```

[Federal Register Volume 80, Number 145 (Wednesday, July 29, 2015)]
[Proposed Rules]
[Pages 45339-45387]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2015-18075]

[[Page 45339]]

Vol. 80

Wednesday,

No. 145

July 29, 2015

Part III

Environmental Protection Agency

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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

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

[[Page 45340]]

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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.

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SUMMARY:  In this action, the Environmental Protection Agency (EPA) 
proposes to revise the Guideline on Air Quality Models (``Guideline''). 
The Guideline has been incorporated into EPA's regulations, satisfying 
a requirement under the Clean Air Act (CAA) section 165(e)(3) 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

[[Page 45341]]

    7. Status of CALPUFF and Assessing Long-Range 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:

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                                                              NAICS \a\
                          Category                               Code
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Federal/state/territorial/local/tribal government..........       924110
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\a\ North American Industry Classification System.

B. 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

[[Page 45342]]

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.1 2 3 4 
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.
---------------------------------------------------------------------------

    \1\ U.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.
    \2\ U.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.
    \3\ U.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.
    \4\ U.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.
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    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.
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    \5\ AECOM, 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.
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    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

[[Page 45343]]

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.
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    \6\ U.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.
    \7\ U.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.
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    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 near-field 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.
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    \8\ Phase 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.
---------------------------------------------------------------------------

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/

[[Page 45344]]

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 non-
mandatory 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.

[[Page 45345]]

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 straight-line, 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.
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    \9\ U.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.
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    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 co-emissions 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.1 2 3 4. 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

[[Page 45346]]

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.
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    \10\ Chu, S.H. and E.L. Meyer, 1991. Use of Ambient Ratios to 
Estimate Impact of NOX Sources on Annual NO2 
Concentrations. Proceedings, 84th Annual Meeting & Exhibition of the 
Air & Waste Management Association, Vancouver, B.C.; 16-21 June 
1991. (16pp.) (Docket No. A-92-65, II-A-9).
    \11\ Podrez, M. 2015. An Update to the Ambient Ratio Method for 
1-h NO2 Air Quality Standards Dispersion Modeling. Atmospheric 
Environment, 103: 163-170.
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    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.
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    \12\ Cole, H.S. and J.E. Summerhays, 1979. A Review of 
Techniques Available for Estimation of Short-Term NO2 
Concentrations. Journal of the Air Pollution Control Association, 
29(8): 812-817.
    \13\ Hanrahan, P.L., 1999. The Polar Volume Polar Ratio Method 
for Determining NO2/NOX Ratios in Modeling--Part I: Methodology. 
Journal of the Air & Waste Management Association, 49: 1324-1331.
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    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\
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    \14\ Benson, Paul E., 1979. CALINE3--A Versatile Dispersion 
Model for Predicting Air Pollutant Levels Near Highways and Arterial 
Streets. Interim Report, Report Number FHWA/CA/TL-79/23. Federal 
Highway Administration, Washington, DC (NTIS No. PB 80-220841).
    \15\ 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.
    \16\ Heist, 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).
    \17\ U.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.
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    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 
well-documented 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.
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    \18\ U.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.
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    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.

[[Page 45347]]

    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, generally-applicable 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\
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    \19\ McMurry, P.H., Shepherd, M.F., Vickery, J.S., 2004. 
Particulate matter science for policy makers: A NARSTO assessment. 
Cambridge University Press.
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    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.
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    \20\ Baker, K.R., Kelly, J.T., 2014. Single source impacts 
estimated with photochemical model source sensitivity and 
apportionment approaches. Atmospheric Environment, 96: 266-274.
    \21\ ENVIRON, 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.
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    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
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    \22\ Zhou, 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.
    \23\ ENVIRON, 2014. User's Guide Comprehensive Air Quality Model 
with Extensions version 6, http://www.camx.com. ENVIRON 
International Corporation, Novato.
    \24\ Byun, 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.
    \25\ Chen, 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.
    \26\ Civerolo, 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.
    \27\ Russell, A.G., 2008. EPA Supersites program-related 
emissions-based particulate matter modeling: initial applications 
and advances. Journal of the Air & Waste Management Association, 58: 
289-302.
    \28\ Tesche, 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.
    \29\ Cai, 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.
    \30\ Hogrefe, 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.
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    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 two-tiered 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 peer-reviewed 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.
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    \31\ U.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.
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    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

[[Page 45348]]

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 secondarily-formed 
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
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    \32\ U.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.
    \33\ U.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.
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    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 case-specific 
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.
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    \34\ Baker, 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.
    \35\ Bergin, 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.
    \36\ Baker, K.R., Kelly, J.T., Fox, T., 2013. Estimating second 
pollutant impacts from single sources (control #27). http://aqmodels.awma.org/conference-proceedings.
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    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

[[Page 45349]]

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 single-source 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., source-receptor 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 long-range transport PSD increment assessments.
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    \37\ U.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.
    \38\ U.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.
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    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 near-field 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 near-field 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.
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    \39\ U.S. EPA, 2015. Interagency Workgroup on Air Quality 
Modeling Phase 3 Summary Report: Near-Field Single Source Secondary 
Impacts. Publication No. EPA 454/P-15-002. Office of Air Quality 
Planning & Standards, Research Triangle Park, North Carolina 27711.
    \40\ U.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.
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    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 source-receptor relationships in the 
near-field 20 21 and over long distances.\41\
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    \41\ ENVIRON, 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.
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    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

[[Page 45350]]

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 long-range 
transport of inert pollutants: 1980 Great Plains Mesoscale Tracer Field 
Experiment, the 1983 Cross-Appalachian 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).
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    \42\ Hegarty, 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.
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    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.
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    \43\ See 70 FR 39104, 39122-23 (July 6, 2005).
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    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 long-range 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 short-term 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

[[Page 45351]]

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.
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    \44\ U.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.
    \45\ U.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.
    \46\ U.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.
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    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 multi-source 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

[[Page 45352]]

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.
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    \47\ U.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.
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    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

[[Page 45353]]

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 two-tiered 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.

[[Page 45354]]

    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

[[Page 45355]]

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

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

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

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

APPENDIX W TO PART 51--Guideline on Air Quality Models Preface

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

[[Page 45356]]

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),1 2 3 conformity analyses,\4\ and other air quality 
assessments required under EPA regulation. Applicable only to 
criteria air pollutants, the Guideline is intended for use by the 
EPA Regional Offices in judging the adequacy of modeling analyses 
performed by the EPA, by state, local, and tribal permitting 
authorities, and by industry. It is appropriate for use by other 
federal government agencies and by state, local, and tribal agencies 
with air quality and land management responsibilities. The Guideline 
serves to identify, for all interested parties, those modeling 
techniques and databases that the EPA considers acceptable. The 
Guideline is not intended to be a compendium of modeling techniques. 
Rather, it should serve as a common measure of acceptable technical 
analysis when supported by sound scientific judgment.
    b. Air quality measurements \5\ are routinely used to 
characterize ambient concentrations of criteria pollutants 
throughout the nation but are rarely sufficient for characterizing 
the ambient impacts of individual sources or demonstrating adequacy 
of emissions limits for an existing source due to limitations in 
spatial and temporal coverage of ambient monitoring networks. The 
impacts of new sources that do not yet exist and modifications to 
existing sources that have yet to be implemented can only be 
determined through modeling. Thus, models have become a primary 
analytical tool in most air quality assessments. Air quality 
measurements can be used in a complementary manner to air quality 
models, with due regard for the strengths and weaknesses of both 
analysis techniques, and are particularly useful in assessing the 
accuracy of model estimates.
    c. It would be advantageous to categorize the various regulatory 
programs and to apply a designated model to each proposed source 
needing analysis under a given program. However, the diversity of 
the nation's topography and climate, and variations in source 
configurations and operating characteristics dictate against a 
strict modeling ``cookbook.'' There is no one model capable of 
properly addressing all conceivable situations even within a broad 
category such as point sources. Meteorological phenomena associated 
with threats to air quality standards are rarely amenable to a 
single mathematical treatment; thus, case-by-case analysis and 
judgment are frequently required. As modeling efforts become more 
complex, it is increasingly important that they be directed by 
highly competent individuals with a broad range of experience and 
knowledge in air quality meteorology. Further, they should be 
coordinated closely with specialists in emissions characteristics, 
air monitoring and data processing. The judgment of experienced 
meteorologists, atmospheric scientists, and analysts is essential.
    d. The model that most accurately estimates concentrations in 
the area of interest is always sought. However, it is clear from the 
needs expressed by the EPA Regional Offices, by state, local, and 
tribal agencies, by many industries and trade associations, and also 
by the deliberations of Congress that consistency in the selection 
and application of models and databases should also be sought, even 
in case-by-case analyses. Consistency ensures that air quality 
control agencies and the general public have a common basis for 
estimating pollutant concentrations, assessing control strategies, 
and specifying emissions limits. Such consistency is not, however, 
promoted at the expense of model and database accuracy. The 
Guideline provides a consistent basis for selection of the most 
accurate models and databases for use in air quality assessments.
    e. Recommendations are made in the Guideline concerning air 
quality models and techniques, model evaluation procedures, and 
model input databases and related requirements. The guidance 
provided here should be followed in air quality analyses relative to 
SIPs, NSR, and in supporting analyses required by the EPA and by 
state, local, and tribal permitting authorities. Specific models are 
identified for particular applications. The EPA may approve the use 
of an alternative model or technique that can be demonstrated to be 
more appropriate than those recommended in the Guideline. In all 
cases, the model or technique applied to a given situation should be 
the one that provides the most accurate representation of 
atmospheric transport, dispersion, and chemical transformations in 
the area of interest. However, to ensure consistency, deviations 
from the Guideline should be carefully documented as part of the 
public record and fully supported by the appropriate reviewing 
authority, as discussed later.
    f. From time to time, situations arise requiring clarification 
of the intent of the guidance on a specific topic. Periodic 
workshops are held with EPA headquarters, EPA Regional 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.

[[Page 45357]]

    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 permit-enforceable emissions, the greater the ability 
to evaluate the source impact and to distinguish the effects of 
various control strategies.
    d. There are three types of models that have historically been 
used in the regulatory demonstrations applicable in the Guideline, 
each having strengths and weaknesses that lend themselves to 
particular regulatory applications.
    i. Gaussian plume models use a ``steady-state'' approximation, 
which assumes that over the model time step, the emissions, 
meteorology and other model inputs, are constant throughout the 
model domain, resulting in a resolved plume with the emissions 
distributed throughout the plume according to a Gaussian 
distribution. This formulation allows Gaussian models to estimate 
near-field impacts of a limited number of sources at a relatively 
high resolution, with temporal scales of an hour and spatial scales 
of meters. However, this formulation allows for only relatively 
inert pollutants, with very limited considerations of transformation 
and removal (e.g., deposition), and further limits the domain for 
which the model may be used. Thus, Gaussian models may not be 
appropriate if model inputs are changing sharply over the model time 
step or within the desired model domain or if more advanced 
considerations of chemistry are needed.
    ii. Lagrangian puff models, on the other hand, are non-steady-
state, and assume that model input conditions are changing over the 
model domain and model time step. Lagrangian models can also be used 
to determine near and far-field impacts from a limited number of 
sources 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 three-
dimensional photochemical grid model.
    iii. Photochemical grid models are three-dimensional Eulerian 
grid-based models that treat chemical and physical processes in each 
grid cell and use diffusion and transport processes to move chemical 
species between grid cells.9 Eulerian models assume that 
emissions are spread evenly throughout each model grid cell. 
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

[[Page 45358]]

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 source-specific air quality impacts. 
In such situations, a screening technique or reduced-form model may 
be viable options for estimating source impacts.
    i. Screening techniques are differentiated from a screening 
model in that screening techniques are approaches that make 
simplified and conservative assumptions about the physical and 
chemical atmospheric processes important to determining source 
impacts while screening models make assumptions about conservative 
inputs to a specific model. The complexity of screening techniques 
ranges from simplified assumptions of chemistry applied to refined 
or screening model output to sophisticated approximations of the 
chemistry applied within a refined model.
    ii. Reduced-form models are computationally efficient simulation 
tools for characterizing the pollutant response to specific types of 
emission reductions for a particular geographic area or background 
environmental conditions that reflect underlying atmospheric science 
of a refined model but reduce the computational resources of running 
a complex, numerical air quality model such as a photochemical grid 
model.

In such situations, an attempt should be made to acquire or improve 
the necessary databases and to develop appropriate analytical 
techniques, but the screening technique or reduced-form model may be 
sufficient in conducting regulatory modeling applications when 
applied in consultation with the EPA Regional Office.
    e. Consistent with the general principle described in paragraph 
2.2(a), the EPA may establish a demonstration tool or method as a 
sufficient means for a user or applicant to make a demonstration 
required by regulation, either by itself or as part of a modeling 
demonstration. To be used for such regulatory purposes, such a tool 
or method must be reflected in a codified regulation or have a well-
documented technical basis and reasoning that is contained or 
incorporated in the record of the regulatory decision in which it is 
applied.

2.3 Availability of Models

    a. For most of the screening and refined models discussed in the 
Guideline, codes, associated documentation and other useful 
information are publicly available for download from the EPA's 
Support Center for Regulatory Atmospheric Modeling (SCRAM) 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

[[Page 45359]]

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 multi-
tiered approach for making a demonstration required under PSD or 
another CAA program. The initial tier or tiers may involve use of 
demonstration tools, screening models, screening techniques, or 
reduced-form models; while the last tier may involve the use of 
demonstration tools, 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 decision-making on the acceptability of an alternative 
model for a given regulatory application. These objective procedures 
may be used for conducting both the technical evaluation of the 
model and the field test or performance evaluation.
    b. This subsection discusses the use of alternate models and 
defines three situations when alternative models may be used. This 
subsection also provides a procedure for implementing 40 CFR 
51.166(l)(2) in PSD permitting. This provision requires written 
approval of the Administrator for any modification or substitution 
of an applicable model. An applicable model for purposes of 40 CFR 
51.166(l) is a preferred model in 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

[[Page 45360]]

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
---------------------------------------------------------------------------

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

    v. A protocol on methods and procedures to be followed has been 
established.
    f. To formally document that the requirements of section 3.2 for 
use of an alternative model are satisfied for a particular 
application or range of applications, a memorandum will be prepared 
by the EPA's Model Clearinghouse through a consultative process with 
the 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 ground-level concentrations at a given hour for a point 
source in flat terrain could be in error by 50 percent due to these 
uncertainties. Errors of 5 to 10 degrees in the measured wind 
direction can result in concentration errors of 20 to 70 percent for 
a particular time and location, depending on stability and station 
location. Such uncertainties do not indicate that an estimated 
concentration does not occur, only that the precise time and 
locations are in doubt. Composite errors in

[[Page 45361]]

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 near-field at a nominal distance 
of 50 km or less. Near-field application is consistent with 
capabilities of Gaussian plume models and, based on the EPA's 
assessment, is sufficient to address whether a source will cause or 
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 multi-source situations using 
screening meteorology or other conservative model assumptions. 
However, the appropriate reviewing authority (paragraph 3.0(b)) 
shall be consulted, and concurrence obtained, on the protocol for 
modeling multiple sources with AERSCREEN or CTSCREEN to ensure that 
the worst case is identified and assessed.
    d. As discussed in section 4.2.3.4, there are also screening 
techniques built into AERMOD that use simplified or limited 
chemistry assumptions for determining the partitioning of NO and 
NO2 for NO2 modeling. These screening 
techniques are part of the EPA's preferred modeling approach for 
NO2 and do not need to be approved as an alternative 
model. However, as with other screening models and techniques, their 
usage shall occur in agreement with the appropriate reviewing 
authority (paragraph 3.0(b)).
    e. 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 command-prompt 
interface.
    b. The MAKEMET program generates a matrix of meteorological 
conditions, in the form of AERMOD-ready surface and profile files, 
based on user-specified surface characteristics, ambient 
temperatures, minimum wind speed, and anemometer height. The 
meteorological matrix is generated based on looping through a range 
of wind speeds, cloud covers, ambient temperatures, solar elevation 
angles, and convective velocity scales (w*, for convective 
conditions only) based on user-specified surface characteristics 
(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 source-receptor-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 ([sigma]v, 
[sigma]w), vertical potential temperature gradient (d[thgr]/dz), 
friction velocity (u*), Monin-Obukhov length (L), mixing height 
(zi) as a function of terrain height, and wind directions 
for both neutral/stable conditions and unstable convective 
conditions. The maximum concentration output from CTSCREEN 
represents a worst-case 1-hour concentration. Time-scaling factors 
of 0.7 for 3-hour, 0.15 for 24-hour and 0.03 for annual 
concentration averages are applied internally by CTSCREEN to the 
highest 1-hour concentration calculated by the model.

4.2.1.3 Screening in Complex Terrain

    a. For applications utilizing AERSCREEN, AERSCREEN automatically 
generates a polar-grid receptor network with spacing determined by 
the maximum distance to model. If the application warrants a 
different receptor network than that generated by AERSCREEN, it may 
be necessary to run AERMOD in screening mode with a user-defined 
network. For CTSCREEN applications or AERMOD in screening mode 
outside of AERSCREEN, placement of receptors requires very careful 
attention when modeling in complex terrain. Often the highest 
concentrations are predicted to occur under very stable conditions, 
when the plume is near, or impinges on, the terrain. 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 
well-known dividing-streamline concept in a simplified simulation of 
the effects of plume-terrain interactions.
    b. The AERMOD modeling system has been extensively evaluated 
across a wide range of scenarios based on numerous field studies, 
including tall stacks in flat and complex terrain settings, sources 
subject to building downwash influences, and low-level non-buoyant 
sources.\27\ These evaluations included several long-term field 
studies associated with operating plants as well as several 
intensive tracer studies. Based on these evaluations, AERMOD has 
shown consistently good performance, with ``errors'' in predicted 
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 post-processor 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 co-emit NO along with NO2 and 
any emitted NO may react with ambient ozone to convert to additional 
NO2 downwind. Thus, comprehensive modeling of 
NO2 would need to consider the ratio of emitted NO and 
NO2, the ambient levels of ozone and subsequent reactions 
between ozone and NO, and the photolysis of NO2 to NO.
    b. Due to the complexity of NO2 modeling, a multi-
tiered 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 in-stack 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 components\58\. 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, 
ground-level ozone and a portion of particulate matter with 
aerodynamic diameter less than 2.5 [mu] 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 build-up of precursors and 
subsequent ozone formation or destruction.
    c. PM2.5 can be either primary (i.e., emitted 
directly from sources) or secondary in nature. The fraction of 
PM2.5 which is primary versus secondary varies by 
location and season. In the United States, PM2.5 is 
dominated by a variety of chemical species or components of 
atmospheric particles, such as ammonium sulfate, ammonium nitrate, 
organic carbon (OC) mass, elemental carbon (EC), and other soil 
compounds and oxidized metals. PM2.5

[[Page 45365]]

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

[[Page 45366]]

(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 case-by-case basis.
    c. The second tier of assessment for secondary PM2.5 
impacts involves those situations where existing technical 
information is not available 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-
by-case 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

[[Page 45367]]

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/multi-source 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/Environmental-Studies/Gulf-of-Mexico-Region/Approved-Air-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.

[[Page 45368]]

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/km\2\, 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 short- lived 
at a given receptor. There are no recommended refined techniques to 
model this phenomenon. There are, however, screening procedures \40\ 
that may be used to approximate the concentrations. Considerable 
care should be exercised in using the results obtained from the 
screening techniques.
    ii. Shoreline fumigation. Fumigation can be an important 
phenomenon on and near the shoreline of bodies of water. This can 
affect both individual plumes and area-wide emissions. When 
fumigation conditions are expected to occur from a source or sources 
with tall stacks located on or just inland of a shoreline, this 
should be addressed in the air quality modeling analysis. 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 low- level emissions sources, 
tends to be minimized, potentially leading to relatively high 
ground-level concentrations. If point sources are of interest, users 
should note the guidance provided in paragraph (a) of this 
subsection. Selection of the appropriate model for applications 
where stagnation is of concern should be determined in consultation 
with the appropriate reviewing authority (paragraph 3.0(b)).

7.2.1.3 Gravitational Settling and Deposition

    a. Gravitational settling and deposition may be directly 
included in a model if either is a significant factor. When 
particulate matter sources can be quantified and settling and dry 
deposition are problems, 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

[[Page 45369]]

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 re-emitted to the far 
wake as a ground-level volume source.
    c. Stack tip downwash generally occurs with poorly constructed 
stacks and when the ratio of the stack exit velocity to wind speed 
is small. An algorithm developed by Briggs \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 
near-source 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

[[Page 45370]]

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\
----------------------------------------------------------------------------------------------------------------
                                                                                              Operating factor
          Averaging time            Emissions limit  (lb/   x   Operating level  (lb/   x     (e.g., hr/yr. hr/
                                          MMBtu) \2\                  MMBtu) \2\                    day)
----------------------------------------------------------------------------------------------------------------
  Stationary Point Source(s) Subject to SIP Emissions Limit(s) Evaluation for Compliance With Ambient Standards
                                       (Including Areawide Demonstrations)
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................  Maximum allowable           Actual or design            Actual operating
                                     emission limit or           capacity (whichever         factor averaged
                                     federal enforceable         is greater), or             over the most
                                     permit limit.               federally permit            recent 2 years.\3\
                                                                 enforceable permit
                                                                 condition.

[[Page 45371]]

 
Short term (throughput) may be used for
  other types of sources.
\3\ Unless it is determined that this period is not representative.
\4\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
  causing the highest concentration.
\5\ If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24-hours) and the
  source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the
  modeled emission rate may be made (e.g., if operation is only 8 a.m. to 4 p.m. each day, only these hours will
  be modeled with emissions from the source. Modeled emissions should not be averaged across non-operating
\6\ See Section 8.3.3.
\7\ Temporally representative operating level could be based on Continuous Emissions Monitoring (CEM) data or
  other information and should be determined through consultation with the appropriate reviewing authority
  (Paragraph 3.0(b)).
\8\ For those permitted sources not in operation or that have not established an appropriate factor, continuous
  operation (i.e., 8760) should be used.
\9\ See Section 8.3.2.

             Table 8-2--Point Source Model Emission Input for NAAQS Compliance in PSD Demonstrations
----------------------------------------------------------------------------------------------------------------
                                                                                              Operating factor
          Averaging time            Emissions limit  (lb/   x   Operating level  (lb/   x     (e.g., hr/yr. hr/
                                          MMBtu) \1\                  MMBtu) \2\                    day)
----------------------------------------------------------------------------------------------------------------
                                      Proposed Major New or Modified Source
----------------------------------------------------------------------------------------------------------------
Annual & quarterly................  Maximum allowable           Design capacity or          Continuous operation
                                     emission limit or           federally                   (i.e., 8760
                                     federal enforceable         enforceable permit          hours).\2\
                                     permit limit.               condition.
Short term (throughput) may be used for
  other types of sources.

[[Page 45372]]

 
\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
\3\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
  causing the highest concentration.
\4\ Includes existing facility to which modification is proposed if the emissions from the existing facility
  will not be affected by the modification. Otherwise use the same parameters as for major modification.
\5\ See Section 8.3.3.
\6\ Unless it is determined that this period is not representative.
\7\ Temporally representative operating level could be based on Continuous Emissions Monitoring (CEM) data or
  other information and should be determined through consultation with the appropriate reviewing authority
  (Paragraph 3.0(b)).
\8\ For those permitted sources not in operation or that have not established an appropriate factor, continuous
  operation (i.e., 8760) should be used.
\9\ See Section 8.3.2.

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, regional-scale photochemical grid 
modeling results.
    b. The monitoring network used for developing background 
concentrations is expected to conform to the same quality assurance 
and other requirements as those networks established for PSD 
purposes.\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[deg] sector 
downwind of the source may be used to determine the area of impact.
    ii. There may be other circumstances which would necessitate 
modifications to the ambient data record. Such cases could include 
removal of data from specific days or hours when a monitor is being 
impacted 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

[[Page 45373]]

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 short-term 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 double-counting of modeled 
and monitored contributions.

8.4 Meteorological Input Data

8.4.1 Discussion

    a. This subsection covers meteorological input data for use in 
dispersion modeling for regulatory applications and is separate from 
recommendations made for photochemical grid modeling. 
Recommendations for meteorological data for photochemical grid 
modeling applications are outlined in the latest version of EPA's 
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 year-to-year variations in 
weather conditions. Where appropriate, data representativeness 
should be viewed in terms of the appropriateness of the data for 
constructing realistic boundary layer profiles and, where 
applicable, three-dimensional meteorological fields, as described in 
paragraphs (c) and (d) of this subsection.
    c. The meteorological data should be adequately representative 
and may be site-specific data, 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 site-specific measurement program. State climatology 
offices, local universities, FAA, military stations, industry and 
pollution control agencies may also be sources of such data. In 
specific cases, prognostic meteorological data may be appropriate 
for use and obtained from similar sources. Some

[[Page 45374]]

recommendations and requirements for the use of each type of data 
are included in this subsection.

8.4.2 Recommendations and Requirements

    a. AERMET \95\ 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: PCRAMMET 
\97\, 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 PCRAMMET \97\ 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 site-
specific, 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.
---------------------------------------------------------------------------

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

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\

[[Page 45375]]

    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 site-specific measurements are 
frequently made ``on-property'' (i.e., on the source's premises), 
acquisition of adequately representative site-specific data does not 
preclude collection of data from a location off property. 
Conversely, collection of meteorological data on a source's property 
does not of itself guarantee adequate representativeness. For help 
in determining representativeness of site-specific measurements, 
technical guidance \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 [sigma]A and 
[sigma]E (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 [sigma]A or 
[sigma]E).
    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 site-specific wind measurements are used, 
AERMOD, at a minimum, requires wind observations at a height above 
ground between seven times the local surface roughness height and 
100 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 
[sigma][thetas], based on four 15-minuite values, to 
minimize ``double counting'' of plume spread associated with 
meander. The calculation of hourly [sigma][thetas] 
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 near-surface measurements of wind speed with 
subjectively determined insolation assessments based on hourly cloud 
cover and ceiling height observations. The wind speed measurements 
are made at or near 10m. The insolation rate is typically assessed 
using observations of cloud cover and ceiling height based on 
criteria outlined by Turner.\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

[[Page 45376]]

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 [sigma]E and 
[sigma]A 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 [sigma]E and 
[sigma]A 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.

[[Page 45377]]

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 case-by-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

[[Page 45378]]

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 multi-stage approach that includes the following two 
stages:
    i. The first stage is referred to as a single-source 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 increment-consuming 
and increment-expanding sources, rather than using past modeling 
analyses of those sources as part of background concentration. This 
would permit the use of newly acquired data or improved modeling 
techniques if such data and/or techniques have become available 
since the last source was permitted.

9.2.3.1 Considerations in Developing Emissions Limits

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

9.2.4 Use of Measured Data in Lieu of Model Estimates

    a. As described throughout the Guideline, modeling is the 
preferred method for demonstrating compliance with the NAAQS and PSD 
increments and for determining the most appropriate emissions limits 
for new and existing sources. When a preferred model or adequately 
justified and approved alternative model is available, model 
results, including the appropriate background, are sufficient for 
air quality demonstrations and establishing emissions limits, if 
necessary. In instances when the modeling technique available is 
only a screening technique, the addition of air quality monitoring 
data to the analysis may lend credence to the model results. 
However, air quality monitoring data alone will normally not be 
acceptable as the sole basis for demonstrating compliance with the 
NAAQS and PSD increments or for determining emissions limits.
    b. There may be rare circumstances where the performance of the 
preferred air quality model will be shown to be less than reasonably 
acceptable when compared with air quality monitoring data measured 
in the vicinity of an existing source. Additionally, there may not 
be an applicable preferred air quality model, screening technique, 
or justifiable alternative model suitable for the situation. In 
these unique instances, there may be the possibility of establishing 
emissions limits and demonstrating compliance with the NAAQS and PSD 
increments solely on the basis of analysis of observed air quality 
data in lieu of an air quality modeling analysis. However, only in 
the case of a modification to an existing source should air quality 
monitoring data alone be a basis for determining adequate emissions 
limits or for demonstration that the modification will not cause or 
contribute to a violation of any NAAQS or PSD increment.
    c. The following items should be considered prior to the 
acceptance of an analysis of measured air quality data as the sole 
basis for an air quality demonstration or determining an emissions 
limit:
    i. Does a monitoring network exist for the pollutants and 
averaging times of concern in the vicinity of the existing source?
    ii. Has the monitoring network been designed to locate points of 
maximum concentration?
    iii. Do the monitoring network and the data reduction and 
storage procedures meet EPA monitoring and quality assurance 
requirements?
    iv. Do the dataset and the analysis allow impact of the most 
important individual sources to be identified if more than one 
source or emission point is involved?
    v. Is at least one full year of valid ambient data available?
    vi. Can it be demonstrated through the comparison of monitored 
data with model results that available air quality models and 
techniques are not applicable?
    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

[[Page 45379]]

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.

10.0 References

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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.

[[Page 45383]]

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 pre-processor codes: AERMET 
processes meteorological data for input to AERMOD, and AERMAP 
processes terrain elevation data and generates receptor and hill 
height information for input to AERMOD.

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 steady- state 
assumptions are appropriate, up to 50km;
     1-hour to annual averaging times; and
     Continuous toxic air emissions.
    (2) For regulatory applications of AERMOD, the regulatory 
default option should be set, i.e., the parameter DFAULT should be 
employed in the MODELOPT record in the Control Pathway. The DFAULT 
option requires the use of terrain elevation data, stack-tip 
downwash, sequential date checking, and does not permit the use of 
the model in the SCREEN mode. In the regulatory default mode, 
pollutant half-life or 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 
site-specific 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 user-specified 
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

[[Page 45384]]

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 re-emitted to 
the far wake as a volume source.
    (4) For elevated terrain, AERMOD incorporates the concept of the 
critical dividing streamline height, in which flow below this height 
remains horizontal, and flow above this height tends to rise up and 
over terrain (Snyder et al., 1985). Plume concentration estimates 
are the weighted sum of these two limiting plume states. However, 
consistent with the steady-state assumption of uniform horizontal 
wind direction over the modeling domain, straight-line plume 
trajectories are assumed, with adjustment in the plume/receptor 
geometry used to account for the terrain effects.

h. Horizontal Winds

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

i. Vertical Wind Speed

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

j. Horizontal Dispersion

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

k. Vertical Dispersion

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

l. Chemical Transformation

    Chemical transformations are generally not treated by AERMOD. 
However, AERMOD does contain an option to treat chemical 
transformation using simple exponential decay, although this option 
is typically not used in regulatory applications, except for sources 
of sulfur dioxide in urban areas. Either a decay coefficient or a 
half-life is input by the user. Note also that the 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 Woodward-Clyde Consultants, Lexington, Massachusetts, 
for American Petroleum Institute, Washington, DC, 20005-4070.
Brode, R.W., 2002. Implementation and Evaluation of PRIME in AERMOD. 
Preprints of the 12th Joint Conference on Applications of Air 
Pollution Meteorology, May 20-24, 2002; American Meteorological 
Society, Boston, MA.
Brode, R.W., 2004. Implementation and Evaluation of Bulk Richardson 
Number Scheme in AERMOD. 13th Joint Conference on Applications of 
Air Pollution Meteorology, August 23-26, 2004; American 
Meteorological Society, Boston, MA.
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 inter-comparison. 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

[[Page 45385]]

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 [sigma]y & [sigma]z values, both flat terrain and hill 
induced (the difference shows the effect of the hill)
--Concentration components due to WRAP, LIFT and FLAT

    (3) If the user selects the TOPN option, a summary table of the 
top four concentrations at each receptor is given. If the ISOR 
option is selected, a source contribution table for every hour will 
be printed.
    (4) A separate output file of predicted (1-hour only) 
concentrations (``CONC'') is written if the user chooses this 
option. Three forms of output are possible:
    (i) A binary file of concentrations, one value for each receptor 
in the hourly sequence as run;
    (ii) A text file of concentrations, one value for each receptor 
in the hourly sequence as run; or
    (iii) A text file as described above, but with a listing of 
receptor information (names, positions, hill number) at the 
beginning of the file.
    (5) Hourly information provided to these files besides the 
concentrations themselves includes the year, month, day, and hour 
information as well as the receptor number with the highest 
concentration.

d. Type of Model

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

e. Pollutant Types

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

f. Source-Receptor Relationship

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

g. Plume Behavior

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

h. Horizontal Winds

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

i. Vertical Wind Speed

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

j. Horizontal Dispersion

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

k. Vertical Dispersion

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

l. Chemical Transformation

    Chemical transformation is not treated by CTDMPLUS.

m. Physical Removal

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

n. Evaluation Studies

Burns, D.J., L.H. Adams and S.G. Perry, 1990. Testing and Evaluation 
of the CTDMPLUS Dispersion Model: Daytime Convective Conditions. 
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.

[[Page 45386]]

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.

[[Page 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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