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Guidance for Ozone and Fine Particulate Matter
Permit Modeling
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EPA-454/P-21-001
September 2021
Guidance for Ozone and Fine Particulate Matter Permit Modeling
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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TABLE OF CONTENTS
I. Introduction 1
II. Guidance Overview 7
II. 1 Significant Emissions Rates for O3 and PM25 10
11.2 Pollutant Applicability for O3 and PM2 5 PSD Air Quality Assessments 11
11.3 Significant Impact Levels for O3 and PM2 5 15
11.4 Source Impact Analysis 16
11.5 Cumulative Impact Analysis 19
II. 5.1 O3 and PM2 5 NAAQ S Compliance 19
II.5.2 PM25 PSD Increments Compliance 20
III. PSD Compliance Demonstrations for the O3 and PM2 5 NAAQS: Source Impact Analysis 23
111.1 O3 NAAQS 23
111.2 PM , NAAQS 24
111.3 Assessing Primary PM2 5 Impacts 26
111.4 Assessing O3 and Secondary PM2 5 Impacts 27
III.4.1 Conceptual Model 27
111.4.2 Tier 1 Assessment Approach 29
111.4.3 Tier 2 Assessment Approach 34
111.5 Comparison to the SIL 38
111.5.1 SIL Comparison for O3 38
111.5.2 SIL Comparison for PM25 39
IV. PSD Compliance Demonstrations for the O3 and PM2 5 NAAQS: Cumulative Impact Analysis
43
IV. 1 Modeling Inventory 45
IV.2 Monitored Background 47
IV.3 Comparison to the NAAQS 48
IV.4 Determining Whether Proposed Source Causes or Contributes to Modeled Violations 56
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V. PSD Compliance Demonstration for the PM2.5 Increments 59
V. 1 Overview of the PSD Increment System 59
V. 1.1 PSD Increments and Baseline Concentration 59
V.1.2 PSD Baseline Area and Key Baseline Dates 61
V.1.3 PSD Increment Expansion 64
V.2 PSD PM2.5 Increments 65
V.3 PSD Compliance Demonstration for the PM2.5 Increments 67
V.3.1 PM2.5 Increments: Source Impact Analysis 68
V.3.2 PM2.5 Increments: Cumulative Analysis 70
V.3.2.1 Assessing Primary PM2.5 Impacts 72
V.3.2.2 Assessing Secondary PM2.5 Impacts 73
V.4. Determining Whether a Proposed Source Will Cause or Contribute to an Increment Violation 74
VI. References 77
Appendix A: Draft Conceptual Description of O3 and PM2.5 Concentrations in the U.S A-l
Appendix B: General Guidance on Use of Dispersion Models for Estimating Primary PM2.5
Concentrations B-l
Appendix C: Example of a Tier 1 Demonstration of the Potential for O3 and Secondary PM2.5 Formation
C-l
Appendix D: Example of the background monitoring data calculations for a Second Level 24-hour
modeling analysis D-l
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I. Introduction
The U.S. Environmental Protection Agency (EPA) is providing this "Guidance for Ozone
and Fine Particulate Matter Permit Modeling" to fulfill a need for additional guidance on
demonstrating compliance with the ozone (O3) and fine particulate matter (PM2.5) National
Ambient Air Quality Standards (NAAQS) and the Prevention of Significant Deterioration (PSD)
increments for PM2.5 in the context of PSD permit applications. Because of the complex
chemistry of secondary formation of O3 and PM2.5, the EPA's judgment in the past was that it
was not technically sound to specify with "reasonable particularity" air quality models that must
be used to assess the impacts of a single source on O3 and secondary PM2.5 concentrations.
Instead, the EPA employed a case-by-case process for determining analytical techniques that
should be used for these secondary pollutants. Under the former process, the EPA recommended
that the "[cjhoice of methods used to assess the impact of an individual source depends on the
nature of the source and its emissions. Thus, model users should consult with their Regional
Office to determine the most suitable approach on a case-by-case basis" (2005 Guideline on Air
Quality Models, U.S. EPA, 2005; hereafter referred to as 2005 Guideline; sections 5.2.1.C and
5.2.2. l.c). As such, under the 2005 Guideline, the appropriate methods for assessing O3 and
secondary PM2.5 impacts were determined as part of the normal consultation process with the
appropriate permitting authority.
On January 4, 2012, the EPA granted a petition submitted on behalf of the Sierra Club on
July 28, 2010 (U.S. EPA, 2012), which requested that the EPA initiate rulemaking regarding the
establishment of air quality models for O3 and PM2.5 for use by all major sources applying for a
PSD permit. In granting that petition, the EPA committed to engage in rulemaking to evaluate
whether updates to the 2005 Guideline were warranted and, as appropriate, incorporate new
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analytical techniques or models for O3 and secondarily formed PM2.5. As discussed in the
preamble of the 2017 revisions to the EPA's Guideline on Air Quality Models (U.S. EPA, 2017a;
hereafter referred to as 2017 Guideline), "the EPA has determined that advances in chemical
transport modeling science indicate it is now reasonable to provide more specific, generally-
applicable guidance that identifies particular models or analytical techniques that may be used
under specific circumstances for assessing the impacts of an individual or single source on ozone
and secondary PM2.5. For assessing secondary pollutant impacts from single sources, the degree
of complexity required to appropriately assess potential impacts varies depending on the nature
of the source, its emissions, and the background environment. In order to provide the user
community flexibility in estimating single-source secondary pollutant impacts that allows for
different approaches to credibly address these different areas, the EPA proposed a two-tiered
demonstration approach for addressing single-source impacts on ozone and secondary PM2.5."
This recommended two-tiered demonstration approach was promulgated as part of the 2017
Guideline revisions.
As presented in section 5.2 of the 2017 Guideline, the first tier involves use of technically
credible relationships between precursor emissions and a source's impacts. Such information
may be published in peer-reviewed literature; developed from modeling that was previously
conducted for an area by a source, a governmental agency, or some other entity that is deemed
sufficient; or generated by a peer-reviewed reduced form model. To assist permitting authorities,
the EPA released the "Guidance on the Development of Modeled Emission Rates for Precursors
(MERPs) as a Tier 1 Demonstration Tool for Ozone and PM2.5 under the PSD Permitting
Program" (U.S. EPA, 2019; hereafter referred to as MERPs Guidance) that provides a
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framework to develop MERPs for consideration and use as a Tier 1 demonstration tool, as
described in the preamble of the 2017 Guideline.
The second tier, also presented in section 5.2 of the 2017 Guideline, involves application
of more sophisticated case-specific chemical transport models (CTMs), e.g., photochemical grid
models, to be determined in consultation with the EPA Regional Offices. The EPA provided
guidance to permitting authorities on procedures for applying CTMs in the "Guidance on the Use
of Models for Assessing the Impacts of Emissions from Single Sources on the Secondarily
Formed Pollutants: Ozone and PM2.5" (U.S. EPA, 2016; hereafter Single-source Modeling
Guidance). The Single-source Modeling Guidance is intended to inform that second tier
approach by providing appropriate technical methods to assess O3 and secondary PM2.5 impacts
associated with the precursor emissions from the new or modifying source. The appropriate tier
for a given application should be selected in consultation with the appropriate permitting
authority and be consistent with EPA guidance.
This guidance provides an update to the previous "Guidance for PM2.5 Permit Modeling"
(U.S. EPA, 2014) to reflect the 2017 revisions to the Guideline and incorporate appropriate
sections for O3. As experience is gained with these types of PSD compliance demonstrations, the
EPA expects to update this and related guidance and provide further specificity on procedures for
assessing the impacts of a single source on O3 and secondary PM2.5 concentrations.
This guidance document is organized in three primary areas:
1. Guidance Overview - Section II provides a general overview of the steps that a
permit applicant should take under the PSD program for demonstrating
compliance with the O3 NAAQS and/or the PM2.5 NAAQS and PSD
increments.
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2. PSD Compliance Demonstrations for the O3 and PM2.5 NAAQS - Sections III and
IV provide a detailed framework for conducting a source impact analysis and
a cumulative impact analysis, respectively, to appropriately address O3 and
PM2.5 impacts from the proposed source1 in determining whether it may cause
or contribute to a NAAQS violation.
3. PSD Compliance Demonstrations for PM2.5 Increments - Section V provides a
detailed discussion of the assessment of primary and secondary PM2.5 impacts
of a new or modifying source with respect to the PM2.5 increments.
This document recommends procedures for permit applicants and permitting authorities
to follow to show that they have satisfied some of the criteria for obtaining or issuing a permit
under applicable PSD regulations. This document is not a rule or regulation, and the guidance it
contains may not apply to a particular situation based upon the individual facts and
circumstances. This guidance does not change or substitute for any law, regulation, or any other
legally binding requirement, may refer to regulatory provisions without repeating them in their
entirety, and is not legally enforceable. The use of non-mandatory language such as "guidance,"
"recommend," "may," "should," and "can," is intended to describe EPA policies and
recommendations. Mandatory terminology such as "must" and "required" are intended to
describe requirements under the terms of the C AA and EPA regulations, but this document does
not establish or alter any legally binding requirements in and of itself.
This guidance does not create any rights or obligations enforceable by any party or
impose binding, enforceable requirements on any PSD permit applicant, PSD permitting
1 The term "proposed source" as used in this guidance document should be read to mean the proposed source or
modification for which the compliance demonstration is being conducted.
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authority, the EPA, or any other person. Since each permitting action will be considered on a
case-by-case basis, this document does not limit or restrict any particular justifiable approach
that permit applicants and permitting authorities may take to conduct the required compliance
demonstrations. Each individual decision to issue a PSD permit must be supported by a record
sufficient to demonstrate that the proposed construction and operation of a stationary source will
not cause or contribute to a violation of the applicable NAAQS and PSD increments. While this
document illustrates a particular approach that the EPA considers appropriate and acceptable as a
general matter, permit applicants and permitting authorities should examine all relevant
information regarding air quality in the area that may be affected by a proposed new or
modifying source and evaluate whether alternative or additional analysis may be necessary in a
given case to demonstrate that the regulatory criteria for a PSD air quality analysis are satisfied.
This document does not represent a conclusion or judgment by the EPA that the technical
approaches recommended in this document will be sufficient to make a successful compliance
demonstration in every permit application or circumstance.
Permitting authorities retain the discretion to address particular issues discussed in this
document in a different manner than the EPA recommends so long as the approach is adequately
justified, supported by the permitting record and relevant technical literature, and consistent with
the applicable requirements in the C AA and implementing regulations, including the terms of an
approved State Implementation Plan (SIP) or Trial Implementation Plan (TIP). Furthermore, this
guidance is not a final agency action and does not determine applicable legal requirements or the
approvability of any particular permit application.
The EPA Regional Offices may seek clarification from the EPA's Office of Air Quality
Planning and Standards (OAQPS) on issues and areas of concern in a modeling protocol or PSD
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compliance demonstration. Through these interactions and subsequent resolutions of specific
issues, clarifications of preferred modeling procedures can become additional EPA guidance.
This can happen in several ways: 1) the preferred procedures are published as regulations or
guidelines; 2) the preferred procedures are formally transmitted as guidance to the Air Division
Directors in the EPA Regional Offices; 3) the preferred procedures are formally transmitted as
guidance to the EPA Regional Office modeling contacts; or 4) the preferred procedures are relied
upon in decisions by the EPA's Model Clearinghouse that establish national precedent that the
approach is technically sound. The Model Clearinghouse is the EPA focal point for the review of
the technical adequacy of pollutant modeling to satisfy regulatory criteria and other NAAQS
compliance demonstration techniques. Model Clearinghouse memoranda involving interpretation
of modeling guidance for specific applications, as well as other clarification memoranda
addressing modeling more generally, are available at the Support Center for Regulatory
Atmospheric Modeling (SCRAM) website at: https://www.epa.gov/scram/air-quality-model-
clearinghouse.
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II. Guidance Overview
This guidance is appropriate for proposed new or modifying sources locating in, or
located in, an area classified as attainment or unclassifiable for O3 and/or PM2.5. It is intended to
provide recommendations on how to conduct compliance demonstrations for the O3 NAAQS and
the PM2.5 NAAQS and PSD increments under the PSD program following the progressive steps
shown in Figure II-l (for O3 and PM2.5 NAAQS) and Figure II-2 (for PM2.5 increments). Since
each permitting action is considered on a case-by-case basis, this guidance does not limit or
restrict any particular justifiable approach that permit applicants and permitting authorities may
take to conduct the required compliance demonstrations. Prospective permit applicants should
recognize the importance of the consultation process with the appropriate permitting authority.
This process will help identify the most appropriate analytical techniques to be used for
conducting a compliance demonstration for the O3 NAAQS and the PM2.5 NAAQS and PSD
increments.
The EPA has historically supported the use of screening tools to facilitate the
implementation of the PSD program and streamline the permitting process in circumstances
where proposed construction is projected to have an insignificant impact on air quality. These
screening tools include significant emissions rates (SERs) and significant impact levels (SILs).
The use of these screening tools at each progressive step, as demonstrated in Figure II-1 and
Figure II-2, is described in more detail throughout Section II.
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Figure II-1. Overview of O3 and PM2.5 NAAQS Compliance Demonstration for New or
Modifying Sources under PSD Programs
processes for assuring NAAQS and PSD Increments compliance (e.g., emissions offsets requirements)
** Any emissions rate or any net emissions increase associated with a major stationary source or major modification, which would
construct within 10 kilometers of a Class I area, and have an impact on such area equal to or greater than 1 ng/m3 (24-hour
average), is considered significant and should proceed with an appropriate air quality assessment. See 40 CFR 52.21(b)(23)(iii).
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Figure II-2. Overview of PM2.5PSD Increments Compliance Demonstration for New or
Modifying Sources under PSD Programs
* State, local, and tribal permit authorities may have specific regulations that require alternative or additional analyses and processes for
assuring NAAQS and PSD Increments compliance (e.g., emissions offsets requirements)
** Any emissions rate or any net emissions increase associated with a major stationary source or major modification, which would
construct within 10 kilometers of a Class I area, and have an impact on such area equal to or greater than 1 ng/m3 (24-hour average), is
considered significant and should proceed with an appropriate air quality assessment. See 40 CFR 52.21(b)(23)(iii).
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II. 1 Significant Emissions Rates for O3 and PM2.5
O3 and PM2.5 are "regulated NSR pollutant[s]" as that term is defined in the PSD
regulations.2 Pursuant to that definition, ambient concentrations of O3 are generally addressed
through the regulation of its two precursors, nitrogen oxides (NOx) and volatile organic
compounds (VOC), while ambient concentrations of PM2.5 are generally addressed through the
regulation of direct PM2.5 and its precursors, NOx and sulfur dioxide (SO2).3 "Significant" is
defined in the EPA regulations at 40 CFR 52.21(b)(23) in reference to a source's potential to
emit (or in the case of a modification, the emissions increase4 and net emissions increase) of a
regulated NSR pollutant. That definition specifies the pollutant and the corresponding emissions
rate that, if equaled or exceeded, would qualify as "significant." For ozone, the significant
emissions rate is defined as 40 tpy of VOC or NOx, and for PM2.5, the significant emission rate is
defined as 10 tpy of direct PM2.5 emissions, 40 tpy of SO2 emissions, or 40 tpy of NOx
emissions.5
2 40 CFR 52.21(b)(50). The regulations at 40 CFR 52.21 apply to the federal PSD program. This guidance document
generally cites those regulations for simplicity, but the guidance reflected here may also be considered when
applying EPA-approved state regulations modeled on 40 CFR 51.166, which contains the PSD program
requirements for an approvable SIP that parallel the requirements of 40 CFR 52.21. This guidance may also cite the
regulations at 40 CFR 51.166 when specifically discussing requirements for state PSD programs.
3 See 73 FR 28321, 28333 (May 16, 2008). The EPA's PSD regulations do not establish a presumption that VOC be
treated as a precursor to PM2.5 in the PSD program. However, a state, or the EPA, may demonstrate that VOC
emissions in a specific area are a significant contributor to that area's ambient PM2.5 concentrations and, thus, should
be treated as a regulated NSR pollutant subject to the PSD permitting requirements. 40 CFR 51 A(>(>(b)(ii))(\)fhjf4j:
40 CFR 52.21(b)(50)(i)(6)(-/).
4 While section 52.21(b)(23) explicitly defines "significant" for purposes of a net emissions increase or potential to
emit, section 52.21(b)(40) defines "significant emissions increase" by reference to the definition of "significant"
found in paragraph (b)(23).
5 A significance rate for VOC as a PM2.5 precursor is not defined in the PSD regulations. However, the preamble to
EPA's final rule on implementing the PSD permitting requirements for PM2.5 and its precursors indicated that any
state making a demonstration under 40 CFR 51.166(b)(49)(i)^/v^ "would be required to adopt the 40-tpy
significant emissions rate [for VOC as a PM2.5 precursor] unless it demonstrates that a more stringent significant
emissions rate (lower rate) is more appropriate." 73 FR at 28333.
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II.2 Pollutant Applicability for O3 and PM2.5 PSD Air Quality Assessments
The EPA's PSD regulations apply specific permitting requirements to regulated New
Source Review (NSR) pollutants that would be emitted in a significant amount by a proposed
new or modified major stationary source.6 For a new major stationary source, PSD permitting
requirements apply to any regulated NSR pollutant for which the source would have the potential
to emit a significant amount. For a modification at an existing major stationary source, PSD
permitting requirements apply to any regulated NSR pollutant for which the modification would
result in a significant emissions increase and a significant net emissions increase (i.e., a "major
modification") of that pollutant.7 Regulated NSR pollutants include pollutants for which a
NAAQS has been promulgated, such as O3 and PM2.5, including any pollutant identified in the
regulations as a constituent or precursor of a pollutant subject to a NAAQS, i.e., NOx and VOC
in the case of O3, and PM2.5 direct emissions, SO2, and NOx in the case of PM2.5.8 As described
in Section II. 1, SERs for direct PM2.5 emissions and each precursor of O3 and PM2.5 are defined
in the regulations.9
The CAA and the EPA's implementing regulations require a PSD permit applicant to
demonstrate that emissions from the proposed source or modification will not cause or contribute
to a violation of any NAAQS or PSD increment and to provide an analysis of the impact of those
6 See 40 CFR 52.21(a)(2) for applicability procedures for new or modified major stationary sources.
7 Elsewhere in this document, simplified language may be used referring to a pollutant emitted in a significant
amount or a source that would emit a significant amount of a pollutant. Where such language is used, it should be
read to apply equally to the potential to emit of a new major stationary source and the emissions increase and net
emissions increase from a modification at an existing major stationary source.
sSee 40 CFR 52.21(b)(50).
9 See 40 CFR 52.21(b)(23)(i). Individual O3 precursors (i.e., NOx and VOC) are not summed when determining a
significant emissions increase for O3. Likewise, emissions of individual PM2.5 precursors (i.e.. SO2 and NOx) are not
summed when determining a significant emissions increase for PM2.5; nor are emissions of a PM2.5 precursor
summed with direct PM2.5 emissions when determining a significant emissions increase for PM2.5. See 57 FR 55620,
55624 (Nov. 25, 1992); 80 FR 65292, 65441 (Oct. 26, 2015); see also 73 FR 28321, 28331 (May 16, 2008).
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emissions on ambient air quality based on monitoring data and air quality modeling.10 The
provisions at 40 CFR 52.21(k)(l) and (m)(l) describe the preconstruction air quality analysis
requirements of the PSD program. Paragraph (k)(l) implements section 165(a)(3) of the CAA
and provides that the owner or operator "shall demonstrate that allowable emission increases
from the proposed source or modification. . . would not cause or contribute to air pollution in
violation of [any NAAQS or PSD increment]" Paragraph (m)(l) implements section 165(e) of
the CAA and provides that any PSD permit application shall contain an analysis of ambient air
quality for each NAAQS pollutant that the source or modification would emit or increase in a
significant amount and for each non-NAAQS pollutant as the Administrator determines
necessary. Paragraph (m)(l)(iii) further provides that, for each NAAQS pollutant, the analysis
shall contain continuous air quality monitoring data for determining whether emissions of that
pollutant would cause or contribute to a violation of any NAAQS or PSD increment. For O3 or
PM2.5, that analysis should examine the impact of the proposed source or modification on
ambient concentrations of the NAAQS pollutant, as opposed to the impact of each individual
precursor or direct component in isolation.
To make the required demonstration, sources should provide a full accounting of the
combined impacts of their allowable precursor (and direct component in the case of PM2.5)
emissions on ambient concentrations of the relevant NAAQS (i.e., O3 or PM2.5) if any
precursor(s) (or the direct component in the case of PM2.5) would be emitted in a significant
amount. In other words, for O3, if either NOx or VOC precursor emissions would be emitted in a
significant amount, then both precursors should be included in the assessment of O3 impacts.
Analogously, for PM2.5, if a source would emit a significant amount of one or more of: NOx,
10 See CAA § 165(a)(3), CAA § 165(e), 40 CFR 52.21(k) and 40 CFR 52.21(m).
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SO2, or direct PM2.5 emissions, then the source should include NOx and SO2 precursor and direct
PM2.5 emissions in the assessment of PM2.5 impacts.11 This approach is supported both
scientifically, because it ensures that the source provides a full accounting of its projected air
quality impacts for the relevant NAAQS, including all precursor (and direct component, in the
case of PM2.5) emissions, and legally, because it is needed to meet the requirements in the PSD
regulations that the owner or operator of a proposed new major stationary source or major
modification demonstrate that it will not cause or contribute to a NAAQS or PSD increment
violation. The definition of "major modification" provides that any significant emissions increase
or net emissions increase at a major stationary source that is significant for VOC or NOx shall be
considered significant for ozone.12 This regulatory definition clearly states that if the emissions
increase from a proposed modification at a major stationary source is significant for either VOC
or NOx, then it shall be treated as significant not solely for the specific precursor that would
exceed the SER but also for ozone in general. For purposes of the air quality assessment, this
means that emissions of both ozone precursors should be evaluated to determine the proposed
source or modification's impact on ambient ozone levels. Also, as discussed in Section II.l, the
SER for ozone is defined as 40 tpy of VOC or NOx.13 Thus, it is EPA's position that both
11 See Tables III-l and III-2 for EPA recommended approaches for assessing ozone and PM2.5 impacts by
assessment case. This holistic approach is necessary for PSD air quality assessments for O3 and PM2.5 but not other
substantive PSD permitting requirements, such as B ACT, that apply directly to source emissions, and are not based
on the source's projected impact on ambient air quality. The EPA regulations and longstanding EPA policy make
clear that BACT limitations apply to directly emitted NAAQS pollutants or precursor pollutants (or both in the case
of PM2.5) that would be emitted from the proposed source (or increased by a modification) in a significant amount.
See 40 CFR 52.21(j)(2), (3) and In re Footprint Power Salem Harbor Development, IP, PSD Appeal No. 14-02
(EAB 2014).
12 See 40 CFR 52.2 l(b)(2)(ii). Similarly, the definition of "major stationary source" provides that a major source that
is major for VOC or NOx shall be considered major for ozone. See 40 CFR 52.21(b)(l)(ii).
13 The SER definition for PM2.5 is less clear because each pollutant-specific value is separated by a semicolon
without using a connector such as "or;" however, the EPA reads the PM2.5 SER definition consistently with the
clearly stated ozone SER definition, meaning that for both ozone and PM2.5, if the emissions of any precursor (or the
direct emissions component in the case of PM2.5) equals or exceeds the respective SER, all precursor emissions (and
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scientific considerations and the regulatory language support a full accounting of the air quality
impact of a pollutant and its precursors that would be emitted by a proposed source or
modification, i.e., all precursors, and the direct component in the case of PM2.5, if any one of
those would be emitted or would increase in a significant amount.
The EPA believes that adopting a narrower approach that would limit the air quality
assessment to only the individual direct or precursor components with emissions equal to or
greater than the corresponding SER, and excluding direct or precursor components with
emissions less than the corresponding SER, would provide an incomplete and potentially
deficient demonstration that the projected emissions from the proposed source or modification
would not cause or contribute to a NAAQS or PSD increment violation. A reviewing authority
considering only the impacts associated with a subset of direct and precursor emissions that
would be emitted by the proposed source or modification may come to an incompletely
supported determination that the required source impact demonstration had been made, whereas
a more complete assessment that includes the impacts of all direct and precursor emissions may
show that the proposed source or modification would cause or contribute to a NAAQS or PSD
increment violation. Such a limited air quality demonstration would be incomplete and therefore
technically and legally flawed. A full accounting of the air quality impact of a direct and
precursor emissions, as applicable, is necessary to make the required demonstration that the
allowable emissions increases would not cause or contribute to a violation of the NAAQS or
PSD increments.
direct emissions in the case of PM2.5) are treated as significant with respect to assessing air quality impacts for the
corresponding NAAQS pollutant.
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II.3 Significant Impact Levels for O3 and PM2.5
The EPA has issued guidance recommending that permitting authorities consider the use
of appropriate pollutant-specific concentration levels known as "significant impact levels" as a
compliance demonstration tool for O3 and PM2.5 air quality assessments on a case-by-case basis
in PSD permitting actions (U.S. EPA, 2018a). The "SILs Guidance" identified recommended SIL
values for the O3 and PM2.5 NAAQS and the PM2.5 PSD increments and included a policy
document, as well as supporting technical and legal analyses, that the EPA and other permitting
authorities may use in case-by-case PSD permitting actions. As explained in the guidance, if a
permitting authority chooses to use a recommended SIL value to support a PSD permitting
decision, it should justify the SIL value and its use in the administrative record for the permitting
action and may choose to rely upon the EPA's SILs Guidance, including the supporting technical
and legal documents, in doing so.
The EPA's recommended SIL values from the SILs Guidance for the O3 and PM2.5
NAAQS are presented in Table II-l and for the PM2.5 PSD increments in Table II-2. It is
important to note that the PM2.5 NAAQS has two averaging periods: 24-hour and annual. There
are no PSD increments established for O3 and, thus, no O3 increment SIL values. For a full
discussion of the basis and purpose of the recommended O3 and PM2.5 SIL values, see the SILs
Guidance and supporting documents (U.S. EPA, 2018a).
Table II-l. EPA Recommended SIL Values for O3 and PM2.5 NAAQS
Criteria Pollutant (NAAQS Level)
NAAQS SIL Concentration
Ozone 8-hour (70 ppb)
1.0 ppb
PM2.5 24-hour (35 ng/m3)
1.2 ng/m3
PMi. <; Annual (12 ng/m3 or 15 ng/m3)
0.2 ng/m3
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Table II-2. EPA Recommended SIL Values for PM2.5 PSD Increments
Criteria Pollutant
PSD Increment SIL Concentration
Class I
Class II
Class III
PM2.5 24-hour
0.27 ng/m3
1.2 ng/m3
1.2 ng/m3
PM2.5 Annual
0.05 ug/m3
0.2 ug/m3
0.2 ug/m3
As explained in the SILs Guidance, SILs are designed to have a role throughout the PSD
air quality compliance demonstration. A permitting authority that chooses to use SILs should
initially compare the modeled concentrations resulting from the proposed source's emissions
increase to the appropriate SIL. The EPA calls this initial comparison the "Source Impact
Analysis." Where the proposed source's predicted impacts on air quality concentrations are
found at this first stage to be greater than or equal to the appropriate SIL, the analysis should
then proceed to a second stage, which involves a cumulative assessment of the air quality in the
affected area. The "Cumulative Impact Analysis" considers the combined impact of the proposed
source or modification and other relevant sources in determining whether there would be a
violation of any NAAQS or PSD increment in the affected area and, if so, whether the proposed
source or modification would cause or contribute to such violation based on the appropriate SIL.
II.4 Source Impact Analysis
As described in section 9.2.3 of the 2017 Guideline, the EPA's recommended procedure
for conducting a PSD air quality assessment is a multi-stage approach. The first stage is a single-
source impact analysis or a source impact analysis.14 This involves assessing whether the
14 This is consistent with the EPA's overall approach for the use of screening techniques in air quality modeling. See
40 CFR part 51, Appendix W, sections 2.2 ("Levels of Sophistication of Air Quality Analyses and Models") and
4.2.1 ("Screening Models and Techniques"). In section 2.2.a, the Guideline observes that "[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
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allowable emissions increase(s) from the proposed new or modifying source could cause or
contribute to a NAAQS or PSD increment violation. As discussed in section II.3, the EPA issued
the SILs Guidance containing recommendations on how to compare the impact from the source
or modification alone to the appropriate SIL.
The owner or operator of a new major stationary source should perform a source impact
analysis to inform the demonstration required for each regulated NSR pollutant that the source
would have the potential to emit in a significant amount. The owner or operator of an existing
source proposing a major modification should perform the analysis to inform the demonstration
requirement for each regulated NSR pollutant that would result in a significant emissions
increase and a significant net emissions increase, as determined by the PSD applicability
procedures (see Section II.2 of this document). For O3 or PM2.5, which can be formed from
precursor emissions, a significant increase of direct (for PM2.5) or any precursor (for O3 or PM2.5)
emissions would mean that the source should perform the required demonstration for all
precursors (for O3 or PM2.5) and the primary pollutant (for PM2.5) emitted. For O3 this should
include both NOx and VOC if either would be emitted in a significant amount. For PM2.5. this
should include direct emissions of PM2.5, as well as emissions of both NOx and SO2 if any one or
more of the three pollutants would be emitted in a significant amount. This holistic approach
ensures that all relevant impacts from a proposed new major stationary source or major
modification (i.e., the combined effect of the source's direct and precursor emissions of O3 or
PM2.5) are accounted for in a demonstration that the proposed new or modified major source will
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."
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not cause or contribute to a NAAQS or PSD increment violation.
It is important to note that in many cases, the emissions rate(s) used for the source impact
analysis should not be the same as the rate calculated for applicability purposes converted into
equivalent short-term model input rates. First, as part of the 2002 NSR Reform Rule, the EPA
made clear that "baseline actual emissions" and the "actual-to-projected-actual applicability test"
should not be used for PSD air quality analyses.15 Instead, for major modifications, the definition
of "actual emissions" at 40 CFR 52.21(b)(21) continues to apply and post-project emissions
should be based on potential to emit or allowable emissions.16 Second, the "allowable emission
increases" that must be evaluated pursuant to 40 CFR 52.2l(k) should correspond with the
averaging time of the applicable standard.17 For major modifications, this may also depend on
the type of emissions unit (new or existing) and the effect the project has on the emissions unit
(e.g., increase in short-term potential to emit vs. increase in annual utilization).
In a source impact analysis, as illustrated in Figure II-l and Figure II-2 and further
explained in this guidance, a permitting authority should compare the modeled concentrations
resulting from the proposed source's emissions increase to an appropriate O3 or PM2.5 SIL. If the
proposed source's maximum modeled impacts are found to be below the level of the O3 or PM2.5
SIL at every modeled receptor, this finding of the source impact analysis may be sufficient to
demonstrate that the source will not cause or contribute to a violation of the O3 NAAQS, PM2.5
NAAQS, or the PM2.5 PSD increment, as necessary to receive a PSD permit. On the other hand,
where the proposed source's predicted impacts on air quality concentrations are estimated to be
15 See 40 CFR 52.2l(b)(2l)(i) and 67 FR 80186 (December 31, 2002) at 80190-91, 80196.
16 "In general, actual emissions as of a particular date shall equal the average rate, in tons per year, at which the unit
actually emitted the pollutant during a consecutive 24-month period which precedes the particular date and which is
representative of normal source operation." 40 CFR 52.21(b)(21)(ii).
17 See Table 8-2 of the Guideline.
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greater than or equal to the level of an appropriate O3 or PM2.5 SIL at any modeled receptor, the
demonstration should proceed to the second stage, conducting a cumulative impact analysis.
II.5 Cumulative Impact Analysis
This section provides an overview of cumulative impact analyses for O3 and PM2.5
NAAQS, as well as PM2.5 PSD increments compliance. The cumulative impact analysis is
illustrated in Figure II-1 and Figure II-2 and further explained in this guidance.
II.5.1 O3 and PM2.5 NAAQS Compliance
For either O3 or PM2.5, where the source impact analysis described in Section II.4 is
insufficient to show that a proposed new or modifying PSD source will not cause or contribute to
a violation of the respective NAAQS, a cumulative impact analysis is then necessary to make the
required NAAQS demonstration, as described in section 9.2.3 of the 2017 Guideline. A
cumulative impact analysis should account for the combined impacts of the following:
1. All direct and precursor emissions of a pollutant (i.e., O3 or PM2.5) from the new or
modifying source if the source would emit any direct or precursor emissions of the
pollutant in a significant amount;18
2. Direct emissions from nearby sources (for primary PM2.5 impacts only), as
appropriate; and
18 For a new major stationary source, this includes all direct and precursor pollutants if the source has the potential to
emit any direct or precursor pollutant in an amount greater than or equal to the SER and for a modification to an
existing major stationary source, it includes all direct and precursor pollutants, if the modification would result in a
significant emissions increase and a significant net emissions increase of any direct or precursor pollutant.
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3. Monitored background levels or concentrations that account for secondary impacts
from regional background sources, secondary impacts from precursor emissions from
nearby sources, and, in the case of primary PM2.5. PM2.5 impacts from direct
emissions from background sources, and nearby sources not explicitly modeled.19
Once the direct and precursor emissions impacts are taken into account, the estimated
cumulative impact should then be compared to the NAAQS to determine if there is a modeled
violation. If not, then the NAAQS compliance demonstration should be sufficient to show that
the proposed source or modification will not cause or contribute to a violation. If there are
predicted NAAQS violations, then the impacts of the direct and precursor emissions increases
from the new or modifying source at those locations can be compared to the appropriate SIL to
determine whether that increase will cause or contribute to the modeled violation of the NAAQS.
Several aspects of the cumulative impact analysis for O3 and PM2.5 will be comparable to
analyses conducted for other criteria pollutants, while other aspects will differ due to the issues
identified earlier.
II.5.2 PM2.5 PSD Increments Compliance
For PM2.5, where the source impact analysis described in Section II.4 is insufficient to
show that a source will not cause or contribute to a violation of any PM2.5 PSD increment, a
cumulative impact analysis is necessary to make the PSD increment demonstration, as described
in section 9.2.3 of the 2017 Guideline. A cumulative impact analysis for an increment differs
19 The emissions impact of any nearby source that has received a permit but is not yet operational should be included
in the air quality assessment. In such cases, consultation with the appropriate permitting authority on the appropriate
assessment approach is recommended.
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from the NAAQS cumulative impact analysis in that the increment assessment only accounts for
the combined impact of the new or modifying source's emissions increase and certain previous
emissions changes from sources (including the modifying source) that affect the PSD increment
under the EPA's PSD regulations. A more complete description of the types of emissions that
affect increment consumption and other aspects of the PSD increment system is contained in
Section V.l of this guidance document. The cumulative impacts are then compared to the
appropriate PM2.5 PSD increments to determine whether the new or modifying source emissions
will cause or contribute to a violation of any PM2.5 PSD increment. The cumulative analysis for
PM2.5 PSD increments is described in greater detail in Section V.3.2.
For PM2.5 PSD increments, since the requirement for calculating the amount of increment
consumed was established relatively recently in comparison to the increments for other
pollutants, a new or modified source being evaluated for PM2.5 PSD increments compliance may
still find that it is the first source, or one of only a few sources, with increment-consuming
emissions in a particular attainment or unclassifiable area. As shown in Figure II-2, for such
situations, a permitting authority may have sufficient reason (based on the approach for
conducting source impact analysis described below) to conclude that the impacts of the new or
modified source may be compared directly to the allowable increments, without the need for a
cumulative modeling analysis. This would be the case where it can be shown that any other
increment-consuming sources in the same baseline area, if any, do not have much or any
overlapping impact with the proposed new or modified source.20
211 The term "increment-consuming source," as used in this guidance, is intended to refer to any type of source whose
emissions changes (increases or decreases) affects the amount of increment consumed or expanded.
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Another important consideration for PM2.5 PSD increments is the differences in the EPA
recommended SIL values for Class I and Class II / III areas, as presented in Table II-2. Given
substantially lower recommended SIL values for Class I areas, there is a greater likelihood that a
proposed new or modifying source would have a predicted impact that equals or exceeds an EPA
recommended SIL for PM2.5 PSD increments in a Class I area, even at distances beyond the
nominal 50 km near-field application distance. Section 4.2 of the 2017 Guideline provides
screening and compliance assessment approaches for near-field (50 km or less) and long-range
transport (beyond 50 km) situations. The MERPs Guidance (i.e., Tier 1 Assessment Approach)
and the Single-source Modeling Guidance (i.e., Tier 2 Assessment Approach) should be
referenced for assessing secondary PM2.5 impacts. There is also distance-weighted empirical
relationship information (i.e., precursor contributions to secondary impacts by distance from
source) provided within the MERPs Guidance that may be particularly useful for assessing
secondary PM2.5 impacts in long-range transport situations. Consultation with the appropriate
permitting authority and the appropriate EPA Regional Office is highly recommended for any
permit applicants demonstrating long-range Class I area increment compliance per the
requirements of section 4.2.c.ii of the 2017 Guideline.
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III. PSD Compliance Demonstrations for the O3 and PJVh.s NAAQS: Source Impact
Analysis
This section provides details regarding the EPA's recommended approaches for
conducting the source impact analysis as part of a PSD compliance demonstration for the O3
and/or PM2.5 NAAQS.
III.1 Os NAAQS
This section provides details regarding the EPA's recommended approaches for
conducting the source impact analysis for the O3 NAAQS associated with each of the two
assessment cases presented in Table III-1. In each of the assessment cases, the analysis should
begin by evaluating whether the impacts of either O3 precursor (VOC or NOx) would be emitted
in a significant amount, i.e., equal to or greater than the respective SER (40 tpy).
Table III-l. EPA Recommended Approaches for Assessing O3 Impacts by Assessment Case
Assessment Case
Description of Assessment Case
Secondary Impacts
Approach*
Case 1:
No Air Quality
Analysis
NOx emissions and VOC emissions < 40 tpy SER
N/A
Case 2*:
Secondary Air
Quality Impacts
NOx emissions or VOC emissions > 40 tpy SER
Include both precursors of
O;,. see Section II.2.
Tier 1 Approach
(e.g., MERPs)
Tier 2 Approach
(e.g., Chemical
Transport Modeling)
* In unique situations (e.g., in parts of Alaska where photochemistry is not possible for portions of the year), it
may be acceptable for the applicant to rely upon a qualitative approach to assess the secondary impacts. Any
qualitative assessments should be justified on a case-by-case basis in consultation with the appropriate
permitting authority and the appropriate EPA Regional Office.
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For Case 1, a modeled O3 NAAQS compliance demonstration is not required since
neither O3 precursor (NOx or VOC) is proposed to be emitted in an amount equal to or greater
than the applicable SER. For Case 2, where either NOx or VOC precursor emissions are greater
than the applicable SER, the permit applicant would need to conduct a compliance demonstration
for secondary impacts for both O3 precursors based on the two-tiered demonstration approach in
the EPA's 2017 Guideline.
III.2 PM2.5 NAAQS
This section provides details regarding the EPA's recommended approaches for
conducting the source impact analysis for the PM2.5 NAAQS associated with each of the two
assessment cases presented in Table III-2. In each of the assessment cases, the analysis should
begin by evaluating whether direct PIVb.swould be emitted in a significant amount, i.e., equal to
or greater than the SER (10 tpy), or whether either precursor (NOx or SO2) would be emitted in a
significant amount, i.e., equal to or greater than the respective SER (40 tpy).
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Table III-2. EPA Recommended Approaches for Assessing Primary and Secondary PM2.5
Impacts by Assessment Case
Assessment
Case
Description of Assessment Case
Primary Impacts
Approach
Secondary Impacts
Approach*
Case 1:
No Air Quality
Analysis
Direct PM2.5 emissions < 10 tpy SER
and
NOx emissions and SO2 emissions < 40 tpy SER
N/A
N/A
Case 2*:
Primary and
Secondary Air
Quality
Impacts
Direct PM2.5 emissions > 10 tpy SER
or
NOx emissions or SO2 emissions > 40 tpy SER
Appendix W
preferred or
approved
alternative
dispersion model
Include both precursors
of PM2.5. see Section
II. 2.
Tier 1 Approach
(e.g., MERPs)
Tier 2 Approach
(e.g., Chemical
Transport Modeling)
* In unique situations (e.g., in parts of Alaska where photochemistry is not possible for portions of the year), it may be
acceptable for the applicant to rely upon a qualitative approach to assess the secondary impacts. Any qualitative assessments
should be justified on a case-by-case basis in consultation with the appropriate EPA Regional Office or other applicable
permitting authority.
A modeled PM2.5 NAAQS compliance demonstration is not required for Case 1 since
neither direct PM2.5, nor any PM2.5 precursor (NOx or SO2), is proposed to be emitted in an
amount equal to or greater than the applicable SER. Case 1 is the only assessment case that does
not require conducting a source impact analysis.
For Case 2, where direct PM2.5 emissions or NOx or SO2 precursor emissions are greater
than or equal to the applicable SER, the primary PM2.5 impacts from direct PM2.5 emissions can
be estimated based on application of AERMOD or another appropriate preferred model listed in
Appendix A of the 2017 Guideline, or an alternative model subject to the provisions of section
3.2 of the 2017 Guideline. However, AERMOD and other preferred models currently listed in
Appendix A of the 2017 Guideline do not account for secondary formation of PM2.5 associated
with the source's precursor emissions. The assessment of NOx and SO2 precursor emission
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impacts on secondary PM2.5 formation should be conducted based on the two-tiered
demonstration approach in EPA's 2017 Guideline.
III.3 Assessing Primary PM2.5 Impacts
The assessment of primary PM2.5 impacts from the proposed new or modifying source is
generally the same for the PM2.5 NAAQS and PSD increments. Section 4.2.3.5 of the 2017
Guideline identifies the AERMOD modeling system as the preferred model for addressing direct
PM2.5 emissions unless another preferred model listed in the Guideline is more appropriate, such
as the Offshore and Coastal Dispersion Model (OCD), or the use of an alternative model is
justified consistent with section 3.2 of the 2017 Guideline.
The AERMOD modeling system includes the following regulatory components:
AERMOD: the dispersion model (U.S. EPA, 2021a);
AERMAP: the terrain processor for AERMOD (U.S. EPA, 2018b); and
AERMET: the meteorological data processor for AERMOD (U.S. EPA, 2021b).
Other components that may be used, depending on the application, are:
BPIPPRIME: the building input processor (U.S. EPA, 2004);
AERSURFACE: the surface characteristics processor for AERMET (U.S. EPA, 2020);
AERSCREEN: a screening version of AERMOD (U.S. EPA, 2021c; U.S. EPA, 201 la);
and
AERMINUTE: a pre-processor to calculate hourly average winds from Automated
Surface Observing System (ASOS) 2-minute observations (U.S. EPA, 2015).
Before applying AERMOD, the applicant should become familiar with the user's guides
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associated with the modeling components listed above and the most recent version of the
AERMOD Implementation Guide (U.S. EPA, 2021d). In addition to these documents, detailed
guidance on the use of the AERMOD modeling system for estimating primary PM2.5 impacts is
provided in Appendix B. Because AERMOD is limited to modeling direct PM2.5 emissions,
additional or alternative approaches are needed to provide an assessment of secondary PM2.5
impacts from the proposed new or modifying source, as discussed in more detail in the following
sections.
III.4 Assessing O3 and Secondary PM2.5 Impacts
This section provides more detail on the EPA's recommended approaches for assessing
the impacts of precursor emissions on O3 and/or secondary PM2.5 formation.
III.4.1 Conceptual Model
Each NAAQS compliance demonstration is unique and may require multiple factors to be
considered and assumptions to be thoroughly justified as a part of the technical assessment. A
well-developed modeling protocol that includes a detailed conceptual description of the current
air pollutant concentrations in the area (see Appendix A for examples of elements of a
conceptual description) and of the nature of the emissions sources within proximity of the new or
modifying emissions source is essential for determining the necessary components of an
acceptable assessment of the impact from O3 and/or secondary PM2.5 formation.21 With timely
21 For more detailed information on the development of such conceptual descriptions for an area, please refer to the
following:
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and appropriate consultation between the applicant and the appropriate permitting authority,
along with the submittal and subsequent approval, if required, of the modeling protocol by the
appropriate permitting authority, many potential problems and unintended oversights in the
technical assessment can be resolved early in the process or avoided all together.
In the development of an appropriate conceptual description to support an assessment, it
is important to fully characterize the current O3 and/or PM2.5 concentrations in the region where
the new or modifying source is to be located and not just the most current design values, which
historically has been used as background concentrations in a cumulative modeling
demonstration. For O3, this characterization should take into consideration episodic high O3
concentrations and any trends in the area. For PM2.5, this characterization should take into
consideration the seasonality and speciated composition of the current PM2.5 concentrations and
any long-term trends that may be occurring. It may also be important to describe the typical
background concentrations of certain chemical species that participate in the photochemical
reactions that form O3 and secondary PM2.5. It is possible that there are mitigating factors for
secondary PM2.5 formation given limitations of other chemical species important in the
photochemical reactions, e.g., minimal ammonia (NH3) in the ambient environment that could
limit any precursor pollutant from readily reacting to form secondary PM2.5. This understanding
of the atmospheric environment will provide important insights on the potential for secondary
Chapter 10 of "Particulate Matter Assessment for Policy Makers: A NARSTO Assessment." P. McMurry, M.
Shepherd, and J. Vickery, eds. Cambridge University Press, Cambridge, England (NARSTO, 2004).
Section 11, "How Do I Get Started? A Conceptual Description"' of "Guidance on the Use of Models and Other
Analyses for Demonstrating Attaimnent of Air Quality Goals for Ozone, PM2 5, and Regional Haze." U.S.
Enviromnental Protection Agency, Research Triangle Park, North Carolina (U.S. EPA, 2007a).
In addition, relevant regional examples include: "Conceptual Model of PM2 5 Episodes in the Midwest," January
2009, Lake Michigan Air Directors Consortium; and "Conceptual Model of Particulate Matter Pollution in the
California San Joaquin Valley," Document Number CP045-1-98, September 8, 1998.
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formation and highlight aspects that will need to be accounted for in the source impact and/or
cumulative impact assessment.
A good conceptual description will also characterize the meteorological conditions that
are representative of the region and are associated with periods and/or seasons of higher and
lower ambient O3 and/or 24-hour PM2.5 concentrations. For example, identification of
meteorological phenomena that typically occur during periods of high daily 8-hour O3 or 24-hour
PM2.5 concentrations, such as low-level temperature inversions, stagnant high pressure systems,
low-level jets, etc., can be extremely important in understanding the importance, or lack thereof,
of photochemistry and secondary PM2.5 formation for the higher ambient O3 and PM2.5
concentrations. The analysis and understanding of meteorological conditions will also inform the
assessment of high O3 episodes and seasonal 24-hour PM2.5 concentrations in the region.
III.4.2 Tier 1 Assessment Approach
As discussed in the section 5.2 of the 2017 Guideline, the EPA has determined that
advances in chemical transport modeling science make it reasonable to provide more specific,
generally-applicable guidance that identifies particular models or analytical techniques that may
be appropriate for use under specific circumstances for assessing the impacts of an individual
proposed source on O3 and secondary PM2.5 concentrations. There is not a preferred model or
technique for estimating O3 or secondary PM2.5 for specific source impacts. Instead, for assessing
secondary pollutant impacts from individual proposed sources, the degree of complexity required
to appropriately assess potential single-source impacts varies depending on the nature of the
source, its proposed emissions, and the background environment. In order to provide the user
community flexibility in estimating single-source secondary pollutant impacts, which allows for
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different approaches to credibly address these different areas, the 2017 Guideline recommends a
two-tiered demonstration approach for addressing single-source impacts on ambient
concentrations of O3 and secondary PM2.5.
To inform a Tier 1 assessment,22 the existing air quality model-based information that is
used should be appropriate in terms of representing the type of source, its precursor emissions,
its geographic location, and a current composition of regional emissions, in addition to those
elements of the conceptual description discussed above. The air quality modeling information
may be available from past or current SIP attainment demonstration modeling, published
modeling studies, or peer-reviewed literature with estimates of model responsiveness to
precursor emissions in contexts that are relevant to the new or modifying source. The estimates
of model responsiveness, such as impact on O3 concentrations per ton of NOx or impact on
PM2.5 concentrations per ton of SO2 emissions, could then be used in conjunction with the
precursor emissions estimates for the proposed new or modifying source to provide a
quantitative estimate of the impact of such precursor emissions on the formation of O3 and/or
secondary PM2.5 concentrations. The estimates of responsiveness should be technically credible
in representing such impacts and it may be advisable for the estimate to reflect an upper bound of
potential impacts.
To assist in the development of appropriate Tier 1 demonstration tools, the EPA
developed the MERPs Guidance to provide a framework for permitting authorities to develop
22 A Tier 1 assessment involves the use of technically credible relationships between precursor emissions and a
source's secondary impacts, e.g., as demonstrated in modeling for a source impact analysis, that may be published in
the peer-reviewed literature, developed from modeling that was previously conducted for an area by a source, a
governmental agency, or some other entity and that is deemed sufficient for evaluating a proposed source's impacts,
or generated by a peer-reviewed reduced form model. In such cases, the EPA expects that existing air quality model-
based information regarding the potential for NOx and VOC precursor emissions to form O3 and for SO2 and NOx
precursor emissions to form secondary PM2.5 concentrations may be used to establish an appropriate estimate of O3
and/or secondary PM2.5 impacts from the proposed new or modifying source.
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area-specific MERPs. The MERPs Guidance illustrates how permitting authorities may
appropriately develop MERPs for specific areas and use them as a Tier 1 compliance
demonstration tool for O3 and secondary PM2.5 under the PSD permitting program. The MERPs
Guidance also addresses the appropriate use of MERPs to reflect the combined ambient impacts
across O3 or PM2.5 precursors and, in the case of PM2.5, the combined primary and secondary
ambient impacts. Such an approach includes flexibility with respect to the use of Tier 1
demonstration tools to generate information relevant for specific regions or areas and
representative of secondary formation in a particular region or area.
Specifically, the MERPs Guidance provides information about how to use CTMs to
estimate single-source impacts on O3 and secondary PM2.5 and how such model simulation
results for specific areas can be used to develop empirical relationships between a source's O3
and PM2.5 precursor emissions and its secondary impacts that may be appropriate for use as a
Tier 1 demonstration tool. It also provides results from EPA photochemical modeling of a set of
more than 100 hypothetical sources across geographic areas and source types that may be used in
developing MERPs as discussed in the guidance. This flexible and scientifically credible
approach allows for the development of area-specific Tier 1 demonstration tools that better
represent the chemical and physical characteristics and secondary pollutant formation within that
region or area.
As discussed in the MERPs Guidance, the EPA's Single-source Modeling Guidance
provides information to stakeholders about how to appropriately address the variety of chemical
and physical characteristics regarding a project scenario and key receptor areas in conducting
photochemical modeling to inform development of MERPs. The development of MERPs for O3
and secondary PM2.5 precursors is just one example of a suitable Tier 1 demonstration tool. The
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EPA will continue to engage with the modeling community to identify credible alternative
approaches for estimating single-source secondary pollutant impacts, which provide flexibility
and are less resource intensive for PSD permit demonstrations.
As an example, a Tier 1 assessment of secondary O3 and PM2.5 impacts was developed by
a permit applicant, the Tennessee Valley Authority (TVA), for a major modification at their
Gleason facility in Tennessee in 2018. The TVA and the Tennessee Department of Environment
and Conservation (TDEC) worked closely with EPA Region 4 to ensure that the ambient impacts
analysis was technically sound and consistent with applicable PSD regulations and EPA
guidance. The PSD air quality modeling analysis was submitted to TDEC in late 2018 using an
approach that was consistent with the MERPs Guidance to relate facility emissions to potential
downwind impacts of secondary O3 and PM2.5. A more detailed discussion of the TVA's
technical assessment is provided in Appendix C.
The National Association of Clean Air Agencies (NACAA) Workgroup final report
(NACAA, 2011) provides details on potential approaches to quantify the secondary PM2.5
impacts from a proposed new or modifying source that may be appropriate to inform a Tier 1
assessment of PM2.5 impacts (see Appendices C and D of NACAA, 2011). One suggested
method in the final report is to convert emissions of precursors into equivalent amounts of direct
PM2.5 emissions using "pollutant offset ratios" and then use a dispersion model to assess the
impacts of the combination of direct PM2.5 emissions and the equivalent direct PM2.5 emissions.
The "pollutant offset ratios" referenced in the NACAA Workgroup report were from the EPA's
2008 "Implementation of the New Source Review (NSR) Program for Particulate Matter Less
Than 2.5 Micrometers (PM2.5)" final rule notice (73 Fed. Reg. 28321, May 16, 2008) concerning
the development and adoption of interpollutant trading (offset) provisions for PM2.5 under state
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nonattainment area NSR programs for PM2.5. The EPA's July 23, 2007, technical analysis titled
"Details on Technical Assessment to Develop Interpollutant Trading Ratios for PM2.5 Offsets"
describes the method used to develop the original "preferred" precursor offset ratios (U.S. EPA,
2007b).23
The EPA does not support using the specific results from the EPA's 2007 technical
assessment in the context of PSD compliance demonstrations without additional technical
demonstration specific to the source(s) and area(s) for which the ratios would be applied. As
described in the EPA's July 21, 2011 memorandum changing its policy on use of the "preferred"
interpollutant offset trading ratios included in the preamble to the 2008 final rule, the EPA
acknowledged that existing models and techniques are adequate to "conduct local
demonstrations leading to the development of area-specific ratios for PM2.5 nonattainment areas"
and provided a general framework for efforts that may be relevant in developing appropriate
"pollutant offset ratios" for use in hybrid qualitative/quantitative assessment of secondary PM2.5
impacts (U.S. EPA, 201 lb). A similar general framework is embodied in the MERPs Guidance
in which the EPA addresses how to conduct modeling to inform the development of a MERP for
a particular area.
The EPA also notes that the NACAA Workgroup "considered, but rejected, other
methods for assessing secondary PM2.5 impacts, including use of a simple emissions divided by
distance (Q/D) metric and use of AERMOD with 100 percent conversion of SO2 and NOx
23 In the preamble to the 2008 final rule, the EPA included preferred or presumptive offset ratios, applicable to
specific PM2.5 precursors that the EPA said at that time state/local air agencies could adopt in conjunction with the
new interpollutant offset provisions for PM2.5, and for which the state could rely on the EPA's technical work to
demonstrate the adequacy of the ratios for use in any PM2.5 nonattainment area. In a July 21, 2011 memorandum,
EPA changed its policy and stated that it no longer supported the ratios provided in the preamble to the 2008 final
rule as presumptively approvable ratios for adoption in SIPs containing nonattainment NSR programs for PM2.5.
Memorandum from Gina McCarthy, Assistant Administrator, to Regional Air Division Directors, "Revised Policy to
Address Reconsideration of Interpollutant Trading Provisions for Fine Particles (PM2.5)" (U.S. EPA, 2011b).
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concentrations to (NH4)2S04 and (NTLONCb " The EPA has reviewed the detailed discussion
provided in Appendix E of the NACAA Workgroup final report and agrees with these technical
conclusions.
III.4.3 Tier 2 Assessment Approach
As discussed in the 2017 Guideline, a Tier 2 assessment involves application of more
sophisticated, case-specific CTMs in consultation with the appropriate permitting authority and
conducted consistent with the recommendations in the most current version of the Single-source
Modeling Guidance. Where it is necessary to estimate O3 and/or secondary PM2.5 impacts with
case-specific air quality modeling, a candidate model should be selected for estimating single-
source impacts on O3 and/or secondarily formed PM2.5 that meets the general criteria for an
"alternative model" where there is no preferred model as outlined in section 3.2.2.e of the 2017
Guideline. The general criteria include:
i. The model has received a scientific peer review;
ii. The model can be demonstrated to be applicable to the problem on a theoretical
basis;
iii. The databases that are necessary to perform the analysis are available and
adequate;
iv. Appropriate performance evaluations of the model have shown that the model is
not biased toward underestimates; and
iv. A protocol on methods and procedures to be followed has been established.
Section 3.2.2 further provides that the appropriate EPA Regional Office, in consultation with the
EPA Model Clearinghouse, is authorized to approve a particular model and approach as an
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alternative model application.
Both Lagrangian puff models and photochemical grid models may be appropriate for this
purpose where those models satisfy alternative model criteria in section 3.2.2 of the 2017
Guideline. That said, the EPA believes photochemical grid models are generally most
appropriate for addressing O3 and secondary PM2.5 impacts because they provide a spatially and
temporally dynamic realistic chemical and physical environment for plume growth and chemical
transformation. Publicly available and documented Eulerian photochemical grid models such as
the Comprehensive Air Quality Model with Extensions (CAMx) (Ramboll Environ, 2018) and
the Community Multiscale Air Quality (CMAQ) (Byun and Schere, 2006) model treat emissions,
chemical transformation, transport, and deposition using time and space variant meteorology.
These modeling systems include primarily emitted species and secondarily formed pollutants
such as O3 and PM2.5 (Chen et al., 2014; Civerolo et al., 2010; Russell, 2008; Tesche et al.,
2006). In addition, these models have been used extensively to support O3 and PM2.5 SIPs and to
explore relationships between inputs and air quality impacts in the United States and elsewhere
(Cai et al., 2011; Civerolo et al., 2010; Hogrefe et al., 2011).
On August 4, 2017, the EPA released a memorandum (U.S. EPA, 2017b) providing
information specific to how the CAMx and the CMAQ model systems were relevant for each of
these elements. This memorandum provides an alternative model demonstration for the CAMx
and CMAQ photochemical transports models establishing their fit for purpose in PSD
compliance demonstrations for O3 and PM2.5 and in NAAQS attainment demonstrations for O3,
PM2.5 and Regional Haze. The memorandum also provides support for their general applicability
for use in PSD compliance demonstrations; however, it does not replace the need for such
demonstrations to provide model protocols describing model application choices or the
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evaluation of model inputs and baseline predictions against measurements relevant for their
specific use by permit applicants and state, local, and tribal air agencies.
For those situations where a refined Tier 2 demonstration is necessary, the EPA has also
provided the Single-source Modeling Guidance that provides recommended, credible procedures
to estimate single-source secondary impacts from sources for permit related assessments.
Extensive peer-reviewed literature demonstrates and documents that photochemical grid models
have been applied for assessing single-source impacts and that the models adequately represent
secondary pollutant impacts from a specific facility, in comparison to near-source downwind in-
plume measurements. The literature shows that these models can clearly differentiate impacts of
a specific facility from those of other sources (Baker and Kelly, 2014; Zhou et al., 2012). Other
peer-reviewed research has clearly shown that photochemical grid models are able to simulate
impacts from single sources on secondarily-formed pollutants (Baker et al., 2015; Bergin et al.,
2008; Kelly et al., 2015). Further, single-source secondary impacts have been provided in
technical reports that further support the utility of these tools for single-source scientific and
regulatory assessments (ENVIRON 2012a; ENVIRON 2012b; Yarwood et al., 2011). The EPA
firmly believes that the peer-reviewed science clearly demonstrates that photochemical grid
models can adequately assess single-source impacts. The EPA recognizes that ongoing
evaluations in this area will lead to continual improvements in science and associated predictive
capabilities of these models.
For the purposes of conducting a Tier 2 assessment, the application of a CTM will
involve case-specific factors that should be part of the consultation process with the appropriate
permitting authority and reflected in the agreed-upon modeling protocol. Consistent with the
Single-source Modeling Guidance and section 9.2.1 of the 2017 Guideline, the EPA recommends
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that the modeling protocols for this purpose should include the following elements:
1. Overview of Modeling/Analysis Project
Participating organizations
Schedule for completion of the project
Description of the conceptual model for the project source/receptor area
Identify how modeling and other analyses will be archived and documented
Identify specific deliverables to the appropriate permitting authority
2. Model and Modeling Inputs
Rationale for the selection of air quality, meteorological, and emissions models
Modeling domain
Horizontal and vertical resolution
Specification of initial and boundary conditions
Episode selection and rationale for episode selection
Rationale for and description of meteorological model setup
Basis for and development of emissions inputs
Methods used to quality assure emissions, meteorological, and other model inputs
3. Model Performance Evaluation
Describe ambient database(s)
Describe evaluation procedures and performance metrics
As stated previously, the EPA expects that the EPA Regional Offices, with assistance
from the OAQPS, may assist reviewing authorities, as necessary, to structure appropriate
technical demonstrations leading to the development of appropriate CTM applications for the
purposes of estimating potential O3 and/or secondary PM2.5 impacts.
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III.5 Comparison to the SIL
This section provides recommendations for source impact analyses where a permit
applicant compares the proposed source's ambient O3 or PM2.5 impacts to an appropriate SIL as
part of the required demonstration that a proposed source or modification will not cause or
contribute to a violation of the O3 or PM2.5 NAAQS. These recommendations are also generally
applicable for demonstrations that a proposed source or modification will not cause or contribute
to a violation of the PM2.5 PSD increments, see Section V.4. The EPA's recommended SIL
values for O3 and PM2.5 NAAQS and PM2.5 PSD increments are listed in Table II-l and Table II-
2. (U.S. EPA, 2018a).
III.5.1 SIL Comparison for O3
For Assessment Case 2, an analysis of secondary O3 impacts should be conducted where
the proposed source's precursor emissions of NOx or VOC are equal to or greater than the
respective SERs. The EPA recommends that the assessment of the combined precursor emissions
impacts on O3 formation be conducted based on the two-tiered demonstration approach specific
to O3 in section 5.3 of the 2017 Guideline. Under the Tier 1 approach, for source impact
analyses, the highest of the multi-season (or episode) averages of the maximum modeled daily 8-
hour O3 concentrations predicted each season (or episode) should be compared to the appropriate
O3 SIL, since this metric represents the maximum potential daily 8-hour O3 impact from the
proposed source or modification. Under the Tier 2 approach, where a CTM is directly applied to
estimate the source impacts, the comparison should be done at each receptor, i.e., each modeled
grid cell. If the source impact is less than the SIL, then the source impact analysis is generally
sufficient to support a finding that the source will not cause or contribute to a NAAQS violation.
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However, if the source impact is equal to or greater than the SIL, then the analysis is insufficient
to show that a source will not cause or contribute to a violation of the NAAQS and a cumulative
impact assessment is necessary.
III.5.2 SIL Comparison for PM2.5
For Assessment Case 2, analyses of both primary and secondary PM2.5 impacts are
necessary because the proposed source's direct PM2.5 emissions or emissions of at least one
PM2.5 precursor are equal to or greater than the respective SERs. In this case, both the primary
and secondary PM2.5 impacts from the proposed source or modification should be included in a
combined comparison to the appropriate PM2.5 SIL in the source impact analysis.
The assessment of the primary PM2.5 concentrations due to direct emissions should be
conducted using the EPA preferred AERMOD dispersion model (or other acceptable preferred or
approved alternative model). The dispersion modeling methods here are similar to the methods
used for other primary pollutants, including the use of maximum allowable emissions, following
Table 8-2 of the 2017 Guideline. However, due to the form of the PM2.5 NAAQS, the EPA
recommends that one of the following be included in the combined PM2.5 SIL comparison for the
source impact analysis, depending on the meteorological data used in the analysis:
The highest of the 5-year averages of the maximum modeled annual 24-hour PM2.5
concentrations (for the 24-hour PM2.5 NAAQS) or highest of the 5-year averages of
the annual average PM2.5 concentrations (for the annual PM2.5 NAAQS) predicted
each year at each receptor, based on 5 years of representative National Weather
Service (NWS) data;
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The highest modeled 24-hour PM2.5 concentration (for the 24-hour PM2.5 NAAQS) or
the highest modeled average PM2.5 concentration (for the annual PM2.5 NAAQS)
predicted at each receptor based on 1 year of site-specific meteorological data; or the
highest of the multi-year averages of the maximum modeled annual 24-hour PM2.5
concentration (for the 24-hour PM2.5 NAAQS) or the highest of the multi-year
averages of the maximum modeled annual average PM2.5 concentrations (for the
annual PM2.5 NAAQS) predicted each year at each receptor, based on 2 or more
years, up to 5 complete years, of available site-specific meteorological data; or
The highest of the 3-year averages of the maximum modeled annual 24-hour PM2.5
concentrations (for the 24-hour PM2.5 NAAQS) or highest of the 3-year averages of
the annual average PM2.5 concentrations (for the annual PM2.5 NAAQS) predicted
each year at each receptor, based on 3 years of prognostic meteorological data.
These metrics represent the maximum potential 24-hour or annual PM2.5 impacts from the
proposed source or modification at any receptor, given the form of the NAAQS, and therefore
provide an appropriate part of the basis for determining whether a cumulative modeling analysis
would be needed.
For the assessment of the precursor emission impacts on PM2.5 formation, the EPA
recommends that this part of the assessment be conducted based on the two-tiered demonstration
approach specific to PM2.5 in section 5.4 of the 2017 Guideline. However, the resulting impact
included in the combined comparison to the PM2.5 SIL will depend on the type of assessment
conducted for the secondary PM2.5 impacts from the source.
In the SIL comparison for Case 2, the primary and secondary PM2.5 impacts may be
combined in various ways that may entail greater or lesser degrees of conservatism. For example,
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combining the peak estimated primary PM2.5 impact with the peak estimated secondary PM2.5
impact, unpaired in time and space, would tend to be a conservative estimate of combined
impacts since, as noted above, peak impacts associated with a source's direct PM2.5 and
precursor emissions are not likely well-correlated in time or space. The conservatism associated
with combining peak estimated primary and secondary impacts for comparison to a SIL makes
this an appropriate initial approach to combining estimated primary and secondary PM2.5
impacts.
Other approaches for combining primary and secondary PM2.5 impacts for comparison to
a SIL will vary based on the degree of temporal and spatial pairing of estimated primary and
secondary PM2.5 impacts. Full temporal and spatial pairing may not be feasible in many cases,
given that the dispersion modeling and chemical transport modeling may be based on different
data periods. Furthermore, full temporal and spatial pairing of primary and secondary PM2.5
impacts may not be appropriate in many cases because photochemical grid modeling represents
gridded concentration estimates whereas dispersion modeling produces estimates at discrete
receptor locations and because of the limitations of both the dispersion model and the
photochemical grid model to accurately predict impacts on a paired in time and space basis. As a
result, consideration of some degree of temporal pairing of primary and secondary PM2.5 impacts
is most appropriate on a seasonal or monthly basis with considerations of spatial pairing that
reflects the general lack of correlation between primary and secondary impacts, i.e., primary
impacts being higher near the source while secondary impacts being higher at some distance
away from the source.
The permitting authority and the permit applicant should thoroughly discuss the details
regarding combining modeled primary and secondary PM2.5 impacts for Case 2 situations and
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should reach agreement during the initial review of the modeling protocol. The permitting
authority should ensure that any approach for combining estimated primary and secondary PM2.5
impacts for comparison to a SIL for Case 2 conforms to the recommendations described above
regarding the form of the modeled estimate. Accordingly, the approach should be based on the
highest of the multi-year averages of the maximum modeled 24-hour or annual PM2.5
concentrations predicted each year at each receptor, which represents the maximum potential
impact from the proposed source or modification.
Ultimately, if the combined primary and secondary PM2.5 impacts are less than the SIL,
then the analysis is generally sufficient to support a finding that the source will not cause or
contribute to aNAAQS violation. However, if the combined primary and secondary PM2.5
impacts are equal to or greater than the SIL, then the analysis is insufficient to show that a source
will not cause or contribute to a violation of the NAAQS and a cumulative impact assessment is
necessary to make the NAAQS compliance demonstration.
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IV. PSD Compliance Demonstrations for the O3 and PM2.5 NAAQS: Cumulative Impact
Analysis
Where the source impact analysis described in Section III is insufficient to show that a
source will not cause or contribute to a violation of the O3 or PM2.5 NAAQS, a cumulative
impact assessment is necessary to make the NAAQS compliance demonstration. A cumulative
assessment accounts for the combined impacts of the proposed new or modifying source's
emissions, emissions from other nearby sources, and representative background levels of O3 or
PM2.5 within the modeling domain. The cumulative impacts are then compared to the O3 or PM2.5
NAAQS to determine whether there is a modeled NAAQS violation. If not, then the NAAQS
compliance demonstration is sufficient. If there are modeled violations, then the source impact at
the location of these predicted violations is compared to the appropriate SIL to determine if the
proposed new or modifying source emissions will cause or contribute to a violation of the
NAAQS. This section provides details on conducting an appropriate cumulative impact
assessment for the O3 and PM2.5 NAAQS.
03
The cumulative impact assessment should include the following components of O3
impacts, as appropriate, for comparison to the NAAQS:
Proposed new or modifying source
o Impacts on O3 from each precursor (NOx and VOC)
Nearby sources
o Impacts on O3 from precursors (NOx and VOC) are typically accounted
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for through representative monitored background24
Monitored background levels of O3 that accounts for O3 impacts from regional
transport and from nearby sources, and O3 impacts from background sources not
included in the modeled inventory, e.g., minor and mobile sources.
PM25
The cumulative impact assessment should include the following components of PM2.5
impacts, as appropriate, for comparison to the NAAQS:
Proposed new or modifying source
o Primary impacts on PM2.5, i.e., from direct PM2.5 emissions
o Secondary impacts on PM2.5 from each precursor (NOx and SO2)
Nearby sources
o Primary impacts on PM2.525
o Impacts on PM2.5 from precursors (NOx and SO2) are typically accounted
for through representative monitored background
Monitored background levels of PM2.5 that accounts for secondary PM2.5 impacts
from regional transport and from nearby sources, and primary PM2.5 impacts from
background sources not included in the modeled inventory, e.g., minor sources.
As with the source impact analysis, the primary impacts of direct PM2.5 emissions from
24 The emissions impact of any nearby source that has received a permit but is not yet operational should be included
in the air quality assessment. In such cases, consultation with the appropriate permitting authority on the appropriate
assessment approach is recommended.
25 The emissions impact of any nearby source that has received a permit but is not yet operational should be included
in the air quality assessment. In such cases, consultation with the appropriate permitting authority on the appropriate
assessment approach is recommended
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the proposed new or modifying source and nearby sources in a cumulative impact analysis
should be estimated based on the AERMOD dispersion model (or other acceptable preferred or
approved alternative model). In addition, EPA recommends that the estimate of secondary PM2.5
impacts from the proposed new or modifying source be conducted based on the two-tiered
demonstration approach described in section 5.2 of the 2017 Guideline. As noted above,
secondary impacts on PM2.5 from regional transport, precursor emissions from nearby sources,
and primary PM2.5 impacts from background sources not included in the modeled inventory
should be accounted for through representative monitored background concentrations.
IV. 1 Modeling Inventory
Section 8 of the 2017 Guideline provides the current required and recommended
approaches for characterizing source emissions and developing the O3 and/or PM2.5 modeling
inventory for purposes of NAAQS compliance modeling in PSD air quality demonstrations.
Section 8.2 and Table 8-2 of the 2017 Guideline address the appropriate emissions limit,
operating level, and operating factor to be modeled, which is the maximum allowable emissions
rate for the proposed new or modifying source in most cases and an allowable emissions rate
adjusted for actual operations for any nearby sources. For applications that require the
assessment of secondarily formed O3 or PM2.5 through a case-specific CTM, information
regarding the development of the appropriate modeling inventory can be found in the Single-
source Modeling Guidance.
Section 8.3.3 of the 2017 Guideline emphasizes the importance of professional judgment
in the identification of nearby and other sources "that are not adequately represented by ambient
monitoring data" that should be included in the modeled emission inventory and identifies "a
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significant concentration gradient in the vicinity of the [proposed] source" as a primary criterion
for this selection. Additionally, the 2017 Guideline suggests that "the number of nearby sources
to be explicitly modeled in the air quality analysis is expected to be few except in unusual
situations" and that "[i]n most cases, the few nearby sources will be located within the first 10 to
20 km from the [proposed] source." The EPA also provided modeling guidance in March 2011
(U.S. EPA, 201 lc) that includes a detailed discussion of the significant concentration gradient
criterion. However, several application-specific factors should be considered when determining
the appropriate inventory of nearby sources to include in the cumulative modeling analysis,
including the potential influence of terrain characteristics on concentration gradients and the
availability and adequacy of ambient monitoring data to account for impacts from nearby sources
as well as other background sources.
Consistent with the 2017 revisions to the Guideline, the EPA cautions against the
application of very prescriptive procedures for identifying which nearby sources should be
included in the modeled emission inventory for NAAQS compliance demonstrations, such as the
procedures described in Chapter C, Section IV.C.l of the draft "New Source Review Workshop
Manual" (U.S. EPA, 1990). Our main concern is that following such procedures in a literal and
uncritical manner may, in many cases, increase the likelihood of double-counting modeled and
monitored concentrations, resulting in cumulative impact assessments that are overly
conservative and would unnecessarily complicate the permitting process. The identification of
which sources to include in the modeled emissions inventory should be addressed in the
modeling protocol and, as necessary, discussed in advance with the permitting authority.
Since modeling of direct PM2.5 emissions has been limited and infrequent, the availability
of an adequate direct PM2.5 emission inventory for nearby sources may not exist in all cases.
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Recommendations for developing PM2.5 emission inventories for use in PSD applications will be
addressed separately, but existing SIP inventories for PM2.5 or statewide PSD inventories of
sources for refined modeling are expected to provide a useful starting point for this effort.
IV.2 Monitored Background
Section 8.3 of the 2017 Guideline provides recommendations for determination of
monitored background concentrations to include in cumulative impact assessments for NAAQS
compliance, which should account for impacts from existing sources that are not explicitly
included in the modeled inventory and natural sources. From newly-acquired, pre-construction
monitoring data and/or existing representative air quality data gathered for purposes of a
permitting analysis, permit applicants should assess and document what the background
monitoring data represent to the extent possible, including any information that may be available
from the state or other agency responsible for siting and maintaining the monitor.26
Determining the monitored background concentrations of O3 and/or PM2.5 to include in
the cumulative impact assessment may entail different considerations from those for other
criteria pollutants lacking secondary formation. Given that the monitored background
determination can be a complex process with many uncertainties based on unique situations,
permit applicants are encouraged to consult with the appropriate permitting authority.
An important aspect of the monitored background concentrations for O3 or PM2.5 is that
the ambient monitoring data should in most cases account for the impact of secondary formation
26 Please note that in the case of an existing source seeking a permit for a modification, there is potential overlap
across secondary impacts from monitored background and from precursor emissions from the existing source. In
such cases, recommendations for excluding monitored values when the source in question is impacting the monitor
in section 8.3.2.C of the 2017 Guideline may need to be modified to avoid overcompensating when the monitored
concentrations are also intended to account for the existing source's impacts on secondary PM2.5. Additionally,
permit applicants should consult with the appropriate permitting authority.
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of either pollutant from precursor emissions of existing sources impacting the modeling domain.
Due to the nature of O3 and secondary PM2.5, monitored background concentrations of O3 and
PM2.5 are more likely to be homogeneous across the modeling domain in most cases compared to
most other pollutants. Additionally, for PM2.5, ambient monitoring data should account for the
component of the background levels of primary PM2.5 from direct PM2.5 emissions of nearby
sources that are not included in the modeled inventory. As with other criteria pollutants,
consideration should also be given to the potential for some double-counting of the impacts from
modeled emissions that may be also included in the background monitored concentrations. This
should generally be of less importance than the representativeness of the monitor for secondary
formation of O3 and PM2.5, unless the monitor is located relatively close to nearby sources of
primary PM2.5 that could be impacting the monitor.
Depending on the nature of local PM2.5 levels within the modeling domain, it may be
appropriate to account for seasonal variations in monitored background PM2.5 levels, which may
not be correlated with seasonal patterns of the modeled primary PM2.5 levels. For example,
maximum modeled primary PM2.5 impacts associated with low-level emission sources are likely
to occur during winter months due to longer periods of stable atmospheric conditions, whereas
maximum ambient levels of secondary PM2.5 typically occur during spring and summer months
due to high levels of sulfates (particularly in the eastern United States). The use of temporally-
varying monitored background concentrations in a cumulative impact analysis is discussed in
more detail in Section IV.3.
IV.3 Comparison to the NAAQS
As indicated in Figure II-1, the first step of a cumulative impact analysis consists of a
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comparison of the combined modeled and monitored concentrations, as discussed above, with
the applicable NAAQS to determine if there are any predicted violations of the O3 and/or PM2.5
NAAQS.
03
Ozone differs from other criteria pollutants because it is secondarily formed by NOx and
VOC precursor emissions and there are not direct O3 emissions to be considered in the NAAQS
compliance demonstration. The O3 design value that is representative for the area, rather than the
overall maximum monitored background concentration, should generally be used as the
monitored component of the cumulative analysis. The O3 design value is based on the 3-year
average of the annual fourth-highest daily maximum 8-hour average O3 concentrations (80 FR
65292).
The EPA recommends that the modeled O3 impacts be added to the monitor-based design
value for comparison to the NAAQS, as appropriate. The monitoring data should be
representative in that it accounts for O3 formation associated with existing sources both within
and outside of the modeling domain. The EPA recommends that modeled O3 impacts be based
on a Tier 1 or 2 assessment that accounts for the source's precursor emissions of NOx and VOC.
The modeled O3 impacts should be based on the average of the predicted annual (or episodic)
fourth-highest daily maximum 8-hour averaged O3 concentrations. The resulting cumulative O3
concentrations should then be compared to the O3 NAAQS (0.070 ppm).
PM25
Combining the modeled and monitored concentrations of PM2.5 for comparison to the 24-
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hour or annual PM2.5 NAAQS entails considerations that differ from those for other criteria
pollutants due to the issues identified at the end of Section IV.2. The discussion below addresses
comparisons to the NAAQS in the context of dispersion modeling of direct PM2.5 emissions and
a Tier 1 or 2 assessment of secondary PM2.5 impacts accounting for the proposed source's PM2.5
precursor emissions.
Given the importance of secondary formation of PM2.5 and the potentially high
background levels relative to the PM2.5 NAAQS, greater emphasis should generally be placed on
the monitored background concentrations relative to the modeled inventory for PM2.5 than for
other pollutants. This is true for both PM2.5 NAAQS and PSD increments assessments. Also,
given the probabilistic form of the PM2.5 NAAQS, careful consideration should be given to how
the monitored and modeled concentrations are combined to estimate the cumulative impact
levels.
The PM2.5 design value that is representative for the area, rather than the overall
maximum monitored background concentration, should generally be used as the monitored
component of the cumulative analysis. The PM2.5 design value for the annual averaging period is
based on the 3-year average of the annual average PM2.5 concentrations, while the PM2.5 design
value for the 24-hour averaging period is based on the 3-year average of the annual 98th
percentile 24-hour average PM2.5 concentrations (78 FR 3086). Details regarding the
determination of the annual 98th percentile monitored 24-hour value based on the number of days
sampled during the year are provided in the data interpretation procedures for the PM2.5 NAAQS
in Appendix N to 40 CFR part 50.
It should be noted here that, although the monitored design values for the PM2.5 standards
are defined in terms of 3-year averages, this definition does not preempt or alter the 2017
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Guideline's requirement for use of 5 years of representative NWS meteorological data, at least 1
year of site-specific data, or at least 3 years of prognostic meteorological data for purposes of
modeling direct emissions of PM2.5.27 The 5-year average based on use of representative NWS
meteorological data, the average across one or more (up to 5) complete years of available site-
specific data, or the average across 3 years of prognostic meteorological data serves as an
unbiased estimate of the 3-year average for purposes of modeling demonstrations of compliance
with the NAAQS. Modeling of "rolling 3-year averages," using years 1 through 3, years 2
through 4, and years 3 through 5, as recommended in the EPA's SIP Modeling Guidance, is not
required.
The EPA recommends that the modeled design concentrations of primary PM2.5 and the
Tier 1 or 2 assessed secondary PM2.5 impacts should be added to the monitor-based design value
for comparison to the NAAQS, as appropriate. The primary PM2.5 modeled design concentration
should be based on:
The 5-year average of the modeled annual 98th percentile 24-hour PM2.5
concentrations (for the 24-hour PM2.5 NAAQS) or 5-year average of the modeled
annual average PM2.5 concentration (for the annual PM2.5 NAAQS) predicted each
year at each receptor, based on 5 years of representative NWS data;
The modeled 98th percentile 24-hour PM2.5 concentrations (for the 24-hour PM2.5
NAAQS) or modeled average PM2.5 concentration (for the annual PM2.5 NAAQS)
predicted at each receptor based on 1 year of site-specific meteorological data, or
the multi-year average of the modeled annual 98th percentile 24-hour PM2.5
27 See 40 CFR part 51, Appendix W, section 8.4.2.e.
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concentrations (for the 24-hour PM2.5 NAAQS) or modeled annual average PM2.5
concentration (for the annual PM2.5 NAAQS) predicted each year at each receptor,
based on 2 or more years, up to 5 complete years, of available site-specific
meteorological data; or
The 3-year average of the modeled annual 98th percentile 24-hour PM2.5
concentrations (for the 24-hour PM2.5 NAAQS) or 3-year average of the modeled
annual average PM2.5 concentration (for the annual PM2.5 NAAQS) predicted each
year at each receptor, based on 3 years of prognostic meteorological data.
The EPA recommends that secondary PM2.5 modeled impacts be based on either a Tier 1
or 2 assessment accounting for the source's PM2.5 precursor emissions of NOx and SO2. The
resulting cumulative PM2.5 concentrations should then be compared to the 24-hour PM2.5
NAAQS (35 (J,g/m3) and/or the annual PM2.5 NAAQS (12 (J,g/m3).
Specifically, the cumulative impact for comparison to the NAAQS should be based on
the combined modeled design concentration for primary PM2.5 impacts based on AERMOD (or
other acceptable preferred or approved alternative model) estimates of the proposed source's and
other nearby sources' direct PM2.5 emissions, the modeled secondary PM2.5 impacts (based on a
Tier 1 or 2 assessment accounting for the proposed source's PM2.5 precursor emissions), and the
monitored design value. The monitor should be representative, in that it accounts for secondary
PM2.5 formation associated with existing sources both within and outside of the modeling
domain, in addition to the background levels of primary PM2.5 associated with nearby and
background sources that are not included in the modeled inventory.
The recommendations provided above constitute a First Level analysis for PM2.5 NAAQS
compliance demonstrations. For applications where impacts from direct PM2.5 emissions are not
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temporally correlated with background PM2.5 levels, combining the modeled and monitored
levels as described above may be overly conservative in some situations. For example, there are
areas of the country where background PM2.5 levels are substantially higher on average during
the summer months as compared to the winter months; however, the predicted impacts from the
new or modified source may be substantially greater in the winter rather than in the summer. In
such cases, a Second Level modeling analysis may be advisable to account for these temporal
relationships. Such an analysis would involve combining the monitored and modeled PM2.5
concentrations on a seasonal (or quarterly) basis, as appropriate. The use of a seasonally-varying
monitored background component is likely to be a more important factor for the 24-hour PM2.5
NAAQS analysis than for the annual PM2.5 NAAQS. Careful evaluation of when model
projections of PM2.5 impacts and background PM2.5 levels peak throughout the year is
recommended before embarking on a Second Level modeling analysis. This is because the First
Level approach may already adequately capture the temporal correlation. As a part of this
process to determine the appropriate level of analysis, the permit applicant should consult with
the appropriate permitting authority and then reflect the appropriate approach in their modeling
protocol.
The AERMOD model provides several options for specifying the monitored background
concentration for inclusion in the cumulative impact assessment. The options that are most
relevant to PM2.5 analyses include:
For First Level 24-hour or annual PM2.5 NAAQS analyses, an option to specify a
single annual background concentration that is applied to each hour of the year,
and
For Second Level 24-hour PM2.5 NAAQS analyses, an option to specify four
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seasonal background values that are combined with modeled concentrations on a
seasonal basis.
The AERMOD model also allows the user to track the effect of background concentrations on
the cumulative modeled design concentration.
For Second Level 24-hour PM2.5 NAAQS modeling analyses, EPA recommends that the
distribution of monitored data equal to and less than the annual 98th percentile be appropriately
divided into seasons (or quarters) for each of the three years that are used to develop the
monitored design value. This will result in data for each year of the multi-year data, which
contains one season (or quarter) with the 98th percentile value and three seasons (quarters) with
maximum values which are less than or equal to the 98th percentile value. The maximum
concentration from each of the seasonal (or quarterly) subsets should then be averaged across
these three years of monitoring data. The resulting average of seasonal (or quarterly) maximums
should then be included as the four seasonal background values within the AERMOD model.
Therefore, the monitored concentrations greater than the 98th percentile in each of the three years
would not be included in the seasonal (or quarterly) subsets. These excluded monitored
concentrations are the same values that are excluded when determining the monitored design
value. An example of the calculations for a Second Level 24-hour PM2.5 NAAQS modeling
analysis is provided in Appendix D.
For a monitor with a daily (1-in-l day monitor) sampling frequency and 100% data
completeness, the highest seven monitored concentrations for each year should be excluded from
the seasonal (or quarterly) subdivided datasets. Similarly, for a monitor with every third day (1-
in-3 day monitor) sampling frequency and 100% data completeness, the highest two monitored
concentrations for each year should be excluded from the seasonal (or quarterly) subdivided
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datasets. The monitored concentrations excluded from the subdivided datasets could primarily
come from one or two seasons (or quarters) each year or could be evenly distributed across all
four seasons (or quarters) each year. Additionally, the monitored concentrations not included in
the subdivided datasets could shift seasonally (or quarterly) from one year to the next. Given the
reason for considering a Second Level 24-hour analysis (i.e., lack of temporal correlation
between modeled and monitored concentrations), it is likely that the monitored data greater than
the 98th percentile would be concentrated in one or two seasons as opposed to evenly distributed
throughout the year. As mentioned earlier, see Appendix N of 40 CFR part 50 in determining the
appropriate 98th percentile rank of the monitored data based on the monitor sampling frequency
and valid number of days sampled during each year.
The EPA does not recommend a "paired sums" approach on an hour-by-hour basis
because of the spatial and temporal variability throughout a typical modeling domain on an
hourly basis and the complexities and limitations of hourly observations from the current PM2.5
ambient monitoring network. The implicit assumption underlying this "paired sums' approach is
that the background monitored levels for each hour are spatially uniform and that the monitored
concentrations are fully representative of background levels at each receptor for each hour. Such
an assumption does not account for the many factors that contribute to the temporal and spatial
variability of ambient PM2.5 concentrations across a typical modeling domain on an hourly
basis.28 Furthermore, the pairing of daily monitored background and 24-hour average modeled
28 The complexity of the PM2.5 ambient monitoring network presents special challenges with a "paired sum"
approach that are not present with other NAAQS pollutants. The Federal Reference Method (FRM) PM2.5
monitoring network is based on 24-hour samples that are taken on average every third day at the l-in-3 day
monitors. The frequency of daily or 1-in-l day PM2.5 monitors is steadily increasing but is relatively limited to the
largest cities and metropolitan regions of the U.S. Various methods to "data fill" the l-in-3 day monitoring database
to create a pseudo-daily dataset have been explored in a few situations, but none of these data filling methods have
been demonstrated to create a representative daily PM2.5 dataset that the EPA would consider acceptable for
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concentrations is not recommended except in rare cases of relatively isolated sources where the
available 1 -in-1 day monitor can be shown to be representative of the ambient concentration
levels in the areas of maximum impact from the proposed new or modifying source. In most
cases, the seasonal (or quarterly) pairing of monitored and modeled concentrations previously
described in the Second Level approach should sufficiently address situations in which the
impacts from direct PM2.5 emissions are not temporally correlated with background PM2.5 levels.
Any monitor-model pairing approach aside from the First or Second Level methods should be
justified on a case-by-case basis in consultation with the appropriate permitting authority and the
appropriate EPA Regional Office.
IV.4 Determining Whether Proposed Source Causes or Contributes to Modeled
Violations
If the cumulative impact assessment following these recommendations results in
predicted violations of the O3 and/or PM2.5 NAAQS, then the permit applicant will need to
demonstrate that the proposed source's emissions do not cause or contribute to the modeled
NAAQS violations. In the SILs Guidance, the EPA explained that the permitting authority may
further evaluate whether the proposed source or modification will cause or contribute to
predicted violations by comparing the proposed source's modeled impacts, paired in time and
space with the predicted violations, to an appropriate SIL. The proposed source or modification
may be considered to not cause or contribute to predicted violations of the O3 or PM2.5 NAAQS
where the modeled impacts of the proposed source or modification at those particular times and
inclusion in a PM2.5 NAAQS compliance demonstration. The use of continuous PM2.5 monitors, which are more
limited in number compared to the FRM monitors and may require careful quality assurance of individual hourly
measurements, may be an option but should be discussed in advance with the appropriate permitting authority.
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locations are less than the appropriate O3 or PM2.5 NAAQS SIL. As explained in the SILs
Guidance, a permitting authority that chooses to use an O3 or PM2.5 SIL value to support a PSD
permitting decision should justify the value and its use in the administrative record for the
permitting action.
A demonstration that a proposed source or modification does not cause or contribute to a
predicted violation should be based on a comparison of the modeled concentrations (primary and
secondary impacts) at the receptor location(s) showing the violation(s) of the O3 or PM2.5
NAAQS to the appropriate O3 or PM2.5 NAAQS SIL. Considering the form of each NAAQS, the
following approaches are recommended:
For a predicted violation of the O3 NAAQS, the average of the predicted annual
(or episodic) fourth-highest daily maximum 8-hour averaged O3 concentrations at
the affected receptor(s) should be compared to an appropriate O3 NAAQS SIL,
e.g., SIL values recommended by EPA in the SILs Guidance (Table II-1).
For a predicted violation of the annual PM2.5 NAAQS, the average of the
predicted annual concentrations at the affected receptor(s) should be compared to
an appropriate PM2.5 annual NAAQS SIL, e.g., SIL values recommended by EPA
in the SILs Guidance (Table II.l).
For a predicted violation of the 24-hour PM2.5 NAAQS, the average of the
predicted annual 98th percentile 24-hour average concentrations at the affected
receptor(s) should be compared to an appropriate PM2.5 24-hour NAAQS SIL,
e.g., SIL values recommended by EPA in the SILs Guidance (Table II-1).
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V. PSD Compliance Demonstration for the PM2.5 Increments
As summarized in Section II of this guidance, CAA section 165(a)(3) requires that
proposed new and modified major stationary sources seeking a PSD permit demonstrate that
their proposed emissions increases will not cause or contribute to a violation of any NAAQS or
PSD increment. Consistent with the flow diagram presented in Figure II-2, this section describes
the EPA's recommendations for completing the required compliance demonstration for the PSD
increments for PM2.5.
V.l Overview of the PSD Increment System
This section provides an overview of the PSD increment system by defining basic terms,
such as increment, baseline concentration, baseline area, trigger date, minor source baseline date,
and major source baseline date. This section also introduces and discusses the concepts of
increment consumption and expansion.
V.l.l PSD Increments and Baseline Concentration
The term "increment" generally refers to what the CAA calls the "maximum allowable
increase over baseline concentrations" with respect to a criteria pollutant. CAA section 169(4)
defines "baseline concentration," generally, as "the ambient concentration levels which exist at
the time of the first application for a [PSD] permit for an area subject to this part... ,"29
Accordingly, an increment analysis is generally concerned with the emissions increases affecting
air quality in a particular PSD area after the date that the first complete PSD application is
29 EPA's regulations at 40 CFR 52.2l(b)( 14)(ii) provide that the application that determines the baseline
concentration is to be a complete PSD application. Hence, the term "complete application" will be used throughout
this section with regard to the minor source baseline date and increment consumption.
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submitted to the permitting authority.30 When comparing the ambient impact of such total
emissions increases against the increment value for a particular pollutant, a cumulative increase
in the ambient concentration of that pollutant that is greater than the increment generally is
considered "significant deterioration." When the cumulative impact analysis identifies significant
deterioration in this way, the permitting authority should determine whether the emissions
increase from the proposed new or modifying source will cause or contribute to the predicted
violation of the PSD increment.
Based on the statutory definition of baseline concentration, as described above, it is
conceptually possible to measure whether there will be significant deterioration in at least two
separate ways. The first way involves comparing a direct modeled projection of the change in air
quality caused by all increment-consuming and expanding emissions to the increment in the area
of concern (known as the baseline area, discussed below in Section V.1.2). The second approach
is to make a determination of whether the current monitored ambient air quality concentration in
the applicable baseline area, supplemented by the modeled impact of the proposed source, will
exceed an allowable ambient air quality ceiling. This latter approach requires comparing such
monitored concentration(s) to the sum of the increment and the baseline concentration for the
baseline area.
Historically, because of the lack of monitoring data to adequately represent the baseline
concentration combined with various other limitations associated with the use of ambient air
311 The EPA also considers emissions decreases occurring after the date of the first PSD application to affect
increment consumption to the extent that such decreases cause an improvement of air quality in the area of concern.
Thus, the concept of increment "expansion" is also discussed in this section.
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quality monitoring data for measuring increment consumption,31 the EPA has recommended that
the required increment analysis be based exclusively on the first approach, which models the
increment-related emissions increases or decreases to determine the resulting ambient air quality
change and compares this value with the increments for a particular pollutant.
V.1.2 PSD Baseline Area and Key Baseline Dates
In order to evaluate in a PSD permit review whether a PSD increment would be violated
by proposed construction of a stationary source, it is necessary to identify (1) the affected
geographic area in which the increment will be tracked and (2) the key baseline dates after which
emissions changes affect increment in that area. The relevant geographic area for determining the
amount of increment consumed is known as the baseline area. The baseline area is established
primarily on the basis of the location of the first major source to submit a complete PSD
application after an established "trigger date" (see discussion of key dates below) and may be
comprised of one or more areas that are designated as "attainment" or "unclassifiable" pursuant
to CAA section 107(d) for a particular pollutant within a state. In accordance with the regulatory
definition of baseline area at 40 CFR 52.21(b)(15), the area is an "intrastate area" and does not
include any area in another state.32 At a minimum, the baseline area is the attainment or
unclassifiable area in which the first PSD applicant after the trigger date proposes to locate, but
additional attainment or unclassifiable areas could be included in a particular baseline area when
31 The EPA described certain limitations associated with the use of ambient air quality monitoring data for
measuring increment consumption in the preamble to its proposed PSD regulations in 1979. For example, the CAA
provides that certain emissions changes should not be considered increment consuming. These limitations generally
continue to apply to the extent that certain emissions changes detected by an ambient monitor are not considered to
consume increment. See 44 Fed. Reg. 51924, 51944 (September 5, 1979).
32 While baseline dates are established on an intrastate basis, once a baseline area is established, emissions changes
from other states may contribute to the amount of increment consumed.
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the proposed source's modeled impact in any such additional areas exceeds certain
concentrations specified in the regulatory definition of baseline area (i.e., concentrations found in
40 CFR 52.21(b)(15)(i)). For PM2.5 this concentration is 0.3 [j,g/m3 on an annual average basis.
Once a baseline area has been established, subsequent PSD applications for sources located in
that area, or sources that could have a significant impact in that area, should rely on the baseline
date associated with that baseline area to determine whether the applicant's proposed emissions
increase, along with other increment-consuming emissions, would cause or contribute to an
increment violation. (See discussion on cumulative increment analysis in Section V.3.2 of this
guidance.)
Within any baseline area, the following three key dates are relevant when conducting the
required increment analysis: (1) trigger date; (2) minor source baseline date; and (3) major
source baseline date. The trigger date is a date fixed by regulation for each pollutant at 40 CFR
52.21(b)(14)(ii). The "minor source baseline date" in a newly established baseline area is the
earliest date after the applicable trigger date on which a proposed new or modified major source
submits a complete PSD application. The minor source baseline date for a baseline area or
adjacent baseline area may also be triggered based on the single source impacts greater than or
equal to specified values for NO2, SO2, PM10, or PM2.5.33 The minor source baseline date is the
date on which tracking of increment consumption must begin. Depending upon the number of
separate attainment and unclassifiable areas that exist for a particular pollutant in a state and the
timing of major source construction within the state, there may be a number of minor source
baseline dates that apply to different baseline areas established in that state. Beginning with the
PSD source whose complete application has established the minor source baseline date in a
33 See 40 CFR 52.2l(b)( 15)(i).
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particular area, any increase or decrease in actual emissions of the pollutant of concern occurring
after the minor source baseline date at any source (minor or major) that will affect air quality in
the baseline area will affect the amount of PSD increment consumed in that baseline area for that
pollutant (in the case of an emissions decrease, see discussion on increment expansion in Section
V.1.3 of this guidance, below).
Finally, the "major source baseline date" is a date fixed by regulation for each pollutant at
52.21(b)(14)(i) and precedes the trigger date. As further explained below, changes in emissions
resulting from construction at major stationary sources only that occur after the major source
baseline date, but before the minor source baseline date, will also affect increment. The
relationship of these three key dates with each other is further illustrated in Figure V-l.
Figure V-l. Determining Baseline Date(s) and When Increment Consumption Starts
Start ^
Major Source Baseline Date Trigger Date Minor Source Baseline Date
Date when actual emissions associated
with construction at major sources affect
increment
S02 and PM10 - 01/06/1975
NOx- 02/08/1988
PM2.s -10/20/2010
Earliest date after which the minor source
baseline date may be established
S02 and PM10 - 08/07/1977
NOx- 02/08/1988
PM2.s - 10/20/2011
Date when actual emissions changes from
all sources affect the available increment
Date of first complete PSD
permit application
Emissions changes occurring before the minor source baseline date generally do not
affect increment in an area (i.e., are not increment-consuming) but are considered to affect the
baseline concentration, which, as explained above, represents the ambient pollutant
concentration levels that exist at the time of the minor source baseline date, or the date of the
first complete application for a PSD permit in an area after the trigger date. However, as noted
above, the CAA provides an exception for certain emissions changes that occur specifically at
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major stationary sources as a result of construction34 that commences after the major source
baseline date. Specifically, for projects at major stationary sources on which construction
commenced on a date prior to the major source baseline date, the changes in emissions from such
projects affect the baseline concentration (not the amount of increment consumed) even if the
emissions change may not actually occur until after the major or minor source baseline dates.
Alternatively, for projects at major stationary sources on which construction commences after
the major source baseline date, the project emissions affect increment, even if the new or
modified source actually begins operation before the minor source baseline date.
V.1.3 PSD Increment Expansion
The "increment consumption" analysis allows permit applicants and permitting
authorities to take into account emissions reductions that occur in the baseline area of concern.
Such emissions reductions are generally said to result in the expansion of increment in the area;
however, not all emissions reductions truly result in an expansion of the increment. Some
emissions reductions, instead, result in a freeing up of increment that had previously been
consumed.
In the case of true "increment expansion," emissions in the area are allowed to increase
by the amount allowed by the original increment plus the amount of actual air quality
improvement (relative to the baseline concentration) achieved by the reduction of emissions
34 CAA section 169(2)(C) indicates that the term "construction," when used in connection with any source or
facility, includes modifications defined in CAA section 111(a)(4). "Modification" is defined at section 111(a)(4) to
mean "any physical change in, or change in the method of operation of a stationary source which increases the
amount of any air pollutant emitted by such source or which results in the emission of any air pollutant not
previously emitted."
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because of its relationship to the established baseline dates for the area.35 In such cases, it is
appropriate to model the actual emissions decrease as a negative amount to effectively lower the
baseline concentration to simulate the expansion of the increment.
On the other hand, in cases where a source's emissions contribute to the amount of
increment consumed, a reduction in such increment-consuming emissions at some later date
results in some amount of the consumed increment being freed up. That is, the resulting air
quality improvement is now available for a source to increase its emissions within the limits of
the original increment level. A subsequent reduction in increment-consuming emissions should
not be modeled as a negative value to determine the amount of increment that has been freed up;
instead, such emissions reductions are simply no longer counted in the increment consumption
analysis.
V.2 PSD PM2.5 Increments
In 2010, the EPA established the PM2.5 increments at the levels shown in Table V-l
through the final rule entitled "Prevention of Significant Deterioration (PSD) for Particulate
Matter Less Than 2.5 Micrometers (PM2.5) - Increments, Significant Impact Levels (SILs) and
Significant Monitoring Concentration (SMC)"36 This 2010 rule established October 20, 2011, as
the trigger date and October 20, 2010, as the major source baseline date for PM2.5 increments.
35 The concept of increment expansion is derived from CAA section 163(a), which provides that a PSD applicant
must assure "that maximum allowable increases over baseline concentrations ... shall not be exceeded." [Emphasis
added.] The target for determining significant deterioration thus becomes the ambient concentration resulting from
the sum of the increment and the baseline concentration. When a decrease in emissions that contributed to the
baseline concentration occurs, an emissions increase that simply "restores" the air quality to the baseline
concentration in a particular baseline area can be allowed, regardless of the amount of increment otherwise being
consumed.
36 See 75 FR 64864 (Oct. 20, 2010).
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The EPA developed the increment system for PM2.5 generally following the same concepts that
were previously applied for development of the increments for PM10, SO2, and nitrogen dioxide
(NO2). As explained above, the framework reflects the statutory concepts set forth in the
statutory definition of baseline concentration that was explained in Section V.l of this guidance.
Table V-l. PM2.5 Increments
Class I
Class II
Class III
3
Increments, |Jg/m
1
4
8
2
9
18
Source: Prevention of Significant Deterioration (PSD) for Particulate Matter Less Than 2.5 Micrometers (PM2.5) - Increments,
Significant Impact Levels (SILs) and Significant Monitoring Concentration (SMC) final rule (75 FR 64864)
The obvious difference between an increment analysis and the NAAQS analysis for
PM2.5 is that the increment analysis is concerned with the degree of change in air quality caused
by a new or modified PSD source rather than the impact of that source on overall air quality (as
defined by the applicable NAAQS) in the area of concern (baseline area). With this in mind, it
should be noted here that an increment analysis is relevant only to the extent that NAAQS
compliance has been ensured. That is, an adequate air quality analysis demonstrating compliance
with the statutory requirements must ensure that the proposed PSD source's emissions will not
cause or contribute to either the NAAQS or PSD increments.37
Another key difference involves the modeling inventory from which the necessary
emissions data is derived. That is, only sources that have PM2.5 emissions (direct and precursor)
that affect the amount of increment consumed in the area of concern should be included in the
37 CAA section 163(b)(4) provides that the maximum allowable concentration of any air pollutant allowed in an area
shall not exceed the concentration allowed by the primary or secondary NAAQS.
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modeling inventory for the increment analysis. Moreover, from such sources, only those specific
emissions changes that affect increment should be included in the actual modeling analysis.
The cumulative impact analysis for PM2.5 increments is also different and based on the
actual emission changes occurring at existing sources in the baseline area after the pertinent
baseline dates (i.e., major and minor source baseline dates), whereas NAAQS analyses are
generally based on the cumulative impact associated with the maximum allowable emissions
from the new or modifying source and other nearby sources (with specific provisions for
operating levels of nearby sources). Furthermore, ambient monitoring data, while useful for
establishing background concentration for the NAAQS analysis, may not be particularly useful
for the typical increment analysis. The limitations associated with using monitoring data for an
increment analysis are discussed in greater detail in Sections V.l and V.3 of this guidance.
It is also important to note that the PM2.5 NAAQS and increments for the 24-hour
averaging period are defined in different forms and therefore must be analyzed differently.38 The
24-hour PM2.5 NAAQS is defined based on the 3-year average of the annual 98th percentile of the
24-hour average concentrations, while the 24-hour PM2.5 increments are based on the second
highest maximum 24-hour concentration.
V.3 PSD Compliance Demonstration for the PM2.5 Increments
The initial steps for the PM2.5 increment analysis, which include the determination of the
allowable emissions increases to model in the source impact analysis and a comparison of the
modeled impacts against the appropriate PM2.5 SILs, may rely, in part, upon the results derived
from the PM2.5 NAAQS analysis described in Sections III and IV of this guidance. Moreover, the
38 The annual NAAQS and increments for PM2.5 are both measured as annual arithmetic mean values.
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technical approach involving the options and alternatives agreed upon for estimating secondary
PM2.5 impacts and combining primary and secondary PM2.5 impacts for the NAAQS analysis
may also be relevant for completing the PM2.5 increment analysis to determine whether the
allowable emissions increase(s) from the proposed source or modification will cause or
contribute to any increment violation.
V.3.1 PM2.5 Increments: Source Impact Analysis
The EPA's recommendations on how to complete the required compliance demonstration
for the PM2.5 PSD increments are based upon the same assessment cases detailed in Section II.4
for PM2.5 NAAQS. As shown in Table V-2, a modeled compliance demonstration is not required
for Case 1 since neither direct PM2.5 emissions nor PM2.5 precursor (NOx or SO2) emissions are
equal to or greater than the respective SERs. Case 1 is the only assessment case that does not
require a modeled compliance demonstration for PM2.5, whereas Case 2 requires a source impact
analysis that should be conducted following the detailed recommendations provided in previous
sections for a NAAQS analysis.
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Table V-2. EPA Recommended Approaches for Assessing Primary and Secondary PM2.5
Impacts by Assessment Case
Assessment
Case
Description of Assessment Case
Primary Impacts
Approach
Secondary Impacts
Approach*
Case 1:
No Air Quality
Analysis
Direct PM2.5 emissions < 10 tpy SER
and
NOx emissions and SO2 emissions < 40 tpy SER
N/A
N/A
Case 2:
Primary and
Secondary Air
Quality
Impacts
Direct PM2.5 emissions > 10 tpy SER
or
NOx emissions or SO2 emissions > 40 tpy SER
Appendix W
preferred or
approved
alternative
dispersion model
Include both precursor of
PM2.5. see Section II.2.
Tier 1 Approach
(e.g.. MERPs)
Tier 2 Approach
(e.g.. Chemical
Transport Modeling)
* In unique situations (e.g.. in parts of Alaska where photochemistry is not possible for portions of the year), it may be
acceptable for the applicant to rely upon a qualitative approach to assess the secondary impacts. Any qualitative assessments
should be justified on a case-by-case basis in consultation with the appropriate EPA Regional Office or other applicable
permitting authority.
A modeling analysis based solely on the PSD applicant's proposed emissions increase
{i.e., source impact analysis) that does not predict an ambient impact equal to or greater than the
appropriate PM2.5 SIL at any location generally will satisfy the requirement for a demonstration
that the source will not cause or contribute to a violation of the PM2.5 increments.
In light of the relatively recent establishment of the fixed dates (i.e., major source
baseline date and trigger date) associated with the PM2.5 increments (compared to comparable
fixed dates for other PSD increments), and the possibility that the minor source baseline date for
a particular area has not yet been set, a proposed new or modified source being evaluated for
compliance with the PM2.5 increments in a particular area may be the first source in the area with
increment-consuming emissions. As indicated in Figure II-2, under this situation, a permitting
authority may have a sufficient basis to conclude that the PM2.5 impacts of the new or modified
PSD source, although greater than the appropriate PM2.5 SILs, may be compared directly to the
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allowable PM2.5 increments without the need for a cumulative analysis (described in Section
V.3.2 of this guidance below). Reliance on this first-in source impact analysis (rather than a
source or cumulative impact analysis that is compared to the appropriate PM2.5 SILs) likely
would be appropriate to assess the amount of increment consumed when the proposed new or
modified source represents the first complete PSD application since the trigger date, thus
establishing the baseline concentration in the area, and there has been no other major source
construction since the major source baseline date.
V.3.2 PM2.5 Increments: Cumulative Analysis
Where the source impact analysis described above is insufficient to show that a proposed
PSD source will not cause or contribute to a violation of the PM2.5 PSD increments, a cumulative
impact assessment is necessary to complete the required increment analysis. A cumulative
assessment of increment consumption accounts for the combined impacts of the following:
1. Direct and precursor allowable emissions from the proposed new or modifying
source;
2. Direct and precursor actual emissions changes that have occurred at existing sources
(including the existing source at which a major modification is being proposed, where
applicable) since the minor source baseline date for the proposed source's baseline
area;
3. Direct and precursor actual emissions from any major stationary source on which
construction commenced after October 20, 2010 (major source baseline date for
PM2.5); and
4. Direct and precursor allowable emissions of permitted sources that are not yet fully
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operative.39
Unlike the guidance provided for the cumulative NAAQS analysis for PM2.5 in Section
IV, it is not typically practical to utilize ambient monitoring data to represent any portion of the
impacts that affect the PM2.5 increments. Therefore, it is usually necessary to model the
applicable emissions from any existing source that will be considered to consume a portion of
the PM2.5 increments in the baseline area(s) of concern. As part of the determination of which
existing sources should be included in the cumulative analysis, it will be necessary to identify the
total area in which a significant impact from the new or modified PSD source will occur. A new
or modified source with an extensive impact area may affect more than one existing baseline
area. Once the affected area has been defined, and the associated minor source baseline dates
have been taken into account, the potential sources can be selected from which increment-
consuming emissions must be quantified. Existing sources whose actual emissions have not
changed substantially since the applicable baseline date may not need to be included for purposes
of increment consumption since, as previously explained, increment is consumed by increases in
actual emissions that occur from existing sources after the baseline date. It is highly
recommended that the PSD applicant work closely with the permitting authority to determine the
existing sources (including newly permitted sources) of direct PM2.5 and precursor emissions that
should be included in the modeling inventory for the increment analysis. Also, if there is reason
to believe that an existing source's actual emissions have decreased since the applicable baseline
date, the PSD applicant may want to check with the permitting authority to ascertain whether the
authority allows for increment expansion to be considered.
39 Regarding the use of allowable emissions, see 40 CFR 52.21(b)(21)(iv).
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Once the modeling inventory for the increment analysis has been developed and
approved, and the increment-consuming emissions have been determined, the modeled
cumulative impacts resulting from the increases and decreases in emissions are then compared to
the PM2.5 increments to determine whether any increment violations will result. This section
provides recommendations on conducting an appropriate cumulative impact assessment for
PM2.5 increments.
V.3.2.1 Assessing Primary PM2.5 Impacts
As explained in Section III.3 of this guidance, the assessment of primary PM2.5 impacts
from the proposed new or modifying PSD source is essentially the same for the PM2.5 NAAQS
and increments. In both cases, the permit applicant must account for the impacts from the
proposed new or modifying source's allowable emissions increase of direct PM2.5.
To assess the impact of direct PM2.5 emissions from existing increment-consuming
sources, actual emissions increases that have occurred since the applicable minor source baseline
date should generally be modeled. Alternatively, existing source impacts from direct PM2.5
emissions may be conservatively modeled using an existing source's allowable emissions where
the PSD applicant determines that such emissions are more readily available and especially when
such allowable emissions are not expected to contribute substantially to the amount of increment
consumed. In the event that an applicant chooses to conduct the cumulative analysis using
allowable emissions and identifies potential problems concerning increment consumption, the
PSD applicant may then rely on more refined data that better represent a particular source's
actual emissions.
The PM2.5 increments analysis should follow the traditional approach involving modeling
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only direct PM2.5 emissions changes that affect the increment and should be based on application
of AERMOD (or other acceptable preferred or approved alternative model), using actual
emission changes associated with any increment-consuming or increment-expanding sources.
The AERMOD model allows for inclusion of these emissions (represented as negative emissions
for the sources expanding increment)40 in the same model run that includes the allowable
increase in emissions from the proposed source and will, therefore, output the net cumulative
concentrations at each receptor established for the modeling domain.41
V.3.2.2 Assessing Secondary PM2.5 Impacts
To assess the secondary impacts from changes in PM2.5 precursor emissions from the new
or modified source, as well as from other increment-consuming sources, the EPA recommends
the analysis for each applicable precursor of PM2.5 be conducted collectively based on the two-
tiered demonstration approach outlined in EPA's 2017 Guideline.
In recent years, several rules promulgated by the EPA have resulted in control
requirements that have significantly reduced NOx and SO2 precursor emissions affecting ambient
PM2.5 concentrations in many areas.42 This is particularly true in the eastern U.S. As a result, in
some cases, the secondary PM2.5 impacts may be addressed by a demonstration that provides
ambient monitoring data that generally confirms a downward trend in precursor emissions
occurring after the applicable PM2.5 minor source baseline date (or the major source baseline
411 See discussion about increment expansion in Section V. 1.3 of this guidance.
41 The "maximum" cumulative impacts will be output as zero if the cumulative impacts computed in the model are
less than zero).
42 Such rules include the following: the Clean Air Interstate Rule (CAIR) Final Rule, 70 FR 25162 (May 12, 2005);
CSAPR Final Rule, 76 FR 48208 (August 8, 2011); CSAPR Update for the 2008 Ozone NAAQS (CSAPR Update)
Final Rule, 81 FR 74504 (October 26, 2016); and the Mercury and Air Toxics Standards Rule (MATS), 77 FR 9304
(February 16,2012).
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date). If it can be confirmed that such emissions reductions have occurred in a particular baseline
area, it may be possible to complete the PM2.5 increments modeling analysis simply by focusing
on potential increment consumption associated with direct PM2.5 emissions. For areas where
PM2.5 precursor emission increases from other increment-consuming sources have occurred since
the major or minor source baseline dates, and are, thus, likely to have added to PM2.5
concentration increases within the baseline area (and, thus, consume PM2.5 increment), the Tier 1
and Tier 2 assessment approaches based on CTMs (using the emissions input data applicable to
increment analyses) discussed in Section III of this guidance may be appropriate for estimating
the portion of PM2.5 increment consumed due to secondary PM2.5 impacts associated with those
increases in precursor emissions.
V.4. Determining Whether a Proposed Source Will Cause or Contribute to an Increment
Violation
When a proposed PSD source predicts, through a cumulative impact analysis, that a
modeled violation of any PM2.5 increment will occur within the baseline area of concern, a closer
examination of the proposed source's individual impact(s) at the violating receptor(s) and the
time(s) of modeled violation become important considerations. The EPA's longstanding policy is
to consider a proposed PSD source to cause or contribute to an increment violation if its impact
(primary and secondary) is significant (equal to or greater than the appropriate PM2.5 SIL) at the
location and time of the modeled violation.43 Accordingly, if a source can demonstrate to the
43 See, e.g., 43 FR 26380 at 26401, June 19, 1978; EPA memo titled "Interpretation of 'Significant Contribution,'"
December 16, 1980; EPA memo titled "Air Quality Analysis for Prevention of Significant Deterioration," July 5,
1988; and more recently, EPA memo titled "Guidance on Significant Impact Levels for Ozone and Fine Particles in
the Prevention of Significant Deterioration Permitting Program," April 17, 2018, Attachment at page 18 ("If the
modeled impact is below the recommended SIL value at the violating receptor during the violation, the EPA
believes this will be sufficient in most cases for a permitting authority .. .to conclude that the source does not cause
or contribute to.. .the predicted violation.")(Emphasis added).
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satisfaction of the permitting authority that significant impacts attributable to the source do not
occur at the location and time of any modeled violation,44 the proposed source or modification
generally may be considered to not cause or contribute to an increment violation. In cases where
a proposed PSD source models impacts that equal or exceed the appropriate PM2.5 SIL and
would cause a new violation of any PM2.5 increment, it is the EPA's longstanding policy to allow
the PSD applicant to obtain sufficient offsets, in the form of emissions reductions internally or
from another existing source, to avoid causing the predicted violation at each affected receptor
where (and when) a violation is modeled. In an area where a proposed PSD source would cause
or contribute to an existing increment violation(s), the PSD source must not be approved for
construction unless such existing violation(s) is entirely corrected at each affected receptor prior
to the operation of the proposed source.45
44 The difficulties associated with combining primary and secondary impacts spatially and temporally were
described in Sections III and IV of this guidance. In the case of a PM2.5 increment analysis, as with the PM2.5
NAAQS analysis, the applicant and permitting authority will need to agree upon an approach that best satisfies the
required compliance demonstration.
45 See. e.g., 43 FR 26380 at 26401, June 19, 1978; 45 FR 52676 at 52678, August 7, 1980; and EPA memo titled
"Air Quality Analysis for Prevention of Significant Deterioration," July 5, 1988. (".. .for any increment violation
(new or existing) for which the proposed source has a significant impact, the permit should not be approved unless
the increment violation is corrected prior to operation of the proposed source.) Note that this policy for the PSD
increments differs from the policy for sources that contribute to an existing NAAQS violation, for which the
proposed sources needs only compensate for its own adverse impact on the NAAQS violation in accordance with 40
CFR 51.165(b)(3).
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VI. References
Baker, K.R., Kelly, J.T., 2014. Single source impacts estimated with photochemical model
source sensitivity and apportionment approaches. Atmospheric Environment 96, 266-274.
Baker, K.R., Kotchenruther, R.A., Hudman, R.C., 2015. Estimating Ozone and Secondary
PM2.5 Impacts from Hypothetical Single Source Emissions in the Central and Eastern
United States. Atmospheric Pollution Research 7, 122-133.
Bergin, M., Russell, A., Odman, T., Cohan, D., and Chameldes, W., 2008. Single Source Impact
Analysis Using Three-Dimensional Air Quality Models. Journal of the Air & Waste
Management Association. 2008; 58, 1351-1359.
Byun and Schere, 2006: Review of the governing equations, computational algorithms, and other
components of the models-3 Community Multiscale Air Quality (CMAQ) modeling
system. D. Byun and K. Schere. Applied Mechanics Reviews. 2006; 59, 51-77.
Cai, C., Kelly, J.T., Avise, J.C., Kaduwela, A.P., Stockwell, W.R., 2011. Photochemical
modeling in California with two chemical mechanisms: model intercomparison and
response to emission reductions. Journal of the Air & Waste Management Association 61,
559-572.
Chen, J., Lu, J., Avise, J.C., DaMassa, J.A., Kleeman, M.J., Kaduwela, A.P., 2014. Seasonal
modeling of PM 2.5 in California's San Joaquin Valley. Atmospheric Environment 92,
182-190.
Civerolo, K, Hogrefe, C., Zalewsky, E., Hao, W., Sistla, G., Lynn, B., Rosenzweig, C., Kinney,
P.L., 2010. Evaluation of an 18-year CMAQ simulation: Seasonal variations and long-
term temporal changes in sulfate and nitrate. Atmospheric Environment 44, 3745-3752.
ENVIRON, 2012a. Comparison of Single-Source Air Quality Assessment Techniques for
Ozone, PM2.5, other Criteria Pollutants and AQRVs, EPA Contract No: EP-D-07-102.
September 2012. 06-20443M6.
ENVIRON, 2012b. Evaluation of Chemical Dispersion Models Using Atmospheric Plume
Measurements from Field Experiments, EPA Contract No: EP-D-07-102. September
2012. 06-20443M6.
Hogrefe, C., Hao, W., Zalewsky, E., Ku, J.-Y., Lynn, B., Rosenzweig, C., Schultz, M., Rast, S.,
Newchurch, M., Wang, L., 2011. An analysis of long-term regional-scale ozone
simulations over the Northeastern United States: variability and trends. Atmospheric
Chemistry and Physics 11, 567-582.
Kelly, J.T., Baker, K.R., Napelenok, S.L., Roselle, S.J., 2015. Examining Single-Source
Secondary Impacts Estimated from Brute-force, Decoupled Direct Method, and
Advanced Plume Treatment Approaches. Atmospheric Environment 111, 10-19.
NACAA, 2011: PM2.5 Modeling Implementation for Projects Subject to National Ambient Air
Quality Demonstration Requirements Pursuant to New Source Review. Report from
NACAA PM2.5 Modeling Implementation Workgroup dated January 7, 2011.
Washington, District of Columbia 20001.
https://gaftp.epa.gov/Air/aamg/SCRAM/conferences/2012 10th Conference On Air O
ualitv Modeling/Review Material/01072011-
NACAAPM2.5ModelingWorkgroupReport-FINAL.pdf.
NARSTO, 2004. Particulate Matter Assessment for Policy Makers: A NARSTO Assessment. P.
McMurry, M. Shepherd, and J. Vickery, eds. Cambridge University Press, Cambridge,
England. ISBN 0 52 184287 5.
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Ramboll Environ, 2018. User's Guide Comprehensive Air Quality Model with Extensions
version 6. ENVIRON International Corporation, Novato, CA. http://www.camx.com.
Russell, 2008: EPA Supersites Program-related emissions-based particulate matter modeling:
Initial applications and advances. A. Russell. Journal of the Air & Waste Management
Association. 2008; 58, 289-302.
Tesche, T., Morris, R., Tonnesen, G., McNally, D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx
annual 2002performance evaluation over the eastern US. Atmospheric Environment 40,
4906-4919.
U.S. EPA, 1990. New Source Review Workshop Manual: Prevention of Significant
Deterioration and Nonattainment Area Permitting - DRAFT. U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711.
https://www.epa.gov/sites/default/files/2015-07/documents/1990wman.pdf.
U.S. EPA, 1992. Protocol for Determining the Best Performing Model. September 1992. EPA-
454/R-92-025. U.S. Environmental Protection Agency, Research Triangle Park, North
Carolina 27711. https://www.epa.gov/sites/production/files/2020-
10/documents/model eval protocol.pdf.
U.S. EPA, 2004. User's Guide to the Building Profile Input Program. EPA-454/R-93-038. U.S.
Environmental Protection Agency, Research Triangle Park, North Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/related/bpip/bpipdup.pdf.
U.S. EPA, 2005. Guideline on Air Quality Models. 40 CFR part 51 Appendix W (70 FR 68218,
Nov. 9, 2005). https://www.epa.gov/sites/default/files/2020-09/documents/appw 05.pdf.
U.S. EPA, 2007a. Guidance on the Use of Models and Other Analyses for Demonstrating
Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. April 2007. EPA-
454/B-07-002. U.S. Environmental Protection Agency, Research Triangle Park, North
Carolina 27711. https://www.epa.gov/sites/production/files/2020-10/documents/final-03-
pm-rh-guidance.pdf.
U.S. EPA, 2007b. Details on Technical Assessment to Develop Interpollutant Trading Ratios for
PM2.5 Offsets. Technical Analysis dated July 23, 2007. U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina 27711.
U.S. EPA, 2010. Modeling Procedures for Demonstrating Compliance with PM2.5 NAAQS.
Stephen Page Memorandum dated March 23, 2010. U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina, 27711.
https://www.epa.gov/sites/default/files/2015-07/documents/pm25memo.pdf.
U.S. EPA, 201 la. AERSCREEN Released as the EPA Recommended Screening Model. Tyler
Fox Memorandum dated April 11, 2011. U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina 27711.
https://www.epa.gov/sites/production/files/2020-
10/documents/20110411 aerscreen release memo.pdf.
U.S. EPA, 201 lb. Revised Policy to Address Reconsideration of Interpollutant Trading
Provisions for Fine Particles (PM2.5). Gina McCarthy Memorandum dated July 21, 2011.
U.S. Environmental Protection Agency, Washington, District of Columbia 20460.
https://www.epa.gov/sites/default/files/2020-10/documents/pm25trade.pdf.
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U.S. EPA, 201 lc. Additional Clarification Regarding Application of Appendix W Modeling
Guidance for the 1-hour NO2 National Ambient Air Quality Standard. Tyler Fox
Memorandum dated March 1, 2011. U.S. Environmental Protection Agency, Research
Triangle Park, North Carolina 27711. https://www.epa.gov/sites/production/files/202Q-
10/documents/additional clarifications appendixw hourly-no2-naaqs final 03-01-
2011.pdf.
U.S. EPA, 2012. Sierra Club Petition Grant. Gina McCarthy Administrative Action dated
January 4, 2012. U.S. Environmental Protection Agency, Washington, District of
Columbia 20460.
https://gaftp.epa.gov/Air/aqmg/SCRAM/conferences/2012 10th Conference On Air Q
ualitv Modeling/Review Material/Sierra Club Petition OAR-ll-0Q2-1093.pdf.
U.S. EPA, 2014. Guidance for PM2.5 Modeling. May 20, 2014. Publication No. EPA-454/B-14-
001. Office of Air Quality Planning and Standards, Research Triangle Park, NC.
https://www.epa.gov/sites/production/files/2020-
09/documents/guidance for pm25 permit modeling.pdf.
U.S. EPA, 2015. AERMINUTE User's Guide. U.S. Environmental Protection Agency, EPA-
454/B-15-006. Research Triangle Park, North Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/met/aerminute/aerminute userguide.pdf.
U.S. EPA, 2016. Guidance on the use of models for assessing the impacts of emissions from
single sources on the secondarily formed pollutants ozone and PM2.5. Publication No.
EPA 454/R-16-005. Office of Air Quality Planning and Standards, Research Triangle
Park, NC. https://www.epa.gov/sites/production/files/2020-09/documents/epa-454 r-16-
005.pdf.
U.S. EPA, 2017a. Guideline on Air Quality Models. 40 CFR part 51 Appendix W (82 FR 5182,
Jan. 17, 2017). https://www.epa.gov/sites/production/files/202Q-
09/documents/appw 17.pdf.
U.S. EPA, 2017b. Use of Photochemical Grid Models for Single-Source Ozone and secondary
PM2.5 impacts for Permit Program Related Assessments and for NAAQS Attainment
Demonstrations for Ozone, PM2.5 and Regional Haze. Tyler Fox Memorandum dated
August 4, 2017. U.S. Environmental Protection Agency, Research Triangle Park, North
Carolina 27711. https://www.epa.gov/sites/production/files/202Q-
10/documents/20170804-photochemical grid model clarification memo.pdf.
U.S. EPA, 2018a. Guidance on Significant Impact Levels for Ozone and Fine Particles in the
Prevention of Significant Deterioration Permitting Program. Office of Air Quality
Planning and Standards, Research Triangle Park, NC.
https://www.epa.gov/nsr/significant-impact-levels-ozone-and-fine-particles.
U.S. EPA, 2018b. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA-
454/B-16-012. U.S. Environmental Protection Agency, Research Triangle Park, North
Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/related/aermap/aermap userguide v!80
81.pdf.
U.S. EPA, 2019. Guidance on the Development of Modeled Emission Rates for Precursors
(MERPs) as a Tier 1 Demonstration Tool for Ozone and PM2.5 under the PSD
Permitting Program. Publication No. EPA 454/R-19-003. Office of Air Quality Planning
and Standards, Research Triangle Park, NC.
https://www.epa.gov/sites/production/files/2020-09/documents/epa-454 r-19-003.pdf.
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U.S. EPA, 2020: AERSURFACE User's Guide. EPA-454/B-20-008. U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/related/aersurface/aersurface ug v2006
O.pdf.
U.S. EPA, 2021a. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA-454/B-
21-001. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/preferred/aermod/aermod userguide.pdf.
U.S. EPA, 2021b. User's Guide for the AERMOD Meteorological Preprocessor (AERMET).
EPA-454/B-21-004. U.S. Environmental Protection Agency, Research Triangle Park, NC
27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/met/aermet/aermet userguide.pdf.
U.S. EPA, 2021c. AERSCREEN User's Guide. EPA-454/B-21-005. U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/screening/aerscreen/aerscreen userguide
.pdf.
U.S. EPA, 202Id. AERMOD Implementation Guide. EPA-454/B-21-006. U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/preferred/aermod/aermod implementati
on guide.pdf.
Yarwood, G., Scorgie, Y., Agapides, N, Tai, E., Karamchandani, P., Due, H., Trieu, T., Bawden,
K., 2011. Ozone Impact Screening Method for New Sources Based on High-order
Sensitivity Analysis of CAMx Simulations for NSW Metropolitan Areas.
Zhou, W., Cohan, D.S., Pinder, R.W., Neuman, J.A., Holloway, J.S., Peischl, J., Ryerson, T.B.,
Nowak, J.B., Flocke, F., Zheng, W.G., 2012. Observation and modeling of the evolution
of Texas power plant plumes. Atmospheric Chemistry and Physics 12, 455-468.
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Appendix A: Draft Conceptual Description of O3 and PM2.5 Concentrations in the U.S.
This appendix provides a brief summary of the current O3 and PM2.5 monitoring
networks. It also characterizes O3 and PM air quality in terms of their precursor emissions and
chemical composition, concentration levels, and spatial and temporal patterns across the nation
based on the ambient data and analyses contained in the EPA's "Integrated Science Assessment
for Ozone and Related Photochemical Oxidants,"46 "The Particle Pollution Report,"47 and
"Particulate Matter Staff Paper."48 Such information may be useful for permit applicants in
preparing conceptual descriptions, as discussed in this guidance. Permit applicants also
encouraged to reference the EPA's "Air Quality Trends" website at https://www.epa.gov/air-
trends for the current O3 and PM2.5 trends and design values.
Conceptual Descriptions of O3
1. O3 Monitoring Networks
To monitor compliance with the NAAQS, state, local, and tribal environmental agencies
operate O3 monitoring sites at various locations, depending on the population of the area and
typical peak O3 concentrations. In 2015, there were over 1,300 O3 monitors reporting O3
concentration data to EPA. All monitors that currently report O3 concentration data to the EPA
use ultraviolet Federal Equivalent Methods (FEMs). Since the highest O3 concentrations tend to
be associated with particular seasons for various locations, EPA requires O3 monitoring during
specific monitoring seasons which vary by state. The O3 monitoring seasons for each state are
listed in Appendix D to 40 CFR part 58.
Figure A-l shows the locations of all U.S. ambient O3 monitoring sites reporting data to
EPA during the 2013-2015 period. The gray dots represent State and Local Ambient Monitoring
Stations (SLAMS) which are operated by state and local governments to meet regulatory
requirements and provide air quality information to public health agencies. SLAMS monitors
make up about 80 percent of the ambient O3 monitoring network in the U.S. The minimum
monitoring requirements to meet the SLAMS O3 network design criteria are specified in
Appendix D to 40 CFR part 58. The requirements are based on both population and ambient
concentration levels for each Metropolitan Statistical Area (MSA). At least one site for each
MSA must be designed to record the maximum concentration for that particular area. The blue
dots highlight two important subsets of monitoring sites within the SLAMS network: the
"National Core" (NCore) network, which consists of about 80 monitoring sites that collect multi-
46 U.S. Environmental Protection Agency (2013). Integrated Science Assessment for Ozone and Related
Photochemical Oxidants. U.S. Enviromnental Protection Agency, Research Triangle Park, NC. EPA/600/R-10/076
(2013 ISA), section 3.2.2 found at https://cfpub.epa.gov/ncea/isa/recordisplav.cfm?deid=247492.
47 The Particle Pollution Report: Current Understanding of Air Quality and Emissions through 2003.
https://www.epa.gov/sites/production/files/2017-l 1/documents/pp report 2003.pdf.
48 Particulate Matter Staff Paper: Review completed in 2012. https://www.epa.gov/naaas/particulate-matter-pm-air-
aualitv-standards-documents-review-completed-2012.
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pollutant measurements on a year-round basis, and the "Photochemical Assessment Monitoring
Stations" (PAMS) network, which consists of about 75 monitoring sites that collect summertime
measurements of various precursor gases involved O3 formation.
The green dots in Figure A-l represent O3 monitoring sites in the Clean Air Status and
Trends Network (CASTNet) which are mostly located in rural areas. There were about 80
CASTNet sites reporting data to EPA in 2015, with sites in the eastern U.S. generally being
operated by the EPA, and sites in the western U.S. generally being operated by the National Park
Service (NPS).
Finally, the black dots in Figure A-l represent "Special Purpose" (SPM) monitoring sites,
which generally collect data for research studies, public health reporting, or other non-regulatory
purposes, and all other O3 monitoring sites which includes monitors operated by tribes, industry,
and other federal agencies such as the U.S. Forest Service (USFS).
Figure A-l. Locations of U.S. Ambient O3 Monitoring Sites in 2013-2015
2. O3 Precursor Emissions and Atmospheric Chemistry
O3 is formed by photochemical reactions of precursor gases and is not directly emitted
from specific sources. In the stratosphere, O3 occurs naturally and provides protection against
harmful solar ultraviolet radiation. In the troposphere, near ground level, O3 forms through
atmospheric reactions involving two main classes of precursor pollutants: volatile organic
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compounds (VOCs) and nitrogen oxides (NOx). Carbon monoxide (CO) and methane (CH4) are
also important for O3 formation over longer time periods.49
Emissions of O3 precursor compounds can be divided into anthropogenic and natural
source categories, with natural sources further divided into biogenic emissions (from vegetation,
microbes, and animals) and abiotic emissions (from biomass burning, lightning, and geogenic
sources). Anthropogenic sources, including mobile sources and power plants, account for the
majority of NOx and CO emissions. Anthropogenic sources are also important for VOC
emissions, though in some locations and at certain times of the year (e.g., southern states during
summer), the majority of VOC emissions come from vegetation.50 In practice, the distinction
between natural and anthropogenic sources is often unclear, as human activities directly or
indirectly affect emissions from what would have been considered natural sources during the
preindustrial era. Thus, emissions from plants, animals, and wildfires could be considered either
natural or anthropogenic, depending on whether emissions result from agricultural practices,
forest management practices, lightning strikes, or other types of events.51
Rather than varying directly with emissions of its precursors, O3 changes in a nonlinear
fashion with the concentrations of its precursors. NOx emissions lead to both the formation and
destruction of O3, depending on the local quantities of NOx, VOC, radicals, and sunlight. In
areas dominated by fresh emissions of NOx, radicals are removed, which lowers the O3
formation rate. In addition, the scavenging of O3 by reaction with NO is called "titration" and is
often found in downtown metropolitan areas, especially near busy streets and roads, as well as in
power plant plumes. This short-lived titration results in localized areas in which O3
concentrations are suppressed compared to surrounding areas, but which contain NO2 that adds
to subsequent O3 formation further downwind. Consequently, O3 response to reductions in NOx
emissions is complex and may include O3 decreases at some times and locations and increases of
O3 at other times and locations. In areas with relatively low NOx concentrations, such as those
found in remote continental areas and rural and suburban areas downwind of urban centers, O3
production typically varies directly with NOx concentrations (e.g., decreases with decreasing
NOx emissions). The NOx titration effect is most pronounced in urban core areas which have
higher volume of mobile source NOx emissions from vehicles than do the surrounding areas. It
should be noted that such locations, which are heavily NOx saturated (or radical limited), tend to
have much lower observed O3 concentrations than downwind areas. As a general rule, as NOx
emissions reductions occur, one can expect lower O3 values to increase while the higher O3
values would be expected to decrease. NOx reductions are expected to result in a compressed O3
distribution, relative to current conditions.
The formation of O3 from precursor emissions is also affected by meteorological
parameters such as the intensity of sunlight and atmospheric mixing. Major episodes of high
ground-level O3 concentrations in the eastern United States are associated with slow-moving
high pressure systems. High pressure systems during the warmer seasons are associated with the
49 2013 ISA, section 3.2.2.
50 2013 ISA, section 3.2.1.
51 2013 ISA, sections 3.2 and 3.7.1.
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sinking of air, resulting in warm, generally cloudless skies, with light winds. The sinking of air
results in the development of stable conditions near the surface which inhibit or reduce the
vertical mixing of O3 precursors. The combination of inhibited vertical mixing and light winds
minimizes the dispersal of pollutants, allowing their concentrations to build up. In addition, in
some parts of the United States (e.g., in Los Angeles), mountain barriers limit mixing and result
in a higher frequency and duration of days with elevated O3 concentrations. Photochemical
activity involving precursors is enhanced during warmer seasons because of the greater
availability of sunlight and higher temperatures.52
3. Spatial and Temporal Patterns in Ambient O3 Concentrations
3.1. Diurnal and Seasonal Patterns
Since O3 formation is a photochemical process, it is not surprising that concentration
levels have strong diurnal and seasonal patterns. Concentration levels tend to be highest at times
when sunlight reaches its highest intensity, namely during the afternoon hours of the late spring
and summer months. However, there are other factors at work, such as the influence of biogenic
VOC emissions and stratospheric intrusions during the spring months, long-range transport, and
traffic patterns which often cause peak NOx emissions to occur during the morning and evening
rush hours.
Figure A-2 shows the diurnal pattern in the hourly O3 concentrations based on ambient
monitoring data from 2000 to 2015. For each monitoring site, the median (top panel) and 95th
percentile (bottom panel) values for each hour of the day were calculated, and each boxplot
shows the range of those values for that particular hour across all monitoring sites. The whiskers
of each boxplot extend to the 5th and 95th percentiles, the box represents the inter-quartile range,
and the centerline represents the median value. The median and 95th percentile values show a
consistent pattern in that O3 levels tend to be lowest during the early AM hours, increasing
rapidly after sunrise. Concentrations typically reach their peak during the afternoon hours, then
decrease at a fairly constant rate throughout the evening and nighttime hours.
Figure A-3 shows the seasonal pattern in the daily maximum 8-hour O3 concentrations
based on ambient monitoring data from 2000 to 2015. For each monitoring site, the median (top
panel) and 95th percentile (bottom panel) values for each month of the year were calculated, and
each boxplot shows the range of those values for that particular month across all monitoring
sites. The whiskers of each boxplot extend to the 5th and 95th percentiles, the box represents the
inter-quartile range, and the centerline represents the median value. Again, the median and 95th
percentile values show a consistent pattern in that O3 levels tend to be highest during the spring
and summer months (April to September), and lower during the fall and winter months (October
to March).
52 2013 ISA, section 3.2.
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Figure A-2. Distribution of Median and 95th Percentile Hourly Os Concentrations by Hour
of the Day based on 2000-2015 Monitoring Data
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Figure A-3. Distribution of Median and 95th Percentile Daily Maximum 8-hour O3
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3.2. Spatial Patterns
To determine whether or not the O3 NAAQS has been met at an ambient monitoring site,
a statistic commonly referred to as a "design value" must be calculated based on three
consecutive years of data collected from that site. The form of the O3 NAAQS design value
statistic is the 3-year average of the annual 4th highest daily maximum 8-hour O3 concentration
in parts per million (ppm). The O3 NAAQS is met at an ambient monitoring site when the design
value is less than or equal to 0.070 ppm. In counties or other geographic areas with multiple
monitors, the area-wide design value is defined as the design value at the highest individual
monitoring site, and the area is said to have met the NAAQS if all monitors in the area are
meeting the NAAQS.
Figure A-4 shows a map of the O3 design values in the U.S. based on data collected
during the 2013-2015 period. The highest design values occur in California and near large
metropolitan areas such as Dallas, Denver, Houston, New York City, and Phoenix. The lowest
design values occur in the Pacific Northwest, the Northern Rockies, the Upper Midwest, and
parts of New England and the Southeast. In general, sparsely populated areas tend to have lower
design values than more urbanized areas.
Figure A-4. Map of 2013-2015 O3 Design Values in parts per billion (ppb)
25-60 ppb (190 sites) G 66 - 70 ppb (327 sites) 76 - 102 ppb (98 sites)
O 61-65 ppb (392 sites) O 71-75 ppb (117 sites)
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3.3. Interannual Variability and Trends
Figure A-5 shows the national trend in the annual 4tr' highest daily maximum 8-hour O3
concentration from 2000 to 2015. The solid black line represents the median value for each year
based on 838 "trends" sites with complete monitoring records, the dashed lines represent the 25th
and 75th percentile values for each year, and the shaded gray area covers the 10th percentile value
up to the 90th percentile value for each year. While there is considerable year-to-year variability,
overall the trend shows an improvement in O3 air quality over the 15-year period. In fact, the
median annual 4th highest value has decreased by 18% since the beginning of the century, and by
24% since 2002.
Figure A-5. National Trend in the Annual 4th Highest Daily Maximum 8-hour O3
Concentration
Trend in Annual 4th Highest Daily Maximum 8-hour 03 Concentrations
National Trend Based on 838 Monitoring Sites
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monitoring site.
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Figure A-6 shows that O3 levels have decreased across much of the eastern U.S. as a
result of regional control programs such as the NOx SIP Call and the Clean Air Interstate Rule
(CAIR). Large reductions have occurred near many urban areas where local control programs
have been implemented in addition to the regional controls. In the western U.S., where control
programs have been more localized, the reductions have occurred mostly in California and near
large urban areas. In other areas most sites have not shown a significant trend, and there are only
a handful of sites have shown an increasing trend.
Figure A-6. Map of site-level O3 trends across the U.S. from 2000 to 2015
Variations in meteorological conditions play an important role in determining O3
concentrations. Ozone is more readily formed on warm, sunny days when the air is stagnant.
Conversely, O3 generation is more limited when it is cool, rainy, cloudy, or windy. EPA uses a
statistical model to adjust for the variability in seasonal average O3 concentrations due to weather
conditions to provide a more accurate assessment of the underlying trend in O3 caused by
emissions.""'3 Figure A-7 shows the national trend in the May to September mean of the daily
ss Louise Camalier, William Cox, and Pat Dolwick (2007). The Effects of Meteorology 011 Ozone in Urban Areas
and their use in Assessing Ozone Trends. Atmospheric Environment Volume 41. Issue 33, October 2007, pages
7127-7137.
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maximum 8-hour O3 concentrations from 2000 to 2015 in 111 urban locations. The dotted red
line shows the trend in observed O3 concentrations at selected monitoring sites, while the solid
blue line shows the underlying O3 trend at those sites after removing the effects of weather. The
solid blue lines represent O3 levels anticipated under "typical" weather conditions and serve as a
more accurate assessment of the trend in O3 due to changes in precursor emissions.
Figure A-7 shows that after adjusting for the year-to-year variability in meteorology, the
overall trend in seasonal average O3 concentrations is much smoother. The adjusted trend clearly
shows that the NOx SIP Call program resulted in a sharp decrease in summertime O3
concentrations starting in 2004. The adjusted trend also indicates that O3 levels decreased
between 2004 and 2009, followed by a small increase from 2009 to 2012, then continued to
decrease after 2012.
Figure A-7. Trend in the May to September mean of the daily maximum 8-hour O3
concentration before (dotted red line) and after (solid blue line) adjusting for year-to-year
variability in meteorology.
National Urban Ozone Trend (111 Locations)
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Conceptual Description ofPM2.5
1. PM2.5 Monitoring Networks
1.1. PMMass Networks
The 1997 promulgation of a fine particulate NAAQS led to deployment of over 1,500
PM2.5 sites (about 1,000 currently in operation) used to determine whether an area complies with
the standard. These sites use a Federal Reference Method (FRM) or Federal Equivalent Method
(FEM), daily sampling over 24-hours, or every third or sixth day. Nearly 200 additional
measurements not meeting FRM or FEM specifications are provided by the chemical speciation
sites (Figure A-8). Approximately 450 stations provide indirect measurements of continuous
FEM (hourly resolution) PM2.5 mass.
1.2. Interagency Monitoring of Protected Visual Environments (IMPROVE) Program
The IMPROVE network, with over 150 sites, has provided nearly a 20+ year record of
major components of PM2.5 (sulfate, nitrate, organic and elemental carbon fractions, and trace
metals) in pristine areas of the United States (Figure A-8). IMPROVE is led by the National Park
Service; various federal and state agencies support its operations. The primary focus of the
network is to track visibility and trends in visibility.
1.3. PM2.5 Chemical Speciation Monitoring
In addition to the IMPROVE network, approximately 200 EPA speciation sites operate in
urban areas of the United States to assist PM2.5 assessment efforts. No FRM exists for particulate
speciation, which is not directly required to determine attainment, and there are slight differences
between monitors and methods used in the Chemical Speciation Network (CSN). However, the
network's coverage (Figure A-8) across urban and rural areas has proved essential for a wide
range of research and analysis. The speciation networks typically collect a 24-hour sample every
three, and sometimes six, days.
Only a handful of sites provide near continuous speciation data, usually limited to some
combination of sulfate, carbon (organic and elemental splits) and nitrate. This enables insight to
diurnal patterns for diagnosing various cause-effect phenomena related to emissions
characterization, source attribution analysis and model evaluation.
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Figure A-8. Locations of chemical speciation sites delineated by program type
2. Composition of PM2.5
Particulate matter (PM) is a highly complex mixture of solid particles and liquid droplets
distributed among numerous atmospheric gases which interact with solid and liquid phases.
Particles range in size from those smaller than 1 nanometer (10"9 meter) to over 100 microns (1
micron is 10"6 meter) in diameter (for reference, a typical strand of human hair is 70 microns and
particles less than about 20 microns generally are not detectable by the human eye). Particles are
classified as PM2.5 and PM10-2.5, corresponding to their size (diameter) range in microns and
referring to total particle mass under 2.5 and between 2.5 and 10 microns, respectively.
Particles span many sizes and shapes and consist of hundreds of different chemicals.
Particles are emitted directly from sources and also are formed through atmospheric chemical
reactions and often are referred to as primary and secondary particles, respectively. Particle
pollution also varies by time of year and location and is affected by several aspects of weather
such as temperature, clouds, humidity, and wind. Further complicating particles is the shifting
between solid/liquid and gaseous phases influenced by concentration and meteorology,
especially temperature.
Particles are made up of different chemical components. The major components, or
species, are carbon, sulfate and nitrate compounds, and crustal materials such as soil and ash
(Figure A-9). The different components that make up particle pollution come from specific
sources and are often formed in the atmosphere. Particulate matter includes both "primary" PM,
which is directly emitted into the air, and "secondary" PM, which forms indirectly from fuel
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combustion and other sources. Primary PM consists of carbon (soot) emitted from cars, trucks,
heavy equipment, forest fires, and burning waste and crustal material from unpaved roads, stone
crushing, construction sites, and metallurgical operations. Secondary PM forms in the
atmosphere from gases. Some of these reactions require sunlight and/or water vapor. Secondary
PM includes:
Sulfates formed from sulfur dioxide emissions from power plants and industrial
facilities;
Nitrates formed from nitrogen oxide emissions from cars, taicks, industrial facilities,
and power plants; and
Carbon formed from reactive organic gas emissions from cars, trucks, industrial
facilities, forest fires, and biogenic sources such as trees.
In addition, ammonia from sources such as fertilizer and animal feed operations is part of
the formation of sulfates and nitrates that exist in the atmosphere as ammonium sulfate and
ammonium nitrate. Note that fine particles can be transported long distances by wind and
weather and can be found in the air thousands of miles from where they were formed.
The chemical makeup of particles varies across the United States (as shown in Figure A-
10). For example, fine particles in the eastern half of the United States contain more sulfates than
those in the West, while fine particles in southern California contain more nitrates than other
areas of the country. Organic carbon is a substantial component of fine particle mass everywhere.
Figure A-9. National Average of Source Impacts on Fine Particle Levels
Cars, trucks, heavy equipment,
wildfires, wood/waste burning,
and biogenics
Suspended soil
and industrial metallurgical
operations
Cars, trucks,
industrial combustion, and
power generation
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Industrial combustion and power
generation
Source: The Particulate Matter Report. EPA-454/R-04-002, Fall 2004. Carbon reflects both organic carbon and
elemental carbon. Organic caibon accounts for automobiles, biogenics, gas-powered off-road, and wildfires.
Elemental carbon is mainly from diesel powered sources.
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Figure A-10. Annual Average PM2.5 Composition grouped by CBSA: 2013-2015
3. Seasonal and Daily Patterns of PM2.5
Fine particles often have a seasonal pattern. Both daily values and quarterly average of
PM2.5 also reveal patterns based on the time of year. Unlike daily O3 levels, which are usually
elevated in the summer, daily PM2.5 values at some locations can be high at any time of the year.
As shown in Figure A-l 1, PM2.5 values in the eastern half of the United States are typically
higher in the third calendar quarter (July-September) when sulfates are more readily formed from
sulfur dioxide (SO2) emissions from power plants in that region and when secondary organic
aerosol is more readily formed in the atmosphere. Fine particle concentrations tend to be higher
in the first calendar quarter (January through March) in the Midwest in part because fine particle
nitrates are more readily formed in cooler weather. PM2.5 values are high during the first
(January through March) and fourth calendar quarter (October through December) in many areas
of the West, in part because of fine particle nitrates and also due to carbonaceous particles which
are directly emitted from wood stove and fireplace use. Average concentration from all locations
reporting PM2.5 with valid design values is shown.
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Figure A-ll. Quarterly Averages of PM2.5 Concentration (fig nr3): 2013-2015
Quarter 1 Quarter 2
The composition of PM2.5 also varies by season and helps explain why mass varies by
season. Figure A-12 shows the average composition by season (spring, summer, fall and winter)
for PM2.5 data collected during 2013-2015. In the eastern United States, sulfate are high in the
spring (March-May) and summer (July-September). Nitrates are most evident in the midwest and
western cities where its percentage is moderately high in the winter and fall. Organic carbon
(OC) is high throughout the year.
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Figure A-12. Quarterly Average PM2.5 Composition grouped by CBSA: 2013-2015
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The composition of the highest daily PM2.5 values may be different than that for the
annual average. Figure A-13 provides 2013-2015 data PM2.5 composition on high mass days
across the United States. Mass is proportioned into six components: sulfates, nitrates, OC,
elemental carbon (EC), crustal material, and sea-salt. Except for the southeast (where there is
little nitrate in PM2.5), nitrates are slightly higher in the top 10 percent of the PM2.5 days. For the
2013-2015 measurements, the percent of sulfates is currently similar or slightly less on the top 10
percent of the days as compared to the annual averages. The portion of OC appears to be similar
on the high days compared to the annual averages, except for the Northern Rockies and Upper
Midwest where the high days are influenced by OC from wood stoves/fireplaces and wildfires.
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Figure A-13, PM2.5 Composition on 10% highest mass concentration days grouped by
CBSA: 2013-2015
sulfate
nitrate
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Appendix B: General Guidance on Use of Dispersion Models for Estimating Primary
PM2.5 Concentrations
This appendix provides general guidance on the application of dispersion models for
estimating ambient concentrations of PM2.5 associated with direct emissions of primary PM2.5.
This guidance is based on and is consistent with the EPA's Guideline on Air Quality Models,
published as Appendix W of 40 CFR part 51, and focuses primarily on the application of
AERMOD, the EPA's preferred dispersion model for most situations. Appendix W is the
primary source of information on the regulatory application of air quality models for State
Implementation Plan (SIP) revisions for existing sources and for New Source Review (NSR) and
Prevention of Significant Deterioration (PSD) programs. There will be applications of dispersion
models unique to specific areas, (i.e., there may be areas of the country where it is necessary to
model unique specific sources or types of sources). In such cases, there should be consultation
with the state or appropriate permitting authority with the appropriate EPA Regional Office
modeling contact to discuss how best to model a particular source.
Recently issued EPA guidance of relevance for consideration in modeling for PM2.5
includes:
"Model Clearinghouse Review of Modeling Procedures for Demonstrating Compliance
with PM2.5NAAQS" February 26, 2010 (U.S. EPA, 2010a);
"Modeling Procedures for Demonstrating Compliance with PM2.5 NAAQS" March 23,
2010 (U.S. EPA, 2010b); and
"Transportation Conformity Guidance for Quantitative Hot-spot Analyses in PM2.5 and
PM10 Nonattainment and Maintenance Areas" November 2015 (U.S. EPA, 2015a).
The guidance listed above, in addition to other relevant support documents can be found on the
SCRAM website at: https://www.epa.gov/scram.
The following sections will refer to the relevant sections of Appendix W and other
existing guidance with summaries as necessary. Please refer to those original guidance
documents for full discussion and consult with the appropriate EPA Regional Office modeling
contact if questions arise about interpretation on modeling techniques and procedures.54
1. Model selection
Preferred air quality models for use in regulatory applications are addressed in Appendix
A of the EPA's Guideline on Air Quality Models. If a model is to be used for a particular
application, the user should follow the guidance on the preferred model for that application.
These models may be used without an area specific formal demonstration of applicability as long
as they are used as indicated in each model summary of Appendix A. Further recommendations
for the application of these models to specific source problems are found in Appendix W. In
54 A list of EPA Regional Office modeling contacts is available on the SCRAM website at:
https://www.epa.gov/scram/air-modeling-regional-contacts.
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2005, the EPA promulgated the American Meteorological Society/Environmental Protection
Agency Regulatory Model (AERMOD) as the Agency's preferred near-field dispersion model
for a wide range of regulatory applications in all types of terrain based on extensive
developmental and performance evaluation. For PSD/NSR modeling under the PM2.5 NAAQS,
AERMOD should be used to model direct PM2.5 emissions unless use of an alternative model
can be justified (section 3.2, Appendix W).
The AERMOD modeling system includes the following components:
AERMOD: the dispersion model (U.S. EPA, 2021a);
AERMAP: the terrain processor for AERMOD (U.S. EPA, 2018a,); and
AERMET: the meteorological data processor for AERMOD (U.S. EPA, 2021b;).
Other components that may be used, depending on the application, are:
BPIPPRIME: the building input processor (U.S. EPA, 2004);
AERSURFACE: the surface characteristics processor for AERMET (U.S. EPA, 2020);
AERSCREEN: a screening version of AERMOD (U.S. EPA, 2021c; U.S. EPA, 2011);
and
AERMINUTE: a pre-processor to calculate hourly average winds from Automated
Surface Observing System (ASOS) 2-minute observations (U.S. EPA, 2015b).
Before running AERMOD, the user should become familiar with the user's guides associated
with the modeling components listed above and the AERMOD Implementation Guide (AIG)
(U.S. EPA, 2021d). The AIG lists several recommendations for applications of AERMOD that
would be applicable for SIP and PSD permit modeling.
1.2. Receptor grid
The model receptor grid is unique to the particular situation and depends on the size of
the modeling domain, the number of modeled sources, and complexity of the terrain. Receptors
should be placed in areas that are considered ambient air (i.e., outside of buildings and where the
public generally has access) and placed out to a distance such that areas of violation can be
detected from the model output to help determine the size of nonattainment areas. Receptor
placement should be of sufficient density to provide resolution needed to detect significant
gradients in the concentrations with receptors placed closer together near the source to detect
local gradients and placed farther apart away from the source. In addition, the user may want to
place receptors at key locations such as around facility "fence lines"55 (which define the ambient
air boundary for a particular source) or monitor locations (for comparison to monitored
55 It should be noted that the term "fence line" for modeling purposes generally makes reference to a source's
property boundary and may not refer literally to the existence of a fence at such boundary. The EPA's "ambient air"
policy does not mandate that public access to a source's property be precluded by a fence; other measures that
effectively preclude public access may be approved for establishing an ambient air exclusion for PSD modeling
purposes.
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concentrations for model evaluation purposes). The receptor network should cover the modeling
domain. States may already have existing receptor placement strategies in place for regulatory
dispersion modeling under NSR/PSD permit programs.
If modeling indicates elevated levels of PM2.5 (near the standard) near the edge of the
receptor grid, consideration should be given to expanding the grid or conducting an additional
modeling run centered on the area of concern. As noted above, terrain complexity should also be
considered when setting up the receptor grid. If complex terrain is included in the model
calculations, AERMOD requires that receptor elevations be included in the model inputs. In
those cases, the AERMAP terrain processor (U.S. EPA, 2018a) should be used to generate the
receptor elevations and hill heights. The latest version of AERMAP (version 09040 or later) can
process either Digitized Elevation Model (DEM) or National Elevation Data (NED) data files.
The AIG recommends the use of NED data since it is more up to date than DEM data, which is
no longer updated (Section 4.3 of the AIG).
2. Source inputs
This section provides guidance on source characterization to develop appropriate inputs
for dispersion modeling with the AERMOD modeling system. Section 2.1 provides guidance on
use of emission, Section 2.2 covers guidance on Good Engineering Practice (GEP) stack heights,
Section 2.3 provides details on source configuration and source types, Section 2.4 provides
details on urban/rural determination of the sources, and Section 2.5 provides general guidance on
source grouping, which may be important for design value calculations.
2.1. Emissions
Consistent with Appendix W, dispersion modeling for the purposes of PSD permitting
should be based on the use of continuous operation at maximum allowable emissions or federally
enforceable permit limits (see Table 8-2 of Appendix W) for the project source for all applicable
averaging periods. Also consistent with past and current guidance, in the absence of maximum
allowable emissions or federally enforceable permit limits, potential to emit emissions (i.e.,
design capacity) should be used. Maximum allowable emissions and continuous operation should
also be assumed for nearby sources included in the modeled inventory for the 24-hr PM2.5
NAAQS, while maximum allowable emissions and the actual operating factor averaged over the
most recent 2 years, unless it is determined that this period is not representative, should be used
for modeled nearby sources for the annual PM2.5 NAAQS.
2.2. Good Engineering Practice (GEP) stack height
Consistent with previous modeling guidance and section 7.2.2.1 of Appendix W, for
stacks with heights that are within the limits of Good Engineering Practice (GEP), actual heights
should be used in modeling. Under the EPA's regulations at 40 CFR 51.100, GEP height, Hg, is
determined to be the greater of:
65 m, measured from the ground-level elevation at the base of the stack;
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for stacks in existence on January 12, 1979, and for which the owner or operator had
obtained all applicable permits or approvals required under 40 CFR parts 51 and 52
Hg=2.5H
provided the owner or operator produces evidence that this equation was actually relied
on in designing the stack or establishing an emission limitation to ensure protection
against downwash;
for all other stacks,
Hg=H + 1.5L,
where H is the height of the nearby structure(s) measured from the ground-level elevation
at the base of the stack and L is the lesser dimension of height or projected width of
nearby structure(s); or
the height demonstrated by a fluid model or a field study approved by the EPA or the
state/local permitting agency which ensures that the emissions from a stack do not result
in excessive concentrations of any air pollutant as a result of atmospheric downwash,
wakes, eddy effects created by the source itself, nearby structures or nearby terrain
features.
For more details about GEP, see the Guideline for Determination of Good Engineering Practice
Stack Height Technical Support Document (U.S. EPA, 1985).
If stack heights exceed GEP, then GEP heights should be used with the individual stack's
other parameters (temperature, diameter, exit velocity). For stacks modeled with actual heights
below GEP that may be subject to building downwash influences, building downwash should be
considered as this can impact concentrations near the source (section 7.2.2.1(b), Appendix W). If
building downwash is being considered, the BPIPPRIME program (U.S. EPA, 2004) should be
used to input building parameters for AERMOD.
2.3. Source configurations and source types
An accurate characterization of the modeled facilities is critical for refined dispersion
modeling, including accurate stack parameters and physical plant layout. Accurate stack
parameters should be determined for the emissions being modeled. Since modeling would be
done with maximum allowable or potential emissions levels at each stack, the stack's parameters
such as exit temperature, diameter, and exit velocity should reflect those emissions levels.
Accurate locations (i.e., latitude and longitude or Universal Transverse Mercator (UTM)
coordinates and datum)56 of the modeled emission sources are also important, as this can affect
the impact of an emission source on receptors, determination of stack base elevation, and relative
56 Latitudes and longitudes to four decimal places position a stack within 30 feet of its actual location and five
decimal places position a stack within three feet of its actual location. Users should use the greatest precision
available.
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location to any nearby building structures. Not only are accurate stack locations needed, but
accurate information for any nearby buildings is important. This information would include
location and orientation relative to stacks and building size parameters (height, and corner
coordinates of tiers) as these parameters are input into BPIPPRIME to calculate building
parameters for AERMOD. If stack locations and or building information are not accurate,
downwash will not be accurately accounted for in AERMOD.
Emission source type characterization within the modeling environment is also important.
As stated in the AERMOD User's Guide (U.S. EPA, 2019a), emissions sources can be
characterized as several different source types: POINT sources, capped stacks (POINTCAP),
horizontal stacks (POINTHOR), VOLUME sources, OPENPIT sources, LINE sources, buoyant
lines sources (BUOYLINE), rectangular AREA sources, circular area sources (AREACIRC),
and irregularly shaped area sources (AREAPOLY). While most sources can be characterized as
POINT sources, some sources, such as fugitive releases or nonpoint sources (emissions from
ports/ships, airports, or smaller point sources with no accurate locations), may be best
characterized as VOLUME or AREA type sources. Sources such as flares can be modeled in
AERMOD using the parameter input methodology described in Section 2.1.2 of the
AERSCREEN User's Guide (U.S. EPA, 2021c). If questions arise about proper source
characterization or typing, users should consult the appropriate EPA Regional Office modeling
contact.
2.4. Urban/rural determination
For any dispersion modeling exercise, the urban or rural determination of a source is
important in determining the boundary layer characteristics that affect the model's prediction of
downwind concentrations. Figure B-l gives example maximum 24-hour concentration profiles
for a 10 meter stack (Figure B-la) and a 100 m stack (Figure B-lb) based on urban vs. rural
designation. The urban population used for the examples is 100,000. In Figure B-la, the urban
concentration is much higher than the rural concentration for distances less than 750 m from the
stack but then drops below the rural concentration beyond 750 m. For the taller stack in Figure
B-lb, the urban concentration is much higher than the rural concentration even as distances
increase from the source. These profiles show that the urban or rural designation of a source can
be quite important.
Determining whether a source is urban or rural can be done using the methodology
outlined in section 7.2.1.1 of Appendix W and recommendations outlined in Sections 5.1 through
5.3 in the AIG (U.S. EPA, 2021d). In summary, there are two methods of urban/rural
classification described in section 7.2.3 of Appendix W.
The first method of urban determination is a land use method (Appendix W, section
7.2.2.1. l(b)(i)). In the land use method, the user analyzes the land use within a 3 km radius of the
source using the meteorological land use scheme described by Auer (1978). Using this
methodology, a source is considered urban if the land use types II (heavy industrial), 12 (light-
moderate industrial), CI (commercial), R2 (common residential), and R3 (compact residential)
are 50 percent or more of the area within the 3 km radius circle. Otherwise, the source is
considered a rural source. The second method uses population density and is described in section
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7.2.2.1. l(b)(ii) of Appendix W. As with the land use method, a circle of 3 km radius is used. If
the population density within the circle is greater than 750 people/km2, then the source is
considered urban. Otherwise, the source is modeled as a rural source. Of the two methods, the
land use method is considered more definitive (section 7.2.1.1.b, Appendix W).
Caution should be exercised with either classification method. As stated in Section 5.1 of
the AIG (U.S. EPA, 2009), when using the land use method, a source may be in an urban area
but located close enough to a body of water or other non-urban land use category to result in an
erroneous rural classification for the source. The AIG in Section 5.1 cautions users against using
the land use scheme on a source by source basis, but advises considering the potential for urban
heat island influences across the full modeling domain. When using the population density
method, section 7.2.2.1.l(b)(ii)of Appendix W states, "Population density should be used with
caution and should not be applied to highly industrialized areas where the population density
may be low and thus a rural classification would be indicated, but the area is sufficiently built-up
so that the urban land use criteria would be satisfied..." With either method, section 7.2.1.1(f) of
Appendix W recommends modeling all sources within an urban complex as urban, even if some
sources within the complex would be considered rural using either the land use or population
density method.
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Figure B-I. Urban (red) and rural (blue) concentration profiles for (a) 10 m buoyant stack
release, and (b) 100 m buoyant stack release
10 m
100 m
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Another consideration that may need attention by the user, and is discussed in Section 5.1
of the AIG, relates to tall stacks located within or adjacent to small to moderate size urban areas.
In such cases, the stack height or effective plume height for very buoyant sources may extend
above the urban boundary layer height. The application of the urban option in AERMOD for
these types of sources may artificially limit the plume height. The use of the urban option may
not be appropriate for these sources, since the actual plume is likely to be transported over the
urban boundary layer. Section 5.1 of the AIG gives details on determining if a tall stack should
be modeled as urban or rural based on comparing the stack or effective plume height to the urban
boundary layer height. The 100 m stack illustrated in Figure B-lb, may be such an example as
the urban boundary layer height for this stack would be 189 m (based on a population of
100,000) and equation 104 of the AERMOD formulation document (Cimorelli, et al., 2004). This
equation is:
^ (B-l)
where ziuo is a reference height of 400 m corresponding to a reference population P0 of 2,000,000
people.
Given that the stack is a buoyant release, the plume may extend above the urban
boundary layer and may be best characterized as a rural source, even if it were near an urban
complex. However, beginning with version 15181 of AERMOD, a formulation bug fix was
incorporated that modified the treatment of plume rise for urban sources, especially for tall
stacks in urban areas. See Section 5.1 of the AIG for more information. Even with the bug fix in
AERMOD 15181, exclusion of these elevated sources from application of the urban option
would need to be justified on a case-by-case basis in consultation with the appropriate permitting
AERMOD requires the input of urban population when utilizing the urban option.
Population can be entered to one or two significant digits (i.e., an urban population of 1,674,365
can be entered as 1,700,000). Users can enter multiple urban areas and populations using the
URBANOPT keyword in the runstream file (U.S. EPA, 2021a). If multiple urban areas are
entered, AERMOD requires that each urban source be associated with a particular urban area or
AERMOD model calculations will abort. Urban populations can be determined by using a
method described in Section 5.2 of the AIG (U.S. EPA, 2021d).
2.5. Source groups
In AERMOD, individual emission sources' concentration results can be combined into
groups using the SRCGROUP keyword (Section 3.3.11 of the AERMOD User's Guide (U.S,
EPA, 2019a). The user can automatically calculate a total concentration (from all sources) using
the SRCGROUP ALL keyword. For the purposes of design value calculations, source group
ALL should be used, especially if all sources in the modeling domain are modeled in one
AERMOD run. Design values should be calculated from the total concentrations (all sources and
background). Individual source impacts on the total concentration may be necessary to determine
the culpability to any NAAQS violations.
iuc
iuo
I>
authority.
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3. Meteorological data
This section gives guidance on the selection of meteorological data for input into
AERMOD. Much of the guidance from section 8.4 of Appendix W is applicable to SIP and PSD
permit modeling and is summarized here. In Section 3.2.1, the use of the tool, AERMINUTE
(U.S. EPA, 2015b), is introduced. AERMINUTE is an AERMET pre-processor that calculates
hourly averaged winds from ASOS 1-minute winds. Section 3.2.4 discusses the use of prognostic
meteorological data.
3.1. Surface characteristics and representativeness
The selection of meteorological data that are input into a dispersion model should be
considered carefully. The selection of data should be based on spatial and climatological
(temporal) representativeness (Appendix W, section 8.4). The representativeness of the data is
based on: 1) the proximity of the meteorological monitoring site to the area under consideration,
2) the complexity of terrain, 3) the exposure of the meteorological site, and 4) the period of time
during which data are collected. Sources of meteorological data are: National Weather Service
(NWS) stations, site-specific or onsite data, and other sources such as universities, Federal
Aviation Administration (FAA), military stations, and others. In specific cases, prognostic
meteorological data may be appropriate for use and obtained from similar sources. Appendix W
addresses spatial representativeness issues in sections 8.4.1.a and 8.4.2.b.
Spatial representativeness of the meteorological data can be adversely affected by large
distances between the source and receptors of interest and the complex topographic
characteristics of the area (Appendix W, sections 8.4.1.a and 8.4.2.b). If the modeling domain is
large enough such that conditions vary drastically across the domain, then the selection of a
single station to represent the domain should be carefully considered. Also, care should be taken
when selecting a station if the area has complex terrain. While a source and meteorological
station may be in close proximity, there may be complex terrain between them such that
conditions at the meteorological station may not be representative of the source. An example
would be a source located on the windward side of a mountain chain with a meteorological
station a few kilometers away on the leeward side of the mountain. Spatial representativeness for
off-site data should also be assessed by comparing the surface characteristics (albedo, Bowen
ratio, and surface roughness) of the meteorological monitoring site and the analysis area. When
processing meteorological data in AERMET (U.S. EPA, 2021b), the surface characteristics of
the meteorological site or the prognostic meteorological model output grid cell should be used
(section 8.4.2.b of Appendix W and the AERSURFACE User's Guide (U.S. EPA, 2020)).
Spatial representativeness should also be addressed for each meteorological variable separately.
For example, temperature data from a meteorological station several kilometers from the analysis
area may be considered adequately representative, while it may be necessary to collect wind data
near the plume height (section 8.4.2.b of Appendix W).
Surface characteristics can be calculated in several ways. For details, see Section 3.1.2 of
the AIG (U.S. EPA, 202Id). The EPA has developed a tool, AERSURFACE (U.S. EPA, 2020)
to aid in the determination of surface characteristics for observed meteorological data. Note that
the use of AERSURFACE is not a regulatory requirement, but the methodology outlined in
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Section 3.1.2 of the AIG should be followed unless an alternative method can be justified. For
prognostic meteorological output, the surface characteristics of the representative grid cell should
be used.
3.2. Meteorological inputs
Appendix W states in section 8.4.2.e that the user should acquire enough meteorological
data to ensure that worst-case conditions are adequately represented in the model results.
Appendix W states that 5 years of NWS meteorological data, at least 1 year of site-specific data,
or at least 3 years of prognostic data should be used and should be adequately representative of
the study area. If 1 or more years of site-specific data are available, those data are preferred.
While the form of the PM2.5 NAAQS contemplates obtaining 3 years of monitoring data, this
does not preempt the use of 5 years of NWS data or at least 1 year of site-specific data in the
modeling. The 5-year average based on the use of NWS data, an average across 3 or more years
of prognostic data, or an average across 1 or more years of available site specific data, serves as
an unbiased estimate of the 3-year average for purposes of modeling demonstrations of
compliance with the NAAQS.
3.2.1. NWS data
NWS data are available from the National Climatic Data Center (NCDC) in many
formats, with the most common one in recent years being the Integrated Surface Hourly data
(ISH). Most available formats can be processed by AERMET. As stated in Section 3.1, when
using data from an NWS station alone or in conjunction with site-specific data, the data should
be spatially and temporally representative of conditions at the modeled sources. Key points
regarding the use of NWS data can be found in the EPA's March 8, 2013 clarification memo
"Use of ASOS meteorological data in AERMOD dispersion modeling" (U.S. EPA, 2013). The
key points are:
The EPA has previously analyzed the effects of ASOS implementation on dispersion
modeling and found that generally AERMOD was less sensitive than ISCST3 to the
implementation of ASOS.
The implementation of the ASOS system over the conventional observation system
should not preclude the consideration of NWS stations in dispersion modeling.
The EPA has implemented an adjustment factor (0.5 knots) in AERMET to adjust for
wind speed truncation in ASOS winds
The EPA has developed the AERMINUTE processor (U.S. EPA, 2015b) to process 2-
minute ASOS winds and calculate an hourly average for input into AERMET. The use of
hourly averaged winds better reflect actual conditions over the hour as opposed to a
single 2-minute observation.
3.2.2. Site-specific data
The use of site-specific meteorological data is the best way to achieve spatial
representativeness. AERMET can process a variety of formats and variables for site-specific
data. The use of site-specific data for regulatory applications is discussed in detail in section
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8.4.4 of Appendix W. Due to the range of data that can be collected onsite and the range of
formats of data input to AERMET, the user should consult Appendix W, the AERMET User's
Guide (U.S. EPA, 2021b), and Meteorological Monitoring Guidance for Regulatory Modeling
Applications (U.S. EPA, 2000). Also, when processing site-specific data for an urban
application, Section 3.3 of the AERMOD Implementation Guide offers recommendations for
data processing. In summary, the guide recommends that site-specific turbulence measurements
should not be used when applying AERMOD's urban option in order to avoid double counting
the effects of enhanced turbulence due to the urban heat island.
3.2.3. Upper air data
AERMET requires full upper air soundings to calculate the convective mixing height. For
AERMOD applications in the U.S., the early morning sounding, usually the 1200 UTC
(Universal Time Coordinate) sounding, is typically used for this purpose. Upper air soundings
can be obtained from the Radiosonde Data of North America CD for the period 1946-1997.
Upper air soundings for 1994 through the present are also available for free download from the
Radiosonde Database Access website. Users should choose all levels or mandatory and
significant pressure levels57 when selecting upper air data. Selecting mandatory levels only
would not be adequate for input into AERMET as the use of just mandatory levels would not
provide an adequate characterization of the potential temperature profile.
3.2.3. Prognostic data
In specific situations where it is infeasible or cost prohibitive to collect adequately representative
site-specific data or there is not a representative NWS or comparable meteorological station
available, it may be appropriate to use prognostic meteorological data, if deemed adequately
representative. However, if prognostic data are not representative of the transport and dispersion
conditions in the area of concern, the collection of site-specific data is necessary (section 8.4.5.1
of Appendix W). To facilitate the use of prognostic meteorological data, EPA has developed a
processor, Mesoscale Model Interface Program, MMIF (Environ, 2015), to process MM5
(Mesoscale Model 5) or WRF (Weather Research Forecast) model data for input to various
models including AERMOD. MMIF can process data for input to AERMET or AERMOD for a
single grid cell or multiple grid cells. For regulatory applications, MMIF should be run to create
inputs for AERMET input as described in section 8.4.5.1.b of Appendix W and MMIF guidance
(U.S. EPA, 2018b). Specific guidance on running MMIF for AERMOD applications can be
found in U.S. EPA, 2018b.
4. Running AERMOD and implications for design value calculations
Recent enhancements to AERMOD include options to aid in the calculation of design
values for comparison with the PM2.5 NAAQS and to aid in determining whether emissions from
the project source caused or contributed to any modeled violations. These enhancements include:
57 By international convention, mandatory levels are in millibars: 1,000, 850, 700, 500, 400, 300, 200, 150, 100, 50,
30, 20, 10, 7 5, 3, 2, and 1. Significant levels may vary depending on the meteorological conditions at the upper-air
station.
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The MAXDCONT option, which shows the impact of each user-specified source group
to the high ranked values for a specified target source group paired in time and space.
The user can specify a range of ranks to analyze or specify an upper bound rank, i.e., 8th
highest, corresponding to the 98th percentile for the 24-hour PM2.5 NAAQS, and a lower
threshold concentration value, such as the NAAQS for the target source group. The
model will process each rank within the range specified, but will stop after the first rank
(in descending order of concentration) that is below the threshold value if specified by the
user. A warning message will be generated if the threshold is not reached within the
range of ranks analyzed (based on the range of ranks specified on the RECTABLE
keyword). This option may be needed to aid in determining which sources should be
considered for controls.
For more details about the enhancements, see the AERMOD User's Guide (U.S. EPA, 2021a).
Ideally, all explicitly modeled sources, receptors, and background should be modeled in
one AERMOD run for all modeled years. In this case, one of the above output options can be
used in AERMOD to calculate design values for comparison to the NAAQS and determine the
area's attainment status and/or inform attainment/nonattainment boundaries. The use of these
options in AERMOD allows AERMOD to internally calculate concentration metrics that can be
used to calculate design values and, therefore, lessen the need for large output files, i.e., hourly
POSTFILES.
However, there may be situations where a single AERMOD run with all explicitly
modeled sources is not possible. These situations often arise due to runtime or storage space
considerations during the AERMOD modeling. Sometimes separate AERMOD runs are done for
each facility or group of facilities, or by year, or the receptor network is divided into separate
sub-networks. In some types of these situations, the MAXDCONT output option may not be an
option for design value calculations, especially if all sources are not included in a single run. If
the user wishes to utilize one of the three output options, then care should be taken in developing
the model inputs to ensure accurate design value calculations.
Situations that would effectively preclude the use of the MAXDCONT option to calculate
meaningful AERMOD design value calculations include the following examples:
Separate AERMOD runs for each source or groups of sources.
o SIP modeling includes 10 facilities for 5 years of NWS data and each facility is
modeled for 5 years in a separate AERMOD run, resulting in ten separate AERMOD
runs.
Separate AERMOD runs for each source and each modeled year.
o 10 facilities are modeled for 5 years of NWS data. Each facility is modeled separately
for each year, resulting in fifty individual AERMOD runs.
In the two situations listed above, the MAXDCONT option would not be useful as the
different AERMOD runs do not include a total concentration with impacts from all facilities. In
these situations, the use of 24-hour POSTFILES, which can be quite large, and external post-
processing would be needed to calculate design values.
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Situations in which the MAXDCONT options may be used but may necessitate some
external post-processing afterwards to calculate a design value include:
The receptor network is divided into sections and an AERMOD run, with all sources and
years, is made for each sub-network.
o A receptor network of 1,000 receptors is divided into four 250 receptor sub-
networks. 10 facilities are modeled with 5 years of NWS data in one AERMOD
run for each receptor network, resulting in four AERMOD runs. After the
AERMOD runs are complete, the MAXDCONT results for each network can be
re-combined into the larger network.
All sources and receptors are modeled in an AERMOD run for each year.
Ten facilities are modeled with 5 years of NWS data. All facilities are modeled with all
receptors for each year individually, resulting in five AERMOD runs. MAXDCONT
output can be used and post-processed to generate the necessary design value
concentrations. The receptor network is divided and each year is modeled separately for
each sub-network with all sources.
Ten facilities are modeled with 5 years of NWS data for 1,000 receptors. The receptor
network is divided into four 250 receptor networks. For each sub-network, all ten
facilities are modeled for each year separately, resulting in twenty AERMOD runs.
MAXDCONT output can be used and post-processed to generate the necessary design
value concentrations.
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5. References
Auer, Jr., A.H., 1978. Correlation of Land Use and Cover with Meteorological Anomalies.
Journal of Applied Meteorology, 17(5), 636-643.
Brode, R., K. Wesson, J. Thurman, and C. Tillerson, 2008: AERMOD Sensitivity to the Choice
of Surface Characteristics, Paper 811, Air And Waste Management Association Annual
Conference.
Cimorelli, A. J., S. G. Perry, A. Venkatram, J. C. Weil, R. J. Paine, R. B. Wilson, R. F. Lee, W.
D. Peters, R. W. Brode, and J. O. Paumier, 2004. AERMOD: Description of Model
Formulation, EPA-454/R-03-004. U.S. Environmental Protection Agency, Research
Triangle Park, NC.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/preferred/aermod/aermod mfd 454-R-
03-004.pdf.
U.S. EPA, 1985. Guideline for Determination of Good Engineering Practice Stack Height
(Technical Support Document for the Stack Height Regulations), Revised. EPA-450/4-
80-023R. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
https://www.epa.gov/sites/production/files/2020-09/documents/gep.pdf.
U.S. EPA, 1992. Screening Procedures for Estimating the Air Quality Impact of Stationary
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Park, NC 27711. https://www.epa.gov/sites/production/files/2020-09/documents/epa-
454r-92-019 ocr.pdf.
U.S. EPA, 1994. SO2 Guideline Document. EPA-452/R-95-008. U.S. Environmental Protection
Agency, Research Triangle Park, NC 27711.
U.S. EPA, 2000. Meteorological Monitoring Guidance for Regulatory Modeling Applications.
EPA-454/R-99-005. U.S. Environmental Protection Agency, Research Triangle Park, NC
27711. https://www.epa.gov/sites/production/files/202Q-10/documents/mmgrma O.pdf.
U.S. EPA, 2004. User's Guide to the Building Profile Input Program. EPA-454/R-93-038. U.S.
Environmental Protection Agency, Research Triangle Park, North Carolina 27711.
U.S. EPA, 2010a. Model Clearinghouse Review of Modeling Procedures for Demonstrating
Compliance with PM2.5 NAAQS. Tyler Fox Memorandum dated February 26, 2010. U.S.
Environmental Protection Agency, Research Triangle Park, North Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/mchisrs/MCmemo Region6 PM25 NAAQS C
ompliance.pdf.
U.S. EPA, 2010b. Modeling Procedures for Demonstrating Compliance with PM2.5 NAAQS.
Stephen Page Memorandum dated March 23, 2010. U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina, 27711.
https://www.epa.gov/sites/production/files/2020-
10/documents/official signed modeling proc for demo compli w pm2.5.pdf.
U.S. EPA, 2011. AERSCREEN Released as the EPA Recommended Screening Model. Tyler
Fox Memorandum dated April 11, 2011. U.S. Environmental Protection Agency,
Research Triangle Park, North Carolina 27711.
https://www.epa.gov/sites/production/files/2020-
10/documents/20110411 aerscreen release memo.pdf.
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U.S. EPA, 2013. Use of ASOS meteorological data in AERMOD dispersion modeling. Tyler Fox
Memorandum dated March 8, 2013. U.S. Environmental Protection Agency, Research
Triangle Park, North Carolina 27711. https://www.epa.gov/sites/production/files/2020-
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U.S. EPA, 2015a. Transportation Conformity Guidance for Quantitative Hot-spot Analyses in
PM2.5 and PM10 Nonattainment and Maintenance Areas. November 2015. EPA-420-B-
15-084. U.S. Environmental Protection Agency, Ann Arbor, Michigan 48105.
http://nepis.epa. gov/Exe/ZvPDF.cgi?Dockev=P100NMXM.pdf. and
http://nepis.epa. gov/Exe/ZvPdf.cgi?Dockev=P100NN22.pdf.
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454/B-15-006. Research Triangle Park, North Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/met/aerminute/aerminute userguide.pdf.
U.S. EPA, 2017. Guideline on Air Quality Models. 40 CFR part 51 Appendix W.
https://www.epa.gov/sites/production/files/2020-09/documents/appw 17.pdf.
U.S. EPA, 2018a. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA-
454/B-18-004. U.S. Environmental Protection Agency, Research Triangle Park, North
Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/related/aermap/aermap userguide v!80
81.pdf.
U.S. EPA, 2018b. Guidance on the Use of the Mesoscale Model Interface Program (MMIF) for
AERMOD Applications. EPA-454/B-18-005. U.S. Environmental Protection Agency,
Research Triangle Park, NC 27711.
https://gaftp.epa.gov/Air/aamg/SCRAM/models/related/mmif/MMIF Guidance.pdf.
U.S. EPA, 2020. AERSURFACE User's Guide. EPA-454/B-20-008. U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/related/aersurface/aersurface ug v2006
O.pdf.
U.S. EPA, 2021a. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA-454/B-
21-001. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/preferred/aermod/aermod userguide.pdf.
U.S. EPA, 2021b. User's Guide for the AERMOD Meteorological Preprocessor (AERMET).
EPA-454/B-21-004. U.S. Environmental Protection Agency, Research Triangle Park, NC
27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/met/aermet/aermet userguide.pdf.
U.S. EPA, 2021c. AERSCREEN User's Guide. EPA-454/B-21-005. U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/screening/aerscreen/aerscreen userguide
.pdf.
U.S. EPA, 202Id. AERMOD Implementation Guide. EPA-454/B-21-006. U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711.
https://gaftp.epa.gov/Air/aqmg/SCRAM/models/preferred/aermod/aermod implementati
on guide.pdf.
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Appendix C: Example of a Tier 1 Demonstration of the Potential for O3 and Secondary
PM2.5 Formation
In 2018, a permit applicant, the Tennessee Valley Authority (TVA) Gleason Combustion
Turbine Plant (GCC), worked closely with the Tennessee Department of Environment and
Conservation (TDEC) and EPA Region 4 to develop a compliance demonstration for a major
facility modification, including the use of a Tier 1 assessment of O3 and secondary PM2.5
impacts. This Tier 1 assessment was based on the application of Modeled Emission Rates for
Precursors (MERPs) and related modeling guidance released by the EPA. In April 2018, the
TDEC published state modeling guidance that can be used by PSD applicants in Tennessee that
largely restated the technical aspects of the guidance presented in the EPA's 2016 Draft MERPs
Guidance. 58 In support of the 2016 Draft MERPs Guidance and subsequently the 2019 MERPs
Guidance, the EPA performed photochemical modeling for four hypothetical sources from
within Tennessee or in close proximity to Tennessee (Shelby County, TN, Giles County, TN,
Barren County, KY and Ashe County, NC), that can be used to represent the O3 and secondary
PM2.5 pollutant formation from other large sources in Tennessee (Figure 1).
FIGURE 1
58 The EPA released a draft version of the "Guidance on the Development of Modeled Emission Rates for Precursors
(MERPs) as a Tier 1 Demonstration Tool for Ozone and PM2.5 under the PSD Permitting Program" 011 December 2,
2016, for public review and comment. Based on the feedback gained from this draft, the EPA released a non-draft or
final version of the "MERPs Guidance" on April 30, 2019. The information in the 2016 draft MERPs Guidance
from which the TDEC based their April 2018 modeling guidance did not substantively change and is representative
of information contained in the current 2019 final version of the MERPs Guidance. The 2019 final MERPs
Guidance is available at: https://www.epa.gov/sites/production/files/2020-09/documents/epa-454 r-19-003.pdf.
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Assessment of PM2.5
Based on information in the EPA's MERPs Guidance, the lowest, most conservative
MERPs from these four hypothetical source locations were established in the TDEC state
modeling guidance as the default MERPs that can be used throughout Tennessee without the
need for further justification (Table 1). The TVA used these default MERPs to assess secondary
PM2.5 impacts for the proposed modification at the GCC facility.
TABLE 1
Default MERPs for Use in TN PSD Applications
Precursor
MERPs for 8-hr O3
MERPs for Daily
MERPs for Annual
(tons/yr)
PM2.5 (tons/year)
PM2.5 (tons/year)
NOx
156
4,000
7,407
S02
-
667
6,061
VOC
1,339
-
-
The combined primary and secondary impacts of PM2.5 for the source impact analysis
were assessed using the highest (AERMOD) modeled primary PM2.5 concentration (HMC), the
Class II SIL, precursor emissions, and the default MERPs. If the sum of the ratios in Equation 1
below is less than 1, then the combined PM2.5 impacts are below the PM2.5 SIL, an adequate
compliance demonstration has been performed, and no additional analyses are necessary.
The following equation was used for this assessment:
EQUATION 1
(HMC\ i ( NOx_Em ^ i ( S02_Em \ ^ ^
\ SIL ) + \NOx_MERP/ + \S02_MERp) < 1
Where:
HMC = Highest modeled primary PM2.5 impact using AERMOD and project
related PM2.5 emissions (|ig/m3)
SIL = Significant Impact Level (|ig/m3)
NOx _Em = Project related NOx Emissions (tons per year - tpy)
NOx MERP = NOx Emissions from Table 1 (tpy)
S02_Em = Project related SO2 Emissions (tpy)
S02_MERP = SO2 Emissions from Table 1 (tpy)
The TVA's 24-hour and annual PM2.5 inputs to Equation 1 are provided in Table 2 below,
and the resulting combined PM2.5 impacts are calculated in Equation 2 and Equation 3 below,
respectively.
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TABLE 2
Primary and Secondary PM2.5 Inputs for the SILs in Class II Areas
Secondary PM2.5 Impacts
24-hr
Average
Annual
Average
Highest Modeled Primary
PM2.5 Concentration ([j,g/m3)111
0.49
0.053
SILs for the NAAQS and PSD Increments in Class II areas ([j,g/m3)|2'
1.2
0.2
GCC NOx Emissions (tons/yr)131
2,270
2,270
Default NOx MERPs [4]
4,000
7,407
GCC SO2 Emissions (tons/yr)131
14.2
14.2
Default S02 MERPs [4]
667
6,061
Notes:
1.
2.
3.
4.
TVA GCC facility project primary PM2.5 modeling results.
SILs for the NAAQS in Class I and Class II areas and for PSD increments
in Class II areas. Based on the April 17, 2018 EPA memo, Guidance on
Significant Impact Levels for Ozone and Fine Particles in the Prevention
of Significant Deterioration Permitting Program.
TVA GCC facility project emissions.
Default MERPs information from Table 1
Combined Impacts for 24-hour PM2.5 for the SIL in Class II Areas:
/0.49\ /z;z70\ /14.z\
(tt) + Uo) + (667 H997
EQUATION 2
r2,270\ /14.2>
Combined Impacts for Annual PM2.5 for the SIL in Class II Areas:
/0.053\ (z;z70\ ( 14.Z \
( 0.2 ) + (7,407) + (606l) ~ 0-573
EQUATION 3
<2,270\ /14.2
Both the 24-hour and annual PM2.5 combined impacts as presented in Equation 2 and
Equation 3 were less than 1, which indicated that 24-hour and annual PM2.5 impacts were
expected to be below the Class II SILs for the NAAQS and PSD increments. From this source
impact PM2.5 assessment, it was determined that emissions from TVA GCC facility would not
cause or contribute to a violation of the PM2.5 NAAQS in Class II areas.
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Assessment of O3
A somewhat more refined analysis was performed to assess the impacts of the proposed
project on O3 concentrations in the area around the TVA GCC facility. Application of the TDEC
default NOx and VOC MERPs for O3 shown in Table 1 above indicated that O3 impacts would
be greater than the 8-hour O3 SIL of 1 ppb and that a cumulative O3 assessment would be
necessary to demonstrate whether the facility modification would cause or contribute to a
violation of a the O3 NAAQS.
IEDMUIND]
II I.INUIS
Paducah
GLEASON
Nashville
MISSOURI
Memphis
NORTH
KiORKINSVIlItlE
Kr'NTI < KN
~ NNKSSI*
30 40 SOMiles
J I I
MISSISSIPPI
W aba m,\
TENNESSEE VALLEY AUTHORITY - GLEASON SIMPLE CYCLE FACILITY
AMBIENT 03 AND N02 MONITORS WITHIN 150 KM OF GCC
N02 MONITOR
03 MONITORS
The O3 assessment first examined ambient O3 concentrations in the region surrounding
the TVA GCC facility. There are no ambient O3 monitors in the immediate vicinity of GCC, but
there are six monitors within 150 km of the facility (Figure 2 and Tables 3 and 4). The Cadiz,
KY, monitor was selected as the most representative background site due to its proximity to
GCC, its comparable levels of precursor emissions in the county, and it has the largest
measurement scale indicating it is representative of regional air quality. The three-year average
(2015- 2017) of the fourth-highest 8-hour O3 concentration was 61 ppb, well below the 70 ppb
NAAQS.
FIGURE 2
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TABLE 3
Ambient O3 Monitors within 150 km of GCC
Site Name
Site ID
Distance
to GCC
(km)
Measurement
Scale (km)
County NOx
Emissions
(tons/year) [11
County VOC
Emissions
(tons/year) [11
Weakley
County
NA
0
NA
1,216
9,061
Jackson
Purchase
21-145-1024
90
0.5 to 4
15,395
6,542
Cadiz
21-221-9991
91
50 to 100
1,424
14,173
Smithland
21-139-0003
103
4 to 50
1,441
5,933
Fairview
47-187-0106
137
4 to50
5,721
13,557
Hopkinsville
21-047-0006
138
50 to 100
3,589
11,806
Edmund
Orgill Park
47-157-1004
147
4 to 50
32,260
38,104
Notes:
1. EPA's National Emissions Inventory, 2014 v.2.
TABLE 4
2015-2017 Ambient O3 Monitoring Data
Site Name
Site ID
3 Year Avg. 4th High 8-Hr
Ozone Cone, (ppb)
Jackson Purchase
21-145-1024
62
Cadiz
21-221-9991
61
Smithland
21-139-0003
64
Fairview
47-187-0106
60
Hopkinsville
21-047-0006
61
Edmund Orgill Park
47-157-1004
65
Notes:
1. EPA Air Quality System (AQS) Data Mart:
https://www.epa.gov/outdoor-air-quality-data.
As previously discussed, in April 2018, TDEC published modeling guidance on the use
of EPA's MERPs in Tennessee that identified four hypothetical sites, located in Shelby County,
TN, Giles County, TN, Barren County, KY and Ashe County, NC, to represent Tennessee
sources (Figure 1). Precursor emissions in these four counties were compared to Weakley
County, where the TVA GCC facility is located. Weakley County precursor emissions are
comparable to emissions in the three rural counties (Giles, Barren and Ashe) and are much lower
than Shelby County which is urban (Table 5). Ashe County is much further from GCC and is
located in mountainous terrain, unlike the relatively flat terrain around GCC. Both Giles County
and Barren County have similar terrain features to Weakley County. NOx MERPs at these two
sites are also lower than in Shelby County and Ashe County, which makes the analysis more
conservative as ozone impacts from GCC are dominated by NOx emissions.
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TABLE 5
Comparison of Weakley County O3 MERPs Sites for Use in TN
County
Distance
to GCC
(km)
Urban/
Elevation
County NOx
Emissions
(tons/year) [1]
County VOC
Emissions
(tons/year) [1]
NOx
MERP
VOC
MERP
Rural
(m)
(ton/year)
P]
(ton/year)
P]
Weakly,
TN
Rural
110
1,216
9,061
NA
NA
Shelby,
TN
177
Urban
94
32,260
38,104
714
1,339
Giles,
TN
188
Rural
240
1,913
11,298
156
4,000
Barren,
KY
257
Rural
256
2,122
7,580
169
3,333
Ashe,
NC
650
Rural
926
730
6,507
267
8,333
Notes:
1. EPA's National Emissions Inventory, 2014 v.2.
2. Lowest, most conservative MERP at each site.
For the two most representative hypothetical sources selected, as part of EPA's MERPs
Guidance, the EPA performed photochemical modeling for two hypothetical source heights (low
and high stack releases) and three hypothetical emission rates (500, 1000, and 3000 tons per
year). As can be seen in Table 6 below, predicted O3 impacts are nonlinear with respect to
precursor emissions. At these hypothetical sources, the amount of O3 formed from 3,000 tons of
NOx is substantially less than six times the amount formed from 500 tons of NOx on a per ton
basis, so using a MERP based on 500 tons of NOx would significantly over-estimate the O3
impacts from GCC. Therefore, this analysis used the most conservative MERPs based on
emission rates most similar to emissions from GCC (hypothetical source emissions of 3,000 tons
per year for NOx and 500 tons per year for VOCs) at the two most representative sites (Giles
County and Barren County) (Table 7).
TABLE 6
Precursor
Pollutant
State
County
FIPS
TPY
Stack
Height
(m)
Cone.
(PPb)
MERP
(tons/year)
NOx
03
Kentucky
Barren
21009
500
10
2.908
172
NOx
03
Kentucky
Barren
21009
500
90
2.946
170
NOx
03
Kentucky
Barren
21009
1000
90
5.026
199
NOx
03
Kentucky
Barren
21009
3000
90
10.687
281
NOx
O3
Tennessee
Giles
47055
500
10
2.616
191
NOx
O3
Tennessee
Giles
47055
500
90
3.208
156
NOx
O3
Tennessee
Giles
47055
1000
90
5.387
186
NOx
O3
Tennessee
Giles
47055
3000
90
10.356
290
TCA GCC project emissions are 2,270for NOx and 158 tpy for VOC.
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TABLE 7
Q3 MERPs for Various Emissions Rates in Giles County and Barren County
NOx
NOx
VOC
VOC
County
Stack
Emissions
MERP
Emissions
MERP
(tons/year)
(ton/year)
(tons/yr)
(ton/year)
Giles, TN
Low
500
163
500
12,500
Giles, TN
High
500
156
500
NA
Giles, TN
Low
1,000
NA
1,000
11,111
Giles, TN
High
1,000
186
1,000
10,000
Giles, TN
High
3,000
290
3,000
4,000
Barren, KY
Low
500
172
500
8,333
Barren, KY
High
500
169
500
8,333
Barren, KY
High
1,000
199
1,000
7,692
Barren, KY
High
3,000
281
3,000
3,333
Most Conservative for
281
8,333
Emissions Similar to GCC
Notes:
1. Hypothetical sources with NOx emissions of 3,000 tons per year and VOC
emissions of 500 tons per year.
The O3 impacts for the source impact assessment were calculated as the sum of the ratio
of precursor emissions to the MERPs. If the sum of the ratios is less than 1, then the O3 impacts
are below the O3 SIL and no cumulative analysis is necessary.
EQUATION 4
( NOx_Em ^ i ( VOC_Em ^
\NOx_MERPJ + \VOC_MERPj < 1
Where:
NOx _Em = Project related NOx Emissions (tons per year - tpy)
NOx MERP = NOx Emissions from Table 7 (tpy)
VOCEm = Project related VOC Emissions (tpy)
S02_MERP = SO2 Emissions from Table 7 (tpy)
The TVA GCC facility's ozone inputs to Equation 4 are provided in Table 8, and the
resulting impacts are calculated in Equation 5 below.
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TABLE 8
Notes:
O3 Precursor
GCC Emissions
(tons/year)
3 Year Avg. 4th High
8-Hr Ozone Cone,
(ppb) W
NOx
2,270
281 [2]
VOC
158
8,333 [3]
1.
2.
3.
TVA GCC facility project emissions.
Most conservative MERP for NOx emissions of 3,000 tons per year at
Giles County or Barren County.
Most conservative MERP for VOC emissions of 500 tons per year at Giles
County or Barren County.
Combined Impacts for O3 for the SIL in Class II Areas:
EQUATION 5
/2,270\ ( 158 \
bar) + tea) = ai°
According to Equation 5, the sum of the ratios was greater than 1, and the combined O3
impacts were above the SIL. Therefore, a cumulative O3 analysis was necessary and performed,
which added background O3 and compared the combined impacts to the NAAQS, as shown in
Equation 6.
Background 03 +
Where:
EQUATION 6
NOx Em \ ( VOC Em
kNOx MERP
\ ( VOC Em \\ \
+ J X SIL < NAAQS
) \VOC_MERP)J )
Background Ozone = 2015-2017 8-hour O3 design value (ppb) for Cadiz monitor
NOx _Em = Project related NOx Emissions (tons per year - tpy)
NOx MERP = NOx Emissions from Table 7 (tpy)
VOCEm = Project related VOC Emissions (tpy)
S02_MERP = SO2 Emissions from Table 7 (tpy)
SIL = 1 ppb O3
NAAQS = 8-hour O3 NAAQS (70 ppb)
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The cumulative O3 impacts from the TCA GCC facility are calculated in Equation 7
below.
Cumulative O3 Impacts:
Using the 3-year 8-hour O3 design value of 61 ppb from Cadiz, KY, the ratios defined in
Equation 5, and the O3 SIL of 1 ppb, the cumulative O3 impacts was calculated to be 69.1 ppb
and did not exceed the O3 NAAQS. From this cumulative O3 assessment, it was determined that
emissions from the TCA GCC facility would not cause or contribute to a violation of the O3
NAAQS.
EQUATION 7
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Appendix D: Example of the background monitoring data calculations for a Second
Level 24-hour modeling analysis
This appendix provides an illustrative example of the calculations and data sorting
recommendations for the background monitoring data to be used in a Second Level 24-hour
PM2.5 modeling analysis. In this example, it was determined through discussion and coordination
with the appropriate permitting authority that the impacts from the project source's direct PM2.5
emissions were most prominent during the cool season and were not temporally correlated with
background PM2.5 levels that were typical highest during the warm season. So, combining the
modeled and monitored levels through a First Level 24-hour PM2.5 modeling analysis was
determined to be potentially overly conservative. Extending the compliance demonstration to a
Second Level analysis allows for a more refined and appropriate assessment of the cumulative
impacts on the direct PM2.5 emissions in this particular situation.
The example provided is from an idealized Federal Reference Method (FRM) PM2.5
monitoring site that operates on a daily (1-in-l day) frequency with 100% data completeness. In
this case, the annual 98th percentile concentration is the 8th highest concentration of the year. In
most cases, the FRM monitoring site will likely operate on a l-and-3 day frequency and will also
likely have missing data due to monitor maintenance or collected data not meeting all of the
quality assurance criteria. Please reference Appendix N to 40 CFR part 50 to determine the
appropriate 98th percentile rank of the monitored data based on the monitor sampling frequency
and valid number of days sampled during each year.
The appropriate seasonal (or quarterly) background concentrations to be included as
inputs to the AERMOD model per a Second Level 24-hour PM2.5 modeling analysis are as
follows:
Step 1 - Start with the most recent 3-years of representative background PM2.5 ambient
monitoring data that are being used to develop the monitored background PM2.5 design
value. In this example, the 3-years of 2008 to 2010 are being used to determine the
monitored design value.
Step 2 - For each year, determine the appropriate rank for the daily 98th percentile PM2.5
concentration. Again, this idealized example is from a 1-in-l day monitor with 100% data
completeness. So, the 8th highest concentration of each year is the 98th percentile PM2.5
concentration. The 98th percentile PM2.5 concentration for 2008 is highlighted in Table E-
1. The full concentration data from 2009 and 2010 are not shown across the steps in this
Appendix for simplicity but would be similar to that of 2008.
Step 3 - Remove from further consideration in this analysis the PM2.5 concentrations
from each year that are greater than the 98th percentile PM2.5 concentration. In the case
presented for a 1-in-l day monitor, the top 7 concentrations are removed. If the monitor
were a l-in-3 day monitor, only the top 2 concentrations would be removed. The resultant
dataset after the top 7 concentrations have been removed from further consideration in
this analysis for 2008 is presented in Table E-2.
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Step 4 - For each year, divide the resultant annual dataset of the monitored data equal to
or less than the 98th percentile PM2.5 concentration into each season (or quarter). For
2008, the seasonal subsets are presented in Table E-3.
Step 5 - Determine the maximum PM2.5 concentration from each of the seasonal (or
quarterly) subsets created in Step 4 for each year. The maximum PM2.5 concentration
from each season for 2008 is highlighted in Table E-3.
Step 6 - Average the seasonal (or quarterly) maximums from Step 5 across the three
years of monitoring data to create the four seasonal background PM2.5 concentrations to
be included as inputs to the AERMOD model. These averages for the 2008 to 2010
dataset used in this example are presented in Table E-4. As noted above, the full
concentration data from 2009 and 2010 are not shown across the steps in this Appendix
for simplicity, but the seasonal maximums from 2009 and 2010 presented in Table E-4
were determined by following the previous five steps similar to that of 2008.
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Table E-l. 2008 Daily PM2.5 Concentrations
Date
Cone.
Date
Cone.
Date
Cone.
Date
Cone.
Date
Cone.
Date
Cone.
Date
Cone.
Date
Cone.
1-Jan
10.4
16-Feb
15.1
2-Apr
10.5
18-May
11.1
3-Jul
17.1
18-Aug
18.7
3-Oct
12.3
18-Nov
4.4
2-Jan
5.4
17-Feb
11.8
3-Apr
8.2
19-May
7.7
4-Jul
19.8
19-Aug
21.5
4-Oct
19.5
19-Nov
8.2
3-Jan
10.0
18-Feb
3.4
4-Apr
9.7
20-May
13.6
5-Jul
14.3
20-Aug
20.1
5-Oct
23.7
20-Nov
11.1
4-Jan
16.4
19-Feb
4.5
5-Apr
6.9
21-May
12.1
6-Jul
11.5
21-Aug
18.4
6-Oct
19.8
21-Nov
5.3
5-Jan
11.2
20-Feb
4.8
6-Apr
6.3
22-May
10.0
7-Jul
14.3
22-Aug
16.7
7-Oet
21.7
22-Nov
8.9
6-Jan
11.1
21-Feb
11.9
7-Apr
7.9
23-May
13.3
8-Jul
12.2
23-Aug
13.8
8-Oct
12.2
23-Nov
14.0
7-Jan
10.2
22-Feb
20.1
8-Apr
9.8
24-May
11.2
9-Jul
11.1
24-Aug
19.0
9-Oct
5.1
24-Nov
12.7
8-Jan
11.4
23-Feb
11.4
9-Apr
16.5
25-May
17.7
10-Jul
9.7
25-Aug
17.6
10-Oet
10.2
25-Nov
9.7
9-Jan
8.1
24-Feb
19.3
10-Apr
13.3
26-May
14.2
11-Jul
16.4
26-Aug
15.4
11-Oct
10.7
26-Nov
12.8
10-Jan
9.4
25-Feb
18.2
11-Apr
11.0
27-May
15.4
12-Jul
21.5
27-Aug
12.6
12-Oct
5.6
27-Nov
16.6
11-Jan
5.7
26-Feb
12.8
12-Apr
8.8
28-May
13.9
13-Jul
25.1
28-Aug
12.1
13-Oct
5.9
28-Nov
17.2
12-Jan
8.9
27-Feb
5.5
13-Apr
6.3
29-May
9.3
14-Jul
11.7
29-Aug
10.1
14-Oct
9.7
29-Nov
16.6
13-Jan
18.1
28-Feb
9.7
14-Apr
5.1
30-May
14.5
15-Jul
18.9
30-Aug
17.2
15-Oct
12.8
30-Nov
4.5
14-Jan
11.0
29-Feb
12.1
15-Apr
7.9
31-May
20.5
16-Jul
28.9
31-Aug
19.9
16-Oct
16.4
1-Dec
7.5
15-Jan
11.8
1-Mar
9.6
16-Apr
8.2
1-Jun
15.3
17-Jul
27.6
1-Sep
19.4
17-Oct
12.0
2-Dec
10.6
16-Jan
10.7
2-Mar
5.6
17-Apr
14.7
2-Jun
11.5
18-Jul
12.8
2-Sep
18.2
18-Oet
7.9
3-Dec
16.7
17-Jan
10.0
3-Mar
12.5
18-Apr
22.5
3-Jun
17.9
19-Jul
6.2
3-Sep
24.0
19-Oet
6.6
4-Dec
12.5
18-Jan
15.6
4-Mar
7.1
19-Apr
12.8
4-Jun
21.1
20-Jul
20.1
4-Sep
15.4
20-Oct
8.1
5-Dec
7.3
19-Jan
18.0
5-Mar
4.9
20-Apr
6.9
5-Jun
17.9
21-Jul
26.5
5-Sep
12.4
21-Oct
12.2
6-Dec
10.4
20-Jan
6.6
6-Mar
9.9
21-Apr
7.5
6-Jun
17.6
22-Jul
16.9
6-Sep
12.5
22-Oct
4.6
7-Dec
13.4
21-Jan
7.4
7-Mar
11.2
22-Apr
6.0
7-Jun
15.0
23-Jul
12.8
7-Sep
15.8
23-Oct
6.1
8-Dec
10.5
22-Jan
13.5
8-Mar
5.5
23-Apr
9.1
8-Jun
22.3
24-Jul
7.9
8-Sep
23.4
24-Oct
4.6
9-Dee
9.3
23-Jan
16.0
9-Mar
8.8
24-Apr
10.3
9-Jun
27.9
25-Jul
15.7
9-Sep
11.5
25-Oct
4.5
10-Dec
6.5
24-Jan
9.4
10-Mar
11.0
25-Apr
12.0
10-Jun
21.6
26-Jul
24.9
10-Sep
6.0
26-Oct
10.5
11-Dee
3.0
25-Jan
12.6
11-Mar
12.1
26-Apr
12.5
11-Jun
19.4
27-Jul
22.2
11-Sep
11.8
27-Oct
6.4
12-Dee
3.5
26-Jan
13.6
12-Mar
9.7
27-Apr
11.3
12-Jun
21.2
28-Jul
17.5
12-Sep
10.7
28-Oct
4.6
13-Dee
10.2
27-Jan
16.1
13-Mar
15.1
28-Apr
7.6
13-Jun
29.1
29-Jul
19.1
13-Sep
7.6
29-Oct
5.6
14-Dee
17.6
28-Jan
10.0
14-Mar
21.6
29-Apr
7.4
14-Jun
15.6
30-Jul
21.1
14-Sep
7.5
30-Oct
7.6
15-Dec
12.4
29-Jan
10.4
15-Mar
16.6
30-Apr
11.4
15-Jun
14.8
31-Jul
18.0
15-Sep
7.1
31-Oet
11.2
16-Dec
9.7
30-Jan
6.9
16-Mar
7.9
1-May
12.6
16-Jun
17.8
1-Aug
16.3
16-Sep
7.7
1-Nov
16.2
17-Dec
7.0
31-Jan
4.9
17-Mar
9.6
2-Mav
10.0
17-Jun
12.6
2-Aug
19.3
17-Sep
11.3
2-Nov
17.3
18-Dec
7.9
1-Feb
5.4
18-Mar
10.3
3-May
11.2
18-Jun
10.5
3-Aug
17.9
18-Sep
16.8
3-Nov
18.3
19-Dec
6.9
2-Feb
7.1
19-Mar
8.4
4-May
10.4
19-Jun
15.0
4-Aug
25.1
19-Sep
14.8
4-Nov
8.9
20-Dec
8.1
3-Feb
10.9
20-Mar
4.9
5-May
15.7
20-Jun
22.7
5-Aug
29.3
20-Sep
8.0
5-Nov
5.8
21-Dec
4.9
4-Feb
12.1
21-Mar
8.7
6-May
16.1
21-Jun
18.7
6-Aug
19.1
21-Sep
10.8
6-Nov
8.6
22-Dec
7.7
5-Feb
17.1
22-Mar
13.3
7-May
16.8
22-Jun
15.2
7-Aug
14.0
22-Sep
14.5
7-Nov
15.0
23-Dec
7.7
6-Feb
10.3
23-Mar
12.2
8-May
14.5
23-Jun
16.8
8-Aug
10.8
23-Sep
21.2
8-Nov
8.3
24-Dec
10.5
7-Feb
4.0
24-Mar
10.3
9-May
11.7
24-Jun
15.1
9-Aug
15.0
24-Sep
8.6
9-Nov
10.0
25-Dec
6.5
8-Feb
9.7
25-Mar
11.9
10-May
9.0
25-Jun
20.7
10-Aug
21.7
25-Sep
1.2
10-Nov
12.8
26-Dec
7.6
9-Feb
11.5
26-Mar
20.1
11-May
6.7
26-Jun
23.0
11-Aug
14.3
26-Sep
16.0
11-Nov
11.8
27-Dec
13.3
10-Feb
3.0
27-Mar
22.5
12-May
7.9
27-Jun
17.8
12-Aug
14.7
27-Sep
12.1
12-Nov
14.8
28-Dec
6.4
11-Feb
5.5
28-Mar
18.2
13-May
8.3
28-Jun
12.4
13-Aug
13.0
28-Sep
18.0
13-Nov
14.5
29-Dec
3.7
12-Feb
18.9
29-Mar
10.8
14-May
12.2
29-Jun
12.7
14-Aug
13.5
29-Sep
17.8
14-Nov
7.7
30-Dec
4.7
13-Feb
17.6
30-Mar
6.4
15-May
13.1
30-Jun
8.9
15-Aug
17.5
30-Sep
16.4
15-Nov
3.6
31-Dec
4.4
14-Feb
11.2
31-Mar
3.3
16-May
8.8
1-Jul
7.1
16-Aug
23.9
1-Oet
12.3
16-Nov
4.6
15-Feb
14.4
1-Apr
7.8
17-May
8.2
2-Jul
13.8
17-Aug
18.4
2-Oct
8.2
17-Nov
7.8
Annual 98th Percentile Concentration = 25.1 lig/m3
D-3
-------
Does not represent final Agency action; Draft final for internal review and comment; 10/26/2020
Table E-2. 2008
Daily PM2.5 Concentrations Less Than or Equal to the 98th Percentile
Date
Cone.
Date
Cone.
Date
Cone.
Date
Cone.
Date
Cone.
Date
Cone.
Date
Cone.
Date
Cone.
1-Jan
10.4
16-Feb
15.1
2-Apr
10.5
18-May
11.1
3-Jul
17.1
18-Aug
18.7
3-Oct
12.3
18-Nov
4.4
2-Jan
5.4
17-Feb
11.8
3-Apr
8.2
19-May
7.7
4-Jul
19.8
19-Aug
21.5
4-Oct
19.5
19-Nov
8.2
3-Jan
10.0
18-Feb
3.4
4-Apr
9.7
20-May
13.6
5-Jul
14.3
20-Aug
20.1
5-Oct
23.7
20-Nov
11.1
4-Jan
16.4
19-Feb
4.5
5-Apr
6.9
21-May
12.1
6-Jul
11.5
21-Aug
18.4
6-Oct
19.8
21-Nov
5.3
5-Jan
11.2
20-Feb
4.8
6-Apr
6.3
22-May
10.0
7-Jul
14.3
22-Aug
16.7
7-Oct
21.7
22-Nov
8.9
6-Jan
11.1
21-Feb
11.9
7-Apr
7.9
23-May
13.3
8-Jul
12.2
23-Aug
13.8
8-Oct
12.2
23-Nov
14.0
7-Jan
10.2
22-Feb
20.1
8-Apr
9.8
24-May
11.2
9-Jul
11.1
24-Aug
19.0
9-Oct
5.1
24-Nov
12.7
8-Jan
11.4
23-Feb
11.4
9-Apr
16.5
25-May
17.7
10-Jul
9.7
25-Aug
17.6
10-Oct
10.2
25-Nov
9.7
9-Jan
8.1
24-Feb
19.3
10-Apr
13.3
26-May
14.2
11-Jul
16.4
26-Aug
15.4
11-Oct
10.7
26-Nov
12.8
10-Jan
9.4
25-Feb
18.2
11-Apr
11.0
27-May
15.4
12-Jul
21.5
27-Aug
12.6
12-Oct
5.6
27-Nov
16.6
11-Jan
5.7
26-Feb
12.8
12-Apr
8.8
28-May
13.9
13-Jul
RC
28-Aug
12.1
13-Oct
5.9
28-Nov
17.2
12-Jan
8.9
27-Feb
5.5
13-Apr
6.3
29-May
9.3
14-Jul
11.7
29-Aug
10.1
14-Oct
9.7
29-Nov
16.6
13-Jan
18.1
28-Feb
9.7
14-Apr
5.1
30-May
14.5
15-Jul
18.9
30-Aug
17.2
15-Oct
12.8
30-Nov
4.5
14-Jan
11.0
29-Feb
12.1
15-Apr
7.9
31-May
20.5
16-Jul
RC
31-Aug
19.9
16-Oct
16.4
1-Dec
7.5
15-Jan
11.8
1-Mar
9.6
16-Apr
8.2
1-Jun
15.3
17-Jul
RC
1-Sep
19.4
17-Oct
12.0
2-Dec
10.6
16-Jan
10.7
2-Mar
5.6
17-Apr
14.7
2-Jun
11.5
18-Jul
12.8
2-Sep
18.2
18-Oct
7.9
3-Dec
16.7
17-Jan
10.0
3-Mar
12.5
18-Apr
22.5
3-Jun
17.9
19-Jul
6.2
3-Sep
24.0
19-Oct
6.6
4-Dec
12.5
18-Jan
15.6
4-Mar
7.1
19-Apr
12.8
4-Jun
21.1
20-Jul
20.1
4-Sep
15.4
20-Oct
8.1
5-Dec
7.3
19-Jan
18.0
5-Mar
4.9
20-Apr
6.9
5-Jun
17.9
21-Jul
RC
5-Sep
12.4
21-Oct
12.2
6-Dec
10.4
20-Jan
6.6
6-Mar
9.9
21-Apr
7.5
6-Jun
17.6
22-Jul
16.9
6-Sep
12.5
22-Oct
4.6
7-Dec
13.4
21-Jan
7.4
7-Mar
11.2
22-Apr
6.0
7-Jun
15.0
23-Jul
12.8
7-Sep
15.8
23-Oct
6.1
8-Dec
10.5
22-Jan
13.5
8-Mar
5.5
23-Apr
9.1
8-Jun
22.3
24-Jul
7.9
8-Sep
23.4
24-Oct
4.6
9-Dec
9.3
23-Jan
16.0
9-Mar
8.8
24-Apr
10.3
9-Jun
RC
25-Jul
15.7
9-Sep
11.5
25-Oct
4.5
10-Dec
6.5
24-Jan
9.4
10-Mar
11.0
25-Apr
12.0
10-Jun
21.6
26-Jul
24.9
10-Sep
6.0
26-Oct
10.5
11-Dec
3.0
25-Jan
12.6
11-Mar
12.1
26-Apr
12.5
11-Jun
19.4
27-Jul
22.2
11-Sep
11.8
27-Oct
6.4
12-Dec
3.5
26-Jan
13.6
12-Mar
9.7
27-Apr
11.3
12-Jun
21.2
28-Jul
17.5
12-Sep
10.7
28-Oct
4.6
13-Dec
10.2
27-Jan
16.1
13-Mar
15.1
28-Apr
7.6
13-Jun
RC
29-Jul
19.1
13-Sep
7.6
29-Oct
5.6
14-Dec
17.6
28-Jan
10.0
14-Mar
21.6
29-Apr
7.4
14-Jun
15.6
30-Jul
21.1
14-Sep
7.5
30-Oct
7.6
15-Dec
12.4
29-Jan
10.4
15-Mar
16.6
30-Apr
11.4
15-Jun
14.8
31-Jul
18.0
15-Sep
7.1
31-Oct
11.2
16-Dec
9.7
30-Jan
6.9
16-Mar
7.9
1-May
12.6
16-Jun
17.8
1-Aug
16.3
16-Sep
7.7
1-Nov
16.2
17-Dec
7.0
31-Jan
4.9
17-Mar
9.6
2-May
10.0
17-Jun
12.6
2-Aug
19.3
17-Sep
11.3
2-Nov
17.3
18-Dec
7.9
1-Feb
5.4
18-Mar
10.3
3-May
11.2
18-Jun
10.5
3-Aug
17.9
18-Sep
16.8
3-Nov
18.3
19-Dec
6.9
2-Feb
7.1
19-Mar
8.4
4-May
10.4
19-Jun
15.0
4-Aug
25.1
19-Sep
14.8
4-Nov
8.9
20-Dec
8.1
3-Feb
10.9
20-Mar
4.9
5-May
15.7
20-Jun
22.7
5-Aug
RC
20-Sep
8.0
5-Nov
5.8
21-Dec
4.9
4-Feb
12.1
21-Mar
8.7
6-May
16.1
21-Jun
18.7
6-Aug
19.1
21-Sep
10.8
6-Nov
8.6
22-Dec
7.7
5-Feb
17.1
22-Mar
13.3
7-May
16.8
22-Jun
15.2
7-Aug
14.0
22-Sep
14.5
7-Nov
15.0
23-Dec
7.7
6-Feb
10.3
23-Mar
12.2
8-May
14.5
23-Jun
16.8
8-Aug
10.8
23-Sep
21.2
8-Nov
8.3
24-Dec
10.5
7-Feb
4.0
24-Mar
10.3
9-May
11.7
24-Jun
15.1
9-Aug
15.0
24-Sep
8.6
9-Nov
10.0
25-Dec
6.5
8-Feb
9.7
25-Mar
11.9
10-May
9.0
25-Jun
20.7
10-Aug
21.7
25-Sep
1.2
10-Nov
12.8
26-Dec
7.6
9-Feb
11.5
26-Mar
20.1
11-May
6.7
26-Jun
23.0
11-Aug
14.3
26-Sep
16.0
11-Nov
11.8
27-Dec
13.3
10-Feb
3.0
27-Mar
22.5
12-May
7.9
27-Jun
17.8
12-Aug
14.7
27-Sep
12.1
12-Nov
14.8
28-Dec
6.4
11-Feb
5.5
28-Mar
18.2
13-May
8.3
28-Jun
12.4
13-Aug
13.0
28-Sep
18.0
13-Nov
14.5
29-Dec
3.7
12-Feb
18.9
29-Mar
10.8
14-May
12.2
29-Jun
12.7
14-Aug
13.5
29-Sep
17.8
14-Nov
7.7
30-Dec
4.7
13-Feb
17.6
30-Mar
6.4
15-May
13.1
30-Jun
8.9
15-Aug
17.5
30-Sep
16.4
15-Nov
3.6
31-Dec
4.4
14-Feb
11.2
31-Mar
3.3
16-May
8.8
1-Jul
7.1
16-Aug
23.9
1-Oct
12.3
16-Nov
4.6
15-Feb
14.4
1-Apr
7.8
17-May
8.2
2-Jul
13.8
17-Aug
18.4
2-Oct
8.2
17-Nov
7.8
Annual 98th Percentile Concentration = 25.1 jig/m
RC = Above 98th Percentile and Removed from Consideration
D-4
-------
Does not represent final Agency action; Draft final for internal review and comment; 10/26/2020
Table E-3. 2008 Daily PM2.5 Concentrations Less Than or Equal to the 98th Percentile by Quarter
Season / Quarter 1
Date
Cone.
Date
Cone.
1-Jan
10.4
16-Feb
15.1
2-Jan
5.4
17-Feb
11.8
3-Jan
10.0
18-Feb
3.4
4-Jan
16.4
19-Feb
4.5
5-Jan
11.2
20-Feb
4.8
6-Jan
11.1
21-Feb
11.9
7-Jan
10.2
22-Feb
20.1
8-Jan
11.4
23-Feb
11.4
9-Jan
8.1
24-Feb
19.3
10-Jan
9.4
25-Feb
18.2
11-Jan
5.7
26-Feb
12.8
12-Jan
8.9
27-Feb
5.5
13-Jan
18.1
28-Feb
9.7
14-Jan
11.0
29-Feb
12.1
15-Jan
11.8
1-Mar
9.6
16-Jan
10.7
2-Mar
5.6
17-Jan
10.0
3-Mar
12.5
18-Jan
15.6
4-Mar
7.1
19-Jan
18.0
5-Mar
4.9
20- Jan
6.6
6-Mar
9.9
21-Jan
7.4
7-Mar
11.2
22-Jan
13.5
8-Mar
5.5
23-Jan
16.0
9-Mar
8.8
24-Jan
9.4
10-Mar
11.0
25-Jan
12.6
11-Mar
12.1
26-Jan
13.6
12-Mar
9.7
27-Jan
16.1
13-Mar
15.1
28-Jan
10.0
14-Mar
21.6
29-Jan
10.4
15-Mar
16.6
30-Jan
6.9
16-Mar
7.9
31-Jan
4.9
17-Mar
9.6
1-Feb
5.4
18-Mar
10.3
2-Feb
7.1
19-Mar
8.4
3-Feb
10.9
20-Mar
4.9
4-Feb
12.1
21-Mar
8.7
5-Feb
17.1
22-Mar
13.3
6-Feb
10.3
23-Mar
12.2
7-Feb
4.0
24-Mar
10.3
8-Feb
9.7
25-Mar
11.9
9-Feb
11.5
26-Mar
20.1
10-Feb
3.0
27-Mar
22.5
11-Feb
5.5
28-Mar
18.2
12-Feb
18.9
29-Mar
10.8
13-Feb
17.6
30-Mar
6.4
14-Feb
11.2
31-Mar
3.3
15-Feb
14.4
Seasonal / Quarterly Maximum
22.5
Season / Quarter 2
Date Cone.
Date Cone.
1-Apr
7.8
17-May
8.2
2-Apr
10.5
18-May
11.1
3-Apr
8.2
19-May
7.7
4-Apr
9.7
20-May
13.6
5-Apr
6.9
21-May
12.1
6-Apr
6.3
22-May
10.0
7-Apr
7.9
23-May
13.3
8-Apr
9.8
24-May
11.2
9-Apr
16.5
25-May
17.7
10-Apr
13.3
26-May
14.2
11-Apr
11.0
27-May
15.4
12-Apr
8.8
28-May
13.9
13-Apr
6.3
29-May
9.3
14-Apr
5.1
30-May
14.5
15-Apr
7.9
31-May
20.5
16-Apr
8.2
1-Jun
15.3
17-Apr
14.7
2-Jun
11.5
18-Apr
22.5
3-Jun
17.9
19-Apr
12.8
4-Jun
21.1
20-Apr
6.9
5-Jun
17.9
21-Apr
7.5
6-Jun
17.6
22-Apr
6.0
7-Jun
15.0
23-Apr
9.1
8-Jun
22.3
24-Apr
10.3
9-Jun
RC
25-Apr
12.0
10-Jun
21.6
26-Apr
12.5
11-Jun
19.4
27-Apr
11.3
12-Jun
21.2
28-Apr
7.6
13-Jun
RC
29-Apr
x7,4
14-Jun
15.6
30-Apr
11.4
15-Jun
14.8
1-May
12.6
16-Jun
17.8
2-May
10.0
17-Jun
12.6
3-May
11.2
18-Jun
10.5
4-May
10.4
19-Jun
15.0
5-May
15.7
20-Jun
22.7
6-May
16.1
21-Jun
18.7
7-May
16.8
22-Jun
15.2
8-May
14.5
23-Jun
16.8
9-May
11.7
24-Jun
15.1
10-May
9.0
25-Jun
20.7
11-May
67
26-Jun
23.0
12-May
7.9
27-Jun
17.8
13-May
8.3
28-Jun
12.4
14-May
12.2
29-Jun
12.7
15-May
13.1
30-Jun
8.9
16-May
8.8
Seasonal / Quarterly Maximum
23.0
Season / Quarter 3
Date Cone.
Date Cone.
1-Jul
7.1
16-Aug
23.9
2-Jul
13.8
17-Aug
18.4
3-Jul
17.1
18-Aug
18.7
4-Jul
19.8
19-Aug
21.5
5-Jul
14.3
20-Aug
20.1
6-Jul
11.5
21-Aug
18.4
7-Jul
14.3
22-Aug
167
8-Jul
12.2
23-Aug
13.8
9-Jul
11.1
24-Aug
19.0
10-Jul
9.7
25-Aug
17.6
11-Jul
16.4
26-Aug
15.4
12-Jul
21.5
27-Aug
12.6
13-Jul
RC
28-Aug
12.1
14-Jul
11.7
29-Aug
10.1
15-Jul
18.9
30-Aug
17.2
16-Jul
RC
31-Aug
19.9
17-Jul
RC
1-Sep
19.4
18-Jul
12.8
2-Sep
18.2
19-Jul
6.2
3-Sep
24.0
20-Jul
20.1
4-Sep
15.4
21-Jul
RC
5-Sep
12.4
22-Jul
16.9
6-Sep
12.5
23-Jul
12.8
7-Sep
15.8
24-Jul
7.9
8-Sep
23.4
25-Jul
15.7
9-Sep
11.5
26-Jul
24.9
10-Sep
6.0
27-Jul
22.2
11-Sep
11.8
28-Jul
17.5
12-Sep
10.7
29-Jul
19.1
13-Sep
7.6
30-Jul
21.1
14-Sep
7.5
31-Jul
18.0
15-Sep
7.1
1-Aug
16.3
16-Sep
7.7
2-Aug
19.3
17-Sep
11.3
3-Aug
17.9
18-Sep
16.8
4-Aug
25.1
19-Sep
14.8
5-Aug
RC
20-Sep
8.0
6-Aug
19.1
21-Sep
10.8
7-Aug
14.0
22-Sep
14.5
8-Aug
10.8
23-Sep
21.2
9-Aug
15.0
24-Sep
8.6
10-Aug
21.7
25-Sep
1.2
11-Aug
14.3
26-Sep
16.0
12-Aug
14.7
27-Sep
12.1
13-Aug
13.0
28-Sep
18.0
14-Aug
13.5
29-Sep
17.8
15-Aug
17.5
30-Sep
16.4
Seasonal / Quarterly Maximum
25.1
Season / Quarter 4
Date Cone.
Date Cone.
1-Oct
12.3
16-Nov
4.6
2-Oct
8.2
17-Nov
7.8
3-Oct
12.3
18-Nov
4.4
4-Oct
19.5
19-Nov
8.2
5-Oct
23.7
20-Nov
11.1
6-Oct
19.8
21-Nov
5.3
7-Oct
21.7
22-Nov
8.9
8-Oct
12.2
23-Nov
14.0
9-Oct
5.1
24-Nov
12.7
10-Oct
10.2
25-Nov
9.7
11-Oct
10.7
26-Nov
12.8
12-Oct
5.6
27-Nov
16.6
13-Oct
5.9
28-Nov
17.2
14-Oct
9.7
29-Nov
16.6
15-Oct
12.8
30-Nov
4.5
16-Oct
16.4
1-Dec
7.5
17-Oct
12.0
2-Dec
10.6
18-Oct
7.9
3-Dec
16.7
19-Oct
6.6
4-Dec
12.5
20-Oct
8.1
5-Dec
7.3
21-Oct
12.2
6-Dec
10.4
22-Oct
4.6
7-Dec
13.4
23-Oct
6.1
8-Dec
10.5
24-Oct
4.6
9-Dec
9.3
25-Oct
4.5
10-Dec
6.5
26-Oct
10.5
11-Dec
3.0
27-Oct
6.4
12-Dec
3.5
28-Oct
4.6
13-Dec
10.2
29-Oct
5.6
14-Dec
17.6
30-Oct
7.6
15-Dec
12.4
31-Oct
11.2
16-Dec
9.7
1-Nov
16.2
17-Dec
7.0
2-Nov
17.3
18-Dec
7.9
3-Nov
18.3
19-Dec
6.9
4-Nov
8.9
20-Dec
8.1
5-Nov
5.8
21-Dec
4.9
6-Nov
8.6
22-Dec
7.7
7-Nov
15.0
23-Dec
7.7
8-Nov
8.3
24-Dec
10.5
9-Nov
10.0
25-Dec
6.5
10-Nov
12.8
26-Dec
7.6
11-Nov
11.8
27-Dec
13.3
12-Nov
14.8
28-Dec
6.4
13-Nov
14.5
29-Dec
3.7
14-Nov
7.7
30-Dec
4.7
15-Nov
3.6
31-Dec
4.4
Seasonal / Quarterly Maximum
23.7
Seasonal / Quarterly Maximum Concentration
RC = Above 98th Percentile and Removed from Consideration
D-5
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Does not represent final Agency action; Draft final for internal review and comment; 10/26/2020
Table E-4. Resulting Average of Seasonal (or Quarterly) Maximums for Inclusion into AERMOD
Seasonal / Quarterly Average Highest Monitored Concentration
(From Annual Data sets Equal To and Less Than the 98 th Percentile)
Ql
Q2
Q3
Q4
2008
22.5
23.0
25.1
23.7
2009
21.1
20.7
21.2
19.8
2010
20.7
22.6
23.5
20.7
Average
21.433
22.100
23.267
21.400
(Note, the complete datasets for 2009 and 2010 are not shown in Appendix D but wouldfollow the same steps as for 2008)
D-6
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/P-21-001
Environmental Protection Air Quality Assessment Division September 2021
Agency Research Triangle Park, NC
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