunction with the 

finalized revisions to section 8 of the 

Guideline, the EPA is also finalizing the 

Guidance on Developing Background 

Concentrations for Use in Modeling 

Demonstrations.10 This guidance 

document details the EPArecommended 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. The EPA requested 

that the public submit comment through 

the docket associated with the October 

2023 proposed revisions to the 

Guideline and received many comments 

requesting clarification or revisions 

which should be incorporated in the 

finalized version of the guidance. A 

majority of the comments were 

generally requests for the EPA to 

include examples and additional details 

in the finalized version of the guidance. 

The requests for additional details 

ranged from minor sentence revisions to 

improve clarity to requests for specific 

metrics that may be used in the process 

and requests for how to implement the 

framework for specific modeling cases. 

The EPA agreed with the commenters 

requesting examples and has 

incorporated hypothetical examples in 

the finalized version of the guidance to 

help the stakeholder community 

implement the framework of stepwise 

considerations. Additionally, the EPA 

has revised the guidance to address 

many of the clarification concerns stated 

by commenters. 

3. Transition Period for Applicability of 

Revisions to the Guideline 

As noted in the DATES section above, 

this rule is effective December 30, 2024. 

For all regulatory applications covered 

under the Guideline, the changes to the 

Addendum A preferred models and 

revisions to the requirements and 

recommendations of the Guideline 

should be integrated into the regulatory 

processes of respective reviewing 

authorities and followed by applicants 

as quickly as practicable. The EPA 

encourages the transition to the revised 

2024 version of the Guideline by no 

later than November 29, 2025. During 

the 1-year period following 

promulgation, protocols for modeling 

analyses based on the 2017 version of 

the Guideline, which are submitted in a 

timely manner, may be approved at the 

discretion of the appropriate reviewing 

authority. 

The EPA notes that some States have 

approved SIP provisions that authorize 

the use of revised versions of the 

Guideline, whereas other States have 

SIP provisions that will require revision 

to provide for the use of a revised 

Guideline, such as the version 

addressed in this notice. States that 

have incorporated an older version of 

the Guideline into their SIPs in order to 

satisfy an infrastructure SIP requirement 

under CAA section 110(a)(2) should 

update their regulations as necessary to 

incorporate this latest version of the 

Guideline as soon as practicable into 

their SIPs, but must do so no later than 


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95040 Federal Register / Vol. 89, No. 230 / Friday, November 29, 2024 / Rules and Regulations 

February 7, 2027, which is the due date 

for 2024 PM2.5 infrastructure SIP 

submittals. For States that have chosen 

to satisfy the modeling and permitting 

requirements of CAA section 110(a)(2) 

by adopting specific versions of the 

Guideline in their State regulations, the 

EPA expects States to update their 

regulations to include this most recent 

version of the Guideline by the 

infrastructure SIP submittal due date. 

The EPA will at that time be evaluating 

infrastructure SIP submissions for 

compliance with applicable 

infrastructure SIP requirements under 

CAA section 110, including CAA 

sections 110(a)(2)(K), (C), (D)(i)(II), and 

(J). However, the need for such an 

update to a State or local regulation 

should not, in most cases, preclude 

regulatory application of the changes to 

the Guideline adopted in this rule in 

regulatory actions. 

All applicants are encouraged to 

consult with their respective reviewing 

authority and EPA Regional office as 

soon as possible to assure acceptance of 

their modeling protocols and/or 

modeling demonstration during this 

period of regulatory transition. 

4. Revisions by Section 

a. Throughout Appendix W to Part 

51—Guideline on Air Quality Models, 

the EPA is revising the phrase 

‘‘Appendix A’’ to ‘‘Addendum A’’ in 

accordance with the requirements of the 

Government Printing Office (GPO). 

b. Section 1.0—Introduction 

During publication, in the first 

sentence of paragraph (i), the phrase 

‘‘Appendix A’’ was separated, thereby 

ending the sentence with ‘‘Appendix’’ 

and inadvertently creating a 

subparagraph (A). The EPA is correcting 

paragraph (i) so that the first sentence 

ends with the phrase ‘‘Addendum A,’’ 

and including the rest of the text from 

the inadvertently created paragraph (A). 

c. Section 3.0—Preferred and 

Alternative Air Quality Models 

The EPA is updating an outdated 

website link in section 3.0(b). 

In sections 3.1.1(c) and 3.1.2(a), the 

phrase ‘‘Appendix A’’ was separated, 

ending the sentences with ‘‘Appendix’’ 

and inadvertently creating a 

subparagraph (A). The EPA is correcting 

these sections by combining the 

inadvertently created subparagraph (A) 

with the sentences that end with 

‘‘Appendix,’’ revising the phrase to 

‘‘Addendum A,’’ and including the rest 

of the text from the inadvertently 

created subparagraphs (A). 

d. Section 4.0—Models for Carbon 

Monoxide, Lead, Sulfur Dioxide, 

Nitrogen Dioxide and Primary 

Particulate Matter 

The EPA is updating reference 

numbers where necessary due to added 

references. 

In sections 4.1(b) and 4.2.2(a), the 

phrase ‘‘Appendix A’’ was separated, 

ending the sentences with ‘‘Appendix’’ 

and inadvertently creating a 

subparagraph (A). The EPA is correcting 

these sections combining the 

inadvertently created subparagraph (A) 

with the sentences that end with 

‘‘Appendix,’’ revising the phrase to 

‘‘Addendum A,’’ and including the rest 

of the text from the inadvertently 

created subparagraphs (A). 

In section 4.2.2.1, the EPA is adding 

a new paragraph (f) regarding the use of 

AERMOD in certain overwater 

situations. A typographical correction is 

made in section 4.2.2.1(b). 

The EPA is amending 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 is revising 

paragraph (e) to adopt the Generic 

Reaction Set Method (GRSM) as a 

regulatory Tier 3 detailed screening 

technique for NO2 modeling 

demonstrations. Sentences in this 

section are being updated to incorporate 

GRSM with the existing regulatory Tier 

3 screening techniques OLM and 

PVMRM. An additional statement is 

made indicating GRSM model 

performance may be better than OLM 

and PVMRM under certain source 

characterization situations. The EPA 

also is adding two references to the 

section including one for the peerreviewed paper on development and 

evaluation of GRSM, and a second 

reference to the EPA Technical Support 

Document (TSD) on GRSM. 

The EPA is revising Table 4–1 in 

section 4.2.3.4(f) to include GRSM as a 

Tier 3 detailed screening option. 

e. Section 5.0—Models for Ozone and 

Secondarily Formed Particulate Matter 

The EPA is updating reference 

numbers where necessary due to added 

references. 

In section 5.2, the EPA is revising 

paragraph (c) to include a reference for 

guidance on the use of models to assess 

the impacts of emissions from single 

sources on secondarily formed ozone 

and PM2.5. 

f. Section 6.0—Modeling for Air Quality 

Related Values and Other Governmental 

Programs 

The EPA is updating reference 

numbers where necessary due to added 

references and is updating an outdated 

website link in section 6.3(a). 

g. Section 7.0—General Modeling 

Considerations 

The EPA is updating reference 

numbers where necessary due to added 

references. 

In section 7.2.3, the EPA is revising 

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 is 

revising 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. 

h. Section 8.0—Model Input Data 

The EPA is updating reference 

numbers where necessary due to added 

references. 

The EPA is revising Table 8–1 and 

Table 8–2 to correct typographical errors 

and update the footnotes in each of the 

tables. 

The EPA is revising 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 provides a stepwise 

framework for modeling isolated single 

sources and multi-source areas as part of 

a cumulative impact analysis. The EPA 

also is removing 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 multisource areas. 

The EPA is removing paragraph (d) in 

section 8.3.2 and renumber paragraphs 

(e) and (f) to (d) and (e), respectively. 

The content of paragraph (d) is being 

included in the revisions of paragraph 

(a) in section 8.3.2. 

In section 8.3.3, the EPA is revising 

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 is removing 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 


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to the lack of definition of this term in 

the context of modeling multi-source 

areas. The EPA is also removing an 

undefined acronym inadvertently 

included in the October 2023 Guideline 

proposal in section 8.3.3(b)(ii). Finally, 

the EPA is revising 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 revisions to 

Tables 8–1 and 8–2. 

In section 8.4.1, the EPA is including 

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 is also revising the 

heading for section 8.4.1(d) to correct a 

capitalization typographical error. 

The EPA is revising 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 is 

revising 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 is revising 

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 is revising paragraph (a) of 

section 8.4.3.2 to remove references to 

specific Web links and to state that 

users should refer to the latest guidance 

documents for Web links. 

The EPA is adding 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 

being replaced with guidance to consult 

the appropriate guidance documents for 

the latest thresholds. 

i. Section 9.0—Regulatory Application 

of Models 

The EPA is updating reference 

numbers where necessary due to added 

references. 

In section 9.2.3, the EPA is revising 

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 revisions to Tables 8–1 and 8–2. 

j. Section 10.0—References 

The EPA is updating 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 is adding 

references to support updates to the 

AERMOD Modeling System described 

in this update to the Guideline. 

5. Revisions to Addendum A to 

Appendix W to Part 51 

a. Section A.0 

The EPA is revising 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 is revising the References 

section to include additional references 

that support our updates to the 

AERMOD Modeling System consistent 

with our October 2023 proposed 

revisions to the Guideline and 

AERMOD. 

In the Abstract section, the EPA is 

adding line type sources as one of the 

source types AERMOD can simulate. 

The EPA is revising 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 removes the need to request an 

alternative model demonstration for 

such applications. The EPA also is 

clarifying elevation data that can be 

used in AERMOD, specifically the 

change in the name of the U.S. 

Geological Survey (USGS) National 

Elevation Dataset (NED) to 3D Elevation 

Program (3DEP). For consistency, 

references to NED are being updated to 

3DEP throughout section A.1. 

The EPA is revising section A.1(b) to 

include prognostic data as 

meteorological input to the AERMOD 

Modeling System, as applicable. 

The EPA is revising section A.1(l) to 

include the Generic Reaction Set 

Method in the discussion on chemical 

transformation in AERMOD. We also are 

clarifying the status of the different 

deposition options in A.1(l). 

The EPA is revising section A.1(n) to 

include references to additional 

evaluation studies to support our 

updates to the AERMOD Modeling 

System. 

The EPA is updating a reference 

added in the October 2023 Guideline 

proposal in section A.1 from a 

manuscript to an existing EPA 

Technical Support Document. 

c. Section A.3 

In section A.3, the EPA is removing 

the reference to the Bureau of Ocean 

Energy Management’s (BOEM) outdated 

guidance. 

IV. Ongoing Model Development 

With the release of AERMOD version 

24142, no additional beta options 

remain within AERMOD. The alpha 

options in version 23132 have all been 

retained in version 24142. The EPA is 

committed to the continued 

maintenance and development of 

AERMOD to expand the model’s 

capabilities and improve performance 

where needed. Ongoing model 

development priorities for model 

improvement, many of which are 

represented in the version 24142 as 

alpha options, are described below. 

• Modifications to PRIME Building 

Downwash 

Beginning with AERMOD version 

19191, two distinct sets of alpha options 

were added that modify the formulation 

of the building downwash algorithm, 

PRIME. The two sets of options, ORD_

DWNW and AWMADWNW, were 

developed independently by the EPA’s 

Office of Development and Research 

(ORD) and the Air & Waste Management 

Association (A&WMA), respectively. 

With a couple of exceptions, the options 

within each set can be employed 

individually or combined with other 

options from each set. In addition to 

these alpha options that modify the 

formulation of PRIME, are the building 

input parameters required by the 

algorithm. In conjunction with the 

assessment and evaluation of these 

alpha options, the EPA is focused on 

improvement of the building 

preprocessor, BPIPPRM, and the 

parameterization of the buildings that is 

input to AERMOD. 

• Offshore Modeling 

To enhance AERMOD’s offshore 

modeling capabilities with the goal of 

replacing the Offshore Coastal 

Dispersion (OCD) dispersion model as 

the EPA’s preferred model for offshore 

dispersion modeling applications, a 

platform downwash alpha option 

(PLATFORM), adapted from OCD, was 

incorporated into AERMOD version 

22112. This model enhancement 

specifically treats building downwash 

effects from raised offshore drilling 

platforms. The PLATFORM option 

continues to undergo refinements and 

evaluation. In addition to the 

PLATFORM alpha option, the EPA is 

implementing a shoreline fumigation 

algorithm into AERMOD, also needed 

for the eventual goal of replacing the 

OCD model. 

• Extended RLINE Source Type 

Including Barriers and Depressed 

Roadways 

The extended RLINE source type 

(RLINEXT) source type was 

implemented in AERMOD version 


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18181 as an alpha option that allows for 

a more refined characterization of an 

individual road segment. It accepts 

separate inputs for the elevations of 

each end of the road segment with 

added capability to model road 

segments that include roadway barriers 

(RBARRIER) and/or are characterized as 

depressed roadways (RDEPRESS). 

RBARRIER and RDEPRESS are also 

alpha options and can only be used in 

conjunction with the RLINEXT source 

type. The development of the RLINEXT 

source type and accompanying options 

to account for barriers and depressed 

roadways is ongoing. 

• Highly Buoyant Plume 

A Highly Buoyant Plume (HBP) 

option was implemented as an alpha 

option beginning with AERMOD version 

23132 to explore and refine AERMOD’s 

treatment of the penetrated plume. A 

penetrated plume occurs when a plume 

is 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. The BLP alpha option is 

only applicable to POINT source types. 

• Aircraft Plume Rise 

Beginning with AERMOD version 

23132, the ARCFTOPT alpha option was 

added with the goal to extend the 

capabilities of AERMOD to 

appropriately model emissions from 

aircraft on the ground and during 

takeoffs and landings. The ARCFTOPT 

option extends the AREA and VOLUME 

source type inputs to account for the 

buoyancy and horizontal momentum of 

aircraft emissions. 

• Low Wind Default Overrides 

(LOW_WIND) 

A LOW_WIND option was first 

implemented as a collection of nonregulatory beta test options in AERMOD 

version 12345 (LOWWIND1 and 

LOWWIND2) and expanded in version 

15481(LOWWIND3), before the alpha/ 

beta framework was implemented. 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 to include a 

number of user defined default 

overrides for wind data parameters. The 

LOW_WIND option in version 24142 

enables the user to override AERMOD 

default values with user-defined values 

for one or more of the following 

parameters: 

Æ Minimum standard deviation of the 

lateral velocity to the average wind 

direction; 

Æ Minimum mean wind speed; 

Æ Minimum and maximum meander 

factor; 

Æ Minimum standard deviation of the 

vertical wind speed; and 

Æ Time scale for random dispersion. 

V. Statutory and Executive Order 

Reviews 

Additional information about these 

statutes and Executive Orders can be 

found at https://www.epa.gov/lawsregulations/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 finalizes 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 an 

unfunded mandate as described in 

UMRA, 2 U.S.C. 1531–1538. 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 final 

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 specifically solicited 

comments on the October 2023 

proposed revisions to the Guideline 

from Tribal officials and did not 

formally receive any Tribal comments 

during the public comment period for 

the rule. Subsequently, the EPA 

provided information regarding this 

final action to the Tribes during a 

monthly National Tribal Air Association 

(NTAA) call earlier in 2024 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. 

G. Executive Order 13045: Protection of 

Children From Environmental Health 

Risks and Safety Risks 

The EPA interprets Executive Order 

13045 as applying only to those 

regulatory actions that concern 

environmental health or safety risks that 

the EPA has reason to believe may 

disproportionately affect children, per 

the definition of ‘‘covered regulatory 

action’’ in section 2–202 of the 

Executive Order. This action does not 

address an environmental health risk or 

safety risk that may disproportionately 

affect children. Therefore, this action is 

not subject to Executive Order 13045. 

The 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 type of 

action cannot be evaluated with respect 

to potentially disproportionate and 

adverse effects on communities with 

environmental justice concerns because 

this final action does not regulate air 

pollutant emissions or establish an 

environmental health or safety standard. 

This action finalizes 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 of environmental 

considerations in required compliance 

demonstrations across various CAA 

programs. 

The EPA identifies and addresses 

environmental justice concerns through 

continuing efforts to improve the 

scientific formulations of the EPA’s air 

quality models, increase model overall 

performance, and reduce uncertainties 

of model projections for regulatory 

applications, which ultimately provides 

for protection of the environment and 

human health. While the EPA does not 

expect this action to directly impact air 

quality, the revisions are important 

because the Guideline 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 and serves as a 

benchmark of consistency across the 

nation. This consistency has value to all 

communities including communities 

with environmental justice concerns. 

K. Congressional Review Act (CRA) 

This action is subject to the 

Congressional Review Act (CRA), and 

the EPA will submit a rule report to 

each House of the Congress and to the 

Comptroller General of the United 

States. This action is not a ‘‘major rule’’ 

as defined by 5 U.S.C. 804(2). 

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 of privately developed 

models to the EPA. 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 Guideline 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 Guideline will be 

proposed and finalized in the Federal 

Register. Information on the current status of 

modeling guidance can always be obtained 

from the EPA’s Regional offices. 

Table of Contents 

List of Tables 

1.0 Introduction 

2.0 Overview of Model Use 

2.1 Suitability of Models 

2.1.1 Model Accuracy and Uncertainty 

2.2 Levels of Sophistication of Air Quality 

Analyses and Models 

2.3 Availability of Models 

3.0 Preferred and Alternative Air Quality 

Models 

3.1 Preferred Models 

3.1.1 Discussion 

3.1.2 Requirements 

3.2 Alternative Models 

3.2.1 Discussion 

3.2.2 Requirements 

3.3 EPA’s Model Clearinghouse 

4.0 Models for Carbon Monoxide, Lead, 

Sulfur Dioxide, Nitrogen Dioxide and 

Primary Particulate Matter 

4.1 Discussion 

4.2 Requirements 

4.2.1 Screening Models and Techniques 

4.2.1.1 AERSCREEN 

4.2.1.2 CTSCREEN 

4.2.1.3 Screening in Complex Terrain 

4.2.2 Refined Models 

4.2.2.1 AERMOD 

4.2.2.2 CTDMPLUS 

4.2.2.3 OCD 

4.2.3 Pollutant Specific Modeling 

Requirements 

4.2.3.1 Models for Carbon Monoxide 

4.2.3.2 Models for Lead 

4.2.3.3 Models for Sulfur Dioxide 

4.2.3.4 Models for Nitrogen Dioxide 

4.2.3.5 Models for PM2.5

4.2.3.6 Models for PM10

5.0 Models for Ozone and Secondarily 

Formed Particulate Matter 

5.1 Discussion 

5.2 Recommendations 

5.3 Recommended Models and Approaches 

for Ozone 

5.3.1 Models for NAAQS Attainment 

Demonstrations and Multi-Source Air 

Quality Assessments 

5.3.2 Models for Single-Source Air 

Quality Assessments 

5.4 Recommended Models and 

Approaches for Secondarily Formed 

PM2.5


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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 

7.2.2.2 Plume Rise 

7.2.3 Mobile Sources 

8.0 Model Input Data 

8.1 Modeling Domain 

8.1.1 Discussion 

8.1.2 Requirements 

8.2 Source Data 

8.2.1 Discussion 

8.2.2 Requirements 

8.3 Background Concentrations 

8.3.1 Discussion 

8.3.2 Recommendations for Isolated 

Single Sources 

8.3.3 Recommendations for Multi-Source 

Areas 

8.4 Meteorological Input Data 

8.4.1 Discussion 

8.4.2 Recommendations and 

Requirements 

8.4.3 National Weather Service Data 

8.4.3.1 Discussion 

8.4.3.2 Recommendations 

8.4.4 Site-Specific Data 

8.4.4.1 Discussion 

8.4.4.2 Recommendations 

8.4.5 Prognostic Meteorological Data 

8.4.5.1 Discussion 

8.4.5.2 Recommendations 

8.4.6 Marine Boundary Layer 

Environments 

8.4.6.1 Discussion 

8.4.6.2 Recommendations 

8.4.7 Treatment of Near-Calms and Calms 

8.4.7.1 Discussion 

8.4.7.2 Recommendations 

9.0 Regulatory Application of Models 

9.1 Discussion 

9.2 Recommendations 

9.2.1 Modeling Protocol 

9.2.2 Design Concentration and Receptor 

Sites 

9.2.3 NAAQS and PSD Increments 

Compliance Demonstrations for New or 

Modified Sources 

9.2.3.1 Considerations in Developing 

Emissions Limits 

9.2.4 Use of Measured Data in Lieu of 

Model Estimates 

10.0 References 

Addendum A to Appendix W of Part 51— 

Summaries of Preferred Air Quality Models 

List of Tables 

Table No. Title 

8–1 ................. Point Source Model Emission Inputs for SIP Revisions of Inert Pollutants. 

8–2 ................. Point Source Model Emission Inputs for NAAQS 

Compliance in PSD Demonstrations. 

1.0 Introduction 

a. The Guideline provides air quality 

modeling techniques that should be applied 

to State Implementation Plan (SIP) submittals 

and revisions, to New Source Review (NSR), 

including new or modifying sources under 

Prevention of Significant Deterioration 

(PSD),123 conformity analyses,4 and other air 

quality assessments required under EPA 

regulation. Applicable only to criteria air 

pollutants, the Guideline is intended for use 

by the EPA Regional offices in judging the 

adequacy of modeling analyses performed by 

the EPA, by State, local, and Tribal 

permitting authorities, and by industry. It is 

appropriate for use by other Federal 

government agencies and by State, local, and 

Tribal agencies with air quality and land 

management responsibilities. The Guideline 

serves to identify, for all interested parties, 

those modeling techniques and databases 

that the EPA considers acceptable. The 

Guideline is not intended to be a 

compendium of modeling techniques. Rather, 

it should serve as a common measure of 

acceptable technical analysis when 

supported by sound scientific judgment. 

b. Air quality measurements 5 are routinely 

used to characterize ambient concentrations 

of criteria pollutants throughout the nation 

but are rarely sufficient for characterizing the 

ambient impacts of individual sources or 

demonstrating adequacy of emissions limits 

for an existing source due to limitations in 

spatial and temporal coverage of ambient 

monitoring networks. The impacts of new 

sources that do not yet exist, and 

modifications to existing sources that have 

yet to be implemented, can only be 

determined through modeling. Thus, models 

have become a primary analytical tool in 

most air quality assessments. Air quality 

measurements can be used in a 

complementary manner to air quality models, 

with due regard for the strengths and 

weaknesses of both analysis techniques, and 

are particularly useful in assessing the 

accuracy of model estimates. 

c. It would be advantageous to categorize 

the various regulatory programs and to apply 

a designated model to each proposed source 

needing analysis under a given program. 

However, the diversity of the nation’s 

topography and climate, and variations in 

source configurations and operating 

characteristics dictate against a strict 

modeling ‘‘cookbook.’’ There is no one model 

capable of properly addressing all 

conceivable situations even within a broad 

category such as point sources. 

Meteorological phenomena associated with 

threats to air quality standards are rarely 

amenable to a single mathematical treatment; 

thus, case-by-case analysis and judgment are 

frequently required. As modeling efforts 

become more complex, it is increasingly 

important that they be directed by highly 

competent individuals with a broad range of 

experience and knowledge in air quality 

meteorology. Further, they should be 

coordinated closely with specialists in 

emissions characteristics, air monitoring and 

data processing. The judgment of 

experienced meteorologists, atmospheric 

scientists, and analysts is essential. 

d. The model that most accurately 

estimates concentrations in the area of 

interest is always sought. However, it is clear 

from the needs expressed by the EPA 

Regional offices, by State, local, and Tribal 

agencies, by many industries and trade 

associations, and also by the deliberations of 

Congress, that consistency in the selection 

and application of models and databases 

should also be sought, even in case-by-case 

analyses. Consistency ensures that air quality 

control agencies and the general public have 

a common basis for estimating pollutant 

concentrations, assessing control strategies, 

and specifying emissions limits. Such 

consistency is not, however, promoted at the 

expense of model and database accuracy. The 

Guideline provides a consistent basis for 

selection of the most accurate models and 

databases for use in air quality assessments. 

e. Recommendations are made in the 

Guideline concerning air quality models and 

techniques, model evaluation procedures, 

and model input databases and related 

requirements. The guidance provided here 

should be followed in air quality analyses 

relative to SIPs, NSR, and in supporting 

analyses required by the EPA and by State, 

local, and Tribal permitting authorities. 

Specific models are identified for particular 

applications. The EPA may approve the use 

of an alternative model or technique that can 

be demonstrated to be more appropriate than 

those recommended in the Guideline. In all 

cases, the model or technique applied to a 

given situation should be the one that 

provides the most accurate representation of 

atmospheric transport, dispersion, and 

chemical transformations in the area of 

interest. However, to ensure consistency, 

deviations from the Guideline should be 

carefully documented as part of the public 

record and fully supported by the 

appropriate reviewing authority, as discussed 

later. 

f. From time to time, situations arise 

requiring clarification of the intent of the 

guidance on a specific topic. Periodic 

workshops are held with EPA headquarters, 

EPA Regional offices, and State, local, and 

Tribal agency modeling representatives to 

ensure consistency in modeling guidance and 

to promote the use of more accurate air 

quality models, techniques, and databases. 

The workshops serve to provide further 

explanations of Guideline requirements to 

the EPA Regional offices and workshop 

materials are issued with this clarifying 

information. In addition, findings from 

ongoing research programs, new model 

development, or results from model 


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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 rules in the Federal Register 

to amend this appendix. The EPA utilizes the 

existing procedures under CAA section 320 

that requires the EPA to conduct a conference 

on air quality modeling at least every 3 years 

(CAA 320, 42 U.S.C. 7620). These modeling 

conferences are intended to develop 

standardized air quality modeling procedures 

and form the basis for associated revisions to 

this Guideline in support of the EPA’s 

continuing effort to prescribe with 

‘‘reasonable particularity’’ air quality models 

and meteorological and emission databases 

suitable for modeling national ambient air 

quality standards (NAAQS) 6 and PSD 

increments. Ample opportunity for public 

comment will be provided for each proposed 

change and public hearings scheduled. 

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 permitenforceable 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 ‘‘steadystate’’ approximation, which assumes that 

over the model time step, the emissions, 

meteorology and other model inputs, are 

constant throughout the model domain, 

resulting in a resolved plume with the 

emissions distributed throughout the plume 

according to a Gaussian distribution. This 

formulation allows Gaussian models to 

estimate near-field impacts of a limited 

number of sources at a relatively high 

resolution, with temporal scales of an hour 

and spatial scales of meters. However, this 

formulation allows for only relatively inert 

pollutants, with very limited considerations 

of transformation and removal (e.g., 

deposition), and further limits the domain for 

which the model may be used. Thus, 

Gaussian models may not be appropriate if 

model inputs are changing sharply over the 

model time step or within the desired model 

domain, or if more advanced considerations 

of chemistry are needed. 

ii. Lagrangian puff models, on the other 

hand, are non-steady-state, and assume that 

model input conditions are changing over the 

model domain and model time step. 

Lagrangian models can also be used to 

determine near- and far-field impacts from a 

limited number of sources. Traditionally, 

Lagrangian models have been used for 

relatively inert pollutants, with slightly more 

complex considerations of removal than 

Gaussian models. Some Lagrangian models 

treat in-plume gas and particulate chemistry. 

However, these models require time and 

space varying concentration fields of 

oxidants and, in the case of fine particulate 

matter (PM2.5), neutralizing agents, such as 

ammonia. Reliable background fields are 

critical for applications involving secondary 

pollutant formation because secondary 

impacts generally occur when in-plume 

precursors mix and react with species in the 

background atmosphere.7 8 These oxidant and 

neutralizing agents are not routinely 

measured, but can be generated with a threedimensional photochemical grid model. 

iii. Photochemical grid models are threedimensional Eulerian grid-based models that 

treat chemical and physical processes in each 

grid cell and use diffusion and transport 

processes to move chemical species between 

grid cells.9 Eulerian models assume that 

emissions are spread evenly throughout each 

model grid cell. At coarse grid resolutions, 

Eulerian models have difficulty with fine 

scale resolution of individual plumes. 

However, these types of models can be 

appropriately applied for assessment of nearfield and regional scale reactive pollutant 

impacts from specific sources7 10 11 12 or all 

sources.13 14 15 Photochemical grid models 

simulate a more realistic environment for 

chemical transformation,7 12 but simulations 

can be more resource intensive than 

Lagrangian or Gaussian plume models. 

e. Competent and experienced 

meteorologists, atmospheric scientists, and 

analysts are an essential prerequisite to the 

successful application of air quality models. 

The need for such specialists is critical when 

sophisticated models are used or the area has 

complicated meteorological or topographic 

features. It is important to note that a model 

applied improperly or with inappropriate 

data can lead to serious misjudgments 

regarding the source impact or the 

effectiveness of a control strategy. 

f. The resource demands generated by use 

of air quality models vary widely depending 

on the specific application. The resources 

required may be important factors in the 

selection and use of a model or technique for 

a specific analysis. These resources depend 

on the nature of the model and its 

complexity, the detail of the databases, the 

difficulty of the application, the amount and 

level of expertise required, and the costs of 

manpower and computational facilities. 

2.1.1 Model Accuracy and Uncertainty 

a. The formulation and application of air 

quality models are accompanied by several 

sources of uncertainty. ‘‘Irreducible’’ 

uncertainty stems from the ‘‘unknown’’ 

conditions, which may not be explicitly 

accounted for in the model (e.g., the 

turbulent velocity field). Thus, there are 

likely to be deviations from the observed 


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concentrations in individual events due to 

variations in the unknown conditions. 

‘‘Reducible’’ uncertainties 16 are caused by: 

(1) uncertainties in the ‘‘known’’ input 

conditions (e.g., emission characteristics and 

meteorological data); (2) errors in the 

measured concentrations; and (3) inadequate 

model physics and formulation. 

b. Evaluations of model accuracy should 

focus on the reducible uncertainty associated 

with physics and the formulation of the 

model. The accuracy of the model is 

normally determined by an evaluation 

procedure which involves the comparison of 

model concentration estimates with 

measured air quality data.17 The statement of 

model accuracy is based on statistical tests or 

performance measures such as bias, error, 

correlation, etc.18 19

c. Since the 1980’s, the EPA has worked 

with the modeling community to encourage 

development of standardized model 

evaluation methods and the development of 

continually improved methods for the 

characterization of model 

performance.16 18 20 21 22 There is general 

consensus on what should be considered in 

the evaluation of air quality models. Namely, 

quality assurance planning, documentation 

and scrutiny should be consistent with the 

intended use and should include: 

• Scientific peer review; 

• Supportive analyses (diagnostic 

evaluations, code verification, sensitivity 

analyses); 

• Diagnostic and performance evaluations 

with data obtained in trial locations; and 

• Statistical performance evaluations in 

the circumstances of the intended 

applications. 

Performance evaluations and diagnostic 

evaluations assess different qualities of how 

well a model is performing, and both are 

needed to establish credibility within the 

client and scientific community. 

d. Performance evaluations allow the EPA 

and model users to determine the relative 

performance of a model in comparison with 

alternative modeling systems. Diagnostic 

evaluations allow determination of a model 

capability to simulate individual processes 

that affect the results, and usually employ 

smaller spatial/temporal scale data sets (e.g., 

field studies). Diagnostic evaluations enable 

the EPA and model users to build confidence 

that model predictions are accurate for the 

right reasons. However, the objective 

comparison of modeled concentrations with 

observed field data provides only a partial 

means for assessing model performance. Due 

to the limited supply of evaluation datasets, 

there are practical limits in assessing model 

performance. For this reason, the conclusions 

reached in the science peer reviews and the 

supportive analyses have particular relevance 

in deciding whether a model will be useful 

for its intended purposes. 

2.2 Levels of Sophistication of Air Quality 

Analyses and Models 

a. It is desirable to begin an air quality 

analysis by using simplified and conservative 

methods followed, as appropriate, by more 

complex and refined methods. The purpose 

of this approach is to streamline the process 

and sufficiently address regulatory 

requirements by eliminating the need of more 

detailed modeling when it is not necessary in 

a specific regulatory application. For 

example, in the context of a PSD permit 

application, a simplified and conservative 

analysis may be sufficient where it shows the 

proposed construction clearly will not cause 

or contribute to ambient concentrations in 

excess of either the NAAQS or the PSD 

increments.2 3

b. There are two general levels of 

sophistication of air quality models. The first 

level consists of screening models that 

provide conservative modeled estimates of 

the air quality impact of a specific source or 

source category based on simplified 

assumptions of the model inputs (e.g., preset, 

worst-case meteorological conditions). In the 

case of a PSD assessment, if a screening 

model indicates that the increase in 

concentration attributable to the source could 

cause or contribute to a violation of any 

NAAQS or PSD increment, then the second 

level of more sophisticated models should be 

applied unless appropriate controls or 

operational restrictions are implemented 

based on the screening modeling. 

c. The second level consists of refined 

models that provide more detailed treatment 

of physical and chemical atmospheric 

processes, require more detailed and precise 

input data, and provide spatially and 

temporally resolved concentration estimates. 

As a result, they provide a more 

sophisticated and, at least theoretically, a 

more accurate estimate of source impact and 

the effectiveness of control strategies. 

d. There are situations where a screening 

model or a refined model is not available 

such that screening and refined modeling are 

not viable options to determine sourcespecific air quality impacts. In such 

situations, a screening technique or reducedform 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 


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techniques, the EPA Regional office will 

coordinate and 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-regionalcontacts), 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 

keep misunderstandings and resource 

expenditures at a minimum. 

d. The identification of preferred models in 

this Guideline should not be construed as a 

determination that the preferred models 

identified here are to be permanently used to 

the exclusion of all others or that they are the 

only models available for relating emissions 

to air quality. The model that most accurately 

estimates concentrations in the area of 

interest is always sought. However, 

designation of specific preferred models is 

needed to promote consistency in model 

selection and application. 

3.1 Preferred Models 

3.1.1 Discussion 

a. The EPA has developed some models 

suitable for regulatory application, while 

other models have been submitted by private 

developers for possible inclusion in the 

Guideline. Refined models that are preferred 

and required by the EPA for particular 

applications have undergone the necessary 

peer scientific reviews 24 25 and model 

performance evaluation exercises 26 27 that 

include statistical measures of model 

performance in comparison with measured 

air quality data as described in section 2.1.1. 

b. An American Society for Testing and 

Materials (ASTM) reference 28 provides a 

general philosophy for developing and 

implementing advanced statistical 

evaluations of atmospheric dispersion 

models, and provides an example statistical 

technique to illustrate the application of this 

philosophy. Consistent with this approach, 

the EPA has determined and applied a 

specific evaluation protocol that provides a 

statistical technique for evaluating model 

performance for predicting peak 

concentration values, as might be observed at 

individual monitoring locations.29

c. When a single model is found to perform 

better than others, it is recommended for 

application as a preferred model and listed 

in Addendum A. If no one model is found 

to clearly perform better through the 

evaluation exercise, then the preferred model 

listed in Addendum A may be selected on 

the basis of other factors such as past use, 

public familiarity, resource requirements, 

and availability. Accordingly, the models 

listed in Addendum A meet these conditions: 

i. The model must be written in a common 

programming language, and the executable(s) 

must run on a common computer platform. 

ii. The model must be documented in a 

user’s guide or model formulation report 

which identifies the mathematics of the 

model, data requirements and program 

operating characteristics at a level of detail 

comparable to that available for other 

recommended models in Addendum A. 

iii. The model must be accompanied by a 

complete test dataset including input 

parameters and output results. The test data 

must be packaged with the model in 

computer-readable form. 

iv. The model must be useful to typical 

users, e.g., State air agencies, for specific air 

quality control problems. Such users should 

be able to operate the computer program(s) 

from available documentation. 

v. The model documentation must include 

a robust comparison with air quality data 

(and/or tracer measurements) or with other 

well-established analytical techniques. 

vi. The developer must be willing to make 

the model and source code available to users 

at reasonable cost or make them available for 

public access through the internet or 

National Technical Information Service. The 

model and its code cannot be proprietary. 

d. The EPA’s process of establishing a 

preferred model includes a determination of 

technical merit, in accordance with the above 

six items, including the practicality of the 

model for use in ongoing regulatory 

programs. Each model will also be subjected 

to a performance evaluation for an 

appropriate database and to a peer scientific 

review. Models for wide use (not just an 

isolated case) that are found to perform better 

will be proposed for inclusion as preferred 

models in future Guideline revisions. 

e. No further evaluation of a preferred 

model is required for a particular application 

if the EPA requirements for regulatory use 

specified for the model in the Guideline are 

followed. Alternative models to those listed 

in Addendum A should generally be 

compared with measured air quality data 

when they are used for regulatory 

applications consistent with 

recommendations in section 3.2. 

3.1.2 Requirements 

a. Addendum A identifies refined models 

that are preferred for use in regulatory 

applications. If a model is required for a 

particular application, the user must select a 

model from Addendum A or follow 

procedures in section 3.2.2 for use of an 

alternative model or technique. Preferred 

models may be used without a formal 

demonstration of applicability as long as they 

are used as indicated in each model summary 

in Addendum A. Further recommendations 

for the application of preferred models to 

specific source applications are found in 

subsequent sections of the Guideline. 

b. If changes are made to a preferred model 

without affecting the modeled 

concentrations, the preferred status of the 

model is unchanged. Examples of 

modifications that do not affect 

concentrations are those made to enable use 

of a different computer platform or those that 

only affect the format or averaging time of the 

model results. The integration of a graphical 

user interface (GUI) to facilitate setting up the 

model inputs and/or analyzing the model 

results without otherwise altering the 

preferred model code is another example of 

a modification that does not affect 

concentrations. However, when any changes 

are made, the Regional Administrator must 

require a test case example to demonstrate 

that the modeled concentrations are not 

affected. 

c. A preferred model must be operated 

with the options listed in Addendum A for 

its intended regulatory application. If the 

regulatory options are not applied, the model 

is no longer ‘‘preferred.’’ Any other 

modification to a preferred model that would 

result in a change in the concentration 

estimates likewise alters its status so that it 

is no longer a preferred model. Use of the 

modified model must then be justified as an 

alternative model on a case-by-case basis to 

the appropriate reviewing authority and 

approved by the Regional Administrator. 

d. Where the EPA has not identified a 

preferred model for a particular pollutant or 

situation, the EPA may establish a multitiered approach for making a demonstration 

required under PSD or another CAA program. 

The initial tier or tiers may involve use of 

demonstration tools, screening models, 

screening techniques, or reduced-form 

models; while the last tier may involve the 

use of demonstration tools, refined models or 

techniques, or alternative models approved 

under section 3.2. 

3.2 Alternative Models 

3.2.1 Discussion 

a. Selection of the best model or techniques 

for each individual air quality analysis is 

always encouraged, but the selection should 

be done in a consistent manner. A simple 

listing of models in this Guideline cannot 

alone achieve that consistency nor can it 

necessarily provide the best model for all 

possible situations. As discussed in section 

3.1.1, the EPA has determined and applied a 

specific evaluation protocol that provides a 

statistical technique for evaluating model 

performance for predicting peak 

concentration values, as might be observed at 

individual monitoring locations.29 This 

protocol is available to assist in developing 

a consistent approach when justifying the use 

of other-than-preferred models recommended 

in the Guideline (i.e., alternative models). 

The procedures in this protocol provide a 

general framework for objective decisionmaking 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 


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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 PM2.5 SIP attainment 

demonstrations and other applications that use the 

model results in a relative sense, the model should 

not be biased toward overestimates. 

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 

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: 

i. If a demonstration can be made that the 

model produces concentration estimates 

equivalent to the estimates obtained using a 

preferred model; 

ii. 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 

iii. If there is no preferred model. 

Any one of these three separate conditions 

may justify use of an alternative model. Some 

known alternative models that are applicable 

for selected situations are listed on the EPA’s 

SCRAM website (section 2.3). However, 

inclusion there does not confer any unique 

status relative to other alternative models 

that are being or will be developed in the 

future. 

c. Equivalency, condition (1) in paragraph 

(b) of this subsection, is established by 

demonstrating that the appropriate regulatory 

metric(s) are within +/¥ 2 percent of the 

estimates obtained from the preferred model. 

The option to show equivalency is intended 

as a simple demonstration of acceptability for 

an alternative model that is nearly identical 

(or contains options that can make it 

identical) to a preferred model that it can be 

treated for practical purposes as the preferred 

model. However, notwithstanding this 

demonstration, models that are not 

equivalent may be used when one of the two 

other conditions described in paragraphs (d) 

and (e) of this subsection are satisfied. 

d. For condition (2) in paragraph (b) of this 

subsection, established statistical 

performance evaluation procedures and 

techniques 28 29 for determining the 

acceptability of a model for an individual 

case based on superior performance should 

be followed, as appropriate. Preparation and 

implementation of an evaluation protocol 

that is acceptable to both control agencies 

and regulated industry is an important 

element in such an evaluation. 

e. Finally, for condition (3) in paragraph (b) 

of this subsection, an alternative model or 

technique may be approved for use provided 

that: 

i. The model or technique has received a 

scientific peer review; 

ii. The model or technique can be 

demonstrated to be applicable to the problem 

on a theoretical basis; 

iii. The databases which are necessary to 

perform the analysis are available and 

adequate; 

iv. Appropriate performance evaluations of 

the model or technique have shown that the 

model or technique is not inappropriately 

biased for regulatory application; a and 

v. A protocol on methods and procedures 

to be followed has been established. 

f. To formally document that the 

requirements of section 3.2 for use of an 

alternative model are satisfied for a particular 

application or range of applications, a 

memorandum will be prepared by the EPA’s 

Model Clearinghouse through a consultative 

process with the EPA Regional office. 

3.3 EPA’s Model Clearinghouse 

a. The Regional Administrator has the 

authority to select models that are 

appropriate for use in a given situation. 

However, there is a need for assistance and 

guidance in the selection process so that 

fairness, consistency, and transparency in 

modeling decisions are fostered among the 

EPA Regional offices and the State, local, and 

Tribal agencies. To satisfy that need, the EPA 

established the Model Clearinghouse 23 to 

serve a central role of coordination and 

collaboration between EPA headquarters and 

the EPA Regional offices. Additionally, the 

EPA holds periodic workshops with EPA 

Headquarters, EPA Regional offices, and 

State, local, and Tribal agency modeling 

representatives. 

b. The appropriate EPA Regional office 

should always be consulted for information 

and guidance concerning modeling methods 

and interpretations of modeling guidance, 

and to ensure that the air quality model user 

has available the latest most up-to-date 

policy and procedures. As appropriate, the 

EPA Regional office may also request 

assistance from the EPA’s Model 

Clearinghouse on other applications of 

models, analytical techniques, or databases 

or to clarify interpretation of the Guideline or 

related modeling guidance. 

c. The EPA Regional office will coordinate 

with the EPA’s Model Clearinghouse after an 

initial evaluation and decision has been 

developed concerning the application of an 

alternative model. The acceptability and 

formal approval process for an alternative 

model is described in section 3.2. 

4.0 Models for Carbon Monoxide, Lead, 

Sulfur Dioxide, Nitrogen Dioxide and 

Primary Particulate Matter 

4.1 Discussion 

a. This section identifies modeling 

approaches generally used in the air quality 

impact analysis of sources that emit the 

criteria pollutants carbon monoxide (CO), 

lead, sulfur dioxide (SO2), nitrogen dioxide 

(NO2), and primary particulates (PM2.5 and 

PM10). 

b. The guidance in this section is specific 

to the application of the Gaussian plume 

models identified in Addendum A. Gaussian 

plume models assume that emissions and 

meteorology are in a steady-state, which is 

typically based on an hourly time step. This 

approach results in a plume that has an 

hourly-averaged distribution of emission 

mass according to a Gaussian curve through 

the plume. Though Gaussian steady-state 

models conserve the mass of the primary 

pollutant throughout the plume, they can 

still take into account a limited consideration 

of first-order removal processes (e.g., wet and 

dry deposition) and limited chemical 

conversion (e.g., OH oxidation). 

c. Due to the steady-state assumption, 

Gaussian plume models are generally 

considered applicable to distances less than 

50 km, beyond which, modeled predictions 

of plume impact are likely conservative. The 

locations of these impacts are expected to be 

unreliable due to changes in meteorology that 

are likely to occur during the travel time. 

d. The applicability of Gaussian plume 

models may vary depending on the 

topography of the modeling domain, i.e., 

simple or complex. Simple terrain is 

considered to be an area where terrain 

features are all lower in elevation than the 

top of the stack(s) of the source(s) in 

question. Complex terrain is defined as 

terrain exceeding the height of the stack(s) 

being modeled. 

e. Gaussian models determine source 

impacts at discrete locations (receptors) for 

each meteorological and emission scenario, 

and generally attempt to estimate 

concentrations at specific sites that represent 

an ensemble average of numerous repetitions 

of the same ‘‘event.’’ Uncertainties in model 

estimates are driven by this formulation, and 

as noted in section 2.1.1, evaluations of 

model accuracy should focus on the 

reducible uncertainty associated with 

physics and the formulation of the model. 

The ‘‘irreducible’’ uncertainty associated 

with Gaussian plume models may be 

responsible for variation in concentrations of 

as much as +/¥ 50 percent.30 ‘‘Reducible’’ 

uncertainties 16 can be on a similar scale. For 

example, Pasquill 31 estimates that, apart 

from data input errors, maximum groundlevel 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 


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indicate that an estimated concentration does 

not occur, only that the precise time and 

locations are in doubt. Composite errors in 

highest estimated concentrations of 10 to 40 

percent are found to be typical.32 33 However, 

estimates of concentrations paired in time 

and space with observed concentrations are 

less certain. 

f. Model evaluations and inter-comparisons 

should take these aspects of uncertainty into 

account. For a regulatory application of a 

model, the emphasis of model evaluations is 

generally placed on the highest modeled 

impacts. Thus, the Cox-Tikvart model 

evaluation approach, which compares the 

highest modeled impacts on several 

timescales, is recommended for comparisons 

of models and measurements and model 

inter-comparisons. The approach includes 

bootstrap techniques to determine the 

significance of various modeled predictions 

and increases the robustness of such 

comparisons when the number of available 

measurements are limited.34 35 Because of the 

uncertainty in paired modeled and observed 

concentrations, any attempts at calibration of 

models based on these comparisons is of 

questionable benefit and shall not be done. 

4.2 Requirements 

a. For NAAQS compliance demonstrations 

under PSD, use of the screening and 

preferred models for the pollutants listed in 

this subsection shall be limited to the nearfield 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 longrange transport assessment beyond 50 km 

necessary for these pollutants if a near-field 

NAAQS compliance demonstration is 

required.36

b. For assessment of PSD increments 

within the near-field distance of 50 km or 

less, use of the screening and preferred 

models for the pollutants listed in this 

subsection shall be limited to the same 

screening and preferred models approved for 

NAAQS compliance demonstrations. 

c. To determine if a compliance 

demonstration for NAAQS and/or PSD 

increments may be necessary beyond 50 km 

(i.e., long-range transport assessment), the 

following screening approach shall be used 

to determine if a significant ambient impact 

will occur with particular focus on Class I 

areas and/or the applicable receptors that 

may be threatened at such distances. 

i. Based on application in the near-field of 

the appropriate screening and/or preferred 

model, determine the significance of the 

ambient impacts at or about 50 km from the 

new or modifying source. If a near-field 

assessment is not available or this initial 

analysis indicates there may be significant 

ambient impacts at that distance, then further 

assessment is necessary. 

ii. For assessment of the significance of 

ambient impacts for NAAQS and/or PSD 

increments, there is not a preferred model or 

screening approach for distances beyond 50 

km. Thus, the appropriate reviewing 

authority (paragraph 3.0(b)) and the EPA 

Regional office shall be consulted in 

determining the appropriate and agreed upon 

screening technique to conduct the second 

level assessment. Typically, a Lagrangian 

model is most appropriate to use for these 

second level assessments, but applicants 

shall reach agreement on the specific model 

and modeling parameters on a case-by-case 

basis in consultation with the appropriate 

reviewing authority (paragraph 3.0(b)) and 

EPA Regional office. When Lagrangian 

models are used in this manner, they shall 

not include plume-depleting processes, such 

that model estimates are considered 

conservative, as is generally appropriate for 

screening assessments. 

d. In those situations where a cumulative 

impact analysis for NAAQS and/or PSD 

increments analysis beyond 50 km is 

necessary, the selection and use of an 

alternative model shall occur in agreement 

with the appropriate reviewing authority 

(paragraph 3.0(b)) and approval by the EPA 

Regional office based on the requirements of 

paragraph 3.2.2(e). 

4.2.1 Screening Models and Techniques 

a. Where a preliminary or conservative 

estimate is desired, point source screening 

techniques are an acceptable approach to air 

quality analyses. 

b. As discussed in paragraph 2.2(a), 

screening models or techniques are designed 

to provide a conservative estimate of 

concentrations. The screening models used 

in most applications are the screening 

versions of the preferred models for refined 

applications. The two screening models, 

AERSCREEN 37 38 and CTSCREEN, are 

screening versions of AERMOD (American 

Meteorological Society (AMS)/EPA 

Regulatory Model) and CTDMPLUS 

(Complex Terrain Dispersion Model Plus 

Algorithms for Unstable Situations), 

respectively. AERSCREEN is the 

recommended screening model for most 

applications in all types of terrain and for 

applications involving building downwash. 

For those applications in complex terrain 

where the application involves a welldefined hill or ridge, CTSCREEN 39 can be 

used. 

c. Although AERSCREEN and CTSCREEN 

are designed to address a single-source 

scenario, there are approaches that can be 

used on a case-by-case basis to address multisource situations using screening 

meteorology or other conservative model 

assumptions. However, the appropriate 

reviewing authority (paragraph 3.0(b)) shall 

be consulted, and concurrence obtained, on 

the protocol for modeling multiple sources 

with AERSCREEN or CTSCREEN to ensure 

that the worst case is identified and assessed. 

d. As discussed in section 4.2.3.4, there are 

also screening techniques built into 

AERMOD that use simplified or limited 

chemistry assumptions for determining the 

partitioning of NO and NO2 for NO2

modeling. These screening techniques are 

part of the EPA’s preferred modeling 

approach for NO2 and do not need to be 

approved as an alternative model. However, 

as with other screening models and 

techniques, their usage shall occur in 

agreement with the appropriate reviewing 

authority (paragraph 3.0(b)). 

e. As discussed in section 4.2(c)(ii), there 

are screening techniques needed for longrange 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 the EPA 

Regional office. 

f. All screening models and techniques 

shall be configured to appropriately address 

the site and problem at hand. Close attention 

must be paid to whether the area should be 

classified urban or rural in accordance with 

section 7.2.1.1. The climatology of the area 

must be studied to help define the worst-case 

meteorological conditions. Agreement shall 

be reached between the model user and the 

appropriate reviewing authority (paragraph 

3.0(b)) on the choice of the screening model 

or technique for each analysis, on the input 

data and model settings, and the appropriate 

metric for satisfying regulatory requirements. 

4.2.1.1 AERSCREEN 

a. Released in 2011, AERSCREEN is the 

EPA’s recommended screening model for 

simple and complex terrain for single sources 

including point sources, area sources, 

horizontal stacks, capped stacks, and flares. 

AERSCREEN runs AERMOD in a screening 

mode and consists of two main components: 

(1) the MAKEMET program which generates 

a site-specific matrix of meteorological 

conditions for input to the AERMOD model; 

and (2) the AERSCREEN command-prompt 

interface. 

b. The MAKEMET program generates a 

matrix of meteorological conditions, in the 

form of AERMOD-ready surface and profile 

files, based on user-specified surface 

characteristics, ambient temperatures, 

minimum wind speed, and anemometer 

height. The meteorological matrix is 

generated based on looping through a range 

of wind speeds, cloud covers, ambient 

temperatures, solar elevation angles, and 

convective velocity scales (w*, for convective 

conditions only) based on user-specified 

surface characteristics for surface roughness 

(Zo), Bowen ratio (Bo), and albedo (r). For 

unstable cases, the convective mixing height 

(Zic) is calculated based on w*, and the 

mechanical mixing height (Zim) is calculated 

for unstable and stable conditions based on 

the friction velocity, u*. 

c. For applications involving simple or 

complex terrain, AERSCREEN interfaces with 

AERMAP. AERSCREEN also interfaces with 

BPIPPRM to provide the necessary building 

parameters for applications involving 

building downwash using the Plume Rise 

Model Enhancements (PRIME) downwash 

algorithm. AERSCREEN generates inputs to 

AERMOD via MAKEMET, AERMAP, and 

BPIPPRM and invokes AERMOD in a 

screening mode. The screening mode of 

AERMOD forces the AERMOD model 

calculations to represent values for the plume 


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centerline, regardless of the source-receptorwind direction orientation. The maximum 

concentration output from AERSCREEN 

represents a worst-case 1-hour concentration. 

Averaging-time scaling factors of 1.0 for 3- 

hour, 0.9 for 8-hour, 0.60 for 24-hour, and 

0.10 for annual concentration averages are 

applied internally by AERSCREEN to the 

highest 1-hour concentration calculated by 

the model for non-area type sources. For area 

type source concentrations for averaging 

times greater than one hour, the 

concentrations are equal to the 1-hour 

estimates.37 40

4.2.1.2 CTSCREEN 

a. CTSCREEN 39 41 can be used to obtain 

conservative, yet realistic, worst-case 

estimates for receptors located on terrain 

above stack height. CTSCREEN accounts for 

the three-dimensional nature of plume and 

terrain interaction and requires detailed 

terrain data representative of the modeling 

domain. The terrain data must be digitized in 

the same manner as for CTDMPLUS and a 

terrain processor is available.42 CTSCREEN is 

designed to execute a fixed matrix of 

meteorological values for wind speed (u), 

standard deviation of horizontal and vertical 

wind speeds (sv, sw), vertical potential 

temperature gradient (dq/dz), friction 

velocity (u*), Monin-Obukhov length (L), 

mixing height (zi) as a function of terrain 

height, and wind directions for both neutral/ 

stable conditions and unstable convective 

conditions. The maximum concentration 

output from CTSCREEN represents a worstcase 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 polargrid 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 userdefined network. For CTSCREEN 

applications or AERMOD in screening mode 

outside of AERSCREEN, placement of 

receptors requires very careful attention 

when modeling in complex terrain. Often the 

highest concentrations are predicted to occur 

under very stable conditions, when the 

plume is near or impinges on the terrain. 

Under such conditions, the plume may be 

quite narrow in the vertical, so that even 

relatively small changes in a receptor’s 

location may substantially affect the 

predicted concentration. Receptors within 

about a kilometer of the source may be even 

more sensitive to location. Thus, a dense 

array of receptors may be required in some 

cases. 

b. For applications involving AERSCREEN, 

AERSCREEN interfaces with AERMAP to 

generate the receptor elevations. For 

applications involving CTSCREEN, digitized 

contour data must be preprocessed 42 to 

provide hill shape parameters in suitable 

input format. The user then supplies receptor 

locations either through an interactive 

program that is part of the model or directly, 

by using a text editor; using both methods to 

select receptor locations will generally be 

necessary to assure that the maximum 

concentrations are estimated by either model. 

In cases where a terrain feature may ‘‘appear 

to the plume’’ as smaller, multiple hills, it 

may be necessary to model the terrain both 

as a single feature and as multiple hills to 

determine design concentrations. 

c. Other screening techniques may be 

acceptable for complex terrain cases where 

established procedures 43 are used. The user 

is encouraged to confer with the appropriate 

reviewing authority (paragraph 3.0(b)) if any 

unforeseen problems are encountered, e.g., 

applicability, meteorological data, receptor 

siting, or terrain contour processing issues. 

4.2.2 Refined Models 

a. Addendum A provides a brief 

description of each preferred model for 

refined applications. Also listed in that 

addendum are availability, the model input 

requirements, the standard options that shall 

be selected when running the program, and 

output options. 

4.2.2.1 AERMOD 

a. For a wide range of regulatory 

applications in all types of terrain, and for 

aerodynamic building downwash, the 

required model is AERMOD.44 45 The 

AERMOD regulatory modeling system 

consists of the AERMOD dispersion model, 

the AERMET meteorological processor, and 

the AERMAP terrain processor. AERMOD is 

a steady-state Gaussian plume model 

applicable to directly emitted air pollutants 

that employs best state-of-practice 

parameterizations for characterizing the 

meteorological influences and dispersion. 

Differentiation of simple versus complex 

terrain is unnecessary with AERMOD. In 

complex terrain, AERMOD employs the wellknown dividing-streamline concept in a 

simplified simulation of the effects of plumeterrain 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 lowlevel non-buoyant sources.27 These 

evaluations included several long-term field 

studies associated with operating plants as 

well as several intensive tracer studies. Based 

on these evaluations, AERMOD has shown 

consistently good performance, with ‘‘errors’’ 

in predicted versus observed peak 

concentrations, based on the Robust Highest 

Concentration (RHC) metric, consistently 

within the range of 10 to 40 percent (cited 

in paragraph 4.1(e)). 

c. AERMOD incorporates the PRIME 

algorithm to account for enhanced plume 

growth and restricted plume rise for plumes 

affected by building wake effects.46 The 

PRIME algorithm accounts for entrainment of 

plume mass into the cavity recirculation 

region, including re-entrainment of plume 

mass into the wake region beyond the cavity. 

d. AERMOD incorporates the Buoyant Line 

and Point Source (BLP) Dispersion model to 

account for buoyant plume rise from line 

sources. The BLP option utilizes the standard 

meteorological inputs provided by the 

AERMET meteorological processor. 

e. The state-of-the-science for modeling 

atmospheric deposition is evolving, new 

modeling techniques are continually being 

assessed, and their results are being 

compared with observations. Consequently, 

while deposition treatment is available in 

AERMOD, the approach taken for any 

purpose shall be coordinated with the 

appropriate reviewing authority (paragraph 

3.0(b)). 

f. The AERMET meteorological processor 

incorporates the COARE algorithms to derive 

marine boundary layer parameters for 

overwater applications of AERMOD.47 48

AERMOD is applicable for some overwater 

applications when platform downwash and 

shoreline fumigation are adequately 

considered in consultation with the Regional 

office and appropriate reviewing authority. 

Where the effects of shoreline fumigation and 

platform downwash need to be assessed, the 

Offshore and Coastal Dispersion (OCD) 

model is the applicable model (paragraph 

4.2.2.3). 

4.2.2.2 CTDMPLUS 

a. If the modeling application involves an 

elevated point source with a well-defined hill 

or ridge and a detailed dispersion analysis of 

the spatial pattern of plume impacts is of 

interest, CTDMPLUS is available. 

CTDMPLUS provides greater resolution of 

concentrations about the contour of the hill 

feature than does AERMOD through a 

different plume-terrain interaction algorithm. 

4.2.2.3 OCD 

a. The OCD (Offshore and Coastal 

Dispersion) model is a straight-line Gaussian 

model that incorporates overwater plume 

transport and dispersion as well as changes 

that occur as the plume crosses the shoreline. 

The OCD model can determine the impact of 

offshore emissions from point, area, or line 

sources on the air quality of coastal regions. 

The OCD model 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


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4.2.3.2 Models for Lead 

a. In January 1999 (40 CFR part 58, 

appendix D), the EPA gave notice that 

concern about ambient lead impacts was 

being shifted away from roadways and 

toward a focus on stationary point sources. 

Thus, models for assessing the impact of lead 

emissions are needed to meet NSR 

requirements to address compliance with the 

lead NAAQS and for SIP attainment 

demonstrations. The EPA has also issued 

guidance on siting ambient monitors in the 

vicinity of stationary point sources.50 For 

lead, the SIP should contain an air quality 

analysis to determine the maximum rolling 3- 

month average lead concentration resulting 

from major lead point sources, such as 

smelters, gasoline additive plants, etc. The 

EPA has developed a post-processor to 

calculate rolling 3-month average 

concentrations from model output.51 General 

guidance for lead SIP development is also 

available.52

b. For major lead point sources, such as 

smelters, which contribute fugitive emissions 

and for which deposition is important, 

professional judgment should be used, and 

there shall be coordination with the 

appropriate reviewing authority (paragraph 

3.0(b)). For most applications, the general 

requirements for screening and refined 

models of section 4.2.1 and 4.2.2 are 

applicable to lead modeling. 

4.2.3.3 Models for Sulfur Dioxide 

a. Models for SO2 are needed to meet NSR 

requirements to address compliance with the 

SO2 NAAQS and PSD increments, for SIP 

attainment demonstrations,53 and for 

characterizing current air quality via 

modeling.54 SO2 is one of a group of highly 

reactive gases known as ‘‘oxides of sulfur’’ 

with largest emissions sources being fossil 

fuel combustion at power plants and other 

industrial facilities. 

b. Given the relatively inert nature of SO2

on the short-term time scales of interest (i.e., 

1-hour) and the sources of SO2 (i.e., 

stationary point sources), the general 

modeling requirements for screening models 

in section 4.2.1 and refined models in section 

4.2.2 are applicable for SO2 modeling 

applications. For urban areas, AERMOD 

automatically invokes a half-life of 4 hours 55

to SO2. Therefore, care must be taken when 

determining whether a source is urban or 

rural (see section 7.2.1.1 for urban/rural 

determination methodology). 

4.2.3.4 Models for Nitrogen Dioxide 

a. Models for assessing the impact of 

sources on ambient NO2 concentrations are 

needed to meet NSR requirements to address 

compliance with the NO2 NAAQS and PSD 

increments. Impact of an individual source 

on ambient NO2 depends, in part, on the 

chemical environment into which the 

source’s plume is to be emitted. This is due 

to the fact that NO2 sources co-emit NO along 

with NO2 and any emitted NO may react with 

ambient ozone to convert to additional NO2

downwind. Thus, comprehensive modeling 

of NO2 would need to consider the ratio of 

emitted NO and NO2, the ambient levels of 

ozone and subsequent reactions between 

ozone and NO, and the photolysis of NO2 to 

NO. 

b. Due to the complexity of NO2 modeling, 

a multi-tiered screening approach is required 

to obtain hourly and annual average 

estimates of NO2.56 Since these methods are 

considered screening techniques, their usage 

shall occur in agreement with the appropriate 

reviewing authority (paragraph 3.0(b)). 

Additionally, since screening techniques are 

conservative by their nature, there are 

limitations to how these options can be used. 

Specifically, modeling of negative emissions 

rates should only be done after consultation 

with the EPA Regional office to ensure that 

decreases in concentrations would not be 

overestimated. Each tiered approach (see 

Figure 4–1) accounts for increasingly 

complex considerations of NO2 chemistry 

and is described in paragraphs c through e 

of this subsection. The tiers of NO2 modeling 

include: 

i. A first-tier (most conservative) ‘‘full’’ 

conversion approach; 

ii. A second-tier approach that assumes 

ambient equilibrium between NO and NO2; 

and 

iii. A third-tier consisting of several 

detailed screening techniques that account 

for ambient ozone and the relative amount of 

NO and NO2 emitted from a source. 

c. For Tier 1, use an appropriate refined 

model (section 4.2.2) to estimate nitrogen 

oxides (NOX) concentrations and assume a 

total conversion of NO to NO2. 

d. For Tier 2, multiply the Tier 1 result(s) 

by the Ambient Ratio Method 2 (ARM2), 

which provides estimates of representative 

equilibrium ratios of NO2/NOX value based 

ambient levels of NO2 and NOX derived from 

national data from the EPA’s Air Quality 

System (AQS).57 The national default for 

ARM2 includes a minimum ambient NO2/ 

NOX ratio of 0.5 and a maximum ambient 

ratio of 0.9. The reviewing agency may 

establish alternative minimum ambient NO2/ 

NOX values based on the source’s in-stack 

emissions ratios, with alternative minimum 

ambient ratios reflecting the source’s in-stack 

NO2/NOX ratios. Preferably, alternative 

minimum ambient NO2/NOX ratios should be 

based on source-specific data which satisfies 

all quality assurance procedures that ensure 

data accuracy for both NO2 and NOX within 

the typical range of measured values. 

However, alternate information may be used 

to justify a source’s anticipated NO2/NOX instack ratios, such as manufacturer test data, 

State or local agency guidance, peer-reviewed 

literature, and/or the EPA’s NO2/NOX ratio 

database. 

e. For Tier 3, a detailed screening 

technique shall be applied on a case-by-case 

basis. Because of the additional input data 

requirements and complexities associated 

with the Tier 3 options, their usage shall 

occur in consultation with the EPA Regional 

office in addition to the appropriate 

reviewing authority. The Ozone Limiting 

Method (OLM),58 the Plume Volume Molar 

Ratio Method (PVMRM),59 and the Generic 

Set Reaction Method (GRSM),60 61 are three 

detailed screening techniques that may be 

used for most sources. These three 

techniques use an appropriate section 4.2.2 

model to estimate NOX concentrations and 

then estimate the conversion of primary NO 

emissions to NO2 based on the ambient levels 

of ozone and the plume characteristics. OLM 

only accounts for NO2 formation based on the 

ambient levels of ozone while PVMRM and 

GRSM also accommodate distance-dependent 

conversion ratios based on ambient ozone. 

GRSM, PVMRM and OLM require explicit 

specification of the NO2/NOX in-stack ratios 

and that ambient ozone concentrations be 

provided on an hourly basis. GRSM requires 

hourly ambient NOX concentrations in 

addition to hourly ozone. 

f. Alternative models or techniques may be 

considered on a case-by-case basis and their 

usage shall be approved by the EPA Regional 

office (section 3.2). Such models or 

techniques should consider individual 

quantities of NO and NO2 emissions, 

atmospheric transport and dispersion, and 

atmospheric transformation of NO to NO2. 

Dispersion models that account for more 

explicit photochemistry may also be 

considered as an alternative model to 

estimate ambient impacts of NOX sources. 


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Figure 4–1: Multi-Tiered Approach for 

Estimating NO2 Concentrations 

4.2.3.5 Models for PM2.5

a. PM2.5 is a mixture consisting of several 

diverse components.62 Ambient PM2.5

generally consists of two components: (1) the 

primary component, emitted directly from a 

source; and (2) the secondary component, 

formed in the atmosphere from other 

pollutants emitted from the source. Models 

for PM2.5 are needed to meet NSR 

requirements to address compliance with the 

PM2.5 NAAQS and PSD increments and for 

SIP attainment demonstrations. 

b. For NSR modeling assessments, the 

general modeling requirements for screening 

models in section 4.2.1 and refined models 

in section 4.2.2 are applicable for the primary 

component of PM2.5, while the methods in 

section 5.4 are applicable for addressing the 

secondary component of PM2.5. Guidance for 

PSD assessments is available for determining 

the best approach to handling sources of 

primary and secondary PM2.5.63

c. For SIP attainment demonstrations and 

regional haze reasonable progress goal 

analyses, effects of a control strategy on PM2.5

are estimated from the sum of the effects on 

the primary and secondary components 

composing PM2.5. Model users should refer to 

section 5.4.1 and associated SIP modeling 

guidance 64 for further details concerning 

appropriate modeling approaches. 

d. The general modeling requirements for 

the refined models discussed in section 4.2.2 

shall be applied for PM2.5 hot-spot modeling 

for mobile sources. Specific guidance is 

available for analyzing direct PM2.5 impacts 

from highways, terminals, and other 

transportation projects.65

4.2.3.6 Models for PM10

a. Models for PM10 are needed to meet NSR 

requirements to address compliance with the 

PM10 NAAQS and PSD increments and for 

SIP attainment demonstrations. 

b. For most sources, the general modeling 

requirements for screening models in section 

4.2.1 and refined models in section 4.2.2 

shall be applied for PM10 modeling. In cases 

where the particle size and its effect on 

ambient concentrations need to be 

considered, particle deposition may be used 

on a case-by-case basis and their usage shall 

be coordinated with the appropriate 

reviewing authority. A SIP development 

guide 66 is also available to assist in PM10

analyses and control strategy development. 

c. Fugitive dust usually refers to dust put 

into the atmosphere by the wind blowing 

over plowed fields, dirt roads, or desert or 

sandy areas with little or no vegetation. 

Fugitive emissions include the emissions 

resulting from the industrial process that are 

not captured and vented through a stack, but 

may be released from various locations 

within the complex. In some unique cases, a 

model developed specifically for the 

situation may be needed. Due to the difficult 

nature of characterizing and modeling 

fugitive dust and fugitive emissions, the 

proposed procedure shall be determined in 

consultation with the appropriate reviewing 

authority (paragraph 3.0(b)) for each specific 

situation before the modeling exercise is 

begun. Re-entrained dust is created by 

vehicles driving over dirt roads (e.g., haul 

roads) and dust-covered roads typically 

found in arid areas. Such sources can be 

characterized as line, area or volume 

sources.65 67 Emission rates may be based on 

site-specific data or values from the general 

literature. 

d. Under certain conditions, recommended 

dispersion models may not be suitable to 

appropriately address the nature of ambient 

PM10. In these circumstances, the alternative 

modeling approach shall be approved by the 

EPA Regional office (section 3.2). 

e. The general modeling requirements for 

the refined models discussed in section 4.2.2 

shall be applied for PM10 hot-spot modeling 

for mobile sources. Specific guidance is 

available for analyzing direct PM10 impacts 

from highways, terminals, and other 

transportation projects.65

5.0 Models for Ozone and Secondarily 

Formed Particulate Matter 

5.1 Discussion 

a. Air pollutants formed through chemical 

reactions in the atmosphere are referred to as 

secondary pollutants. For example, groundlevel ozone and a portion of PM2.5 are 

secondary pollutants formed through 

photochemical reactions. Ozone and 

secondarily formed particulate matter are 

closely related to each other in that they 

share common sources of emissions and are 

formed in the atmosphere from chemical 

reactions with similar precursors. 

b. Ozone formation is driven by emissions 

of NOX and volatile organic compounds 

(VOCs). Ozone formation is a complicated 

nonlinear process that requires favorable 

meteorological conditions in addition to VOC 

and NOX emissions. Sometimes complex 

terrain features also contribute to the buildup of precursors and subsequent ozone 

formation or destruction. 

c. PM2.5 can be either primary (i.e., emitted 

directly from sources) or secondary in nature. 

The fraction of PM2.5 which is primary versus 

secondary varies by location and season. In 

the United States, PM2.5 is dominated by a 

variety of chemical species or components of 

atmospheric particles, such as ammonium 

sulfate, ammonium nitrate, organic carbon 

mass, elemental carbon, and other soil 

compounds and oxidized metals. PM2.5

sulfate, nitrate, and ammonium ions are 

predominantly the result of chemical 

reactions of the oxidized products of SO2 and 

NOX emissions with direct ammonia 

emissions.68

d. Control measures reducing ozone and 

PM2.5 precursor emissions may not lead to 

proportional reductions in ozone and PM2.5. 

Modeled strategies designed to reduce ozone 

or PM2.5 levels typically need to consider the 

chemical coupling between these pollutants. 

This coupling is important in understanding 

processes that control the levels of both 

pollutants. Thus, when feasible, it is 

important to use models that take into 

account the chemical coupling between 

ozone and PM2.5. In addition, using such a 

multi-pollutant modeling system can reduce 

the resource burden associated with applying 

and evaluating separate models for each 

pollutant and promotes consistency among 

the strategies themselves. 

e. PM2.5 is a mixture consisting of several 

diverse chemical species or components of 


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atmospheric particles. Because chemical and 

physical properties and origins of each 

component differ, it may be appropriate to 

use either a single model capable of 

addressing several of the important 

components or to model primary and 

secondary components using different 

models. Effects of a control strategy on PM2.5

is estimated from the sum of the effects on 

the specific components comprising PM2.5. 

5.2 Recommendations 

a. Chemical transformations can play an 

important role in defining the concentrations 

and properties of certain air pollutants. 

Models that take into account chemical 

reactions and physical processes of various 

pollutants (including precursors) are needed 

for determining the current state of air 

quality, as well as predicting and projecting 

the future evolution of these pollutants. It is 

important that a modeling system provide a 

realistic representation of chemical and 

physical processes leading to secondary 

pollutant formation and removal from the 

atmosphere. 

b. Chemical transport models treat 

atmospheric chemical and physical processes 

such as deposition and motion. There are two 

types of chemical transport models, Eulerian 

(grid based) and Lagrangian. These types of 

models are differentiated from each other by 

their frame of reference. Eulerian models are 

based on a fixed frame of reference and 

Lagrangian models use a frame of reference 

that moves with parcels of air between the 

source and receptor point.9 Photochemical 

grid models are three-dimensional Eulerian 

grid-based models that treat chemical and 

physical processes in each grid cell and use 

diffusion and transport processes to move 

chemical species between grid cells.9 These 

types of models are appropriate for 

assessment of near-field and regional scale 

reactive pollutant impacts from specific 

sources 7 10 11 12 or all sources.13 14 15 In some 

limited cases, the secondary processes can be 

treated with a box model, ideally in 

combination with a number of other 

modeling techniques and/or analyses to treat 

individual source sectors. 

c. Regardless of the modeling system used 

to estimate secondary impacts of ozone and/ 

or PM2.5, model results should be compared 

to observation data to generate confidence 

that the modeling system is representative of 

the local and regional air quality. For ozone 

related projects, model estimates of ozone 

should be compared with observations in 

both time and space. For PM2.5, model 

estimates of speciated PM2.5 components 

(such as sulfate ion, nitrate ion, etc.) should 

be compared with observations in both time 

and space.69

d. Model performance metrics comparing 

observations and predictions are often used 

to summarize model performance. These 

metrics include mean bias, mean error, 

fractional bias, fractional error, and 

correlation coefficient.69 There are no 

specific levels of any model performance 

metric that indicate ‘‘acceptable’’ model 

performance. The EPA’s preferred approach 

for providing context about model 

performance is to compare model 

performance metrics with similar 

contemporary applications.64 69 Because 

model application purpose and scope vary, 

model users should consult with the 

appropriate reviewing authority (paragraph 

3.0(b)) to determine what model performance 

elements should be emphasized and 

presented to provide confidence in the 

regulatory model application. 

e. There is no preferred modeling system 

or technique for estimating ozone or 

secondary PM2.5 for specific source impacts 

or to assess impacts from multiple sources. 

For assessing secondary pollutant impacts 

from single sources, the degree of complexity 

required to assess potential impacts varies 

depending on the nature of the source, its 

emissions, and the background environment. 

The EPA recommends a two-tiered approach 

where the first tier consists of using existing 

technically credible and appropriate 

relationships between emissions and impacts 

developed from previous modeling that is 

deemed sufficient for evaluating a source’s 

impacts. The second tier consists of more 

sophisticated case-specific modeling 

analyses. The appropriate tier for a given 

application should be selected in 

consultation with the appropriate reviewing 

authority (paragraph 3.0(b)) and be consistent 

with EPA guidance.70

5.3 Recommended Models and Approaches 

for Ozone 

a. Models that estimate ozone 

concentrations are needed to guide the 

choice of strategies for the purposes of a 

nonattainment area demonstrating future 

year attainment of the ozone NAAQS. 

Additionally, models that estimate ozone 

concentrations are needed to assess impacts 

from specific sources or source complexes to 

satisfy requirements for NSR and other 

regulatory programs. Other purposes for 

ozone modeling include estimating the 

impacts of specific events on air quality, 

ozone deposition impacts, and planning for 

areas that may be attaining the ozone 

NAAQS. 

5.3.1 Models for NAAQS Attainment 

Demonstrations and Multi-Source Air 

Quality Assessments 

a. Simulation of ozone formation and 

transport is a complex exercise. Control 

agencies with jurisdiction over areas with 

ozone problems should use photochemical 

grid models to evaluate the relationship 

between precursor species and ozone. Use of 

photochemical grid models is the 

recommended means for identifying control 

strategies needed to address high ozone 

concentrations in such areas. Judgment on 

the suitability of a model for a given 

application should consider factors that 

include use of the model in an attainment 

test, development of emissions and 

meteorological inputs to the model, and 

choice of episodes to model. Guidance on the 

use of models and other analyses for 

demonstrating attainment of the air quality 

goals for ozone is available.63 64 Users should 

consult with the appropriate reviewing 

authority (paragraph 3.0(b)) to ensure the 

most current modeling guidance is applied. 

5.3.2 Models for Single-Source Air Quality 

Assessments 

a. Depending on the magnitude of 

emissions, estimating the impact of an 

individual source’s emissions of NOX and 

VOC on ambient ozone is necessary for 

obtaining a permit. The simulation of ozone 

formation and transport requires realistic 

treatment of atmospheric chemistry and 

deposition. Models (e.g., Lagrangian and 

photochemical grid models) that integrate 

chemical and physical processes important 

in the formation, decay, and transport of 

ozone and important precursor species 

should be applied. Photochemical grid 

models are primarily designed to characterize 

precursor emissions and impacts from a wide 

variety of sources over a large geographic 

area but can also be used to assess the 

impacts from specific sources.7 11 12

b. The first tier of assessment for ozone 

impacts involves those situations where 

existing technical information is available 

(e.g., results from existing photochemical 

grid modeling, published empirical estimates 

of source specific impacts, or reduced-form 

models) in combination with other 

supportive information and analysis for the 

purposes of estimating secondary impacts 

from a particular source. The existing 

technical information should provide a 

credible and representative estimate of the 

secondary impacts from the project source. 

The appropriate reviewing authority 

(paragraph 3.0(b)) and appropriate EPA 

guidance 70 71 should be consulted to 

determine what types of assessments may be 

appropriate on a case-by-case basis. 

c. The second tier of assessment for ozone 

impacts involves those situations where 

existing technical information is not 

available or a first tier demonstration 

indicates a more refined assessment is 

needed. For these situations, chemical 

transport models should be used to address 

single-source impacts. Special considerations 

are needed when using these models to 

evaluate the ozone impact from an individual 

source. Guidance on the use of models and 

other analyses for demonstrating the impacts 

of single sources for ozone is available.70

This guidance document provides a more 

detailed discussion of the appropriate 

approaches to obtaining estimates of ozone 

impacts from a single source. Model users 

should use the latest version of the guidance 

in consultation with the appropriate 

reviewing authority (paragraph 3.0(b)) to 

determine the most suitable refined approach 

for single-source ozone modeling on a caseby-case basis. 

5.4 Recommended Models and Approaches 

for Secondarily Formed PM2.5

a. Models that estimate PM2.5

concentrations are needed to guide the 

choice of strategies for the purposes of a 

nonattainment area demonstrating future 

year attainment of the PM2.5 NAAQS. 

Additionally, models that estimate PM2.5

concentrations are needed to assess impacts 

from specific sources or source complexes to 

satisfy requirements for NSR and other 

regulatory programs. Other purposes for 

PM2.5 modeling include estimating the 

impacts of specific events on air quality, 


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visibility, deposition impacts, and planning 

for areas that may be attaining the PM2.5

NAAQS. 

5.4.1 Models for NAAQS Attainment 

Demonstrations and Multi-Source Air 

Quality Assessments 

a. Models for PM2.5 are needed to assess the 

adequacy of a proposed strategy for meeting 

the annual and 24-hour PM2.5 NAAQS. 

Modeling primary and secondary PM2.5 can 

be a multi-faceted and complex problem, 

especially for secondary components of PM2.5

such as sulfates and nitrates. Control 

agencies with jurisdiction over areas with 

secondary PM2.5 problems should use models 

that integrate chemical and physical 

processes important in the formation, decay, 

and transport of these species (e.g., 

photochemical grid models). Suitability of a 

modeling approach or mix of modeling 

approaches for a given application requires 

technical judgment as well as professional 

experience in choice of models, use of the 

model(s) in an attainment test, development 

of emissions and meteorological inputs to the 

model, and selection of days to model. 

Guidance on the use of models and other 

analyses for demonstrating attainment of the 

air quality goals for PM2.5 is available.63 64

Users should consult with the appropriate 

reviewing authority (paragraph 3.0(b)) to 

ensure the most current modeling guidance 

is applied. 

5.4.2 Models for Single-Source Air Quality 

Assessments 

a. Depending on the magnitude of 

emissions, estimating the impact of an 

individual source’s emissions on secondary 

particulate matter concentrations may be 

necessary for obtaining a permit. Primary 

PM2.5 components shall be simulated using 

the general modeling requirements in section 

4.2.3.5. The simulation of secondary 

particulate matter formation and transport is 

a complex exercise requiring realistic 

treatment of atmospheric chemistry and 

deposition. Models should be applied that 

integrate chemical and physical processes 

important in the formation, decay, and 

transport of these species (e.g., Lagrangian 

and photochemical grid models). 

Photochemical grid models are primarily 

designed to characterize precursor emissions 

and impacts from a wide variety of sources 

over a large geographic area and can also be 

used to assess the impacts from specific 

sources.7 10 For situations where a project 

source emits both primary PM2.5 and PM2.5

precursors, the contribution from both 

should be combined for use in determining 

the source’s ambient impact. Approaches for 

combining primary and secondary impacts 

are provided in appropriate guidance for 

single source permit related 

demonstrations.70

b. The first tier of assessment for secondary 

PM2.5 impacts involves those situations 

where existing technical information is 

available (e.g., results from existing 

photochemical grid modeling, published 

empirical estimates of source specific 

impacts, or reduced-form models) in 

combination with other supportive 

information and analysis for the purposes of 

estimating secondary impacts from a 

particular source. The existing technical 

information should provide a credible and 

representative estimate of the secondary 

impacts from the project source. The 

appropriate reviewing authority (paragraph 

3.0(b)) and appropriate EPA guidance 70 71

should be consulted to determine what types 

of assessments may be appropriate on a caseby-case basis. 

c. The second tier of assessment for 

secondary PM2.5 impacts involves those 

situations where existing technical 

information is not available or a first tier 

demonstration indicates a more refined 

assessment is needed. For these situations, 

chemical transport models should be used for 

assessments of single-source impacts. Special 

considerations are needed when using these 

models to evaluate the secondary particulate 

matter impact from an individual source. 

Guidance on the use of models and other 

analyses for demonstrating the impacts of 

single sources for secondary PM2.5 is 

available.70 This guidance document 

provides a more detailed discussion of the 

appropriate approaches to obtaining 

estimates of secondary particulate matter 

concentrations from a single source. Model 

users should use the latest version of this 

guidance in consultation with the 

appropriate reviewing authority (paragraph 

3.0(b)) to determine the most suitable singlesource modeling approach for secondary 

PM2.5 on a case-by-case basis. 

6.0 Modeling for Air Quality Related 

Values and Other Governmental Programs 

6.1 Discussion 

a. Other Federal government agencies and 

State, local, and Tribal agencies with air 

quality and land management responsibilities 

have also developed specific modeling 

approaches for their own regulatory or other 

requirements. Although such regulatory 

requirements and guidance have come about 

because of EPA rules or standards, the 

implementation of such regulations and the 

use of the modeling techniques is under the 

jurisdiction of the agency issuing the 

guidance or directive. This section covers 

such situations with reference to those 

guidance documents, when they are 

available. 

b. When using the model recommended or 

discussed in the Guideline in support of 

programmatic requirements not specifically 

covered by EPA regulations, the model user 

should consult the appropriate Federal, State, 

local, or Tribal agency to ensure the proper 

application and use of the models and/or 

techniques. These agencies have developed 

specific modeling approaches for their own 

regulatory or other requirements. Most of the 

programs have, or will have when fully 

developed, separate guidance documents that 

cover the program and a discussion of the 

tools that are needed. The following 

paragraphs reference those guidance 

documents, when they are available. 

6.2 Air Quality Related Values 

a. The 1990 CAA Amendments give FLMs 

an ‘‘affirmative responsibility’’ to protect the 

natural and cultural resources of Class I areas 

from the adverse impacts of air pollution and 

to provide the appropriate procedures and 

analysis techniques. The CAA identifies the 

FLM as the Secretary of the department, or 

their designee, with authority over these 

lands. Mandatory Federal Class I areas are 

defined in the CAA as international parks, 

national parks over 6,000 acres, and 

wilderness areas and memorial parks over 

5,000 acres, established as of 1977. The FLMs 

are also concerned with the protection of 

resources in federally managed Class II areas 

because of other statutory mandates to 

protect these areas. Where State or Tribal 

agencies have successfully petitioned the 

EPA and lands have been redesignated to 

Class I status, these agencies may have 

equivalent responsibilities to that of the 

FLMs for these non-Federal Class I areas as 

described throughout the remainder of 

section 6.2. 

b. The FLM agency responsibilities include 

the review of air quality permit applications 

from proposed new or modified major 

pollution sources that may affect these Class 

I areas to determine if emissions from a 

proposed or modified source will cause or 

contribute to adverse impacts on air quality 

related values (AQRVs) of a Class I area and 

making recommendations to the FLM. 

AQRVs are resources, identified by the FLM 

agencies, that have the potential to be 

affected by air pollution. These resources 

may include visibility, scenic, cultural, 

physical, or ecological resources for a 

particular area. The FLM agencies take into 

account the particular resources and AQRVs 

that would be affected; the frequency and 

magnitude of any potential impacts; and the 

direct, indirect, and cumulative effects of any 

potential impacts in making their 

recommendations. 

c. While the AQRV notification and impact 

analysis requirements are outlined in the 

PSD regulations at 40 CFR 51.166(p) and 40 

CFR 52.21(p), determination of appropriate 

analytical methods and metrics for AQRV’s 

are determined by the FLM agencies and are 

published in guidance external to the general 

recommendations of this paragraph. 

d. To develop greater consistency in the 

application of air quality models to assess 

potential AQRV impacts in both Class I areas 

and protected Class II areas, the FLM 

agencies have developed the Federal Land 

Managers’ Air Quality Related Values Work 

Group Phase I Report (FLAG).72 FLAG 

focuses upon specific technical and policy 

issues associated with visibility impairment, 

effects of pollutant deposition on soils and 

surface waters, and ozone effects on 

vegetation. Model users should consult the 

latest version of the FLAG report for current 

modeling guidance and with affected FLM 

agency representatives for any application 

specific guidance which is beyond the scope 

of the Guideline. 

6.2.1 Visibility 

a. Visibility in important natural areas (e.g., 

Federal Class I areas) is protected under a 

number of provisions of the CAA, including 

sections 169A and 169B (addressing impacts 

primarily from existing sources) and section 

165 (new source review). Visibility 

impairment is caused by light scattering and 

light absorption associated with particles and 

gases in the atmosphere. In most areas of the 

country, light scattering by PM2.5 is the most 


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significant component of visibility 

impairment. The key components of PM2.5

contributing to visibility impairment include 

sulfates, nitrates, organic carbon, elemental 

carbon, and crustal material.72

b. Visibility regulations (40 CFR 51.300 

through 51.309) require State, local, and 

Tribal agencies to mitigate current and 

prevent future visibility impairment in any of 

the 156 mandatory Federal Class I areas 

where visibility is considered an important 

attribute. In 1999, the EPA issued revisions 

to the regulations to address visibility 

impairment in the form of regional haze, 

which is caused by numerous, diverse 

sources (e.g., stationary, mobile, and area 

sources) located across a broad region (40 

CFR 51.308 through 51.309). The state of 

relevant scientific knowledge has expanded 

significantly since that time. A number of 

studies and reports 73 74 have concluded that 

long-range transport (e.g., up to hundreds of 

kilometers) of fine particulate matter plays a 

significant role in visibility impairment 

across the country. Section 169A of the CAA 

requires States to develop SIPs containing 

long-term strategies for remedying existing 

and preventing future visibility impairment 

in the 156 mandatory Class I Federal areas, 

where visibility is considered an important 

attribute. In order to develop long-term 

strategies to address regional haze, many 

State, local, and Tribal agencies will need to 

conduct regional-scale modeling of fine 

particulate concentrations and associated 

visibility impairment. 

c. The FLAG visibility modeling 

recommendations are divided into two 

distinct sections to address different 

requirements for: (1) near field modeling 

where plumes or layers are compared against 

a viewing background, and (2) distant/multisource modeling for plumes and aggregations 

of plumes that affect the general appearance 

of a scene.72 The recommendations 

separately address visibility assessments for 

sources proposing to locate relatively near 

and at farther distances from these areas.72

6.2.1.1 Models for Estimating Near-Field 

Visibility Impairment 

a. To calculate the potential impact of a 

plume of specified emissions for specific 

transport and dispersion conditions (‘‘plume 

blight’’) for source-receptor distances less 

than 50 km, a screening model and guidance 

are available.72 75 If a more comprehensive 

analysis is necessary, a refined model should 

be selected. The model selection, procedures, 

and analyses should be determined in 

consultation with the appropriate reviewing 

authority (paragraph 3.0(b)) and the affected 

FLM(s). 

6.2.1.2 Models for Estimating Visibility 

Impairment for Long-Range Transport 

a. Chemical transformations can play an 

important role in defining the concentrations 

and properties of certain air pollutants. 

Models that take into account chemical 

reactions and physical processes of various 

pollutants (including precursors) are needed 

for determining the current state of air 

quality, as well as predicting and projecting 

the future evolution of these pollutants. It is 

important that a modeling system provide a 

realistic representation of chemical and 

physical processes leading to secondary 

pollutant formation and removal from the 

atmosphere. 

b. Chemical transport models treat 

atmospheric chemical and physical processes 

such as deposition and motion. There are two 

types of chemical transport models, Eulerian 

(grid based) and Lagrangian. These types of 

models are differentiated from each other by 

their frame of reference. Eulerian models are 

based on a fixed frame of reference and 

Lagrangian models use a frame of reference 

that moves with parcels of air between the 

source and receptor point.9 Photochemical 

grid models are three-dimensional Eulerian 

grid-based models that treat chemical and 

physical processes in each grid cell and use 

diffusion and transport processes to move 

chemical species between grid cells.9 These 

types of models are appropriate for 

assessment of near-field and regional scale 

reactive pollutant impacts from specific 

sources 7 10 11 12 or all sources.13 14 15

c. Development of the requisite 

meteorological and emissions databases 

necessary for use of photochemical grid 

models to estimate AQRVs should conform to 

recommendations in section 8 and those 

outlined in the EPA’s Modeling Guidance for 

Demonstrating Attainment of Air Quality 

Goals for Ozone, PM2.5, and Regional Haze.64

Demonstration of the adequacy of prognostic 

meteorological fields can be established 

through appropriate diagnostic and statistical 

performance evaluations consistent with 

recommendations provided in the 

appropriate guidance.64 Model users should 

consult the latest version of this guidance 

and with the appropriate reviewing authority 

(paragraph 3.0(b)) for any applicationspecific guidance that is beyond the scope of 

this subsection. 

6.2.2 Models for Estimating Deposition 

Impacts 

a. For many Class I areas, AQRVs have 

been identified that are sensitive to 

atmospheric deposition of air pollutants. 

Emissions of NOX, sulfur oxides, NH3, 

mercury, and secondary pollutants such as 

ozone and particulate matter affect 

components of ecosystems. In sensitive 

ecosystems, these compounds can acidify 

soils and surface waters, add nutrients that 

change biodiversity, and affect the ecosystem 

services provided by forests and natural 

areas.72 To address the relationship between 

deposition and ecosystem effects, the FLM 

agencies have developed estimates of critical 

loads. A critical load is defined as, ‘‘A 

quantitative estimate of an exposure to one 

or more pollutants below which significant 

harmful effects on specified sensitive 

elements of the environment do not occur 

according to present knowledge.’’ 76

b. The FLM deposition modeling 

recommendations are divided into two 

distinct sections to address different 

requirements for: (1) near field modeling, and 

(2) distant/multi-source modeling for 

cumulative effects. The recommendations 

separately address deposition assessments for 

sources proposing to locate relatively near 

and at farther distances from these areas.72

Where the source and receptors are not in 

close proximity, chemical transport (e.g., 

photochemical grid) models generally should 

be applied for an assessment of deposition 

impacts due to one or a small group of 

sources. Over these distances, chemical and 

physical transformations can change 

atmospheric residence time due to different 

propensity for deposition to the surface of 

different forms of nitrate and sulfate. Users 

should consult the latest version of the FLAG 

report 72 and relevant FLM representatives 

for guidance on the use of models for 

deposition. Where source and receptors are 

in close proximity, users should contact the 

appropriate FLM for application-specific 

guidance. 

6.3 Modeling Guidance for Other 

Governmental Programs 

a. Dispersion and photochemical grid 

modeling may need to be conducted to 

ensure that individual and cumulative 

offshore oil and gas exploration, 

development, and production plans and 

activities do not significantly affect the air 

quality of any State as required under the 

Outer Continental Shelf Lands Act (OCSLA). 

Air quality modeling requires various input 

datasets, including emissions sources, 

meteorology, and pre-existing pollutant 

concentrations. For sources under the 

reviewing authority of the Department of 

Interior, Bureau of Ocean Energy 

Management (BOEM), guidance for the 

development of all necessary Outer 

Continental Shelf (OCS) air quality modeling 

inputs and appropriate model selection and 

application is available from the BOEM’s 

website: https://www.boem.gov/about-boem/ 

regulations-guidance/guidance-portal. 

b. The Federal Aviation Administration 

(FAA) is the appropriate reviewing authority 

for air quality assessments of primary 

pollutant impacts at airports and air bases. 

The Aviation Environmental Design Tool 

(AEDT) is developed and supported by the 

FAA, and is appropriate for air quality 

assessment of primary pollutant impacts at 

airports or air bases. AEDT has adopted 

AERMOD for treating dispersion. Application 

of AEDT is intended for estimating the 

change in emissions for aircraft operations, 

point source, and mobile source emissions on 

airport property and quantify the associated 

pollutant level- concentrations. AEDT is not 

intended for PSD, SIP, or other regulatory air 

quality analyses of point or mobile sources at 

or peripheral to airport property that are 

unrelated to airport operations. The latest 

version of AEDT may be obtained from the 

FAA at: https://aedt.faa.gov. 

7.0 General Modeling Considerations 

7.1 Discussion 

a. This section contains recommendations 

concerning a number of different issues not 

explicitly covered in other sections of the 

Guideline. The topics covered here are not 

specific to any one program or modeling area, 

but are common to dispersion modeling 

analyses for criteria pollutants. 

7.2 Recommendations 

7.2.1 All Sources 

7.2.1.1 Dispersion Coefficients 

a. For any dispersion modeling exercise, 

the urban or rural determination of a source 


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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 77 in rural areas and 

McElroy- Pooler 78 in urban areas. These 

coefficients are still incorporated in the BLP 

and OCD models. However, the AERMOD 

model incorporates a more up-to-date 

characterization of the atmospheric boundary 

layer using continuous functions of 

parameterized horizontal and vertical 

turbulence based on Monin-Obukhov 

similarity (scaling) relationships.44 Another 

key feature of AERMOD’s formulation is the 

option to use directly observed variables of 

the boundary layer to parameterize 

dispersion.44 45

b. The selection of rural or urban 

dispersion coefficients in a specific 

application should follow one of the 

procedures suggested by Irwin 79 to 

determine whether the character of an area is 

primarily urban or rural (of the two methods, 

the land use procedure is considered more 

definitive.): 

i. Land Use Procedure: (1) Classify the land 

use within the total area, Ao, circumscribed 

by a 3 km radius circle about the source 

using the meteorological land use typing 

scheme proposed by Auer; 80 (2) if land use 

types I1, I2, C1, R2, and R3 account for 50 

percent or more of Ao, use urban dispersion 

coefficients; otherwise, use appropriate rural 

dispersion coefficients. 

ii. Population Density Procedure: (1) 

Compute the average population density, p 

per square kilometer with Ao as defined 

above; (2) If p is greater than 750 people per 

square kilometer, use urban dispersion 

coefficients; otherwise use appropriate rural 

dispersion coefficients. 

c. Population density should be used with 

caution and generally not be applied to 

highly industrialized areas where the 

population density may be low and, thus, a 

rural classification would be indicated. 

However, the area is likely to be sufficiently 

built-up so that the urban land use criteria 

would be satisfied. Therefore, in this case, 

the classification should be ‘‘urban’’ and 

urban dispersion parameters should be used. 

d. For applications of AERMOD in urban 

areas, under either the Land Use Procedure 

or the Population Density Procedure, the user 

needs to estimate the population of the urban 

area affecting the modeling domain because 

the urban influence in AERMOD is scaled 

based on a user-specified population. For 

non-population oriented urban areas, or areas 

influenced by both population and industrial 

activity, the user will need to estimate an 

equivalent population to adequately account 

for the combined effects of industrialized 

areas and populated areas within the 

modeling domain. Selection of the 

appropriate population for these applications 

should be determined in consultation with 

the appropriate reviewing authority 

(paragraph 3.0(b)) and the latest version of 

the AERMOD Implementation Guide.81

e. It should be noted that AERMOD allows 

for modeling rural and urban sources in a 

single model run. For analyses of whole 

urban complexes, the entire area should be 

modeled as an urban region if most of the 

sources are located in areas classified as 

urban. For tall stacks located within or 

adjacent to small or moderate sized urban 

areas, the stack height or effective plume 

height may extend above the urban boundary 

layer and, therefore, may be more 

appropriately modeled using rural 

coefficients. Model users should consult with 

the appropriate reviewing authority 

(paragraph 3.0(b)) and the latest version of 

the AERMOD Implementation Guide 81 when 

evaluating this situation. 

f. Buoyancy-induced dispersion (BID), as 

identified by Pasquill,82 is included in the 

preferred models and should be used where 

buoyant sources (e.g., those involving fuel 

combustion) are involved. 

7.2.1.2 Complex Winds 

a. Inhomogeneous local winds. In many 

parts of the United States, the ground is 

neither flat nor is the ground cover (or land 

use) uniform. These geographical variations 

can generate local winds and circulations, 

and modify the prevailing ambient winds 

and circulations. Typically, geographic 

effects are more apparent when the ambient 

winds are light or calm, as stronger synoptic 

or mesoscale winds can modify, or even 

eliminate the weak geographic circulations.83

In general, these geographically induced 

wind circulation effects are named after the 

source location of the winds, e.g., lake and 

sea breezes, and mountain and valley winds. 

In very rugged hilly or mountainous terrain, 

along coastlines, or near large land use 

variations, the characteristics of the winds 

are a balance of various forces, such that the 

assumptions of steady-state straight-line 

transport both in time and space are 

inappropriate. In such cases, a model should 

be chosen to fully treat the time and space 

variations of meteorology effects on transport 

and dispersion. The setup and application of 

such a model should be determined in 

consultation with the appropriate reviewing 

authority (paragraph 3.0(b)) consistent with 

limitations of paragraph 3.2.2(e). The 

meteorological input data requirements for 

developing the time and space varying threedimensional 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 shortlived at a given receptor. There are no 

recommended refined techniques to model 

this phenomenon. There are, however, 

screening procedures 40 that may be used to 

approximate the concentrations. 

Considerable care should be exercised in 

using the results obtained from the screening 

techniques. 

ii. Shoreline fumigation. Fumigation can be 

an important phenomenon on and near the 

shoreline of bodies of water. This can affect 

both individual plumes and area-wide 

emissions. When fumigation conditions are 

expected to occur from a source or sources 

with tall stacks located on or just inland of 

a shoreline, this should be addressed in the 

air quality modeling analysis. The EPA has 

evaluated several coastal fumigation models, 

and the evaluation results of these models are 

available for their possible application on a 

case-by-case basis when air quality estimates 

under shoreline fumigation conditions are 

needed.84 Selection of the appropriate model 

for applications where shoreline fumigation 

is of concern should be determined in 

consultation with the appropriate reviewing 

authority (paragraph 3.0(b)). 

iii. Stagnation. Stagnation conditions are 

characterized by calm or very low wind 

speeds, and variable wind directions. These 

stagnant meteorological conditions may 

persist for several hours to several days. 

During stagnation conditions, the dispersion 

of air pollutants, especially those from lowlevel emissions sources, tends to be 

minimized, potentially leading to relatively 

high ground-level concentrations. If point 

sources are of interest, users should note the 

guidance provided in paragraph (a) of this 

subsection. Selection of the appropriate 

model for applications where stagnation is of 

concern should be determined in 

consultation with the appropriate reviewing 

authority (paragraph 3.0(b)). 

7.2.1.3 Gravitational Settling and 

Deposition 

a. Gravitational settling and deposition 

may be directly included in a model if either 

is a significant factor. When particulate 

matter sources can be quantified and settling 

and dry deposition are problems, use 

professional judgment along with 

coordination with the appropriate reviewing 

authority (paragraph 3.0(b)). AERMOD 

contains algorithms for dry and wet 

deposition of gases and particles.85 For other 

Gaussian plume models, an ‘‘infinite halflife’’ 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.88 Since 

the definition of GEP stack height defines 

excessive concentrations as a maximum 

ground-level concentration due in whole or 

in part to downwash of at least 40 percent 

in excess of the maximum concentration 

without downwash, the potential air quality 

impacts associated with cavity and wake 

effects should also be considered for stacks 

that equal or exceed the EPA formula height 

for GEP. The AERSCREEN model can be used 

to obtain screening estimates of potential 

downwash influences, based on the PRIME 

downwash algorithm incorporated in the 

AERMOD model. If more refined 

concentration estimates are required, 

AERMOD should be used (section 4.2.2). 

7.2.2.2 Plume Rise 

a. The plume rise methods of Briggs 90 91

are incorporated in many of the preferred 

models and are recommended for use in 

many modeling applications. In 

AERMOD,44 45 for the stable boundary layer, 

plume rise is estimated using an iterative 

approach, similar to that in the CTDMPLUS 

model. In the convective boundary layer, 

plume rise is superposed on the 

displacements by random convective 

velocities.92 In AERMOD, plume rise is 

computed using the methods of Briggs, 

except in cases involving building 

downwash, in which a numerical solution of 

the mass, energy, and momentum 

conservation laws is performed.93 No explicit 

provisions in these models are made for 

multistack plume rise enhancement or the 

handling of such special plumes as flares. 

b. Gradual plume rise is generally 

recommended where its use is appropriate: 

(1) in AERMOD; (2) in complex terrain 

screening procedures to determine close-in 

impacts; and (3) when calculating the effects 

of building wakes. The building wake 

algorithm in AERMOD incorporates and 

exercises the thermodynamically based 

gradual plume rise calculations as described 

in paragraph (a) of this subsection. If the 

building wake is calculated to affect the 

plume for any hour, gradual plume rise is 

also used in downwind dispersion 

calculations to the distance of final plume 

rise, after which final plume rise is used. 

Plumes captured by the near wake are reemitted to the far wake as a ground-level 

volume source. 

c. Stack tip downwash generally occurs 

with poorly constructed stacks and when the 

ratio of the stack exit velocity to wind speed 

is small. An algorithm developed by Briggs 91

is the recommended technique for this 

situation and is used in preferred models for 

point sources. 

d. On a case-by-case basis, refinements to 

the preferred model may be considered for 

plume rise and downwash effects and shall 

occur in agreement with the appropriate 

reviewing authority (paragraph 3.0(b)) and 

approval by the EPA Regional office based on 

the requirements of section 3.2.2. 

7.2.3 Mobile Sources 

a. Emissions of primary pollutants from 

mobile sources can be modeled with an 

appropriate model identified in section 4.2. 

Screening of mobile sources can be 

accomplished by using screening 

meteorology, e.g., worst-case meteorological 

conditions. Maximum hourly concentrations 

computed from screening modeling can be 

converted to longer averaging periods using 

the scaling ratios specified in the 

AERSCREEN User’s Guide.37

b. Mobile sources can be modeled in 

AERMOD as either line (i.e., elongated area) 

sources or as a series of volume sources. Line 

sources can be represented in AERMOD with 

the following source types: LINE, AREA, 

VOLUME or RLINE. However, since mobile 

source modeling usually includes an analysis 

of very near-source impacts, the results can 

be highly sensitive to the characterization of 

the mobile emissions. Important 

characteristics for both line/area and volume 

sources include the plume release height, 

source width, and initial dispersion 

characteristics, and should also take into 

account the impact of traffic-induced 

turbulence that can cause roadway sources to 

have larger initial dimensions than might 

normally be used for representing line 

sources. 

c. The EPA’s quantitative PM hot-spot 

guidance 65 and Haul Road Workgroup Final 

Report 67 provide guidance on the 

appropriate characterization of mobile 

sources as a function of the roadway and 

vehicle characteristics. The EPA’s 

quantitative PM hot-spot guidance includes 

important considerations and should be 

consulted when modeling roadway links. 

Area and line sources, which can be 

characterized as AREA, LINE, and RLINE 

source types in AERMOD, or volume sources, 

may be used for modeling mobile sources. 

However, experience in the field has shown 

that area sources (characterized as AREA, 

LINE, or RLINE source types) may be easier 

to characterize correctly compared to volume 

sources. If volume sources are used, it is 

particularly important to ensure that roadway 

emissions are appropriately spaced when 

using volume source so that the emissions 

field is uniform across the roadway. 

Additionally, receptor placement is 

particularly important for volume sources 

that have ‘‘exclusion zones’’ where 

concentrations are not calculated for 

receptors located ‘‘within’’ the volume 

sources, i.e., less than 2.15 times the initial 

lateral dispersion coefficient from the center 

of the volume.65 Therefore, placing receptors 

in these ‘‘exclusion zones’’ will result in 

underestimates of roadway impacts. 

8.0 Model Input Data 

a. Databases and related procedures for 

estimating input parameters are an integral 

part of the modeling process. The most 

appropriate input data available should 

always be selected for use in modeling 

analyses. Modeled concentrations can vary 

widely depending on the source data or 

meteorological data used. This section 

attempts to minimize the uncertainty 

associated with database selection and use by 

identifying requirements for input data used 

in modeling. More specific data requirements 

and the format required for the individual 

models are described in detail in the user’s 

guide and/or associated documentation for 

each model. 

8.1 Modeling Domain 

8.1.1 Discussion 

a. The modeling domain is the geographic 

area for which the required air quality 

analyses for the NAAQS and PSD increments 

are conducted. 

8.1.2 Requirements 

a. For a NAAQS or PSD increments 

assessment, the modeling domain or project’s 

impact area shall include all locations where 

the emissions of a pollutant from the new or 

modifying source(s) may cause a significant 

ambient impact. This impact area is defined 

as an area with a radius extending from the 

new or modifying source to: (1) the most 

distant location where air quality modeling 

predicts a significant ambient impact will 

occur, or (2) the nominal 50 km distance 

considered applicable for Gaussian 

dispersion models, whichever is less. The 

required air quality analysis shall be carried 

out within this geographical area with 

characterization of source impacts, nearby 

source impacts, and background 

concentrations, as recommended later in this 

section. 

b. For SIP attainment demonstrations for 

ozone and PM2.5, or regional haze reasonable 

progress goal analyses, the modeling domain 

is determined by the nature of the problem 

being modeled and the spatial scale of the 

emissions that impact the nonattainment or 

Class I area(s). The modeling domain shall be 

designed so that all major upwind source 

areas that influence the downwind 

nonattainment area are included in addition 

to all monitor locations that are currently or 

recently violating the NAAQS or close to 

violating the NAAQS in the nonattainment 

area. Similarly, all Class I areas to be 

evaluated in a regional haze modeling 

application shall be included and sufficiently 

distant from the edge of the modeling 

domain. Guidance on the determination of 

the appropriate modeling domain for 

photochemical grid models in demonstrating 

attainment of these air quality goals is 

available.64 Users should consult the latest 

version of this guidance for the most current 

modeling guidance and the appropriate 

reviewing authority (paragraph 3.0(b)) for any 

application specific guidance that is beyond 

the scope of this section. 

8.2 Source Data 

8.2.1 Discussion 

a. Sources of pollutants can be classified as 

point, line, area, and volume sources. Point 

sources are defined in terms of size and may 

vary between regulatory programs. The line 

sources most frequently considered are 

roadways and streets along which there are 

well-defined movements of motor vehicles. 

They may also be lines of roof vents or 

stacks, such as in aluminum refineries. Area 


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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, PM2.5, and regional 

haze reasonable progress goal analyses, 

emissions which reflect actual emissions 

during the base modeling year time period 

should be input to models for base year 

modeling. Emissions projections to future 

years should account for key variables such 

as growth due to increased or decreased 

activity, expected emissions controls due to 

regulations, settlement agreements or consent 

decrees, fuel switches, and any other relevant 

information. Guidance on emissions 

estimation techniques (including future year 

projections) for SIP attainment 

demonstrations is available.64 95

b. For the purpose of SIP revisions for 

stationary point sources, the regulatory 

modeling of inert pollutants shall use the 

emissions input data shown in Table 8–1 for 

short-term and long-term NAAQS. To 

demonstrate compliance and/or establish the 

appropriate SIP emissions limits, Table 8–1 

generally provides for the use of ‘‘allowable’’ 

emissions in the regulatory dispersion 

modeling of the stationary point source(s) of 

interest. In such modeling, these source(s) 

should be modeled sequentially with these 

loads for every hour of the year. As part of 

a cumulative impact analysis, Table 8–1 

allows for the model user to account for 

actual operations in developing the 

emissions inputs for dispersion modeling of 

nearby sources, while other sources are best 

represented by air quality monitoring data. 

Consultation with the appropriate reviewing 

authority (paragraph 3.0(b)) is advisable on 

the establishment of the appropriate 

emissions inputs for regulatory modeling 

applications with respect to SIP revisions for 

stationary point sources. 

c. For the purposes of demonstrating 

NAAQS compliance in a PSD assessment, the 

regulatory modeling of inert pollutants shall 

use the emissions input data shown in Table 

8–2 for short and long-term NAAQS. The 

new or modifying stationary point source 

shall be modeled with ‘‘allowable’’ emissions 

in the regulatory dispersion modeling. As 

part of a cumulative impact analysis, Table 

8–2 allows for the model user to account for 

actual operations in developing the 

emissions inputs for dispersion modeling of 

nearby sources, while other sources are best 

represented by air quality monitoring data. 

For purposes of situations involving 

emissions trading, refer to current EPA policy 

and guidance to establish input data. 

Consultation with the appropriate reviewing 

authority (paragraph 3.0(b)) is advisable on 

the establishment of the appropriate 

emissions inputs for regulatory modeling 

applications with respect to PSD assessments 

for a proposed new or modifying source. 

d. For stationary source applications, 

changes in operating conditions that affect 

the physical emission parameters (e.g., 

release height, initial plume volume, and exit 

velocity) shall be considered to ensure that 

maximum potential impacts are 

appropriately determined in the assessment. 

For example, the load or operating condition 

for point sources that causes maximum 

ground-level concentrations shall be 

established. As a minimum, the source 

should be modeled using the design capacity 

(100 percent load). If a source operates at 

greater than design capacity for periods that 

could result in violations of the NAAQS or 

PSD increments, this load should be 

modeled. Where the source operates at 

substantially less than design capacity, and 

the changes in the stack parameters 

associated with the operating conditions 

could lead to higher ground level 

concentrations, loads such as 50 percent and 

75 percent of capacity should also be 

modeled. Malfunctions which may result in 

excess emissions are not considered to be a 

normal operating condition. They generally 

should not be considered in determining 

allowable emissions. However, if the excess 

emissions are the result of poor maintenance, 

careless operation, or other preventable 

conditions, it may be necessary to consider 

them in determining source impact. A range 

of operating conditions should be considered 

in screening analyses. The load causing the 

highest concentration, in addition to the 

design load, should be included in refined 

modeling. 

e. Emissions from mobile sources also have 

physical and temporal characteristics that 

should be appropriately accounted. For 

example, an appropriate emissions model 

shall be used to determine emissions profiles. 

Such emissions should include speciation 

specific for the vehicle types used on the 

roadway (e.g., light duty and heavy duty 

trucks), and subsequent parameterizations of 

the physical emissions characteristics (e.g., 

release height) should reflect those emissions 

sources. For long-term standards, annual 

average emissions may be appropriate, but 

for short-term standards, discrete temporal 

representation of emissions should be used 

(e.g., variations in weekday and weekend 

traffic or the diurnal rush-hour profile typical 

of many cities). Detailed information and 

data requirements for modeling mobile 

sources of pollution are provided in the 

user’s manuals for each of the models 

applicable to mobile sources.65 67

TABLE 8–1—POINT SOURCE MODEL EMISSION INPUTS FOR SIP REVISIONS OF INERT POLLUTANTS 1

Averaging time Emissions limit 

(lb/MMBtu) 2 × Operating level (MMBtu/hr) 2 × Operating factor 

(e.g., hr/yr, hr/day) 

Stationary Point Sources(s) Subject to SIP Emissions Limit(s) Evaluation for Compliance with Ambient Standards 

(Including Areawide Demonstrations) 

Annual & quarterly ... Maximum allowable emission 

limit or federally enforceable 

permit limit.

Actual or design capacity 

(whichever is greater), or federally enforceable permit condition.3

Actual operating factor averaged over the most recent 2 

years.4


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TABLE 8–1—POINT SOURCE MODEL EMISSION INPUTS FOR SIP REVISIONS OF INERT POLLUTANTS 1—Continued 

Averaging time Emissions limit 

(lb/MMBtu) 2 × Operating level 

(MMBtu/hr) 2 × Operating factor (e.g., hr/yr, hr/day) 

Short term (≤24 

hours).

Maximum allowable emission 

limit or federally enforceable 

permit limit.

Actual or design capacity 

(whichever is greater), or federally enforceable permit condition.3

Continuous operation, i.e., all 

hours of each time period 

under consideration (for all 

hours of the meteorological 

database).5

Nearby Source(s) 5

Annual & quarterly ... Maximum allowable emission 

limit or federally enforceable 

permit limit.6

Annual level when actually operating, averaged over the 

most recent 2 years.4

Actual operating factor averaged over the most recent 2 

years.4 8

Short term (≤24 

hours).

Maximum allowable emission 

limit or federally enforceable 

permit limit.6

Temporarily representative level 

when actually operating, reflective of the most recent 2 

years.4 7

Continuous operation, i.e., all 

hours of each time period 

under consideration (for all 

hours of the meteorological 

database).5

Other Source(s) 6 9

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. 

1For 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. 2Terminology applicable to fuel burning sources; analogoous terminology (e.g., lb/throughput) may be used for other types of sources. 3Operating levels such as 50 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.) 6See Section 8.3.3. 

7Temporarily representative operating level could be based on Continuous Emissions Monitoring (CEM) data or other informtation and should 

be determined through consultation with the appropriate reviewing authority (Paragraph 3.0(b)). 8For those permitted sources not in operation or that have not established an appropriate factor, continuous operation, (i.e., 8760) should be 

used. 9See Section 8.3.2. 

TABLE 8–2—POINT SOURCE MODEL EMISSION INPUTS FOR NAAQS COMPLIANCE IN PSD DEMONSTRATIONS 1

Averaging time Emissions limit 

(lb/MMBtu) 1 × Operating level (MMBtu/hr) 1 × Operating factor (e.g., hr/yr, hr/day) 

Proposed Major New or Modified Source 

Annual & quarterly ... Maximum allowable emission 

limit or federally enforceable 

permit limit.

Design capacity or federally enforceable permit condition.2

Continuous operation, (i.e., 

8760 hours.3

Short term (≤24 

hours).

Maximum allowable emission 

limit or federally enforceable 

permit limit.

Design capacity or federally enforceable permit condition.2Continuous operation, i.e., all 

hours of each time period 

under consideration (for all 

hours of the meteorological 

database).3

Nearby Source(s) 4 5

Annual & quarterly ... Maximum allowable emission 

limit or federally enforceable 

permit limit.5

Annual level when actually operating, averaged over the 

most recent 2 years 6.

Actual operating factor averaged over the most recent 2 

years.6 8

Short term (≤24 

hours).

Maximum allowable emission 

limit or federally enforceable 

permit limit.5

Temporarily representative level 

when actually operating, reflective of the most recent 2 

years.6 7

Continuous operation, i.e., all 

hours of each time period 

under consideration (for all 

hours of the meteorological 

database).3

Other Source(s) 5 9

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. 

1Terminology applicable to fuel burning sources; analogous terminology (e.g., lb/throughput) may be used for other types of sources. 2Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load causing the highest concentration. 


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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. 5See Section 8.3.3. 

6 Unless it is determined that this period is not representative. 

7Temporarily representative operating level could be based on Continuous Emissions Monitoring (CEM) data or other informtation and should 

be determined through consultation with the appropriate reviewing authority (Paragraph 3.0(b)). 8For those permitted sources not in operation or that have not established an appropriate factor, continuous operation, (i.e., 8760) should be 

used. 9See Section 8.3.2. 

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, the EPA has developed the 

Draft Guidance on Developing Background 

Concentrations for Use in Modeling 

Demonstrations.96 The guidance provides a 

recommended framework composed of steps 

that should be used in parallel with the 

recommendations made in this section. 

Generally, background air quality should not 

include the ambient impacts of the project 

source under consideration. Instead, it 

should include: 

i. Nearby sources: These are individual 

sources located in the vicinity of the 

source(s) under consideration for emissions 

limits that are not adequately represented by 

ambient monitoring data. The ambient 

contributions from these nearby sources are 

thereby accounted for by explicitly modeling 

their emissions (section 8.2). 

ii. Other sources: That portion of the 

background attributable to natural sources, 

other unidentified sources in the vicinity of 

the project, and regional transport 

contributions from more distant sources 

(domestic and international). The ambient 

contributions from these sources are typically 

accounted for through use of ambient 

monitoring data or, in some cases, regionalscale 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 

procedures. These data should be adequately 

representative of the area to inform 

calculation of the design concentration for 

comparison to the applicable NAAQS 

(section 9.2.2). 

c. For photochemical grid modeling 

conducted in SIP attainment demonstrations 

for ozone, PM2.5 and regional haze, the 

emissions from nearby and other sources are 

included as model inputs and fully 

accounted for in the modeling application 

and predicted concentrations. The concept of 

adding individual components to develop a 

design concentration, therefore, do not apply 

in these SIP applications. However, such 

modeling results may then be appropriate for 

consideration in characterizing background 

concentrations for other regulatory 

applications. Also, as noted in section 5, this 

modeling approach does provide for an 

appropriate atmospheric environment to 

assess single-source impacts for ozone and 

secondary PM2.5. 

d. For NAAQS assessments and SIP 

attainment demonstrations for inert 

pollutants, the development of the 

appropriate background concentration for a 

cumulative impact analysis involves proper 

accounting of each contribution to the design 

concentration and will depend upon whether 

the project area’s situation consists of either 

an isolated single source(s) or a multitude of 

sources. For PSD increment assessments, all 

impacts after the appropriate baseline dates 

(i.e., trigger date, major source baseline date, 

and minor source baseline date) from all 

increment-consuming and incrementexpanding 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 the EPA’s recommended 

framework for determining an appropriate 

background concentration should be 

consistent with appropriate EPA modeling 

guidance 63 96 and justified in the modeling 

protocol that is vetted with the appropriate 

reviewing authority (paragraph 3.0(b)). 

b. The EPA recommends use of the most 

recent quality assured air quality monitoring 

data collected in the vicinity of the source to 

determine the background concentration for 

the averaging times of concern. In most cases, 

the EPA recommends using data from the 

monitor closest to and upwind of the project 

area. If several monitors are available, 

preference should be given to the monitor 

with characteristics that are most similar to 

the project area. If there are no monitors 

located in the vicinity of the new or 

modifying source, a ‘‘regional site’’ may be 

used to determine background 

concentrations. A regional site is one that is 

located away from the area of interest but is 

impacted by similar or adequately 

representative sources. 

c. Many of the challenges related to 

cumulative impact analyses arise in the 

context of defining the appropriate metric to 

characterize background concentrations from 

ambient monitoring data and determining the 

appropriate method for combining this 

monitor-based background contribution to 

the modeled impact of the project and other 

nearby sources. For many cases, the best 

starting point would be use of the current 

design value for the applicable NAAQS as a 

uniform monitored background contribution 

across the project area. However, there are 

cases in which the current design value may 

not be appropriate. Such cases include but 

are not limited to: 

i. For situations involving a modifying 

source where the existing facility is 

determined to impact the ambient monitor, 

the background concentration at each 

monitor can be determined by excluding 

values when the source in question is 

impacting the monitor. In such cases, 

monitoring sites inside a 90° 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 


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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. The 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 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 

situations. The determination of nearby 

sources through the application of the EPA’s 

recommended framework calls for the 

exercise of professional judgment by the 

appropriate reviewing authority (paragraph 

3.0(b)) and should be consistent with 

appropriate EPA modeling guidance.63 96 This 

guidance is not intended to alter the exercise 

of that judgment or to comprehensively 

prescribe which sources should be included 

as nearby sources. 

c. For cumulative impact analyses of shortterm 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 doublecounting 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 the EPA’s Modeling 

Guidance for Demonstrating Attainment of 

Air Quality Goals for Ozone, PM2.5, and 

Regional Haze.64 In cases where Lagrangian 

models are applied for regulatory purposes, 

appropriate meteorological inputs should be 

determined in consultation with the 

appropriate reviewing authority (paragraph 

3.0(b)). 

b. The meteorological data used as input to 

a dispersion model should be selected on the 

basis of spatial and climatological (temporal) 

representativeness as well as the ability of 

the individual parameters selected to 

characterize the transport and dispersion 

conditions in the area of concern. The 

representativeness of the measured data is 

dependent on numerous factors including, 

but not limited to: (1) the proximity of the 

meteorological monitoring site to the area 

under consideration; (2) the complexity of 

the terrain; (3) the exposure of the 

meteorological monitoring site; and (4) the 

period of time during which data are 

collected. The spatial representativeness of 

the data can be adversely affected by large 

distances between the source and receptors 

of interest and the complex topographic 

characteristics of the area. Temporal 

representativeness is a function of the yearto-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 sitespecific 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 sitespecific 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 


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bFormerly the National Climatic Data Center 

(NCDC). 

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. AERMET100 shall be used to preprocess 

all meteorological data, be it observed or 

prognostic, for use with AERMOD in 

regulatory applications. The AERMINUTE 101

processor, in most cases, should be used to 

process 1-minute ASOS wind data for input 

to AERMET when processing NWS ASOS 

sites in AERMET. When processing 

prognostic meteorological data for AERMOD, 

the Mesoscale Model Interface Program 

(MMIF) 109 should be used to process data for 

input to AERMET, both for land-based 

applications and overwater applications. 

Other methods of processing prognostic 

meteorological data for input to AERMET 

should be approved by the appropriate 

reviewing authority. Additionally, the 

following meteorological preprocessors are 

recommended by the EPA: PCRAMMET,102

MPRM,103 and METPRO.104 PCRAMMET is 

the recommended meteorological data 

preprocessor for use in applications of OCD 

employing hourly NWS data. MPRM is the 

recommended meteorological data 

preprocessor for applications of OCD 

employing site-specific meteorological data. 

METPRO is the recommended meteorological 

data preprocessor for use with 

CTDMPLUS.105

b. Regulatory application of AERMOD 

necessitates careful consideration of the 

meteorological data for input to AERMET. 

Data representativeness, in the case of 

AERMOD, means utilizing data of an 

appropriate type for constructing realistic 

boundary layer profiles. Of particular 

importance is the requirement that all 

meteorological data used as input to 

AERMOD should be adequately 

representative of the transport and dispersion 

within the analysis domain. Where surface 

conditions vary significantly over the 

analysis domain, the emphasis in assessing 

representativeness should be given to 

adequate characterization of transport and 

dispersion between the source(s) of concern 

and areas where maximum design 

concentrations are anticipated to occur. The 

EPA recommends that the surface 

characteristics input to AERMET should be 

representative of the land cover in the 

vicinity of the meteorological data, i.e., the 

location of the meteorological tower for 

measured data or the representative grid cell 

for prognostic data. Therefore, the model user 

should apply the latest version 

AERSURFACE,106 107 where applicable, for 

determining surface characteristics when 

processing measured land-based 

meteorological data through AERMET. In 

areas where it is not possible to use 

AERSURFACE output, surface characteristics 

can be determined using techniques that 

apply the same analysis as AERSURFACE. In 

the case of measured meteorological data for 

overwater applications, AERMET calculates 

the surface characteristics and AERSURFACE 

outputs are not needed. In the case of 

prognostic meteorological data, the surface 

characteristics associated with the prognostic 

meteorological model output for the 

representative grid cell should be used.108 109

Furthermore, since the spatial scope of each 

variable could be different, 

representativeness should be judged for each 

variable separately. For example, for a 

variable such as wind direction, the data 

should ideally be collected near plume 

height to be adequately representative, 

especially for sources located in complex 

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 PCRAMMET102 or MPRM,103 that 

provides hourly stability class, wind 

direction and speed, ambient temperature, 

and mixing height, are required. Data over 

water requires hourly mixing height, relative 

humidity, air temperature, and water surface 

temperature. Missing winds are substituted 

with the surface winds. Vertical wind 

direction shear, vertical temperature 

gradient, and turbulence intensities are 

optional. 

e. The model user should acquire enough 

meteorological data to ensure that worst-case 

meteorological conditions are adequately 

represented in the model results. The use of 

5 years of adequately representative NWS or 

comparable meteorological data, at least 1 

year of site-specific (either land-based or 

overwater based), or at least 3 years of 

prognostic meteorological data, are required. 

If 1 year or more, up to 5 years, of sitespecific data are available, these data are 

preferred for use in air quality analyses. 

Depending on completeness of the data 

record, consecutive years of NWS, sitespecific, or prognostic data are preferred. 

Such data must be subjected to quality 

assurance procedures as described in section 

8.4.4.2. 

f. Objective analysis in meteorological 

modeling is to improve meteorological 

analyses (the ‘‘first guess field ’’) used as 

initial conditions for prognostic 

meteorological models by incorporating 

information from meteorological 

observations. Direct and indirect (using 

remote sensing techniques) observations of 

temperature, humidity, and wind from 

surface and radiosonde reports are commonly 

employed to improve these analysis fields. 

For long-range transport applications, it is 

recommended that objective analysis 

procedures, using direct and indirect 

meteorological observations, be employed in 

preparing input fields to produce prognostic 

meteorological datasets. The length of record 

of observations should conform to 

recommendations outlined in paragraph 

8.4.2(e) for prognostic meteorological model 

datasets. 

8.4.3 National Weather Service Data 

8.4.3.1 Discussion 

a. The NWS meteorological data are 

routinely available and familiar to most 

model users. Although the NWS does not 

provide direct measurements of all the 

needed dispersion model input variables, 

methods have been developed and 

successfully used to translate the basic NWS 

data to the needed model input. Site-specific 

measurements of model input parameters 

have been made for many modeling studies, 

and those methods and techniques are 

becoming more widely applied, especially in 

situations such as complex terrain 

applications, where available NWS data are 

not adequately representative. However, 

there are many modeling applications where 

NWS data are adequately representative and 

the applications still rely heavily on the NWS 

data. 

b. Many models use the standard hourly 

weather observations available from the 

National Centers for Environmental 

Information (NCEI).b These observations are 

then preprocessed before they can be used in 

the models. Prior to the advent of ASOS in 

the early 1990’s, the standard ‘‘hourly’’ 

weather observation was a human-based 

observation reflecting a single 2-minute 

average generally taken about 10 minutes 

before the hour. However, beginning in 

January 2000 for first-order stations and in 

March 2005 for all stations, the NCEI has 

archived the 1-minute ASOS wind data (i.e., 

the rolling 2-minute average winds) for the 

NWS ASOS sites. The AERMINUTE 

processor 101 was developed to reduce the 

number of calm and missing hours in 

AERMET processing by substituting standard 

hourly observations with full hourly average 

winds calculated from 1-minute ASOS wind 

data. 

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 


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be used when processing standard hourly 

NWS data for use in the preferred EPA 

models to ensure a lack of bias in wind 

direction assignments within the models. 

d. Beginning with year 2000, NCEI began 

archiving 2-minute winds, reported every 

minute to the nearest degree for NWS ASOS 

sites. The AERMINUTE processor was 

developed to read those winds and calculate 

hourly average winds for input to AERMET. 

When such data are available for the NWS 

ASOS site being processed, the AERMINUTE 

processor should be used, in most cases, to 

calculate hourly average wind speed and 

direction when processing NWS ASOS data 

for input to AERMOD.99

e. Data from universities, FAA, military 

stations, industry and pollution control 

agencies may be used if such data are 

equivalent in accuracy and detail (e.g., siting 

criteria, frequency of observations, data 

completeness, etc.) to the NWS data, they are 

judged to be adequately representative for the 

particular application, and have undergone 

quality assurance checks. 

f. After valid data retrieval requirements 

have been met,110 large number of hours in 

the record having missing data should be 

treated according to an established data 

substitution protocol provided that 

adequately representative alternative data are 

available. Data substitution guidance is 

provided in section 5.3 of reference 110. 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 sitespecific measurements are frequently made 

‘‘on-property’’ (i.e., on the source’s premises), 

acquisition of adequately representative sitespecific 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 sitespecific measurements, technical 

guidance 110 is available. Site-specific data 

should always be reviewed for 

representativeness and adequacy by an 

experienced meteorologist, atmospheric 

scientist, or other qualified scientist in 

consultation with the appropriate reviewing 

authority (paragraph 3.0(b)). 

8.4.4.2 Recommendations 

a. The EPA guidance 110 provides 

recommendations on the collection and use 

of site-specific meteorological data. 

Recommendations on characteristics, siting, 

and exposure of meteorological instruments 

and on data recording, processing, 

completeness requirements, reporting, and 

archiving are also included. This publication 

should be used as a supplement to other 

limited guidance on these subjects.5 97 111 112

Detailed information on quality assurance is 

also available.113 As a minimum, site-specific 

measurements of ambient air temperature, 

transport wind speed and direction, and the 

variables necessary to estimate atmospheric 

dispersion should be available in 

meteorological datasets to be used in 

modeling. Care should be taken to ensure 

that meteorological instruments are located 

to provide an adequately representative 

characterization of pollutant transport 

between sources and receptors of interest. 

The appropriate reviewing authority 

(paragraph 3.0(b)) is available to help 

determine the appropriateness of the 

measurement locations. 

i. Solar radiation measurements. Total 

solar radiation or net radiation should be 

measured with a reliable pyranometer or net 

radiometer sited and operated in accordance 

with established site-specific meteorological 

guidance.110 113

ii. Temperature measurements. 

Temperature measurements should be made 

at standard shelter height (2m) in accordance 

with established site-specific meteorological 

guidance.110

iii. Temperature difference measurements. 

Temperature difference (DT) measurements 

should be obtained using matched 

thermometers or a reliable thermocouple 

system to achieve adequate accuracy. Siting, 

probe placement, and operation of DT 

systems should be based on guidance found 

in Chapter 3 of reference 110 and such 

guidance should be followed when obtaining 

vertical temperature gradient data. AERMET 

may employ the Bulk Richardson scheme, 

which requires measurements of temperature 

difference, in lieu of cloud cover or 

insolation data. To ensure correct application 

and acceptance, AERMOD users should 

consult with the appropriate reviewing 

authority (paragraph 3.0(b)) before using the 

Bulk Richardson scheme for their analysis. 

iv. Wind measurements. For simulation of 

plume rise and dispersion of a plume emitted 

from a stack, characterization of the wind 

profile up through the layer in which the 

plume disperses is desirable. This is 

especially important in complex terrain and/ 

or complex wind situations where wind 

measurements at heights up to hundreds of 

meters above stack base may be required in 

some circumstances. For tall stacks when 

site-specific data are needed, these winds 

have been obtained traditionally using 

meteorological sensors mounted on tall 

towers. A feasible alternative to tall towers is 

the use of meteorological remote sensing 

instruments (e.g., acoustic sounders or radar 

wind profilers) to provide winds aloft, 

coupled with 10-meter towers to provide the 

near-surface winds. Note that when sitespecific 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 nearsurface measurements of wind speed with 

subjectively determined insolation 

assessments based on hourly cloud cover and 

ceiling height observations. The wind speed 

measurements are made at or near 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 

sE and sA methods) may be used. 

iii. The SRDT method, described in section 

6.4.4.2 of reference 110, is modified slightly 

from that published from earlier work 114 and 

has been evaluated with three site-specific 

databases.115 The two methods of stability 

classification that use wind fluctuation 

statistics, the sE and sA methods, are also 

described in detail in section 6.4.4 of 

reference 110 (note applicable tables in 

section 6). For additional information on the 

wind fluctuation methods, several references 

are available.116 117 118 119

c. Missing data substitution. After valid 

data retrieval requirements have been met,110

hours in the record having missing data 

should be treated according to an established 

data substitution protocol provided that 

adequately representative alternative data are 

available. Such protocols are usually part of 

the approved monitoring program plan. Data 

substitution guidance is provided in section 

5.3 of reference 110. If no representative 

alternative data are available for substitution, 

the absent data should be coded as missing, 

using missing data codes appropriate to the 

applicable meteorological pre-processor. 


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Appropriate model options for treating 

missing data, if available in the model, 

should be employed. 

8.4.5 Prognostic meteorological data 

8.4.5.1 Discussion 

a. For some modeling applications, there 

may not be a representative NWS or 

comparable meteorological station available 

(e.g., complex terrain), and it may be cost 

prohibitive or infeasible to collect adequately 

representative site-specific data. For these 

cases, it may be appropriate to use prognostic 

meteorological data, if deemed adequately 

representative, in a regulatory modeling 

application. However, if prognostic 

meteorological data are not representative of 

transport and dispersion conditions in the 

area of concern, the collection of site-specific 

data is necessary. 

b. The EPA has developed a processor, the 

MMIF,108 to process MM5 (Mesoscale Model 

5) or WRF (Weather Research and 

Forecasting) model data for input to various 

models including AERMOD. MMIF can 

process data for input to AERMET or 

AERMOD for a single grid cell or multiple 

grid cells. MMIF output has been found to 

compare favorably against observed data 

(site-specific or NWS).120 Specific guidance 

on processing MMIF for AERMOD can be 

found in reference 109. 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 

models is contingent upon the concurrence 

with the appropriate reviewing authority 

(paragraph 3.0(b)) that the data are of 

acceptable quality. 

8.4.6 Marine Boundary Layer Environments 

8.4.6.1 Discussion 

a. Calculations of boundary layer 

parameters for the marine boundary layer 

present special challenges as the marine 

boundary layer can be very different from the 

boundary layer over land. For example, 

convective conditions can occur in the 

overnight hours in the marine boundary layer 

while typically over land, stable conditions 

occur at night. Also, surface roughness in the 

marine environment is a function of wave 

height and wind speed and less static with 

time than surface roughness over land. 

b. While the Offshore and Coastal 

Dispersion Model (OCD) is the preferred 

model for overwater applications, there are 

applications where the use of AERMOD is 

applicable. These include applications that 

utilize features of AERMOD not included in 

OCD (e.g., NO2 chemistry). Such use of 

AERMOD would require consultation with 

the Regional Office and appropriate 

reviewing authority to ensure that platform 

downwash and shoreline fumigation are 

adequately considered in the modeling 

demonstration. 

c. For the reasons stated above, a 

standalone pre-processor to AERMOD, called 

AERCOARE 47 was developed to use the 

Coupled Ocean Atmosphere Response 

Experiment (COARE) bulk-flux algorithms 48

to bypass AERMET and calculate the 

boundary layer parameters for input to 

AERMOD for the marine boundary layer. 

AERCOARE can process either measurements 

from water-based sites such as buoys or 

prognostic data. To better facilitate the use of 

the COARE algorithms for AERMOD, EPA 

has included the COARE algorithms into 

AERMET thus eliminating the need for a 

standalone pre-processor and ensuring the 

algorithms are updated as part of routine 

AERMET updates. 

8.4.6.2 Recommendations 

a. Measured data. For applications in the 

marine environment that require the use of 

AERMOD, measured surface data, such as 

from a buoy or other offshore platform, 

should be processed in AERMET with the 

COARE processing option following 

recommendations in the AERMET User’s 

Guide 100 and AERMOD Implementation 

Guide.81 For applications in the marine 

environment that require the use of OCD, 

users should use the recommended 

meteorological pre-processor MPRM. 

b. Prognostic data. For applications in the 

marine environment that require the use of 

AERMOD and prognostic data, the prognostic 

data should be processed via MMIF for input 

to AERMET following recommendations in 

paragraph 8.4.5.1(b) and the guidance found 

in reference 109. 

8.4.7 Treatment of Near-Calms and Calms 

8.4.7.1 Discussion 

a. Treatment of calm or light and variable 

wind poses a special problem in modeling 

applications since steady-state Gaussian 

plume models assume that concentration is 

inversely proportional to wind speed, 

depending on model formulations. 

Procedures have been developed to prevent 

the occurrence of overly conservative 

concentration estimates during periods of 

calms. These procedures acknowledge that a 

steady-state Gaussian plume model does not 

apply during calm conditions, and that our 

knowledge of wind patterns and plume 

behavior during these conditions does not, at 

present, permit the development of a better 

technique. Therefore, the procedures 

disregard hours that are identified as calm. 

The hour is treated as missing and a 

convention for handling missing hours is 

recommended. With the advent of the 

AERMINUTE processor, when processing 

NWS ASOS data, the inclusion of hourly 

averaged winds from AERMINUTE will, in 

some instances, dramatically reduce the 

number of calm and missing hours, 

especially when the ASOS wind are derived 

from a sonic anemometer. To alleviate 

concerns about these issues, especially those 

introduced with AERMINUTE, the EPA 

implemented a wind speed threshold in 

AERMET for use with ASOS derived 

winds.99 100 Winds below the threshold will 

be treated as calms. 

b. AERMOD, while fundamentally a 

steady-state Gaussian plume model, contains 

algorithms for dealing with low wind speed 

(near calm) conditions. As a result, AERMOD 

can produce model estimates for conditions 

when the wind speed may be less than 

1 m/s, but still greater than the instrument 

threshold. Required input to AERMET for 

site-specific data, the meteorological 

processor for AERMOD, includes a threshold 

wind speed and a reference wind speed. The 

threshold wind speed is the greater of the 

threshold of the instrument used to collect 

the wind speed data or wind direction 

sensor.110 The reference wind speed is 

selected by the model as the lowest level of 

non-missing wind speed and direction data 

where the speed is greater than the wind 

speed threshold, and the height of the 

measurement is between seven times the 

local surface roughness length and 100 m. If 

the only valid observation of the reference 

wind speed between these heights is less 

than the threshold, the hour is considered 

calm, and no concentration is calculated. 

None of the observed wind speeds in a 

measured wind profile that are less than the 

threshold speed are used in construction of 

the modeled wind speed profile in AERMOD. 

8.4.7.2 Recommendations 

a. Hourly concentrations calculated with 

steady-state Gaussian plume models using 

calms should not be considered valid; the 

wind and concentration estimates for these 

hours should be disregarded and considered 

to be missing. Model predicted 


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concentrations for 3-, 8-, and 24-hour 

averages should be calculated by dividing the 

sum of the hourly concentrations for the 

period by the number of valid or non-missing 

hours. If the total number of valid hours is 

less than 18 for 24-hour averages, less than 

6 for 8-hour averages, or less than 3 for 3- 

hour averages, the total concentration should 

be divided by 18 for the 24-hour average, 6 

for the 8-hour average, and 3 for the 3-hour 

average. For annual averages, the sum of all 

valid hourly concentrations is divided by the 

number of non-calm hours during the year. 

AERMOD has been coded to implement these 

instructions. For hours that are calm or 

missing, the AERMOD hourly concentrations 

will be zero. For other models listed in 

Addendum A, a post-processor computer 

program, CALMPRO 121 has been prepared, is 

available on the EPA’s SCRAM website 

(section 2.3), and should be used. 

b. Stagnant conditions that include 

extended periods of calms often produce 

high concentrations over wide areas for 

relatively long averaging periods. The 

standard steady-state Gaussian plume models 

are often not applicable to such situations. 

When stagnation conditions are of concern, 

other modeling techniques should be 

considered on a case-by-case basis (see also 

section 7.2.1.2). 

c. When used in steady-state Gaussian 

plume models other than AERMOD, 

measured site-specific wind speeds of less 

than 1 m/s but higher than the response 

threshold of the instrument should be input 

as 1 m/s; the corresponding wind direction 

should also be input. Wind observations 

below the response threshold of the 

instrument should be set to zero, with the 

input file in ASCII format. For input to 

AERMOD, no such adjustment should be 

made to the site-specific wind data, as 

AERMOD has algorithms to account for light 

or variable winds as discussed in section 

8.4.6.1(a). For NWS ASOS data, see the 

AERMET User’s Guide 100 for guidance on 

wind speed thresholds. For prognostic data, 

see the latest guidance 109 for thresholds. 

Observations with wind speeds less than the 

threshold are considered calm, and no 

concentration is calculated. In all cases 

involving steady-state Gaussian plume 

models, calm hours should be treated as 

missing, and concentrations should be 

calculated as in paragraph (a) of this 

subsection. 

9.0 Regulatory Application of Models 

9.1 Discussion 

a. Standardized procedures are valuable in 

the review of air quality modeling and data 

analyses conducted to support SIP submittals 

and revisions, NSR, or other EPA 

requirements to ensure consistency in their 

regulatory application. This section 

recommends procedures specific to NSR that 

facilitate some degree of standardization 

while at the same time allowing the 

flexibility needed to assure the technically 

best analysis for each regulatory application. 

For SIP attainment demonstrations, refer to 

the appropriate EPA guidance 53 64 for the 

recommended procedures. 

b. Air quality model estimates, especially 

with the support of measured air quality 

data, are the preferred basis for air quality 

demonstrations. A number of actions have 

been taken to ensure that the best air quality 

model is used correctly for each regulatory 

application and that it is not arbitrarily 

imposed. 

• First, the Guideline clearly recommends 

that the most appropriate model be used in 

each case. Preferred models are identified, 

based on a number of factors, for many uses. 

• 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 caseby-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 shortterm (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 


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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 multistage 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. 

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 


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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 

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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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 appendix 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 


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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 intercomparison. Transportation Research 

Part D: Transport and Environment, 25: 

pp 93–105. 

U.S. Environmental Protection Agency, 2023. 

Incorporation and Evaluation of the 

RLINE Source Type in AERMOD For 

Mobile Source Applications. Publication 

No. EPA–454/R–23–011. Office of Air 

Quality Planning and Standards, 

Research Triangle Park, NC. 

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 preprocessor 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. 

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 steadystate 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, directionspecific 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 userspecified 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 


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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., sigmatheta 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 

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 reemitted to the far wake as a volume source. 

(4) For elevated terrain, AERMOD 

incorporates the concept of the critical 

dividing streamline height, in which flow 

below this height remains horizontal, and 

flow above this height tends to rise up and 

over terrain (Snyder, et al., 1985). Plume 

concentration estimates are the weighted sum 

of these two limiting plume states. However, 

consistent with the steady-state assumption 

of uniform horizontal wind direction over the 

modeling domain, straight-line plume 

trajectories are assumed, with adjustment in 

the plume/receptor geometry used to account 

for the terrain effects. 

h. Horizontal Winds 

Vertical profiles of wind are calculated for 

each hour based on measurements and 

surface-layer similarity (scaling) 

relationships. At a given height above 

ground, for a given hour, winds are assumed 

constant over the modeling domain. The 

effect of the vertical variation in horizontal 

wind speed on dispersion is accounted for 

through simple averaging over the plume 

depth. 

i. Vertical Wind Speed 

In convective conditions, the effects of 

random vertical updraft and downdraft 

velocities are simulated with a bi-Gaussian 

probability density function. In both 

convective and stable conditions, the mean 

vertical wind speed is assumed equal to zero. 

j. Horizontal Dispersion 

Gaussian horizontal dispersion coefficients 

are estimated as continuous functions of the 

parameterized (or measured) ambient lateral 

turbulence and also account for buoyancyinduced and building wake-induced 

turbulence. Vertical profiles of lateral 

turbulence are developed from measurements 

and similarity (scaling) relationships. 

Effective turbulence values are determined 

from the portion of the vertical profile of 

lateral turbulence between the plume height 

and the receptor height. The effective lateral 

turbulence is then used to estimate 

horizontal dispersion. 

k. Vertical Dispersion 

In the stable boundary layer, Gaussian 

vertical dispersion coefficients are estimated 

as continuous functions of parameterized 

vertical turbulence. In the convective 

boundary layer, vertical dispersion is 

characterized by a bi-Gaussian probability 

density function and is also estimated as a 

continuous function of parameterized 

vertical turbulence. Vertical turbulence 

profiles are developed from measurements 

and similarity (scaling) relationships. These 

turbulence profiles account for both 

convective and mechanical turbulence. 

Effective turbulence values are determined 

from the portion of the vertical profile of 

vertical turbulence between the plume height 

and the receptor height. The effective vertical 

turbulence is then used to estimate vertical 

dispersion. 

l. Chemical Transformation 

Chemical transformations are generally not 

treated by AERMOD. However, AERMOD 

does contain an option to treat chemical 

transformation using simple exponential 

decay, although this option is typically not 

used in regulatory applications except for 

sources of sulfur dioxide in urban areas. 

Either a decay coefficient or a half-life is 

input by the user. Note also that the Generic 

Reaction Set Method, Plume Volume Molar 

Ratio Method and the Ozone Limiting 

Method (section 4.2.3.4) for NO2 analyses are 

available. 

m. Physical Removal 

AERMOD can be used to treat dry and wet 

deposition for both gases and particles. 

Currently, Method 1 particle deposition is 

available for regulatory applications. Method 

2 particle deposition and gas deposition are 

currently alpha options and not available for 

regulatory applications 

n. Evaluation Studies 

American Petroleum Institute, 1998. 

Evaluation of State of the Science of Air 

Quality Dispersion Model, Scientific 

Evaluation, prepared by WoodwardClyde Consultants, Lexington, 

Massachusetts, for American Petroleum 

Institute, Washington, DC, 20005–4070. 


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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 intercomparison. Transportation Research 

Part D: Transport and Environment, 25: 

pp 93–105. 

U.S. Environmental Protection Agency, 2023. 

Incorporation and Evaluation of the 

RLINE Source Type in AERMOD For 

Mobile Source Applications. Publication 

No. EPA–454/R–23–011. Office of Air 

Quality Planning and Standards, 

Research Triangle Park, NC. 

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 NonDefault 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 

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., 

Æ Distance in along-flow and cross flow 

direction 

Æ Effective plume-receptor height 

difference 

Æ Effective sy & sz values, both flat terrain 

and hill induced (the difference shows the 

effect of the hill) 

Æ 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 nonreactive, primary pollutants. 

f. Source-Receptor Relationship 

Up to 40 point sources, 400 receptors and 

25 hills may be used. Receptors and sources 

are allowed at any location. Hill slopes are 

assumed not to exceed 15°, so that the 

linearized equation of motion for Boussinesq 

flow are applicable. Receptors upwind of the 

impingement point, or those associated with 

any of the hills in the modeling domain, 

require separate treatment. 


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g. Plume Behavior 

(1) As in CTDM, the basic plume rise 

algorithms are based on Briggs’ (1975) 

recommendations. 

(2) A central feature of CTDMPLUS for 

neutral/stable conditions is its use of a 

critical dividing-streamline height (Hc) to 

separate the flow in the vicinity of a hill into 

two separate layers. The plume component in 

the upper layer has sufficient kinetic energy 

to pass over the top of the hill while 

streamlines in the lower portion are 

constrained to flow in a horizontal plane 

around the hill. Two separate components of 

CTDMPLUS compute ground-level 

concentrations resulting from plume material 

in each of these flows. 

(3) The model calculates on an hourly (or 

appropriate steady averaging period) basis 

how the plume trajectory (and, in stable/ 

neutral conditions, the shape) is deformed by 

each hill. Hourly profiles of wind and 

temperature measurements are used by 

CTDMPLUS to compute plume rise, plume 

penetration (a formulation is included to 

handle penetration into elevated stable 

layers, based on Briggs (1984)), convective 

scaling parameters, the value of Hc, and the 

Froude number above Hc. 

h. Horizontal Winds 

CTDMPLUS does not simulate calm 

meteorological conditions. Both scalar and 

vector wind speed observations can be read 

by the model. If vector wind speed is 

unavailable, it is calculated from the scalar 

wind speed. The assignment of wind speed 

(either vector or scalar) at plume height is 

done by either: 

• Interpolating between observations 

above and below the plume height, or 

• Extrapolating (within the surface layer) 

from the nearest measurement height to the 

plume height. 

i. Vertical Wind Speed 

Vertical flow is treated for the plume 

component above the critical dividing 

streamline height (Hc); see ‘‘Plume 

Behavior.’’ 

j. Horizontal Dispersion 

Horizontal dispersion for stable/neutral 

conditions is related to the turbulence 

velocity scale for lateral fluctuations, sv, for 

which a minimum value of 0.2 m/s is used. 

Convective scaling formulations are used to 

estimate horizontal dispersion for unstable 

conditions. 

k. Vertical Dispersion 

Direct estimates of vertical dispersion for 

stable/neutral conditions are based on 

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. 

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 


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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, DC and S.R. Hanna, 1989. OCD: 

The Offshore and Coastal Dispersion 

Model. Volume I: User’s Guide. Sigma 

Research Corporation, Westford, MA. 

Hanna, S.R., L.L. Schulman, R.J. Paine and 

J.E. Pleim, 1984. The Offshore and 

Coastal Dispersion (OCD) Model User’s 

Guide, Revised. OCS Study, MMS 84– 

0069. Environmental Research & 

Technology, Inc., Concord, MA. (NTIS 

No. PB 86–159803). 

Hanna, S.R., L.L. Schulman, R.J. Paine, J.E. 

Pleim and M. Baer, 1985. Development 

and Evaluation of the Offshore and 

Coastal Dispersion (OCD) Model. Journal 

of the Air Pollution Control Association, 

35: 1039–1047. 

Hanna, S.R. and DC DiCristofaro, 1988. 

Development and Evaluation of the 

OCD/API Model. Final Report, API Pub. 

4461, American Petroleum Institute, 

Washington, DC. 

[FR Doc. 2024–27636 Filed 11–27–24; 8:45 am] 

BILLING CODE 6560–50–P 


khammond on DSK9W7S144PROD with RULES3