Vol. 80 Wednesday, 

No. 145 July 29, 2015 

Part III 

Environmental Protection Agency 

40 CFR Part 51 

Revision to the Guideline on Air Quality Models: Enhancements to the 

AERMOD Dispersion Modeling System and Incorporation of Approaches To 

Address Ozone and Fine Particulate Matter; Proposed Rule 

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45340 Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules 

ENVIRONMENTAL PROTECTION 

AGENCY 

40 CFR Part 51 

[EPA–HQ–OAR–2015–0310; FRL–9930–11– 

OAR] 

RIN 2060–AS54 

Revision to the Guideline on Air 

Quality Models: Enhancements to the 

AERMOD Dispersion Modeling System 

and Incorporation of Approaches To 

Address Ozone and Fine Particulate 

Matter 

AGENCY: Environmental Protection 

Agency (EPA). 

ACTION: Proposed rule; notice of 

conference. 

SUMMARY: In this action, the 

Environmental Protection Agency (EPA) 

proposes to revise the Guideline on Air 

Quality Models (‘‘Guideline’’). The 

Guideline has been incorporated into 

EPA’s regulations, satisfying a 

requirement under the Clean Air Act 

(CAA) section 165(e)(3) for the EPA to 

specify, with reasonable particularity 

models to be used in the Prevention of 

Significant Deterioration (PSD) program. 

It provides EPA-preferred models and 

other recommended techniques, as well 

as guidance for their use in predicting 

ambient concentrations of air pollutants. 

The proposed revisions to the Guideline 

include enhancements to the 

formulation and application of the 

EPA’s AERMOD near-field dispersion 

modeling system and the incorporation 

of a tiered demonstration approach to 

address the secondary chemical 

formation of ozone and fine particulate 

matter (PM2.5) associated with precursor 

emissions from single sources. 

Additionally, the EPA proposes various 

editorial changes to update and 

reorganize information throughout the 

Guideline to streamline the compliance 

assessment process. 

Within this action, the EPA is also 

announcing the Eleventh Conference on 

Air Quality Modeling and invites the 

public to participate in the conference. 

The conference will focus on the 

proposed revisions to the Guideline and 

part of the conference will also serve as 

the public hearing for these revisions. 

DATES: Comments must be received on 

or before October 27, 2015. 

Public hearing and conference: The 

public hearing for this action and the 

Eleventh Conference on Air Quality 

Modeling will be held August 12–13, 

2015, from 8:30 a.m. to 5:00 p.m. 

ADDRESSES: Submit your comments, 

identified by Docket ID No. EPA–HQ– 

OAR–2015–0310, by one of the 

following methods: 

• Federal eRulemaking Portal: http:// 

www.regulations.gov. Follow the online 

instructions for submitting comments. 

• Email: A-and-R-Docket@epa.gov. 

Include docket ID No. EPA–HQ–OAR–

2015–0310 in the subject line of the 

message. 

• Fax: (202) 566–9744. 

• Mail: Environmental Protection 

Agency, Mail code 28221T, Attention 

Docket No. EPA–HQ–OAR–2015–0310, 

1200 Pennsylvania Ave. NW., 

Washington, DC 20460. Please include a 

total of two copies. 

• Hand/Courier Delivery: EPA Docket 

Center, Room 3334, EPA WJC West 

Building, 1301 Constitution Ave. NW., 

Washington, DC. Such deliveries are 

only accepted during the Docket’s 

normal hours of operation, and special 

arrangements should be made for 

deliveries of boxed information. 

Instructions: Direct your comments to 

Docket ID No. EPA–HQ–OAR–2015– 

0310. The EPA’s policy is that all 

comments received will be included in 

the public docket without change and 

may be made available online at http:// 

www.regulations.gov, including any 

personal information provided, unless 

the comment includes information 

claimed to be Confidential Business 

Information (CBI) or other information 

whose disclosure is restricted by statute. 

Do not submit information that you 

consider to be CBI or otherwise 

protected through http://

www.regulations.gov or email. The 

www.regulations.gov Web site is an 

‘‘anonymous access’’ system, which 

means the EPA will not know your 

identity or contact information unless 

you provide it in the body of your 

comment. If you send an email 

comment directly to the EPA without 

going through http://

www.regulations.gov, your email 

address will be automatically captured 

and included as part of the comment 

that is placed in the public docket and 

made available on the Internet. If you 

submit an electronic comment, the EPA 

recommends that you include your 

name and other contact information in 

the body of your comment and with any 

disk or CD ROM you submit. If the EPA 

cannot read your comment due to 

technical difficulties and cannot contact 

you for clarification, the EPA may not 

be able to consider your comment. 

Electronic files should avoid the use of 

special characters, any form of 

encryption, and be free of any defects or 

viruses. For additional information 

about the EPA’s public docket, visit the 

EPA Docket Center homepage at http:// 

www.epa.gov/epahome/dockets.htm. 

Docket: All documents in the docket 

are listed in the http://

www.regulations.gov index. Although 

listed in the index, some information is 

not publicly available, e.g., CBI or other 

information whose disclosure is 

restricted by statute. Certain other 

material, such as copyrighted material, 

will be publicly available only in hard 

copy. Publicly available docket 

materials are available either 

electronically in www.regulations.gov or 

in hard copy at the Air and Radiation 

Docket and Information Center, EPA/

DC, Room 3334, WJC West Building, 

1301 Constitution Ave. NW., 

Washington, DC. The Public Reading 

Room is open from 8:30 a.m. to 4:30 

p.m., Monday through Friday, excluding 

legal holidays. The telephone number 

for the Public Reading Room is (202) 

566–1744 and the telephone number for 

the Air and Radiation Docket and 

Information Center is (202) 566–1742. 

Public hearing and conference: The 

public hearing for this action and the 

Eleventh Conference on Air Quality 

Modeling will be held in the EPA 

Auditorium, Room C111, 109 T.W. 

Alexander Drive, Research Triangle 

Park, NC 27711. 

FOR FURTHER INFORMATION CONTACT: Mr. 

George M. Bridgers, Air Quality 

Assessment Division, Office of Air 

Quality Planning and Standards, U.S. 

Environmental Protection Agency, Mail 

code C439–01, Research Triangle Park, 

NC 27711; telephone: (919) 541–5563; 

fax: (919) 541–0044; email: 

Bridgers.George@epa.gov. 

SUPPLEMENTARY INFORMATION: 

Table of Contents 

The following topics are discussed in 

this preamble: 

I. General Information 

A. Does this action apply to me? 

B. What should I consider as I prepare my 

comments for the EPA? 

C. Where can I get a copy of this 

document? 

II. Background 

A. The Guideline on Air Quality Models 

and EPA Modeling Conferences 

B. The Tenth Conference on Air Quality 

Modeling 

III. Public Participation Regarding Revisions 

to the Guideline and Notice of Eleventh 

Conference on Air Quality Modeling 

IV. Proposed Changes to the Guideline 

A. Proposed Actions 

1. Clarifications To Distinguish 

Requirements From Recommendations 

2. Updates to EPA’s AERMOD Modeling 

System 

3. Status of AERSCREEN 

4. Updates to 3-Tiered Demonstration 

Approach for NO2

5. Status of CALINE3 Models 

6. Addressing Single-Source Impacts on 

Ozone and Secondary PM2.5

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7. Status of CALPUFF and Assessing LongRange Transport for PSD Increment and 

Regional Haze 

8. Role of EPA’s Model Clearinghouse 

9. Updates to Modeling Procedures for 

Cumulative Impact Analysis 

10. Updates on Use of Meteorological Input 

Data for Regulatory Dispersion Modeling 

11. Transition Period for Applicability of 

Revisions to the Guideline 

B. Proposed Editorial Changes 

V. Statutory and Executive Order Reviews 

A. Executive Order 12866: Regulatory 

Planning and Review and Executive 

Order 13563: Improving Regulation and 

Regulatory Review 

B. Paperwork Reduction Act 

C. Regulatory Flexibility Act 

D. Unfunded Mandates Reform Act 

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 

and Safety Risks 

H. Executive Order 13211: Actions 

Concerning Regulations That 

Significantly Affect Energy Supply, 

Distribution, or Use 

I. National Technology Transfer and 

Advancement Act 

J. Executive Order 12898: Federal Actions 

To Address Environmental Justice in 

Minority Populations and Low-Income 

Populations 

I. General Information 

A. Does this action apply to me? 

This action applies to federal, state, 

territorial, and local air quality 

management programs that conduct air 

quality modeling as part of State 

Implementation Plan (SIP) submittals 

and revisions, New Source Review 

(NSR), including new or modifying 

industrial sources under Prevention of 

Significant Deterioration (PSD), 

Conformity, and other air quality 

assessments required under EPA 

regulation. Categories and entities 

potentially regulated by this action 

include: 

Category NAICSa

Code 

Federal/state/territorial/local/

tribal government .................. 924110 

a North American Industry Classification 

System. 

B. What should I consider as I prepare 

my comments for the EPA? 

1. Submitting CBI. Do not submit this 

information to the EPA through http:// 

www.regulations.gov or email. Clearly 

mark any of the information that you 

claim to be CBI. For CBI information in 

a disk or CD ROM that you mail to the 

EPA, mark the outside of the disk or CD 

ROM as CBI and then identify 

electronically within the disk or CD 

ROM the specific information that is 

claimed as CBI. In addition to one 

complete version of the comment that 

includes information claimed as CBI, a 

copy of the comment that does not 

contain the information claimed as CBI 

must be submitted for inclusion in the 

public docket. Information so marked 

will not be disclosed except in 

accordance with procedures set forth in 

40 CFR part 2. 

2. Tips for preparing your comments. 

When submitting comments, remember 

to: 

• Follow directions—The agency may 

ask you to respond to specific questions 

or organize comments by referencing a 

CFR part or section number. 

• Explain why you agree or disagree, 

suggest alternatives, and substitute 

language for your requested changes. 

• Describe any assumptions and 

provide any technical information and/ 

or data that you used. 

• If you estimate potential costs or 

burdens, explain how you arrived at 

your estimate in sufficient detail to 

allow for it to be reproduced. 

• Provide specific examples to 

illustrate your concerns and suggest 

alternatives. 

• Explain your views as clearly as 

possible, avoiding the use of profanity 

or personal threats. 

• Make sure to submit your 

comments by the comment period 

deadline identified. 

C. Where can I get a copy of this 

document? 

In addition to being available in the 

docket, an electronic copy of this 

proposed rule will also be available on 

the Worldwide Web (WWW) through 

the EPA’s Technology Transfer Network 

(TTN). Following signature, a copy of 

this proposed rule will be posted on the 

TTN’s Support Center for Regulatory 

Atmospheric Modeling (SCRAM) Web 

site at the following address: http://

www.epa.gov/ttn/scram. The TTN 

provides information and technology 

exchange in various areas of air 

pollution control. 

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 new source permits, 

source permit modifications, SIP 

submittals and revisions, conformity, 

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 40 CFR 51.112, 51.117, 

51.150, 51.160, 51.165, 51.166, 52.21, 

93.116, 93.123, and 93.150. 

The EPA originally published the 

Guideline in April 1978 (EPA–450/2– 

78–027), and it was incorporated by 

reference in the regulations for the PSD 

program in June 1978. The EPA revised 

the Guideline in 1986 (51 FR 32176), 

and updated it with supplement A in 

1987 (53 FR 32081), supplement B in 

July 1993 (58 FR 38816), and 

supplement C in August 1995 (60 FR 

40465). The EPA published the 

Guideline as appendix W to 40 CFR part 

51 when the EPA issued supplement B. 

The EPA republished the Guideline in 

August 1996 (61 FR 41838) to adopt the 

CFR system for labeling paragraphs. The 

publication and incorporation of the 

Guideline by reference into the EPA’s 

PSD regulations satisfies the 

requirement under the CAA section 

165(e)(3) 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 1977–1988, 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. 

In October 1988, we held the Fourth 

Conference on Air Quality Modeling to 

advise the public on new modeling 

techniques and to solicit comments to 

guide our consideration of any 

rulemaking needed to further revise the 

Guideline. We held the Fifth Conference 

in March 1991, which also served as a 

public hearing for the proposed 

revisions to the Guideline. In August 

1995, we held the Sixth Conference as 

a forum to update our available 

modeling tools with state-of-the-science 

techniques and for the public to offer 

new ideas. 

The Seventh Conference was held in 

June 2000, and also served as a public 

hearing for another round of proposed 

changes to the recommended air quality 

models in the Guideline. 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 

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1U.S. EPA, 2010. Applicability of Appendix W 

Modeling Guidance for the 1-hour NO2 NAAQS. 

Tyler Fox Memorandum dated June 28, 2010, Office 

of Air Quality Planning & Standards, Research 

Triangle Park, North Carolina 27711. http://

www.epa.gov/ttn/scram/guidance/clarification/

ClarificationMemo_AppendixW_Hourly-NO2- 

NAAQS_FINAL_06-28-2010.pdf. 

2U.S. EPA, 2010. Applicability of Appendix W 

Modeling Guidance for the 1-hour SO2 NAAQS. 

Tyler Fox Memorandum dated August 23, 2010, 

Office of Air Quality Planning & Standards, 

Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/guidance/

clarification/ClarificationMemo_AppendixW_

Hourly-SO2-NAAQS_FINAL_08-23-2010.pdf. 

3U.S. EPA, 2010. Guidance Concerning the 

Implementation of the 1-hour SO2 NAAQS for the 

Prevention of Significant Deterioration Program. 

Stephen D. Page, Memorandum dated August 23, 

2010, Office of Air Quality Planning & Standards, 

Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/region07/air/nsr/nsrmemos/

appwso2.pdf. 

4U.S. EPA, 2010. Guidance Concerning the 

Implementation of the 1-hour NO2 NAAQS for the 

Prevention of Significant Deterioration Program. 

Stephen D. Page, Memorandum dated June 29, 

2010, Office of Air Quality Planning & Standards, 

Research Triangle Park, North Carolina 27711. 

http://www.epa.gov/ttn/scram/guidance/

clarification/ClarificationMemo_AppendixW_

Hourly-SO2-NAAQS_FINAL_08-23-2010.pdf. 

5AECOM, 2010. AERMOD low wind speed 

evaluation study results, http://

mycommittees.api.org/rasa/amp/

Modeling%20Documents/AECOM%202009%

20Low%20Wind%20Speed%20Evaluation%

20Study%20Report.pdf. 

of emissions from 50 to several hundred 

kilometers and to make various editorial 

changes to update and reorganize 

information and remove obsolete 

models. 

We held the Eighth Conference on Air 

Quality Modeling in September 2005. 

This conference provided details on 

changes to the preferred air quality 

models, including available methods for 

model performance evaluation and the 

notice of data availability that the EPA 

published in September 2003, related to 

the incorporation of the PRIME 

downwash algorithm in the AERMOD 

dispersion model (in response to 

comments received from the Seventh 

Conference). Additionally, at the Eighth 

Conference, a panel of experts discussed 

the use of state-of-the-science prognostic 

meteorological data for informing the 

dispersion models. 

The EPA further revised the Guideline 

on November 9, 2005 (70 FR 68218), to 

adopt AERMOD as the preferred model 

for near-field dispersion of emissions for 

distances up to 50 kilometers. 

The Ninth Conference on Air Quality 

Modeling was held in October 2008, and 

emphasized the following topics: 

Reinstituting the Model Clearinghouse, 

review of non-guideline applications of 

dispersion models, regulatory status 

updates of AERMOD and CALPUFF, 

continued discussions on the use of 

prognostic meteorological data for 

informing dispersion models, and 

presentations reviewing the available 

model evaluation methods. 

B. The Tenth Conference on Air Quality 

Modeling 

The most recent EPA modeling 

conference was the Tenth Conference on 

Air Quality Modeling held in March 

2012. This conference covered multiple 

topics which have been vital in the 

development of the proposed revisions 

to the Guideline. 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 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. A 

transcript of the conference proceedings 

and a document that summarizes the 

public comments received are available 

at EPA’s SCRAM Web site at http://

www.epa.gov/ttn/scram/

10thmodconf.htm. 

The EPA promulgated a new 1-hour 

NAAQS for NO2 in January 2010, and a 

new 1-hour NAAQS for SO2 in June 

2010. Although AERMOD evaluations 

that formed the basis of its promulgation 

as the EPA’s preferred dispersion model 

demonstrated that AERMOD provides 

generally unbiased estimates of ambient 

concentrations, the increased stringency 

of these new standards resulted in 

increased scrutiny by the modeling 

community of AERMOD model 

performance. In response, the EPA 

issued several guidance memoranda to 

clarify the applicability of the Guideline 

and address initial issues with use of 

current models and procedures under 

PSD permitting.1234 However, the 

situation also necessitated the EPA and 

the modeling community to more 

closely evaluate the science and model 

formulation of AERMOD to better 

understand the issues being experienced 

by stakeholders and to address 

performance issues in its use for PSD 

permitting under these new standards. 

As part of this effort, the EPA 

reconvened the AERMOD 

Implementation Workgroup (AIWG) 

with state and local agency modelers to 

evaluate AERMOD across a variety of 

hypothetical sources and results from 

this assessment were also presented at 

this conference to inform the modeling 

community of potential implications 

and areas for improvement in the model 

and guidance on their use. 

Several presentations at the Tenth 

Modeling Conference addressed issues 

and challenges associated with 

demonstrating compliance with these 

new 1-hour NAAQS for NO2 and SO2. 

This included results from a study 

sponsored by the American Petroleum 

Institute (API) that evaluated AERMOD 

model performance under low wind 

speed conditions using additional 

National Oceanic and Atmospheric 

Administration (NOAA) field studies at 

Oak Ridge, TN, and Idaho Falls, ID, 

which were not included in the original 

17 databases used to support 

AERMOD’s promulgation in 2005. The 

API low wind study 5 showed 

significant overprediction of observed 

concentrations, especially for the Oak 

Ridge study where observed wind 

speeds were below 0.5 m/s for 10 of the 

11 tracer tests, and included wind 

speeds as low as 0.15 m/s. The API low 

wind study also included proposed 

modifications to the AERMET 

meteorological processor and AERMOD 

model to address this bias toward 

overprediction under stable/light wind 

conditions. 

Prior to the promulgation of the 1- 

hour NO2 NAAQS, compliance with the 

previous annual NO2 NAAQS was 

routinely demonstrated based on the 

Tier 1 assumption of full conversion or 

a Tier 2 option based on an ambient 

ratio of 75 percent conversion of 

nitrogen oxides (NOX) to NO2, referred 

to as the Ambient Ratio Method (ARM). 

However, compliance with the new 1- 

hour NAAQS has typically required a 

more refined treatment of NOX

conversion to NO2. Therefore, several 

presentations at the Tenth Modeling 

Conference focused on issues associated 

with demonstrating compliance with 

the new 1-hour NO2 NAAQS. 

These presentations included an 

overview of an API funded study to 

develop a Tier 2 ambient ratio method 

for the 1-hour NO2 NAAQS, referred to 

as ARM2. The ARM2 approach was 

developed based on an extensive 

analysis of ambient ratios of NO2/NOX

that were analyzed by land use (urban 

vs. rural) and geographical areas. Based 

on these analyses of the ambient NO2/ 

NOX ratios, an empirical relationship 

between ambient concentrations of NO2

and NOX was developed. The EPA 

subsequently reviewed and evaluated 

this ARM2 approach and then 

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6U.S. EPA, 2012. Gina McCarthy Letter to Robert 

Ukeiley dated January 4, 2012, Washington, DC 

20460. http://www.epa.gov/ttn/scram/

10thmodconf/review_material/Sierra_Club_

Petition_OAR-11-002-1093.pdf. 

7U.S. EPA, 2014. Guidance for PM2.5 Modeling. 

May 20, 2014, EPA–454/B–14–001. Office of Air 

Quality Planning & Standards, Research Triangle 

Park, NC. http://www.epa.gov/ttn/scram/guidance/

guide/Guidance_for_PM25_Permit_Modeling.pdf. 

8Phase 1 of the IWAQM effort focused on review 

of respective regional modeling programs, 

development of an organizational framework, and 

formulating reasonable objectives and plans that 

were presented to EPA management for support and 

commitment. Phase 2 of the IWAQM process 

continued this work and largely concluded in 1998 

with the publication of the Interagency Workgroup 

on Air Quality Modeling (IWAQM) Phase 2 

Summary Report and Recommendations for 

Modeling Long Range Transport Impacts (EPA–454/ 

R–98–019) (USEPA, 1998). This report provided a 

series of recommendations concerning the 

application of the CALPUFF model for use in 

regulatory long-range transport (LRT) modeling that 

supported the revisions in 2003 to the Guideline. 

incorporated this screening technique as 

a non-Default/Beta option in version 

13350 of AERMOD in December 2013. 

Another issue associated with NO2

NAAQS compliance presented at this 

conference focused on the use of 

relative (instantaneous) dispersion 

coefficients to define the plume volume 

which determines the amount of ozone 

available to convert nitrogen (NO) to 

NO2 using the Plume Volume Molar 

Ratio Method (PVMRM) option in 

AERMOD. The relative dispersion 

coefficients originally incorporated in 

AERMOD for PVMRM are best 

representative of daytime convective 

conditions and may tend to 

overestimate plume volumes during 

stable conditions. Such overestimation 

of the plume volume will tend to result 

in PVMRM to overestimate 

concentrations of NO2. 

In addition, modeling of single-source 

impacts for ozone and secondarily 

formed PM2.5 was a topic of discussion 

at the Tenth Modeling Conference. On 

January 4, 2012, the EPA granted a 

petition submitted on behalf of the 

Sierra Club on July 28, 2010 6 and 

committed to engage in rulemaking to 

evaluate whether updates to the 

Guideline are warranted and, as 

appropriate, incorporate new analytical 

techniques or models for ozone and 

secondarily formed PM2.5. As a part of 

satisfying this commitment, there were 

presentations of ongoing research at the 

Tenth Modeling Conference regarding 

single-source plume chemistry and 

photochemical grid modeling 

techniques, as well as several public 

forums. In addition, the EPA presented 

an overview along with a panel 

discussion of its Draft Guideline for 

PM2.5 Permit Modeling that addressed 

the need for consideration of secondary 

PM2.5 in demonstrating compliance with 

the PM2.5 NAAQS.7 Subsequently, 

written comments pertaining to such 

modeling were submitted to the EPA. 

As introduced at the Tenth Modeling 

Conference, the Interagency Workgroup 

on Air Quality Modeling (IWAQM) 

process was formally reinitiated in June 

2013 to inform the EPA’s process of 

updating the Guideline to address 

chemically reactive pollutants in nearfield and long-range transport 

applications. The IWAQM, which 

consists of representatives from the 

EPA, the U.S. Forest Service, the 

National Park Service, and the U.S. Fish 

and Wildlife Service, was initially 

formed to support development of 

technically sound recommendations 

regarding assessment of air pollutant 

source impacts on Federal Class I parks 

and wilderness areas. Comments 

received from stakeholders at the Tenth 

Modeling Conference supported 

reinitiating this interagency 

collaborative effort (as ‘‘Phase 3’’) to 

provide additional guidance for 

modeling single-source impacts on 

secondarily formed pollutants (e.g., 

ozone and PM2.5) in the near-field and 

for long-range transport. Stakeholder 

comments also support the idea of this 

collaborative effort working in parallel 

with stakeholders to further model 

development and evaluation. This 

renewed 8 effort included the 

establishment of two separate working 

groups, one focused on long-range 

transport of primary and secondary 

pollutants and the other on near-field 

single-source impacts of secondary 

pollutants. The primary objectives of 

this phase of IWAQM include reviewing 

existing approaches for estimating 

single-source secondary pollutant 

impacts, developing revisions to the 

Guideline, and the development of 

guidance for using technical methods to 

estimate downwind secondary pollutant 

impacts. 

III. Public Participation Regarding 

Revisions to the Guideline and Notice 

of Eleventh Conference on Air Quality 

Modeling 

Interested persons may provide the 

EPA with their views on the proposed 

revisions to the Guideline in several 

ways. This includes submitting written 

comments to the EPA, participating in 

the Eleventh Conference on Air Quality 

Modeling, and speaking at the public 

hearing that will be conducted as part 

of the conference. Additional 

information on how to submit written 

comments on the proposed revisions to 

the Guideline is provided in the 

ADDRESSES section above. 

The public hearing for this action and 

the Eleventh Conference on Air Quality 

Modeling will be held August 12–13, 

2015, from 8:30 a.m. to 5:00 p.m., in the 

EPA Auditorium, Room C111, 109 T.W. 

Alexander Drive, Research Triangle 

Park, NC 27711. On August 12, 2015, 

the first half of the conference will 

consist of a structured agenda with 

presentations. The second half of the 

first and all of the second day (August 

13, 2015), is reserved for the public 

hearing on this proposed rule. Advance 

requests for reserved time to speak 

during the public hearing should be 

submitted by August 7, 2015, to Mr. 

George M. Bridgers, Air Quality 

Assessment Division, Office of Air 

Quality Planning and Standards, U.S. 

Environmental Protection Agency, Mail 

code C439–01, Research Triangle Park, 

NC 27711; telephone: (919) 541–5563; 

fax: (919) 541–0044; email: 

Bridgers.George@epa.gov. The EPA will 

also provide an opportunity for oral 

presentations by individuals that sign 

up at the public hearing. Information 

submitted to the EPA during the 

conference will be placed in the docket 

for this rule proposing revisions to the 

Guideline. 

Background information: 

Preregistration details, additional 

background information, and a more 

detailed agenda for the Eleventh 

Conference on Air Quality Modeling are 

electronically available at http://

www.epa.gov/ttn/scram/

11thmodconf.htm. Preregistration for 

the conference, while not required, is 

strongly recommended due to 

heightened security protocols at the 

EPA–RTP facility. 

REAL ID Act: Because this hearing is 

being held at a U.S. government facility, 

individuals planning to attend the 

hearing should be prepared to show 

valid picture identification to the 

security staff in order to gain access to 

the meeting room. Please note that the 

REAL ID Act, passed by Congress in 

2005, established new requirements for 

entering federal facilities. These 

requirements took effect July 21, 2014. 

If your driver’s license is issued by 

Alaska, American Samoa, Arizona, 

Kentucky, Louisiana, Maine, 

Massachusetts, Minnesota, Montana, 

New York, Oklahoma, or the state of 

Washington, you must present an 

additional form of identification to enter 

the federal buildings where the public 

hearings will be held. Acceptable 

alternative forms of identification 

include: Federal employee badges, 

passports, enhanced driver’s licenses 

and military identification cards. We 

will list any additional acceptable forms 

of identification at: http://www.epa.gov/ 

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45344 Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules 

ttn/scram/11thmodconf.htm. In 

addition, you will need to obtain a 

property pass for any personal 

belongings you bring with you. Upon 

leaving the building, you will be 

required to return this property pass to 

the security desk. No large signs will be 

allowed in the building, cameras may 

only be used outside of the building and 

demonstrations will not be allowed on 

federal property for security reasons. 

Conference and Public Hearing: The 

Eleventh Conference on Air Quality 

Modeling will be open to the public. No 

registration fee is charged. The 

conference will be conducted informally 

and chaired by an EPA official. As 

required under CAA section 320, a 

verbatim transcript of the conference 

proceedings will be produced and 

placed in the docket for this proposed 

rule. 

The Eleventh Conference on Air 

Quality Modeling will begin with 

introductory remarks by the presiding 

EPA official. The EPA staff will then 

provide an overview of the revisions to 

the Guideline as proposed in this 

document and present on the research 

that supports those revisions and 

supports formulation updates to the 

preferred models. The following topics 

will be presented: 

I. Overview of the Eleventh Conference 

on Air Quality Modeling; 

II. Review of the proposed revisions to 

the Guideline; and 

III. Review of the proposed revisions to 

the preferred air quality models. 

At the conclusion of the 

presentations, the EPA will convene the 

public hearing on the proposed 

revisions to the Guideline. The public 

hearing will span the second half of the 

first day and throughout the second day 

of the conference. 

Those wishing to reserve time to 

speak at the public hearing, whether to 

volunteer a presentation on a special 

topic or to offer general comment on any 

of the modeling techniques scheduled 

for presentation, should contact us at 

the address given in the FOR FURTHER

INFORMATION CONTACT section (note the 

cutoff date). Such persons should 

identify the organization (if any) on 

whose behalf they are speaking and the 

length of the presentation. If a 

scheduled presentation is projected to 

be longer than 10 minutes, the presenter 

should also state why a longer period is 

needed. Scheduled speakers should 

bring extra copies of their presentation 

for inclusion in the docket and for the 

convenience of the recorder. Scheduled 

speakers will also be permitted to enter 

additional written comments into the 

record. 

Any person in attendance wishing to 

speak at the public hearing who has not 

reserved time prior to the conference 

may provide oral comments on the 

proposed revisions to the Guideline 

during time allotted on the last day of 

the conference. These parties will need 

to sign up to speak on the second day 

of the hearing and the EPA may need to 

limit the duration of presentations to 

allow all participants to be heard. 

Additional written statements or 

comments on the proposed revisions 

should be sent to the OAR Regulatory 

Docket (see ADDRESSES section). A 

transcript of the conference proceedings 

and a copy of all written comments will 

be maintained in Docket ID No. EPA– 

HQ–OAR–2015–0310, which will 

remain open until October 27, 2015, for 

the purpose of receiving additional 

comments after the conference and the 

public hearing on the proposed 

revisions to the Guideline. 

IV. Proposed Changes to the Guideline 

In this action, the EPA is proposing 

two type of revisions to the Guideline. 

The first involve substantive changes to 

address various topics, including those 

presented and discussed at the Tenth 

Modeling Conference. These proposed 

revisions to the Guideline include 

enhancements to the formulation and 

application of the EPA’s preferred 

dispersion modeling system, AERMOD, 

and the incorporation of a tiered 

demonstration approach to address the 

secondary chemical formation of ozone 

and PM2.5 associated with precursor 

emissions from single sources. The 

second type of revision involves 

editorial changes to update and 

reorganize information throughout the 

Guideline. These revisions are not 

intended to meaningfully change the 

substance of the Guideline, but rather to 

make the Guideline easier to use and to 

streamline the compliance assessment 

process. 

A. Proposed Actions 

This section provides a detailed 

overview of the substantive proposed 

changes to the Guideline that are 

intended to improve the science of the 

models and approaches used in 

regulatory assessments. 

1. Clarifications To Distinguish 

Requirements From Recommendations 

The EPA’s PSD permitting regulations 

specify that ‘‘[a]ll applications of air 

quality modeling involved in this 

subpart shall be based on the applicable 

models, data bases, and other 

requirements specified in appendix W 

of this part (Guideline on Air Quality 

Models).’’ 40 CFR 51.166(l); see also 40 

CFR 52.21(l). The applicable models are 

the preferred models listed in appendix 

A to appendix W to 40 CFR part 51. 

However, there has been some 

ambiguity in the past with respect to the 

‘‘other requirements’’ specified in the 

Guideline that must be used in PSD 

permitting analysis and other regulatory 

modeling assessments. 

Ambiguity can result because the 

Guideline generally contains 

‘‘recommendations’’ and these 

recommendations are expressed in nonmandatory language. For instance, the 

Guideline frequently uses ‘‘should’’ and 

‘‘may’’ rather than ‘‘shall’’ and ‘‘must.’’ 

This approach is generally preferred 

throughout the Guideline because of the 

need to exercise expert judgment in air 

quality analysis and the reasons 

discussed in the Guideline that ‘‘dictate 

against a strict modeling ‘cookbook’.’’ 

(40 CFR part 51, appendix W, section 

1.0(c)) 

Considering the non-mandatory 

language used throughout the Guideline, 

the EPA’s Environmental Appeals Board 

has correctly observed the following: 

‘‘Although appendix W has been 

promulgated as codified regulatory text, 

appendix W provides permit issuers broad 

latitude and considerable flexibility in 

application of air quality modeling. 

Appendix W is replete with references to 

‘‘recommendations,’’ ‘‘guidelines,’’ and 

reviewing authority discretion.’’ 

In Re Prairie State Generating Company, 

13 E.A.D. 1, 99 (EAB 2005) (internal 

citations omitted). 

Although this approach is typical 

throughout the Guideline, there are 

instances where the EPA does not 

believe permit issues should have broad 

latitude. Some principles of air quality 

modeling described in the Guideline 

must always be applied to produce an 

acceptable analysis. Thus, to promote 

clarity in the use and interpretation of 

the revised Guideline, we have, in these 

cases used mandatory language, and 

made specific reference to 

‘‘requirements’’ throughout the 

proposed text where appropriate to 

distinguish requirements from 

recommendations in the application of 

models for regulatory purposes. We 

solicit comment regarding the 

appropriateness of these revisions in 

providing the necessary clarity on the 

requirements under the proposed 

revisions to the Guideline as distinct 

from the recommendations in the 

revised text while noting the continued 

flexibilities provided for within the 

Guideline including but not limited to 

use and approval of alternative models. 

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Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules 45345 

9U.S. EPA, 1993. ‘‘Proposal for Calculating Plume 

Rise for Stacks with Horizontal Releases or Rain 

Caps for Cookson Pigment, Newark, New Jersey’’, 

Memorandum from Joseph A. Tikvart, U.S. EPA/

OAQPS, Research Triangle Park, NC. July 9, 1993. 

http://www.epa.gov/ttn/scram/guidance/mch/new_

mch/R1076_TIKVART_9_JUL_93.pdf. 

10Chu, S.H. and E.L. Meyer, 1991. Use of 

Ambient Ratios to Estimate Impact of NOX Sources 

on Annual NO2 Concentrations. Proceedings, 84th 

Annual Meeting & Exhibition of the Air & Waste 

Management Association, Vancouver, B.C.; 16–21 

June 1991. (16pp.) (Docket No. A–92–65, II–A–9). 

11Podrez, M. 2015. An Update to the Ambient 

Ratio Method for 1-h NO2 Air Quality Standards 

Dispersion Modeling. Atmospheric Environment, 

103: 163–170. 

2. Updates to EPA’s AERMOD Modeling 

System 

Based on studies presented and 

discussed at the Tenth Modeling 

Conference, and additional relevant 

research since 2010, 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. We propose the following 

updates to the AERMOD modeling 

system to address a number of technical 

concerns expressed by stakeholders: 

1. A proposed option incorporated in 

AERMET to adjust the surface friction 

velocity (u*) to address issues with 

AERMOD model overprediction under 

stable, low wind speed conditions. This 

proposed option is selected by the user 

with the METHOD STABLEBL ADJ_U* 

record in the AERMET Stage 3 input 

file. 

2. A proposed low wind option in 

AERMOD to address issues with model 

overprediction under low wind speed 

conditions. The low wind option will 

increase the minimum value of the 

lateral turbulence intensity (sigma-v) 

from 0.2 to 0.3 and adjusts the 

dispersion coefficient to account for the 

effects of horizontal plume meander on 

the plume centerline concentration. It 

also eliminates upwind dispersion 

which is incongruous with a straightline, steady-state plume dispersion 

model such as AERMOD. The proposed 

option is selected by specifying 

‘‘LOWWIND3’’ on the CO MODELOPT 

keyword in the AERMOD input file. 

3. Modifications to AERMOD 

formulation to address issues with 

overprediction for applications 

involving relatively tall stacks located 

near relatively small urban areas (no 

user input is required). 

4. Proposed regulatory default options 

in AERMOD to address plume rise for 

horizontal and capped stacks based on 

the July 9, 1993, Model Clearinghouse 

memorandum,9 with adjustments to 

account for the PRIME algorithm for 

sources subject to building downwash. 

These options are selected by the model 

user specifying ‘‘POINTCAP’’ or 

‘‘POINTHOR’’ for source type on the SO 

LOCATION keyword in the AERMOD 

input file. 

5. A proposed buoyant line source 

option, based on the BLP model, has 

been incorporated in AERMOD. This 

proposed option is selected by the 

model user with the SOURCE type 

‘‘BOUYLINE’’ to specify the individual 

buoyant line source locations and 

emissions and the new ‘‘BLAVGVAL’’ 

keyword to specify average parameters 

for a composite buoyant line. 

6. Proposed updates to the NO2 Tier 

2 and Tier 3 screening techniques coded 

within AERMOD as described more 

fully later in this preamble section. 

Model performance evaluation and 

peer scientific review references for the 

updated AERMOD modeling system are 

cited, as appropriate. An updated user’s 

guide and model formulation 

documents for version 15181 have been 

placed in the docket. We have updated 

the summary description of the 

AERMOD modeling system to appendix 

A of the Guideline to reflect these 

proposed updates. The essential codes, 

preprocessors, and test cases have been 

updated and posted to the EPA’s 

SCRAM Web site, http://www.epa.gov/

ttn/scram. 

We invite comments on whether we 

have reasonably addressed the technical 

concerns expressed by the stakeholder 

community and are on sound footing to 

recommend these updates to the 

regulatory default version of the 

AERMOD modeling system which 

includes its replacement of BLP as an 

appendix A model for the intended 

regulatory applications. 

3. Status of AERSCREEN 

In the preamble of the 2005 Guideline, 

we stated that a screening version of 

AERMOD called AERSCREEN was being 

developed and, in the meantime, 

SCREEN3 may be used until 

AERSCREEN was available. In 2011, the 

EPA released AERSCREEN, a program 

that creates inputs and runs AERMOD 

in screening mode. AERSCREEN also 

interfaces with AERMOD’s terrain 

processor, AERMAP, the building 

processor for AERMOD, BPIPPRIME, 

and can use AERSURFACE surface 

characteristics in the generation of 

meteorological data for AERMOD via 

the MAKEMET utility. In an April 2011 

memorandum, the EPA stated that 

AERSCREEN was the recommended 

screening model for simple and 

complex terrain and replaced SCREEN3. 

Since AERSCREEN invokes AERMOD, 

AERSCREEN represents the state of the 

science in screening dispersion models. 

As part of this proposed update to 

AERSCREEN, AERSCREEN now 

includes inversion break-up and coastal 

fumigation, features that were part of 

SCREEN3. These fumigation algorithms 

also take advantage of AERMOD’s 

boundary layer parameterizations for 

calculating variables needed by the 

algorithms. 

We invite comment on incorporation 

of AERSCREEN into the Guideline as 

the screening model for AERMOD that 

may be applicable in applications in all 

types of terrain and for applications 

involving building downwash. 

4. Updates to 3-Tiered Demonstration 

Approach for NO2

Section 5.2.4 of the 2005 Guideline 

details a 3-tiered approach for assessing 

NOX sources, which was recommended 

to obtain annual average estimates of 

NO2 from point sources for purposes of 

NSR analysis, including the PSD 

program and SIP planning purposes. 

This 3-tiered approach addresses the coemissions of NO and NO2 and the 

subsequent conversion of NO to NO2 in 

the atmosphere. The tiered levels 

include: (1) Assuming that all NO is 

converted to NO2 (full conversion), (2) 

using the Ambient Ratio Method (ARM), 

which applies an assumed equilibrium 

ratio of NO2 to NOX, based on observed 

ambient conditions, to the annual 

results from the Tier 1 full conversion, 

and (3) detailed screening options 

focused on determining site-specific 

ratios of NO2 to NOX. 

In January 2010, a new 1-hour NO2

standard was promulgated. Prior to the 

adoption of the 1-hour NO2 standard, 

few PSD permit applications required 

the use of Tier 3 options and guidance 

available at the time did not fully 

address the modeling needs for a 1-hour 

standard, i.e., tiered approaches for NO2

in the 2005 Guideline specifically 

targeted an annual standard. As a result, 

several guidance memoranda have been 

issued by the EPA to further inform 

modeling procedures for sources 

demonstrating compliance with the new 

1-hour standard.1234. In response to the 

1-hour NO2 standard, the EPA is 

proposing several modifications to the 

Tier 2 and 3 NO2 screening techniques 

incorporated into AERMOD. 

For the Tier 2 technique, the EPA is 

proposing to replace the existing ARM 

with a revised Ambient Ratio Method 2 

(ARM2). The existing Tier 2 technique, 

ARM, was based on a study that focused 

exclusively on long-term averages.10 A 

recently published study 11 presented a 

new analysis of national levels of 

ambient ratios of NO2 to NOX based on 

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45346 Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules 

12Cole, H.S. and J.E. Summerhays, 1979. A 

Review of Techniques Available for Estimation of 

Short-Term NO2 Concentrations. Journal of the Air 

Pollution Control Association, 29(8): 812–817. 

13Hanrahan, P.L., 1999. The Polar Volume Polar 

Ratio Method for Determining NO2/NOX Ratios in 

Modeling—Part I: Methodology. Journal of the Air 

& Waste Management Association, 49: 1324–1331. 

14Benson, Paul E., 1979. CALINE3—A Versatile 

Dispersion Model for Predicting Air Pollutant 

Levels Near Highways and Arterial Streets. Interim 

Report, Report Number FHWA/CA/TL–79/23. 

Federal Highway Administration, Washington, DC 

(NTIS No. PB 80–220841). 

15Cimorelli, 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. 

16Heist, D., V. Isakov, S. Perry, M. Snyder, A. 

Venkatram, C. Hood, J. Stocker, D. Carruthers, S. 

Arunachalam, AND C. Owen. Estimating near-road 

pollutant dispersion: a model inter-comparison. 

Transportation Research Part D: Transport and 

Environment. Elsevier BV, AMSTERDAM, 

Netherlands, 25:93–105, (2013). 

17U.S. EPA, 2013, Transportation Conformity 

Guidance for Quantitative Hot-Spot Analyses in 

PM2.5 and PM10 Nonattainment and Maintenance 

Areas. Publication No. EPA–420–B–13–053, Office 

of Transportation and Air Quality, Ann Arbor, MI. 

http://www.epa.gov/otaq/stateresources/transconf/

policy/420b13053-sec.pdf. 

18U.S. EPA, 2012. Gina McCarthy Letter to Robert 

Ukeiley dated January 4, 2012, Washington, DC 

20460. http://www.epa.gov/ttn/scram/

10thmodconf/review_material/Sierra_Club_

Petition_OAR-11-002-1093.pdf. 

hourly data from the EPA’s Air Quality 

System (AQS). Based on this analysis, a 

new second tier NO2 screening 

technique, ARM2, has been developed 

and incorporated into AERMOD. 

Because ARM2 is based on hourly 

measurements of the NO2 to NOX ratios 

and provides more detailed estimates of 

this ratio based on the total NOX

present, the EPA is proposing to 

incorporate a modified version of ARM2 

as the new preferred second tier NOX

modeling approach. 

For the Tier 3 technique, the EPA 

proposes that the existing detailed 

screening options of the Ozone Limiting 

Method (OLM) 12 and PVMRM 13 be 

formally incorporated into the 

regulatory version of AERMOD. Both 

OLM and PVMRM have been available 

as non-regulatory, non-default options 

in AERMOD for many years, but their 

usage in a NAAQS compliance 

demonstration required approval by the 

reviewing authority. Based on the EPA’s 

evaluation and external studies 

available on their performance, which 

show that OLM and PVMRM are 

capable of modeling 1-hour NO2

impacts and NO and NO2 speciation 

with reasonable accuracy when applied 

appropriately, both OLM and PVMRM 

are being proposed as preferred Tier 3 

screening methods for NO2 modeling. In 

addition, the EPA is proposing to 

incorporate a revised version of the 

PVMRM option, referred to as 

PVMRM2, that utilizes relative 

dispersion coefficients to estimate 

plume volume during convective 

conditions and total dispersion 

coefficients during stable conditions. 

These adjustments to the calculation of 

plume volume are intended to mitigate 

potential overprediction of NO2

conversion in multisource applications, 

especially during stable meteorological 

conditions. The EPA is proposing to 

replace the existing PVMRM with the 

new PVMRM2 with both versions being 

made available in the proposed version 

of AERMOD to facilitate testing and 

evaluation of the EPA’s proposed 

replacement of PVMRM option with 

new PVMRMR2 option. 

We invite comments on whether we 

have reasonably addressed technical 

concerns regarding the 3-tiered 

demonstration approach and specific 

NO2 screening techniques within 

AERMOD and whether we are on sound 

foundation to recommend the updates 

described above. 

5. Status of CALINE3 Models 

The 2005 Guideline identified 

CALINE3 14 and its variants (CAL3QHC 

and CAL3QHCR) as the preferred model 

for mobile source modeling for carbon 

monoxide (CO), particulate matter (PM), 

and lead. CALINE3 was developed in 

the late 1970’s using P–G stability 

classes as the basis for the dispersion 

algorithms. AERMOD, on the other 

hand, uses a planetary boundary layer 

scaling parameter to characterize 

stability and determine dispersion rates, 

which has been found to be superior to 

dispersion parameterizations based on 

P–G stability classes.15 In addition, the 

LINE and AREA source options in 

AERMOD implement a full numerical 

integration of emissions across the LINE 

or AREA sources, whereas the CALINE3 

family of models incorporate a much 

less refined approach. Thus, AERMOD 

provides a more scientifically credible 

and accurate characterization of plume 

dispersion than CALINE3. Recent model 

performance studies 16 have shown that 

the CALINE models performed poorly 

when compared to AERMOD and other 

modern dispersion models which also 

employ state-of-the-science dispersion 

parameters. AERMOD is also able to 

model multiple years in a single model 

run, while the CALINE3 variants are 

limited to either a single meteorological 

condition (CALINE3 and CAL3QHC) or 

a single year of meteorological data 

(CAL3QHCR). Additionally, AERMOD 

is able to utilize more recent, and more 

representative, meteorological 

observations than are readily available 

for modeling with CAL3QHCR. Based 

on the more scientifically sound basis 

for AERMOD, improved model 

performance over CALINE3, and the 

availability of more representative 

meteorological data, the EPA proposes 

replacing CALINE3 with AERMOD as 

the preferred appendix A model for 

determining near-field impacts for 

primary emissions from mobile sources, 

including PM2.5, PM10, and CO hot-spot 

analyses.17

We solicit comments on our proposal 

to identify AERMOD as a replacement 

for CALINE3 as an appendix A model 

for its intended regulatory applications. 

6. Addressing Single-Source Impacts on 

Ozone and Secondary PM2.5

On January 4, 2012, the EPA granted 

a petition submitted on behalf of the 

Sierra Club on July 28, 2010,18 that 

requested the EPA initiate rulemaking to 

establish air quality models for ozone 

and PM2.5 for use by all major sources 

applying for a PSD permit. In granting 

that petition, the EPA explained that the 

‘‘complex chemistry of ozone and 

secondary formation of PM2.5 are welldocumented and have historically 

presented significant challenges to the 

designation of particular models for 

assessing the impacts of individual 

stationary sources on the formation of 

these air pollutants’’ and further 

explained that ‘‘[b]ecause of these 

considerations, the EPA’s judgment in 

the past has been that it was not 

technically sound to designate with 

particularity specific models that must 

be used to assess the impacts of a single 

source on ozone concentrations,’’ but 

rather the EPA had established a process 

for determining on a case-by-case basis 

the analytical techniques that should be 

used for ozone, as well as for secondary 

formation of PM2.5. 

In the petition grant, the EPA 

committed to engage in rulemaking to 

evaluate whether updates to the 

Guideline are warranted and, as 

appropriate, incorporate new analytical 

techniques or models for ozone and 

secondarily formed PM2.5. This 

rulemaking satisfies the EPA’s 

commitment in the petition grant. As a 

part of this commitment and in 

compliance with CAA section 320, the 

EPA conducted the Tenth Modeling 

Conference in March 2012, where there 

were presentations of ongoing research 

of single-source plume chemistry and 

photochemical grid modeling 

techniques, as well as several public 

forums, and the EPA subsequently 

received written comments pertaining to 

such modeling. 

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19McMurry, P.H., Shepherd, M.F., Vickery, J.S., 

2004. Particulate matter science for policy makers: 

A NARSTO assessment. Cambridge University 

Press. 

20Baker, K.R., Kelly, J.T., 2014. Single source 

impacts estimated with photochemical model 

source sensitivity and apportionment approaches. 

Atmospheric Environment, 96: 266–274. 

21ENVIRON, 2012. Evaluation of chemical 

dispersion models using atmospheric plume 

measurements from field experiments, EPA 

Contract No: EP–D–07–102. September 2012. 06– 

20443M6. http://www.epa.gov/ttn/scram/reports/

Plume_Eval_Final_Sep_2012v5.pdf. 

22Zhou, W., Cohan, D.S., Pinder, R.W., Neuman, 

J.A., Holloway, J.S., Peischl, J., Ryerson, T.B., 

Nowak, J.B., Flocke, F., Zheng, W.G., 2012. 

Observation and modeling of the evolution of Texas 

power plant plumes. Atmospheric Chemistry and 

Physics, 12: 455–468. 

23ENVIRON, 2014. User’s Guide Comprehensive 

Air Quality Model with Extensions version 6, 

http://www.camx.com. ENVIRON International 

Corporation, Novato. 

24Byun, D., Schere, K.L., 2006. Review of the 

governing equations, computational algorithms, and 

other components of the models-3 Community 

Multiscale Air Quality (CMAQ) modeling system. 

Applied Mechanics Reviews, 59: 51–77. 

25Chen, J., Lu, J., Avise, J.C., DaMassa, J.A., 

Kleeman, M.J., Kaduwela, A.P., 2014. Seasonal 

modeling of PM 2.5 in California’s San Joaquin 

Valley. Atmospheric Environment, 92: 182–190. 

26Civerolo, K., Hogrefe, C., Zalewsky, E., Hao, W., 

Sistla, G., Lynn, B., Rosenzweig, C., Kinney, P.L., 

2010. Evaluation of an 18-year CMAQ simulation: 

Seasonal variations and long-term temporal changes 

in sulfate and nitrate. Atmospheric Environment, 

44: 3745–3752. 

27Russell, A.G., 2008. EPA Supersites programrelated emissions-based particulate matter 

modeling: initial applications and advances. 

Journal of the Air & Waste Management 

Association, 58: 289–302. 

28Tesche, T., Morris, R., Tonnesen, G., McNally, 

D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx 

annual 2002 performance evaluation over the 

eastern US. Atmospheric Environment, 40: 4906– 

4919. 

29Cai, C., Kelly, J.T., Avise, J.C., Kaduwela, A.P., 

Stockwell, W.R., 2011. Photochemical modeling in 

California with two chemical mechanisms: model 

intercomparison and response to emission 

reductions. Journal of the Air & Waste Management 

Association, 61: 559–572. 

30Hogrefe, C., Hao, W., Zalewsky, E., Ku, J.-Y., 

Lynn, B., Rosenzweig, C., Schultz, M., Rast, S., 

Newchurch, M., Wang, L., 2011. An analysis of 

long-term regional-scale ozone simulations over the 

Northeastern United States: variability and trends. 

Atmospheric Chemistry and Physics, 11: 567–582. 

31U.S. EPA, 2015. Guidance on the use of models 

for assessing the impacts from single sources on 

secondarily formed pollutants ozone and PM2.5. 

Publication No. EPA 454/P–15–001. Office of Air 

Quality Planning & Standards, Research Triangle 

Park, North Carolina 27711. 

The EPA initiated Phase 3 of the 

IWAQM process in June 2013 to inform 

this process to update the Guideline to 

address chemically reactive pollutants 

for near-field and long-range transport 

applications. Comments received from 

stakeholders at the Tenth Modeling 

Conference supported this collaborative 

effort to provide additional guidance for 

modeling single-source impacts of 

secondarily formed pollutants in the 

near-field and for long-range transport. 

Stakeholder comments also supported 

the idea of this collaborative effort 

occurring in parallel with stakeholders’ 

efforts to further model development 

and evaluation. The EPA’s 

recommended revisions to the Guideline 

are largely based on detailed review and 

assessment of this input. 

For this proposed revision to the 

Guideline, the EPA has determined that 

advances in photochemical modeling 

science indicate it is now reasonable to 

provide more specific, generallyapplicable guidance that identifies 

particular models or analytical 

techniques that may be used under 

specific circumstances for assessing the 

impacts of an individual source on 

ozone and secondary PM2.5. 

Quantifying secondary pollutant 

formation requires simulating chemical 

reactions and thermodynamic 

partitioning in a realistic chemical and 

physical environment. Chemical 

transport models treat atmospheric 

chemical and physical processes such as 

deposition and transport. There are two 

types of chemical transport models, 

which are differentiated based on a 

fixed frame of reference (i.e., Eulerian 

models, specifically photochemical grid 

models) or a frame of reference that 

moves with parcels of air between the 

source and receptor point (i.e., 

Lagrangian models).19

Comparing these two types of 

chemical transport models, 

photochemical grid models are 

integrated, three-dimensional grid-based 

models that treat chemical and physical 

processes in each grid cell and use 

Eulerian diffusion and transport 

processes to move chemical species to 

other grid cells.19 While some 

Lagrangian models also treat in-plume 

gas and particulate chemistry, to do so 

these models require time and space 

varying oxidant concentrations, and in 

the case of PM2.5, neutralizing agents 

such as ammonia, because important 

secondary impacts happen when plume 

edges start to interact with the 

surrounding chemical environment.20 21

These oxidant and neutralizing agents 

are not routinely measured, but can be 

generated with a three-dimensional 

photochemical transport model and 

subsequently input to a Lagrangian 

modeling system. 

In light of these differences between 

photochemical grid models and 

Lagrangian models that address 

chemistry, the EPA believes 

photochemical grid models are 

generally most appropriate for 

addressing ozone and secondary PM2.5

because they provide a spatially and 

temporally dynamic realistic chemical 

and physical environment for plume 

growth and chemical 

transformation.20 22 Publically available 

and documented Eulerian 

photochemical grid models such as the 

Comprehensive Air Quality Model with 

Extensions (CAMx) 23 and the 

Community Multiscale Air Quality 

(CMAQ) 24 model treat emissions, 

chemical transformation, transport, and 

deposition using time and space variant 

meteorology. These modeling systems 

include primarily emitted species and 

secondarily formed pollutants such as 

ozone and PM2.5.25 26 27 28 These models 

have been used extensively to support 

ozone and PM2.5 SIPs and to explore 

relationships between inputs and air 

quality impacts in the United States and 

elsewhere.26 29 30

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. In 

order to provide the user community 

flexibility in estimating single-source 

secondary pollutant impacts and given 

the emphasis on the use of 

photochemical grid models for these 

purposes, the EPA is proposing a twotiered demonstration approach for 

addressing single-source impacts on 

ozone and secondary PM2.5. The first 

tier involves use of technically credible 

relationships between precursor 

emissions and a source’s impacts that 

may be published in the peer-reviewed 

literature; developed from modeling that 

was previously conducted for an area by 

a source, a governmental agency, or 

some other entity and that is deemed 

sufficient; or generated by a peerreviewed reduced form model. The 

second tier involves application of more 

sophisticated case-specific chemical 

transport models (e.g., photochemical 

grid models) to be determined in 

consultation with the EPA Regional 

Office and conducted consistent with 

new EPA single-source modeling 

guidance.31 The appropriate tier for a 

given application should be selected in 

consultation with the appropriate 

reviewing authority and be consistent 

with EPA guidance. 

To fully implement these proposed 

changes to the Guideline related to 

addressing ozone and secondary PM2.5

impacts, the EPA intends to pursue a 

separate rulemaking to establish a 

technical basis and new values for PM2.5

Significant Impact Levels (SILs) and to 

introduce a new demonstration tool for 

ozone and PM2.5 precursors referred to 

as Model Emissions Rates for Precursors 

(MERP). When completed, this rule 

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32U.S. EPA, 2015. ‘‘Proposed Approach for 

Demonstrating PM2.5 PSD Compliance’’, 

Memorandum to Docket No. EPA–HQ–OAR–2015– 

0310 by Tyler J Fox, U.S. EPA/OAQPS, Research 

Triangle Park, NC. June 30, 2015. 

33U.S. EPA, 2015. ‘‘Proposed Approach for 

Demonstrating Ozone PSD Compliance’’, 

Memorandum to Docket No. EPA–HQ–OAR–2015– 

0310 by Tyler J Fox, U.S. EPA/OAQPS, Research 

Triangle Park, NC. June 30, 2015. 

34Baker, K.R., Foley, K.M., 2011. A nonlinear 

regression model estimating single source 

concentrations of primary and secondarily formed 

PM2.5. Atmospheric Environment, 45: 3758–3767. 

35Bergin, M.S., Russell, A.G., Odman, M.T., 

Cohan, D.S., Chameldes, W.L., 2008. Single-Source 

Impact Analysis Using Three-Dimensional Air 

Quality Models. Journal of the Air & Waste 

Management Association, 58: 1351–1359. 

36Baker, K.R., Kelly, J.T., Fox, T., 2013. 

Estimating second pollutant impacts from single 

sources (control #27). http://aqmodels.awma.org/

conference-proceedings. 

would differ from the current process 

recommended in the EPA’s Guidance 

for PM2.5 Permit Modeling.7 A MERP 

would neither replace the existing 

Significant Emissions Rates (SERs) for 

these pollutants nor serve as the basis 

for the applicability of PSD 

requirements to sources with emissions 

above the SER. However, a MERP would 

represent a level of emissions of 

precursors that is not expected to 

contribute significantly to 

concentrations of ozone or secondarilyformed PM2.5. Our present 

understanding of the atmospheric 

science of ozone and secondary PM2.5

formation indicates that MERP values 

will likely be higher than the SERs and 

more appropriate for evaluating the 

impacts of these criteria pollutants as 

precursors to ozone and PM2.5

formation. As part of the separate 

rulemaking, the EPA intends to 

demonstrate that a source with 

precursor emissions (e.g., NOX and SO2

for PM2.5) below the MERP level will 

have ambient impacts that will be less 

than the SIL and, thereby, provide a 

sufficient demonstration that the source 

will not cause or contribute to a 

violation of the PM2.5 NAAQS or PSD 

increments. The EPA’s Guidance for 

PM2.5 Permit Modeling 7 provides for a 

three-tiered approach to address 

secondary PM2.5 with (1) a qualitative 

assessment; (2) a hybrid qualitative/

quantitative assessment utilizing 

existing technical work; and (3) a full 

quantitative modeling exercise. The 

EPA expects that MERPs as a 

demonstration tool will replace the first 

tier of a qualitative assessment as 

sources that currently would provide a 

qualitative assessment are expected to 

have precursor emissions levels below 

the MERP. The second and third tier of 

assessment will then be consistent with 

the EPA’s proposed two-tiered 

demonstration approach for PM2.5

reflected in this proposed revisions to 

the Guideline. To specifically assist the 

public in commenting on this rule 

within the overall context of the NSR 

program, including PSD, the EPA has 

added two separate memoranda to the 

docket of this proposed rule. These 

memoranda provide more details on 

how this future approach to PSD 

compliance demonstrations will work 

for secondary PM2.5 and also describe 

our expectations for how such an 

approach might work for ozone based 

on a future, separate action to similarly 

establish a SIL and MERPs (for VOC and 

NOX precursors) for ozone using 

approaches similar to those for 

PM2.5.32 33

While the development of MERPs for 

ozone and secondary PM2.5 precursors is 

expected to address a number of PSD 

permitting situations, the EPA believes 

that most of the remaining situations in 

which a source must demonstrate 

compliance under the proposed 

Guideline will be addressed sufficiently 

under the proposed first tier where 

existing technical information could be 

used 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. In these situations, a 

more refined approach for estimating 

secondary pollutant impacts from 

project sources may not be necessary. 

The EPA has been compiling and 

reviewing screening approaches that are 

based on technically credible tools (e.g., 

photochemical grid models) that relate 

source precursor emissions to secondary 

impacts. In review of existing 

approaches detailed in peer reviewed 

journal articles and non-peer reviewed 

forms (e.g., technical reports, conference 

presentations), it is not clear that a 

single approach has been clearly 

proposed to and evaluated by the 

modeling community for estimating 

screening level secondary impacts from 

single sources. Other screening level 

alternatives to photochemical grid 

model application may include the use 

of existing credible photochemical 

model impacts for sources deemed to be 

similar in terms of emission rates, 

release parameters, and background 

environment. The EPA will continue to 

engage with the modeling community to 

identify credible alternative approaches 

for estimating single-source secondary 

pollutant impacts which provide 

flexibility and are less resource 

intensive for permit demonstration 

purposes. 

For those situations for which existing 

modeling or screening estimates are not 

available or appropriate, the second tier 

proposed by the EPA would apply and 

involve use of more sophisticated casespecific chemical transport models (e.g., 

photochemical grid models) to be 

determined in consultation with the 

appropriate EPA Regional Office based 

upon new EPA single-source modeling 

guidance.31 Based on several scientific 

studies, the EPA proposes to determine 

that photochemical grid models are 

appropriate for assessment of near-field 

and regional scale reactive pollutant 

impacts from specific sources 20 22 34 35 or 

a group of multiple sources impacting 

an area.25 27 28 Even though single-source 

emissions are injected into a grid 

volume, photochemical transport 

models have been shown to adequately 

capture single-source impacts when 

compared with downwind in-plume 

measurements.20 22 Where set up 

appropriately for the purposes of 

assessing the contribution of single 

sources to primary and secondarily 

formed pollutants, photochemical grid 

models can be used with a variety of 

approaches to estimate these impacts. 

These approaches generally fall into the 

category of source sensitivity (how air 

quality changes due to changes in 

emissions) and source apportionment 

(what air quality impacts are related to 

certain emissions). Source 

apportionment has been used to 

differentiate the contribution from 

single sources on model predicted 

ozone and PM2.5 concentrations.20 34 The 

direct decoupled method (DDM) has 

also been used to estimate ozone and 

PM2.5 impacts from specific sources 20 35

as well as the simpler brute-force 

sensitivity approach.20 22 35 Limited 

comparison of single-source impacts 

between models 36 and approaches to 

differentiate single-source impacts 20 36

show generally similar downwind 

spatial gradients and impacts. 

Near-source in-plume aircraft based 

measurement field studies provide an 

opportunity for evaluating model 

estimates of (near-source) downwind 

transport and chemical impacts from 

single stationary point sources.21

Photochemical grid model source 

apportionment and source sensitivity 

simulation of a single source downwind 

impacts compare well against field 

study primary and secondary ambient 

measurements made in Tennessee and 

Texas.20 21 This work indicates 

photochemical grid models and source 

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37U.S. EPA, 2009. Reassessment of the 

Interagency Workgroup on Air Quality Modeling 

(IWAQM) Phase 2 Summary Report; Revisions to 

Phase 2 Recommendations. Draft. Office of Air 

Quality Planning & Standards, Research Triangle 

Park, North Carolina 27711. http://www.epa.gov/

ttn/scram/guidance/reports/Draft_IWAQM_

Reassessment_052709.pdf. 

38U.S. EPA, 2015. ‘‘Summary of CALPUFF 

Ownership Since 2003 Promulgation’’, 

Memorandum to Docket No. EPA–HQ–OAR–2015– 

0310 by Tyler J Fox, U.S. EPA/OAQPS, Research 

Triangle Park, NC. June 30, 2015. 

39U.S. EPA, 2015. Interagency Workgroup on Air 

Quality Modeling Phase 3 Summary Report: NearField Single Source Secondary Impacts. Publication 

No. EPA 454/P–15–002. Office of Air Quality 

Planning & Standards, Research Triangle Park, 

North Carolina 27711. 

40U.S. EPA, 2015. Interagency Workgroup on Air 

Quality Modeling Phase 3 Summary Report: Long 

Range Transport and Air Quality Related Values. 

Publication No. EPA 454/P–15–003. Office of Air 

Quality Planning & Standards, Research Triangle 

Park, North Carolina 27711. 

41ENVIRON, 2012. Documentation of the 

Evaluation of CALPUFF and Other Long Range 

Transport Models using Tracer Field Experiment 

Data, EPA Contract No: EP–D–07–102. February 

2012. 06–20443M4. http://www.epa.gov/ttn/scram/

reports/EPA-454_R-12-003.pdf. 

apportionment and source sensitivity 

approaches provide meaningful 

estimates of single-source impacts on 

ozone and secondarily-formed PM2.5. 

Additional evaluations for longer time 

periods and more diverse environments, 

both physical and chemical, would be 

valuable to generate broader confidence 

in these approaches for this purpose. 

We invite comments on whether the 

proposed two-tiered demonstration 

approach and related EPA guidance is 

appropriately based on sound science 

and practical application of available 

models and tools to address singlesource impacts on ozone and secondary 

PM2.5. 

7. Status of CALPUFF and Assessing 

Long-Range Transport for PSD 

Increment and Regional Haze 

The 2003 Guideline recommended 

CALPUFF as the preferred model for 

long-range transport (i.e., sourcereceptor distances of 50 to several 

hundred kilometers) of emissions from 

point, volume, area, and line sources for 

primary criteria pollutants (e.g., PM and 

SO2). Since that time, as discussed 

previously in this preamble, the EPA 

has received input from stakeholders 

and has worked through the IWAQM 

process on analytical techniques to 

address chemically reactive pollutants 

for near-field and long-range transport 

applications. As a result, in order to 

provide the user community flexibility 

in estimating single-source secondary 

pollutant impacts and given the 

availability of more appropriate 

modeling techniques, such as 

photochemical transport models (which 

address limitations of models like 

CALPUFF 37), the EPA is proposing that 

the Guideline no longer contain 

language that requires the use of 

CALPUFF or another Lagrangian puff 

model for long-range transport 

assessments. Additionally, the EPA is 

proposing to remove the CALPUFF 

modeling system as an EPA-preferred 

model for long-range transport due to 

concerns about the management and 

maintenance of the model code given 

the frequent change in ownership of the 

model code since promulgation in the 

previous version of the Guideline.38 The 

EPA recognizes that long-range 

transport assessments may be necessary 

in certain limited situations for PSD 

increment. For these situations, the EPA 

is proposing a screening approach 

where CALPUFF along with other 

appropriate screening tools and 

methods may be used to support longrange transport PSD increment 

assessments. 

To determine if a Class I PSD 

increment analyses may be necessary 

beyond 50 km (i.e., long-range transport 

assessment), the EPA is recommending 

a screening approach to determine if a 

significant impact will occur with 

particular focus on Class I areas that 

may be threatened at such distances. 

The first step relies upon the near-field 

application of the appropriate screening 

and/or preferred model to determine the 

significance of ambient impact at or 

about 50 km from the new of modifying 

source. If this initial analysis indicates 

there may be significant ambient 

impacts at that distance, then further 

analysis is necessary. For assessment of 

Class I ambient impacts, under the 

proposed Guideline, there will not a 

preferred model for distances beyond 50 

km. Typically, a Lagrangian model is 

the type of model appropriate to use for 

these screening assessments; however, 

applicants should establish approaches 

(models and modeling parameters) on a 

case-by-case basis in consultation with 

the appropriate reviewing authority, 

Regional Office, and the affected Federal 

Land Manager(s) (FLM(s)). If a 

cumulative increment analysis is 

necessary, for these limited situations, 

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 section 3.2.2(e). 

As previously noted, Phase 3 of the 

IWAQM process was reinitiated in June 

2013 to inform the EPA’s commitment 

to update the Guideline to address 

chemically reactive pollutants in nearfield and long-range transport 

applications. This Phase 3 effort 

included the establishment of a working 

group composed of EPA and FLM 

technical staff focused on long-range 

transport of primary and secondary 

pollutants with an emphasis on use of 

consistent approaches to those being 

developed and applied to meet nearfield assessment needs for ozone and 

secondarily-formed PM2.5. The EPA 

expects that such approaches will be 

focused on state of the science chemical 

transport models (CTMs) as detailed in 

IWAQM reports 39 40 and published 

literature. 

To inform future consideration of 

visibility modeling in regulatory 

applications consistent with proposed 

changes for addressing chemistry for 

single-source impact on ozone and 

secondary PM2.5, the final report 40 of 

the IWAQM long-range transport 

subgroup identified that modern CTMs 

have evolved sufficiently and provide a 

credible platform for estimating 

potential visibility impacts from a single 

or small group of emission sources. 

Chemical transport models are well 

suited for the purpose of estimating 

long-range impacts of secondary 

pollutants, such as PM2.5, that 

contribute to regional haze and other 

secondary pollutants, such as ozone, 

that contribute to negative impacts on 

vegetation through deposition 

processes. These multiple needs require 

a full chemistry photochemical model 

capable of representing both gas, 

particle, and aqueous phase chemistry 

for PM2.5, haze, and ozone. 

Photochemical transport models are 

suitable for estimating visibility and 

deposition since important physical and 

chemical processes related to the 

formation and transport of PM are 

realistically treated. Source sensitivity 

and apportionment techniques 

implemented in photochemical grid 

models have evolved sufficiently and 

provide the opportunity for estimating 

potential visibility and deposition 

impacts from one or a small group of 

emission sources using a full science 

photochemical grid model. 

Photochemical grid models using 

meteorology output from prognostic 

meteorological models have 

demonstrated skill in estimating sourcereceptor relationships in the nearfield 20 21 and over long distances.41

It is important that modeling tools 

used for single-source long-range 

transport impacts assessments 

demonstrate skill in adequately 

replicating source-receptor relationships 

that are not in close proximity. For 

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45350 Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules 

42Hegarty, J., Draxler, R.R., Stein, A.F., Brioude, 

J., Mountain, M., Eluszkiewicz, J., Nehrkorn, T., 

Ngan, F., Andrews, A., 2013. Evaluation of 

Lagrangian particle dispersion models with 

measurements from controlled tracer releases. 

Journal of Applied Meteorology and Climatology, 

52: 2623–2637. 

43See 70 FR 39104, 39122–23 (July 6, 2005). 

source-receptor distances greater than 

50 km, regional scale photochemical 

grid models may be applied for the 

assessment of visibility impacts due to 

one or a small group of sources. Skill in 

estimating source-receptor relationships 

on this scale can be illustrated by 

evaluating modeling systems against 

regional scale inert tracer release 

experiments. Historically, several 

regional tracer release experiments have 

been used to demonstrate skill in longrange transport of inert pollutants: 1980 

Great Plains Mesoscale Tracer Field 

Experiment, the 1983 CrossAppalachian Tracer Experiment 

(CAPTEX), the 1987 Across North 

American Tracer Experiment 

(ANATEX), and 1994 European Tracer 

Experiment (ETEX).41 42 Photochemical 

grid models have been shown to 

demonstrate similar skill to Lagrangian 

models for pollutant transport when 

compared to measurements made from 

multiple mesoscale field experiments.41

Use of CTMs for Air Quality Related 

Values (AQRV) analysis requirements, 

while not subject to specific EPA model 

approval requirements outlined in 40 

CFR 51.166(l)(2) and 40 CFR 52.21(l)(2), 

should be justified for each application 

following the general recommendations 

outlined in section 3.2, and concurrence 

sought with the affected FLM(s). 

In 2005, the EPA issued guidelines for 

implementation of the best available 

retrofit technology (BART) requirements 

under the Regional Haze Rule. In these 

BART Guidelines, the EPA addressed 

the question of how states could best 

predict a single source’s contribution to 

visibility impairment.43 At the time, the 

EPA recognized that CALPUFF had not 

yet been fully evaluated for secondary 

pollutant formation, but the EPA still 

considered CALPUFF to be the best 

application for assessing a single 

source’s impact on visibility in a Class 

I area for purposes of the regional haze 

program. The EPA took note of the 

limitations of CALPUFF for this purpose 

but concluded that CALPUFF was the 

best modeling application for use in 

evaluating BART, especially given how 

the modeling results would be used. 

Based on this assessment, the EPA 

recommended that the states use 

CALPUFF. The EPA also made clear, 

however, that states could use other 

alternative approaches, including 

photochemical grid models, if done in 

consultation with the appropriate EPA 

Regional Office. 

The current version of the Guideline 

does not contain any explicit 

recommendation regarding the use of 

CALPUFF in the regional haze program, 

but in advising states and in making its 

own BART determinations, the EPA has 

looked to the Guideline to resolve 

questions regarding the proper 

application of the model. In particular, 

the EPA has guided states to use the 

applicable regulatory version of 

CALPUFF for such assessments. 

Following the EPA’s recommendations, 

states have used the EPA-preferred 

version of CALPUFF in hundreds of 

BART determinations since 2005. 

Although most assessments of BART are 

now complete, a handful of BART 

determinations remain outstanding. We 

expect most of the remaining actions 

addressing the BART requirements to be 

completed within the next two years. 

The proposed changes to the 

Guideline do not affect the EPA’s 

recommendation in the 2005 BART 

Guidelines to use CALPUFF in the 

BART determination process. Given that 

the overwhelming majority of BART 

determinations have been made using 

CALPUFF, we consider it appropriate 

for states (or the EPA) to continue to use 

this application for the remaining 

assessments under the current Guideline 

with approved protocols. This approach 

assures consistency across and within 

states in the regional haze program. In 

addition, in many instances, the 

modeling of visibility impacts has 

already been completed even though the 

BART determination process is not yet 

done. Allowing states to continue to rely 

on CALPUFF avoids additional time 

and expense in developing a new 

assessment of visibility impacts for a 

SIP initially due in 2007. We intend to 

continue to advise states with respect to 

the EPA-preferred version of CALPUFF 

that should be used in specific BART 

cases. Consistent with the BART 

Guidelines, states may also use 

alternative modeling approaches, in 

consultation with the appropriate EPA 

Regional Office. 

The EPA is seeking comment on its 

proposed screening approach to address 

long-range transport for purposes of 

assessing PSD increments; its decision 

to remove CALPUFF as a preferred 

model in appendix A for such longrange transport assessments; and its 

decision to consider CALPUFF as a 

screening technique along with other 

Lagrangian models to be used in 

consultation with the appropriate 

reviewing authority. It is important to 

note that the EPA’s proposed action to 

remove CALPUFF as an appendix A 

model in this Guideline does not affect 

its use under the FLM’s guidance 

regarding AQRV assessments (FLAG 

2010) nor previous use of this model as 

part of regulatory modeling applications 

required under the CAA. Similarly, this 

proposed action does not affect EPA’s 

recommendation that States use 

CALPUFF to determine the applicability 

and level of BART in regional haze 

implementation plans.43

8. Role of EPA’s Model Clearinghouse 

The EPA’s Model Clearinghouse has 

been a fundamental aspect of 

communication between the EPA 

Region Offices and with the broader 

permitting community on technical 

modeling and compliance 

demonstration issues for almost three 

decades. The Model Clearinghouse 

serves a critical role in helping resolve 

issues that arise from unique situations 

that are not specifically addressed in the 

Guideline or necessitate the 

consideration of an alternative model or 

technique for a specific application or 

range of applications. The Model 

Clearinghouse ensures that fairness, 

consistency, and transparency in 

modeling decisions are fostered among 

the Regional Offices and the state, local, 

and tribal agencies. 

In this action, we are proposing to 

codify the long-standing process of the 

Regional Offices consulting and 

coordinating with the Model 

Clearinghouse on all approvals of 

alternative models or techniques. While 

the Regional Administrators are the 

delegated authority to issue such 

approvals under section 3.2 of the 

Guideline, all alternative model 

approvals will only be issued after 

consultation with the EPA’s Model 

Clearinghouse and formal 

documentation through a concurrence 

memorandum which demonstrates that 

the requirements within section 3.2 for 

use of an alternative model have been 

met. 

We invite comment on our proposal 

to codify existing practice of requiring 

consultation and coordination between 

the EPA Regional Offices and the EPA’s 

Model Clearinghouse on all approvals 

under section 3.2 of alternative models 

or techniques. 

9. Updates to Modeling Procedures for 

Cumulative Impact Analysis 

Based on input from the Tenth 

Modeling Conference and recent permit 

modeling experiences under new shortterm NAAQS for SO2 and NO2, the EPA 

is proposing to make modifications to 

section 8 of the Guideline regarding 

model inputs and background 

concentrations to provide much needed 

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44U.S. EPA, 2011. Additional Clarification 

Regarding Applicability of Appendix W Modeling 

Guidance for the 1-hour NO2 NAAQS. Tyler Fox 

Memorandum dated March 1, 2011, Office of Air 

Quality Planning & Standards, Research Triangle 

Park, North Carolina 27711.http://www.epa.gov/ttn/ 

scram/guidance/clarification/NO2_Clarification_

Memo-20140930.pdf. 

45U.S. EPA, 2014. Clarification on the Use of 

AERMOD Dispersion Modeling for Demonstrating 

Compliance with the NO2 National Ambient Air 

Quality Standard. R. Chris Owen and Roger Brode 

Memorandum dated September 30, 2014, Office of 

Air Quality Planning & Standards, Research 

Triangle Park, North Carolina 27711. http://

www.epa.gov/ttn/scram/guidance/clarification/

NO2_Clarification_Memo-20140930.pdf. 

46U.S. EPA, 1990. New Source Review Workshop 

Manual: Prevention of Significant Deterioration and 

Nonattainment Area Permitting (Draft). Office of Air 

Quality Planning & Standards, Research Triangle 

Park, North Carolina 27711. http://www.epa.gov/

nsr. 

clarity associated with input and 

database selection for use in PSD and 

SIP modeling. Many of these revisions 

are based on the EPA clarification 

memoranda issued since 2010 that were 

intended to provide the necessary 

clarification regarding applicability of 

the Guideline to PSD modeling for these 

new standards.1 2 44 45 The EPA has 

specifically cautioned against the literal 

and uncritical application of very 

prescriptive procedures for conducting 

NAAQS and PSD modeling compliance 

demonstrations as described in chapter 

C of the draft New Source Review 

Workshop Manual.46 Our main concern 

is that following such procedures in a 

literal and uncritical manner has led to 

practices that are overly conservative 

and unnecessarily complicate the 

permitting process. The proposed 

changes to section 8 are intended to 

modify these practices and provide a 

more appropriate basis for selection and 

use of modeling inputs through the 

Guideline itself and supporting 

guidance. 

We have provided a more definitive 

definition of the appropriate modeling 

domain and how to best characterize the 

various contributions to air quality 

concentrations within that domain. 

Specifically, we provide the following 

recommendations: 

• Definition and/or factors to 

consider in determining appropriate 

modeling domain for NAAQS and PSD 

increment assessments and for SIP 

attainment demonstrations (see section 

8.1). 

• Revised requirements on how to 

characterize emissions from nearby 

sources to be explicitly modeled for 

purposes of a cumulative impact 

assessment under PSD and new 

language regarding how to characterize 

direct and precursor emissions from 

modeled sources for SIP attainment 

demonstrations for ozone, PM2.5, and 

regional haze (see section 8.2). 

• Revised recommendations on how 

to determine background concentrations 

in constructing the design 

concentration, or total air quality 

concentration, as part of a cumulative 

impact analysis for NAAQS and PSD 

increments. Specific recommendations 

are proposed for situations involving 

isolated single-source(s) and multisource areas (see section 8.3) with an 

emphasis on how to determine which 

nearby sources to explicitly model 

based on the concept of significant 

concentration gradients and the use of 

monitored background to adequately 

represent ‘‘other sources’’ (i.e., 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)). It is important to 

note the interconnectedness of these 

issues as the question of which nearby 

sources to include in cumulative 

modeling is inextricably linked with the 

question of what ambient monitoring 

data are available and what these data 

represent for a specific application. 

More specific data requirements and the 

format required for the individual 

models are described in detail in the 

users’ guide and/or associated 

documentation for each model. 

Given the added complexity of the 

technical issues that arise in the context 

of demonstrating compliance with 

NAAQS through dispersion modeling, 

we strongly encourage adherence to the 

recommendations in section 9.2.1 of the 

proposed Guideline regarding 

development of a modeling protocol, 

i.e., that ‘‘[e]very effort should be made 

by the Regional Office to meet with all 

parties involved in either a SIP revision 

or a PSD permit application prior to the 

start of any work on such a project. 

During this meeting, a protocol should 

be established between the preparing 

and reviewing parties to define the 

procedures to be followed, the data to be 

collected, the model to be used, and the 

analysis of the source and concentration 

data.’’ We expect by providing more 

clarity in the Guideline of the factors to 

be considered in the cumulative impact 

assessment, permit applicants and 

permitting authorities will be able to 

find the proper balance of the 

competing factors that contribute to 

these analyses. 

We invite comments on whether the 

updates proposed in section 8 of the 

Guideline and associated guidance are 

appropriate and sufficient to provide the 

necessary clarification in selecting and 

establishing the model inputs for 

conducting the regulatory modeling for 

PSD and SIP applications. 

10. Updates on Use of Meteorological 

Input Data for Regulatory Dispersion 

Modeling 

For near-field dispersion modeling 

applications using National Weather 

Service (NWS) Automated Surface 

Observing Stations (ASOS), the EPA 

released a pre-processor to AERMET, 

called AERMINUTE, in 2011 that 

calculates hourly averaged winds from 

2-minute winds reported every minute 

at NWS ASOS sites. AERMET 

substitutes these hourly averaged winds 

for the standard hourly observations, 

thus reducing the number of calms and 

missing winds for input into AERMOD. 

The presence of calms and missing 

winds were due to the METAR reporting 

methodology of surface observations. In 

March 2013, the EPA released a 

memorandum regarding the use of 

ASOS data in AERMOD as well as the 

use of AERMINUTE. When using 

meteorological data from ASOS sites for 

input to AERMOD, hourly averaged 

winds from AERMINUTE should be 

used in most cases. 

For a near-field dispersion modeling 

application where there is no 

representative NWS station, and it is 

prohibitive or not feasible to collect 

adequately representative site-specific 

data, it may be necessary to use 

prognostic meteorological data for the 

application. The EPA released the 

MMIF program that converts the 

prognostic meteorological data into a 

format suitable for dispersion modeling 

applications. The most recent 3 years of 

prognostic data are preferred. Use of the 

prognostic data is contingent on the 

concurrence of the appropriate 

reviewing authorities and collaborating 

agencies that the data are of acceptable 

quality and representative of the 

modeling application. 

We solicit comments on our proposed 

updates regarding use of meteorological 

input data for regulatory application of 

dispersion models. 

11. Transition Period for Applicability 

of Revisions to the Guideline 

In previous rulemakings to revise the 

Guideline, we have traditionally 

communicated that it would be 

appropriate to provide 1 year to 

transition to the use of new models, 

techniques and procedures in the 

context of PSD permit applications and 

other regulatory modeling applications. 

We invite comments whether it would 

be appropriate to apply a 1-year 

transition after promulgation of the 

revised Guideline (i.e., from its effective 

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47U.S. EPA, 2015. Technical Support Document 

(TSD) for Replacement of CALINE3 with AERMOD 

for Transportation Related Air Quality Analyses. 

Publication No. EPA–454/B–15–002. Office of Air 

Quality Planning & Standards, Research Triangle 

Park, NC. 

date) such that applications conducted 

under the current Guideline with 

approved protocols would be acceptable 

during that period, but new 

requirements and recommendations 

should be used for applications 

submitted after that period or protocols 

approved after that period. 

The EPA believes such a transition 

period is appropriate to avoid the time 

and expense of revisiting modeling that 

is substantially complete, which would 

cause undue delays to permit 

applications that are pending when the 

proposed revisions to the Guideline are 

finalized. The revisions that the EPA is 

proposing to the Guideline are intended 

as incremental improvements to the 

Guideline, and such improvements do 

not necessarily invalidate past practices 

under the previous edition of the 

Guideline. The requirements and 

recommendations in the current (2005) 

version of the Guideline were 

previously identified as acceptable by 

the EPA, and they will continue to be 

acceptable for air quality assessments 

during the period of transition to the 

revised version of the Guideline. 

Where a proposed revision to the 

Guideline does raise questions about the 

acceptability of a requirement or 

recommendation that it replaces, model 

users and applicants are encouraged to 

consult with the appropriate reviewing 

authority as soon as possible to assure 

the acceptability of modeling used to 

support permit applications during this 

period. 

B. Proposed Editorial Changes 

The EPA is proposing to make 

editorial changes to update and 

reorganize information throughout the 

Guideline. These revisions are not 

intended to meaningfully change the 

substance of the Guideline, but rather to 

make the Guideline easier to use. One 

way this is accomplished is by grouping 

topics together in a more logical manner 

to make related content easier to find. 

This in turn should streamline the 

compliance assessment process. 

Editorial changes are described below 

for each affected section. We invite 

comment on any of the changes 

proposed below for the Guideline text. 

1. Preface 

Only a few minor text revisions are 

proposed to this section for consistency 

with the remainder of the Guideline. 

2. Section 1 

The EPA propose to update the 

introduction section to reflect the 

reorganized nature of the revised 

Guideline. Minor text revisions are 

proposed throughout this section for 

additional clarity. Additional 

information is provided regarding the 

importance of CAA section 320 to 

amendments of the Guideline. 

3. Section 2 

The EPA proposes to revise section 2 

to more appropriately discuss the 

process by which models are evaluated 

and considered for use in particular 

applications. We propose to incorporate 

information from the previous section 9 

pertaining to model accuracy and 

uncertainty within this section to clarify 

how model performance evaluation is 

critical in determining the suitability of 

models for particular application. 

We also propose to provide a 

discussion in section 2.1 (Model 

Accuracy and Uncertainty) of the three 

types of models historically used for 

regulatory demonstrations. For each 

type of model, some strengths and 

weaknesses are listed to assist readers in 

the understanding of the particular 

regulatory applications to which they 

are most appropriate. 

In addition, the EPA proposes 

revisions to section 2.2 with respect to 

the recommended practice of 

progressing from simplified and 

conservative air quality analysis toward 

more complex and refined analysis. In 

this section, the EPA proposes to clarify 

distinctions between various types of 

models that have previously been 

described as screening models. In 

addition, this section clarifies 

distinctions between models used for 

screening purposes and screening 

techniques and demonstration tools that 

may be acceptable in certain 

applications. 

4. Section 3 

The EPA proposes minor 

modifications to section 3 to more 

accurately reflect current EPA practices 

and by moving the discussion of the 

EPA’s Model Clearinghouse to a revised 

section 3.3 for ease of reference and 

prominence within the Guideline. A 

change is proposed to require Regional 

Office consultation with the Model 

Clearinghouse on all alternative model 

approvals. Previously, section 3 

included various requirements under 

recommendation subheading that were 

not clearly identified as requirements. 

Accordingly, the EPA is proposing to 

modify section 3 with the incorporation 

of requirement subsections to eliminate 

any ambiguity. 

5. Section 4 

The EPA proposes to significantly 

revise section 4 to incorporate the 

modeling approaches recommended for 

air quality impact analyses for the 

criteria pollutants of CO, lead, SO2, 

NO2, and primary PM2.5 and PM10. In 

many respects, the proposed revisions 

to section 4 are a combination of the 

previous sections 4 and 5, reflecting 

inert criteria pollutants only. The EPA 

also proposes to modify section 4 to 

incorporate requirement subsections to 

provide clarity of the various 

requirements where previously sections 

4 and 5 included various requirements 

under recommendation subheadings. 

As proposed, this section provides an 

in-depth discussion of screening and 

refined models, including the 

introduction of AERSCREEN as the 

recommended screening model for 

simple and complex terrain for single 

sources and options for multi-source 

screening with AERMOD.47 The EPA 

proposes to include a clear discussion of 

each appendix A preferred model in 

section 4.3 (Refined Models). The EPA 

also proposes to modify the discussion 

for each preferred model (i.e., AERMOD 

Modeling System, CTDMPLUS, and 

OCD) from the previous section 4 with 

appropriate edits and some streamlining 

based on information available in the 

respective model formulation 

documentation and users guides. 

The EPA is proposing to add a 

subsection specifically addressing the 

modeling recommendations for SO2

where, previously, section 4 of the 

Guideline was generally understood to 

be applicable for SO2. Minor updates are 

proposed with respect to the modeling 

recommendations for each of the other 

inert criteria pollutants that were 

previously found in section 5. For NO2, 

the ARM2 is proposed to be added as a 

Tier 2 option, and the Tier 3 options of 

OLM and PVMRM are proposed to 

become part of the regulatory version of 

AERMOD. For any pollutant that had 

significant emissions from mobile 

sources, our previous recommendation 

to use the CALINE3 models is proposed 

to be replaced with AERMOD. 

6. Section 5 

As already stated, much of the 

previous section 5 with respect to the 

inert criteria pollutants is proposed to 

be incorporated into the revised section 

4. As proposed, the revised section 5 is 

now focused only on the modeling 

approaches recommended for ozone and 

secondary PM2.5. 

Both ozone and secondary PM2.5 are 

formed through chemical reactions in 

the atmosphere and are not 

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appropriately modeled with traditional 

steady-state Gaussian plume models, 

such as AERMOD. Chemical transport 

models are necessary to appropriately 

assess the single-source air quality 

impacts of precursor pollutants on the 

formation of ozone or secondary PM2.5. 

While the proposed revision to 

section 5 do not specify a particular 

EPA-preferred model or technique for 

use in air quality assessments, a twotiered screening approach is proposed 

for ozone and secondary PM2.5 with 

appropriate references to the EPA’s new 

single-source modeling guidance. The 

first tier consists of technically credible 

and appropriate relationships between 

emissions and the impacts developed 

from existing modeling simulations. If 

existing technical information is not 

available or appropriate, then a second 

tier approach would apply, involving 

use of sophisticated chemical transport 

models (e.g., photochemical grid 

models) as determined in consultation 

with the appropriate EPA Regional 

Office on a case-by-case basis based 

upon the EPA’s new single-source 

modeling guidance. 

7. Section 6 

Revisions to section 6 are proposed to 

more clearly address the modeling 

recommendations of other federal 

agencies, such as the FLM(s), that have 

been developed in response to EPA 

rules or standards. While no attempt is 

made to comprehensively discuss each 

topic, the EPA proposes to provide 

appropriate references to the respective 

federal agency guidance documents. 

The proposed revision to section 6 

focus primarily on AQRVs, including 

near-field and long-range transport 

assessments for visibility impairment 

and deposition. The interests of the 

Bureau of Ocean Energy and 

Management for Outer Continental Shelf 

permitting situations and of the Federal 

Aviation Administration for airport and 

air base permitting situations are 

represented in proposed section 6.3 

(Modeling Guidance for Other 

Governmental Programs). 

The discussion of Good Engineering 

Practices (GEP) for stack height 

consideration is proposed to be 

modified and moved to section 7. The 

EPA proposed to remove the discussion 

of long-range transport for PSD Class I 

increment and references to the 

previously preferred long-range 

transport model, CALPUFF, in 

accordance with the more detailed 

discussion in the Proposed Actions 

section of this Preamble. 

8. Section 7 

We propose to revise section 7 to be 

more streamlined and appropriate to the 

variety of general modeling issues and 

considerations that are not already been 

covered in sections 4, 5, and 6 of the 

Guideline. The EPA proposes to move 

the information concerning design 

concentrations and receptor sites to 

section 9. The discussion of stability 

categories is proposed to be removed 

from section 7 since it is specifically 

addressed in the model formulation 

documentation and guidance for the 

dispersion models that require stability 

categories to be defined. As already 

stated, the GEP discussion from the 

previous section 6 is proposed to be 

incorporated into this section. 

The EPA proposes to expand the 

recommendations for determining rural 

or urban dispersion coefficients to 

provide more clarity with respect to 

appropriate characterization within 

AERMOD, including a discussion on the 

existence of highly industrialized areas 

where population density is low that 

may be best treated with urban rather 

than rural dispersion coefficients. 

References to CALPUFF in the Complex 

Winds subsection are proposed to be 

removed due to technical issues 

described in the Proposed Actions 

section of this preamble. As proposed, 

if necessary for special complex wind 

situations, the setup and application of 

an alternative model should now be 

determined in consultation with the 

appropriate reviewing authority. 

Finally, the EPA proposes to revise 

section 7 to include a new discussion of 

modeling considerations specific to 

mobile sources. 

9. Section 8 

The EPA propose extensive updates 

and modifications to section 8 to reflect 

current EPA practices, requirements, 

and recommendations for determining 

the appropriate modeling domain and 

model input data from new or 

modifying source(s) or sources under 

consideration for a revised permit limit, 

from background concentrations 

(including air quality monitoring data 

and nearby and others sources), and 

from meteorology. As with earlier 

sections, the EPA proposes to modify 

section 8 to incorporate requirement 

subsections where previously section 8 

ambiguously included various 

requirements under recommendation 

subheadings. 

The Background Concentration 

subsection is proposed to be 

significantly modified from the existing 

Guideline to include a more clear and 

comprehensive discussion of nearby 

and other sources. This is intended to 

eliminate confusion of how to identify 

nearby sources that should be explicitly 

modeled and all other sources that 

should be generally represented by air 

quality monitoring data. In addition to 

air quality monitoring data, a brief 

discussion on the use of photochemical 

grid modeling to appropriately 

characterize background concentrations 

has been included in this proposed 

section. Updates to Tables 8–1 and 8– 

2 are proposed per changes in the 

considerations for nearby sources, as 

discussed in the Proposed Actions 

section of this Preamble. 

The use of prognostic mesoscale 

meteorological models to provide 

meteorological input for regulatory 

dispersion modeling applications is 

proposed to be incorporated throughout 

the Meteorological Input Data 

subsection, including the introduction 

of the MMIF as a tool to inform 

regulatory model applications. Other 

than additional minor modifications to 

the recommendations through this 

subsection based on current EPA 

practices, the most substantive proposed 

edits relate to the recommendation to 

use the AERMINUTE meteorological 

data processor to calculate hourly 

average wind speed and direction when 

processing NWS ASOS data for 

developing AERMET meteorological 

inputs to the AERMOD dispersion 

model. 

10. Section 9 

The EPA proposes to move all of the 

information previously in section 9 

related to model accuracy and 

evaluation into other sections in the 

revised Guideline (primarily to the 

revised section 2 and some to the 

revised section 4). This provides for 

greater clarity in those topics as applied 

to selection of models under the 

Guideline. However, the EPA proposes 

to remove subsection on the ‘‘Use of 

Uncertainty in Decision Making.’’. After 

removing this content, the EPA 

proposes to totally revise section 9 to 

focus on the regulatory application of 

models, which would include the 

majority of the information found 

previously in section 10. 

The EPA proposes to revise the 

discussion portion of section 9 to more 

clearly summarize the general concepts 

presented in earlier sections of the 

Guideline and to set the stage for the 

appropriate regulatory application of 

models and/or, in rare circumstances, 

air quality monitoring data. The 

importance of developing and vetting a 

modeling protocol is more prominently 

presented in a separate subsection. 

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The information related to design 

concentrations is proposed to be 

updated and unified from previous 

language found in sections 7 and 10. An 

expanded discussion of receptor sites is 

proposed based on language from the 

previous section 7 and new 

considerations given past practices of 

model users tending to define an 

excessively large and inappropriate 

number of receptors based on vague 

guidance. 

The recommendations for NAAQS 

and PSD increment compliance 

demonstrations are proposed to be 

overhauled to more clearly and 

accurately reflect the long-standing EPA 

recommendation and practice of 

performing a single-source impact 

analysis as a first stage of the NAAQS 

and PSD increment compliance 

demonstration and, as necessary, 

conducting a more comprehensive 

cumulative impact analysis as the 

second stage. The appropriate 

considerations and applications of 

screening and/or refined model are 

described in each stage. 

Finally, the section on Use of 

Measured Data in Lieu of Model 

Estimates subsection is proposed to be 

revised to provide more details on the 

process for determining the rare 

circumstances in which air quality 

monitoring data may be considered for 

determining the most appropriate 

emissions limit for a modification to an 

existing source. As with other portions 

of the revised section 9, the language 

throughout this subsection is proposed 

to be updated to reflect current EPA 

practices, as appropriate. 

11. Section 10 

As discussed, the majority of the 

information found previously in section 

10 is proposed to be incorporated into 

the revised section 9. As proposed, 

section 10 consists of the references that 

were in the previous section 12. We also 

propose to update each reference, as 

appropriate, based on the text revisions 

throughout the Guideline. 

12. Section 11 

In a streamlining effort, the EPA 

proposes to remove this bibliography 

section from the Guideline. 

13. Section 12 

As stated earlier, this references 

section is now proposed as section 10 

with appropriate updates. 

14. Appendix A to the Guideline 

The EPA proposes to revise appendix 

A to the Guideline to remove the 

Buoyant Line and Point Source 

Dispersion Model (BLP), CALINE3, and 

CALPUFF as refined air quality models 

preferred for specific regulator 

applications. The rational for the 

removal of these air quality models from 

the preferred status can be found in the 

Proposed Actions section of this 

Preamble. 

V. Statutory and Executive Order 

Reviews 

A. Executive Order 12866: Regulatory 

Planning and Review and Executive 

Order 13563: Improving Regulation and 

Regulatory Review 

This proposed action is not a 

‘‘significant regulatory action’’ under 

the terms of Executive Order 12866 (58 

FR 51735, October 4, 1993) and is, 

therefore not subject to OMB review 

under Executive Orders 12866 and 

13563 (76 FR 3821, January 21, 2011). 

B. Paperwork Reduction Act 

This proposed action does not impose 

an information collection burden 

subject to OMB review under the 

provisions of the Paperwork Reduction 

Act, 44 U.S.C. 3501 et seq. 

C. Regulatory Flexibility Act 

The Regulatory Flexibility Act (RFA) 

generally requires an agency to prepare 

a regulatory flexibility analysis of any 

rule subject to notice and comment 

rulemaking requirements under the 

Administrative Procedure Act or any 

other statute unless the agency certifies 

that the rule will not have a significant 

economic impact on a substantial 

number of small entities. Small entities 

include small businesses, small 

organizations, and small governmental 

jurisdictions. 

For purposes of assessing the impacts 

of this rule on small entities, small 

entity is defined as (1) a small business 

as defined by the Small Business 

Administration’s (SBA) regulations at 13 

CFR 121.201; (2) a small governmental 

jurisdiction that is a government of a 

city, county, town, school district or 

special district with a population of less 

than 50,000; and (3) a small 

organization that is any not-for-profit 

enterprise which is independently 

owned and operated and is not 

dominant in its field. 

The modeling techniques described in 

this proposed action are primarily used 

by air agencies and by industries 

owning major sources subject to NSR 

permitting requirements. To the extent 

that any small entities would have to 

conduct air quality assessments, using 

the models and/or techniques described 

in this proposed action are not expect to 

pose any additional burden (compared 

to the existing models and/or 

techniques) on these entities. The 

proposal features updates to the existing 

EPA-preferred model, AERMOD, that 

serves to increase efficiency and 

accuracy by changing only 

mathematical formulations and specific 

data elements. Also, this proposed 

action will streamline resources 

necessary to conduct necessary 

modeling with AERMOD by 

incorporating model algorithms from 

the BLP model and replacing CALINE3 

for mobile source applications. 

Although this proposed action calls for 

new models and/or techniques for use 

in addressing ozone and secondary 

PM2.5, we expect most small entities 

will generally be able to rely on existing 

modeling simulations; so, we expect 

minimal burden associated with these 

assessments. Therefore, we do not 

believe that that this proposal poses a 

significant or unreasonable burden on 

any small entities. 

After considering the economic 

impacts of this rule on small entities, I 

certify that this action will not have a 

significant economic impact on a 

substantial number of small entities. We 

continue to be interested in the 

potential impacts of the proposed rule 

on small entities and welcome 

comments on issues related to such 

impacts. 

D. Unfunded Mandates Reform Act 

This proposed action contains no 

federal mandates under the provisions 

of Title II of the Unfunded Mandates 

Reform Act of 1995 (UMRA), 2 U.S.C. 

1531–1538 for state, local, or tribal 

governments or the private sector. This 

action imposes no enforceable duty on 

any state, local or tribal governments or 

the private sector. Therefore, this action 

is not subject to the requirements of 

sections 202 or 205 of the UMRA. This 

action is also not subject to the 

requirements of section 203 of UMRA 

because it contains no regulatory 

requirements that might significantly or 

uniquely affect small governments. 

E. Executive Order 13132: Federalism 

This proposed 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, as specified in 

Executive Order 13132. This rule does 

not create a mandate on state, local or 

tribal governments nor does it impose 

any enforceable duties on these entities. 

This action would add better, more 

accurate techniques for conducting air 

quality assessments and does not add 

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any additional requirements for any of 

the affected parties covered under 

Executive Order 13132. Thus, the 

requirements of section 6 of the 

Executive Order do not apply to this 

proposal. In the spirit of Executive 

Order 13132, and consistent with the 

EPA policy to promote communications 

between the EPA and state and local 

governments, the EPA specifically 

solicits comment on this proposed rule 

from state and local officials. 

F. Executive Order 13175: Consultation 

and Coordination With Indian Tribal 

Governments 

This proposed action does not have 

tribal implications, as specified in 

Executive Order 13175 (65 FR 67249, 

November 9, 2000). This proposed rule 

imposes no requirements on tribal 

governments. Accordingly, Executive 

Order 13175 does not apply to this 

action. In the spirit of Executive Order 

13175, the EPA specifically solicits 

additional comment on this proposed 

action from tribal officials. 

G. Executive Order 13045: Protection of 

Children From Environmental Health 

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 is not 

subject to Executive Order 13045 

because it does not concern an 

environmental health risk or safety risk. 

H. Executive Order 13211: Actions 

Concerning Regulations That 

Significantly Affect Energy Supply, 

Distribution, or Use 

This action is not a ‘‘significant 

energy action’’ as defined in Executive 

Order 13211 (66 FR 28355 (May 22, 

2001)), because it is not likely to have 

a significant adverse effect on the 

supply, distribution, or use of energy. 

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 

The EPA has determined that this 

proposed rule will not have 

disproportionately high and adverse 

human health or environmental effects 

on minority or low-income populations 

because it does not affect the level of 

protection provided to human health or 

the environment. 

List of Subjects in 40 CFR Part 51 

Environmental protection, 

Administrative practice and procedure, 

Air pollution control, Carbon monoxide, 

Intergovernmental relations, Nitrogen 

oxides, Ozone, Particulate Matter, 

Reporting and recordkeeping 

requirements, Sulfur oxides. 

Dated: July 14, 2015. 

Gina McCarthy, 

Administrator. 

For the reasons stated in the 

preamble, title 40, chapter I of the Code 

of Federal Regulations is proposed to be 

amended as follows: 

PART 51—REQUIREMENTS FOR 

PREPARATION, ADOPTION, AND 

SUBMITTAL OF IMPLEMENTATION 

PLANS 

■ 1. The authority citation for part 51 

continues to read as follows: 

Authority: 23 U.S.C. 101; 42 U.S.C. 7401– 

7671q. 

■ 2. Appendix W to part 51 is revised 

to read as follows: 

APPENDIX W TO PART 51—Guideline 

on Air Quality Models Preface 

a. Industry and control agencies have long 

expressed a need for consistency in the 

application of air quality models for 

regulatory purposes. In the 1977 Clean Air 

Act (CAA), Congress mandated such 

consistency and encouraged the 

standardization of model applications. The 

Guideline on Air Quality Models (hereafter, 

Guideline) was first published in April 1978 

to satisfy these requirements by specifying 

models and providing guidance for their use. 

The Guideline provides a common basis for 

estimating the air quality concentrations of 

criteria pollutants used in assessing control 

strategies and developing emissions limits. 

b. The continuing development of new air 

quality models in response to regulatory 

requirements and the expanded requirements 

for models to cover even more complex 

problems have emphasized the need for 

periodic review and update of guidance on 

these techniques. Historically, three primary 

activities have provided direct input to 

revisions of the Guideline. The first is a series 

of periodic EPA workshops and modeling 

conferences conducted for the purpose of 

ensuring consistency and providing 

clarification in the application of models. 

The second activity was the solicitation and 

review of new models from the technical and 

user community. In the March 27, 1980, 

Federal Register, a procedure was outlined 

for the submittal to the EPA of privately 

developed models. After extensive evaluation 

and scientific review, these models, as well 

as those made available by the EPA, have 

been considered for recognition in the 

Guideline. The third activity is the extensive 

on-going research efforts by the EPA and 

others in air quality and meteorological 

modeling. 

c. Based primarily on these three activities, 

new sections and topics have been included 

as needed. The EPA does not make changes 

to the guidance on a predetermined schedule, 

but rather on an as-needed basis. The EPA 

believes that revisions of the Guideline 

should be timely and responsive to user 

needs and should involve public 

participation to the greatest possible extent. 

All future changes to the guidance will be 

proposed and finalized in the Federal 

Register. Information on the current status of 

modeling guidance can always be obtained 

from EPA’s Regional Offices. 

Table of Contents 

List of Tables 

1.0 Introduction 

2.0 Overview of Model Use 

2.1 Suitability of Models 

2.1.1 Model Accuracy and Uncertainty 

2.2 Levels of Sophistication of Air Quality 

Analyses and Models 

2.3 Availability of Models 

3.0 Preferred and Alternative Air Quality 

Models 

3.1 Preferred Models 

3.1.1 Discussion 

3.1.2 Requirements 

3.2 Alternative Models 

3.2.1 Discussion 

3.2.2 Requirements 

3.3 EPA’s Model Clearinghouse 

4.0 Models for Carbon Monoxide, Lead, 

Sulfur Dioxide, Nitrogen Dioxide and 

Primary Particulate Matter 

4.1 Discussion 

4.2 Requirements 

4.2.1 Screening Models and Techniques 

4.2.1.1 AERSCREEN 

4.2.1.2 CTSCREEN 

4.2.1.3 Screening in Complex Terrain 

4.2.2 Refined Models 

4.2.2.1 AERMOD 

4.2.2.2 CTDMPLUS 

4.2.2.3 OCD 

4.2.3 Pollutant Specific Modeling 

Requirements 

4.2.3.1 Models for Carbon Monoxide 

4.2.3.2 Models for Lead 

4.2.3.3 Models for Sulfur Dioxide 

4.2.3.4 Models for Nitrogen Dioxide 

4.2.3.5 Models for PM2.5

4.2.3.6 Models for PM10

5.0 Models for Ozone and Secondarily 

Formed Particulate Matter 

5.1 Discussion 

5.2 Recommendations 

5.3 Recommended Models and Approaches 

for Ozone 

5.3.1 Models for NAAQS Attainment 

Demonstrations and Multi-Source Air 

Quality Assessments 

5.3.2 Models for Single-Source Air 

Quality Assessments 

5.4 Recommended Models and Approaches 

for Secondarily Formed PM2.5

5.4.1 Models for NAAQS Attainment 

Demonstrations and Multi-Source Air 

Quality Assessments 

5.4.2 Models for Single-Source Air 

Quality Assessments 

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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 Source 

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 Treatment of Near-Calms and Calms 

8.4.6.1 Discussion 

8.4.6.2 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 

Appendix 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 

Input for SIP Revisions of Inert 

Pollutants. 

8–2 ......... Point Source Model Emission 

Input for NAAQS Compliance in 

PSD Demonstrations. 

1.0 Introduction 

a. The Guideline recommends 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 Office, and state, local, and 

tribal agency modeling representatives to 

ensure consistency in modeling guidance and 

to promote the use of more accurate air 

quality models, techniques, and databases. 

The workshops serve to provide further 

explanations of Guideline requirements to 

the EPA Regional Offices and workshop 

materials are issued with this clarifying 

information. In addition, findings from 

ongoing research programs, new model 

development, or results from model 

evaluations and applications are 

continuously evaluated. Based on this 

information, changes in the applicable 

guidance may be indicated and appropriate 

revisions to the Guideline may be considered. 

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g. All changes to the Guideline must follow 

rulemaking requirements since the Guideline 

is codified in appendix W to 40 Code of 

Federal Regulations (CFR) part 51. The EPA 

will promulgate proposed and final rules in 

the Federal Register to amend this appendix. 

The EPA utilizes the existing procedures 

under CAA section 320 that requires EPA to 

conduct a Conference on Air Quality 

Modeling at least every 3 years. 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 (CAA 320, 42 U.S.C. 7620). 

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 appendix: Appendix A. Thus, when 

reference is made to ‘‘appendix A’’ in this 

document, it refers to appendix A to 

appendix W to 40 CFR part 51. Appendix A 

contains summaries of refined air quality 

models that are ‘‘preferred’’ for 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 at a high 

resolution. 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. Typically, Eulerian models 

have difficulty with fine scale resolution of 

individual plumes. However, these types of 

models can be appropriately applied for 

assessment of near-field and regional scale 

reactive pollutant impacts from specific 

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

Photochemical gird 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 

the more sophisticated models are used or 

the area being investigated 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, noise, 

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 date 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 or conservative 

methods (or both) 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 or 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 concentration 

contributed by 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. 

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) 

Web site at http://www.epa.gov/ttn/scram. 

This is a Web site 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 available 

from the National Technical Information 

Service (NTIS), http://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 Web site (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 and PSD permitting under programs 

approved into SIPs. In some EPA regions, 

federal authority to administer NSR and PSD 

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 regions. Therefore, 

depending on the circumstances, the 

appropriate reviewing authority may be an 

EPA Regional Office, a state, local, or tribal 

agency, or perhaps the Federal Land Manager 

(FLM). In some cases, the Guideline requires 

review and approval of the use of an 

alternative model by the EPA Regional Office 

(sometimes stated as ‘‘Regional 

Administrator’’). For all approvals of 

alternative models or techniques, the EPA 

Regional Office will coordinate and shall 

seek concurrence with the EPA’s Model 

Clearinghouse. If there is any question as to 

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the appropriate reviewing authority, you 

should contact the EPA Regional Office 

modeling contact (http://www.epa.gov/ttn/

scram/guidance_cont_regions.htm), 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 appendix A. If no one model is found to 

clearly perform better through the evaluation 

exercise, then the preferred model listed in 

appendix A may be selected on the basis of 

other factors such as past use, public 

familiarity, resource requirements, and 

availability. Accordingly, the models listed 

in appendix A meet these conditions: 

i. The model must be written in a common 

programming language, and the executable(s) 

must run on a common computer platform. 

ii. The model must be documented in a 

user’s guide 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 appendix 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 appendix 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. Appendix 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 appendix 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 

appendix 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 model 

kernel 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 

concentration are not affected. 

c. A preferred model must be operated 

with the options listed in appendix A for its 

intended regulatory application. If other 

options are exercised, the model is no longer 

‘‘preferred.’’ Any other modification to a 

preferred model that would result in a 

change in the concentration estimates 

likewise alters its status 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, refinded 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 

appendix 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 

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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 

note be biased toward overestimates. 

40 CFR 51.166(l)(2). The Regional 

Administrators are delegated authority to 

issue such approvals under section 3.2 of the 

Guideline, provided that such approval is 

issued after consultation with EPA’s Model 

Clearinghouse and formally documented in a 

concurrence memorandum from 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 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: 

1. If a demonstration can be made that the 

model produces concentration estimates 

equivalent to the estimates obtained using a 

preferred model; 

2. If a statistical performance evaluation 

has been conducted using measured air 

quality data and the results of that evaluation 

indicate the alternative model performs 

better for the given application than a 

comparable model in appendix A; or 

3. If 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 Web site (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 maximum or highest, 

second highest concentrations are within 

+/¥ 2 percent of the estimates obtained from 

the preferred model. The option to show 

equivalency is intended as a simple 

demonstration of acceptability for an 

alternative model that is so nearly identical 

(or contains options that can make it 

identical) to a preferred model that it can be 

treated for practical purposes as the preferred 

model. 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 

which is acceptable to both control agencies 

and regulated industry is an important 

element in such an evaluation. 

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

of this subsection, an alternative 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 Region 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 Office, and state, 

local, and tribal agency modeling 

representatives. 

b. The 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 appendix 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, as used 

here, is considered to be an area where 

terrain features are all lower in elevation than 

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

question. Complex terrain is defined as 

terrain exceeding the height of the stack 

being modeled. 

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 

indicate that an estimated concentration does 

not occur, only that the precise time and 

locations are in doubt. Composite errors in 

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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 

contribution to ambient concentrations in 

excess to a NAAQS. In most cases, maximum 

source impacts of inert pollutant are 

anticipated to occur within 10 to 20 km from 

the source. Therefore, the EPA does not 

consider a long-range transport assessment 

beyond 50 km necessary for these 

pollutants.36

b. For assessment of PSD increments 

within the near-field nominal 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 Class I PSD increment 

analyses may be necessary beyond 50 km 

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

following screening approach shall be used 

to determine if a significant impact will 

occur with particular focus on Class I areas 

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 this initial step 

indicates there may be significant ambient 

impacts at that distance or such near-field 

assessment is not available, then further 

assessment is necessary. 

ii. For assessment of Class I significance of 

ambient impacts and cumulative increment 

analyses, there is not a preferred model or 

screening approach for distances beyond 50 

km. Thus, the EPA Regional Office shall be 

consulted in determining the appropriate and 

agreed upon modeling approach to conduct 

the second level assessment. Typically a 

Lagrangian model may be the type of model 

used for this second level assessment, but 

applicants shall reach agreed upon 

approaches (models and modeling 

parameters) on a case-by-case basis. When 

Lagrangian models are used in this manner, 

they shall not include plume-depleting 

reactions, such that model estimates are 

considered conservative, as is generally 

appropriate for screening assessments. 

d. In those limited situations where a 

cumulative increment 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 preferred 

screening model for most applications in all 

types of terrain and for applications 

involving building downwash. For those 

applications in complex terrain where the 

application involves a well-defined hill or 

ridge, CTSCREEN 39 can be used. 

c. Although AERSCREEN and CTSCREEN 

are designed to address a single-source 

scenario, there are approaches that can be 

used on a case-by-case basis to address 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. 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 into the AERMOD 

model; and (2) the AERSCREEN commandprompt 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 (Zo, Bo, 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 

BIPPRM to provide the necessary building 

parameters for applications involving 

building downwash using the PRIME 

downwash algorithm. AERSCREEN generates 

inputs to AERMOD via MAKEMET, 

AERMAP, and BPIPPRM and invokes 

AERMOD in a screening mode. The screening 

mode of AERMOD forces the AERMOD 

model calculations to represent values for the 

plume centerline, regardless of the sourcereceptor-wind direction orientation. The 

maximum concentration output from 

AERSCREEN represents a worst-case 1-hour 

concentration. Averaging-time scaling factors 

of 0.9 for 3-hour, 0.7 for 8-hour, 0.40 for 24- 

hour, and 0.08 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), 

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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. 

The plume under such conditions may be 

quite narrow in the vertical, so that even 

relatively small changes in a receptor’s 

location may substantially affect the 

predicted concentration. Receptors within 

about a kilometer of the source may be even 

more sensitive to location. Thus, a dense 

array of receptors may be required in some 

cases. 

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 

receptors either through an interactive 

program that is part of the model or directly, 

by using a text editor; using both methods to 

select receptors will generally be necessary to 

assure that the maximum concentrations are 

estimated by either model. In cases where a 

terrain feature may ‘‘appear to the plume’’ as 

smaller, multiple hills, it may be necessary 

to model the terrain both as a single feature 

and as multiple hills to determine design 

concentrations. 

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 

unresolvable problems are encountered, e.g., 

applicability, meteorological data, receptor 

siting, or terrain contour processing issues. 

4.2.2 Refined Models 

a. A brief description of each preferred 

model for refined applications is found in 

appendix A. Also listed in that appendix 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 

recommended 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 low-level nonbuoyant 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 vs. 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(g). 

c. AERMOD incorporates the Plume Rise 

Model Enhancements (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 within AERMOD 

utilizes the standard meteorological inputs 

provided by the AERMET meteorological 

processor. 

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

atmospheric deposition is evolving and 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)). 

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. If the modeling application involves 

determining the impact of offshore emissions 

from point, area, or line sources on the air 

quality of coastal regions, the recommended 

model is the OCD (Offshore and Coastal 

Dispersion) Model. OCD is a straight-line 

Gaussian model that incorporates overwater 

plume transport and dispersion as well as 

changes that occur as the plume crosses the 

shoreline. OCD 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, including PSD, 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. However, 

since the screening model specified in 

section 4.2.1 (AERSCREEN) can only handle 

one source at a time, a section 4.2.2 model 

may be used with screening meteorology 

(e.g., generated with MAKEMET) to conduct 

screening assessments of CO projects 

involving more than one source (e.g., 

roadway hotspot assessments).47

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, including PSD, 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.48 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 postprocessor to calculate rolling 3-month 

average concentrations from model output.49

General guidance for lead SIP development is 

also available.50

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. 

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4.2.3.3 Models for Sulfur Dioxide 

a. Models for SO2 are needed to meet NSR 

requirements, including PSD, to address 

compliance with the SO2 NAAQS and PSD 

increments, for SIP attainment 

demonstrations,51 and for characterizing 

current air quality via modeling.52 SO2 is one 

of a group of highly reactive gasses 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 53

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, including 

PSD, 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 coemit 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 approach is required to obtain 

hourly and annual average estimates of 

NO2.54 Since these methods are considered 

screening, 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, 

negative emissions should not be modeled 

because decreases in concentrations would 

be overestimated. Each tiered approach (see 

Figure 4–1) accounts for increasing complex 

considerations of NO2 chemistry and is 

described in paragraphs b through d 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 section 

4.2.2 refined model to estimate nitrogen 

oxides (NOX) concentrations and assume a 

total conversion of NO to NO2. If the 

resulting design concentrations exceed the 

NAAQS or PSD increments for NO2, proceed 

to Tier 2. 

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

by the Ambient Ratio Method 2 (ARM2), 

which provides estimates of representative 

equilibrium ratios of NO2/NOX value based 

ambient levels of NO2 and NOX derived from 

national data from the EPA’s Air Quality 

System (AQS).55 The national default for 

ARM2 will include a minimum NO2/NOX

ratio of 0.5 and a maximum ratio of 0.9. The 

reviewing agency may establish alternative 

default minimum NO2/NOX values based on 

the source’s in-stack emissions ratios, with 

alternative minimum values reflecting the 

source’s in-stack NO2/NOX ratios. Preferably, 

alternative default NO2/NOX values 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, 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) 56 and the Plume Volume 

Molar Ratio Method (PVMRM) 57 are two 

detailed screening techniques that may be 

used for most sources. These two 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 also 

accommodates distance-dependent 

conversion ratios based on ambient ozone. 

Both PVMRM and OLM require that ambient 

ozone concentrations be provided on an 

hourly basis and explicit specification of the 

speciation of the NO2/NOX in-stack ratios. 

PVMRM works best for relatively isolated 

and elevated point source modeling while 

OLM works best for large groups of sources, 

area sources, and near-surface releases, 

including road-way sources. 

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 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 applied to estimate ambient 

impacts of NOX sources. 

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4.2.3.5 Models for PM2.5

a. The PM2.5 NAAQS, promulgated on July 

18, 1997, includes particles with an 

aerodynamic diameter nominally less than or 

equal to 2.5 micrometers. PM2.5 is a mixture 

consisting of several diverse components58. 

Ambient PM2.5 generally consists of two 

components, the primary component, 

emitted directly from a source, and the 

secondary component, which is formed in 

the atmosphere from other pollutants emitted 

from the source. Models for PM2.5 are needed 

to meet NSR requirements, including PSD, to 

address compliance with the PM2.5 NAAQS 

and PSD increments and for SIP attainment 

demonstrations. 

b. For NSR, including PSD, modeling 

assessments, the refined methods in section 

4.2.2 are required for modeling the primary 

component of PM2.5, while the methods in 

section 5.4 are recommended 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 PM2.5.59

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 60 for further details concerning 

appropriate modeling approaches. 

d. The general modeling requirements for 

the refined models discussed in section 4.2.2 

should 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 projects.61

4.2.3.6 Models for PM10

a. The NAAQS for PM10 was promulgated 

on July 1, 1987. The EPA promulgated 

regulations for PSD increment measured as 

PM10 in a document published on June 3, 

1993. Models for PM10 are needed to meet 

NSR requirements, including PSD, 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 

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

shall be approved by the EPA Regional Office 

(section 3.2). A SIP development guide 62 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.61 63 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 

should be applied for PM10 hot-spot 

modeling for mobile sources. Specific 

guidance is available for analyzing direct 

PM10 impacts from highways, terminals, and 

other projects.61

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 particulate 

matter with aerodynamic diameter less than 

2.5 m m (PM2.5 or fine PM) 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 or 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 

(OC) mass, elemental carbon (EC), and other 

soil compounds and oxidized metals. PM2.5


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sulfate, nitrate, and ammonium ions are 

predominantly the result of chemical 

reactions of the oxidized products of sulfur 

dioxide (SO2) and NOX emissions with direct 

ammonia (NH3) emissions.64

d. Modeled strategies designed to reduce 

ozone or PM2.5 levels typically need to 

consider the chemical coupling between 

these pollutants. Control measures reducing 

ozone and PM2.5 precursor emissions may 

not lead to proportional reductions in ozone 

and PM2.5. 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 

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 composing 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. These 

types of models are appropriate for 

assessment of near-field and regional scale 

reactive pollutant impacts from specific 

sources7 10 11 12 or all sources.13 14 15 In some 

limited cases, the secondary processes can be 

treated with a box model, potentially 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.65

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.65 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.60 65 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.66

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, including PSD, 

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.60 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 ozone concentrations is necessary for 

obtaining a permit. The simulation of ozone 

formation and transport requires realistic 

treatment of atmospheric chemistry and 

deposition. Models should be applied which 

integrate chemical and physical processes 

important in the formation, decay, and 

transport of ozone and important precursor 

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 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 66 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 such that chemical transport 

models (e.g., photochemical grid 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.66 This 

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 this guidance in consultation 

with the appropriate reviewing authority 

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(paragraph 3.0(b)) to determine the most 

suitable single-source ozone modeling 

approach on a case-by-case basis. 

5.4 Recommended Models and Approaches 

for Secondarily Formed PM 2.5

a. Models are needed to guide the choice 

of strategies to address an observed PM2.5

problem in an area not attaining the PM2.5

NAAQS. Additionally, models are needed to 

assess PM2.5 impacts from specific sources or 

industrial source complexes to satisfy 

requirements for NSR, including PSD, and 

other regulatory programs. Other purposes 

for PM2.5 modeling include estimating the 

impacts of specific events on air quality, 

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/or 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 

which 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.59 60

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 is necessary 

for obtaining a permit. Primary PM2.5

components shall be simulated using 

AERMOD (see section 4.2.2). 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 

which 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

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 66

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 such that 

chemical transport models (e.g., 

photochemical grid 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.66 This document provides a more 

detailed discussion of the appropriate 

approaches to obtaining estimates of 

secondary particulate matter concentrations 

from a single source. Model users should use 

the latest version of this guidance in 

consultation with the appropriate reviewing 

authority (paragraph 3.0(b)) to determine the 

most suitable single-source modeling 

approach for secondary PM2.5 on a case-bycase basis. 

6.0 Modeling for Air Quality Related 

Values and Other Governmental Programs 

6.1 Discussion 

a. Other federal agencies 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 or 

state agency to ensure the proper application 

and use of the models and/or techniques. 

Other federal 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. No 

attempt has been made to provide a 

comprehensive discussion of each topic since 

the reference documents were designed to do 

that. 

6.2 Air Quality Related Values 

a. The 1997 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 Act 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. 

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, which 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) 67. 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.67

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 the 1997 CAA 

Amendments. A number of studies and 

reports 68 69 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. CAA section 169A 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.67 The recommendations 

separately address visibility assessments for 

sources proposing to locate relatively near 

and at farther distances from these areas.67

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.67 70 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.60

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.60 Model users should 

consult the latest version of this guidance 

and with the appropriate reviewing authority 

(paragraph 3.0(b)) for any application specific 

guidance which 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.67 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.’’ 71

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.67

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 67 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 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 BOEMS’s 

Web site: http://www.boem.gov/

Environmental-Stewardship/EnvironmentalStudies/Gulf-of-Mexico-Region/ApprovedAir-Quality-Models-for-the-GOMR.aspx. 

b. The Federal Aviation Administration 

(FAA) is the appropriate reviewing authority 

for air quality assessments of primary 

pollutant impacts at airports and air bases. 

Air quality application for this purpose is 

intended for estimating the collective impact 

of changes in aircraft operations, point 

source, and mobile source emissions at 

airports on pollutant concentrations. The 

latest version of the Aviation Environmental 

Design Tool (AEDT), is developed and is 

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 collective impact of changes in 

aircraft operations, point source, and mobile 

source emissions on pollutant 

concentrations. It is not intended for PSD, 

SIP, or other regulatory air quality analyses 

of point or mobile sources at or peripheral to 

airport property that are unrelated to airport 

operations. The latest version of AEDT may 

be obtained from FAA at its Web site: 

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. 

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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 

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 72 in rural areas and 

McElroy- Pooler 73 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 74 to 

determine whether the character of an area is 

primarily urban or rural: 

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

use within the total area, Ao, circumscribed 

by a 3km radius circle about the source using 

the meteorological land use typing scheme 

proposed by Auer; 75 (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/km2, 

use urban dispersion coefficients; otherwise 

use appropriate rural dispersion coefficients. 

(Of the two methods, the land use procedure 

is considered more definitive.) 

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.76

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)) when evaluating this 

situation and the latest version of the 

AERMOD Implementation Guide.76

f. Buoyancy-induced dispersion (BID), as 

identified by Pasquill,77 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. Geographic effects are most 

apparent when the ambient winds are light 

or calm.78 In general these geographically 

induced wind circulation effects are named 

after the source location of the winds, e.g., 

lake and sea breezes, and mountain and 

valley winds. In very rugged hilly or 

mountainous terrain, along coastlines, or 

near large land use variations, the 

characterization of the winds is a balance of 

various forces, such that the assumptions of 

steady-state straight-line transport both in 

time and space are inappropriate. In such 

cases, a model should be chosen to fully treat 

the time and space variations of meteorology 

effects on transport and dispersion. The 

setup and application of such a model should 

be determined in consultation with the 

appropriate reviewing authority (paragraph 

3.0(b)) consistent with limitations of 

paragraph 3.2.2(e). The meteorological input 

data requirements for developing the time 

and space varying three-dimensional winds 

and dispersion meteorology for these 

situations are discussed in paragraph 

8.4.1.2(c). Examples of inhomogeneous 

winds include, but are not limited to, 

situations described in the following 

paragraphs: 

i. Inversion breakup fumigation. Inversion 

breakup fumigation occurs when a plume (or 

multiple plumes) is emitted into a stable 

layer of air and that layer is subsequently 

mixed to the ground through convective 

transfer of heat from the surface or because 

of advection to less stable surroundings. 

Fumigation may cause excessively high 

concentrations but is usually rather 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. 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.79 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, 

professional judgment should be used, and 

there should be coordination with the 

appropriate reviewing authority (paragraph 

3.0(b)). AERMOD contains algorithms for dry 

and wet deposition of gases and particles.80

For other Gaussian plume models, an 

‘‘infinite half-life’’ may be used for estimates 

of particle concentrations when only 

exponential decay terms are used for treating 

settling and deposition. Lagrangian models 

have varying degrees of complexity for 

dealing with settling and deposition and the 

selection of a parameterization for such 

should be included in the approval process 

for selecting a Lagrangian model. Eulerian 

grid models tend to have explicit 

parameterizations for gravitational settling 

and deposition as well as wet deposition 

parameters already included as part of the 

chemistry scheme. 

7.2.2 Stationary Sources 

7.2.2.1 Good Engineering Practice Stack 

Height 

a. The use of stack height credit in excess 

of Good Engineering Practice (GEP) stack 

height or credit resulting from any other 

dispersion technique is prohibited in the 

development of emissions limits by 40 CFR 

51.118 and 40 CFR 51.164. The definition of 

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GEP stack height and dispersion technique 

are contained in 40 CFR 51.100. Methods and 

procedures for making the appropriate stack 

height calculations, determining stack height 

credits and an example of applying those 

techniques are found in several 

references,81 82 83 84 which provide a great 

deal of additional information for evaluating 

and describing building cavity and wake 

effects. 

b. If stacks for new or existing major 

sources are found to be less than the height 

defined by 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.83 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, the 

recommended steady-state plume dispersion 

model in section 4.2.2, AERMOD, should be 

used. 

7.2.2.2 Plume Rise 

a. The plume rise methods of Briggs 85 86

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.87 In AERMOD, plume rise is 

computed using the methods of Briggs except 

cases involving building downwash, in 

which a numerical solution of the mass, 

energy, and momentum conservation laws is 

performed.88 No explicit provisions in these 

models are made for multistack plume rise 

enhancement or the handling of such special 

plumes as flares; these problems should be 

considered on a case-by-case basis. 

b. Gradual plume rise is generally 

recommended where its use is appropriate: 

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

screening procedures to determine close-in 

impacts and (3) when calculating the effects 

of building wakes. The building wake 

algorithm in AERMOD incorporates and 

exercises the thermodynamically based 

gradual plume rise calculations as described 

in 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 86

is the recommended technique for this 

situation and is used in preferred models for 

point sources. 

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, such as that generated by the 

MAKEMET component of AERSCREEN, 

which can generate a range of meteorological 

scenarios using site-specific characteristics, 

such as albedo, Bowen ratio, and surface 

roughness. Maximum hourly concentrations 

computed from screening runs can be 

converted to longer averaging periods using 

the scaling ratios specific 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. 

However, since mobile source modeling 

usually includes an analysis of very nearsource impacts (e.g., hot-spot modeling, 

which can include receptors within 5–10 

meters of the roadway), the results can be 

highly sensitive to the characterization of the 

mobile emissions. When modeling roadway 

links, such as highway and arterial links, the 

EPA recommends that line/area sources 

instead of volume sources be used whenever 

possible, as it is easier to characterize them 

correctly. Important characteristics for both 

line/area and volume sources include the 

plume release height, source width, and 

initial dispersion characteristics, which 

should also take into account the impact of 

traffic-induced turbulence, which 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 61 and Haul Road Workgroup Final 

Report 63 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. 

Line or area sources are recommended for 

mobile sources. However, if volume sources 

are used, it is particularly important to insure 

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, 

which 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.61 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 users’ 

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 increment 

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 point source 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 which 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.60 Users should consult the latest 

version of this guidance for the most current 

modeling guidance and with the appropriate 

reviewing authority (paragraph 3.0(b)) for any 

application specific guidance which 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 

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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 

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–42.89

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.60 90

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’’ emission 

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 increment, 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. 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.61 63

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

Averaging time Emissions limit 

(lb/MMBtu) 2 × Operating level 

(lb/MMBtu) 2 × Operating factor 

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

Stationary Point Source(s) Subject to SIP Emissions Limit(s) Evaluation for Compliance With Ambient Standards 

(Including Areawide Demonstrations) 

Annual & quarterly ..................... Maximum allowable emission 

limit or federal enforceable 

permit limit.

Actual or design capacity 

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

Actual operating factor averaged over the most recent 2 

years.3

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

Averaging time Emissions limit 

(lb/MMBtu) 2 × Operating level 

(lb/MMBtu) 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.4

Continuous operation, i.e., all 

hours of each time period 

under consideration (for all 

hours of the meteorological 

database).5

Nearby Source(s).6

Annual & quarterly ..................... Maximum allowable emission 

limit or federal enforceable 

permit limit.5

Annual level when actually operating, averaged over the 

most recent 2 years.3

Actual operating factor averaged over the most recent 2 

years.3 8

Short term (≤24 hours) .............. Maximum allowable emission 

limit or federal enforceable 

permit limit.6

Temporally representative level 

when actually operating, reflective of the most recent 2 

years.3 7

Continuous operation, i.e., all 

hours of each time period 

under consideration (for all 

hours of the meteorological 

database).5

Other Source(s) 8 9

The ambient impacts from Non-nearby or Other Sources (e.g., natural sources, minor sources and, distant major source 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; analogous terminology (e.g., lb/throughput) may be used for other types of sources. 3 Unless it is determined that this period is not representative. 

4Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load causing the highest concentration. 5 If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24–hours) and the source operation is constrained by a 

federally enforceable permit condition, an appropriate adjustment to the modeled emission rate may be made (e.g., if operation is only 8 a.m. to 

4 p.m. each day, only these hours will be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating 6See Section 8.3.3. 

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

be determined through consultation with the appropriate reviewing authority (Paragraph 3.0(b)). 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 INPUT FOR NAAQS COMPLIANCE IN PSD DEMONSTRATIONS

Averaging time Emissions limit 

(lb/MMBtu) 1 × Operating level (lb/MMBtu) 2 × Operating factor (e.g., hr/yr. hr/day) 

Proposed Major New or Modified Source 

Annual & quarterly ..................... Maximum allowable emission 

limit or federal enforceable 

permit limit.

Design capacity or federally enforceable permit condition. Continuous operation (i.e., 8760 

hours).2

Short term (≤24 hours) .............. Maximum allowable emission 

limit or federal enforceable 

permit limit.

Design capacity 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).2

Nearby Source(s) 4 5

Annual & quarterly ..................... Maximum allowable emission 

limit or federal 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 federal enforceable 

permit limit.5

Annual level when actually operating, averaged over 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).2

Other Source(s) 5 9

The ambient impacts from Non-nearby or Other Sources (e.g., natural sources, minor sources and,distant major sources, 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. 

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2 If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24–hours) and the source operation is constrained by a 

federally enforceable permit condition, an appropriate adjustment to the modeled emission rate may be made (e.g., if operation is only 8 a.m. to 

4 p.m. each day, only these hours will be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating 3Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load causing the highest concentration. 4 Includes existing facility to which modification is proposed if the emissions from the existing facility will not be affected by the modification. 

Otherwise use the same parameters as for major modification. 5See Section 8.3.3. 

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

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

be determined through consultation with the appropriate reviewing authority (Paragraph 3.0(b)). 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 9.2.4). 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 in the vicinity of the source(s) under 

consideration for emissions limits that are 

not adequately represented by ambient 

monitoring data. Typically, sources that 

cause a significant concentration gradient in 

the vicinity of the source(s) under 

consideration for emissions limits are not 

adequately represented by background 

ambient monitoring. 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.91

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-sources impacts for ozone and 

secondary PM2.5. 

d. For PSD assessments in general 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. 

8.3.2 Recommendations for Isolated Single 

Source 

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. 

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 the most similar characteristics as the 

project area. If there are no monitors located 

in the vicinity of the new or modify 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 

activities that are not typical or expected to 

occur again in the future (e.g., construction, 

roadway repairs, forest fires, or unusual 

agricultural activities). There may also be 

cases where scaling (multiplying the 

monitored concentrations with a scaling 

factor) or adjusting (adding or subtracting a 

constant value the monitored concentrations) 

of data from specific days or hours. Such 

adjustments would make the monitored 

background concentrations more temporally 

and/or spatially representative of area around 

the new or modifying source for the purposes 

of the regulator 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).92

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. Determination of the appropriate 

background concentrations should be 

consistent with appropriate EPA modeling 

guidance 59 92 and justified in the modeling 

protocol that is vetted with the appropriate 

reviewing authority (paragraph 3.0(b)). 

e. Considering the spatial and temporal 

variability throughout a typical modeling 

domain on an hourly basis and the 

complexities and limitations of hourly 

observations from the ambient monitoring 

network, the EPA does not recommend 

hourly or daily pairing of monitored 

background and modeled concentrations 

except in rare cases of relatively isolated 

sources where the available monitor can be 

shown to be representative of the ambient 

concentration levels in the areas of maximum 

impact from the proposed new source. The 

implicit assumption underlying hourly 

pairing is that the background monitored 

levels for each hour are spatially uniform and 

that the monitored values are fully 

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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. 

f. 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) identification and 

characterization of contributions from nearby 

sources through explicit modeling, and (2) 

characterization of contributions from other 

sources through adequately representative 

ambient monitoring data. 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. Since an 

ambient monitor is limited to characterizing 

air quality at a fixed location, sources that 

causes a significant concentration gradient in 

the vicinity of the source(s) under 

consideration for emissions limits are not 

likely to be adequately characterized by the 

monitored data due to the high degree of 

variability of the source’s impact. 

i. The pattern of concentration gradients 

can vary significantly based on the averaging 

period being assessed. In general, 

concentration gradients will be smaller and 

more spatially uniform for annual averages 

than for short-term averages, especially for 

hourly averages. The spatial distribution of 

annual impacts around a source will often 

have a single peak downwind of the source 

based on the prevailing wind direction, 

except in cases where terrain or other 

geographic effects are important. By contrast, 

the spatial distribution of peak short-term 

impacts will typically show several localized 

concentration peaks with more significant 

gradient. 

ii. Concentration gradients associated with 

a particular source will generally be largest 

between that source’s location and the 

distance to the maximum ground-level 

concentrations from that source. Beyond the 

maximum impact distance, concentration 

gradients will generally be much smaller and 

more spatially uniform. Thus, the magnitude 

of a concentration gradient will be greatest in 

the proximity of the source and will 

generally not be significant at distances 

greater than 10 times the height of the 

stack(s) at that source without consideration 

of terrain influences. 

iii. The number of nearby sources to be 

explicitly modeled in the air quality analysis 

is expected to be few except in unusual 

situations. In most cases, the few nearby 

sources will be located within 10 to 20 km 

from the source(s) under consideration. 

Owing to both the uniqueness of each 

modeling situation and the large number of 

variables involved in identifying nearby 

sources, no attempt is made here to 

comprehensively define a ‘‘significant 

concentration gradient.’’ Rather, 

identification of nearby sources calls for the 

exercise of professional judgement by the 

appropriate reviewing authority (paragraph 

3.0(b)). This guidance is not intended to alter 

the exercise of that judgement or to 

comprehensively 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 appendix 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). 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, 

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 sources, minor and distance 

major 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 EPA’s Guidance on the Use 

of Models and Other Analyses for 

Demonstrating Attainment of Air Quality 

Goals for Ozone, PM2.5, and Regional Haze 93. 

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, data from a nearby National 

Weather Service (NWS) or comparable 

station, or prognostic meteorological data. 

The implementation of ASOS (automated 

surface observing stations) in recent years 

should not preclude the use of NWS–ASOS 

data if such a station is determined to be 

representative of the modeled area.94

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 

use and obtained from similar sources. Some 

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bFormerly the National Climatic Data Center 

(NCDC). 

recommendations and requirements for the 

use of each type of data are included in this 

subsection. 

8.4.2 Recommendations and Requirements 

a. AERMET95 shall be used to preprocess 

all meteorological data, be it observed or 

prognostic, for use with AERMOD in 

regulatory applications. The AERMINUTE 96

processor, in most cases, should be used to 

process 1-minute ASOS wind data for input 

into AERMET when processing NWS ASOS 

sites in AERMET. When processing 

prognostic meteorological data for AERMOD, 

the Mesoscale Model Interface Program 

(MMIF) 93 should be used to process data for 

input into AERMET. Other methods of 

processing prognostic meteorological data for 

input into AERMET should be approved by 

the appropriate reviewing authority. 

Additionally, the following meteorological 

preprocessors are recommended by the EPA: 

PCRAMMET97, MPRM 98, and METPRO 99. 

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.100

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 101 102, where applicable, for 

determining surface characteristics when 

processing measured meteorological data 

through AERMET. In areas where it is not 

possible to use AERSURFACE output, surface 

characteristics can determined using 

techniques that apply the same analysis as 

AERSURFACE. 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.103 104

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 76. 

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 PCRAMMET97 which 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 

meteorological data, at least 1 year of sitespecific, or at least 3 years of prognostic 

meteorological data are required. If 1 year or 

more, up to 5 years, of site-specific data is 

available, these data are preferred for use in 

air quality analyses. Such data should have 

been subjected to quality assurance 

procedures as described in 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 LRT 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 ‘‘hourly’’ weather 

observation was a human observer-based 

observation reflecting a single 2-minute 

average generally taken about 10 minutes 

before the hour. However, beginning with 

January 2000 for first-order stations and 

March 2005 for all stations, NCEI has 

archived the rolling 2-minute average winds 

at every minute for ASOS sites. The 

AERMINUTE processor 96 was developed to 

reduce calm and missing hours by taking 

advantage of the availability of the 1-minute 

ASOS wind data to calculate full hourly 

average winds to replace standard hourly 

observations and reduce the number of calm 

and missing winds in AERMET processing. 

8.4.3.2 Recommendations 

a. The preferred models listed in appendix 

A all accept as input the NWS meteorological 

data preprocessed into model compatible 

form. If NWS data are judged to be 

adequately representative for a specific 

modeling application, they may be used. 

NEIS makes available surface 105 106 and 

upper air 107 meteorological data online and 

in CD–ROM format. Upper air data are also 

available at the Earth System Research 

Laboratory Global Systems Divisions Web 

site (http://esrl.noaa.gov/gsd). 

b. Although most NWS wind 

measurements are made at a standard height 

of 10 meters, 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. A 

specific set of randomly generated numbers 

has been developed for use with the 

preferred EPA models and should be used 

with standard NWS data to ensure a lack of 

bias in wind direction assignments within 

the models. 

d. Beginning with year 2000, NCDC began 

archiving 2-minute winds, reported every 

minute for NWS ASOS sites. The 

AERMINUTE processor was developed to 

read those winds and calculate hourly 

average winds for input into 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.94

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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,108 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 108. 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 108 is available. Site-specific data 

should always be reviewed for 

representativeness and adequacy by an 

experienced meteorologist, atmospheric 

scientist, or other qualified scientist. 

8.4.4.2 Recommendations 

a. The EPA guidance 108 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 91 109 110

Detailed information on quality assurance is 

also available.111 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. 

b. All processed site-specific data should 

be in the form of hourly averages for input 

into the dispersion model. These data 

include surface wind speed, transport 

direction, dilution wind speed, and 

turbulence measurements sA and sE (for use 

in stability determinations and direct input 

into the dispersion model). The hourly 

average turbulence measurements should be 

the square root of the arithmetic average of 

the 15-minute average variances (square of sA

or sE). 

c. Missing data substitution. After valid 

data retrieval requirements have been met,108

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 108. If no representative 

alternative data are available for substitution, 

the absent data should be coded as missing 

using missing data codes appropriate to the 

applicable meteorological pre-processor. 

Appropriate model options for treating 

missing data, if available in the model, 

should be employed. 

d. Solar radiation measurements. Total 

solar radiation or net radiation should be 

measured with a reliable pyranometer or net 

radiometer, sited and operated in accordance 

with established site-specific meteorological 

guidance.108 111

e. Temperature measurements. 

Temperature measurements should be made 

at standard shelter height (2m) in accordance 

with established site-specific meteorological 

guidance.108

f. 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 108 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. 

g. 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 meters. (For 

additional requirements for AERMOD and 

CTDMPLUS, see appendix A.) Specifications 

for wind measuring instruments and systems 

are contained in reference 108. 

h. Turbulence. There are several dispersion 

models that are capable of using direct 

measurements of turbulence (wind 

fluctuations) in the characterization of the 

vertical and lateral dispersion (e.g., 

CTDMPLUS, AERMOD). For specific 

requirements for CTDMPLUS, AERMOD, see 

appendix A. For technical guidance on 

measurement and processing of turbulence 

parameters, see reference 108. When 

turbulence data are used in this manner to 

directly characterize the vertical and lateral 

dispersion, the averaging time for the 

turbulence measurements should be 1 hour. 

However, since AERMOD incorporates an 

algorithm to account for horizontal plume 

meander under low wind conditions, the 

methodology outlined in paragraph 8.4.4.2(b) 

should be used to calculate hourly averages 

of sq, based on four 15-minuite values, to 

minimize ‘‘double counting’’ of plume spread 

associated with meander. The calculation of 

hourly sq discussed above is automatically 

applied within AERMET when sub-hourly 

data are processed. There are other 

dispersion models that employ P–G stability 

categories for the characterization of the 

vertical and lateral dispersion. Methods for 

using site-specific turbulence data for the 

characterization of P–G stability categories 

are discussed in reference 108. When 

turbulence data are used in this manner to 

determine the P–G stability category, the 

averaging time for the turbulence 

measurements should be 15 minutes, with 

hourly averaged values based on 

methodology in paragraph 8.4.4.2(b). 

i. Stability categories. For dispersion 

models that employ P–G stability categories 

for the characterization of the vertical and 

lateral dispersion, the P–G stability 

categories, as originally defined, couple 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 10m. The 

insolation rate is typically assessed using 

observations of cloud cover and ceiling 

height based on criteria outlined by Turner.72

It is recommended that the P–G stability 

category be estimated using the Turner 

method with site-specific wind speed 

measured at or near 10m and representative 

cloud cover and ceiling height. 

Implementation of the Turner method, as 

well as considerations in determining 

representativeness of cloud cover and ceiling 

height in cases for which site-specific cloud 

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observations are unavailable, may be found 

in section 6 of reference 108. 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. 

j. The SRDT method, described in section 

6.4.4.2 of reference 108, is modified slightly 

from that published from earlier work 112 and 

has been evaluated with three site-specific 

databases.113 The two methods of stability 

classification which use wind fluctuation 

statistics, the sE and sA methods, are also 

described in detail in section 6.4.4 of 

reference 108 (note applicable tables in 

section 6). For additional information on the 

wind fluctuation methods, several references 

are available.114 115 116 117

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 necessary to use prognostic 

meteorological data in a regulatory modeling 

application. 

b. The EPA has developed a processor, the 

MMIF (Mesoscale Model Interface Program) 

to process MM5 (Mesoscale Model 5) or WRF 

(Weather Research and Forecasting) model 

data for input into various models including 

AERMOD. MMIF can process data for input 

into 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).118

Specific guidance on processing MMIF for 

AERMOD can be found in reference 104. 

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 into AERMOD. If an 

alternative method of processing data for 

input into 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 should be devoted to the 

process of evaluating the prognostic 

meteorological data. The modeling data 

should be compared to NWS observational 

data in an effort to show that the data are 

accurately 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.93

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.93

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 judgement 

may be needed to select the appropriate grid 

cell to use. In such cases, the selected grid 

cell 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 Treatment of Near-Calms and Calms 

8.4.6.1 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 which 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 low winds, especially those 

introduced with AERMINUTE, the EPA 

implemented a wind speed threshold in 

AERMET for use with ASOS derived 

winds.96 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 typically the 

threshold of the instrument used to collect 

the wind speed data. The reference wind 

speed is selected by the model as the lowest 

level of non-missing wind speed and 

direction data where the speed is greater than 

the wind speed threshold, and the height of 

the measurement is between seven times the 

local surface roughness and 100 meters. If the 

only valid observation of the reference wind 

speed between these heights is less than the 

threshold, the hour is considered calm, and 

no concentration is calculated. None of the 

observed wind speeds in a measured wind 

profile that are less than the threshold speed 

are used in construction of the modeled wind 

speed profile in AERMOD. 

8.4.6.2 Recommendations 

a. Hourly concentrations calculated with 

steady-state Gaussian plume models using 

calms should not be considered valid; the 

wind and concentration estimates for these 

hours should be disregarded and considered 

to be missing. Critical concentrations for 

3-, 8-, and 24-hour averages should be 

calculated by dividing the sum of the hourly 

concentrations for the period by the number 

of valid or non-missing hours. If the total 

number of valid hours is less than 18 for 24- 

hour averages, less than 6 for 8-hour averages 

or less than 3 for 3-hour averages, the total 

concentration should be divided by 18 for the 

24-hour average, 6 for the 8-hour average and 

3 for the 3-hour average. For annual averages, 

the sum of all valid hourly concentrations is 

divided by the number of non-calm hours 

during the year. AERMOD has been coded to 

implement these instructions. For hours that 

are calm or missing, the AERMOD hourly 

concentrations will be zero. For other models 

listed in appendix A, a post-processor 

computer program, CALMPRO 119 has been 

prepared, is available on the EPA’s SCRAM 

Web site (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, measured site-specific wind 

speeds of less than 1 m/s but higher than the 

response threshold of the instrument should 

be input as 1 m/s; the corresponding wind 

direction should also be input. Wind 

observations below the response threshold of 

the instrument should be set to zero, with the 

input file in ASCII format. For input to 

AERMOD, no adjustment should be made to 

the site-specific wind data. For NWS ASOS 

data, especially data using the 1-minute 

ASOS winds, a wind speed threshold option 

is allowed with a recommended speed of 0.5 

m/s. 94 When using prognostic data processed 

by MMIF, a 0.5 m/s threshold is also invoked 

by MMIF for input into AERMET. 

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. 

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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, including PSD, or other 

EPA requirements to ensure consistency in 

their regulatory application. This section 

recommends procedures specific to NSR, 

including PSD, 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 51 60 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 peer scientific review. 

Statistical performance measures, including 

measures of difference (or residuals) such as 

bias, variance of difference and gross 

variability of the difference, and measures of 

correlation such as time, space, and time and 

space combined as 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). 

The Guideline, therefore, provides 

objective methods that allow a determination 

to be made as to what air quality model or 

technique is most appropriate for a particular 

application. 

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 Web site at 

http://www.epa.gov/air/criteria.html. Under 

the CAA, the NAAQS are subjected to 

extensive review every 5 years and the 

standards, including the level and the form, 

may be revised as part of that review. The 

criteria pollutants have either long-term 

(annual or quarterly) and/or short-term (24- 

hour or less) forms that are not to be 

exceeded more than a certain frequency over 

a period of time (e.g., no exceedance on a 

rolling 3-month average, no more than once 

per year, or no more than once per year 

averaged over 3 years), are averaged over a 

period of time (e.g., an annual mean or an 

annual mean averaged over 3 years), or are 

some percentile that is averaged over a 

period of time (e.g., annual 99th or 98th 

percentile averaged over 3 years). The 3-year 

period for ambient monitoring design values 

does not dictate the length of the data periods 

recommended for modeling (i.e., 5 years of 

NWS meteorological data, at least 1 year of 

site-specific, or at least 3 years of prognostic 

meteorological data). 

e. This section discusses general 

recommendations on the regulatory 

application of models for the purposes of 

NSR, including PSD permitting, and 

particularly for estimating design 

concentration(s), appropriately comparing 

these estimates to NAAQS and PSD 

increment, and developing emissions limits. 

Lastly, this section provides the criteria 

necessary for considering use of 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 Web site (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 this 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 source. The NAAQS design 

concentration is then compared to the 

applicable NAAQS. 

ii. For a PSD increment assessment, the 

design concentration includes impacts after 

the appropriate baseline date from all 

increment consuming and increment 

expanding sources. The PSD increment 

design concentration is then compared to the 

applicable PSD increment. 

b. The specific form of the NAAQS for the 

pollutant(s) of concern will also influence 

how the background and modeled data 

should be combined for appropriate 

comparison with the respective NAAQS in 

such a modeling demonstration. Given the 

potential for revision of the form of the 

NAAQS and the complexities of combining 

background and modeled data, specific 

details on this process can be found in 

applicable modeling guidance available on 

the EPA’s SCRAM Web site (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 should be less than 

or equal to the permitted increment. The 

modeled annual averages should 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 increment 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 

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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 nearby 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 

Modified Sources 

a. As described in this subsection, the 

recommended procedure for conducting 

either a NAAQS or PSD increment 

assessment under PSD permitting is a multistage approach that includes the following 

two stages: 

i. The first stage is referred to as a singlesource impact analysis, since only the new or 

modifying source is considered in the 

analysis. 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 second stage is referred to 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, it includes consideration of 

background, which includes contributions 

from natural, nearby, and unknown sources. 

b. Each stage involves increasing 

complexity and details, as required to fully 

demonstrate a new or modifying source will 

not cause of contribution to a violation of any 

NAAQS or PSD increment. As such, starting 

with a single-source impact analysis 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, begins 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 there will not be a 

significant impact, then the analysis is 

sufficient for the required demonstration 

under PSD. If the ambient concentration 

estimates indicate that significant impacts 

may occur, 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 estimated ambient 

concentrations indicate that there will not be 

a significant impact, then the analysis is 

generally sufficient to demonstrate that the 

source will not cause or contribute to an 

exceedance. However, if the concentration 

estimates from the refined modeling analysis 

indicate that significant impacts may occur, 

then a cumulative impact analysis should be 

undertaken. The receptors that indicate the 

location of significant 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 are 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) the significance of the 

source’s contribution, in a temporal and 

spatial sense, to any modeled violation, i.e., 

where and when the predicted design 

concentration is greater than the NAAQS. For 

PSD increment, 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 incrementconsuming and increment-expanding 

sources, rather than using past modeling 

analyses of those sources as part of 

background concentration. This would 

permit the use of newly acquired data or 

improved modeling techniques if such data 

and/or techniques have become available 

since the last source was permitted. 

9.2.3.1 Considerations in Developing 

Emissions Limits 

a. Emissions limits and resulting control 

requirements should be established to 

provide for compliance with each applicable 

NAAQS (and averaging period) and PSD 

increment. It is possible that multiple 

emissions limits will be required for a source 

to demonstrate compliance with several 

criteria pollutants (and averaging periods) 

and PSD increments. Case-by-case 

determinations must be made as to the 

appropriate form of the limits, i.e., whether 

the emissions limits restrict the emission 

factor (e.g., limiting lb/MMBTU), the 

emission rate (e.g., lb/hr), or both. The 

appropriate reviewing authority (paragraph 

3.0(b)) and appropriate EPA guidance should 

be consulted to determine the appropriate 

emissions limits on a case-by-case basis. 

9.2.4 Use of Measured Data in Lieu of 

Model Estimates 

a. As described throughout the Guideline, 

modeling is the preferred method for 

demonstrating compliance with the NAAQS 

and PSD increments and for determining the 

most appropriate emissions limits for new 

and existing sources. When a preferred 

model or adequately justified and approved 

alternative model is available, model results, 

including the appropriate background, are 

sufficient for air quality demonstrations and 

establishing emissions limits, if necessary. In 

instances when the modeling technique 

available is only a screening technique, the 

addition of air quality monitoring data to the 

analysis may lend credence to the model 

results. However, air quality monitoring data 

alone will normally not be acceptable as the 

sole basis for demonstrating compliance with 

the NAAQS and PSD increments or for 

determining emissions limits. 

b. There may be rare circumstances where 

the performance of the preferred air quality 

model will be shown to be less than 

reasonably acceptable when compared with 

air quality monitoring data measured in the 

vicinity of an existing source. Additionally, 

there may not be an applicable preferred air 

quality model, screening technique, or 

justifiable alternative model suitable for the 

situation. In these unique instances, there 

may be the possibility of establishing 

emissions limits and demonstrating 

compliance with the NAAQS and PSD 

increments solely on the basis of analysis of 

observed air quality data in lieu of an air 

quality modeling analysis. However, only in 

the case of a modification to an existing 

source should air quality monitoring data 

alone be a basis for determining adequate 

emissions limits or for demonstration that the 

modification will not cause or contribute to 

a violation of any NAAQS or PSD increment. 

c. The following items should be 

considered prior to the acceptance of an 

analysis of measured air quality data as the 

sole basis for an air quality demonstration or 

determining an emissions limit: 

i. Does a monitoring network exist for the 

pollutants and averaging times of concern in 

the vicinity of the existing source? 

ii. Has the monitoring network been 

designed to locate points of maximum 

concentration? 

iii. Do the monitoring network and the data 

reduction and storage procedures meet EPA 

monitoring and quality assurance 

requirements? 

iv. Do the dataset and the analysis allow 

impact of the most important individual 

sources to be identified if more than one 

source or emission point is involved? 

v. Is at least one full year of valid ambient 

data available? 

vi. Can it be demonstrated through the 

comparison of monitored data with model 

results that available air quality models and 

techniques are not applicable? 

c. 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. 

d. Sources should obtain approval from the 

appropriate reviewing authority (paragraph 

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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. 

e. 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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Insley, 1989. User’s Guide to the 

Complex Terrain Dispersion Model Plus 

Algorithms for Unstable Situations 

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(CTDMPLUS). Volume 1: Model 

Descriptions and User Instructions. EPA 

Publication No. EPA–600/8–89–041. 

Environmental Protection Agency, 

Research Triangle Park, NC. http://

www.epa.gov/ttn/scram/dispersion_

prefrec.htm#ctdmplus. (NTIS No. PB 89– 

181424) 

101. Environmental Protection Agency, 2008. 

AERSURFACE User’s Guide. Publication 

No. EPA–454/B–08–001. Office of Air 

Quality Planning & Standards, Research 

Triangle Park, NC. http://www.epa.gov/

ttn/scram/dispersion_

related.htm#aersurface. 

102. Wesson, K., R. Brode, W. Peters, and C. 

Tillerson, 2006. AERMOD Sensitivity to 

the Choice of Surface Characteristics. 

A&WMA presentation. 

103. Environ, 2014. The Mesocale Model 

Interface Program (MMIF) Version 3.1 

User’s Manual. 

104. Environmental Protection Agency, 2015 

Guidance on the Use of the Mesoscale 

Model Interface Program (MMIF) for 

AERMOD Applications. Publication No. 

EPA–454/B–15–001. Office of Air 

Quality Planning & Standards, Research 

Triangle Park, NC. 

105. Solar and Meteorological Surface 

Observation Network, 1961–1990; 3- 

volume CD–ROM. Version 1.0, 

September 1993. Produced jointly by 

National Climatic Data Center and 

National Renewable Energy Laboratory. 

Can be ordered from NOAA National 

Data Center’s Web site at http://

www.nndc.noaa.gov. 

106. Hourly United States Weather 

Observations, 1990–1995 (CD–ROM). 

October 1997. Produced jointly by 

National Climatic Data Center and 

Environmental Protection Agency. Can 

be ordered from NOAA National Data 

Center’s Web site at http://

www.ncdc.noaa.gov. 

107. Radiosonde Data of North America, 

1946–1996; 4-volume CD–ROM. 

August1996. Produced jointly by 

Forecast Systems laboratory and 

National Climatic Data Center. Can be 

ordered from NOAA National Data 

Center’s Web site at http://

lwf.ncdc.noaa.gov/oa/ncdc.html. 

108. Environmental Protection Agency, 2000. 

Meteorological Monitoring Guidance for 

Regulatory Modeling Applications. 

Publication No. EPA–454/R–99–005. 

Office of Air Quality Planning & 

Standards, Research Triangle Park, NC. 

(NTIS No. PB 2001–103606) 

109. ASTM D5527: Standard Practice for 

Measuring Surface Winds and 

Temperature by Acoustic Means. (2011) 

110. ASTM D5741: Standard Practice for 

Characterizing Surface Wind Using Wind 

Vane and Rotating Anemometer. (2011) 

111. Environmental Protection Agency, 1995. 

Quality Assurance for Air Pollution 

Measurement Systems, Volume IV— 

Meteorological Measurements. 

Publication No. EPA600/R–94/038d. 

Office of Air Quality Planning & 

Standards, Research Triangle Park, NC. 

Note: For copies of this handbook, you 

may make inquiry to ORD Publications, 

26 West Martin Luther King Dr., 

Cincinnati, OH 45268. 

112. Bowen, B.M., J.M. Dewart and A.I. Chen, 

1983. Stability Class Determination: A 

Comparison for One Site. Proceedings, 

Sixth Symposium on Turbulence and 

Diffusion. American Meteorological 

Society, Boston, MA; pp. 211–214. 

(Docket No. A–92–65, II–A–7) 

113. Environmental Protection Agency, 1993. 

An Evaluation of a Solar Radiation/

Delta-T (SRDT) Method for Estimating 

Pasquill-Gifford (P–G) Stability 

Categories. Publication No. EPA–454/R– 

93–055. Office of Air Quality Planning & 

Standards, Research Triangle Park, NC. 

(NTIS No. PB 94–113958) 

114. Irwin, J.S., 1980. Dispersion Estimate 

Suggestion #8: Estimation of Pasquill 

Stability Categories. Office of Air Quality 

Planning & Standards, Research Triangle 

Park, NC. (Docket No. A–80–46, II–B–10) 

115. Mitchell, Jr., A.E. and K.O. Timbre, 

1979. Atmospheric Stability Class from 

Horizontal Wind Fluctuation. Presented 

at 72nd Annual Meeting of Air Pollution 

Control Association, Cincinnati, OH; 

June 24–29, 1979. (Docket No. A–80–46, 

II–P–9) 

116. Smedman—Hogstrom, A. and V. 

Hogstrom, 1978. A Practical Method for 

Determining Wind Frequency 

Distributions for the Lowest 200m from 

Routine Meteorological Data. Journal of 

Applied Meteorology, 17(7): 942–954. 

117. Smith, T.B. and S.M. Howard, 1972. 

Methodology for Treating Diffusivity. 

MRI 72 FR–1030. Meteorology Research, 

Inc., Altadena, CA. (Docket No. A–80– 

46, II–P–8) 

118. Environmental Protection Agency, 2015. 

Evaluation of Prognostic Meteorological 

Data in AERMOD Applications. 

Publication No. EPA–454/R–15–004. 

Office of Air Quality Planning & 

Standards, Research Triangle Park, NC. 

119. Environmental Protection Agency, 1984. 

Calms Processor (CALMPRO) User’s 

Guide. Publication No. EPA–901/9–84– 

001. Office Of Air Quality Planning & 

Standards, Region I, Boston, MA. (NTIS 

No. PB 84–229467) 

Appendix A to Appendix W of Part 

51—Summaries of Preferred Air 

Quality Models 

Table of Contents 

A.0 Introduction and Availability 

A.1 AERMOD (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) Many of these models have been 

subjected to a performance evaluation using 

comparisons with observed air quality data. 

Where possible, several of the models 

contained herein have been subjected to 

evaluation exercises, including (1) statistical 

performance tests recommended by the 

American Meteorological Society and (2) 

peer scientific reviews. The models in this 

appendix have been selected on the basis of 

the results of the model evaluations, 

experience with previous use, familiarity of 

the model to various air quality programs, 

and the costs and resource requirements for 

use. 

(3) Codes and documentation for all 

models listed in this appendix are available 

from the EPA’s Support Center for Regulatory 

Air Models (SCRAM) Web site at http://

www.epa.gov/ttn/scram. Codes and 

documentation may also available from the 

National Technical Information Service 

(NTIS), http://www.ntis.gov, and, when 

available, is referenced with the appropriate 

NTIS accession number. 

A.1 AERMOD (AMS/EPA Regulatory 

Model) 

References 

Environmental Protection Agency, 2004. 

AERMOD: Description of Model 

Formulation. Publication No. EPA–454/

R–03–004. Office of Air Quality Planning 

& Standards, Research Triangle Park, NC; 

September 2004. http://www.epa.gov/

ttn/scram/7thconf/aermod/aermod_

mfd.pdf. 

Cimorelli, A., et al., 2005. AERMOD: A 

Dispersion Model for Industrial Source 

Applications. Part I: General Model 

Formulation and Boundary Layer 

Characterization. Journal of Applied 

Meteorology, 44(5): 682–693. 

Perry, S. et al., 2005. AERMOD: A Dispersion 

Model for Industrial Source 

Applications. Part II: Model Performance 

against 17 Field Study Databases. Journal 

of Applied Meteorology, 44(5): 694–708. 

Environmental Protection Agency, 2004. 

User’s Guide for the AMS/EPA 

Regulatory Model—AERMOD. 

Publication No. EPA–454/B–03–001. 

Office of Air Quality Planning & 

Standards, Research Triangle Park, NC; 

September 2004. http://www.epa.gov/

ttn/scram/dispersion_

prefrec.htm#aermod. 

Environmental Protection Agency, 2004. 

User’s Guide for the AERMOD 

Meteorological Preprocessor (AERMET). 

Publication No. EPA–454/B–03–002. 

Office of Air Quality Planning & 

Standards, Research Triangle Park, NC; 

November 2004. http://www.epa.gov/ttn/ 

scram/metobsdata_

procaccprogs.htm#aermet. 

User’s Guide for the AERMOD Terrain 

Preprocessor (AERMAP). Publication No. 

EPA–454/B–03–003. Office of Air 

Quality Planning & Standards, Research 

Triangle Park, NC; October 2004. 

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http://www.epa.gov/ttn/scram/

dispersion_related.htm#aermap. 

Schulman, L. L., D.G. Strimaitis and J.S. 

Scire, 2000. Development and evaluation 

of the PRIME plume rise and building 

downwash model. Journal of the Air and 

Waste Management Association, 50: 

378–390. 

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 Web site (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, or volume sources 

based on an up-to-date characterization of the 

atmospheric boundary layer. Sources may be 

located in rural or urban areas, and receptors 

may be located in simple or complex terrain. 

AERMOD accounts for building wake effects 

(i.e., plume downwash) based on the PRIME 

building downwash algorithms. The model 

employs hourly sequential preprocessed 

meteorological data to estimate 

concentrations for averaging times from 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. Recommendations for 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 (line) 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 

50km; 

• 1-hour to annual averaging times; and 

• Continuous toxic air emissions. 

(2) For regulatory applications of 

AERMOD, the regulatory default option 

should be set, i.e., the parameter DFAULT 

should be employed in the MODELOPT 

record in the Control Pathway. The DFAULT 

option requires the use of terrain elevation 

data, stack-tip downwash, sequential date 

checking, and does not permit the use of the 

model in the SCREEN mode. In the 

regulatory default mode, pollutant half-life or 

decay options are not employed, except in 

the case of an urban source of sulfur dioxide 

where a four-hour half-life is applied. Terrain 

elevation data from the U.S. Geological 

Survey 7.5-Minute Digital Elevation Model 

(DEM) or equivalent (approx. 30-meter 

resolution) should be used in all 

applications. Starting in 2011, data from the 

National Elevation Dataset (NED, http://

ned.usgs.gov) can also be used in AERMOD, 

which includes a range of resolutions, 

ranging from 1-m to 2 arc seconds and such 

high resolution would always be preferred. In 

some cases, exceptions of 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 input includes 

source type, location, emission rate, stack 

height, stack inside diameter, stack gas exit 

velocity, stack gas temperature, area and 

volume source dimensions, and source 

elevation. Building dimensions and variable 

emission rates are optional. 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. 

(2) Meteorological data: The AERMET 

meteorological preprocessor requires input of 

surface characteristics, including surface 

roughness (zo), Bowen ratio, and albedo, as 

well as, hourly observations of wind speed 

between 7zo and 100m (reference wind speed 

measurement from which a vertical profile 

can be developed), wind direction, cloud 

cover, and temperature between zo and 100m 

(reference temperature measurement from 

which a vertical profile can be developed). 

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 (if 

applicable) and upper air meteorological 

stations are required. The wind speed 

starting threshold is also required in 

AERMET for applications involving sitespecific data). When using prognostic data, 

modeled profiles of temperature and winds 

are input into 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, 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 insure that the wind, 

temperature and turbulence profiles derived 

by AERMOD are both laterally and vertically 

representative of the source area. The 

adequacy of input data should be judged 

independently for each variable. The values 

for surface roughness, Bowen ratio, and 

albedo should reflect the surface 

characteristics in the vicinity of the 

meteorological tower 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 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 terrain data produced by the 

U.S. Geological Survey (USGS), or other 

equivalent data. AERMAP can be used 

optionally to estimate source elevations. 

c. Output 

Printed output options include input 

information, high concentration summary 

tables by receptor for user-specified 

averaging periods, maximum concentration 

summary tables, and concurrent values 

summarized by receptor for each day 

processed. Optional output files can be 

generated for: A listing of occurrences of 

exceedances of user-specified threshold 

value; a listing of concurrent (raw) results at 

each receptor for each hour modeled, suitable 

for post-processing; a listing of design values 

that can be imported into graphics software 

for plotting contours; 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 

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normalized concentrations, suitable for 

model evaluation studies. 

d. Type of Model 

AERMOD is a steady-state plume model, 

using Gaussian distributions in the vertical 

and horizontal for stable conditions, and in 

the horizontal for convective conditions. The 

vertical concentration distribution for 

convective conditions results from an 

assumed bi-Gaussian probability density 

function of the vertical velocity. 

e. Pollutant Types 

AERMOD is applicable to primary 

pollutants and continuous releases of toxic 

and hazardous waste pollutants. Chemical 

transformation is treated by simple 

exponential decay. 

f. Source-Receptor Relationships 

AERMOD applies user-specified locations 

for sources and receptors. Actual separation 

between each source-receptor pair is used. 

Source and receptor elevations are user input 

or are determined by AERMAP using USGS 

DEM terrain data. Receptors may be located 

at user-specified heights above ground level. 

g. Plume Behavior 

(1) In the convective boundary layer (CBL), 

the transport and dispersion of a plume is 

characterized as the superposition of three 

modeled plumes: The direct plume (from the 

stack), the indirect plume, and the penetrated 

plume, where the indirect plume accounts 

for the lofting of a buoyant plume near the 

top of the boundary layer, and the penetrated 

plume accounts for the portion of a plume 

that, due to its buoyancy, penetrates above 

the mixed layer, but can disperse downward 

and re-enter the mixed layer. In the CBL, 

plume rise is superposed on the 

displacements by random convective 

velocities (Weil et al., 1997). 

(2) In the stable boundary layer, plume rise 

is estimated using an iterative approach 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 Plume 

Volume Molar Ratio Method and the Ozone 

Limiting Method (section 4.2.3.4) and for 

point-source NO2 analyses are available. 

m. Physical Removal 

AERMOD can be used to treat dry and wet 

deposition for both gases and particles. 

n. Evaluation Studies 

American Petroleum Institute, 1998. 

Evaluation of State of the Science of Air 

Quality Dispersion Model, Scientific 

Evaluation, prepared by WoodwardClyde Consultants, Lexington, 

Massachusetts, for American Petroleum 

Institute, Washington, DC, 20005–4070. 

Brode, R.W., 2002. Implementation and 

Evaluation of PRIME in AERMOD. 

Preprints of the 12th Joint Conference on 

Applications of Air Pollution 

Meteorology, May 20–24, 2002; 

American Meteorological Society, 

Boston, MA. 

Brode, R.W., 2004. Implementation and 

Evaluation of Bulk Richardson Number 

Scheme in AERMOD. 13th Joint 

Conference on Applications of Air 

Pollution Meteorology, August 23–26, 

2004; American Meteorological Society, 

Boston, MA. 

Environmental Protection Agency, 2003. 

AERMOD: Latest Features and 

Evaluation Results. Publication No. 

EPA–454/R–03–003. Office of Air 

Quality Planning & Standards, Research 

Triangle Park, NC. http://www.epa.gov/

ttn/scram/7thconf/aermod/aermod_

mep.pdf. 

Heist, D., et al, 2013. Estimating near-road 

pollutant dispersion: A model intercomparison. Transportation Research 

Part D: Transport and Environment, 25: 

pp 93–105. 

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. 

Environmental Protection Agency, 

Research Triangle Park, NC. http://

www.epa.gov/ttn/scram/dispersion_

prefrec.htm#ctdmplus. (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 Web site (paragraph A.0(3)). 

Abstract 

CTDMPLUS is a refined point source 

Gaussian air quality model for use in all 

stability conditions for complex terrain 

applications. The model contains, in its 

entirety, the technology of CTDM for stable 

and neutral conditions. However, 

CTDMPLUS can also simulate daytime, 

unstable conditions, and has a number of 

additional capabilities for improved user 

friendliness. Its use of meteorological data 

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and terrain information is different from 

other EPA models; considerable detail for 

both types of input data is required and is 

supplied by preprocessors specifically 

designed for CTDMPLUS. CTDMPLUS 

requires the parameterization of individual 

hill shapes using the terrain preprocessor and 

the association of each model receptor with 

a particular hill. 

a. Recommendation for Regulatory Use 

CTDMPLUS is appropriate for the 

following applications: 

• Elevated point sources; 

• Terrain elevations above stack top; 

• Rural or urban areas; 

• Transport distances less than 50 

kilometers; and 

• 1-hour to annual averaging times when 

used with a post-processor program such as 

CHAVG. 

b. Input Requirements 

(1) Source data: For each source, user 

supplies source location, height, stack 

diameter, stack exit velocity, stack exit 

temperature, and emission rate; if variable 

emissions are appropriate, the user supplies 

hourly values for emission rate, stack exit 

velocity, and stack exit temperature. 

(2) Meteorological data: For applications of 

CTDMPLUS, multiple level (typically three 

or more) measurements of wind speed and 

direction, temperature and turbulence (wind 

fluctuation statistics) are required to create 

the basic meteorological data file 

(‘‘PROFILE’’). Such measurements should be 

obtained up to the representative plume 

height(s) of interest (i.e., the plume height(s) 

under those conditions important to the 

determination of the design concentration). 

The representative plume height(s) of interest 

should be determined using an appropriate 

complex terrain screening procedure (e.g., 

CTSCREEN) and should be documented in 

the monitoring/modeling protocol. The 

necessary meteorological measurements 

should be obtained from an appropriately 

sited meteorological tower augmented by 

SODAR and/or RASS if the representative 

plume height(s) of interest is above the levels 

represented by the tower measurements. 

Meteorological preprocessors then create a 

SURFACE data file (hourly values of mixed 

layer heights, surface friction velocity, 

Monin-Obukhov length and surface 

roughness length) and a RAWINsonde data 

file (upper air measurements of pressure, 

temperature, wind direction, and wind 

speed). 

(3) Receptor data: Receptor names (up to 

400) and coordinates, and hill number (each 

receptor must have a hill number assigned). 

(4) Terrain data: User inputs digitized 

contour information to the terrain 

preprocessor which creates the TERRAIN 

data file (for up to 25 hills). 

c. Output 

(1) When CTDMPLUS is run, it produces 

a concentration file, in either binary or text 

format (user’s choice), and a list file 

containing a verification of model inputs, i.e., 

• Input meteorological data from 

‘‘SURFACE’’ and ‘‘PROFILE’’, 

• Stack data for each source, 

• Terrain information, 

• Receptor information, and 

• Source-receptor location (line printer 

map). 

(2) In addition, if the case-study option is 

selected, the listing includes: 

• Meteorological variables at plume height, 

• Geometrical relationships between the 

source and the hill, and 

• Plume characteristics at each receptor, 

i.e., 

—Distance in along-flow and cross flow 

direction 

—Effective plume-receptor height difference 

—Effective sy & sz values, both flat terrain 

and hill induced (the difference shows the 

effect of the hill) 

—Concentration components due to WRAP, 

LIFT and FLAT 

(3) If the user selects the TOPN option, a 

summary table of the top four concentrations 

at each receptor is given. If the ISOR option 

is selected, a source contribution table for 

every hour will be printed. 

(4) A separate output file of predicted (1- 

hour only) concentrations (‘‘CONC’’) is 

written if the user chooses this option. Three 

forms of output are possible: 

(i) A binary file of concentrations, one 

value for each receptor in the hourly 

sequence as run; 

(ii) A text file of concentrations, one value 

for each receptor in the hourly sequence as 

run; or 

(iii) A text file as described above, but with 

a listing of receptor information (names, 

positions, hill number) at the beginning of 

the file. 

(5) Hourly information provided to these 

files besides the concentrations themselves 

includes the year, month, day, and hour 

information as well as the receptor number 

with the highest concentration. 

d. Type of Model 

CTDMPLUS is a refined steady-state, point 

source plume model for use in all stability 

conditions for complex terrain applications. 

e. Pollutant Types 

CTDMPLUS may be used to model 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. 

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. Environmental 

Protection Agency, Research Triangle 

Park, NC. 

Paumier, J.O., S.G. Perry and D.J. Burns, 

1990. An Analysis of CTDMPLUS Model 

Predictions with the Lovett Power Plant 

Data Base. Environmental Protection 

Agency, Research Triangle Park, NC. 

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45386 Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules 

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, D.C. and S.R. Hanna, 1989. 

OCD: The Offshore and Coastal 

Dispersion Model, Version 4. Volume I: 

User’s Guide, and Volume II: 

Appendices. Sigma Research 

Corporation, Westford, MA. http://

www.epa.gov/ttn/scram/dispersion_

prefrec.htm#ocd. (NTIS Nos. PB 93– 

144384 and PB 93–144392) 

Availability 

The model codes and associated 

documentation are available on EPA’s 

SCRAM Web site (paragraph A.0(3)). Official 

contact at Minerals Management Service: Mr. 

Dirk Herkhof, Parkway Atrium Building, 381 

Elden Street, Herndon, VA 20170, Phone: 

(703) 787–1735. 

Abstract 

(1) OCD is a straight-line Gaussian model 

developed to determine the impact of 

offshore emissions from point, area or line 

sources on the air quality of coastal regions. 

OCD incorporates overwater plume transport 

and dispersion as well as changes that occur 

as the plume crosses the shoreline. Hourly 

meteorological data are needed from both 

offshore and onshore locations. These 

include water surface temperature, overwater 

air temperature, mixing height, and relative 

humidity. 

(2) Some of the key features include 

platform building downwash, partial plume 

penetration into elevated inversions, direct 

use of turbulence intensities for plume 

dispersion, interaction with the overland 

internal boundary layer, and continuous 

shoreline fumigation. 

a. Recommendations for Regulatory Use 

OCD has been recommended for use by the 

Minerals Management Service for emissions 

located on the Outer Continental Shelf (50 FR 

12248; 28 March 1985). OCD is applicable for 

overwater sources where onshore receptors 

are below the lowest source height. Where 

onshore receptors are above the lowest 

source height, offshore plume transport and 

dispersion may be modeled on a case-by-case 

basis in consultation with the appropriate 

reviewing authority (paragraph 3.0(b)). 

b. Input Requirements 

(1) Source data: Point, area or line source 

location, pollutant emission rate, building 

height, stack height, stack gas temperature, 

stack inside diameter, stack gas exit velocity, 

stack angle from vertical, elevation of stack 

base above water surface and gridded 

specification of the land/water surfaces. As 

an option, emission rate, stack gas exit 

velocity and temperature can be varied 

hourly. 

(2) Meteorological data (over water): Wind 

direction, wind speed, mixing height, relative 

humidity, air temperature, water surface 

temperature, vertical wind direction shear 

(optional), vertical temperature gradient 

(optional), turbulence intensities (optional). 

(3) Meteorological data: 

Over land: Surface weather data from a 

preprocessor such as PCRAMMET which 

provides hourly stability class, wind 

direction, wind speed, ambient temperature, 

and mixing height are required. 

Over water: Hourly values for mixing 

height, relative humidity, air temperature, 

and water surface temperature are required; 

if wind speed/direction are missing, values 

over land will be used (if available); vertical 

wind direction shear, vertical temperature 

gradient, and turbulence intensities are 

optional. 

(4) 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) As in ISC, the basic plume rise 

algorithms are based on Briggs’ 

recommendations. 

(2) Momentum rise includes consideration 

of the stack angle from the vertical. 

(3) The effect of drilling platforms, ships, 

or any overwater obstructions near the source 

are used to decrease plume rise using a 

revised platform downwash algorithm based 

on laboratory experiments. 

(4) Partial plume penetration of elevated 

inversions is included using the suggestions 

of Briggs (1975) and Weil and Brower (1984). 

(5) Continuous shoreline fumigation is 

parameterized using the Turner method 

where complete vertical mixing through the 

thermal internal boundary layer (TIBL) 

occurs as soon as the plume intercepts the 

TIBL. 

h. Horizontal Winds 

(1) Constant, uniform wind is assumed for 

each hour. 

(2) Overwater wind speed can be estimated 

from overland wind speed using relationship 

of Hsu (1981). 

(3) Wind speed profiles are estimated using 

similarity theory (Businger, 1973). Surface 

layer fluxes for these formulas are calculated 

from bulk aerodynamic methods. 

i. Vertical Wind Speed 

Vertical wind speed is assumed equal to 

zero. 

j. Horizontal Dispersion 

(1) Lateral turbulence intensity is 

recommended as a direct estimate of 

horizontal dispersion. If lateral turbulence 

intensity is not available, it is estimated from 

boundary layer theory. For wind speeds less 

than 8 m/s, lateral turbulence intensity is 

assumed inversely proportional to wind 

speed. 

(2) Horizontal dispersion may be enhanced 

because of obstructions near the source. A 

virtual source technique is used to simulate 

the initial plume dilution due to downwash. 

(3) Formulas recommended by Pasquill 

(1976) are used to calculate buoyant plume 

enhancement and wind direction shear 

enhancement. 

(4) At the water/land interface, the change 

to overland dispersion rates is modeled using 

a virtual source. The overland dispersion 

rates can be calculated from either lateral 

turbulence intensity or Pasquill-Gifford 

curves. The change is implemented where 

the plume intercepts the rising internal 

boundary layer. 

k. Vertical Dispersion 

(1) Observed vertical turbulence intensity 

is not recommended as a direct estimate of 

vertical dispersion. Turbulence intensity 

should be estimated from boundary layer 

theory as default in the model. For very 

stable conditions, vertical dispersion is also 

a function of lapse rate. 

(2) Vertical dispersion may be enhanced 

because of obstructions near the source. A 

virtual source technique is used to simulate 

the initial plume dilution due to downwash. 

(3) Formulas recommended by Pasquill 

(1976) are used to calculate buoyant plume 

enhancement. 

(4) At the water/land interface, the change 

to overland dispersion rates is modeled using 

a virtual source. The overland dispersion 

rates can be calculated from either vertical 

turbulence intensity or the Pasquill-Gifford 

coefficients. The change is implemented 

where the plume intercepts the rising 

internal boundary layer. 

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. 

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Federal Register / Vol. 80, No. 145 / Wednesday, July 29, 2015 / Proposed Rules 45387 

n. Evaluation Studies 

DiCristofaro, D.C. and S.R. Hanna, 1989. 

OCD: The Offshore and Coastal 

Dispersion Model. Volume I: User’s 

Guide. Sigma Research Corporation, 

Westford, MA. 

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

J.E. Pleim, 1984. The Offshore and 

Coastal Dispersion (OCD) Model User’s 

Guide, Revised. OCS Study, MMS 84– 

0069. Environmental Research & 

Technology, Inc., Concord, MA. (NTIS 

No. PB 86–159803). 

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

Pleim and M. Baer, 1985. Development 

and Evaluation of the Offshore and 

Coastal Dispersion (OCD) Model. Journal 

of the Air Pollution Control Association, 

35: 1039–1047. 

Hanna, S.R. and D.C. DiCristofaro, 1988. 

Development and Evaluation of the 

OCD/API Model. Final Report, API Pub. 

4461, American Petroleum Institute, 

Washington, DC. 

[FR Doc. 2015–18075 Filed 7–28–15; 8:45 am] 

BILLING CODE 6560–50–P 

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