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.

    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
<IP_ADDRESS>(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

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

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.

    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

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

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
    <IP_ADDRESS> AERSCREEN
    <IP_ADDRESS> CTSCREEN
    <IP_ADDRESS> Screening in Complex Terrain
    4.2.2 Refined Models
    <IP_ADDRESS> AERMOD
    <IP_ADDRESS> CTDMPLUS
    <IP_ADDRESS> OCD
    4.2.3 Pollutant Specific Modeling Requirements
    <IP_ADDRESS> Models for Carbon Monoxide
    <IP_ADDRESS> Models for Lead
    <IP_ADDRESS> Models for Sulfur Dioxide
    <IP_ADDRESS> Models for Nitrogen Dioxide
    <IP_ADDRESS> Models for PM2.5
    <IP_ADDRESS> 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
    <IP_ADDRESS> Models for Estimating Near-Field Visibility Impairment
    <IP_ADDRESS> 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
    <IP_ADDRESS> Dispersion Coefficients
    <IP_ADDRESS> Complex Winds
    <IP_ADDRESS> Gravitational Settling and Deposition
    7.2.2 Stationary Sources
    <IP_ADDRESS> Good Engineering Practice Stack Height
    <IP_ADDRESS> 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
    <IP_ADDRESS> Discussion
    <IP_ADDRESS> Recommendations
    8.4.4 Site-specific data
    <IP_ADDRESS> Discussion
    <IP_ADDRESS> Recommendations
    8.4.5 Prognostic meteorological data
    <IP_ADDRESS> Discussion
    <IP_ADDRESS> Recommendations
    8.4.6 Treatment of Near-Calms and Calms
    <IP_ADDRESS> Discussion
    <IP_ADDRESS> 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
    <IP_ADDRESS> 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

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

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

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

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.

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 <IP_ADDRESS>, there are also screening
techniques built into AERMOD that use simplified or limited
chemistry assumptions for determining the partitioning of NO and
NO2 for NO2 modeling. These screening
techniques are part of the EPA's preferred modeling approach for
NO2 and do not need to be approved as an alternative
model. However, as with other screening models and techniques, their
usage shall occur in agreement with the appropriate reviewing
authority (paragraph 3.0(b)).

    e. As discussed in section 4.2(c)(ii), there are screening
techniques needed for 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 <IP_ADDRESS>. 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.

<IP_ADDRESS> 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

<IP_ADDRESS> 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.

<IP_ADDRESS> 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.

<IP_ADDRESS> 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.

    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)).

<IP_ADDRESS> 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.

<IP_ADDRESS> 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

<IP_ADDRESS> 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

<IP_ADDRESS> 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.

<IP_ADDRESS> 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 <IP_ADDRESS> for urban/rural determination
methodology).

<IP_ADDRESS> 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

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

<IP_ADDRESS> 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\

<IP_ADDRESS> 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

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