Document ID: EPA-HQ-OAR-2015-0310-0154
Agency: epa
Document Type: Rule
Title: Guideline on Air Quality Models: Enhancements to AERMOD Dispersion Modeling System and Incorporation of Approaches to Address Ozone and Fine Particulate Matter
Posted Date: 2017-01-17T05:00Z

[Federal Register Volume 82, Number 10 (Tuesday, January 17, 2017)]
[Rules and Regulations]
[Pages 5182-5235]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2016-31747]

[[Page 5181]]

Vol. 82

Tuesday,

No. 10

January 17, 2017

Part IV

 Environmental Protection Agency

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 40 CFR Part 51

 Revisions 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; Final Rule

  Federal Register / Vol. 82 , No. 10 / Tuesday, January 17, 2017 / 
Rules and Regulations  

[[Page 5182]]

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ENVIRONMENTAL PROTECTION AGENCY

40 CFR Part 51

[EPA-HQ-OAR-2015-0310; FRL-9956-23-OAR]
RIN 2060-AS54

Revisions 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: Final rule.

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SUMMARY: In this action, the Environmental Protection Agency (EPA) 
promulgates revisions to the Guideline on Air Quality Models 
(``Guideline''). The Guideline provides EPA's preferred models and 
other recommended techniques, as well as guidance for their use in 
estimating ambient concentrations of air pollutants. It is incorporated 
into the EPA's regulations, satisfying a requirement under the Clean 
Air Act (CAA) for the EPA to specify with reasonable particularity 
models to be used in the Prevention of Significant Deterioration (PSD) 
program. This action includes enhancements to the formulation and 
application of the EPA's preferred near-field dispersion modeling 
system, AERMOD (American Meteorological Society (AMS)/EPA Regulatory 
Model), 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. The EPA is changing the preferred status of and 
removing several air quality models from appendix A of the Guideline. 
The EPA is also making various editorial changes to update and 
reorganize information throughout the Guideline to streamline the 
compliance assessment process.

DATES: This rule is effective February 16, 2017. For all regulatory 
applications covered under the Guideline, except for transportation 
conformity, the changes to the appendix A preferred models and 
revisions to the requirements and recommendations of the Guideline must 
be integrated into the regulatory processes of respective reviewing 
authorities and followed by applicants by no later than January 17, 
2018. During the 1-year period following promulgation, protocols for 
modeling analyses based on the 2005 version of the Guideline, which are 
submitted in a timely manner, may be approved at the discretion of the 
appropriate reviewing authority.
    This final rule also starts a 3-year transition period that ends on 
January 17, 2020 for transportation conformity purposes. Any refined 
analyses that are started before the end of this 3-year period, with a 
preferred appendix A model based on the 2005 version of the Guideline, 
can be completed after the end of the transition period, similar to 
implementation of the transportation conformity grace period for new 
emissions models. See the discussion in section IV.A.4 of this preamble 
for details on how this transition period will be implemented.
    All applicants are encouraged to consult with their respective 
reviewing authority as soon as possible to assure acceptance of their 
modeling protocols and/or modeling demonstration during either of these 
periods.

ADDRESSES: The EPA has established a docket for this action under 
Docket ID No. EPA-HQ-OAR-2015-0310. All documents in the docket are 
listed on the https://www.regulations.gov Web site. Although listed in 
the index, some information is not publicly available, e.g., 
Confidential Business Information (CBI) or other information whose 
disclosure is restricted by statute. Certain other material, such as 
copyrighted material, is not placed on the Internet and will be 
publicly available only in hard copy form. Publicly available docket 
materials are available electronically through https://www.regulations.gov.

FOR FURTHER INFORMATION CONTACT: Mr. George M. Bridgers, 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. Where can I get a copy of this rule and related information?
    C. Judicial Review
    D. List of Acronyms
II. Background
III. The Tenth and Eleventh Conferences on Air Quality Modeling and 
Public Hearing
IV. Discussion of Public Comments on the Proposed Changes to the 
Guideline
    A. Final Action
    1. Clarifications To Distinguish Requirements From 
Recommendations
    2. Updates to EPA's AERMOD Modeling System
    3. Status of AERSCREEN
    4. Status of CALINE3 Models
    5. Addressing Single-Source Impacts on Ozone and Secondary 
PM2.5
    6. Status of CALPUFF and Assessing Long-Range Transport for PSD 
Increments and Regional Haze
    7. Role of EPA's Model Clearinghouse (MCH)
    8. Updates to Modeling Procedures for Cumulative Impact Analysis
    9. Updates on Use of Meteorological Input Data for Regulatory 
Dispersion Modeling
    B. Final Editorial Changes
    1. Preface
    2. Section 1
    3. Section 2
    4. Section 3
    5. Section 4
    6. Section 5
    7. Section 6
    8. Section 7
    9. Section 8
    10. Section 9
    11. Section 10
    12. Section 11
    13. Section 12
    14. Appendix A to the Guideline
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 (PRA)
    C. Regulatory Flexibility Act (RFA)
    D. Unfunded Mandates Reform Act (UMRA)
    E. Executive Order 13132: Federalism
    F. Executive Order 13175: Consultation and Coordination With 
Indian Tribal Governments
    G. Executive Order 13045: Protection of Children From 
Environmental Health 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
    K. Congressional Review Act (CRA)

I. General Information

A. Does this action apply to me?

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

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

B. Where can I get a copy of this rule and related information?

    In addition to being available in the docket, electronic copies of 
the rule and related materials will also be available on the Worldwide 
Web (WWW) through the EPA's Support Center for Regulatory Atmospheric 
Modeling (SCRAM) Web site at https://www.epa.gov/scram.

C. Judicial Review

    This final rule is nationally applicable, as it revises the 
Guideline on Air Quality Models, 40 CFR part 51, appendix W. Under 
section 307(b)(1) of the Clean Air Act (CAA), judicial review of this 
final rule is available by filing a petition for review in the U.S. 
Court of Appeals for the District of Columbia Circuit by March 20, 
2017. Moreover, under section 307(b)(2) of the CAA, the requirements 
established by this action may not be challenged separately in any 
civil or criminal proceedings brought by the EPA to enforce these 
requirements. This rule is also subject to section 307(d) of the CAA.

D. List of Acronyms

AEDT Aviation Environmental Design Tool
AERMET Meteorological data preprocessor for AERMOD
AERMINUTE Pre-processor to AERMET to read 1-minute ASOS data to 
calculate hourly average winds for input into AERMET
AERMOD American Meteorological Society (AMS)/EPA Regulatory Model
AERSCREEN Program to run AERMOD in screening mode
AERSURFACE Land cover data tool in AERMET
AQRV Air Quality Related Value
AQS Air Quality System
ARM Ambient Ratio Method
ARM2 Ambient Ratio Method 2
ASOS Automated Surface Observing Stations
ASTM American Society for Testing and Materials
Bo Bowen ratio
BART Best available retrofit technology
BID Buoyancy-induced dispersion
BLP Buoyant Line and Point Source model
BOEM Bureau of Ocean Energy Management
BPIPPRM Building Profile Input Program for PRIME
BUKLRN Bulk Richardson Number
CAA Clean Air Act
CAL3QHC Screening version of the CALINE3 model
CAL3QHCR Refined version of the CALINE3 model
CALINE3 CAlifornia LINE Source Dispersion Model
CALMPRO Calms Processor
CALPUFF California Puff model
CALTRANS99 California Department of Transportation Highway 99 Tracer 
Experiment
CAMx Comprehensive Air Quality Model with Extensions
CFR Code of Federal Regulations
CMAQ Community Multiscale Air Quality
CO Carbon monoxide
CTDMPLUS Complex Terrain Dispersion Model Plus Algorithms for 
Unstable Situations
CTSCREEN Screening version of CTDMPLUS
CTM Chemical transport model
d[thgr]/dz Vertical potential temperature gradient
DT Temperature difference
EDMS Emissions and Dispersion Modeling System
EPA Environmental Protection Agency
FAA Federal Aviation Administration
FLAG Federal Land Managers' Air Quality Related Values Work Group 
Phase I Report
FLM Federal Land Manager
GEP Good engineering practice
GUI Graphical user interface
IBL Inhomogeneous boundary layer
ISC Industrial Source Complex model
IWAQM Interagency Workgroup on Air Quality Modeling
km kilometer
L Monin-Obukhov length
m meter
m/s meter per second
MAKEMET Program that generates a site-specific matrix of 
meteorological conditions for input to AERMOD
MAR Minimum ambient ratio
MCH Model Clearinghouse
MCHISRS Model Clearinghouse Information Storage and Retrieval System
MERPs Model Emissions Rates for Precursors
METPRO Meteorological Processor for dispersion models
MM5 Mesoscale Model 5
MMIF Mesoscale Model Interface program
MPRM Meteorological Processor for Regulatory Models
NAAQS National Ambient Air Quality Standards
NCEI National Centers for Environmental Information
NH3 Ammonia
NO Nitric oxide
NOAA National Oceanic and Atmospheric Administration
NOX Nitrogen oxides
NO2 Nitrogen dioxide
NSR New Source Review
NTI National Technical Information Service
NWS National Weather Service
OCD Offshore and Coastal Dispersion Model
OCS Outer Continental Shelf
OCSLA Outer Continental Shelf Lands Act
OLM Ozone Limiting Method
PCRAMMET Meteorological Processor for dispersion models
P-G stability Pasquill-Gifford stability
PM2.5 Particles less than or equal to 2.5 micrometers in 
diameter
PM10 Particles less than or equal to 10 micrometers in 
diameter
PRIME Plume Rise Model Enhancements algorithm
PSD Prevention of Significant Deterioration
PVMRM Plume Volume Molar Ratio Method
r Albedo
RHC Robust Highest Concentration
RLINE Research LINE source model for near-surface releases
SCICHEM Second-order Closure Integrated Puff Model
SCRAM Support Center for Regulatory Atmospheric Modeling
SCREEN3 A single source Gaussian plume model which provides maximum 
ground-level concentrations for point, area, flare, and volume 
sources
SDM Shoreline Dispersion Model
SILs Significant impact levels
SIP State Implementation Plan
SMAT Software for Model Attainment Test
SO2 Sulfur dioxide
SRDT Solar radiation/delta-T method
TSD Technical support document
u Values for wind speed
u* Surface friction velocity
VOC Volatile organic compound
w* Convective velocity scale
WRF Weather Research and Forecasting model
zi Mixing height
Zo Surface roughness
Zic Convective mixing height
Zim Mechanical mixing height
[sigma]v, [sigma]w Horizontal and vertical 
wind speeds

II. Background

    The Guideline is used by the EPA, other federal, state, 
territorial, local, and tribal air quality agencies, and industry to 
prepare and review new or modified source permits, SIP submittals or 
revisions, conformity, and other air quality assessments required under 
the CAA and EPA regulations. The Guideline serves as a means by which 
national consistency is maintained in air quality analyses for 
regulatory activities under 40 CFR (Code of Federal Regulations) 
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. Subsequently, the EPA revised the Guideline on 
April 15, 2003 (68 FR

[[Page 5184]]

18440), to adopt CALPUFF as the preferred model for long-range 
transport of emissions from 50 to several hundred kilometers (km) and 
to make various editorial changes to update and reorganize information 
and remove obsolete 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 km. The 
publication and incorporation of the Guideline into the EPA's PSD 
regulations satisfies the requirement under 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.
    On July 29, 2015, we proposed revisions to the Guideline in the 
Federal Register (80 FR 45340). The proposed revisions to the Guideline 
and preferred models are based upon stakeholder input received during 
the Tenth Conference on Air Quality Modeling. These proposed revisions 
were presented at the Eleventh Conference on Air Quality Modeling that 
included the public hearing for the proposed action. The conferences 
and public hearing are briefly described in section III of this 
preamble.
    Section IV provides a brief discussion of comments received and our 
responses that support the changes to the Guideline being finalized 
through this action. A more comprehensive discussion of the public 
comments received and our responses are provided in the Response to 
Comments document that is included in the docket for this action.

III. The Tenth and Eleventh Conferences on Air Quality Modeling and 
Public Hearing

    To inform the development of our proposed revisions to the 
Guideline and in compliance with CAA section 320, we held the Tenth 
Conference on Air Quality Modeling in March 2012. The conference 
addressed updates on: The regulatory status and future development of 
AERMOD and CALPUFF, review of the Mesoscale Model Interface (MMIF) 
prognostic meteorological data processing tool for dispersion models, 
draft modeling guidance for compliance demonstrations of the 
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. Based 
on comments received from stakeholders at the Tenth Modeling 
Conference, ``Phase 3'' of the Interagency Workgroup on Air Quality 
Modeling (IWAQM) was formalized in June 2013 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. A transcript of the conference proceedings and a 
summary of the public comments received are available in the docket for 
the Tenth Modeling Conference.\1\ Additionally, all of the material 
associated with this conference are available on the EPA's SCRAM Web 
site at https://www3.epa.gov/ttn/scram/10thmodconf.htm.
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    \1\ See Docket ID No. EPA-HQ-OAR-2015-0310.
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    The Eleventh Conference on Air Quality Modeling was held August 12-
13, 2015, in continuing compliance with CAA section 320. The Eleventh 
Modeling Conference included the public hearing for this action. The 
conference began with a thorough overview of the proposed revisions to 
the Guideline, including presentations from EPA staff on the 
formulation updates to the preferred models and the research and 
technical evaluations that support these and other revisions. 
Specifically, there were presentations summarizing the proposed updates 
to the AERMOD modeling system, replacement of CALINE3 with AERMOD for 
modeling of mobile sources, incorporation of prognostic meteorological 
data for use in dispersion modeling, the proposed screening approach 
for long-range transport for NAAQS and PSD increments assessments with 
use of CALPUFF as a screening technique rather than an EPA-preferred 
model, the proposed 2-tiered screening approach to address ozone and 
PM2.5 in PSD compliance demonstrations, the status and role 
of the Model Clearinghouse, and updates to procedures for single-source 
and cumulative modeling analyses (e.g., modeling domain, source input 
data, background data, and compliance demonstration procedures).
    At the conclusion of these presentations, the public hearing on the 
proposed revisions to the Guideline was convened. The public hearing 
was held on the second half of the first day and on the second day of 
the conference. There were 26 presentations by stakeholders and 
interested parties. The EPA presentations and the presentations from 
the public hearing are provided in the docket for this action. A 
transcript of the conference proceedings is also available in the 
docket. Additionally, all of the material associated with the Eleventh 
Modeling Conference and the public hearing are available on the EPA's 
SCRAM Web site at https://www3.epa.gov/ttn/scram/11thmodconf.htm.

IV. Discussion of Public Comments on the Proposed Changes to the 
Guideline

    In this action, the EPA is finalizing two types of revisions to the 
Guideline. The first type involves substantive changes to address 
various topics, including those presented and discussed at the Tenth 
and Eleventh Modeling Conferences. These 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 latter 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.
    The EPA recognizes that the scope and extent of the final changes 
to the Guideline may not address all of the current concerns identified 
by the stakeholder community or emerging science issues. The EPA is 
committed to ensuring in the future that the Guideline and associated 
modeling guidance reflect the most up-to-date science and will provide 
appropriate and timely updates. Adhering to the existing procedures 
under CAA section 320, which requires the EPA to conduct a conference 
on air quality modeling at least every 3 years, the Twelfth Conference 
on Air Quality Modeling will occur within the next 2 years to provide a 
public forum for the EPA and the stakeholder community to engage on 
technical issues, introduce new air quality modeling research and 
techniques, and discuss recommendations on future areas of air quality 
model development and subsequent revisions to the Guideline. A formal 
notice announcing the next Conference on Air Quality Modeling will be 
published in the Federal Register at the appropriate time and will 
provide information to the stakeholder community on how to register to 
attend and/or present at the conference.

[[Page 5185]]

A. Final Action

    In this section, we offer summaries of the substantive comments 
received and our responses and explain the final changes to the 
Guideline in terms of the main technical and policy concerns addressed 
by the EPA. A more comprehensive discussion of the public comments 
received and our responses is provided in the Response to Comments 
document located in the docket for this action.
    Air quality modeling involves estimating ambient concentrations 
using scientific methodologies selected from a range of possible 
methods, and should utilize the most advanced practical technology that 
is available at a reasonable cost to users, keeping in mind the 
intended uses of the modeling and ensuring transparency to the public. 
With these revisions, we believe that the Guideline continues to 
reflect scientific advances in the field and balances these important 
considerations for regulatory assessments. This action amends appendix 
W of 40 CFR part 51 as detailed below:
1. Clarifications To Distinguish Requirements From Recommendations
    We proposed revisions to the Guideline to provide clarity in 
distinguishing requirements from recommendations while noting the 
continued flexibilities provided within the Guideline, including but 
not limited to use and approval of alternative models. The vast 
majority of the public comments were supportive of the overall proposed 
reorganization and revisions to the regulatory text. There were only a 
few comments specific to the distinction between requirements and 
recommendations. All but one of these comments commended the EPA for 
providing this level of clarity of what is required in regulatory 
modeling demonstrations and where there is appropriate flexibility in 
the technique or approach. One comment expressed a concern that 
allowing for flexibility is critical when regulations, standards, and 
modeling techniques are constantly evolving. In this final action, the 
EPA reaffirms that significant flexibility and adaptability remain in 
the Guideline, while the revisions we are adopting serve to provide 
clarity in portions of the Guideline that have caused confusion in the 
past.
    As discussed in the preamble to the proposed rule, 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)(1); see 
also 40 CFR 52.21(l)(1). The ``applicable models'' are the preferred 
models listed in appendix A to appendix W to 40 CFR part 51. However, 
there was 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 could arise 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 observed:

    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 appears throughout the Guideline, there are 
instances where the EPA does not believe permit issuers 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 are finalizing the specific use of mandatory language, as 
proposed, along with references to ``requirements,'' where appropriate, 
to distinguish requirements from recommendations in the application of 
models for regulatory purposes.
2. Updates to EPA's AERMOD Modeling System
    In our proposed action, we invited comments on the proposed 
scientific updates to the regulatory version of the AERMOD modeling 
system, including:
    1. A proposed ``ADJ_U*'' option incorporated in AERMET to adjust 
the surface friction velocity (u*) to address issues with AERMOD model 
tendency to overprediction from some sources under stable, low wind 
speed conditions.
    2. A proposed ``LOWWIND3'' option in AERMOD to address issues with 
model tendency to overprediction under low wind speed conditions. The 
low wind option increases 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.
    3. Modifications to AERMOD formulation to address issues with model 
tendency to overprediction for applications involving relatively tall 
stacks located near relatively small urban areas.
    4. Proposed regulatory options in AERMOD to address plume rise for 
horizontal and capped stacks based on the July 9, 1993, Model 
Clearinghouse memorandum,\2\ with adjustments to account for the Plume 
Rise Model Enhancements (PRIME) algorithm for sources subject to 
building downwash.
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    \2\ U.S. Environmental Protection Agency, 1993. Proposal for 
Calculating Plume Rise for Stacks with Horizontal Releases or Rain 
Caps for Cookson Pigment, Newark, New Jersey. Memorandum dated July 
9, 1993, Office of Air Quality Planning and Standards, Research 
Triangle Park, NC. https://www3.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 Buoyant Line 
and Point Source (BLP) model, incorporated in AERMOD.
    6. Proposed updates to the NO2 Tier 2 and Tier 3 
screening techniques coded within AERMOD.
    The EPA's final action related to each of these proposed updates is 
discussed below.
Incorporation of the ADJ_U* Option Into AERMET
    The EPA has integrated the ADJ_U* option into the AERMET 
meteorological processor for AERMOD to address issues with model 
overprediction of ambient concentrations from some sources associated 
with underprediction of the surface friction velocity (u*) during light 
wind, stable conditions. The proposed update to AERMET included 
separate ADJ_U* algorithms for applications with and without the Bulk 
Richardson Number (BULKRN) option in AERMET. The ADJ_U* option with 
BULKRN utilizes measured vertical temperature difference data (i.e., 
delta-T data) and is based on Luhar and Rayner (2009, BLM v.132). The 
ADJ_U*

[[Page 5186]]

option without BULKRN does not utilize delta-T data and is based on 
Qian and Venkatram (2011, BLM v. 138). These studies also include 
meteorological evaluations of predicted versus observed values of u*, 
which demonstrate improved skill in predicting u* during stable light 
wind conditions, and we consider these meteorological evaluations as 
key components of the overall technical assessment of these model 
formulation changes.
    The majority of public comments supported the adoption of the 
ADJ_U* option in AERMET, while a few commenters expressed concern 
regarding the potential for the ADJ_U* option to underestimate ambient 
concentrations. Some commenters also expressed concern regarding the 
appropriateness of the field study databases used in the EPA model 
evaluations. We acknowledge the issues and potential challenges 
associated with conducting field studies for use in model performance 
evaluations, especially during stable light wind conditions, given the 
potentially high degree of variability that may exist across the 
modeling domain and the increased potential for microscale influences 
on plume transport and dilution. This variability is one of the reasons 
that we discourage placing too much weight on modeled versus predicted 
concentrations paired in time and space in model performance 
evaluations. This also highlights the advantages of conducting field 
studies that utilize circular arcs of monitors at several distances to 
minimize the potential influence of uncertainties associated with the 
plume transport direction on model-to-monitor comparisons. The 1974 
Idaho Falls, Idaho, and 1974 Oak Ridge, Tennessee, field 
studies,3 4 conducted by the National Oceanic and 
Atmospheric Administration (NOAA), are two of the key databases 
included in the evaluation of the ADJ_U* option in AERMET (as well as 
the LOWWIND3 option in AERMOD), and both utilized circular arcs of 
monitors at 100 meter (m), 200 m, and 400 m downwind of the tracer 
release point.
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    \3\ NOAA Technical Memorandum ERL ARL-52, 1974. Diffusion under 
Low Wind Speed, Inversion Conditions. Sagendorf, J.F., C. Dickson. 
Air Resources Laboratory, Idaho Falls, Idaho.
    \4\ NOAA Technical Memorandum ERL ARL-61, 1976. Diffusion under 
Low Wind Speed Conditions near Oak Ridge, Tennessee. Wilson, R.B., 
G. Start, C. Dickson, N. Ricks. Air Resources Laboratory, Idaho 
Falls, Idaho.
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    Initial evaluations of the ADJ_U* option in AERMET and LOWWIND 
options in AERMOD were first presented as ``beta'' options in appendix 
F of the AERMOD User's Guide Addendum for version 12345. This included 
results for the Idaho Falls and Oak Ridge field studies. Updated 
evaluations based on these NOAA studies were included in the AERMOD 
User's Guide Addendum for v15181, along with additional evaluations for 
the Lovett database involving a tall stack with nearby complex terrain. 
Additional evaluations of these proposed modifications to AERMET and 
AERMOD were also presented at the Eleventh Modeling Conference, 
including an evaluation based on the 1993 Cordero Mine PM10 
field study in Wyoming, as summarized in the Response to Comments 
document.
    One commenter provided a detailed modeling assessment of the 
proposed ADJ_U* option in AERMET (as well as the proposed LOWWIND3 
option in AERMOD) across a number of field studies to support their 
position that the proposed model updates will ``reduce model accuracy'' 
and ``in some cases quite significantly reduce[s] modeled impacts, 
particularly so in the case of the Tracy validation study data.'' The 
EPA's review of the modeling results provided by the commenter 
indicated almost no influence of the ADJ_U* option on those field 
studies associated with tall stacks in flat terrain, including the 
Baldwin and Kincaid field studies. These results are expected since the 
``worst-case'' meteorological conditions for tall stacks in flat 
terrain generally occur during daytime convective conditions that are 
not affected by the ADJ_U* option. In addition, the commenter's 
modeling results presented for the Lovett field study, a tall stack 
with nearby complex terrain, appear to show improved performance (with 
less underprediction) with the ADJ_U* option as compared to the default 
option in AERMET, thereby supporting use of the ADJ_U* option in 
appropriate situations.
    The commenter also stated that the issue of underprediction with 
the ADJ_U* option is ``particularly so in the case of the Tracy 
validation study.'' The Tracy field study involved a tall stack located 
with nearby terrain similar to the Lovett field study; however, the 
Tracy field study differs from the Lovett and other complex terrain 
field studies in that Tracy had the most extensive set of site-specific 
meteorological data, including several levels of wind speed, wind 
direction, ambient temperature, and turbulence parameters (i.e., sigma-
theta and/or sigma-w), extending from 10 m above ground up to 400 m 
above ground for some parameters. The Tracy field study also included 
the largest number of ambient monitors of any complex terrain study 
used in evaluating AERMOD performance, including 106 monitors extending 
across a domain of about 75 square kilometers, and used sulfur 
hexafluoride (SF6) as a tracer which reduces uncertainty in 
evaluating model performance by minimizing the influence of background 
concentrations on the model-to-monitor comparisons. The EPA's review of 
the commenter's results for the Tracy database confirms their finding 
of a bias toward underprediction by almost a factor of two with the 
ADJ_U* option in AERMET, compared to relatively unbiased results with 
the default option in AERMET based on the full set of meteorological 
inputs. However, there was no diagnostic performance evaluation 
included with the commenter's analysis that could provide the necessary 
clarity regarding the potential connection between the ADJ U* option 
and cause for the bias to underpredict concentrations.
    After proposal, the EPA received several requests through its Model 
Clearinghouse (MCH) for alternative model approval of the ADJ U* option 
under section 3.2.2 of the Guideline. The EPA issued two MCH 
concurrences on February 10, 2016, for the Donlin Gold, LLC mining 
facility in EPA Region 10 (i.e., ground level, fugitive emissions of 
particulate matter from sources with low release heights during periods 
of low-wind/stable conditions), and on April 29, 2016, for the Schiller 
Station facility in EPA Region 1 (i.e., SO2 emissions from 
tall stack sources with impacts on distant complex terrain, during low-
wind/stable conditions). In both cases, the request memoranda from the 
EPA Regions to the MCH noted the potential for underprediction by 
AERMOD with the ADJ U* option in situations where turbulence data from 
site-specific meteorological data inputs were also used. Through the 
MCH concurrence for each case, the EPA acknowledged the potential for 
this underprediction and effectively communicated to the stakeholder 
community that these turbulence data were not used in the approved 
alternative model. There was no detailed diagnostic performance 
evaluation included with the MCH requests to provide insights regarding 
the potential connection between the ADJ U* option and use of on-site 
turbulence data.
    To evaluate the public comments in light of these MCH concurrences, 
the EPA has conducted additional meteorological data degradation 
analyses for the Tracy field study and

[[Page 5187]]

the 1972 Idaho Falls field study for a ground-level release in flat 
terrain to provide a better understanding of the nature of the tendency 
to underpredict concentrations when applying the ADJ_U* option with 
site-specific turbulence measurements. The full meteorological dataset 
available for the Tracy field study provides a robust case study for 
this assessment because it includes several levels of turbulence data, 
i.e., sigma-theta (the standard deviation of horizontal wind direction 
fluctuations) and/or sigma-w (the standard deviation of the vertical 
wind speed fluctuations), in addition to several levels of wind speed, 
direction and temperature. The 1972 Idaho Falls field study also 
included a robust set of meteorological data to assess this potential 
issue for ground-level sources.
    The results of this EPA study confirm good performance for the 
Tracy field study using the full set of meteorological inputs with the 
default options (i.e., without the ADJ_U* option in AERMET and without 
any LOWWIND option in AERMOD). Including the ADJ_U* option in AERMET 
with full meteorological data results in an underprediction of about 40 
percent. On the other hand, AERMOD results without the ADJ_U* option in 
AERMET and without the observed profiles of temperature and turbulence 
(i.e., mimicking standard airport meteorological inputs) results in 
significant overprediction by about a factor of 4. However, using the 
ADJ_U* option with the degraded meteorological data shows very good 
agreement with observations, comparable to or slightly better than the 
results with full meteorological inputs. Full results from this study 
to assess the use of the ADJ_U* option with various levels of 
meteorological data inputs are detailed in our Response to Comments 
document provided in the docket for this action. The Response to 
Comments document also provides evidence of this potential bias toward 
underprediction when the ADJ_U* option is applied for applications that 
also include site-specific meteorological data with turbulence 
parameters based on the 1972 Idaho Falls study. As with the Tracy field 
study, the Idaho Falls field study results with site-specific 
turbulence data do not show a bias toward underprediction without the 
ADJ_U* option, but do show a bias toward underprediction using 
turbulence data with the ADJ_U* option.
    Based on these detailed findings, the public cannot be assured that 
the proposed ADJ_U* option, when used with site-specific meteorological 
inputs including turbulence data (i.e., sigma-theta and/or sigma-w), 
would not bias model predictions towards underestimation, which would 
be inconsistent with section 3.2.2 of the Guideline. Therefore, the EPA 
has determined that the ADJ_U* option should not be used in AERMET in 
combination with use of measured turbulence data because of the 
observed tendency for model underpredictions resulting from the 
combined influences of the ADJ_U* and the turbulence parameters within 
the current model formulation.
    While these findings suggest that the ADJ_U* option is not 
appropriate for use in regulatory applications involving site-specific 
meteorological data that include measured turbulence parameters, the 
model performance and diagnostic evaluations strongly support the 
finding that the ADJ_U* option provides for an appropriate adjustment 
to the surface friction velocity parameter when standard National 
Weather Service (NWS) airport meteorological data, site-specific 
meteorological data without turbulence parameters, or prognostic 
meteorological input data are used for the regulatory application.
    Therefore, based on these findings of improved model performance 
with the ADJ_U* option for sources where peak impacts are likely to 
occur during low wind speed and stable conditions, as well as the peer-
reviewed studies demonstrating improved estimates of the surface 
friction velocity (u*) based on these options, the EPA is adopting the 
proposed ADJ_U* option in AERMET as a regulatory option for use in 
AERMOD for sources using standard NWS airport meteorological data, 
site-specific meteorological data without turbulence parameters, or 
prognostic meteorological inputs derived from prognostic meteorological 
models.
Incorporation of the LOWWIND3 Option Into AERMOD
    In addition to the ADJ_U* option in AERMET, the EPA also proposed 
the incorporation of LOWWIND3 as a regulatory option in AERMOD to 
address issues with model overprediction for some sources under low 
wind speed conditions. Beginning with version 12345 of AERMOD, two 
LOWWIND ``beta'' options were included in AERMOD (i.e., LOWWIND1 and 
LOWWIND2), and a third option, LOWWIND3, was incorporated at the time 
of proposal in version 15181 of AERMOD. The LOWWIND options modify the 
minimum value of sigma-v, the lateral turbulence intensity, which is 
used to determine the lateral plume dispersion coefficient (i.e., 
sigma-y). With respect to the specific issue of setting a minimum value 
of sigma-v, the LOWWIND options can be considered as empirical options 
based on applicable parameter specifications from the scientific 
literature. However, the LOWWIND options go beyond this empirical 
specification of the minimum sigma-v parameter to address the 
horizontal meander component in AERMOD that also contributes to lateral 
plume spread, especially during low wind, stable conditions. 
Furthermore, since the horizontal meander component in AERMOD is a 
function of the ``effective'' sigma-v value, lateral plume dispersion 
may be further enhanced under the LOWWIND3 option by increased meander, 
beyond the influence of the minimum sigma-v value alone.
    The current default option in AERMOD uses a minimum sigma-v of 0.2 
meters per second (m/s). Setting a higher minimum value of sigma-v 
would tend to increase lateral dispersion during low wind conditions 
and, therefore, could reduce predicted ambient concentrations. It is 
also worth noting that the values of sigma-v derived in AERMOD are 
based on the dispersion parameters generated in AERMET (i.e., the 
surface friction velocity (u*) and the convective velocity scale (w*)), 
as well as the user-specified surface characteristics (i.e., the 
surface roughness length, Bowen ratio, and albedo) used in processing 
the meteorological inputs through AERMET. As a result, application of 
the ADJ_U* option in AERMET will tend to increase sigma-v values in 
AERMOD and generally tend to lower predicted peak concentrations, 
separate from application of the LOWWIND options. Unlike the proposed 
ADJ_U* option in AERMET that adjusts u* under stable conditions, the 
LOWWIND options in AERMOD are applied for both stable and unstable/
convective conditions. However, since atmospheric turbulence will 
generally be higher during unstable/convective conditions than for 
stable conditions, the potential influence of the minimum sigma-v value 
on plume dispersion is likely to be much less important during 
unstable/convective conditions.
    The majority of commenters supported the EPA's proposal to 
incorporate the LOWWIND3 option into the regulatory version of AERMOD 
because they believed it would provide a more realistic treatment of 
low wind situations and reduce the potential for overprediction of the 
current regulatory version of AERMOD for such conditions. However, one 
commenter indicated that the proposed

[[Page 5188]]

LOWWIND3 option in AERMOD will ``reduce model accuracy'' along with 
model results, showing a tendency for underprediction across a number 
of evaluation databases. As discussed in the Response to Comments 
document, the influence of the LOWWIND3 option on model performance is 
mixed, and has shown a tendency toward underprediction with increasing 
distance in some cases, especially when LOWWIND3 is applied in 
conjunction with the ADJ_U* option in AERMET. The EPA's reassessment of 
model performance confirmed this finding of underprediction with 
increasing distance, in particular for the 1972 Idaho Falls field study 
database (discussed previously) and the Prairie Grass, Kansas, field 
study, which involved a near-surface tracer release in flat terrain. As 
noted above, there is an interaction between the ADJ_U* option and 
LOWWIND options because the values of sigma-v derived in AERMOD are 
based on the surface friction velocity (u*) parameter generated in 
AERMET. As a result, the ADJ_U* option in conjunction with the LOWWIND3 
option influences the AERMOD derived sigma-v parameter and, in some 
cases, may exacerbate the tendency for AERMOD with LOWWIND3 to 
underpredict at higher concentrations, as shown in the commenter's 
assessment and the EPA's reassessment.
    Another aspect of the AERMOD model formulation that may contribute 
to an increasing bias toward underprediction with distance is the 
treatment of the ``inhomogeneous boundary layer'' (IBL) that accounts 
for changes in key parameters such as wind speed and temperature with 
height above ground. The IBL approach determines ``effective'' values 
of wind speed, temperature, and turbulence that are averaged across a 
layer of the plume between the plume centerline height and the height 
of the receptor. The extent of this layer depends on the vertical 
dispersion coefficient (i.e., sigma-z). Therefore, as the plume grows 
downwind of the source, the extent of the layer used to calculate the 
effective parameters will increase (up to specified limits). The 
potential influence of this aspect of AERMOD formulation on modeled 
concentrations will depend on several factors, including source 
characteristic, meteorological condition, and the topographic 
characteristics of the modeling domain.
    Several commenters recommended that the EPA's proposed revisions to 
AERMOD be further evaluated given either the lack or paucity of peer-
reviewed literature upon which they are based. Specifically, one 
commenter noted that ``while this overprediction phenomenon can occur 
under certain conditions, additional studies produced by a more diverse 
group of organizations should be evaluated.'' Unlike the situation with 
the ADJ_U* option, the EPA does not have a published, peer-reviewed 
model formulation update with supporting model performance evaluations 
that fully address the complex issues of concern for the LOWWIND 
options. Therefore, the EPA agrees with commenters that additional 
study and evaluation is warranted for the proposed LOWWIND3 option, as 
well as other low wind options, in order to gain the understanding 
across the modeling community that is necessary to determine whether it 
would be appropriate to incorporate it into an EPA-preferred model used 
to inform regulatory decisions. The EPA will continue to work with the 
modeling community to further assess the theoretical considerations and 
model performance results under relevant conditions to inform 
considerations for appropriate adjustments to the default minimum value 
of sigma-v from 0.2 m/s that, as noted by some commenters, may be 
considered separate from any specific LOWWIND option.
    Based on EPA's review of public comments and further consideration 
of the issues, the public cannot be assured that the proposed LOWWIND3 
option does not have a tendency to bias model predictions towards 
underestimation (especially in combination with the ADJ_U* option and/
or site-specific turbulence parameters), which would be inconsistent 
with section 3.2.2 of the Guideline. Therefore, lacking sufficient 
evidence to support adoption of LOWWIND3 (or other LOWWIND options) as 
a regulatory option in AERMOD, we are not incorporating LOWWIND3 as a 
regulatory option in AERMOD at this time, and we are deferring action 
on the LOWWIND options in general pending further analysis and 
evaluation in conjunction with the modeling community.
Modifications to AERMOD Formulation for Tall Stack Applications Near 
Small Urban Areas
    As proposed, the EPA recognized the need to address observed 
overpredictions by AERMOD when applied to situations involving tall 
stacks located near small urban areas. The tendency to overpredict 
concentrations results from an unrealistic limit on plume rise imposed 
within the dispersion model. The EPA received broad support in the 
public comments for these proposed modifications to the AERMOD 
formulation that appropriately address overprediction for applications 
involving relatively tall stacks located near small urban areas. The 
EPA is finalizing this model formulation update, as proposed, into the 
regulatory version of AERMOD.
Address Plume Rise for Horizontal and Capped Stacks in AERMOD
    As proposed, the EPA updated the regulatory options in AERMOD to 
address plume rise for horizontal and capped stacks based on the July 
9, 1993, MCH memorandum,\2\ with adjustments to account for the PRIME 
algorithm for sources subject to building downwash. There was broad-
based support for this model update across the public comments. One 
commenter noted that the use of this proposed option for horizontal 
stacks, although a better method than the previous version, can lead to 
extremely high concentrations for sources with building downwash in 
complex terrain. Despite the noted improved performance of the proposed 
option in the case of building downwash, the EPA recognizes the ongoing 
issues with this option in the presence of building downwash and with 
its inherent complexities and its particular application in such 
situations with complex terrain. The EPA also recognizes that the 
appropriateness of this option for that particular situation would be a 
matter of consultation with the appropriate reviewing authority. 
However, given the broad support stated in public comments for the 
improved treatment, the EPA is finalizing this formulation update, as 
proposed, as a regulatory option within AERMOD.
Incorporation of the BLP Model Into AERMOD
    As proposed, the EPA has integrated the BLP model into the AERMOD 
modeling system and removed BLP from appendix A as a preferred model. 
The comments received on the BLP integration into AERMOD are summarized 
in four categories: (1) Strongly supportive of the integration and 
replacement of BLP; (2) supportive of the integration, but with 
concerns that the integration of BLP is not fully consistent with the 
dispersion algorithms in AERMOD; (3) supportive of the integration, but 
suggestive that more time is needed to evaluate the implementation and 
that BLP should remain in appendix A until more evaluation can be made 
of the new code; and (4) concerned that modeled concentrations between 
the original BLP and BLP integrated in AERMOD are not identical. 
Despite the concerns expressed, all the comments received

[[Page 5189]]

were supportive of the concept of integrating the two models and 
removing BLP from appendix A.
    The EPA's integration of BLP into AERMOD was not intended to update 
the model science within BLP into AERMOD. Thus, while the comments 
relating to inconsistencies between AERMOD (e.g., based on Monin-
Obukhov length and similarity profiling) and BLP (e.g., based on 
Pasquill-Gifford stability classes) are largely accurate, they do not 
affect the status of the proposed BLP integration. Many of the comments 
on the proposal suggested that the EPA needs to more quickly integrate 
updates to the AERMOD modeling system to address these inconsistencies. 
However, the EPA does not find it appropriate to delay the release of 
the integrated model, particularly since the stated purposed of the 
integration and evaluation is to assure equivalency and not a 
fundamental update to the BLP model science to be consistent with that 
of AERMOD, which would require additional time and effort to 
appropriately inform a possible future EPA action. The EPA appreciates 
the comments identifying potential issues where model equivalency was 
not fully demonstrated. These instances have been further evaluated and 
corrections have been made to the code to sufficiently address these 
issues. The details of these corrections, along with the comments 
relating to inconsistencies in underlying dispersion science, are 
addressed in detail in the Response to Comments document located in the 
docket for this action.
    Therefore, the EPA is integrating the BLP model into the AERMOD 
modeling system, is removing BLP from appendix A as an EPA-preferred 
model, and is updating the summary description of the AERMOD modeling 
system to appendix A of the Guideline as proposed.
Updates to the NO2 Tier 2 and Tier 3 Screening Techniques in 
AERMOD
    In the proposed action, we solicited 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 were on sound foundation to recommend the 
proposed updates. Section 5.2.4 of the 2005 version of the Guideline 
details a 3-tiered approach for assessing nitrogen oxides 
(NOX) sources, which was recommended to obtain annual 
average estimates of NO2 concentrations from point sources 
for purposes of NSR analyses and for SIP planning purposes. This 3-
tiered approach addresses the co-emissions of nitric oxide (NO) and 
NO2 and the subsequent conversion of NO to NO2 in 
the atmosphere. In January 2010, the EPA promulgated a new 1-hour 
NO2 NAAQS (75 FR 6474). 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 version of the Guideline 
specifically targeted an annual standard). In response to the 1-hour 
NO2 standard, the EPA proposed the incorporation of several 
modifications to the Tier 2 and Tier 3 NO2 screening 
techniques as regulatory options in AERMOD, so that alternative model 
approval would no longer be needed.
    The proposed modifications specifically included: (1) Replacing the 
existing Tier 2 Ambient Ratio Method (ARM) \5\ with a revised Ambient 
Ratio Method 2 (ARM2) \6\ approach; and (2) incorporating the existing 
detailed screening option of the Ozone Limiting Method (OLM) \7\ and 
updated version of the Plume Volume Molar Ratio Method (PVMRM) \8\ as 
regulatory options in AERMOD as preferred Tier 3 screening methods for 
NO2 modeling. The vast majority of the public comments 
supported the proposed changes to these methods. However, there were 
two subsets of comments that required additional response.
---------------------------------------------------------------------------

    \5\ 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, June 16-21 1991, Vancouver, B.C.
    \6\ Podrez, M. 2015. An Update to the Ambient Ratio Method for 
1-h NO2 Air Quality Standards Dispersion Modeling. 
Atmospheric Environment, 103: 163-170.
    \7\ 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.
    \8\ 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.
---------------------------------------------------------------------------

    First, several commenters stated that the proposed default 
NO2/NOX minimum ambient ratio (MAR) of 0.5, for 
use with the ARM2 approach, was too high and that a MAR of 0.2 should 
be used instead. The MAR is the lowest NO2/NOX 
ratio used in the ARM2 method at the highest NOX levels. The 
MAR increases from this minimum level to a maximum NO2/
NOX ratio of 0.9 at the lowest NOX levels. While 
commenters believe that the MAR of 0.2 is more representative of 
ambient data, the EPA maintains that consistency in the tiered approach 
for NO2 modeling, with the Tier 2 methods being more 
conservative than the Tier 3 methods, is needed and that national 
default model inputs need to be conservative, in line with the CAA's 
objective to prevent potential NAAQS violations. The revised text 
allows for alternative MARs that should not be overly difficult to 
justify to the appropriate reviewing authority when lower MARs are 
appropriate. The EPA reaffirms that site-specific data are always 
preferred, but provides the national default model inputs when these 
data are unavailable.
    Second, several commenters noted that the specific version of 
PVMRM2 intended for regulatory use was not entirely clear. Version 
15181 of AERMOD included both PVMRM and PVMRM2 with the proposal 
preamble text indicating that we would be promulgating PVMRM2; however, 
the proposed regulatory text identified PVMRM, which caused confusion. 
The methodology employed in the ``PVMRM2'' option in AERMOD version 
15181 is now the ``PVMRM'' regulatory option in AERMOD, and the 
methodology employed in the ``PVMRM'' option in AERMOD version 15181 
has been removed entirely from the model. The basis for this decision 
is that the updated PVMRM2 is a more complete implementation of the 
PVMRM approach outlined by Hanrahan (1999) than the original PVMRM 
implementation in AERMOD.
    Therefore, the EPA is updating the regulatory version of the AERMOD 
modeling system to reflect these changes for NO2 modeling 
and has updated the related descriptions of the AERMOD modeling system 
in section 4.2.3.4 of the Guideline as proposed.
EPA's Preferred Version of the AERMOD Modeling System
    As described throughout section IV.A.2 of this preamble, we are 
revising the summary description of the AERMOD modeling system in 
appendix A of the Guideline to reflect these updates. Model performance 
evaluation and scientific peer review references for the updated AERMOD 
modeling system are cited, as appropriate. An updated user's guide and 
model formulation documents for version 16216 are located in the docket 
for this action. The essential codes, preprocessors, and test cases 
have been updated and posted on the EPA's SCRAM Web site at https://www.epa.gov/scram/air-quality-dispersion-modeling-preferred-and-recommended-models#aermod.

[[Page 5190]]

3. Status of AERSCREEN
    In our proposed action, we invited comment on the incorporation of 
AERSCREEN into the Guideline as the recommended screening model for 
AERMOD that may be suitable for applications in all types of terrain 
and for applications involving building downwash. AERSCREEN uses the 
EPA's preferred near-field dispersion model AERMOD in screening mode 
and represents the state of the science versus the outdated algorithms 
of SCREEN3 that are based on the Industrial Source Complex model (ISC).
    We received some comments that SCREEN3 should be retained as it is 
simpler to use than AERSCREEN. The EPA disagrees with those comments 
and reminds users that AERSCREEN is already being utilized by much of 
the stakeholder community and represents the state of the science as 
stated in the paragraph above. Given the preferred status of AERMOD 
over ISC and the fact that AERSCREEN is now incorporating fumigation, 
an option available in SCREEN3, we feel that there are no valid 
technical reasons to retain SCREEN3 as a recommended screening model.
    We also received comments expressing concerns about the fumigation 
options and conservatism of the fumigation outputs. The fumigation 
options implemented in AERSCREEN are the same algorithms used in 
SCREEN3, such that the current capabilities in that screening model are 
now available in AERSCREEN. However, these fumigation options take 
advantage of the AERMOD equations for the dispersion parameters sigma-y 
and sigma-z that are needed for the fumigation calculations. AERSCREEN 
also takes advantage of the meteorological data generated by MAKEMET to 
calculate those parameters based on the boundary layer algorithms 
included in AERMET, as opposed to using standard dispersion curves used 
by SCREEN3. Some commenters suggested that the Shoreline Dispersion 
Model (SDM) algorithms be investigated for fumigation calculations. We 
agree with these commenters and will investigate the incorporation of 
the SDM algorithms in AERSCREEN for a future release. One commenter 
noted a bug in building outputs when running AERSCREEN with downwash 
and user-supplied BPIPPRM input files. The commenter stated that 
AERSCREEN takes the maximum and minimum dimensions over the 36 
directions output by BPIPPRM for use in modeling. For some directions, 
there may be no building influence and AERSCREEN erroneously takes a 
zero dimension as a building width. The EPA has determined that this is 
not a bug in AERSCREEN. Rather, it is a product of the output of 
BPIPPRM, which may report a value of zero for building widths and, 
thus, AERSCREEN reports a value of zero as a minimum building width. To 
address this issue, we have modified AERSCREEN to only output non-zero 
widths.
    Finally, several commenters pointed out a typographical error in 
the AERSCREEN conversion factors from 1-hour to 3-, 8-, and 24-hour and 
annual results in section 4.2.1.1 of the Guideline. The original text 
reported the SCREEN3 factors and not the AERSCREEN factors listed in 
the AERSCREEN user's guide. These factors have been corrected in the 
final revisions to the Guideline to reflect the AERSCREEN factors. 
Another commenter also found a typographical error in section 
4.2.1.1(c) where BPIPPRM was misspelled. This too was corrected. We 
also received a comment that the term ``unresolvable'' in section 
4.2.1.3(c) implies that a problem cannot be solved. Suggested language 
of ``unforeseen challenges'' was suggested. We agreed that the 
``unresolvable'' is erroneous and changed the term to ``unforeseen.''
    Therefore, the EPA is incorporating AERSCREEN into the Guideline as 
the recommended screening model for AERMOD that may be used in 
applications across all types of terrain and for applications involving 
building downwash.
4. Status of CALINE3 Models
    We solicited comment on our proposal to replace CALINE3 \9\ with 
AERMOD as the preferred appendix A model for its intended regulatory 
applications, primarily determining near-field impacts for primary 
emissions from mobile sources for PM2.5, PM10, 
and carbon monoxide (CO) hot-spot analyses.\10\ This proposed action 
was based on the importance of reflecting the latest science in AERMOD, 
its improved model performance over CALINE3, and the availability of 
more representative meteorological data for use in AERMOD. The EPA's 
proposal also set forth a 1-year transition period for the adoption of 
AERMOD for all regulatory applications.
---------------------------------------------------------------------------

    \9\ 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).
    \10\ U.S. Environmental Protection Agency, 2015, Transportation 
Conformity Guidance for Quantitative Hot-Spot Analyses in 
PM2.5 and PM10 Nonattainment and Maintenance 
Areas. Publication No. EPA-420-B-15-084, Office of Transportation 
and Air Quality, Ann Arbor, MI.
---------------------------------------------------------------------------

    The mobile source modeling applications under the CAA requirements 
that are most affected by the replacement of CALINE3 with AERMOD are 
transportation conformity hot-spot analyses for PM2.5, 
PM10, and CO.\11\ To date, PM hot-spot analyses have 
involved a refined analysis that can be accomplished with either AERMOD 
or CAL3QHCR (a variant of CALINE3).\10\ For CO hot-spot analyses, 
screening analyses are typically conducted with CAL3QHC (a variant of 
CALINE3).\12\
---------------------------------------------------------------------------

    \11\ Transportation conformity is required under Clean Air Act 
section 176(c) for federally funded or approved transportation 
projects in nonattainment and maintenance areas; EPA's 
transportation conformity regulations can be found at 40 CFR part 
93.
    \12\ U.S. Environmental Protection Agency, 1992, Guideline for 
Modeling Carbon Monoxide from Roadway Intersections, EPA-454/R-92-
005, Office of Air Quality Planning and Standards, RTP, NC.
---------------------------------------------------------------------------

    The EPA received several comments supporting and several comments 
opposed to the proposed replacement of CALINE3 with AERMOD as the 
preferred appendix A model for mobile source emissions. The commenters 
who supported the proposed replacement agreed with the reasons set 
forth in the proposal, mainly that AERMOD reflects the state-of-the-
science for Gaussian plume dispersion models, with on-going updates and 
enhancements supported by the EPA, has more accurate performance and is 
more flexible and can be applied to more project types than other 
dispersion models, can utilize more recent and more representative 
meteorological data, and that a single model will generally streamline 
the process of conducting and securing approval of model 
demonstrations.\13\ Alternatively, the commenters who did not support 
the proposal believed: that the science indicating AERMOD has more 
accurate performance is unclear; that AERMOD would increase the time 
required to complete hot-spot analyses, particularly for CO screening; 
and that a longer transition period, such as a 3-year period, would be 
needed for the adoption of new models for conformity analyses.
---------------------------------------------------------------------------

    \13\ U.S. Environmental Protection Agency, 2016. Technical 
Support Document (TSD) for Replacement of CALINE3 with AERMOD for 
Transportation Related Air Quality Analyses. Publication No. EPA-
454/B-16-006. Office of Air Quality Planning and Standards, Research 
Triangle Park, NC.
---------------------------------------------------------------------------

    The adverse comments related to the sufficiency of the EPA's 
technical and scientific basis for the replacement of

[[Page 5191]]

CALINE3 with AERMOD included statements that AERMOD does not have an 
explicit line-source algorithm; that the peer-reviewed literature shows 
mixed results for model assessments; and that AERMOD performance for 
roadways has not been as well documented for an array of transportation 
projects.
    First, the EPA notes that, based on implementation of conformity 
requirements to date, the majority of PM hot-spot analyses have been 
conducted with AERMOD and its existing algorithms have been used to 
perform these analyses. While it is true that AERMOD does not have an 
explicit line-source algorithm, it does have a LINE source input 
pathway that mimics the input requirements for CALINE3 and simplifies 
using elongated area sources such as roadways. While roadway sources 
are often described as ``line sources,'' they are in fact three-
dimensional entities. The roadway width is one of the model inputs for 
CALINE3 and the width of a roadway is frequently many times the 
distance from the edge of the roadway to the closest receptor. The 
actual formulation of these source types is not as explicit as the 
names suggest. For example, LINE source in AERMOD performs an explicit 
numerical integration of emissions from the LINE source, whereas CALINE 
uses a rough integration based on a series of finite line segments. 
Thus, an elongated area source in AERMOD is likely to represent the 
distribution of roadway emissions more accurately than the approach 
taken in CALINE3. In fact, the body of literature focused on roadway 
emissions suggests that the formulation of the Gaussian plume (i.e., 
line, area or volume) is not as important as the appropriate settings 
of the source characteristics and the quality of the emissions and 
meteorological inputs (see discussion in the Response to Comments 
document in the docket for this action).
    These commenters also believed that the Heist (2013) journal 
article \14\ cited primarily as supporting the proposal was too limited 
in scope. The quality of the emissions inputs, in particular, is one of 
the reasons the EPA focused on Heist (2013) to support the proposal. 
The EPA reviewed current model assessments in the literature and found 
that the majority used traffic counts and an emissions model to 
estimate emissions (see the Response to Comments document for more 
details). Although this approach introduces significant uncertainty in 
the model evaluation, this uncertainty was not addressed in these types 
of studies. Studies that use tracer emissions rather than traffic 
counts and emissions models remove this uncertainty and allow an 
evaluation of the dispersion model itself, rather than a joint 
evaluation of the emissions model and the dispersion model. The studies 
based on tracer releases rather than modeled emissions are limited to 
the CALTRANS99 and the 2008 Idaho Falls field studies examined in Heist 
(2013), and its robust model performance evaluations of these two 
studies. Thus, Heist (2013) was the primary literature the EPA 
considered in making a determination regarding AERMOD replacing 
CALINE3, rather than the small number of other recent model evaluations 
available in the peer-reviewed literature. Since the CALTRANS99 field 
campaign evaluated by Heist (2013) included an emission measurement 
system attached to vehicles driving on an operational highway, the 
results are fully representative of operational highways. The Heist 
(2013) study compared a developmental line-source model, RLINE, to 
AERMOD with volume and line sources as well as CALINE3 and CALINE4. 
RLINE showed nearly equivalent performance to the area and volume 
formulations from AERMOD. CALINE3 was clearly the worst performing 
model from the six model formulations evaluated. While CALINE4 had 
better performance than CALINE3, CALINE4 was still the second-worst 
performing model. It should also be noted that most recent literature 
only evaluates the CALINE4 model rather than the CALINE3 model, which 
further highlights that the CALINE3 model is outdated in its science, 
even within its own class of models.
---------------------------------------------------------------------------

    \14\ Heist, D., V. Isakov, S. Perry, M. Snyder, A. Venkatram, C. 
Hood, J. Stocker, D. Carruthers, S. Arunachalam, and R.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).
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    In terms of regulatory applications, AERMOD has been demonstrated 
to be useful for a range of transportation applications and is 
generally relied on over CAL3QHCR for more complicated projects because 
of its greater flexibility in source types (e.g., CAL3QHCR is unable to 
model certain types of projects or project features such as intermodal 
terminals or tunnels) and meteorological processing. Additionally, the 
Federal Aviation Administration (FAA) replaced CALINE3 with AERMOD in 
2005 in its Emissions and Dispersion Modeling System (EDMS) to expand 
its capability and improve its accuracy in evaluating airport 
impacts.\15\ This, along with the fact that AERMOD has been used for 
many years already for PM hot-spot analyses for transportation 
conformity determinations, shows that AERMOD is more than capable of 
being useful for a wide variety of transportation projects and that the 
performance has been more than adequate for even the most complicated 
projects.
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    \15\ 70 FR 68218, Revision to the Guideline on Air Quality 
Models: Adoption of a Preferred General Purpose (Flat and Complex 
Terrain) Dispersion Model and Other Revisions, November 9, 2005.
---------------------------------------------------------------------------

    Comments were also made with respect to potential longer AERMOD 
model run times and the time necessary to set up model files and obtain 
meteorological data. These statements are not entirely reflective of 
the EPA's experience to date in implementing the PM hot-spot 
requirement. The EPA believes that AERMOD has been used for more 
complicated projects, since PM hot-spot analyses are completed for 
projects that are often very large and involve different project 
components that significantly increase the number of diesel vehicles. 
By their nature, these types of transportation projects involve more 
time to set up and complete and few transportation modelers have 
actually run both CAL3QHCR and AERMOD for equivalent projects.\16\ In 
addition, volume sources have frequently been selected by implementers 
for AERMOD demonstrations, and this approach involves more time and 
effort in setting up the model runs, and more sources to be used than 
would be necessary with area sources. In addition, since AERMOD is 
already used in all 50 states for NSR purposes, meteorological input 
data for AERMOD are frequently prepared as a matter of course by the 
state and local air agencies and often made publicly available for 
download. Therefore, the EPA's understanding and experience is that the 
amount of time and resources necessary to create model inputs and 
complete PM hot-spot model simulations for AERMOD versus CAL3QHCR is 
not distinguishable from the overall process of running a traffic 
model, developing design alternatives for multiple purposes beyond 
conformity, and running the emissions model for the scenarios. In 
addition, as stated above and in the EPA's existing guidance, AERMOD 
has several advantages when conducting a PM hot-spot analysis: The 
ability to model a

[[Page 5192]]

variety of different transportation project types; the reliance on 
existing and more recent AERMET meteorological datasets obtained 
through the interagency consultation process; and additional 
capabilities that reduce the number of steps in conducting a PM hot-
spot analyses.\17\
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    \16\ Quantitative PM hot-spot analyses are not required for most 
new projects in PM nonattainment and maintenance areas, and most 
state departments of transportation have not been required to 
complete such an analysis to date for transportation conformity.
    \17\ See Sections 7 and 9 of EPA's 2015 Transportation 
Conformity Guidance for Quantitative Hot-Spot Analyses in 
PM2.5 and PM10 Nonattainment and Maintenance 
Areas. For example, Exhibit 7-2 in this guidance highlights that 
AERMOD can be used for all project types that require PM hot-spot 
analyses under the transportation conformity rule, and Exhibit 7-3 
clarifies the number of runs typically necessary for a PM hot-spot 
analysis with AERMOD (1-5 runs) versus CAL3QHCR (20 runs).
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    In response to the comments received and based on the analysis 
conducted by the EPA, the following actions are being taken in the 
final rulemaking:
     The EPA is replacing CALINE3 with AERMOD as the appendix A 
preferred model for refined modeling for mobile source applications. 
The EPA has reviewed the available literature and conducted its own 
analysis\13\ that demonstrates AERMOD provides superior performance to 
that of CALINE3 for refined applications. The EPA emphasizes that 
AERMOD has been the only model that is applicable to all types of 
projects, including highway interchanges and intersections; transit, 
freight, and other terminal projects; intermodal projects; and projects 
in which nearby sources also need to be modeled.\10\
     The EPA acknowledges that the implementation of AERMOD for 
all refined modeling may take time, as many state transportation 
departments are not yet experienced with the AERMOD modeling system. 
Many states may have attended one of the EPA's multiple trainings but 
have not been involved in a quantitative PM hot-spot analysis to date. 
Thus, we are providing an extended 3-year transition period before 
AERMOD is required as the sole dispersion model for refined modeling in 
transportation conformity determinations. In addition, any refined 
analyses for which the air quality modeling was begun before the end of 
this 3-year period with a CALINE3-based model can be completed after 
the end of the transition period with that model, similar to the way 
the transportation conformity grace period for new emissions models is 
implemented.
     The EPA acknowledges that there are limited demonstrations 
of using AERMOD for multi-source screening and that additional 
development work is necessary to develop an AERMOD-based screening 
approach for CO that satisfies the need for this type of analysis. 
Thus, we have modified section 4.2.3.1(b) of the Guideline to reference 
the EPA's 1992 CO guidance that employs CAL3QHC for CO screening 
analysis.\12\ This technical guidance will remain in place as the 
recommended approach for CO screening until such time that the EPA (1) 
develops a new CO screening approach based on AERMOD or another 
appropriate model and (2) updates the Guideline to include the new CO 
screening approach. The use of CAL3QHC for CO screening does not need 
to undergo the review process discussed in the Guideline section 
2.2(d). That review process is not necessary for CAL3QHC because its 
use is already well-established for CO hot-spot analyses and the review 
criteria have already been met.
     Finally, the EPA has formally recommended the 
establishment of a standing air quality modeling workgroup with the 
U.S. Department of Transportation, including the Federal Highway 
Administration, Federal Transit Administration, and FAA, to continue to 
evaluate and develop modeling practices for the transportation sector 
to ensure that future updates to dispersion models and methods reflect 
the latest available science and implementation.
    See the docket for this action for the Response to Comments 
document for this part of the proposal as well as the EPA's latest 
technical support document (TSD) for using AERMOD for CO hot-spot 
screening analyses.
5. Addressing Single-Source Impacts on Ozone and Secondary 
PM2.5
    As discussed in our proposed action, on January 4, 2012, the EPA 
granted a petition submitted on behalf of the Sierra Club on July 28, 
2010,\18\ which requested that the EPA initiate rulemaking regarding 
the establishment of 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 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 final action completes the 
rulemaking process described in the EPA's granting of the Sierra Club 
petition. As discussed in the proposal, the EPA has determined that 
advances in chemical transport 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 
or single source on ozone and secondary PM2.5. For assessing 
secondary pollutant impacts from single sources, the degree of 
complexity required to appropriately 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 
that allows for different approaches to credibly address these 
different areas, the EPA proposed a two-tiered demonstration approach 
for addressing single-source impacts on ozone and secondary 
PM2.5.
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    \18\ U.S. Environmental Protection Agency, 2012. Sierra Club 
Petition Grant. Administrative Action dated January 4, 2012, U.S. 
Environmental Protection Agency, Washington, District of Columbia 
20460. https://www3.epa.gov/ttn/scram/10thmodconf/review_material/Sierra_Club_Petition_OAR-11-002-1093.pdf.
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    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 (CTMs) (e.g., photochemical grid models) to be 
determined in consultation with the EPA Regional Offices and conducted 
consistent with the EPA single-source modeling guidance.\19\ The 
appropriate tier for a given application should be selected in 
consultation with the appropriate reviewing authority and be consistent 
with EPA guidance. We invited comments on whether our proposed two-
tiered demonstration approach and related EPA technical guidance are 
appropriately based on sound science and practical application of 
available models and tools to address single-source impacts on ozone 
and secondary PM2.5.
---------------------------------------------------------------------------

    \19\ U.S. Environmental Protection Agency, 2016. Guidance on the 
use of models for assessing the impacts of emissions from single 
sources on the secondarily formed pollutants ozone and 
PM2.5. Publication No. EPA 454/R-16-005. Office of Air 
Quality Planning and Standards, Research Triangle Park, NC.
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    Multiple commenters expressed support for the two-tiered approach 
for estimating single-source secondary impacts for permit-related 
programs, while other commenters did not support

[[Page 5193]]

a multi-tiered approach for this purpose. Commenters also sought 
flexibility in the first tier to allow for area-specific 
demonstrations, thereby avoiding the second tier assessments where 
chemical transport modeling may be part of the demonstration. Most 
commenters support the idea of developing Model Emissions Rates for 
Precursors (MERPs) for use as a Tier 1 demonstration tool, as described 
in the preamble of the proposed rule. However, some commenters 
expressed the need for more specific information about Tier 1 
demonstration tools, particularly MERPs. Furthermore, one commenter 
expressed concern about the particular use of demonstration tools, such 
as MERPs, not reflecting the combined ambient impacts across precursors 
and, in the context of PM2.5, in combining primary and 
secondary ambient impacts.
    The EPA has issued draft guidance for use by permitting authorities 
and permit applicants and deferred rulemaking at this time to address 
how permitting authorities may develop and use significant impact 
levels (SILs) for ozone and PM2.5. In addition, we are not 
establishing a single set of national MERPs through rulemaking as we 
had anticipated in the preamble of the proposed rule. Instead, the EPA 
developed a draft technical guidance document to provide a framework 
for permitting authorities to develop area-specific MERPs consistent 
with the Guidance on Significant Impact Levels for Ozone and Fine 
Particles in the Prevention of Significant Deterioration Permitting 
Program.\20\ Through this process, the EPA believes it has provided 
sufficient information regarding Tier 1 demonstration tools, such as 
MERPs. The draft MERPs technical guidance document \21\ illustrates how 
permitting authorities may appropriately develop MERPs for specific 
areas and use them as a Tier 1 demonstration tool for permit-related 
programs. This draft guidance also explicitly addresses the commenter 
concern regarding the appropriate use of MERPs such that their use 
reflects the combined ambient impacts across precursors and, in the 
case of PM2.5, the combined primary and secondary ambient 
impacts. This approach provides the flexibility requested by many 
commenters with respect to Tier 1 demonstration tools, such as MERPs, 
to generate information relevant for specific regions or areas rather 
than a single, national level that may not be representative of 
secondary formation in a particular region or area.
---------------------------------------------------------------------------

    \20\ U.S. Environmental Protection Agency, 2016. Guidance on 
Significant Impact Levels for Ozone and Fine Particles in the 
Prevention of Significant Deterioration Permitting Program. Office 
of Air Quality Planning and Standards, Research Triangle Park, NC.
    \21\ U.S. Environmental Protection Agency, 2016. Guidance on the 
Use of Modeled Emission Rates for Precursors (MERPs) as a Tier 1 
Demonstration Tool for Permit Related Programs. Publication No. EPA 
454/R-16-006. Office of Air Quality Planning and Standards, Research 
Triangle Park, NC.
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    Specifically, the draft MERPs technical guidance provides 
information about how to use CTMs to estimate single-source impacts on 
ozone and secondary PM2.5 and how these model simulation 
results can be used to develop empirical relationships for specific 
areas that may be appropriate as a Tier 1 demonstration tool. It also 
provides results from EPA photochemical modeling of multiple 
hypothetical situations across geographic areas and source types that 
may be used in developing MERPs consistent with the guidance or with 
supplemental modeling in situations where the EPA's modeling may not be 
representative. This flexible and scientifically credible approach 
allows for the development of area-specific Tier 1 demonstration tools 
that better represent the chemical and physical characteristics and 
secondary pollutant formation within that region or area.
    The draft MERPs technical guidance \21\ and the EPA's draft single-
source modeling guidance \19\ provide information to stakeholders about 
how to appropriately address the variety of chemical and physical 
characteristics regarding a project scenario and key receptor areas 
that should be addressed in conducting additional modeling to inform 
development of MERPs. The development of MERPs for ozone and secondary 
PM2.5 precursors is just one example of a suitable Tier 1 
demonstration tool. 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 demonstrations.
    Commenters also stated that requiring chemical transport modeling 
as a Tier 2 demonstration tool places undue burden financially on the 
states, as they do not have the expertise to run or review such models, 
and that the regulated community does not have the expertise to run 
such models. Commenters requested a clearer rationale and procedure for 
applying CTMs for the purposes of estimating single-source secondary 
impacts for permit-related programs. In response, the EPA believes that 
its technical guidance on single-source modeling provides both the 
clarity necessary to conduct such modeling and the flexibility 
appropriate to address such situations.
    First, based on peer-reviewed assessments of models used for 
estimating ozone and secondary PM2.5 for single-source 
impacts, the EPA continues to recommend that CTMs (including 
photochemical grid models or Lagrangian models) be used where a more 
refined Tier 2 demonstration for ozone or secondary PM2.5 
may be necessary. Given interest in the stakeholder community in 
different types of CTMs for the purposes of estimating single-source 
impacts for permit-related programs, and that these models, where 
applied appropriately, are fit for this purpose, selection of a single 
model for preferred status under the Guideline would impede sources 
from using a model or technique deemed most appropriate for specific 
situations, recognizing the diversity in chemical and physical 
environments across the United States.
    Second, as discussed above, the EPA expects that the use of MERPs 
(or a similarly credible screening approach) as a Tier 1 demonstration 
tool will be sufficient for most sources to satisfy their compliance 
demonstration. For those situations where a refined Tier 2 
demonstration is necessary, the EPA has provided detailed single-source 
modeling guidance with clear and credible procedures for estimating 
single-source secondary impacts from sources doing permit related 
assessments. The EPA has future plans to provide a module as part of 
its Software for Model Attainment Test (SMAT) tool, a publicly 
available, Windows-based program, that will allow users to work with 
output generated from CTMs to provide a consistent approach for 
estimating single-source ozone or secondary PM2.5 impacts 
consistent with EPA guidance and the Guideline.
    Multiple commenters do not agree that photochemical grid models can 
adequately assess single-source impacts. A commenter recognized that 
photochemical grid model evaluations using in-plume traverses are 
encouraging as documented in the IWAQM reports, but stated that more 
work is needed to generate additional confidence in the technique, and 
further requests that the EPA use newer field study data from 2013 to 
evaluate CTM performance against in-plume transects of ozone and 
secondary PM2.5.
    As referenced in the preamble to the proposal, the EPA has relied 
upon extensive peer-reviewed literature showing that photochemical grid 
models have been applied for single-

[[Page 5194]]

source impacts and, compared with near-source downwind in-plume 
measurements, that the models adequately represent secondary pollutant 
impacts from a specific facility. The literature shows that these 
models can clearly differentiate impacts of a specific facility from 
those of other sources.22 23 Other peer-reviewed research 
has clearly shown that photochemical grid models are able to simulate 
impacts from single sources on secondarily-formed 
pollutants.24 25 26 Further, single-source secondary impacts 
have been provided in technical reports that further support the 
utility of these tools for single-source scientific and regulatory 
assessments.27 28 29 The EPA firmly believes that the peer-
reviewed science clearly demonstrates that photochemical grid models 
can adequately assess single-source impacts. The EPA recognizes that 
ongoing evaluations in this area that will lead to continual 
improvements in the applicability of these models, such as the work 
underway to compare photochemical grid model estimates of single-source 
impacts with in-plume aircraft measurements made as part of the 2013 
SENEX field campaign.\30\
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    \22\ Baker, K.R., Kelly, J.T., 2014. Single Source Impacts 
Estimated with Photochemical Model Source Sensitivity and 
Apportionment Approaches. Atmospheric Environment 96, 266-274.
    \23\ 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.
    \24\ Baker, K.R., Kotchenruther, R.A., Hudman, R.C., 2015. 
Estimating Ozone and Secondary PM2.5 Impacts from 
Hypothetical Single Source Emissions in the Central and Eastern 
United States. Atmospheric Pollution Research 7, 122-133.
    \25\ 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.
    \26\ Kelly, J.T., Baker, K.R., Napelenok, S.L., Roselle, S.J., 
2015. Examining Single-Source Secondary Impacts Estimated from 
Brute-force, Decoupled Direct Method, and Advanced Plume Treatment 
Approaches. Atmospheric Environment 111, 10-19.
    \27\ ENVIRON, 2012a. Comparison of Single-Source Air Quality 
Assessment Techniques for Ozone, PM2.5, other Criteria 
Pollutants and AQRVs, EPA Contract No: EP-D-07-102. September 2012. 
06-20443M6.
    \28\ ENVIRON, 2012b. Evaluation of Chemical Dispersion Models 
Using Atmospheric Plume Measurements from Field Experiments, EPA 
Contract No: EP-D-07-102. September 2012. 06-20443M6.
    \29\ Yarwood, G., Scorgie, Y., Agapides, N., Tai, E., 
Karamchandani, P., Duc, H., Trieu, T., Bawden, K., 2011. Ozone 
Impact Screening Method for New Sources Based on High-order 
Sensitivity Analysis of CAMx Simulations for NSW Metropolitan Areas.
    \30\ National Oceanic & Atmospheric Administration. Southeast 
Nexus (SENEX) 2013. Studying the Interactions Between Natural and 
Anthropogenic Emissions at the Nexus of Climate Change and Air 
Quality. http://www.esrl.noaa.gov/csd/projects/senex.
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    Commenters requested that the EPA consider Lagrangian CTMs for use 
in assessing single-source secondary impacts. A commenter proposed that 
the Second-order Closure Integrated Puff Model (SCICHEM) can provide an 
alternative modeling platform for all single-source regulatory 
applications including ozone and secondary PM2.5 impacts. 
Commenters note that SCICHEM does not suffer from limitations of other 
Lagrangian puff models with respect to overlapping puffs having similar 
access to background species as noted in the EPA's single-source 
modeling guidance.
    The proposed revisions to the Guideline and EPA's single-source 
modeling guidance clearly indicate that CTMs are appropriate for 
estimating single-source impacts on ozone and secondary 
PM2.5 as a Tier 2 demonstration tool or as means to develop 
a Tier 1 demonstration tool. Both Lagrangian puff models and 
photochemical grid models may be appropriate for this purpose where 
those models fulfill alternative model criteria detailed in section 
3.2.2 of the Guideline. Furthermore, the single-source modeling 
guidance has been updated to reflect the difference in treatment of 
overlapping puffs and background in SCICHEM compared to other 
Lagrangian puff models. However, 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.23 34 Publicly 
available and documented Eulerian photochemical grid models such as the 
Comprehensive Air Quality Model with Extensions (CAMx) \31\ and the 
Community Multiscale Air Quality (CMAQ) \32\ 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 
PM2.5.33 34 35 36 In addition, 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.23 37 38
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    \31\ ENVIRON, 2014. User's Guide Comprehensive Air Quality Model 
with Extensions version 6, http://www.camx.com. ENVIRON 
International Corporation, Novato.
    \32\ 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.
    \33\ Chen, J., Lu, J., Avise, J.C., DaMassa, J.A., Kleeman, 
M.J., Kaduwela, A.P., 2014. Seasonal Modeling of PM2.5 in 
California's San Joaquin Valley. Atmospheric Environment, 92: 182-
190.
    \34\ 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.
    \35\ 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.
    \36\ 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.
    \37\ 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.
    \38\ 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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    The EPA is promulgating the two-tiered demonstration approach as 
described in section 5 of the Guideline and updating EPA technical 
guidance that was released at the time of proposal in response to 
public comments. These revisions to the Guideline and supporting 
technical guidance are based on sound science and practical application 
of available models and tools to address single-source impacts on ozone 
and secondary PM2.5. In particular, the EPA has updated its 
previous PM2.5 modeling guidance for permitting \39\ to 
reflect these changes and also incorporated appropriate sections for 
ozone in releasing its Guidance for Ozone and PM2.5 Permit 
Modeling \40\ with this final rule.
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    \39\ U.S. Environmental Protection Agency, 2014. Guidance for 
PM2.5 Modeling. May 20, 2014. Publication No. EPA-454/B-
14-001. Office of Air Quality Planning and Standards, Research 
Triangle Park, NC.
    \40\ U.S. Environmental Protection Agency, 2016. Guidance for 
Ozone and PM2.5 Permit Modeling. Publication No. EPA-454/
B-16-005. Office of Air Quality Planning and Standards, Research 
Triangle Park, NC.
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6. Status of CALPUFF and Assessing Long-Range Transport for PSD 
Increments and Regional Haze
    The EPA proposed a 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

[[Page 5195]]

to consider CALPUFF as a screening technique along with other 
Lagrangian models to be used in consultation with the appropriate 
reviewing authority. 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 grid models (which address limitations of models like 
CALPUFF \41\), the EPA proposed that the Guideline no longer contain 
language that requires the use of CALPUFF or another Lagrangian puff 
model for long-range transport assessments. The EPA did recognize that 
long-range transport assessments may be necessary in certain limited 
situations for PSD increments, particularly for Class I areas. For 
these situations, the EPA proposed a screening approach where CALPUFF, 
along with other appropriate screening tools and methods, may be used 
to support long-range transport assessments of PSD increments.
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    \41\ U.S. Environmental Protection Agency, 2016. Reassessment of 
the Interagency Workgroup on Air Quality Modeling Phase 2 Summary 
Report; Revisions to Phase 2 Recommendations. Publication No. EPA-
454/R-16-007. Office of Air Quality Planning and Standards, Research 
Triangle Park, NC.
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    We received comment that there may also be certain situations where 
long-range transport assessments of NAAQS compliance may be necessary 
because either near-field NAAQS compliance is not required or the 
nearest receptors of concern are greater than 50 km (e.g., many Outer 
Continental Shelf sources). We agree with this comment and are amending 
the proposed screening approach in section 4.2 of the Guideline to also 
include a long-range assessment of NAAQS compliance, when appropriate. 
Specifically, to determine if NAAQS or PSD increments analyses may be 
necessary beyond 50 km (i.e., long-range transport assessment), the EPA 
is updating its recommended screening approach to cases where near-
field NAAQS compliance is not required or the nearest receptors of 
concern are greater than 50 km away.
    Some commenters also expressed concern about the appropriateness of 
the EPA's technical basis for establishing the long-range transport 
screening assessment and, in particular, the appropriateness of the 
ambient levels used as benchmarks for evaluating the hypothetical 
source impacts. To support the EPA's proposed approach for long-range 
transport, we provided a TSD that demonstrated the level of single-
source impacts from a variety of facility types.\42\ The facility 
impacts were compared to benchmark ambient values for NO2, 
SO2, PM10, and PM2.5 in order to 
determine which facility types and pollutants might have impacts above 
these levels at 50 km from the source. The comments on the proposal 
indicated confusion about which values were applied in the TSD and, in 
particular, confusion about values used for Class I areas for both 
NAAQS and PSD increments. The EPA believes that because each NAAQS is 
uniform throughout the class areas, no class-specific protection is 
necessary when assessing whether a source causes or contributes to a 
violation of the NAAQS. Thus, for all NAAQS analyses, a uniform set of 
benchmark ambient values were used in the TSD across all class areas. 
However, the EPA recognizes that, historically, Congress has provided 
special protections to Class I areas, via more protective PSD 
increments. Thus, for all PSD increments analyses detailed in the TSD, 
more conservative benchmark ambient values applicable to Class I areas 
for PSD increments were used. The EPA has updated the TSD to more 
clearly reflect these conditions and alleviate the confusion on behalf 
of the commenters. These modifications do not affect the results or 
conclusions from the analysis or the finalization of the EPA's approach 
for long-range transport screening.
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    \42\ U.S. Environmental Protection Agency, 2015. Technical 
Support Document (TSD) for AERMOD-Based Assessments of Long-Range 
Transport Impacts for Primary Pollutants. Publication No. EPA-454/B-
15-003. Office of Air Quality Planning and Standards, Research 
Triangle Park, NC.
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    A number of commenters expressed concern about the EPA's proposed 
removal of CALPUFF as the preferred long-range transport model in 
appendix A and do not support its removal without replacement. Other 
commenters indicated that a lack of an EPA-preferred long-range 
transport model increases uncertainty in performing Class I PSD 
increment analyses or could lead to inconsistent modeling approaches 
for such analyses. Also, many of these same commenters expressed 
concerns about the need for its approval as an alternative model and 
the additional time that such a process would entail.
    The EPA has presented a well-reasoned and technically sound 
screening approach for long-range transport assessments for NAAQS and 
PSD increments that streamlines the time and resources necessary to 
conduct such analyses and provides for appropriate flexibility in the 
use of CALPUFF or other Lagrangian models as a screening technique. To 
address concerns by commenters related to the approval of CALPUFF or 
other Lagrangian model in this screening approach, the EPA has modified 
section 4.2.1 of the Guideline to specifically recognize the use of 
Lagrangian models as an appropriate screening technique, for this 
purpose, that does not need to be approved by the EPA as an alternative 
model. Rather, the selection of specific model and model parameters 
must be done in consultation with the appropriate reviewing authority 
and EPA Regional Office. We consider the flexibility in selection of 
the appropriate screening technique provided by this long-range 
screening approach to be critically important for applicants to apply 
the most suitable technical basis to inform these complex situations. 
To the extent that a cumulative impact analysis is necessary at 
distances beyond 50 km, then the use of a Lagrangian or other model is 
subject to approval under section 3.2.2(e) of the Guideline. In 
response to commenter concerns about the additional time and potential 
delays associated with such approvals, as discussed in more detail 
later in this preamble, the EPA disagrees with such contentions and 
notes that the recently observed average response time of MCH 
concurrences on alternative models is less than a month.
    Some commenters also stated that the EPA had not provided 
sufficient scientific or technical justification for removal of CALPUFF 
in appendix A, while other commenters supported the removal of CALPUFF 
as a preferred model. One commenter provided detailed information 
documenting the inconsistent nature of CALPUFF performance to more 
fully support the EPA's proposed action to remove it as a preferred 
model. As detailed in the Response to Comments document, the EPA has 
fully documented the past and current concerns related to the 
regulatory use of the CALPUFF modeling system and believes that these 
concerns, including the well-documented scientific and technical issues 
with the modeling system, support the EPA's decision to remove it as a 
preferred model in appendix A of the Guideline. In addition, there was 
no substantive or technical information submitted in the public 
comments that would lead the EPA to reconsider its documented concerns 
about the CALPUFF modeling system and its regulatory use.
    In addition, a few commenters recommended that the EPA consider 
Lagrangian CTMs to address long-range transport from single sources. In 
this regard, some commenters mentioned the

[[Page 5196]]

more advanced version of CALPUFF for consideration here and 
specifically proposed that the SCICHEM model can also provide an 
alternative modeling platform for all single-source regulatory 
applications including ozone and secondary PM2.5 impacts. In 
addition, they noted that SCICHEM does not suffer from limitations of 
other Lagrangian puff models with respect to overlapping puffs having 
similar access to background species as noted in the EPA's single-
source modeling guidance. While the information provided by commenters 
is not sufficient for the EPA to adopt a replacement to CALPUFF as an 
appendix A model for long-range transport, this information clearly 
indicates that there are other models available and potentially 
suitable for use in these situations. Given the EPA's determination 
regarding the appropriateness of using current models and tools to 
address single-source impacts on ozone and secondary PM2.5, 
we will continue to work with the modeling community on the development 
and evaluation of models that may be suitable for future consideration 
as preferred models to meet long-range assessment needs, as well as 
broader use in demonstrating compliance with NAAQS and PSD increments. 
Such developments would further strengthen the scientific credibility 
of the models and approaches used under the Guideline and continue to 
streamline their regulatory application through use of integrated 
models with capabilities to address multiple pollutants.
    As previously noted in the proposed rule, Phase 3 of the IWAQM 
process was reinitiated in June 2013 to further 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 workgroup composed of EPA and Federal 
Land Managers (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 CTMs as detailed in IWAQM reports 43 44 and 
published literature.
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    \43\ U.S. Environmental Protection Agency, 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 and Standards, Research Triangle 
Park, NC.
    \44\ U.S. Environmental Protection Agency, 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 and Standards, Research 
Triangle Park, NC.
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    To inform future consideration of visibility modeling in regulatory 
applications consistent with the EPA's guidance for addressing 
chemistry for single-source impact on ozone and secondary 
PM2.5, the final report \44\ 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. 
Such CTMs 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 gas, particle, and aqueous 
phase chemistry for PM2.5, haze, and ozone.
    Photochemical grid 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 \24\ \27\ and over long 
distances.\45\
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    \45\ 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. 
https://www3.epa.gov/ttn/scram/reports/EPA-454_R-12-003.pdf.
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    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.\45\ 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 
3.2.2 of the Guideline, and concurrence sought with the affected 
FLM(s).
    As proposed, with revisions discussed above, we are taking final 
action to codify the screening approach to address long-range transport 
for purposes of assessing NAAQS and/or PSD increments; removing CALPUFF 
as a preferred model in appendix A for such long-range transport 
assessments; and confirming our recommendation to consider CALPUFF as a 
screening technique along with other Lagrangian models that may be used 
as part of this screening approach without alternative model approval. 
As detailed in the preamble of the proposed rule, it is important to 
note that the EPA's final action to remove CALPUFF as a preferred 
appendix A model in this Guideline does not affect its use under the 
FLM's guidance regarding AQRV assessments (FLAG 2010) nor any previous 
use of this model as part of regulatory modeling applications required 
under the CAA. Similarly, this final action does not affect the EPA's 
recommendation that states use CALPUFF to determine the applicability 
and level of best available retrofit technology in regional haze 
implementation plans.\46\ It is also important to note that the use of 
CALPUFF in the near-field as an alternative model for situations 
involving complex terrain and complex winds is not changed by removal 
of CALPUFF as a preferred model in appendix A. The EPA recognizes that 
AERMOD, as a Gaussian plume dispersion model, may be limited in its 
ability to appropriately address such situations, and that CALPUFF or 
other Lagrangian model may be more suitable, so we continue to provide 
the flexibility of alternative model approvals (as has been in place 
since the 2003 revisions to the Guideline).
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    \46\ See 70 FR 39104, 39122-23 (July 6, 2005).
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7. Role of EPA's Model Clearinghouse (MCH)
    We proposed to codify our existing practice of requiring 
consultation and coordination between the EPA Regional Offices and the 
EPA's MCH on all approvals (under section 3.2.2 of the Guideline) of 
alternative models or techniques. This coordination process has been in 
practice for almost three decades during which the MCH has served a 
critical role in helping resolve issues that have arisen from unique 
situations that were not specifically addressed in the Guideline or 
necessitated the consideration of an alternative model or technique for 
a

[[Page 5197]]

specific application or range of applications. However, the most 
comprehensive documentation of this coordination process was a 1988 EPA 
memorandum to the EPA Regional Offices defining the Model Clearinghouse 
Operational Plan,\47\ which was not widely available to the regulated 
modeling community until it is was included in the docket for the 
proposed rule. In response to the proposal and docketed information, 
the EPA received a wide range of comments regarding the MCH and the 
related proposed revisions to the Guideline.
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    \47\ U.S. Environmental Protection Agency, 2016. Model 
Clearinghouse: Operational Plan. Publication No. EPA-454/B-16-008. 
Office of Air Quality Planning and Standards, Research Triangle 
Park, NC.
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    The majority of the commenters expressed varying levels of concern 
with the potential for significant delay to the permit review process 
if all the EPA Regional Office alternative model approvals were 
required to seek concurrence from the MCH. Several commenters suggested 
that the current process, as defined in the existing Guideline, is 
appropriate and should not be changed. Other commenters stated that the 
current MCH process is slow, cumbersome, and in many ways, not needed. 
Certain industry commenters recommended the establishment of specific 
timeline requirements for the EPA Regional Office and MCH alternative 
model approvals. Other industry comments recommended the establishment 
of an external review committee for alternative model approvals and/or 
an external advisory group to recommend additional changes to the MCH 
process. Finally, there were a few comments expressing concern that the 
MCH process is not well-known and that decisions by the MCH are not 
widely disseminated.
    With regard to comments about possible delay to the approval 
process for an alternative model, it is important to point out that the 
revisions to the Guideline are only codifying an existing process 
between the EPA Regional Offices and the Model Clearinghouse. 
Therefore, the administrative processing time for these approvals 
should not be affected by codifying the existing process. In fact, we 
anticipate that this action will further streamline the process by 
clarifying it for the regulatory modeling community. Additionally, the 
revisions will ensure fairness, consistency, and transparency in 
modeling decisions across all EPA Regional Offices. Additional 
important aspects of these revisions were noted and supported through 
comment by several state air permitting agencies, an organization 
representing the state agencies, and a large industrial trade 
organization.
    It is important to note that the EPA's MCH has formally accepted 
and concurred with five alternative model requests from the EPA 
Regional Offices since proposal of this rule. The average MCH response 
time for those five requests was 28 days. There was some variability in 
the timing of these formal concurrences with one of the concurrences 
being completed within less than a day; three of the concurrences 
taking approximately 22 days; and one of the more complex requests 
taking slightly longer than 2 months. The range of MCH response times 
over the past year is indicative of applicants that have either engaged 
early with their respective EPA Regional Office through vetting of a 
modeling protocol and the identification and coordination of 
significant issues prior to submittal of their modeling compliance 
demonstration, or applicants that have performed a substantial amount 
of modeling work and justification documentation prior to any 
engagement with the EPA Regional Office or MCH.
    When applicants do not engage with the EPA early in the process, 
additional time is often needed for the justification of the 
alternative model or options selected and/or remodeling of their 
facility based on issues realized through review by the EPA. In a few 
cases, the approach desired by an applicant had to be completely 
reworked from the beginning, which created significant delays in the 
permit review and approval process. Early engagement with the EPA will 
result in the shortest amount of time needed for any alternative model 
approval by the Agency. However, complex situations involving 
facilities with unique issues, and requesting a completely new or novel 
alternative model approach, will require additional time for the 
applicant, the appropriate reviewing authority, the EPA Regional 
Office, and the EPA's MCH to collaboratively work together through an 
informed and iterative process to achieve an approvable alternative 
model submittal. For these reasons and the recently observed response 
time of MCH concurrences on alternative models of less than a month, we 
believe that it is unwarranted to impose a regulatory time limit on the 
MCH concurrence process. The revised Model Clearinghouse Operational 
Plan outlines the MCH process by defining the roles and 
responsibilities of all parties, providing thorough descriptions and 
flow diagrams, referencing the current databases that store all formal 
MCH decisions, making available templates for request memoranda and 
other pertinent information, and providing ``best practice'' examples 
of request memoranda that highlight how to best inform the MCH process. 
We believe these enhancements will increase clarity and understanding 
of this process and make the imposition of a regulatory time limit 
unnecessary. This Model Clearinghouse Operational Plan is included in 
the docket and available on the EPA's SCRAM Web site.
    The suggestion by commenters to use an external review committee 
for alternative model approvals is unnecessary and inappropriate. The 
CAA requires that air quality models are specified by the EPA 
Administrator. Any modification or substitution of a regulatory model 
under the Guideline can only be made with written approval of the 
Administrator. The delegation of this preferred model or alternative 
model approval process can only occur within the EPA. Also, an external 
review committee would add another layer of review and coordination to 
the prerequisite EPA processes and would ultimately result in delays in 
the overall permit review and approval process. Aside from future 
regulatory revisions of the Guideline, the EPA is required per CAA 
section 320 to conduct a Conference on Air Quality Modeling at least 
every 3 years, at which time formal public comment on the MCH process 
or any other aspect of the Guideline can be provided. The EPA believes 
that the current process demonstrates our continued commitment to 
provide the regulatory community with scientifically credible models 
and techniques developed through collaborative efforts, which are 
provided in updates to the Guideline.
    In this action, as proposed, we are codifying the long-standing 
process of the EPA Regional Offices consulting and coordinating with 
the MCH on all approvals of alternative models or techniques. While the 
Regional Administrators are the delegated authority to issue such 
approvals under section 3.2.2 of the Guideline, all alternative model 
approvals will be issued only after consultation with the EPA's MCH and 
formal documentation through a concurrence memorandum that indicates 
that the alternative model requirements in section 3.2.2 have been met.
8. Updates to Modeling Procedures for Cumulative Impact Analysis
    As discussed in the preamble to our proposed action, based on input 
from the Tenth Modeling Conference and

[[Page 5198]]

recent permit modeling experiences under the 1-hour NAAQS for 
SO2 and NO2, we proposed revisions in section 8 
of the Guideline and associated guidance to provide the necessary 
clarification in selecting and establishing the model domain and inputs 
for conducting the regulatory modeling for PSD and SIP applications. In 
addition to solicited public feedback on section 8, we received 
numerous public comments with respect to section 9 of the Guideline, 
which is revised to more clearly summarize the general concepts 
represented throughout the Guideline and set the stage for appropriate 
regulatory application of models and/or, in rare circumstance, air 
quality monitoring data.
    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.48 49 50 51 The EPA has specifically 
cautioned against the literal and uncritical application of very 
prescriptive procedures for conducting NAAQS and PSD increments 
modeling compliance demonstrations as described in chapter C of the 
1990 draft New Source Review Workshop Manual.\52\ Following such 
procedures in a literal and uncritical manner has led to practices that 
are overly conservative and unnecessarily complicate the permitting 
process.
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    \48\ U.S. Environmental Protection Agency, 2010. Applicability 
of Appendix W Modeling Guidance for the 1-hour NO2 NAAQS. Memorandum 
dated June 28, 2010, Office of Air Quality Planning and Standards, 
Research Triangle Park, NC. https://www3.epa.gov/ttn/scram/guidance/clarification/ClarificationMemo_AppendixW_Hourly-NO2-NAAQS_FINAL_06-28-2010.pdf.
    \49\ U.S. Environmental Protection Agency, 2010. Applicability 
of Appendix W Modeling Guidance for the 1-hour SO2 NAAQS. 
Memorandum dated August 23, 2010, Office of Air Quality Planning and 
Standards, Research Triangle Park, NC. https://www3.epa.gov/ttn/scram/guidance/clarification/ClarificationMemo_AppendixW_Hourly-SO2-NAAQS_FINAL_08-23-2010.pdf.
    \50\ U.S. Environmental Protection Agency, 2011. Additional 
Clarification Regarding Applicability of Appendix W Modeling 
Guidance for the 1-hour NO2 NAAQS. Memorandum dated March 
1, 2011, Office of Air Quality Planning and Standards, Research 
Triangle Park, NC. https://www3.epa.gov/ttn/scram/guidance/clarification/Additional_Clarifications_AppendixW_Hourly-NO2-NAAQS_FINAL_03-01-2011.pdf.
    \51\ U.S. Environmental Protection Agency, 2014. Clarification 
on the Use of AERMOD Dispersion Modeling for Demonstrating 
Compliance with the NO2 National Ambient Air Quality 
Standard. Memorandum dated September 30, 2014, Office of Air Quality 
Planning and Standards, Research Triangle Park, NC. https://www3.epa.gov/ttn/scram/guidance/clarification/NO2_Clarification_Memo-20140930.pdf.
    \52\ U.S. Environmental Protection Agency, 1990. New Source 
Review Workshop Manual: Prevention of Significant Deterioration and 
Nonattainment Area Permitting (Draft). Office of Air Quality 
Planning and Standards, Research Triangle Park, NC. https://www.epa.gov/nsr.
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    Commenters were supportive of the addition of the definition of the 
modeling domain, including the appropriate factors to consider, for 
NAAQS and PSD increments assessments and for SIP attainment 
demonstrations in section 8 of the Guideline. However, several 
commenters stated that the discussion in the proposed Guideline could 
result in conservatively large modeling domains regularly extending to 
50 km. A typographical error was identified in that discussion that may 
have caused this confusion and is corrected in this final rule. With 
this correction, it is now clear that the modeling domain or proposed 
project's impact area is defined as an area with a radius extending 
from the new or modifying source to: (1) The most distant location 
where air quality modeling predicts a significant ambient impact will 
occur, or (2) the nominal 50 km distance considered applicable for 
Gaussian dispersion models, whichever is less [emphasis added]. In most 
situations, the extent to which a significant ambient impact could 
occur from a new or modifying source likely will be considerably less 
than 50 km.
    Commenters also were supportive of the expanded discussion of 
receptor sites in section 9 of the Guideline. There were several 
requests for additional considerations for the potential exclusion of 
receptors from the modeling domain based on various factors. Along 
these lines, a few commenters requested that we add a formal definition 
of ``ambient air'' into the Guideline and provide specific exceptions 
to allow for the exclusion of certain receptors. The definition of 
``ambient air'' and related provisions are provided in 40 CFR 50.1(e). 
Principles for justifying exclusion of particular areas from this 
definition of ``ambient air'' are discussed in EPA guidance for the PSD 
program. The EPA has not proposed to revise this definition or how the 
EPA has interpreted it in guidance. Thus, we do not believe it is 
necessary to address this topic within the Guideline.
    There was overwhelming support by the stakeholder community for 
revisions to the Guideline that would bring additional clarity and 
flexibility concerning the process of determining background 
concentrations used in constructing the design concentration, or total 
air quality concentration, as a part of a cumulative impact analysis 
for NAAQS and PSD increments. There were, however, numerous specific 
public comments highlighting typographical errors or requesting 
additional clarifications on particular details of this process. Where 
appropriate, revisions were made to the Guideline to address many of 
these comments. A few of the public comments identified concerns that 
we have already addressed within other portions of the Guideline or 
desired more technical detail than is necessary in regulatory text and 
are best addressed through updates to existing technical guidance.
    In particular, there were numerous requests to further clarify the 
analysis of significant concentration gradients from ``nearby 
sources,'' as used in the selection of which nearby sources should be 
explicitly modeled in a cumulative impact assessment under PSD. In the 
proposed revisions to the Guideline, we expanded the concept of 
significant concentration gradients from the previous version of the 
Guideline. Given the uniqueness of each modeling situation and the 
large number of variables involved in identifying nearby sources, we 
continue to believe that comprehensively defining significant 
concentration gradients in the Guideline is inappropriate and could be 
unintentionally and excessively restrictive. Rather, the identification 
of nearby sources to be explicitly modeled is regarded as an exercise 
of professional judgment to be accomplished jointly by the applicant 
and the appropriate reviewing authority. Following this final action, 
we will continue to work with the stakeholder community to clarify and 
improve upon the existing technical guidance and associated approaches 
that could be used to develop and analyze significant concentrations 
gradients from nearby sources.
    We received numerous comments from the stakeholder community 
supporting the proposed revisions to Tables 8-1 and 8-2 that allow for 
the modeling of nearby sources using a representation of average actual 
emissions based on the most recent 2 years of normal source operation. 
Typographical errors were noted in the public comments and have 
subsequently been corrected in both of these tables. The public 
comments also include additional recommendations for alternate 
procedures to develop or calculate actual emissions; however, these 
commenters either did not include substantive technical support for 
these recommendations or they were inconsistent with the required 
application of the preferred appendix A model.

[[Page 5199]]

    Several commenters from the industrial sector suggested that the 
Guideline should be further amended to allow modeling approaches that 
account for emissions variability in NSR permitting for new and 
modifying sources. Additionally, there was public comment that highly 
intermittent sources should be categorically excluded from NAAQS 
assessments for statistically-based short-term standards. The emissions 
variability approaches and exclusion of highly intermittent sources 
would be a significant departure from long-standing EPA policy in the 
NSR program and are not addressed in the Guideline. If there are future 
revisions to the NSR program that would allow for such considerations, 
then appropriate revisions to the Guideline would be considered at that 
time.
    A few public comments expressed concern with our recommendation of 
using the current monitored design value as the background ambient 
concentration to be included with any explicitly modeled nearby sources 
and the estimated modeled impact of the source for comparison to the 
appropriate NAAQS in PSD assessments. The concern expressed in the 
comments is that this practice is exceedingly conservative and results 
in very unrealistic characterizations of the design concentration. We 
agree that certain combinations of monitored background data and 
modeled concentrations can lead to overly conservative assessments. 
However, we also point out that section 8.3.2(c) of the Guideline 
clearly states that the best starting point for many cases is the use 
of the current design value, but there are many cases in which the 
current design value may not be appropriate. We then provide four 
example cases where the use of the current monitored design value is 
not appropriate and further state that this list of examples is not 
exhaustive such that other cases could be considered on a case-by-case 
basis with approval by the appropriate reviewing authority.
    The modeling protocols discussion at the beginning of section 9 of 
the Guideline received a few public comments. One commenter wanted the 
discussion to be less prescriptive and not require involvement of the 
EPA Regional office for every protocol. Another commenter wanted the 
EPA to establish specific deadlines for approvals (or disapprovals) of 
modeling protocols. We are aware that the discussion on modeling 
protocols does not contain any specific requirements for applicants or 
permit reviewing authorities. Rather, the modeling protocol discussion 
is provided to recommend best practices to streamline the regulatory 
modeling process and avoid unnecessary work and additional permit 
delays. Given the added complexity of the technical issues that arise 
in the context of demonstrating regulatory compliance through air 
quality modeling, we strongly encourage the development of 
comprehensive modeling protocols by the applicants and a thorough 
vetting of these protocols by the appropriate reviewing authority prior 
to the start of any work on a project. In circumstances where 
alternative models or non-Guideline procedures are being considered, it 
is advisable to also include the EPA Regional Office in the initial 
protocol meeting if it is not the primary permit reviewing authority.
    Finally, there were a few general comments on the discussion of 
NAAQS and PSD increments compliance demonstrations within section 9 of 
the Guideline. Some of those comments offered additional suggestions 
for revisions to the Guideline that are addressed in the Response to 
Comments document located in the docket for this action. In particular, 
one commenter criticized the multi-stage process recommended by the 
EPA, which has been applied in the PSD program for more than 25 years. 
The commenter argued that a cumulative impact analysis must always be 
conducted and that there was no other rational way to show that a new 
or modifying source will not cause or contribute to a violation of the 
NAAQS or PSD increments. In this context, the commenter argued against 
the use of ``significant impact levels'' to show, based on a single-
source analysis, that an individual source does not cause or contribute 
to a violation of the NAAQS or PSD increments. The EPA has revised 
section 9.2.3 of the proposed Guideline to make more clear that this 
two-stage approach is a recommendation and not a requirement. To the 
extent this recommendation is followed, interested parties retain the 
opportunity to comment on the adequacy of a single-source analysis and 
to call for a cumulative impact analysis to make the required 
demonstration in the context of individual permits.
    Further, the EPA is not establishing SILs in this rulemaking and 
did not intend to codify the use of these values in the Guideline. Our 
use of the term ``significant impact'' was intended to carry forward 
principles previously reflected in sections 10.2.1(b), 10.2.1(c) and 
10.2.3.2(a) of the 2005 version of the Guideline. To make clear that 
this rule is not codifying the application of SILs and is only 
describing the outline of a recommended multi-stage process for making 
the required demonstration, we have removed the term ``significant 
impact'' from many parts of section 9.2.3. In a separate guidance,\20\ 
the EPA has provided a legal and technical rationale that permitting 
authorities may consider adopting to support the use of ``significant 
impact levels'' to quantify a degree of concentration impact below 
which a source does not have the potential to cause or contribute to a 
violation. This rationale, which is not adopted by the EPA in this 
rule, differs in material respects from the basis for a prior EPA 
rulemaking to adopt SILs that this commenter criticized.
    As proposed, we are finalizing revisions to sections 8 and 9 of the 
Guideline to add necessary clarity where requested by public commenters 
and to correct typographical errors. The EPA fully expects that, by 
providing more clarity in the Guideline of the factors to be considered 
in conducting both the single-source impact and cumulative impact 
assessments, permit applicants and permitting authorities will find the 
proper balance across the various competing factors that contribute to 
these analyses.
9. Updates on Use of Meteorological Input Data for Regulatory 
Dispersion Modeling
    The EPA solicited comments on the proposed updates regarding use of 
meteorological input data for regulatory application of dispersion 
models, including the use of 2-minute Automated Surface Observing 
Stations (ASOS) for hourly average winds to replace standard hourly 
observations, and the use of prognostic meteorological data for areas 
where there is no representative NWS data and it is infeasible or 
prohibitive to collect site-specific data.
    For near-field dispersion modeling applications using NWS ASOS 
sites, 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, and thus reduces 
the number of calms and missing winds for input to 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

[[Page 5200]]

use of ASOS data in AERMOD,\53\ 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.
---------------------------------------------------------------------------

    \53\ U.S. Environmental Protection Agency, 2013. Use of ASOS 
Meteorological Data in AERMOD Dispersion Modeling. Memorandum dated 
March 8, 2013, Office of Air Quality Planning and Standards, 
Research Triangle Park, NC. https://www3.epa.gov/ttn/scram/guidance/clarification/20130308_Met_Data_Clarification.pdf.
---------------------------------------------------------------------------

    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 are contingent on the concurrence of the 
appropriate reviewing authority and collaborating agencies that the 
data are of acceptable quality and representative of the modeling 
application.
    We received many comments favorable to the use of prognostic 
meteorological data. While supporting the use of prognostic 
meteorological data, many commenters also requested additional guidance 
on running the prognostic meteorological models, assessing the 
suitability of the model output, and the use of MMIF to generate the 
meteorological data needed for AERMET and AERMOD. Based on the comments 
received, the EPA has updated the guidance \54\ on use of the 
prognostic meteorological data.
---------------------------------------------------------------------------

    \54\ U.S. Environmental Protection Agency, 2016 Guidance on the 
Use of the Mesoscale Model Interface Program (MMIF) for AERMOD 
Applications. Publication No. EPA-454/B-16-003. Office of Air 
Quality Planning and Standards, Research Triangle Park, NC.
---------------------------------------------------------------------------

    Therefore, as proposed, the EPA is updating the Guideline to 
recommend that AERMINUTE output should be routinely used in most cases 
when meteorological data from NWS ASOS sites are used for input to 
AERMOD and that representative prognostic meteorological data are 
appropriate for use in dispersion modeling within areas where there is 
no representative NWS data, or it is infeasible or prohibitive to 
collect site-specific meteorological data.

B. Final Editorial Changes

    In this action, the EPA is making editorial changes to update and 
reorganize information throughout the Guideline. These revisions are 
intended to make the Guideline easier to use, without meaningfully 
changing the substance of the Guideline, 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.
    We describe these editorial changes below for each affected section 
of the Guideline, as well as changes associated with the resolution of 
the comments and issues discussed in section IV.A. of this preamble and 
the correction of typographical errors identified in our proposal. For 
ease of reference, we are publishing the entire text of appendix W and 
its appendix A, as revised through today's action.
1. Preface
    As proposed, the preface is updated to reflect minor text revisions 
for consistency with the remainder of the Guideline.
2. Section 1
    The introduction section is updated to reflect the reorganized 
nature of the revised Guideline as proposed. Additional information is 
provided regarding the importance of CAA section 320 to amendments of 
the Guideline.
3. Section 2
    As proposed, section 2 is revised to more appropriately discuss the 
process by which models are evaluated and considered for use in 
particular applications. Information from the previous section 9 
pertaining to model accuracy and uncertainty is incorporated within 
this section to clarify how model performance evaluation is critical in 
determining the suitability of models for particular application.
    A discussion is provided in section 2.1 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 
understanding the particular regulatory applications to which they are 
most appropriate.
    In addition, we revised 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, we 
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.
    A few typographical corrections were made in this section based on 
public comment and additional review of the proposed regulatory text. 
Also, based on public comment, clarity was added to the description of 
the modeling process to indicate that an applicant may choose to 
implement controls or operational limits based on screening modeling 
rather than performing additional refined modeling.
4. Section 3
    There were minor modifications, including a few typographical 
corrections, made to section 3 based on public comment to more 
accurately reflect current EPA practices. As proposed, the discussion 
of the EPA's MCH is moved to a revised section 3.3 for ease of 
reference and prominence within the Guideline. With this action, EPA 
Regional Office consultation with and concurrence by the MCH is 
required on all alternative model approvals. Previously, section 3 
included various requirements under a recommendation subheading that 
were not clearly identified as requirements. Accordingly, we modified 
section 3 with the incorporation of requirement subsections to 
eliminate any ambiguity. Finally, the metric used to demonstrate 
equivalency of models (section 3.2.2) is modified based on public 
comment to be more appropriate for both deterministic and probabilistic 
based standards.
5. Section 4
    As proposed, section 4 is revised to incorporate the modeling 
approaches recommended for air quality impact analyses for the 
following criteria pollutants: CO, lead, SO2, 
NO2, and primary PM2.5 and PM10. The 
revised section 4 is now a combination of the previous sections 4 and 
5, reflecting inert criteria pollutants only. We also modified section 
4 to incorporate requirement subsections that provide clarity to the 
various requirements where, previously, sections 4 and 5 included 
various requirements under recommendation subheadings.
    Section 4 now 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. We included a clear discussion of each appendix A preferred 
model in section 4.3. We modified the discussion for each preferred 
model (i.e., AERMOD Modeling System, CTDMPLUS, and OCD) from the 
previous section 4 with

[[Page 5201]]

appropriate edits and some streamlining based on information available 
in the respective model formulation documentation and user's guides.
    We added a subsection specifically addressing the modeling 
recommendations for SO2 where, previously, section 4 of the 
Guideline was generally understood to be applicable for SO2. 
We made minor updates 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 added as a Tier 2 option, 
and the Tier 3 options of OLM and PVMRM are now regulatory options in 
AERMOD. For refined modeling of mobile sources, we have revised our 
previous language regarding the use of the CALINE3 models and are now 
listing AERMOD, where appropriate. As previously discussed in section 
IV.A.4 of this preamble, the section on CO modeling has been revised to 
reference existing guidance for CO screening rather than discussing 
screening approaches with AERMOD.
    Throughout section 4, typographical errors in our proposal were 
noted by commenters. We have corrected those errors and made some minor 
revisions for additional clarity addressing some confusion that was 
expressed in several public comments. Of note, modifications to the 
requirements discussion of section 4.2 from our proposal were made to 
account for the potential need for a NAAQS compliance demonstration for 
long-range transport situations where a near-field assessment for NAAQS 
is not available or indicates a significant ambient impact at or about 
50 km.
6. Section 5
    As stated above, much of the previous section 5 (i.e., the portions 
pertaining to the inert criteria pollutants) is now incorporated into 
the revised section 4. As proposed, the revised section 5 focuses only 
on the modeling approaches recommended for ozone and secondary 
PM2.5. Other than addressing a few typographical errors 
based on public comment, the only additions to section 5 from proposal 
are a few transitional statements that were added for additional 
clarity.
    Both ozone and secondary PM2.5 are formed through 
chemical reactions in the atmosphere and are not 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 revisions to section 5 do not specify a particular EPA-
preferred model or technique for use in air quality assessments, we 
have established a two-tiered screening approach 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 CTMs (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
    As proposed, section 6 is revised to more clearly address the 
modeling recommendations of other federal agencies, such as the FLMs, 
that have been developed in response to EPA rules or standards. Based 
on public comment from a tribal association and several tribes, we have 
added clarifying language that indicates that other state, local, or 
tribal agencies with air quality and land management responsibilities 
may also have specific modeling approaches for their own regulatory or 
other requirements. While no attempt was made to comprehensively 
discuss each topic, we provide appropriate references to the respective 
federal agency guidance documents.
    The revisions 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 (BOEM) for Outer Continental Shelf (OCS) permitting 
situations and the FAA for airport and air base permitting situations 
are represented in section 6.3.
    The discussion of Good Engineering Practices (GEP) for stack height 
consideration is modified and moved to section 7. We have removed the 
discussion of long-range transport for PSD Class I increments and the 
references to the previously preferred long-range transport model, 
CALPUFF, in accordance with the more detailed discussion in section 
IV.A.6 of this preamble.
8. Section 7
    As proposed, we revised section 7 to be more streamlined and 
appropriate to the variety of general modeling issues and 
considerations that are not covered in sections 4, 5, and 6 of the 
Guideline. Information concerning design concentrations and receptor 
sites is moved to section 9. The discussion of stability categories has 
been removed from section 7 because it is specifically addressed in the 
model formulation documentation and guidance for the dispersion models 
that require stability categories to be defined. As stated above, the 
GEP discussion from the previous section 6 is now incorporated into 
this section. Based on public comment, we added a statement to the 
plume rise discussion to clarify that refinements to the preferred 
model may be considered for plume rise and downwash effects only with 
agreement from the appropriate reviewing authority and approval by the 
EPA Regional Office.
    We expanded 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, which may be best treated with urban rather than rural 
dispersion coefficients. References to CALPUFF in the Complex Winds 
subsection have been removed in keeping with our approach to not 
explicitly name models that are not listed in appendix A, so as to not 
imply any preferential status vis-a-vis other available models. 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, we 
revised section 7, as proposed, to include a new discussion of modeling 
considerations specific to mobile sources.
9. Section 8
    We made extensive updates and modifications to section 8, as 
proposed, 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, we 
modified section 8 to incorporate requirement subsections where 
previously section 8 ambiguously included various requirements under 
recommendation subheadings. Commenters identified typographical errors 
that have been corrected along with appropriate clarifications in this 
section.

[[Page 5202]]

    The Background Concentration subsection has been significantly 
modified from the existing Guideline to include a clearer and more 
comprehensive discussion of ``nearby'' and ``other'' sources. This is 
intended to eliminate confusion over 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, a 
brief discussion on the use of photochemical grid modeling to 
appropriately characterize background concentrations has been included 
in this section. Updates to Tables 8-1 and 8-2 are made per changes in 
the considerations for nearby sources, as discussed in section IV.A.8 
of this preamble. Based on several public comments, Table 8-2 was 
further updated to correctly state that the operational level for 
nearby sources for short-term average times is the ``temporally 
representative level when actually operating, reflective of the most 
recent 2 years.''
    The use of prognostic mesoscale meteorological models to provide 
meteorological input for regulatory dispersion modeling applications 
has been incorporated throughout the ``Meteorological Input Data'' 
subsection, including the introduction of the MMIF as a tool to inform 
regulatory model applications. We made additional minor modifications 
to the recommendations in this subsection based on current EPA 
practices, of which the most substantive edit was 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
    As proposed, we moved 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 greater clarity in those topics as 
applied to selection of models under the Guideline. We removed a 
subsection on the ``Use of Uncertainty in Decision Making.'' Also, we 
revised section 9 to focus on the regulatory application of models, 
which includes the majority of the information found previously in 
section 10.
    We revised 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 in lieu of modeling. The importance of developing and 
vetting a modeling protocol is more prominently presented in a separate 
subsection.
    The information related to design concentrations is updated and 
unified from previous language found in sections 7 and 10. An expanded 
discussion of receptor sites is 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.
    We added the recommendations for NAAQS and PSD increments 
compliance demonstrations that had been in section 10. In additions, we 
updated the recommendations to more clearly and accurately reflect the 
long-standing practice of performing a single-source impact analysis as 
a first stage of the NAAQS and PSD increments 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, we revised the ``Use of Measured Data in Lieu of Model 
Estimates'' subsection 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 updated 
to reflect current EPA practices, as appropriate.
11. Section 10
    As proposed, we incorporated the majority of the information found 
previously in section 10 into the revised section 9. Section 10 now 
consists of the references that were in the previous section 12. Each 
reference is updated, as appropriate, based on the text revisions 
throughout the Guideline.
12. Section 11
    In a streamlining effort, we removed the bibliography section from 
the Guideline as proposed.
13. Section 12
    As stated earlier, this references section is now section 10 with 
appropriate updates.
14. Appendix A to the Guideline
    As proposed, we revised appendix A to the Guideline to remove the 
BLP model, CALINE3, and CALPUFF as refined air quality models preferred 
for specific regulatory applications. The rationale for the removal of 
these air quality models from the preferred status can be found in 
section IV.A.2, section IV.A.4, and section IV.A.6 of this preamble. 
Finally, we made minor modifications, including a few typographical 
corrections, to appendix A based on public comment and additional 
review of the proposed regulatory text.

V. Statutory and Executive Order Reviews

A. Executive Order 12866: Regulatory Planning and Review and Executive 
Order 13563: Improving Regulation and Regulatory Review

    This action is a significant regulatory action that was submitted 
to the Office of Management and Budget (OMB) for review. The OMB 
determined that this regulatory action could potentially interfere with 
an action taken or planned by another agency. Any changes made in 
response to OMB recommendations have been documented in the docket.

B. Paperwork Reduction Act (PRA)

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

C. Regulatory Flexibility Act (RFA)

    I certify that this action will not have a significant economic 
impact on a substantial number of small entities under the RFA. In 
making this determination, the impact of concern is any significant 
adverse economic impact on small entities. An agency may certify that a 
rule will not have a significant economic impact on a substantial 
number of small entities if the rule relieves regulatory burden, has no 
net burden or otherwise has a positive economic effect on the small 
entities subject to the rule.
    The modeling techniques described in this 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 action are not expected to pose any 
additional burden on these entities. The

[[Page 5203]]

revisions to the existing EPA-preferred model, AERMOD, serve to 
increase efficiency and accuracy by changing only mathematical 
formulations and specific data elements. Also, this action will 
streamline resources necessary to conduct modeling with AERMOD by 
incorporating model algorithms from the BLP model. Although this final 
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. We 
have, therefore, concluded that this action will have no net regulatory 
burden for all directly regulated small entities.

D. Unfunded Mandates Reform Act (UMRA)

    This action does not contain an unfunded mandate of $100 million or 
more as described in UMRA, 2 U.S.C. 1531-1538 and does not 
significantly or uniquely affect small governments. This action imposes 
no enforceable duty on any state, local or tribal governments or the 
private sector beyond those imposed by the existing NSR requirements.

E. Executive Order 13132: Federalism

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

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

    This action does not have tribal implications, as specified in 
Executive Order 13175. The final rule provides revisions to the 
Guideline which is used by the EPA, other federal, state, territorial, 
local, and tribal air quality agencies, and industry to prepare and 
review new source permits, source permit modifications, SIP submittals 
and revisions, conformity, and other air quality assessments required 
under EPA regulation. The Tribal Air Rule implements the provisions of 
section 301(d) of the CAA authorizing eligible tribes to implement 
their own tribal air program. Thus, Executive Order 13175 does not 
apply to this action. In the spirit of Executive Order 13175, the EPA 
provided an informational webinar with the National Tribal Air 
Association (NTAA) on September 10, 2015, and also received comment on 
the proposed action from the NTAA and several individual tribes. These 
comments and our responses are included in the docket for this action.

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. Further, we have concluded that this 
action is not likely to have any adverse energy effects because its 
purpose is to streamline the procedures by which stakeholders apply air 
quality modeling and technique in conducting their air quality 
assessments required under the CAA and, also, increases the scientific 
credibility and accuracy of the models and techniques used for 
conducting these assessments.

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 believes that this action is not subject to Executive Order 
12898 (59 FR 7629, February 16, 1994) because it does not establish an 
environmental health or safety standard. This regulatory action 
provides updates and clarifications to the Guideline and does not have 
any impact on human health or the environment.

K. Congressional Review Act (CRA)

    This action is subject to the Congressional Review Act (CRA), and 
the EPA will submit a rule report to each House of the Congress and to 
the Comptroller General of the United States. This action is not a 
``major rule'' as defined by 5 U.S.C. 804(2).

List of Subjects in 40 CFR Part 51

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

    Dated: December 20, 2016.
Gina McCarthy,
Administrator.

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

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

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

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

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

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

Preface

    a. Industry and control agencies have long expressed a need for 
consistency in the application of air quality models for regulatory 
purposes. In the 1977 Clean Air Act (CAA), Congress mandated such 
consistency and encouraged the standardization of model 
applications. The Guideline on Air Quality Models (hereafter, 
Guideline) was first published in April 1978 to satisfy these 
requirements by specifying models and providing guidance for their 
use. The Guideline provides a common basis for estimating the air 
quality concentrations of criteria pollutants used in assessing 
control strategies and developing emissions limits.
    b. The continuing development of new air quality models in 
response to regulatory requirements and the expanded requirements 
for models to cover even more complex problems have emphasized the 
need for periodic review and update of guidance on these techniques. 
Historically, three primary activities have provided direct input to 
revisions of the Guideline. The first is a series of periodic EPA 
workshops and modeling conferences conducted for the purpose of 
ensuring consistency and providing clarification in the application 
of models. The second activity was the solicitation and review of 
new models from the technical and user community. In the March 27, 
1980, Federal Register, a procedure was outlined for the submittal 
to the EPA of privately developed models. After extensive evaluation 
and scientific review, these models, as well as those made available 
by the EPA, have been considered for recognition in the Guideline. 
The third activity is the extensive on-going research efforts by the 
EPA and

[[Page 5204]]

others in air quality and meteorological modeling.
    c. Based primarily on these three activities, new sections and 
topics have been included as needed. The EPA does not make changes 
to the guidance on a predetermined schedule, but rather on an as-
needed basis. The EPA believes that revisions of the Guideline 
should be timely and responsive to user needs and should involve 
public participation to the greatest possible extent. All future 
changes to the guidance will be proposed and finalized in the 
Federal Register. Information on the current status of modeling 
guidance can always be obtained from the EPA's Regional Offices.

Table of Contents

List of Tables

1.0 Introduction
2.0 Overview of Model Use
2.1 Suitability of Models
    2.1.1 Model Accuracy and Uncertainty
2.2 Levels of Sophistication of Air Quality Analyses and Models
2.3 Availability of Models
3.0 Preferred and Alternative Air Quality Models
3.1 Preferred Models
    3.1.1 Discussion
    3.1.2 Requirements
3.2 Alternative Models
    3.2.1 Discussion
    3.2.2 Requirements
3.3 EPA's Model Clearinghouse
4.0 Models for Carbon Monoxide, Lead, Sulfur Dioxide, Nitrogen 
Dioxide and Primary Particulate Matter
4.1 Discussion
4.2 Requirements
    4.2.1 Screening Models and Techniques
    4.2.1.1 AERSCREEN
    4.2.1.2 CTSCREEN
    4.2.1.3 Screening in Complex Terrain
    4.2.2 Refined Models
    4.2.2.1 AERMOD
    4.2.2.2 CTDMPLUS
    4.2.2.3 OCD
    4.2.3 Pollutant Specific Modeling Requirements
    4.2.3.1 Models for Carbon Monoxide
    4.2.3.2 Models for Lead
    4.2.3.3 Models for Sulfur Dioxide
    4.2.3.4 Models for Nitrogen Dioxide
    4.2.3.5 Models for PM2.5
    4.2.3.6 Models for PM10
5.0 Models for Ozone and Secondarily Formed Particulate Matter
5.1 Discussion
5.2 Recommendations
5.3 Recommended Models and Approaches for Ozone
    5.3.1 Models for NAAQS Attainment Demonstrations and Multi-
Source Air Quality Assessments
    5.3.2 Models for Single-Source Air Quality Assessments
    5.4 Recommended Models and Approaches for Secondarily Formed 
PM2.5
    5.4.1 Models for NAAQS Attainment Demonstrations and Multi-
Source Air Quality Assessments
    5.4.2 Models for Single-Source Air Quality Assessments
6.0 Modeling for Air Quality Related Values and Other Governmental 
Programs
6.1 Discussion
6.2 Air Quality Related Values
    6.2.1 Visibility
    6.2.1.1 Models for Estimating Near-Field Visibility Impairment
    6.2.1.2 Models for Estimating Visibility Impairment for Long-
Range Transport
    6.2.2 Models for Estimating Deposition Impacts
6.3 Modeling Guidance for Other Governmental Programs
7.0 General Modeling Considerations
7.1 Discussion
7.2 Recommendations
    7.2.1 All sources
    7.2.1.1 Dispersion Coefficients
    7.2.1.2 Complex Winds
    7.2.1.3 Gravitational Settling and Deposition
    7.2.2 Stationary Sources
    7.2.2.1 Good Engineering Practice Stack Height
    7.2.2.2 Plume Rise
    7.2.3 Mobile Sources
8.0 Model Input Data
8.1 Modeling Domain
    8.1.1 Discussion
    8.1.2 Requirements
8.2 Source Data
    8.2.1 Discussion
    8.2.2 Requirements
8.3 Background Concentrations
    8.3.1 Discussion
    8.3.2 Recommendations for Isolated Single Sources
    8.3.3 Recommendations for Multi-Source Areas
8.4 Meteorological Input Data
    8.4.1 Discussion
    8.4.2 Recommendations and Requirements
    8.4.3 National Weather Service Data
    8.4.3.1 Discussion
    8.4.3.2 Recommendations
    8.4.4 Site-specific data
    8.4.4.1 Discussion
    8.4.4.2 Recommendations
    8.4.5 Prognostic meteorological data
    8.4.5.1 Discussion
    8.4.5.2 Recommendations
    8.4.6 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 Inputs
                                     for SIP Revisions of Inert
                                     Pollutants.
8-2...............................  Point Source Model Emission Inputs
                                     for NAAQS Compliance in PSD
                                     Demonstrations.
------------------------------------------------------------------------

1.0 Introduction

    a. The Guideline provides air quality modeling techniques that 
should be applied to State Implementation Plan (SIP) submittals and 
revisions, to New Source Review (NSR), including new or modifying 
sources under Prevention of Significant Deterioration 
(PSD),1 2 3 conformity analyses,\4\ and other air quality 
assessments required under EPA regulation. Applicable only to 
criteria air pollutants, the Guideline is intended for use by the 
EPA Regional Offices in judging the adequacy of modeling analyses 
performed by the EPA, by state, local, and tribal permitting 
authorities, and by industry. It is appropriate for use by other 
federal government agencies and by state, local, and tribal agencies 
with air quality and land management responsibilities. The Guideline 
serves to identify, for all interested parties, those modeling 
techniques and databases that the EPA considers acceptable. The 
Guideline is not intended to be a compendium of modeling techniques. 
Rather, it should serve as a common measure of acceptable technical 
analysis when supported by sound scientific judgment.
    b. Air quality measurements \5\ are routinely used to 
characterize ambient concentrations of criteria pollutants 
throughout the nation but are rarely sufficient for characterizing 
the ambient impacts of individual sources or demonstrating adequacy 
of emissions limits for an existing source due to limitations in 
spatial and temporal coverage of ambient monitoring networks. The 
impacts of new sources that do not yet exist, and modifications to 
existing sources that have yet to be implemented, can only be 
determined through modeling. Thus, models have become a primary 
analytical tool in most air quality assessments. Air quality 
measurements can be used in a complementary manner to air quality 
models, with due regard for the strengths and weaknesses of both 
analysis techniques, and are particularly useful in assessing the 
accuracy of model estimates.
    c. It would be advantageous to categorize the various regulatory 
programs and to apply a designated model to each proposed source 
needing analysis under a given program. However, the diversity of 
the nation's topography and climate, and variations in source 
configurations and operating characteristics dictate against a 
strict modeling ``cookbook.'' There is no one model capable of 
properly addressing all conceivable situations even within a broad 
category such as point sources. Meteorological phenomena associated 
with threats to air quality standards are rarely amenable to a 
single mathematical treatment; thus, case-by-case analysis and 
judgment are frequently required. As modeling efforts become more 
complex, it is increasingly important that they be directed by 
highly competent individuals with a broad range of experience and 
knowledge in air quality

[[Page 5205]]

meteorology. Further, they should be coordinated closely with 
specialists in emissions characteristics, air monitoring and data 
processing. The judgment of experienced meteorologists, atmospheric 
scientists, and analysts is essential.
    d. The model that most accurately estimates concentrations in 
the area of interest is always sought. However, it is clear from the 
needs expressed by the EPA Regional Offices, by state, local, and 
tribal agencies, by many industries and trade associations, and also 
by the deliberations of Congress, that consistency in the selection 
and application of models and databases should also be sought, even 
in case-by-case analyses. Consistency ensures that air quality 
control agencies and the general public have a common basis for 
estimating pollutant concentrations, assessing control strategies, 
and specifying emissions limits. Such consistency is not, however, 
promoted at the expense of model and database accuracy. The 
Guideline provides a consistent basis for selection of the most 
accurate models and databases for use in air quality assessments.
    e. Recommendations are made in the Guideline concerning air 
quality models and techniques, model evaluation procedures, and 
model input databases and related requirements. The guidance 
provided here should be followed in air quality analyses relative to 
SIPs, NSR, and in supporting analyses required by the EPA and by 
state, local, and tribal permitting authorities. Specific models are 
identified for particular applications. The EPA may approve the use 
of an alternative model or technique that can be demonstrated to be 
more appropriate than those recommended in the Guideline. In all 
cases, the model or technique applied to a given situation should be 
the one that provides the most accurate representation of 
atmospheric transport, dispersion, and chemical transformations in 
the area of interest. However, to ensure consistency, deviations 
from the Guideline should be carefully documented as part of the 
public record and fully supported by the appropriate reviewing 
authority, as discussed later.
    f. From time to time, situations arise requiring clarification 
of the intent of the guidance on a specific topic. Periodic 
workshops are held with EPA headquarters, EPA Regional Offices, and 
state, local, and tribal agency modeling representatives to ensure 
consistency in modeling guidance and to promote the use of more 
accurate air quality models, techniques, and databases. The 
workshops serve to provide further explanations of Guideline 
requirements to the EPA Regional Offices and workshop materials are 
issued with this clarifying information. In addition, findings from 
ongoing research programs, new model development, or results from 
model evaluations and applications are continuously evaluated. Based 
on this information, changes in the applicable guidance may be 
indicated and appropriate revisions to the Guideline may be 
considered.
    g. All changes to the Guideline must follow rulemaking 
requirements since the Guideline is codified in appendix W to 40 
Code of Federal Regulations (CFR) part 51. The EPA will promulgate 
proposed and final rules in the Federal Register to amend this 
appendix. The EPA utilizes the existing procedures under CAA section 
320 that requires the EPA to conduct a Conference on Air Quality 
Modeling at least every 3 years (CAA 320, 42 U.S.C. 7620). These 
modeling conferences are intended to develop standardized air 
quality modeling procedures and form the basis for associated 
revisions to this Guideline in support of the EPA's continuing 
effort to prescribe with ``reasonable particularity'' air quality 
models and meteorological and emission databases suitable for 
modeling National Ambient Air Quality Standards (NAAQS) \6\ and PSD 
increments. Ample opportunity for public comment will be provided 
for each proposed change and public hearings scheduled.
    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

[[Page 5206]]

limited number of sources. Traditionally, Lagrangian models have 
been used for relatively inert pollutants, with slightly more 
complex considerations of removal than Gaussian models. Some 
Lagrangian models treat in-plume gas and particulate chemistry. 
However, these models require time and space varying concentration 
fields of oxidants and, in the case of fine particulate matter 
(PM2.5), neutralizing agents, such as ammonia. Reliable 
background fields are critical for applications involving secondary 
pollutant formation because secondary impacts generally occur when 
in-plume precursors mix and react with species in the background 
atmosphere.7 8 These oxidant and neutralizing agents are 
not routinely measured, but can be generated with a three-
dimensional photochemical grid model.
    iii. Photochemical grid models are three-dimensional Eulerian 
grid-based models that treat chemical and physical processes in each 
grid cell and use diffusion and transport processes to move chemical 
species between grid cells.\9\ Eulerian models assume that emissions 
are spread evenly throughout each model grid cell. At coarse grid 
resolutions, Eulerian models have difficulty with fine scale 
resolution of individual plumes. However, these types of models can 
be appropriately applied for assessment of near-field and regional 
scale reactive pollutant impacts from specific sources 
7 10 11 12 or all sources.13 14 15 
Photochemical grid models simulate a more realistic environment for 
chemical transformation,7 12 but simulations can be more 
resource intensive than Lagrangian or Gaussian plume models.
    e. Competent and experienced meteorologists, atmospheric 
scientists, and analysts are an essential prerequisite to the 
successful application of air quality models. The need for such 
specialists is critical when sophisticated models are used or the 
area has complicated meteorological or topographic features. It is 
important to note that a model applied improperly or with 
inappropriate data can lead to serious misjudgments regarding the 
source impact or the effectiveness of a control strategy.
    f. The resource demands generated by use of air quality models 
vary widely depending on the specific application. The resources 
required may be important factors in the selection and use of a 
model or technique for a specific analysis. These resources depend 
on the nature of the model and its complexity, the detail of the 
databases, the difficulty of the application, the amount and level 
of expertise required, and the costs of manpower and computational 
facilities.

2.1.1 Model Accuracy and Uncertainty

    a. The formulation and application of air quality models are 
accompanied by several sources of uncertainty. ``Irreducible'' 
uncertainty stems from the ``unknown'' conditions, which may not be 
explicitly accounted for in the model (e.g., the turbulent velocity 
field). Thus, there are likely to be deviations from the observed 
concentrations in individual events due to variations in the unknown 
conditions. ``Reducible'' uncertainties \16\ are caused by: (1) 
Uncertainties in the ``known'' input conditions (e.g., emission 
characteristics and meteorological data); (2) errors in the measured 
concentrations; and (3) inadequate model physics and formulation.
    b. Evaluations of model accuracy should focus on the reducible 
uncertainty associated with physics and the formulation of the 
model. The accuracy of the model is normally determined by an 
evaluation procedure which involves the comparison of model 
concentration estimates with measured air quality data.\17\ The 
statement of model accuracy is based on statistical tests or 
performance measures such as bias, error, correlation, 
etc.18 19
    c. Since the 1980's, the EPA has worked with the modeling 
community to encourage development of standardized model evaluation 
methods and the development of continually improved methods for the 
characterization of model performance.16 18 20 21 22 
There is general consensus on what should be considered in the 
evaluation of air quality models; namely, quality assurance 
planning, documentation and scrutiny should be consistent with the 
intended use and should include:
     Scientific peer review;
     Supportive analyses (diagnostic evaluations, code 
verification, sensitivity analyses);
     Diagnostic and performance evaluations with data 
obtained in trial locations; and
     Statistical performance evaluations in the 
circumstances of the intended applications.

Performance evaluations and diagnostic evaluations assess different 
qualities of how well a model is performing, and both are needed to 
establish credibility within the client and scientific community.
    d. Performance evaluations allow the EPA and model users to 
determine the relative performance of a model in comparison with 
alternative modeling systems. Diagnostic evaluations allow 
determination of a model capability to simulate individual processes 
that affect the results, and usually employ smaller spatial/temporal 
scale data sets (e.g., field studies). Diagnostic evaluations enable 
the EPA and model users to build confidence that model predictions 
are accurate for the right reasons. However, the objective 
comparison of modeled concentrations with observed field data 
provides only a partial means for assessing model performance. Due 
to the limited supply of evaluation datasets, there are practical 
limits in assessing model performance. For this reason, the 
conclusions reached in the science peer reviews and the supportive 
analyses have particular relevance in deciding whether a model will 
be useful for its intended purposes.

2.2 Levels of Sophistication of Air Quality Analyses and Models

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

In such situations, an attempt should be made to acquire or improve 
the necessary databases and to develop appropriate analytical 
techniques, but the screening technique or reduced-form model may be 
sufficient in conducting regulatory modeling applications when 
applied in consultation with the EPA Regional Office.
    e. Consistent with the general principle described in paragraph 
2.2(a), the EPA may establish a demonstration tool or method as a 
sufficient means for a user or applicant to

[[Page 5207]]

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 https://www.epa.gov/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 be 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 permitting under programs approved into 
SIPs. In some EPA regions, federal authority to administer NSR 
permitting and related activities has been delegated to state or 
local agencies. In these cases, such agencies ``stand in the shoes'' 
of the respective EPA Region. Therefore, depending on the 
circumstances, the appropriate reviewing authority may be an EPA 
Regional Office, a state, local, or tribal agency, or perhaps the 
Federal Land Manager (FLM). In some cases, the Guideline requires 
review and approval of the use of an alternative model by the EPA 
Regional Office (sometimes stated as ``Regional Administrator''). 
For all approvals of alternative models or techniques, the EPA 
Regional Office will coordinate and shall seek concurrence with the 
EPA's Model Clearinghouse. If there is any question as to the 
appropriate reviewing authority, you should contact the EPA Regional 
Office modeling contact (https://www3.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

[[Page 5208]]

preferred model code is another example of a modification that does 
not affect concentrations. However, when any changes are made, the 
Regional Administrator must require a test case example to 
demonstrate that the modeled concentrations are not affected.
    c. A preferred model must be operated with the options listed in 
appendix A for its intended regulatory application. If the 
regulatory options are not applied, the model is no longer 
``preferred.'' Any other modification to a preferred model that 
would result in a change in the concentration estimates likewise 
alters its status so that it is no longer a preferred model. Use of 
the modified model must then be justified as an alternative model on 
a case-by-case basis to the appropriate reviewing authority and 
approved by the Regional Administrator.
    d. Where the EPA has not identified a preferred model for a 
particular pollutant or situation, the EPA may establish a multi-
tiered approach for making a demonstration required under PSD or 
another CAA program. The initial tier or tiers may involve use of 
demonstration tools, screening models, screening techniques, or 
reduced-form models; while the last tier may involve the use of 
demonstration tools, refined models or techniques, or alternative 
models approved under section 3.2.

3.2 Alternative Models

3.2.1 Discussion

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

3.2.2 Requirements

    a. Determination of acceptability of an alternative model is an 
EPA Regional Office responsibility in consultation with the EPA's 
Model Clearinghouse as discussed in paragraphs 3.0(b) and 3.2.1(b). 
Where the Regional Administrator finds that an alternative model is 
more appropriate than a preferred model, that model may be used 
subject to the approval of the EPA Regional Office based on the 
requirements of this subsection. This finding will normally result 
from a determination that: (1) A preferred air quality model is not 
appropriate for the particular application; or (2) a more 
appropriate model or technique is available and applicable.
    b. An alternative model shall be evaluated from both a 
theoretical and a performance perspective before it is selected for 
use. There are three separate conditions under which such a model 
may be approved for use:
    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 appropriate 
regulatory metric(s) are within  2 percent of the 
estimates obtained from the preferred model. The option to show 
equivalency is intended as a simple demonstration of acceptability 
for an alternative model that is nearly identical (or contains 
options that can make it identical) to a preferred model that it can 
be treated for practical purposes as the preferred model. However, 
notwithstanding this demonstration, models that are not equivalent 
may be used when one of the two other conditions described in 
paragraphs (d) and (e) of this subsection are satisfied.
    d. For condition (2) in paragraph (b) of this subsection, 
established statistical performance evaluation procedures and 
techniques 28 29 for determining the acceptability of a 
model for an individual case based on superior performance should be 
followed, as appropriate. Preparation and implementation of an 
evaluation protocol that is acceptable to both control agencies and 
regulated industry is an important element in such an evaluation.
    e. Finally, for condition (3) in paragraph (b) of this 
subsection, an alternative model or technique may be approved for 
use provided that:
    i. The model or technique has received a scientific peer review;
    ii. The model or technique can be demonstrated to be applicable 
to the problem on a theoretical basis;
    iii. The databases which are necessary to perform the analysis 
are available and adequate;
    iv. Appropriate performance evaluations of the model or 
technique have shown that the model or technique is not 
inappropriately biased for regulatory application \a\; and
---------------------------------------------------------------------------

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

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

3.3 EPA's Model Clearinghouse

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

[[Page 5209]]

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 is considered to be an area where terrain features 
are all lower in elevation than the top of the stack(s) of the 
source(s) in question. Complex terrain is defined as terrain 
exceeding the height of the stack(s) being modeled.
    e. Gaussian models determine source impacts at discrete 
locations (receptors) for each meteorological and emission scenario, 
and generally attempt to estimate concentrations at specific sites 
that represent an ensemble average of numerous repetitions of the 
same ``event.'' Uncertainties in model estimates are driven by this 
formulation, and as noted in section 2.1.1, evaluations of model 
accuracy should focus on the reducible uncertainty associated with 
physics and the formulation of the model. The ``irreducible'' 
uncertainty associated with Gaussian plume models may be responsible 
for variation in concentrations of as much as  50 
percent.\30\ ``Reducible'' uncertainties \16\ can be on a similar 
scale. For example, Pasquill \31\ estimates that, apart from data 
input errors, maximum ground-level concentrations at a given hour 
for a point source in flat terrain could be in error by 50 percent 
due to these uncertainties. Errors of 5 to 10 degrees in the 
measured wind direction can result in concentration errors of 20 to 
70 percent for a particular time and location, depending on 
stability and station location. Such uncertainties do not indicate 
that an estimated concentration does not occur, only that the 
precise time and locations are in doubt. Composite errors in highest 
estimated concentrations of 10 to 40 percent are found to be 
typical.32 33 However, estimates of concentrations paired 
in time and space with observed concentrations are less certain.
    f. Model evaluations and inter-comparisons should take these 
aspects of uncertainty into account. For a regulatory application of 
a model, the emphasis of model evaluations is generally placed on 
the highest modeled impacts. Thus, the Cox-Tikvart model evaluation 
approach, which compares the highest modeled impacts on several 
timescales, is recommended for comparisons of models and 
measurements and model inter-comparisons. The approach includes 
bootstrap techniques to determine the significance of various 
modeled predictions and increases the robustness of such comparisons 
when the number of available measurements are 
limited.34 35 Because of the uncertainty in paired 
modeled and observed concentrations, any attempts at calibration of 
models based on these comparisons is of questionable benefit and 
shall not be done.

4.2 Requirements

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

4.2.1 Screening Models and Techniques

    a. Where a preliminary or conservative estimate is desired, 
point source screening techniques are an acceptable approach to air 
quality analyses.
    b. As discussed in paragraph 2.2(a), screening models or 
techniques are designed to provide a conservative estimate of 
concentrations. The screening models used in most applications are 
the screening versions of the preferred models for refined 
applications. The two screening models, AERSCREEN 37 38 
and CTSCREEN, are screening versions of AERMOD (American 
Meteorological Society (AMS)/EPA Regulatory Model) and CTDMPLUS 
(Complex Terrain Dispersion Model Plus Algorithms for Unstable 
Situations), respectively. AERSCREEN is the recommended screening 
model for most applications in all types of terrain and for 
applications involving building downwash. For those applications in 
complex terrain where the application involves a well-defined hill 
or ridge, CTSCREEN \39\ can be used.
    c. Although AERSCREEN and CTSCREEN are designed to address a 
single-source scenario, there are approaches that can be used on a 
case-by-case basis to address multi-source situations using 
screening meteorology or other conservative model assumptions. 
However, the appropriate reviewing authority (paragraph 3.0(b)) 
shall be consulted, and concurrence obtained, on the protocol for 
modeling multiple sources with AERSCREEN or CTSCREEN to ensure that 
the worst case is identified and assessed.
    d. As discussed in section 4.2.3.4, there are also screening 
techniques built into AERMOD that use simplified or limited 
chemistry assumptions for determining the partitioning of NO and 
NO2 for NO2 modeling. These screening 
techniques are part of the EPA's preferred modeling approach for 
NO2 and do not need to be approved as an alternative 
model. However, as with other screening models and techniques, their 
usage shall occur in agreement with the appropriate reviewing 
authority (paragraph 3.0(b)).

[[Page 5210]]

    e. As discussed in section 4.2(c)(ii), there are screening 
techniques needed for long-range transport assessments that will 
typically involve the use of a Lagrangian model. Based on the long-
standing practice and documented capabilities of these models for 
long-range transport assessments, the use of a Lagrangian model as a 
screening technique for this purpose does not need to be approved as 
an alternative model. However, their usage shall occur in 
consultation with the appropriate reviewing authority (paragraph 
3.0(b)) and EPA Regional Office.
    f. All screening models and techniques shall be configured to 
appropriately address the site and problem at hand. Close attention 
must be paid to whether the area should be classified urban or rural 
in accordance with section 7.2.1.1. The climatology of the area must 
be studied to help define the worst-case meteorological conditions. 
Agreement shall be reached between the model user and the 
appropriate reviewing authority (paragraph 3.0(b)) on the choice of 
the screening model or technique for each analysis, on the input 
data and model settings, and the appropriate metric for satisfying 
regulatory requirements.

4.2.1.1 AERSCREEN

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

4.2.1.2 CTSCREEN

    a. CTSCREEN 39 41 can be used to obtain conservative, 
yet realistic, worst-case estimates for receptors located on terrain 
above stack height. CTSCREEN accounts for the three-dimensional 
nature of plume and terrain interaction and requires detailed 
terrain data representative of the modeling domain. The terrain data 
must be digitized in the same manner as for CTDMPLUS and a terrain 
processor is available.\42\ CTSCREEN is designed to execute a fixed 
matrix of meteorological values for wind speed (u), standard 
deviation of horizontal and vertical wind speeds ([sigma]v, 
[sigma]w), vertical potential temperature gradient (d[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. Under such 
conditions, the plume may be quite narrow in the vertical, so that 
even relatively small changes in a receptor's location may 
substantially affect the predicted concentration. Receptors within 
about a kilometer of the source may be even more sensitive to 
location. Thus, a dense array of receptors may be required in some 
cases.
    b. For applications involving AERSCREEN, AERSCREEN interfaces 
with AERMAP to generate the receptor elevations. For applications 
involving CTSCREEN, digitized contour data must be preprocessed \42\ 
to provide hill shape parameters in suitable input format. The user 
then supplies receptor locations either through an interactive 
program that is part of the model or directly, by using a text 
editor; using both methods to select receptor locations will 
generally be necessary to assure that the maximum concentrations are 
estimated by either model. In cases where a terrain feature may 
``appear to the plume'' as smaller, multiple hills, it may be 
necessary to model the terrain both as a single feature and as 
multiple hills to determine design concentrations.
    c. Other screening techniques may be acceptable for complex 
terrain cases where established procedures 43 are used. 
The user is encouraged to confer with the appropriate reviewing 
authority (paragraph 3.0(b)) if any unforeseen problems are 
encountered, e.g., applicability, meteorological data, receptor 
siting, or terrain contour processing issues.

4.2.2 Refined Models

    a. A brief description of each preferred model for refined 
applications is found in 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 required model 
is AERMOD.44 45 The AERMOD regulatory modeling system 
consists of the AERMOD dispersion model, the AERMET meteorological 
processor, and the AERMAP terrain processor. AERMOD is a steady-
state Gaussian plume model applicable to directly emitted air 
pollutants that employs best state-of-practice parameterizations for 
characterizing the meteorological influences and dispersion. 
Differentiation of simple versus complex terrain is unnecessary with 
AERMOD. In complex terrain, AERMOD employs the well-known dividing-
streamline concept in a simplified simulation of the effects of 
plume-terrain interactions.
    b. The AERMOD modeling system has been extensively evaluated 
across a wide range of scenarios based on numerous field studies, 
including tall stacks in flat and complex terrain settings, sources 
subject to building downwash influences, and low-level non-buoyant 
sources.27 These evaluations included several long-term 
field studies associated with operating plants as well as several 
intensive tracer studies. Based on these evaluations, AERMOD has 
shown consistently good performance, with ``errors'' in predicted 
versus observed peak concentrations, based on the Robust Highest 
Concentration (RHC) metric, consistently within the range of 10 to 
40 percent (cited in paragraph 4.1(e)).
    c. AERMOD incorporates the PRIME algorithm to account for 
enhanced plume growth and restricted plume rise for plumes affected 
by building wake effects.46 The PRIME algorithm accounts 
for entrainment of plume mass into the cavity recirculation region, 
including re-entrainment of plume mass into the wake region beyond 
the cavity.
    d. AERMOD incorporates the Buoyant Line and Point Source (BLP) 
Dispersion model to account for buoyant plume rise from line 
sources. The BLP option utilizes the standard meteorological inputs 
provided by the AERMET meteorological processor.

[[Page 5211]]

    e. The state-of-the-science for modeling atmospheric deposition 
is evolving, new modeling techniques are continually being assessed, 
and their results are being compared with observations. 
Consequently, while deposition treatment is available in AERMOD, the 
approach taken for any purpose shall be coordinated with the 
appropriate reviewing authority (paragraph 3.0(b)).

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

4.2.3.3 Models for Sulfur Dioxide

    a. Models for SO2 are needed to meet NSR requirements 
to address compliance with the SO2 NAAQS and PSD 
increments, for SIP attainment demonstrations,51 and for 
characterizing current air quality via modeling.52 
SO2 is one of a group of highly reactive gases known as 
``oxides of sulfur'' with largest emissions sources being fossil 
fuel combustion at power plants and other industrial facilities.
    b. Given the relatively inert nature of SO2 on the 
short-term time scales of interest (i.e., 1-hour) and the sources of 
SO2 (i.e., stationary point sources), the general 
modeling requirements for screening models in section 4.2.1 and 
refined models in section 4.2.2 are applicable for SO2 
modeling applications. For urban areas, AERMOD automatically invokes 
a half-life of 4 hours 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 to 
address compliance with the NO2 NAAQS and PSD increments. 
Impact of an individual source on ambient NO2 depends, in 
part, on the chemical environment into which the source's plume is 
to be emitted. This is due to the fact that NO2 sources 
co-emit NO along with NO2 and any emitted NO may react 
with ambient ozone to convert to additional NO2 downwind. 
Thus, comprehensive modeling of NO2 would need to 
consider the ratio of emitted NO and NO2, the ambient 
levels of ozone and subsequent reactions between ozone and NO, and 
the photolysis of NO2 to NO.
    b. Due to the complexity of NO2 modeling, a multi-
tiered screening approach is required to obtain hourly and annual 
average estimates of NO2.54 Since these 
methods are considered screening techniques, their usage shall occur 
in agreement with the appropriate reviewing authority (paragraph 
3.0(b)). Additionally, since screening techniques are conservative 
by their nature, there are limitations to how these options can be 
used. Specifically, modeling of negative emissions rates should only 
be done after consultation with the EPA Regional Office to ensure 
that decreases in concentrations would not be overestimated. Each 
tiered approach (see Figure 4-1) accounts for increasingly complex 
considerations of NO2 chemistry and is described in 
paragraphs c through e of this subsection. The tiers of 
NO2 modeling include:
    i. A first-tier (most conservative) ``full'' conversion 
approach;
    ii. A second-tier approach that assumes ambient equilibrium 
between NO and NO2; and
    iii. A third-tier consisting of several detailed screening 
techniques that account for ambient ozone and the relative amount of 
NO and NO2 emitted from a source.
    c. For Tier 1, use an appropriate refined model (section 4.2.2) 
to estimate nitrogen oxides (NOX) concentrations and 
assume a total conversion of NO to NO2.
    d. For Tier 2, multiply the Tier 1 result(s) by the Ambient 
Ratio Method 2 (ARM2), which provides estimates of representative 
equilibrium ratios of NO2/NOX value based 
ambient levels of NO2 and NOX derived from 
national data from the EPA's Air Quality System (AQS).\55\ The 
national default for ARM2 includes a minimum ambient NO2/
NOX ratio of 0.5 and a maximum ambient ratio of 0.9. The 
reviewing agency may establish alternative minimum ambient 
NO2/NOX values based on the source's in-stack 
emissions ratios, with alternative minimum ambient ratios reflecting 
the source's in-stack NO2/NOX ratios. 
Preferably, alternative minimum ambient NO2/
NOX ratios should be based on source-specific data which 
satisfies all quality assurance procedures that ensure data accuracy 
for both NO2 and NOX within the typical range 
of measured values. However, alternate information may be used to 
justify a source's anticipated NO2/NOX in-
stack ratios, such as manufacturer test data, state or local agency 
guidance, peer-reviewed literature, and/or the EPA's NO2/
NOX ratio database.
    e. For Tier 3, a detailed screening technique shall be applied 
on a case-by-case basis. Because of the additional input data 
requirements and complexities associated with the Tier 3 options, 
their usage shall occur in consultation with the EPA Regional Office 
in addition to the appropriate reviewing authority. The Ozone 
Limiting Method (OLM) \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 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 roadway 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 models or

[[Page 5212]]

techniques should consider individual quantities of NO and 
NO2 emissions, atmospheric transport and dispersion, and 
atmospheric transformation of NO to NO2. Dispersion 
models that account for more explicit photochemistry may also be 
considered as an alternative model to estimate ambient impacts of 
NOX sources.
[GRAPHIC] [TIFF OMITTED] TR17JA17.000

4.2.3.5 Models for PM2.5

    a. PM2.5 is a mixture consisting of several diverse 
components.\58\ Ambient PM2.5 generally consists of two 
components: (1) The primary component, emitted directly from a 
source; and (2) the secondary component, formed in the atmosphere 
from other pollutants emitted from the source. Models for 
PM2.5 are needed to meet NSR requirements to address 
compliance with the PM2.5 NAAQS and PSD increments and 
for SIP attainment demonstrations.
    b. For NSR modeling assessments, the general modeling 
requirements for screening models in section 4.2.1 and refined 
models in section 4.2.2 are applicable for the primary component of 
PM2.5, while the methods in section 5.4 are applicable 
for addressing the secondary component of PM2.5. Guidance 
for PSD assessments is available for determining the best approach 
to handling sources of primary and secondary PM2.5.\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 shall be applied for PM2.5 
hot-spot modeling for mobile sources. Specific guidance is available 
for analyzing direct PM2.5 impacts from highways, 
terminals, and other transportation projects.\61\

4.2.3.6 Models for PM10

    a. Models for PM10 are needed to meet NSR 
requirements to address compliance with the PM10 NAAQS 
and PSD increments and for SIP attainment demonstrations.
    b. For most sources, the general modeling requirements for 
screening models in section 4.2.1 and refined models in section 
4.2.2 shall be applied for PM10 modeling. In cases where 
the particle size and its effect on ambient concentrations need to 
be considered, particle deposition may be used on a case-by-case 
basis and their usage shall be coordinated with the appropriate 
reviewing authority. A SIP development guide \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 shall be applied for PM10 hot-
spot modeling for mobile sources. Specific guidance is available for 
analyzing direct PM10 impacts from highways, terminals, 
and other transportation projects.\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 PM2.5 are secondary 
pollutants formed through photochemical reactions. Ozone and 
secondarily formed particulate matter are closely related to each 
other in that they share common sources of emissions and are formed 
in the atmosphere from chemical reactions with similar precursors.
    b. Ozone formation is driven by emissions of NOX and 
volatile organic compounds (VOCs). Ozone formation is a complicated 
nonlinear process that requires favorable meteorological conditions 
in addition to VOC and NOX emissions. Sometimes complex 
terrain features also contribute to the build-up of precursors and 
subsequent ozone formation or destruction.
    c. PM2.5 can be either primary (i.e., emitted 
directly from sources) or secondary in nature. The fraction of 
PM2.5 which is primary versus secondary varies by 
location and season. In the United States, PM2.5 is 
dominated by a variety of chemical species or components of 
atmospheric particles, such as ammonium sulfate, ammonium nitrate, 
organic carbon mass, elemental carbon, and other soil compounds and 
oxidized metals. PM2.5 sulfate, nitrate, and ammonium 
ions are predominantly the result of chemical reactions of the 
oxidized products of SO2 and NOX emissions 
with direct ammonia emissions.\64\
    d. Control measures reducing ozone and PM2.5 
precursor emissions may not lead to proportional reductions in ozone 
and PM2.5. Modeled strategies designed to reduce ozone

[[Page 5213]]

or PM2.5 levels typically need to consider the chemical 
coupling between these pollutants. This coupling is important in 
understanding processes that control the levels of both pollutants. 
Thus, when feasible, it is important to use models that take into 
account the chemical coupling between ozone and PM2.5. In 
addition, using such a multi-pollutant modeling system can reduce 
the resource burden associated with applying and evaluating separate 
models for each pollutant and promotes consistency among the 
strategies themselves.
    e. PM2.5 is a mixture consisting of several diverse 
chemical species or components of atmospheric particles. Because 
chemical and physical properties and origins of each component 
differ, it may be appropriate to use either a single model capable 
of addressing several of the important components or to model 
primary and secondary components using different models. Effects of 
a control strategy on PM2.5 is estimated from the sum of 
the effects on the specific components comprising PM2.5.

5.2 Recommendations

    a. Chemical transformations can play an important role in 
defining the concentrations and properties of certain air 
pollutants. Models that take into account chemical reactions and 
physical processes of various pollutants (including precursors) are 
needed for determining the current state of air quality, as well as 
predicting and projecting the future evolution of these pollutants. 
It is important that a modeling system provide a realistic 
representation of chemical and physical processes leading to 
secondary pollutant formation and removal from the atmosphere.
    b. Chemical transport models treat atmospheric chemical and 
physical processes such as deposition and motion. There are two 
types of chemical transport models, Eulerian (grid based) and 
Lagrangian. These types of models are differentiated from each other 
by their frame of reference. Eulerian models are based on a fixed 
frame of reference and Lagrangian models use a frame of reference 
that moves with parcels of air between the source and receptor 
point.\9\ Photochemical grid models are three-dimensional Eulerian 
grid-based models that treat chemical and physical processes in each 
grid cell and use diffusion and transport processes to move chemical 
species between grid cells.\9\ These types of models are appropriate 
for assessment of near-field and regional scale reactive pollutant 
impacts from specific sources 7 10 11 12 or all 
sources.13 14 15 In some limited cases, the secondary 
processes can be treated with a box model, ideally in combination 
with a number of other modeling techniques and/or analyses to treat 
individual source sectors.
    c. Regardless of the modeling system used to estimate secondary 
impacts of ozone and/or PM2.5, model results should be 
compared to observation data to generate confidence that the 
modeling system is representative of the local and regional air 
quality. For ozone related projects, model estimates of ozone should 
be compared with observations in both time and space. For 
PM2.5, model estimates of speciated PM2.5 
components (such as sulfate ion, nitrate ion, etc.) should be 
compared with observations in both time and space.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 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. 59 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 ambient ozone is necessary for obtaining a permit. The simulation 
of ozone formation and transport requires realistic treatment of 
atmospheric chemistry and deposition. Models (e.g., Lagrangian and 
photochemical grid models) that integrate chemical and physical 
processes important in the formation, decay, and transport of ozone 
and important precursor species should be applied. Photochemical 
grid models are primarily designed to characterize precursor 
emissions and impacts from a wide variety of sources over a large 
geographic area but can also be used to assess the impacts from 
specific sources. 7 11 12
    b. The first tier of assessment for ozone impacts involves those 
situations where existing technical information is available (e.g., 
results from existing photochemical grid modeling, published 
empirical estimates of source specific impacts, or reduced-form 
models) in combination with other supportive information and 
analysis for the purposes of estimating secondary impacts from a 
particular source. The existing technical information should provide 
a credible and representative estimate of the secondary impacts from 
the project source. The appropriate reviewing authority (paragraph 
3.0(b)) and appropriate EPA guidance 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 or a first tier demonstration indicates a more refined 
assessment is needed. For these situations, chemical transport 
models should be used to address single-source impacts. Special 
considerations are needed when using these models to evaluate the 
ozone impact from an individual source. Guidance on the use of 
models and other analyses for demonstrating the impacts of single 
sources for ozone is available. 66 This guidance document 
provides a more detailed discussion of the appropriate approaches to 
obtaining estimates of ozone impacts from a single source. Model 
users should use the latest version of the guidance in consultation 
with the appropriate reviewing authority (paragraph 3.0(b)) to 
determine the most suitable refined approach for single-source ozone 
modeling on a case-by-case basis.

[[Page 5214]]

5.4 Recommended Models and Approaches for Secondarily Formed 
PM2.5

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

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

    a. Models for PM2.5 are needed to assess the adequacy 
of a proposed strategy for meeting the annual and 24-hour 
PM2.5 NAAQS. Modeling primary and secondary 
PM2.5 can be a multi-faceted and complex problem, 
especially for secondary components of PM2.5 such as 
sulfates and nitrates. Control agencies with jurisdiction over areas 
with secondary PM2.5 problems should use models that 
integrate chemical and physical processes important in the 
formation, decay, and transport of these species (e.g., 
photochemical grid models). Suitability of a modeling approach or 
mix of modeling approaches for a given application requires 
technical judgment as well as professional experience in choice of 
models, use of the model(s) in an attainment test, development of 
emissions and meteorological inputs to the model, and selection of 
days to model. Guidance on the use of models and other analyses for 
demonstrating attainment of the air quality goals for 
PM2.5 is available.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 may be necessary for obtaining a permit. 
Primary PM2.5 components shall be simulated using the 
general modeling requirements in section 4.2.3.5. The simulation of 
secondary particulate matter formation and transport is a complex 
exercise requiring realistic treatment of atmospheric chemistry and 
deposition. Models should be applied that integrate chemical and 
physical processes important in the formation, decay, and transport 
of these species (e.g., Lagrangian and photochemical grid models). 
Photochemical grid models are primarily designed to characterize 
precursor emissions and impacts from a wide variety of sources over 
a large geographic area and can also be used to assess the impacts 
from specific sources.7 10 For situations where a project 
source emits both primary PM2.5 and PM2.5 
precursors, the contribution from both should be combined for use in 
determining the source's ambient impact. Approaches for combining 
primary and secondary impacts are provided in appropriate guidance 
for single source permit related demonstrations. 66
    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 or a first tier demonstration indicates 
a more refined assessment is needed. For these situations, chemical 
transport models should be used for assessments of single-source 
impacts. Special considerations are needed when using these models 
to evaluate the secondary particulate matter impact from an 
individual source. Guidance on the use of models and other analyses 
for demonstrating the impacts of single sources for secondary 
PM2.5 is available. 66 This guidance document 
provides a more detailed discussion of the appropriate approaches to 
obtaining estimates of secondary particulate matter concentrations 
from a single source. Model users should use the latest version of 
this guidance in consultation with the appropriate reviewing 
authority (paragraph 3.0(b)) to determine the most suitable single-
source modeling approach for secondary PM2.5 on a case-
by-case basis.

6.0 Modeling for Air Quality Related Values and Other Governmental 
Programs

6.1 Discussion

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

6.2 Air Quality Related Values

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

[[Page 5215]]

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 
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 that time. 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. 
Section 169A of the CAA requires states to develop SIPs containing 
long-term strategies for remedying existing and preventing future 
visibility impairment in the 156 mandatory Class I Federal areas, 
where visibility is considered an important attribute. In order to 
develop long-term strategies to address regional haze, many state, 
local, and tribal agencies will need to conduct regional-scale 
modeling of fine particulate concentrations and associated 
visibility impairment.
    c. The FLAG visibility modeling recommendations are divided into 
two distinct sections to address different requirements for: (1) 
Near field modeling where plumes or layers are compared against a 
viewing background, and (2) distant/multi-source modeling for plumes 
and aggregations of plumes that affect the general appearance of a 
scene.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 that is beyond the scope of this 
subsection.

6.2.2 Models for Estimating Deposition Impacts

    a. For many Class I areas, AQRVs have been identified that are 
sensitive to atmospheric deposition of air pollutants. Emissions of 
NOX, sulfur oxides, NH3, mercury, and 
secondary pollutants such as ozone and particulate matter affect 
components of ecosystems. In sensitive ecosystems, these compounds 
can acidify soils and surface waters, add nutrients that change 
biodiversity, and affect the ecosystem services provided by forests 
and natural areas.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 may need to be 
conducted to ensure that individual and cumulative offshore oil and 
gas exploration, development, and production plans and activities do 
not significantly affect the air quality of any state as required 
under the Outer Continental Shelf Lands Act (OCSLA). Air quality 
modeling requires various input datasets, including emissions 
sources, meteorology, and pre-existing pollutant concentrations. For 
sources under the reviewing authority of the Department of Interior, 
Bureau of Ocean Energy Management (BOEM), guidance for the 
development of all necessary Outer Continental Shelf (OCS) air 
quality modeling inputs and appropriate model selection and 
application is available from the BOEM's Web site: https://www.boem.gov/GOMR-Environmental-Compliance.
    b. The Federal Aviation Administration (FAA) is the appropriate 
reviewing authority for air quality assessments of primary pollutant 
impacts at airports and air bases. The Aviation Environmental Design 
Tool (AEDT) is developed and supported by the FAA, and is 
appropriate for air quality assessment of primary pollutant impacts 
at airports or air bases. AEDT has adopted AERMOD for treating 
dispersion. Application of AEDT is intended for estimating the 
change in emissions for aircraft operations, point source, and 
mobile source emissions on airport property and quantify the 
associated pollutant level- concentrations. AEDT is not intended for 
PSD, SIP, or other regulatory air quality analyses of point or 
mobile sources at or peripheral to airport property that are 
unrelated to airport operations. The latest version of AEDT may be 
obtained from the FAA at: https://aedt.faa.gov.

[[Page 5216]]

7.0 General Modeling Considerations

7.1 Discussion

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

7.2 Recommendations

7.2.1 All Sources

7.2.1.1 Dispersion Coefficients

    a. For any dispersion modeling exercise, the urban or rural 
determination of a source 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 (of the two methods, the land use 
procedure is considered more definitive.):
    i. Land Use Procedure: (1) Classify the land use within the 
total area, Ao, circumscribed by a 3 km radius circle 
about the source using the meteorological land use typing scheme 
proposed by Auer; 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 per square 
kilometer, use urban dispersion coefficients; otherwise use 
appropriate rural dispersion coefficients.
    c. Population density should be used with caution and generally 
not be applied to highly industrialized areas where the population 
density may be low and, thus, a rural classification would be 
indicated. However, the area is likely to be sufficiently built-up 
so that the urban land use criteria would be satisfied. Therefore, 
in this case, the classification should be ``urban'' and urban 
dispersion parameters should be used.
    d. For applications of AERMOD in urban areas, under either the 
Land Use Procedure or the Population Density Procedure, the user 
needs to estimate the population of the urban area affecting the 
modeling domain because the urban influence in AERMOD is scaled 
based on a user-specified population. For non-population oriented 
urban areas, or areas influenced by both population and industrial 
activity, the user will need to estimate an equivalent population to 
adequately account for the combined effects of industrialized areas 
and populated areas within the modeling domain. Selection of the 
appropriate population for these applications should be determined 
in consultation with the appropriate reviewing authority (paragraph 
3.0(b)) and the latest version of the AERMOD Implementation 
Guide.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)) 
and the latest version of the AERMOD Implementation Guide 
76 when evaluating this situation.
    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. Typically, geographic effects are more apparent when 
the ambient winds are light or calm, as stronger synoptic or 
mesoscale winds can modify, or even eliminate the weak geographic 
circulations.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 characteristics of the winds are a 
balance of various forces, such that the assumptions of steady-state 
straight-line transport both in time and space are inappropriate. In 
such cases, a model should be chosen to fully treat the time and 
space variations of meteorology effects on transport and dispersion. 
The setup and application of such a model should be determined in 
consultation with the appropriate reviewing authority (paragraph 
3.0(b)) consistent with limitations of paragraph 3.2.2(e). The 
meteorological input data requirements for developing the time and 
space varying three-dimensional winds and dispersion meteorology for 
these situations are discussed in paragraph 8.4.1.2(c). Examples of 
inhomogeneous winds include, but are not limited to, situations 
described in the following paragraphs:
    i. Inversion breakup fumigation. Inversion breakup fumigation 
occurs when a plume (or multiple plumes) is emitted into a stable 
layer of air and that layer is subsequently mixed to the ground 
through convective transfer of heat from the surface or because of 
advection to less stable surroundings. Fumigation may cause 
excessively high concentrations, but is usually rather short-lived 
at a given receptor. There are no recommended refined techniques to 
model this phenomenon. There are, however, screening procedures 
40 that may be used to approximate the concentrations. 
Considerable care should be exercised in using the results obtained 
from the screening techniques.
    ii. Shoreline fumigation. Fumigation can be an important 
phenomenon on and near the shoreline of bodies of water. This can 
affect both individual plumes and area-wide emissions. When 
fumigation conditions are expected to occur from a source or sources 
with tall stacks located on or just inland of a shoreline, this 
should be addressed in the air quality modeling analysis. The EPA 
has evaluated several coastal fumigation models, and the evaluation 
results of these models are available for their possible application 
on a case-by-case basis when air quality estimates under shoreline 
fumigation conditions are needed.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, use professional judgment along with 
coordination with the appropriate reviewing authority (paragraph 
3.0(b)). AERMOD contains algorithms for dry and wet deposition of 
gases and particles.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.

[[Page 5217]]

7.2.2 Stationary Sources

7.2.2.1 Good Engineering Practice Stack Height

    a. The use of stack height credit in excess of Good Engineering 
Practice (GEP) stack height or credit resulting from any other 
dispersion technique is prohibited in the development of emissions 
limits by 40 CFR 51.118 and 40 CFR 51.164. The definition of GEP 
stack height and dispersion technique are contained in 40 CFR 
51.100. Methods and procedures for making the appropriate stack 
height calculations, determining stack height credits and an example 
of applying those techniques are found in several 
references,81 82 83 84 that provide a great deal of 
additional information for evaluating and describing building cavity 
and wake effects.
    b. If stacks for new or existing major sources are found to be 
less than the height defined by the EPA's refined formula for 
determining GEP height, then air quality impacts associated with 
cavity or wake effects due to the nearby building structures should 
be determined. The EPA refined formula height is defined as H + 
1.5L.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, AERMOD should be used (section 
4.2.2).

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 
in 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.
    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.
    d. On a case-by-case basis, refinements to the preferred model 
may be considered for plume rise and downwash effects and shall 
occur in agreement with the appropriate reviewing authority 
(paragraph 3.0(b)) and approval by the EPA Regional Office based on 
the requirements of section 3.2.2.

7.2.3 Mobile Sources

    a. Emissions of primary pollutants from mobile sources can be 
modeled with an appropriate model identified in section 4.2. 
Screening of mobile sources can be accomplished by using screening 
meteorology, e.g., worst-case meteorological conditions. Maximum 
hourly concentrations computed from screening modeling can be 
converted to longer averaging periods using the scaling ratios 
specified in the AERSCREEN User's Guide.\37\
    b. Mobile sources can be modeled in AERMOD as either line (i.e., 
elongated area) sources or as a series of volume sources. 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 (m) of the roadway), the results can be 
highly sensitive to the characterization of the mobile emissions. 
Important characteristics for both line/area and volume sources 
include the plume release height, source width, and initial 
dispersion characteristics, and should also take into account the 
impact of traffic-induced turbulence that can cause roadway sources 
to have larger initial dimensions than might normally be used for 
representing line sources.
    c. The EPA's quantitative PM hot-spot guidance \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. Area, line or volume sources may be used for 
modeling mobile sources. However, experience in the field has shown 
that area sources may be easier to characterize correctly compared 
to volume sources. If volume sources are used, it is particularly 
important to ensure that roadway emissions are appropriately spaced 
when using volume source so that the emissions field is uniform 
across the roadway. Additionally, receptor placement is particularly 
important for volume sources that have ``exclusion zones'' where 
concentrations are not calculated for receptors located ``within'' 
the volume sources, i.e., less than 2.15 times the initial lateral 
dispersion coefficient from the center of the volume.\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 user's guide and/or associated documentation for each model.

8.1 Modeling Domain

8.1.1 Discussion

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

8.1.2 Requirements

    a. For a NAAQS or PSD increments assessment, the modeling domain 
or project's impact area shall include all locations where the 
emissions of a pollutant from the new or modifying source(s) may 
cause a significant ambient impact. This impact area is defined as 
an area with a radius extending from the new or modifying source to: 
(1) The most distant location where air quality modeling predicts a 
significant ambient impact will occur, or (2) the nominal 50 km 
distance considered applicable for Gaussian dispersion models, 
whichever is less. The required air quality analysis shall be 
carried out within this geographical area with characterization of 
source impacts, nearby source impacts, and background 
concentrations, as recommended later in this section.
    b. For SIP attainment demonstrations for ozone and 
PM2.5, or regional haze reasonable progress goal 
analyses, the modeling domain is determined by the nature of the 
problem being modeled and the spatial scale of the emissions that 
impact the nonattainment or Class I area(s). The modeling domain 
shall be designed so that all major upwind source areas that 
influence the downwind nonattainment area are included in addition 
to all monitor locations that are currently or recently violating 
the NAAQS or close to violating the NAAQS in the nonattainment area. 
Similarly, all Class I areas to be evaluated in a regional haze 
modeling application shall be included and sufficiently distant from 
the edge of the modeling domain. Guidance on the determination of 
the appropriate modeling domain for photochemical grid models in 
demonstrating attainment of these air quality goals is 
available.\60\ Users should consult the latest version of this 
guidance for the most current modeling guidance and the appropriate 
reviewing authority (paragraph 3.0(b)) for any application specific 
guidance that is beyond the scope of this section.

[[Page 5218]]

8.2 Source Data

8.2.1 Discussion

    a. Sources of pollutants can be classified as point, line, area, 
and volume sources. Point sources are defined in terms of size and 
may vary between regulatory programs. The line sources most 
frequently considered are roadways and streets along which there are 
well-defined movements of motor vehicles. They may also be lines of 
roof vents or stacks, such as in aluminum refineries. Area 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'' emissions in the regulatory dispersion modeling. 
As part of a cumulative impact analysis, Table 8-2 allows for the 
model user to account for actual operations in developing the 
emissions inputs for dispersion modeling of nearby sources, while 
other sources are best represented by air quality monitoring data. 
For purposes of situations involving emissions trading, refer to 
current EPA policy and guidance to establish input data. 
Consultation with the appropriate reviewing authority (paragraph 
3.0(b)) is advisable on the establishment of the appropriate 
emissions inputs for regulatory modeling applications with respect 
to PSD assessments for a proposed new or modifying source.
    d. For stationary source applications, changes in operating 
conditions that affect the physical emission parameters (e.g., 
release height, initial plume volume, and exit velocity) shall be 
considered to ensure that maximum potential impacts are 
appropriately determined in the assessment. For example, the load or 
operating condition for point sources that causes maximum ground-
level concentrations shall be established. As a minimum, the source 
should be modeled using the design capacity (100 percent load). If a 
source operates at greater than design capacity for periods that 
could result in violations of the NAAQS or PSD increments, this load 
should be modeled. Where the source operates at substantially less 
than design capacity, and the changes in the stack parameters 
associated with the operating conditions could lead to higher ground 
level concentrations, loads such as 50 percent and 75 percent of 
capacity should also be modeled. Malfunctions which may result in 
excess emissions are not considered to be a normal operating 
condition. They generally should not be considered in determining 
allowable emissions. However, if the excess emissions are the result 
of poor maintenance, careless operation, or other preventable 
conditions, it may be necessary to consider them in determining 
source impact. A range of operating conditions should be considered 
in screening analyses. The load causing the highest concentration, 
in addition to the design load, should be included in refined 
modeling.
    e. Emissions from mobile sources also have physical and temporal 
characteristics that should be appropriately accounted. For example, 
an appropriate emissions model shall be used to determine emissions 
profiles. Such emissions should include speciation specific for the 
vehicle types used on the roadway (e.g., light duty and heavy duty 
trucks), and subsequent parameterizations of the physical emissions 
characteristics (e.g., release height) should reflect those 
emissions sources. For long-term standards, annual average emissions 
may be appropriate, but for short-term standards, discrete temporal 
representation of emissions should be used (e.g., variations in 
weekday and weekend traffic or the diurnal rush-hour profile typical 
of many cities). Detailed information and data requirements for 
modeling mobile sources of pollution are provided in the user's 
manuals for each of the models applicable to mobile 
sources.61 63

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8.3 Background Concentrations

8.3.1 Discussion

    a. Background concentrations are essential in constructing the 
design concentration, or total air quality concentration, as part of 
a cumulative impact analysis for NAAQS and PSD increments (section 
9.2.3). Background air quality should not include the ambient 
impacts of the project source under consideration. Instead, it 
should include:
    i. Nearby sources: These are individual sources located in the 
vicinity of the source(s) under consideration for emissions limits 
that are not adequately represented by ambient monitoring data. 
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

[[Page 5221]]

assess single-source impacts for ozone and secondary 
PM2.5.
    d. For NAAQS assessments and SIP attainment demonstrations for 
inert pollutants, the development of the appropriate background 
concentration for a cumulative impact analysis involves proper 
accounting of each contribution to the design concentration and will 
depend upon whether the project area's situation consists of either 
an isolated single source(s) or a multitude of sources. For PSD 
increment assessments, all impacts after the appropriate baseline 
dates (i.e., trigger date, major source baseline date, and minor 
source baseline date) from all increment-consuming and increment-
expanding sources should be considered in the design concentration 
(section 9.2.2).

8.3.2 Recommendations for Isolated Single Sources

    a. In areas with an isolated source(s), determining the 
appropriate background concentration should focus on 
characterization of contributions from all other sources through 
adequately representative ambient monitoring data.
    b. The EPA recommends use of the most recent quality assured air 
quality monitoring data collected in the vicinity of the source to 
determine the background concentration for the averaging times of 
concern. In most cases, the EPA recommends using data from the 
monitor closest to and upwind of the project area. If several 
monitors are available, preference should be given to the monitor 
with characteristics that are most similar to the project area. If 
there are no monitors located in the vicinity of the new or 
modifying source, a ``regional site'' may be used to determine 
background concentrations. A regional site is one that is located 
away from the area of interest but is impacted by similar or 
adequately representative sources.
    c. Many of the challenges related to cumulative impact analyses 
arise in the context of defining the appropriate metric to 
characterize background concentrations from ambient monitoring data 
and determining the appropriate method for combining this monitor-
based background contribution to the modeled impact of the project 
and other nearby sources. For many cases, the best starting point 
would be use of the current design value for the applicable NAAQS as 
a uniform monitored background contribution across the project area. 
However, there are cases in which the current design value may not 
be appropriate. Such cases include but are not limited to:
    i. For situations involving a modifying source where the 
existing facility is determined to impact the ambient monitor, the 
background concentration at each monitor can be determined by 
excluding values when the source in question is impacting the 
monitor. In such cases, monitoring sites inside a 90[deg] sector 
downwind of the source may be used to determine the area of impact.
    ii. There may be other circumstances which would necessitate 
modifications to the ambient data record. Such cases could include 
removal of data from specific days or hours when a monitor is being 
impacted by activities that are not typical or not expected to occur 
again in the future (e.g., construction, roadway repairs, forest 
fires, or unusual agricultural activities). There may also be cases 
where it may be appropriate to scale (multiplying the monitored 
concentrations with a scaling factor) or adjust (adding or 
subtracting a constant value the monitored concentrations) data from 
specific days or hours. Such adjustments would make the monitored 
background concentrations more temporally and/or spatially 
representative of the area around the new or modifying source for 
the purposes of the regulatory assessment.
    iii. For short-term standards, the diurnal or seasonal patterns 
of the air quality monitoring data may differ significantly from the 
patterns associated with the modeled concentrations. When this 
occurs, it may be appropriate to pair the air quality monitoring 
data in a temporal manner that reflects these patterns (e.g., 
pairing by season and/or hour of day).\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 60 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 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 cause 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 the first 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 judgment by the appropriate 
reviewing authority (paragraph 3.0(b)). This guidance is not 
intended to alter the exercise of that judgment or to

[[Page 5222]]

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) or could have impact on the averaging period of 
concern. Accordingly, it is not necessary to model impacts of a 
nearby source that does not, by its nature, operate at the same time 
as the primary source or could have impact on the averaging period 
of concern, regardless of an identified significant concentration 
gradient from the nearby source. The burden is on the permit 
applicant to adequately justify the exclusion of nearby sources to 
the satisfaction of the appropriate reviewing authority (paragraph 
3.0(b)). The following examples illustrate two cases in which a 
nearby source may be shown not to operate at the same time as the 
primary source(s) being modeled: (1) Seasonal sources (only used 
during certain seasons of the year). Such sources would not be 
modeled as nearby sources during times in which they do not operate; 
and (2) Emergency backup generators, to the extent that they do not 
operate simultaneously with the sources that they back up. Such 
emergency equipment would not be modeled as nearby sources.
    d. Other sources. That portion of the background attributable to 
all other sources (e.g., natural sources, minor and distant 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 
Modeling Guidance for Demonstrating Attainment of Air Quality Goals 
for Ozone, PM2.5, and Regional Haze.\60\ 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 NWS Automated Surface Observing Stations 
(ASOS) in the early 1990's should not preclude the use of NWS ASOS 
data if such a station is determined to be representative of the 
modeled area.\93\
    d. Model input data are normally obtained either from the NWS or 
as part of a site-specific measurement program. State climatology 
offices, local universities, FAA, military stations, industry, and 
pollution control agencies may also be sources of such data. In 
specific cases, prognostic meteorological data may be appropriate 
for use and obtained from similar sources. Some recommendations and 
requirements for the use of each type of data are included in this 
subsection.

8.4.2 Recommendations and Requirements

    a. AERMET \94\ shall be used to preprocess all meteorological 
data, be it observed or prognostic, for use with AERMOD in 
regulatory applications. The AERMINUTE \95\ processor, in most 
cases, should be used to process 1-minute ASOS wind data for input 
to AERMET when processing NWS ASOS sites in AERMET. When processing 
prognostic meteorological data for AERMOD, the Mesoscale Model 
Interface Program (MMIF) \103\ should be used to process data for 
input to AERMET. Other methods of processing prognostic 
meteorological data for input to AERMET should be approved by the 
appropriate reviewing authority. Additionally, the following 
meteorological preprocessors are recommended by the EPA: 
PCRAMMET,\96\ MPRM,\97\ and METPRO.\98\ 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.\99\
    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,100 101 
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 be 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.102 103 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.

[[Page 5223]]

The over land or surface data, processed through PCRAMMET \96\ or 
MPRM,\97\ that provides hourly stability class, wind direction and 
speed, ambient temperature, and mixing height, are required. Data 
over water requires hourly mixing height, relative humidity, air 
temperature, and water surface temperature. Missing winds are 
substituted with the surface winds. Vertical wind direction shear, 
vertical temperature gradient, and turbulence intensities are 
optional.
    e. The model user should acquire enough meteorological data to 
ensure that worst-case meteorological conditions are adequately 
represented in the model results. The use of 5 years of adequately 
representative NWS or comparable meteorological data, at least 1 
year of site-specific, or at least 3 years of prognostic 
meteorological data, are required. If 1 year or more, up to 5 years, 
of site-specific data are available, these data are preferred for 
use in air quality analyses. Depending on completeness of the data 
record, consecutive years of NWS, site-specific, or prognostic data 
are preferred. Such data must be subjected to quality assurance 
procedures as described in section 8.4.4.2.
    f. Objective analysis in meteorological modeling is to improve 
meteorological analyses (the ``first guess field'') used as initial 
conditions for prognostic meteorological models by incorporating 
information from meteorological observations. Direct and indirect 
(using remote sensing techniques) observations of temperature, 
humidity, and wind from surface and radiosonde reports are commonly 
employed to improve these analysis fields. For long-range transport 
applications, it is recommended that objective analysis procedures, 
using direct and indirect meteorological observations, be employed 
in preparing input fields to produce prognostic meteorological 
datasets. The length of record of observations should conform to 
recommendations outlined in paragraph 8.4.2(e) for prognostic 
meteorological model datasets.

8.4.3 National Weather Service Data

8.4.3.1 Discussion

    a. The NWS meteorological data are routinely available and 
familiar to most model users. Although the NWS does not provide 
direct measurements of all the needed dispersion model input 
variables, methods have been developed and successfully used to 
translate the basic NWS data to the needed model input. Site-
specific measurements of model input parameters have been made for 
many modeling studies, and those methods and techniques are becoming 
more widely applied, especially in situations such as complex 
terrain applications, where available NWS data are not adequately 
representative. However, there are many modeling applications where 
NWS data are adequately representative and the applications still 
rely heavily on the NWS data.
    b. Many models use the standard hourly weather observations 
available from the National Centers for Environmental Information 
(NCEI).\b\ These observations are then preprocessed before they can 
be used in the models. Prior to the advent of ASOS in the early 
1990's, the standard ``hourly'' weather observation was a human-
based observation reflecting a single 2-minute average generally 
taken about 10 minutes before the hour. However, beginning in 
January 2000 for first-order stations and in March 2005 for all 
stations, the NCEI has archived the 1-minute ASOS wind data (i.e., 
the rolling 2-minute average winds) for the NWS ASOS sites. The 
AERMINUTE processor \95\ was developed to reduce the number of calm 
and missing hours in AERMET processing by substituting standard 
hourly observations with full hourly average winds calculated from 
1-minute ASOS wind data.
---------------------------------------------------------------------------

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

8.4.3.2 Recommendations

    a. The preferred models listed in 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. The NCEI makes available surface 104 105 and upper 
air \106\ 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 m, the actual anemometer height should be used as input 
to the preferred meteorological processor and model.
    c. Standard hourly NWS wind directions are reported to the 
nearest 10 degrees. Due to the coarse resolution of these data, a 
specific set of randomly generated numbers has been developed by the 
EPA and should be used when processing standard hourly NWS data for 
use in the preferred EPA models to ensure a lack of bias in wind 
direction assignments within the models.
    d. Beginning with year 2000, NCEI began archiving 2-minute 
winds, reported every minute to the nearest degree for NWS ASOS 
sites. The AERMINUTE processor was developed to read those winds and 
calculate hourly average winds for input to AERMET. When such data 
are available for the NWS ASOS site being processed, the AERMINUTE 
processor should be used, in most cases, to calculate hourly average 
wind speed and direction when processing NWS ASOS data for input to 
AERMOD.\93\
    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,\107\ 
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.\107\ 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 \107\ is available. 
Site-specific data should always be reviewed for representativeness 
and adequacy by an experienced meteorologist, atmospheric scientist, 
or other qualified scientist in consultation with the appropriate 
reviewing authority (paragraph 3.0(b)).

8.4.4.2 Recommendations

    a. The EPA guidance 107 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 91 108 109 Detailed 
information on quality assurance is also available.110 As 
a minimum, site-specific measurements of ambient air temperature, 
transport wind speed and direction, and the variables necessary to 
estimate atmospheric dispersion should be available in 
meteorological datasets to be used in modeling. Care should be taken 
to ensure that meteorological instruments are located to provide an 
adequately representative characterization of pollutant transport 
between sources and receptors of interest. The appropriate reviewing 
authority (paragraph 3.0(b)) is available to help determine the 
appropriateness of the measurement locations.
    i. Solar radiation measurements. Total solar radiation or net 
radiation should be measured with a reliable pyranometer or net 
radiometer sited and operated in accordance with established site-
specific meteorological guidance.107 110

[[Page 5224]]

    ii. Temperature measurements. Temperature measurements should be 
made at standard shelter height (2m) in accordance with established 
site-specific meteorological guidance.107
    iii. Temperature difference measurements. Temperature difference 
(DT) measurements should be obtained using matched thermometers or a 
reliable thermocouple system to achieve adequate accuracy. Siting, 
probe placement, and operation of DT systems should be based on 
guidance found in Chapter 3 of reference 107 and such guidance 
should be followed when obtaining vertical temperature gradient 
data. AERMET may employ the Bulk Richardson scheme, which requires 
measurements of temperature difference, in lieu of cloud cover or 
insolation data. To ensure correct application and acceptance, 
AERMOD users should consult with the appropriate reviewing authority 
(paragraph 3.0(b)) before using the Bulk Richardson scheme for their 
analysis.
    iv. Wind measurements. For simulation of plume rise and 
dispersion of a plume emitted from a stack, characterization of the 
wind profile up through the layer in which the plume disperses is 
desirable. This is especially important in complex terrain and/or 
complex wind situations where wind measurements at heights up to 
hundreds of meters above stack base may be required in some 
circumstances. For tall stacks when site-specific data are needed, 
these winds have been obtained traditionally using meteorological 
sensors mounted on tall towers. A feasible alternative to tall 
towers is the use of meteorological remote sensing instruments 
(e.g., acoustic sounders or radar wind profilers) to provide winds 
aloft, coupled with 10-meter towers to provide the near-surface 
winds. Note that when site-specific wind measurements are used, 
AERMOD, at a minimum, requires wind observations at a height above 
ground between seven times the local surface roughness height and 
100 m. (For additional requirements for AERMOD and CTDMPLUS, see 
appendix A.) Specifications for wind measuring instruments and 
systems are contained in reference 107.
    b. All processed site-specific data should be in the form of 
hourly averages for input to the dispersion model.
    i. Turbulence data. There are several dispersion models that are 
capable of using direct measurements of turbulence (wind 
fluctuations) in the characterization of the vertical and lateral 
dispersion (e.g., CTDMPLUS or AERMOD). When turbulence data are used 
to directly characterize the vertical and lateral dispersion, the 
averaging time for the turbulence measurements should be 1 hour. For 
technical guidance on processing of turbulence parameters for use in 
dispersion modeling, refer to the user's guide to the meteorological 
processor for each model (see section 8.4.2(a)).
    ii. Stability categories. For dispersion models that employ P-G 
stability categories for the characterization of the vertical and 
lateral dispersion, the P-G stability categories, as originally 
defined, couple near-surface measurements of wind speed with 
subjectively determined insolation assessments based on hourly cloud 
cover and ceiling height observations. The wind speed measurements 
are made at or near 10 m. The insolation rate is typically assessed 
using observations of cloud cover and ceiling height based on 
criteria outlined by Turner.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 10 m and representative 
cloud cover and ceiling height. Implementation of the Turner method, 
as well as considerations in determining representativeness of cloud 
cover and ceiling height in cases for which site-specific cloud 
observations are unavailable, may be found in section 6 of reference 
107. In the absence of requisite data to implement the Turner 
method, the solar radiation/delta-T (SRDT) method or wind 
fluctuation statistics (i.e., the [sigma]E and 
[sigma]A methods) may be used.
    iii. The SRDT method, described in section 6.4.4.2 of reference 
107, is modified slightly from that published from earlier 
work111 and has been evaluated with three site-specific 
databases.112 The two methods of stability classification 
that use wind fluctuation statistics, the [sigma]E and 
[sigma]A methods, are also described in detail in section 
6.4.4 of reference 107 (note applicable tables in section 6). For 
additional information on the wind fluctuation methods, several 
references are available.113 114 115 116
    c. Missing data substitution. After valid data retrieval 
requirements have been met,107 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 107. If no representative 
alternative data are available for substitution, the absent data 
should be coded as missing, using missing data codes appropriate to 
the applicable meteorological pre-processor. Appropriate model 
options for treating missing data, if available in the model, should 
be employed.

8.4.5 Prognostic Meteorological Data

8.4.5.1 Discussion

    a. For some modeling applications, there may not be a 
representative NWS or comparable meteorological station available 
(e.g., complex terrain), and it may be cost prohibitive or 
infeasible to collect adequately representative site-specific data. 
For these cases, it may be appropriate to use prognostic 
meteorological data, if deemed adequately representative, in a 
regulatory modeling application. However, if prognostic 
meteorological data are not representative of transport and 
dispersion conditions in the area of concern, the collection of 
site-specific data is necessary.
    b. The EPA has developed a processor, the MMIF,102 to 
process MM5 (Mesoscale Model 5) or WRF (Weather Research and 
Forecasting) model data for input to various models including 
AERMOD. MMIF can process data for input to AERMET or AERMOD for a 
single grid cell or multiple grid cells. MMIF output has been found 
to compare favorably against observed data (site-specific or 
NWS).117 Specific guidance on processing MMIF for AERMOD 
can be found in reference 103. When using MMIF to process prognostic 
data for regulatory applications, the data should be processed to 
generate AERMET inputs and the data subsequently processed through 
AERMET for input to AERMOD. If an alternative method of processing 
data for input to AERMET is used, it must be approved by the 
appropriate reviewing authority (paragraph 3.0(b)).

8.4.5.2 Recommendations

    a. Prognostic model evaluation. Appropriate effort by the 
applicant should be devoted to the process of evaluating the 
prognostic meteorological data. The modeling data should be compared 
to NWS observational data or other comparable data in an effort to 
show that the data are adequately replicating the observed 
meteorological conditions of the time periods modeled. An 
operational evaluation of the modeling data for all model years 
(i.e., statistical, graphical) should be completed.\60\ 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.\60\
    b. Representativeness. When processing MMIF data for use with 
AERMOD, the grid cell used for the dispersion modeling should be 
adequately spatially representative of the analysis domain. In most 
cases, this may be the grid cell containing the emission source of 
interest. Since the dispersion modeling may involve multiple sources 
and the domain may cover several grid cells, depending on grid 
resolution of the prognostic model, professional judgment may be 
needed to select the appropriate grid cell to use. In such cases, 
the selected grid cells should be adequately representative of the 
entire domain.
    c. Grid resolution. The grid resolution of the prognostic 
meteorological data should be considered and evaluated 
appropriately, particularly for projects involving complex terrain. 
The operational evaluation of the modeling data should consider 
whether a finer grid resolution is needed to ensure that the data 
are representative. The use of output from prognostic mesoscale 
meteorological models is contingent upon the concurrence with the 
appropriate reviewing authority (paragraph 3.0(b)) that the data are 
of acceptable quality.

8.4.6 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

[[Page 5225]]

behavior during these conditions does not, at present, permit the 
development of a better technique. Therefore, the procedures 
disregard hours that are identified as calm. The hour is treated as 
missing and a convention for handling missing hours is recommended. 
With the advent of the AERMINUTE processor, when processing NWS ASOS 
data, the inclusion of hourly averaged winds from AERMINUTE will, in 
some instances, dramatically reduce the number of calm and missing 
hours, especially when the ASOS wind are derived from a sonic 
anemometer. To alleviate concerns about these issues, especially 
those introduced with AERMINUTE, the EPA implemented a wind speed 
threshold in AERMET for use with ASOS derived winds.93 94 
Winds below the threshold will be treated as calms.
    b. AERMOD, while fundamentally a steady-state Gaussian plume 
model, contains algorithms for dealing with low wind speed (near 
calm) conditions. As a result, AERMOD can produce model estimates 
for conditions when the wind speed may be less than 1 m/s, but still 
greater than the instrument threshold. Required input to AERMET for 
site-specific data, the meteorological processor for AERMOD, 
includes a threshold wind speed and a reference wind speed. The 
threshold wind speed is the greater of the threshold of the 
instrument used to collect the wind speed data or wind direction 
sensor.\107\ The reference wind speed is selected by the model as 
the lowest level of non-missing wind speed and direction data where 
the speed is greater than the wind speed threshold, and the height 
of the measurement is between seven times the local surface 
roughness length and 100 m. If the only valid observation of the 
reference wind speed between these heights is less than the 
threshold, the hour is considered calm, and no concentration is 
calculated. None of the observed wind speeds in a measured wind 
profile that are less than the threshold speed are used in 
construction of the modeled wind speed profile in AERMOD.

8.4.6.2 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. Model predicted concentrations for 3-, 
8-, and 24-hour averages should be calculated by dividing the sum of 
the hourly concentrations for the period by the number of valid or 
non-missing hours. If the total number of valid hours is less than 
18 for 24-hour averages, less than 6 for 8-hour averages, or less 
than 3 for 3-hour averages, the total concentration should be 
divided by 18 for the 24-hour average, 6 for the 8-hour average, and 
3 for the 3-hour average. For annual averages, the sum of all valid 
hourly concentrations is divided by the number of non-calm hours 
during the year. AERMOD has been coded to implement these 
instructions. For hours that are calm or missing, the AERMOD hourly 
concentrations will be zero. For other models listed in appendix A, 
a post-processor computer program, CALMPRO 118 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 other than 
AERMOD, measured site-specific wind speeds of less than 1 m/s but 
higher than the response threshold of the instrument should be input 
as 1 m/s; the corresponding wind direction should also be input. 
Wind observations below the response threshold of the instrument 
should be set to zero, with the input file in ASCII format. For 
input to AERMOD, no such adjustment should be made to the site-
specific wind data, as AERMOD has algorithms to account for light or 
variable winds as discussed in section 8.4.6.1(a). For NWS ASOS 
data, especially data using the 1-minute ASOS winds, a wind speed 
threshold option is allowed with a recommended speed of 0.5 m/
s.93 When using prognostic data processed by MMIF, a 0.5 
m/s threshold is also invoked by MMIF for input to 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.

9.0 Regulatory Application of Models

9.1 Discussion

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

9.2 Recommendations

9.2.1 Modeling Protocol

    a. Every effort should be made by the appropriate reviewing 
authority (paragraph

[[Page 5226]]

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 the pre-application meeting. The protocol should be written and 
agreed upon by the parties concerned, although it is not intended 
that this protocol be a binding, formal legal document. Changes in 
such a protocol or deviations from the protocol are often necessary 
as the data collection and analysis progresses. However, the 
protocol establishes a common understanding of how the demonstration 
required to meet regulatory requirements will be made.

9.2.2 Design Concentration and Receptor Sites

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

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

    a. As described in this subsection, the recommended procedure 
for conducting either a NAAQS or PSD increments assessment under PSD 
permitting is a multi-stage approach that includes the following two 
stages:
    i. The EPA describes the first stage as a single-source impact 
analysis, since this stage involves considering only the impact of 
the new or modifying source. There are two possible levels of detail 
in conducting a single-source impact analysis with the model user 
beginning with use of a screening model and proceeding to use of a 
refined model as necessary.
    ii. The EPA describes the second stage as a cumulative impact 
analysis, since it takes into account all sources affecting the air 
quality in an area. In addition to the project source impact, this 
stage includes consideration of background, which includes 
contributions from nearby sources and other sources (e.g., natural, 
minor, and distant major sources).
    b. Each stage should involve increasing complexity and details, 
as required, to fully demonstrate that a new or modifying source 
will not cause or contribute to a violation of any NAAQS or PSD 
increment. As such, starting with a single-source impact analysis is 
recommended because, where the analysis at this stage is sufficient 
to demonstrate that a source will not cause or contribute to any 
potential violation, this may alleviate the need for a more time-
consuming and comprehensive cumulative modeling analysis.
    c. The single-source impact analysis, or first stage of an air 
quality analysis, should begin by determining the potential of a 
proposed new or modifying source to cause or contribute to a NAAQS 
or PSD increment violation. In certain circumstances, a screening 
model or technique may be used instead of the preferred model 
because it will provide estimated worst-case ambient impacts from 
the proposed new or modifying source. If these worst case ambient 
concentration estimates indicate that the source will not cause or 
contribute to any potential violation of a NAAQS or PSD increment, 
then the screening analysis should generally be sufficient for the 
required demonstration under PSD. If the ambient concentration 
estimates indicate that the source's emissions have the potential to 
cause or contribute to a violation, then the use of a refined model 
to estimate the source's impact should be pursued. The refined 
modeling analysis should use a model or technique consistent with 
the Guideline (either a preferred model or technique or an 
alternative model or technique) and follow the requirements and 
recommendations for model inputs outlined in section 8. If the 
ambient concentration increase predicted with refined modeling 
indicates that the source will not cause or contribute to any 
potential violation of a NAAQS or PSD increment, then the refined 
analysis should generally be sufficient for the required 
demonstration under PSD. However, if the ambient concentration 
estimates from the refined modeling analysis indicate that the 
source's emissions have the potential to cause or contribute to a 
violation, then a cumulative impact analysis should be undertaken. 
The receptors that indicate the location of significant ambient 
impacts should be used to define the modeling domain for use in the 
cumulative impact analysis (section 8.2.2).
    d. The cumulative impact analysis, or the second stage of an air 
quality analysis, should be conducted with the same refined model or 
technique to characterize the project source and then include the 
appropriate background concentrations (section 8.3). The resulting 
design concentrations should be used to determine whether the source 
will cause or contribute to a NAAQS or PSD increment violation. This 
determination should be based on: (1) The appropriate design 
concentration for each applicable NAAQS (and averaging period); and 
(2) whether the source's emissions cause or contribute to a 
violation at the time and location of any modeled

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violation (i.e., when and where the predicted design concentration 
is greater than the NAAQS). For PSD increments, the cumulative 
impact analysis should also consider the amount of the air quality 
increment that has already been consumed by other sources, or, 
conversely, whether increment has expanded relative to the baseline 
concentration. Therefore, the applicant should model the existing or 
permitted nearby increment-consuming and increment-expanding 
sources, rather than using past modeling analyses of those sources 
as part of background concentration. This would permit the use of 
newly acquired data or improved modeling techniques if such data 
and/or techniques have become available since the last source was 
permitted.

9.2.3.1 Considerations in Developing Emissions Limits

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

9.2.4 Use of Measured Data in Lieu of Model Estimates

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

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87. Weil, J.C., L.A. Corio and R.P. Brower, 1997. A PDF dispersion 
model for buoyant plumes in the convective boundary layer. Journal 
of Applied Meteorology, 36: 982-1003.
88. L.L. Schulman, D.G. Strimaitis and J.S. Scire, 2002. Development 
and evaluation of the PRIME plume rise and building downwash model. 
Journal of the Air & Waste Management Association, 50: 378-390.
89. U.S. Environmental Protection Agency, 1995. Compilation of Air 
Pollutant Emission Factors, Volume I: Stationary Point and Area 
Sources (Fifth Edition, AP-42: GPO Stock No. 055-000-00500-1), and 
Supplements A-D. Volume I can be downloaded from EPA's Web site at 
https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emission-factors.
90. U.S. Environmental Protection Agency, 2014. Draft Emissions 
Inventory Guidance for Implementation of Ozone and Particulate 
Matter National Ambient Air Quality Standards (NAAQS) and Regional 
Haze Regulations. Office of Air Quality Planning and Standards, 
Research Triangle Park, NC. https://www.epa.gov/sites/production/files/2014-10/documents/2014revisedeiguidance_0.pdf.
91. U.S. Environmental Protection Agency, 1987. Ambient Air 
Monitoring Guidelines for Prevention of Significant Deterioration 
(PSD). Publication No. EPA-450/4-87-007. Office of Air Quality 
Planning and Standards, Research Triangle Park, NC. (NTIS No. PB 90-
168030).
92. U.S. Environmental Protection Agency, 2011. Additional 
Clarification Regarding Application of Appendix W Modeling Guidance 
for the 1-hour NO2 National Ambient Air Quality Standard. 
Office of Air Quality Planning and Standards, Research Triangle 
Park, NC. https://www3.epa.gov/ttn/scram/guidance/clarification/Additional_Clarifications_AppendixW_Hourly-NO2-NAAQS_FINAL_03-01-2011.pdf.
93. U.S. Environmental Protection Agency, 2013. Use of ASOS 
meteorological data in AERMOD dispersion modeling. Memorandum dated 
March 8, 2013, Office of Air Quality Planning and Standards, 
Research Triangle Park, NC. https://www3.epa.gov/ttn/scram/guidance/clarification/20130308_Met_Data_Clarification.pdf.
94. U.S. Environmental Protection Agency, 2016. User's Guide for the 
AERMOD Meteorological Preprocessor (AERMET). Publication No. EPA-
454/B-16-010. Office of Air Quality Planning and Standards, Research 
Triangle Park, NC.
95. U.S Environmental Protection Agency. 2016. AERMINUTE User's 
Guide. Publication No. EPA-454/B-15-006. Office of Air Quality 
Planning and Standards, Research Triangle Park, NC.
96. U.S. Environmental Protection Agency, 1993. PCRAMMET User's 
Guide. Publication No. EPA-454/R-96-001. Office of Air Quality 
Planning and Standards, Research Triangle Park, NC. (NTIS No. PB 97-
147912).
97. U.S. Environmental Protection Agency, 1996. Meteorological 
Processor for Regulatory Models (MPRM). Publication No. EPA-454/R-
96-002. Office of Air Quality Planning and Standards, Research 
Triangle Park, NC. (NTIS No. PB 96-180518).
98. Paine, R.J., 1987. User's Guide to the CTDM Meteorological 
Preprocessor Program. Publication No. EPA-600/8-88-004. Office of 
Research and Development, Research Triangle Park, NC. (NTIS No. PB-
88-162102).
99. 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. Publication No. EPA-600/8-89-041. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
No. PB 89-181424).
100. U.S. Environmental Protection Agency, 2008. AERSURFACE User's 
Guide. Publication No. EPA-454/B-08-001. Office of Air Quality 
Planning and Standards, Research Triangle Park, NC.
101. Brode, R., K. Wesson, J. Thurman, and C. Tillerson, 2008. 
AERMOD Sensitivity to the Choice of Surface Characteristics. Paper 
#811 presented at the 101st Air and Waste Management Association 
Annual Conference and Exhibition, June 24-27, 2008, Portland, OR.
102. Environ, 2015. The Mesoscale Model Interface Program (MMIF) 
Version 3.2 User's Manual.
103. U.S. Environmental Protection Agency, 2016. Guidance on the Use 
of the Mesoscale Model Interface Program (MMIF) for AERMOD 
Applications. Publication No. EPA-454/B-16-003. Office of Air 
Quality Planning and Standards, Research Triangle Park, NC.
104. 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.ncdc.noaa.gov.
105. 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.
106. Radiosonde Data of North America, 1946-1996; 4-volume CD-ROM. 
August 1996. Produced jointly by Forecast Systems laboratory and 
National Climatic Data Center. Can be ordered from NOAA National 
Data Center's Web site at http://www.ncdc.noaa.gov.
107. U.S. Environmental Protection Agency, 2000. Meteorological 
Monitoring Guidance for Regulatory Modeling Applications. 
Publication No. EPA-454/R-99-005. Office of Air Quality Planning and 
Standards, Research Triangle Park, NC. (NTIS No. PB 2001-103606).
108. ASTM D5527: Standard Practice for Measuring Surface Winds and 
Temperature by Acoustic Means. (2011).
109. ASTM D5741: Standard Practice for Characterizing Surface Wind 
Using Wind Vane and Rotating Anemometer. (2011).
110. U.S. 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 and 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.
111. 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).
112. U.S. 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 and Standards, Research Triangle 
Park, NC. (NTIS No. PB 94-113958).
113. Irwin, J.S., 1980. Dispersion Estimate Suggestion #8: 
Estimation of Pasquill Stability Categories. U.S. Environmental 
Protection Agency, Office of Air Quality Planning and Standards, 
Research Triangle Park, NC. (Docket No. A-80-46, II-B-10).
114. 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).
115. Smedman-Hogstrom, A. and V. Hogstrom, 1978. A Practical Method 
for Determining Wind Frequency Distributions for the Lowest 200 m 
from Routine Meteorological Data. Journal of Applied Meteorology, 
17(7): 942-954.
116. 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).
117. U.S. Environmental Protection Agency, 2016. Evaluation of 
Prognostic Meteorological Data in AERMOD Applications. Publication 
No. EPA-454/R-16-004. Office of Air Quality Planning and Standards, 
Research Triangle Park, NC.

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118. U.S. Environmental Protection Agency, 1984. Calms Processor 
(CALMPRO) User's Guide. Publication No. EPA-901/9-84-001. Office of 
Air Quality Planning and Standards, Region I, Boston, MA. (NTIS No. 
PB 84-229467).

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 https://www.epa.gov/scram. Codes and 
documentation may also available from the National Technical 
Information Service (NTIS), http://www.ntis.gov, and, when 
available, are referenced with the appropriate NTIS accession 
number.

A.1 AERMOD (AMS/EPA Regulatory Model)

References

U.S. Environmental Protection Agency, 2016. AERMOD Model 
Formulation. Publication No. EPA-454/B-16-014. Office of Air Quality 
Planning and Standards, Research Triangle Park, NC.
Cimorelli, A., et al., 2005. AERMOD: A Dispersion Model for 
Industrial Source Applications. Part I: General Model Formulation 
and Boundary Layer Characterization. Journal of Applied Meteorology, 
44(5): 682-693.
Perry, S. et al., 2005. AERMOD: A Dispersion Model for Industrial 
Source Applications. Part II: Model Performance against 17 Field 
Study Databases. Journal of Applied Meteorology, 44(5): 694-708.
U.S. Environmental Protection Agency, 2016. User's Guide for the 
AMS/EPA Regulatory Model (AERMOD). Publication No. EPA-454/B-16-011. 
Office of Air Quality Planning and Standards, Research Triangle 
Park, NC.
U.S. Environmental Protection Agency, 2016. User's Guide for the 
AERMOD Meteorological Preprocessor (AERMET). Publication No. EPA-
454/B-16-010. Office of Air Quality Planning and Standards, Research 
Triangle Park, NC.
U.S. Environmental Protection Agency, 2016. User's Guide for the 
AERMOD Terrain Preprocessor (AERMAP). Publication No. EPA-454/B-16-
012. U.S. Environmental Protection Agency, Office of Air Quality 
Planning and Standards, Research Triangle Park, NC.
Schulman, L. L., D.G. Strimaitis and J.S. Scire, 2000. Development 
and evaluation of the PRIME plume rise and building downwash model. 
Journal of the Air 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. Regulatory Use

    (1) AERMOD is appropriate for the following applications:
     Point, volume, and area sources;
     Buoyant, elevated line sources (e.g., aluminum 
reduction plants);
     Mobile sources;
     Surface, near-surface, and elevated releases;
     Rural or urban areas;
     Simple and complex terrain;
     Transport distances over which steady- state 
assumptions are appropriate, up to 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 meteorological data processed with the 
regulatory options in AERMET, the use of terrain elevation data 
processed through the AERMAP terrain processor, stack-tip downwash, 
sequential date checking, and does not permit the use of the model 
in the SCREEN mode. In the regulatory default mode, pollutant half-
life or decay options are not employed, except in the case of an 
urban source of sulfur dioxide where a 4-hour half-life is applied. 
Terrain elevation data from the U.S. Geological Survey (USGS) 7.5-
Minute Digital Elevation Model (DEM), or equivalent (approx. 30-
meter resolution), (processed through AERMAP) should be used in all 
applications. Starting in 2011, data from the National Elevation 
Dataset (NED, https://nationalmap.gov/elevation.html) can also be 
used in AERMOD, which includes a range of resolutions, from 1-m to 2 
arc seconds and such high resolution would always be preferred. In 
some cases, exceptions from the terrain data requirement may be made 
in consultation with the appropriate reviewing authority (paragraph 
3.0(b)).

b. Input Requirements

    (1) Source data: Required inputs include source type, location, 
emission rate, stack height, stack inside diameter, stack gas exit 
velocity, stack gas exit temperature, area and volume source 
dimensions, and source base elevation. For point sources subject to 
the influence of building downwash, direction-specific building 
dimensions (processed through the BPIPPRM building processor) should 
be input. Variable emission rates are optional. Buoyant line sources 
require coordinates of the end points of the line, release height, 
emission rate, average line source width, average building width, 
average spacing between buildings, and average line source buoyancy 
parameter. For mobile sources, traffic volume; emission factor, 
source height, and mixing zone width are needed to determine 
appropriate model inputs.
    (2) Meteorological data: The AERMET meteorological preprocessor 
requires input of surface characteristics, including surface 
roughness (zo), Bowen ratio, and albedo, as well as, hourly 
observations of wind speed between 7zo and 100 m (reference wind 
speed measurement from which a vertical profile can be developed), 
wind direction, cloud cover, and temperature between zo and 100 m 
(reference temperature measurement from which a vertical profile can 
be developed). Meteorological data can be in the

[[Page 5232]]

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 to AERMET. These can be 
hourly or a time that represents a morning sounding. Additionally, 
measured profiles of wind, temperature, vertical and lateral 
turbulence may be required in certain applications (e.g., in complex 
terrain) to adequately represent the meteorology affecting plume 
transport and dispersion. Optionally, measurements of solar and/or 
net radiation may be input to AERMET. Two files are produced by the 
AERMET meteorological preprocessor for input to the AERMOD 
dispersion model. When using observed data, the surface file 
contains observed and calculated surface variables, one record per 
hour. For applications with multi-level site-specific meteorological 
data, the profile contains the observations made at each level of 
the meteorological tower (or remote sensor). When using prognostic 
data, the surface file contains surface variables calculated by the 
prognostic model and AERMET. The profile file contains the 
observations made at each level of a meteorological tower (or remote 
sensor), the one-level observations taken from other representative 
data (e.g., National Weather Service surface observations), one 
record per level per hour, or in the case of prognostic data, the 
prognostic modeled values of temperature and winds at user-specified 
levels.
    (i) Data used as input to AERMET should possess an adequate 
degree of representativeness to ensure that the wind, temperature 
and turbulence profiles derived by AERMOD are both laterally and 
vertically representative of the source impact area. The adequacy of 
input data should be judged independently for each variable. The 
values for surface roughness, Bowen ratio, and albedo should reflect 
the surface characteristics in the vicinity of the meteorological 
tower or representative grid cell when using prognostic data, and 
should be adequately representative of the modeling domain. Finally, 
the primary atmospheric input variables, including wind speed and 
direction, ambient temperature, cloud cover, and a morning upper air 
sounding, should also be adequately representative of the source 
area when using observed data.
    (ii) For applications involving the use of site-specific 
meteorological data that includes turbulences parameters (i.e., 
sigma-theta and/or sigma-w), the application of the ADJ_U* option in 
AERMET would require approval as an alternative model application 
under section 3.2.
    (iii) For recommendations regarding the length of meteorological 
record needed to perform a regulatory analysis with AERMOD, see 
section 8.4.2.
    (3) Receptor data: Receptor coordinates, elevations, height 
above ground, and hill height scales are produced by the AERMAP 
terrain preprocessor for input to AERMOD. Discrete receptors and/or 
multiple receptor grids, Cartesian and/or polar, may be employed in 
AERMOD. AERMAP requires input of DEM or NED terrain data produced by 
the USGS, or other equivalent data. AERMAP can be used optionally to 
estimate source elevations.

c. Output

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

d. Type of Model

    AERMOD is a steady-state plume model, using Gaussian 
distributions in the vertical and horizontal for stable conditions, 
and in the horizontal for convective conditions. The vertical 
concentration distribution for convective conditions results from an 
assumed bi-Gaussian probability density function of the vertical 
velocity.

e. Pollutant Types

    AERMOD is applicable to primary pollutants and continuous 
releases of toxic and hazardous waste pollutants. Chemical 
transformation is treated by simple exponential decay.

f. Source-Receptor Relationships

    AERMOD applies user-specified locations for sources and 
receptors. Actual separation between each source-receptor pair is 
used. Source and receptor elevations are user input or are 
determined by AERMAP using USGS DEM or NED terrain data. Receptors 
may be located at user-specified heights above ground level.

g. Plume Behavior

    (1) In the convective boundary layer (CBL), the transport and 
dispersion of a plume is characterized as the superposition of three 
modeled plumes: (1) The direct plume (from the stack); (2) the 
indirect plume; and (3) the penetrated plume, where the indirect 
plume accounts for the lofting of a buoyant plume near the top of 
the boundary layer, and the penetrated plume accounts for the 
portion of a plume that, due to its buoyancy, penetrates above the 
mixed layer, but can disperse downward and re-enter the mixed layer. 
In the CBL, plume rise is superposed on the displacements by random 
convective velocities (Weil et al., 1997).
    (2) In the stable boundary layer, plume rise is estimated using 
an iterative approach to account for height-dependent lapse rates, 
similar to that in the CTDMPLUS model (see A.2 in this appendix).
    (3) Stack-tip downwash and buoyancy induced dispersion effects 
are modeled. Building wake effects are simulated for stacks subject 
to building downwash using the methods contained in the PRIME 
downwash algorithms (Schulman, et al., 2000). For plume rise 
affected by the presence of a building, the PRIME downwash algorithm 
uses a numerical solution of the mass, energy and momentum 
conservation laws (Zhang and Ghoniem, 1993). Streamline deflection 
and the position of the stack relative to the building affect plume 
trajectory and dispersion. Enhanced dispersion is based on the 
approach of Weil (1996). Plume mass captured by the cavity is well-
mixed within the cavity. The captured plume mass is 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.

[[Page 5233]]

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) 
for 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.
U.S. Environmental Protection Agency, 2003. AERMOD: Latest Features 
and Evaluation Results. Publication No. EPA-454/R-03-003. Office of 
Air Quality Planning and Standards, Research Triangle Park, NC.
Heist, D., et al, 2013. Estimating near-road pollutant dispersion: A 
model inter-comparison. Transportation Research Part D: Transport 
and Environment, 25: pp 93-105.

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

References

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis, M.T. 
Mills, D.G. Strimaitis, R.J. Yamartino and E.M. Insley, 1989. User's 
Guide to the Complex Terrain Dispersion Model Plus Algorithms for 
Unstable Situations (CTDMPLUS). Volume 1: Model Descriptions and 
User Instructions. EPA Publication No. EPA-600/8-89-041. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
No. PB 89-181424).
Perry, S.G., 1992. CTDMPLUS: A Dispersion Model for Sources near 
Complex Topography. Part I: Technical Formulations. Journal of 
Applied Meteorology, 31(7): 633-645.

Availability

    The model codes and associated documentation are available on 
the EPA's SCRAM 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 and terrain information is different from other 
EPA models; considerable detail for both types of input data is 
required and is supplied by preprocessors specifically designed for 
CTDMPLUS. CTDMPLUS requires the parameterization of individual hill 
shapes using the terrain preprocessor and the association of each 
model receptor with a particular hill.

a. Regulatory Use

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

b. Input Requirements

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

c. Output

    (1) When CTDMPLUS is run, it produces a concentration file, in 
either binary or text format (user's choice), and a list file 
containing a verification of model inputs, i.e.,
     Input meteorological data from ``SURFACE'' and 
``PROFILE,''
     Stack data for each source,
     Terrain information,
     Receptor information, and
     Source-receptor location (line printer map).
    (2) In addition, if the case-study option is selected, the 
listing includes:
     Meteorological variables at plume height,
     Geometrical relationships between the source and the 
hill, and
     Plume characteristics at each receptor, i.e.,

--Distance in along-flow and cross flow direction
--Effective plume-receptor height difference
--Effective [sigma]y & [sigma]z values, both flat terrain and hill 
induced (the difference shows the effect of the hill)
--Concentration components due to WRAP, LIFT and FLAT.

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

[[Page 5234]]

    (5) Hourly information provided to these files besides the 
concentrations themselves includes the year, month, day, and hour 
information as well as the receptor number with the highest 
concentration.

d. Type of Model

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

e. Pollutant Types

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

f. Source-Receptor Relationship

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

g. Plume Behavior

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

h. Horizontal Winds

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

i. Vertical Wind Speed

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

j. Horizontal Dispersion

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

k. Vertical Dispersion

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

l. Chemical Transformation

    Chemical transformation is not treated by CTDMPLUS.

m. Physical Removal

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

n. Evaluation Studies

Burns, D.J., L.H. Adams and S.G. Perry, 1990. Testing and Evaluation 
of the CTDMPLUS Dispersion Model: Daytime Convective Conditions. 
U.S. Environmental Protection Agency, Research Triangle Park, NC.
Paumier, J.O., S.G. Perry and D.J. Burns, 1990. An Analysis of 
CTDMPLUS Model Predictions with the Lovett Power Plant Data Base. 
U.S. Environmental Protection Agency, Research Triangle Park, NC.
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. 
(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)).

Abstract

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

a. Regulatory Use

    OCD has been recommended for use by the Bureau of Ocean Energy 
Management 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: PCRAMMET is the recommended 
meteorological data preprocessor for use in applications of OCD 
employing hourly NWS data. MPRM is the recommended meteorological 
data preprocessor for applications of OCD employing site-specific 
meteorological data.
    (i) Over land: Surface weather data including hourly stability 
class, wind direction, wind speed, ambient temperature, and mixing 
height are required.
    (ii) Over water: Hourly values for mixing height, relative 
humidity, air temperature, and water surface temperature are 
required; if wind speed/direction are missing, values over land will 
be used (if available); vertical wind direction shear, vertical 
temperature gradient, and turbulence intensities are optional.
    (3) Receptor data: Location, height above local ground-level, 
ground-level elevation above the water surface.

c. Output

    (1) All input options, specification of sources, receptors and 
land/water map including locations of sources and receptors.
    (2) Summary tables of five highest concentrations at each 
receptor for each averaging period, and average concentration for 
entire run period at each receptor.
    (3) Optional case study printout with hourly plume and receptor 
characteristics. Optional table of annual impact assessment from 
non-permanent activities.
    (4) Concentration output files can be used by ANALYSIS 
postprocessor to produce the highest concentrations for each 
receptor, the cumulative frequency distributions for each receptor, 
the tabulation of all concentrations exceeding a given threshold, 
and the manipulation of hourly concentration files.

[[Page 5235]]

d. Type of Model

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

e. Pollutant Types

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

f. Source-Receptor Relationship

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

g. Plume Behavior

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

h. Horizontal Winds

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

i. Vertical Wind Speed

    Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

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

k. Vertical Dispersion

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

l. Chemical Transformation

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

m. Physical Removal

    Physical removal is also treated using exponential decay.

n. Evaluation Studies

DiCristofaro, D.C. and S.R. Hanna, 1989. OCD: The Offshore and 
Coastal Dispersion Model. Volume I: User's Guide. Sigma Research 
Corporation, Westford, MA.
Hanna, S.R., L.L. Schulman, R.J. Paine and J.E. Pleim, 1984. The 
Offshore and Coastal Dispersion (OCD) Model User's Guide, Revised. 
OCS Study, MMS 84-0069. 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. 2016-31747 Filed 1-13-17; 8:45 am]
BILLING CODE 6560-50-P