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PM2.5 Precursor Demonstration Guidance

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EPA-454/R-19-004
May 2019
PM2.5 Precursor Demonstration Guidance
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Air Quality Policy Division
Research Triangle Park, NC

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i of Contents
Acronyms Used in this Guidance	5
1.0 Introduction	6
1.1 Precursor Demonstrations	7
1.1.1	Comprehensive and Major Stationary Source Precursor Demonstrations.. 7
1.1.2	Nonattainment NSR Precursor Demonstration	8
Policy Guidance	10
2.0 Overview	10
2.1	Interpretation of "Contribute Significantly" In Section 189(e) of the Clean Air
Act and the PM2.5 SIP Requirements Rule	10
2.2	Criteria for Identifying a Contribution	13
2.3	Evaluating Whether a Contribution is "Significant"- Considering Additional
Information	18
2.4	Locations at Which to Evaluate Air Quality Changes	19
Technical Guidance	20
3.0 Concentration-Based Analysis	20
3.1	Ambient Data Analysis of Secondarily-Formed PM2.5	20
3.1.1	Ammonium Sulfate	21
3.1.2	Ammonium Nitrate	21
3.1.3	SOA	22
3.1.4	Role of NOx and SO2 in Secondary PM Chemistry	22
3.1.5	Assigning PM2.5 Species to Precursors - Summary	23
3.1.6	Evaluating Concentration Based Analysis Results	25
3.1.7	Additional Information	25
3.2	Air Quality Modeling	26
3.2.1 Evaluating Modeling Results	27
4.0 Sensitivity Based Analysis	27
4.1 Modeling for Sensitivity Demonstrations	28
4.1.1	Emissions Reductions for Sensitivity Analyses	28
4.1.2	Evaluating Sensitivity Modeling Results	32
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5.0 Attainment Plan Precursor Demonstrations	32
5.1	Modeling for Attainment Plan Precursor Demonstrations	33
5.1.1	Air Quality Modeling Process	33
5.1.2	Modeling Approaches	35
5.2	Base Year and Future Year Model Assessments	35
5.3	Calculating the Modeled Impact from Precursors	36
5.3.1	Estimating the Annual PM2.5 Impact from Precursors	38
5.3.2	Estimating the Daily PM2.5 Impact from Precursors	38
6.0 Nonattainment New Source Review (NNSR) Precursor Demonstration	39
6.1	NNSR Demonstrations	40
6.2	Modeling for NNSR Demonstrations	42
6.2.1	Types of Models	43
6.2.2	Modeling for Major Stationary Sources	43
6.2.3	Modeling Approaches	44
6.2.4	Horizontal Grid Resolution	44
6.3	Location and Source Characteristics of Potential Major Stationary Source
Growth	46
6.4	Base Year and Future Year Model Assessments	46
6.5	Calculating the Modeled Impact from Precursors	47
6.5.1	Estimating the Annual PM2.5 Impact from Precursors	47
6.5.2	Estimating the Daily PM2.5 Impact from Precursors	47
7.0 References	49
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Acron\
BACT
Best Available Control Technology
CAA
Clean Air Act
CAMx
Comprehensive Air Quality Model with Extensions
CI
Confidence Interval
CMAQ
Community Multiscale Air Quality model
CSN
Chemical Speciation Network
CTM
Chemical transport model
DDM
Direct Decoupled Method
DV
Design Value
EC
Elemental carbon
EGU
Electric Generating Units
EPA
Environmental Protection Agency
FEM
Federal Equivalent Method
FRM
Federal Reference Method
IMPROVE
Interagency Monitoring of Protected Visual Environments
LAER
Lowest Achievable Emissions Rate
NNSR
Nonattainment New Source Review
NSR
New Source Review
OM
Organic matter
PM
Particulate matter
PMio
Particulate matter with diameter 10 microns or less
PM2.5
Particulate matter with diameter 2.5 microns or less
PSD
Prevention of Significant Deterioration
RACM
Reasonably Available Control Measures
RACT
Reasonably Available Control Technology
RFP
Reasonable Further Progress
RRF
Relative response factor
SANDWICH
Sulfate, adjusted nitrate, derived water, inferred carbonaceous balance
SILs
Significant Impact Levels
SIP
State Implementation Plan
SMAT
Software for the Modeled Attainment Test
SOA
Secondary Organic Aerosol
VOC
Volatile Organic Compound
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1.0 iiiiiM 11 1
In 2016, the Environmental Protection Agency (EPA) finalized the fine particulate matter
(PM2.5) State Implementation Plan (SIP) Requirements Rule,1 which contains details on
planning requirements that apply to areas designated nonattainment for any PM2.5
national ambient air quality standard (NAAQS). The PM2.5 SIP Requirements Rule
addresses the statutory SIP requirements for state, local and tribal air agencies
(hereafter known as "air agencies") such as: general requirements for attainment plan
due dates and attainment dates; emissions inventories; attainment demonstrations;
provisions for demonstrating reasonable further progress (RFP); quantitative
milestones; contingency measures; and nonattainment New Source Review (NNSR)
permitting programs.
The PM2.5 SIP Requirements Rule identifies the four main PM2.5 precursor pollutants
(sulfur dioxide (SO2), nitrogen oxides (NOx), volatile organic compounds (VOC), and
ammonia (NH3)) that are required to be addressed in all PM2.5 nonattainment area SIPs.
The rule establishes that PM2.5 precursors must be evaluated for potential control
measures in any PM2.5 attainment plan and any NNSR program. The rule does not
include any national presumption that excludes sources of emissions of a particular
precursor from further analysis for attainment plan or NNSR control requirements.
However, the rule indicates that air agencies may choose to submit an optional
precursor demonstration designed to show that for a specific PM2.5 nonattainment area,
emissions of a particular precursor from sources within the nonattainment area do not
or would not contribute significantly to PM2.5 levels that exceed the standard. If the EPA
approves the demonstration, the attainment plan for a particular PM2.5 nonattainment
area may exclude that precursor from certain control requirements under the Clean Air
Act (CAA or Act) (e.g., reasonably available control measures [RACM], reasonably
available control technology [RACT], RFP, NNSR), depending on the type of
demonstration provided.
This guidance document is designed to assist air agencies that may wish to submit PM2.5
precursor demonstrations as allowed by the PM2.5 SIP Requirements Rule. This guidance
is intended for use by air agencies; the EPA Headquarters and Regional offices; and the
public. This document does not substitute for provisions or regulations of the CAA, nor
is it a regulation itself. As the term "guidance" suggests, it provides recommendations or
guidelines, as authorized under CAA section 189(e), that will be useful to air agencies in
developing the precursor demonstrations by which the EPA can ultimately determine
whether sources of one or more precursors contribute significantly to PM2.5 levels that
exceed the standard in a particular nonattainment area. Thus, it does not impose
binding, enforceable requirements on any party, nor does it ensure that the EPA will
approve a precursor demonstration in all instances where the guidance is followed, as
1 See Fine Particulate Matter National Ambient Air Quality Standards: State Implementation Plan
Requirements (PM2.5SIP Requirements Rule), 81 FR 58009 (Aug. 24, 2016).
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the guidance may not apply to a particular situation based upon the facts and
circumstances of a particular nonattainment area.
Where appropriate, and consistent with the requirements of the PM2.5 SIP Requirements
Rule, air agencies retain the discretion to develop precursor demonstrations on a case-
by-case basis that differ from this guidance. Final decisions by the EPA to approve a
particular precursor demonstration as part of a plan submission will only be made based
on the requirements of the statute and applicable regulations, and will only be made
following an air agency's final submission of the precursor demonstration to the EPA,
and after appropriate notice and opportunity for public review and comment. Interested
parties may raise comment about the appropriateness of the application of this
guidance to a particular nonattainment area during the approval process for a SIP
submittal that includes a precursor demonstration. The EPA and air agencies should
consider whether or not the recommendations in this guidance are appropriate for each
situation.
1.1 Precursor Demonstrations
The PM2.5 SIP Requirements Rule permits states to submit precursor demonstrations to
exclude sources of one or more precursors from control requirements under either the
attainment plan or the NNSR program. Below are brief descriptions of comprehensive
and major stationary source precursor demonstrations (collectively referred to as
attainment plan precursor demonstrations) and NNSR precursor demonstrations.
1.1.1 Comprehensive and Major Stationary Source Precursor Demonstrations
For any Moderate or Serious area plan2, an air agency could choose to provide an
optional precursor demonstration showing that existing emissions of a particular
precursor "do not contribute significantly to PM2.5 levels that exceed the standard in the
area."3 In the preamble to the PM2.5 SIP Requirements Rule, the EPA described two
potential steps in this analytical process (a concentration-based analysis, and a
sensitivity analysis).
Concentration-Based Analysis. The first step of the analysis would be to determine
whether all emissions of the relevant precursor "contribute significantly" to total PM2.5
concentrations (a "concentration-based analysis"). This can be in the form of (1) a
"comprehensive precursor demonstration," in which the state would need to show that
emissions of a particular PM2.5 precursor from all existing emissions sources , do not
contribute significantly to PM2.5 levels that exceed the standard in the area; or (2) a
"major stationary source precursor demonstration," which the state would need to
2	Such plans may include an attainment demonstration or an impracticability demonstration. See
40 CFR 51.1011(a)(4)(i) and 40 CFR 51.1011(a)(4)(ii).
3	See 40 CFR 51.1006(a)(1).
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show that emissions of a particular precursor from all existing major stationary sources
located in the nonattainment area, do not contribute significantly to PM2.5 levels that
exceed the standard in the area. This analysis can be documented through the
assessment of recent air quality monitoring data for PM2.5 component species in the
area, emissions inventory information, and/or air quality modeling.
Sensitivity-Based Analysis. If the concentration-based analysis does not support a finding
of insignificant contribution, then the air agency could still choose to conduct an
optional "sensitivity-based analysis."4 Through air quality modeling, this sensitivity
analysis can either evaluate the effect of reducing emissions of the precursor (by a
certain percentage) from all existing emissions sources of the precursor on PM2.5 levels
in the area, or it can evaluate the effect of reducing emissions from only existing major
stationary sources on PM2.5 levels in the area.
If the EPA approves a comprehensive precursor demonstration for a particular
nonattainment area, the air agency would not be required to control emissions of the
relevant precursor, for any existing source, in the attainment plan for the area.5 If the
EPA approves a major stationary source precursor demonstration for a particular
nonattainment area, the air agency would not be required to control emissions of the
relevant precursor from existing major stationary sources in the attainment plan for the
area.6
1.1.2 Nonattainment NSR Precursor Demonstration
NNSR Analysis. Under the final rule, a separate optional analysis is available for air
agencies that seek to demonstrate that new or modified major stationary sources of a
particular precursor would not contribute significantly to PM2.5 levels that exceed the
standard in the nonattainment area.7 For this demonstration, an air agency would need
to provide a separate NNSR precursor demonstration that evaluates "the sensitivity of
PM2.5 levels in the nonattainment area to an increase in emissions of a particular
precursor in order to determine whether the resulting air quality changes are
significant."8 If the EPA approves this type of demonstration for a particular
nonattainment area, the air agency would be able to exempt such new major stationary
sources and major modifications at existing sources from the NNSR requirements for
that PM2.5 precursor in 40 CFR 51.165.9
4	See 40 CFR 51.1006(a)(l)(ii) and 51.1006(a)(2)(ii).
5	See 40 CFR 51.1006(a)(l)(iii).
6	See 40 CFR 51.1006(a)(2)(iii).
7	See 40 CFR 51.1006(a)(3).
8	See 40 CFR 51.1006(a)(3)(i).
9	See 40 CFR 51.1006(a)(3)(ii).
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This guidance document contains additional details on the recommended procedures
for completing each of the three types of PM2.5 precursor demonstrations defined in the
final rule, including techniques for conducting these analyses and recommended
contribution thresholds for this purpose. See the PM2.5 SIP Requirements Rule for more
information on these precursor demonstrations, including details on the specific SIP
elements that do not need to be addressed based on the approval of a particular
precursor demonstration.10
10See PM2.5SIP Requirements Rule at 81 FR 58017.
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dance
2.0	i
As discussed above, the PM2.5SIP Requirements Rule establishes that SO2, NOx, VOC,
and NH3 are precursors for which sources are to be presumptively evaluated for
potential control measures in an attainment plan or any NNSR program for any PM2.5
nonattainment area. The rule also indicates that an air agency may choose to submit an
optional precursor demonstration designed to show that, for a particular PM2.5
nonattainment area, emissions of one or more precursors from sources within the
nonattainment area do not or would not contribute significantly to PM2.5 levels that
exceed the standard. This section discusses the factors that EPA recommends using to
determine the degree of impact that reflects a significant contribution on annual and
24-hour PM2.5 concentrations. Later sections of the guidance describe specific details
about how to conduct recommended technical analyses for the three types of precursor
demonstrations (attainment plan—comprehensive; attainment plan-major stationary
source; and NNSR) included in the final rule.
2.1	Interpretation of "Contribute Significantly" in Section 189(e) of the Clean
Air Act and the PM2.5 -II requirement- I uk
Section 189(e) of the CAA requires that control requirements "for major stationary
sources of PM10 shall also apply to major stationary sources of PM10 precursors, except
where the Administrator determines that such sources do not contribute significantly to
PM10 levels which exceed the standard in the area." Consistent with the decision of the
United States Court of Appeals for the District of Columbia Circuit (D.C. Circuit) in NRDC
v. EPA, 706 F.3d 428 (2013), this provision also applies to the regulation of sources of
PM2.5 precursors in designated PM2.5 nonattainment areas. To implement the exception
provided by this provision, the PM2.5 SIP Requirements Rule permits states to submit
precursor demonstrations intended to exclude sources of one or more precursors from
control requirements under either the attainment plan or the NNSR program.11
Consistent with the statute, regulatory language at 40 CFR 51.1006 states that an
attainment plan precursor demonstration must show that sources "do not contribute
11 See PM2.5 SIP Requirements Rule, 81 FR 58009 (August 24, 2016). Page 58018 states: "Even
though CAA section 189(e) only explicitly contemplates exceptions to control requirements for
PM2.5 precursors from major stationary sources in nonattainment areas, the EPA believes that by
analogy it has authority to promulgate regulations that allow states to determine that it is not
necessary to regulate PM2.5 precursors from other sources in nonattainment areas as well, under
appropriate circumstances."
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significantly to PM2.5 levels that exceed the standard in the area."12 In addition, an NNSR
precursor demonstration must show that sources will not "contribute significantly to
PM2.5 levels that exceed the standard in the nonattainment area."13
The phrase "contribute significantly" and the included terms "contribute" and
"significantly" are not defined in section 189, section 302 or any other part of the CAA.
Courts have observed that the absence of a statutory definition does not by itself
establish that a term is ambiguous. NRDC v. EPA, 489 F.3d 1250, 1258 (D.C. Cir. 2007). In
the absence of a definition, the ordinary meaning of a term should govern. Petit v. Dep't
of Education, 675 F.3d 769, 781 (D.C. Cir. 2012). But courts have also observed that the
meaning of a statutory term depends on the context in which it is used. Bell Atlantic
Telephone Co. v. FCC, 131 F.3d 1044, 1047 (D.C. Cir. 1997). EPA's regulations likewise do
not include a definition of the term "contribute significantly."14
One federal appeals court has recognized, based in part on competing dictionary
definitions, that the term "contribute" does not itself have a consistent, ordinary
meaning. Catawba County, N.C. v. EPA, 571 F.3d 20, 39 (D.C. Cir. 2009). In two different
contexts under the CAA, the United States Court of Appeals for the District of Columbia
Circuit has observed that the term "contribute" is ambiguous with respect to the degree
of air quality effect to which it applies. Id. at 38-39; EDF v. EPA, 82 F.3d 451, 459,
amended by 92 F.3d 1209 (D.C. Cir. 1996).
In the Catawba County case, the court considered the use of this term in section 107(d)
of the CAA, which governs EPA actions to designate specific areas as in attainment or
nonattainment with the NAAQS. Under this provision, a nonattainment area must
include any area that does not meet the NAAQS or "that contributes to ambient air
quality in a nearby area that does not meet" the NAAQS. The Petitioners argued that the
EPA was required to interpret the word "contribute" in this context to require a
"significant causal relationship" in order to include a nearby area in a nonattainment
area. The Petitioners also argued that the EPA must establish a quantified amount of
impact that qualifies as a contribution before the EPA could include a nearby area in a
nonattainment area. Id. The court held that "section 107(d) is ambiguous as to how EPA
should measure contribution and what degree of contribution is sufficient to deem an
area nonattainment." In doing so, the court noted the Petitioners' citation of one
dictionary definition and the EPA's citation of other dictionary definitions of the term
"contribute" and concluded that "[t]his alone suggests an ambiguity." Catawba County,
571 F.3d at 39. Consequently, the court held that the EPA was not compelled to apply
the Petitioners' preferred meaning of the term "contribute" in the context of section
107(d). The court recognized that the EPA had the discretion to interpret the term
12	See 40 CFR 51.1006(a)(1) and 51.1006(a)(2).
13	See 40 CFR 51.1006(a)(3)(i).
14	EPA's New Source Review Permitting regulations contain a definition of the term "significant,"
but this definition does not modify the term "contribute" and applies in a different context. See
e.g., 40 CFR 51.166(b)(2), (b)(23), (j)(2)-(3).
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"contribute" in section 107(d) of the CAA to mean "sufficiently contribute" and that EPA
could use a multi-factor test, rather than a quantified threshold, to determine when a
nearby area contributed to a NAAQS violation. Likewise, in another case, the court
reasoned that "contribute to" in section 176(c) of the CAA is ambiguous and "leaves
wide open the question of how large a reduction in emissions must be to constitute a
contribution." EDFv. EPA, 82 F.3d 451, 459, amended by 92 F.3d 1209 (D.C. Cir. 1996).
Section 189(e) is one of several provisions in the CAA that uses the term "contribute" or
similar forms of this term. The reasoning of the Catawba County opinion supports the
view that EPA has the discretion under section 189(e) to exercise judgment to
determine the degree of impact that "contributes" to adverse air quality conditions
based on the particular context in which the term "contribute" is used in the CAA. See
571 F.3d at 39.15 Furthermore, this opinion supports EPA's discretion to identify
qualitative or quantitative criteria or factors that may be used to determine whether
something "contributes," as long as the Agency provides a reasoned basis to justify
using such criteria or factors to represent a "contribution."
Although there is ambiguity regarding the degree of impact that "contributes" to an air
quality condition, Congress has provided at least some direction regarding the degree of
contribution that is required under section 189(e) of the CAA. In this provision, Congress
included the term "significantly" after the word "contributes." This indicates that
Congress intended to allow for the exemption of sources of a PM2.5 precursor from
control requirements even where there is an impact greater than a simple
"contribution," but how much greater is not specified.
The D.C. Circuit has also observed that the term "significant" is ambiguous and may be
subject to different meanings in different contexts. Michigan v. EPA, 213 F.3d 663, 677-
(D.C. Cir. 2000). In this case, the court considered the use of this term in section
110(a)(2)(D)(i)(l) of the CAA, which requires state plans to prohibit those emissions
which "contribute significantly" to nonattainment of a NAAQS in a downwind state. The
EPA defined the amount of emissions from each state that "contribute significantly" to
nonattainment as those emissions exceeding a specified threshold and which could be
reduced using "highly cost-effective controls." Id. at 675. Petitioners challenged the
EPA's reliance on cost effectiveness to define the level of upwind state contribution that
qualified as "significant." Petitioners presented conflicting arguments to the court as to
whether the statute permitted any consideration of cost, and, as such, the court
determined that it could, therefore, discern no clear congressional intent to preclude
the consideration of cost. Id. at 676-77. The court explained that "[t]he term 'significant'
does not itself convey a thought that significance should be measured only in one
15 See also Environmental Defense v. Duke Energy Corp., 549 U.S. 561 (2007) (where the term
"modification" and its definition appear, by cross-reference, in two places in the CAA, the EPA
may interpret the term differently in the two contexts, so long as it does so in a reasonable
manner consistent with the statutory definition).
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dimension - here, in the petitioners' view, health alone." Id. at 677. Rather, the court
explained that the meaning of "significant" may depend on its context and can, in some
contexts, "beg a consideration of costs." Id. Thus, the court held that "nothing in the
text, structure, or history of [section] 110(a)(2)(D). . . bars EPA from considering costs in
its application." Id. at 679. Consistent with the reasoning in Michigan, the use of the
term "significant" in section 189(e) is ambiguous and is subject to a reasonable
interpretation based on the context of the term's use. Thus, it is within the Agency's
discretion to identify additional qualitative or quantitative criteria or factors to
determine whether a contribution is "significant," as long as the Agency provides a
reasoned basis to justify using such additional criteria or factors.
2.2 Criteria for IIclentiif	itiriibutiion
This guidance document on optional precursor demonstrations for the PM2.5 SIP
Requirements Rule describes the factors that the EPA recommends that states consider
when seeking to demonstrate that sources of PM2.5 precursors "do not contribute
significantly" to PM2.5 levels that exceed the NAAQS, for the specific purpose of
attainment plan and NNSR program implementation for nonattainment areas. These
factors include quantitative "contribution" values based on the Agency's April 17, 2018,
memorandum titled "Guidance on Significant Impact Levels for Ozone and Fine Particles
in the Prevention of Significant Deterioration Program" (USEPA, 2018a). These
"significant impact levels" were derived from a statistical analysis described in the EPA
document, "Technical Basis for the EPA's Development of Significant Impact Thresholds
for PM2.5 and Ozone" (USEPA, 2018b), hereafter referred to as the "Technical Basis
Document."
The EPA first began developing these quantitative threshold values for use in the
Prevention of Significant Deterioration (PSD) permitting program to implement section
165(a)(3) of the CAA, which requires that an applicant for a PSD permit demonstrate
that the proposed source will not "cause or contribute" to a violation of any NAAQS or
PSD increment. The statistical methods and analysis detailed in the Technical Basis
Document focus on using the concept of statistical significance to identify levels of
change in air quality concentrations that are not considered to represent a contribution
to air quality degradation. The EPA believes the values derived through this method may
be used as quantified levels of air quality change that would not "cause or contribute"
to an exceedance of the NAAQS. Conversely, an impact above any such level may be
viewed as a change that would contribute to an exceedance of the NAAQS.
Since section 189(e) of the CAA also uses the term "contribute," these values may also
have relevance in this context. However, as discussed above, in section 189(e) of the
CAA the term "contribute" is modified by the term "significantly." As a result, the EPA
believes that, for purposes of the PM2.5 precursor demonstration, other factors may be
considered in determining whether an air quality change or impact would "contribute
significantly" to PM2.5 levels that exceed the applicable NAAQS in an area. Under the
PM2.5 SIP Requirements Rule, the significance of a precursor's contribution is to be
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determined "based on the facts and circumstances of the area."16 This section of the
guidance document discusses how a state may determine whether emissions of a
precursor would "contribute" to a violation of the NAAQS, and section 2.3 discusses
factors that may inform a determination of whether a contribution should be treated as
significant.
The discussion that follows in this section considers the concept of "statistical
significance" that the EPA has used to evaluate whether an impact on ambient air
quality should be considered to be a "contribution" under the statute. We strive to
promote clarity when applying the results of this statistical analysis within the context of
statutory provisions that use the terms "contribute" and "contribute significantly." This
guidance document uses the term "contribution" to describe a degree of change in air
quality that EPA's statistical analysis shows to be more than "negligible" or "trivial" and
thus can be regarded as an impact that "contributes" to air quality concentrations.17
This is not to be confused with the use of the term "contribute significantly" in the CAA,
which the EPA interprets to encompass considering additional factors beyond those
used to identify a "contribution." Given that Congress gave more specific direction that
we consider whether precursor emissions "contribute significantly" in the context of
CAA section 189(e), we have endeavored in this guidance to use the term "contribute
significantly" or "significant contribution" only when discussing whether the criteria in
section 189(e) for a precursor exemption has been satisfied.
The concept of statistical significance is well established, with a basis in commonly
accepted scientific and mathematical theory. The Technical Basis Document notes that
the statistical methods and data reflected in that analysis may be applicable for multiple
regulatory applications where EPA seeks to identify a level of change in air quality that is
either significant or insignificant. As described below, EPA believes a precursor
demonstration for a PM2.5 SIP is another regulatory application where the analysis EPA
conducted in the PSD context can be applied. However, this analysis is only the first step
of developing quantitative thresholds to initially determine whether there is a
"contribution" before looking at other factors to determine if the contribution is
"significant."
The Technical Basis Document describes that compliance with the NAAQS is determined
by comparing the measured "design value" (DV) at an air quality monitor to the level of
the NAAQS for the relevant pollutant.18 The EPA believes that an insignificant change in
ambient air quality can be defined and quantified based on characterizing the observed
variability of ambient air quality levels. The Technical Basis Document analysis has been
designed to take into account the ambient data used to determine DVs for both the
16	See 40 CFR 51.1006.
17	Here we use "contribution" to be analogous to the term "significant impact" in the PSD
permitting program.
18	A design value is a statistic that describes the air quality status of a given location to be
compared to the level of the NAAQS. More information may be found at
https://www.epa.gov/air-trends.
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annual and 24-hour PM2.5 NAAQS. The EPA's technical approach, referred to as the "Air
Quality Variability" approach, relies upon the fact that there is spatial and temporal
variability in the observed ambient data and then uses statistical theory and methods to
identify a level of change in DVs that is not statistically discernible from the original DV,
thereby representing an "insignificant" change in air quality.
Based on these observed ambient data, the EPA has identified changes in air quality
levels of PM2.5 that may be considered an insignificant impact through applying a well-
established statistical technique known as bootstrapping. Bootstrapping is a method
that allows one to determine the accuracy of sample statistics (e.g., mean, percentiles)
for a population of data (Efron, 1979 and 2003). The bootstrap approach applied in the
Technical Basis Document uses a non-parametric, random resampling with replacement,
which recreates the sample dataset (e.g., in this case, the ambient data underlying the
DVs), resulting in many resampled datasets. This approach allows one to determine
measures of accuracy of sample statistics based on these resampled datasets when the
underlying distribution of the statistic is not known (Efron, 1993).
The bootstrap technique, as applied in the air quality variability analysis, quantifies the
degree of air quality variability in an area and then allows one to determine appropriate
confidence intervals (CIs), i.e., statistical measures of the variability associated with the
monitor-based DVs, to inform the degree of air quality change that may be considered
"insignificant." This approach for quantifying a degree of impact that contributes to
PM2.5 air quality is fundamentally based on the concept that an anthropogenic
perturbation of air quality that is within a specified CI may be considered
indistinguishable from the inherent variability in the measured atmospheric
concentrations and is, from a statistical standpoint, not significant at the given
confidence interval. (USEPA, 2018b)
Specifically, the analysis in the Technical Basis Document uses 17 years (2000-2016) of
nationwide ambient PM2.5 measurement data to generate a large number of resampled
datasets for PM2.5 DVs at each monitor. These resampled datasets were used to
determine statistical CIs that provide a measure of the inherent variability in air quality
at the monitor location. This variability may be driven by the frequency of various types
of meteorological and/or emissions conditions affecting a particular location. The
analysis estimates a range of CIs for each monitor. In the analysis for the PSD permitting
program, EPA selected the 50 percent CI to quantify the level of air quality change that
can be considered "insignificant" for the purposes of meeting requirements under that
program. The Agency's April 2018 memorandum (USEPA, 2018a) explains the analysis
design and describes how the results are applied to develop Significant Impact Levels
(SILs) for use in the PSD program.
We believe the air quality variability analysis described in the PSD memorandum (and
documented in more detail in the Technical Basis Document) is also suitable for
determining in the first instance whether emissions of a PM2.5 precursor "contribute" to
PM2.5 levels that exceed the NAAQS, as part of a precursor demonstration under the
PM2.5 SIP Requirements Rule. The concept of significance as expressed in the Technical
15

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Basis Document is that an anthropogenic perturbation of air quality that is less than the
inherent variability in the measured atmospheric concentration is, from a statistical
standpoint, not significant. The statistical analysis of ambient air quality variability is not
dependent on the source of the anthropogenic perturbation (e.g., a single stationary
source, versus multiple sources across an area). The analysis is based on the variability
in ambient data measurements, which is driven by the variability in meteorology and
emissions from all sources. This includes near source and long-range impacts from single
sources and groups of sources (including major stationary sources, cars, minor sources,
etc.). Accordingly, the analysis EPA initially conducted for PSD purposes is equally
applicable to a number of circumstances. In this case, PM2.5 precursor demonstrations
attempt to show that a particular perturbation in anthropogenic emissions does not
contribute significantly to PM2.5 levels which exceed the standard in the area. The
derived insignificance level from the statistical analysis is not specific to the PSD
program. The analysis examines and defines a perturbation in ambient air
measurements. EPA used the statistical analysis to define an air quality threshold, below
which is considered an insignificant impact. Because the threshold is an ambient air-
based value that defines a contribution, it is similarly applicable to both PSD analyses
and PM2.5 precursor demonstrations. This includes both comprehensive demonstrations
(examining the impacts from all emissions sources) and major stationary source
demonstrations (examining the impacts from only major stationary sources).
As noted above, the 50 percent CI was selected to quantify the level of air quality
change that can be considered "insignificant" for the purposes of meeting requirements
under the PSD program. The 50 percent level was chosen as a protective (low) level,
below which would clearly represent an insignificant impact on air quality. We believe
the same logic applies to identifying an impact that "contributes" in the context of
precursor demonstrations and, therefore, we recommend use of the same 50 percent CI
(and numerical thresholds) for precursor demonstrations. The threshold can be
considered a value below which air quality impacts (from both emissions decreases and
increases) are not significant or meaningful and thus, do not represent a contribution
and, therefore, do not "contribute significantly" to PM2.5 concentrations that exceed the
standard. Note however that merely exceeding the threshold does not necessarily mean
that the precursor contributes significantly, additional analysis may be required to make
that determination. (See section 2.3)
In addition, the statistical significance analysis calculates the inherent variability in the
ambient data both above and below the median observed concentrations. In this way,
the variability analysis is equally applicable in examining the impact of both emissions
increases (which would generally lead to higher observed or modeled concentrations)
and emissions decreases (which would generally lead to lower observed or modeled
concentrations), relative to a base case.
The Agency's April 2018 memorandum (USEPA, 2018a) recommends specific
concentration values that represent the change in PM2.5 air quality that can serve to
quantify air quality impacts that "contribute" to PM2.5 concentrations in each area. As
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explained in more detail above, even though PM precursor demonstrations and the PSD
program serve different purposes, the definition of an air quality "contribution" is
viewed in the same manner, using the same ambient data based statistical analysis.
Therefore, consistent with the April 2018 memorandum and the accompanying
Technical Basis Document analysis, the EPA recommends using the following values for
this purpose as part of an optional precursor demonstration under the PM2.5 SIP
Requirements Rule:
•	> 0.2 |-ig/m3 for the annual PM2.5 NAAQS, and
•	> 1.5 |-ig/m3 for the 24-hour PM2.5 NAAQS.19
The EPA believes that these recommended thresholds are appropriate guidelines for
identifying an air quality impact level that below which is "insignificant" and thus, for
purposes of the precursor demonstration, does not "contribute" to PM2.5 concentrations
subject to the current PM2.5 NAAQS.20
Depending on the type of precursor demonstration conducted, the "perturbation" in air
quality can be represented in different ways: as the precursor's impact on ambient
PM2.5 levels due to emissions from all sources or all major stationary sources in the
nonattainment area; a decrease in precursor emissions from all sources or all major
stationary sources in the nonattainment area; or, in the case of an NNSR demonstration,
as an increase in precursor emissions from major stationary sources. As explained
above, the thresholds should be appropriate for interpreting the significance of the
perturbation for each of these analyses, regardless of whether the evaluation involves
the impact of one or more new sources intending to locate in the nonattainment area,
or examining the combined impact on PM2.5 concentrations from multiple existing
sources of emissions.
19	The recommended 24-hour NAAQS threshold for PM2.5 precursor demonstrations is 1.5 ng/m3,
which is the 50 percent CI value from the air quality variability analysis documented in the
technical basis document. However, due to the fact that 40 CFR 51.165(b)(2) still lists 1.2 ng/m3
as the significance level for the 24-hour PM2.5 NAAQS for PSD purposes, it is EPA's intent to
apply the 40 CFR 51.165(b)(2) threshold for PSD actions covered by that rule pending further
evaluation.
20	As described in the Technical Basis Document, the monitoring site variability is first calculated
as a percentage of the measured PM2.5. Then the median percent variability from all sites is
multiplied by the level of the NAAQS to get the threshold concentrations. Therefore, these
thresholds represent a percentage of the 2006 24-hour NAAQS (35 ng/m3) and the 2012 annual
NAAQS (12 ng/m3). Different thresholds may be applicable to other levels and/or forms of the
NAAQS (either past or future).
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2,3 Evaluating Whether a Contribution is "Significant"- Considering Additional
Information
An approvable precursor demonstration must show that the air quality change at
relevant locations (as described in section 2.4 below) does not "contribute significantly"
to PM2.5 levels that exceed the standard. The EPA generally expects that a precursor
demonstration will be adequate to support exempting sources of a precursor from
control requirements if the analysis shows that the air quality impact at all relevant
locations does not exceed the recommended contribution thresholds (i.e., 0.2 |-ig/m3 for
the annual PM2.5 NAAQS, and 1.5 |-ig/m3 for the 24-hour PM2.5 NAAQS).21
If the estimated air quality impact is greater than or equal to the recommended
contribution threshold, this fact does not necessarily preclude approval of the precursor
demonstration. There may be cases where it could be determined that precursor
emissions have an impact above the recommended contribution thresholds, yet "do not
contribute significantly" to levels that exceed the standard in the area (pursuant to
section 189(e), emphasis added). Under the PM2.5SIP Requirements Rule, the
significance of a precursor's contribution is to be determined "based on the facts and
circumstances of the area."
Air agencies may thus provide the EPA with information related to other factors they
believe should be considered in determining whether the contribution of emissions of a
particular precursor to levels that exceed the NAAQS is "significant" or not. Such factors
may include: the amount by which a precursor's impact exceeds the recommended
contribution threshold(s); the amount by which the cumulative impact from all modeled
precursors exceeds the recommended threshold(s); the severity of nonattainment at
relevant monitors and/or grid cell locations in the area; whether an area is measuring
clean data and the amount by which the current DV is below the NAAQS;22 the percent
of emissions reduction analyzed; source characteristics (e.g., source type, stack height,
location); anticipated growth or loss of sources; analyses of speciation data and
precursor emission inventories; chemical tracer studies; special intensive measurement
21	The criteria pollutant of concern is total PM2.5, for which there are multiple precursors that
impact PM2.5 concentrations. Therefore, if a precursor demonstration is submitted that intends
to show that multiple precursors do not contribute significantly to PM2.5 levels that exceed the
standard in the area, the individual impact and the cumulative impact of all modeled precursors
should be calculated and documented. A cumulative precursor impact that is above the
recommended threshold may be an important consideration in assessing, on a case-by-case
basis, if one or more precursors does not "contribute significantly" to PM2.5 levels that exceed
the standard in the area.
22	Note that some areas remain designated nonattainment, even though recent DVs may be
below the NAAQS. While NNSR permitting requirements still apply until the area is formally
redesignated to attainment, the fact that recent measured DVs are close to or even below the
NAAQS can be used as a factor in considering precursor impacts.
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studies to evaluate specific atmospheric chemistry in the area; or trends in ambient
speciation data and precursor emissions.
We are not recommending one particular approach to evaluating additional factors, and
the air agency may provide other information not listed here as well. Any air agency
providing additional information should provide a clear rationale explaining how such
information supports their claim that the precursor does or does not contribute
significantly to PM2.5 levels that exceed the standard. The EPA will consider such
additional information and evaluate each demonstration on a case-by-case basis.
2,4 Locations at Which to Evaluate Air Quality Changes
For the comprehensive or major stationary source precursor demonstrations, the EPA
believes that air quality changes of concern should be evaluated at existing or relevant
historical PM2.5 monitor locations (i.e., as part of an air quality modeling analysis)
because it is at those locations where NAAQS compliance will be determined. The
evaluation of air quality changes at monitor locations for attainment plan precursor
demonstrations is consistent with the PM2.5 SIP Requirements Rule's treatment of
monitor locations for modeled attainment demonstrations for PM2.5 nonattainment
areas.23
For an NNSR precursor demonstration, the EPA believes that the analysis should
evaluate the projected air quality change from potential future major stationary sources
in all parts of the nonattainment area (i.e., all grid cells in an air quality modeling
analysis) rather than just at existing monitor locations. While a monitor-based analysis is
appropriate for nonattainment area planning, where the existing PM2.5 ambient
monitoring network is designed to represent air quality based on the geographic
orientation and magnitude of existing sources, this contrasts with NNSR, where new
major stationary sources might locate in parts of the nonattainment area that are not
currently well represented by the current monitoring network. The overall objective of
the NSR program is to prevent future violations. Therefore, it is important to examine
the sensitivity of the entire nonattainment area to potential increases in precursor
emissions to support a request to exempt sources of that precursor from NNSR
permitting. This recommendation is consistent with how new major stationary sources
are treated in modeling analyses required to be conducted for the PSD program (USEPA,
2014).
23 See PM2.5SIP Requirements Rule at 81 FR 58051.
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Technical Guidance
3.0	¦ "i i ..i 11[¦ i-'ii i • >1 Analysis
If an air agency chooses to perform a precursor demonstration for an attainment plan
(either a comprehensive or major stationary source analysis), the final rule requires that
the demonstration must include a concentration-based analysis.24 This demonstration
can consist of analyses using ambient data or it could optionally include air quality
modeling. The goal of the comprehensive plan analysis is to examine the overall impact
on PM2.5 air quality in the nonattainment area as a result of emissions of a particular
precursor from all existing sources (including stationary, area, and mobile sources). A
major stationary source analysis examines the overall impact on PM2.5 air quality from
emissions of a particular precursor from all existing major stationary sources.
The recommended starting point for such an analysis is an evaluation of all available
ambient air quality monitoring data for the area (and possibly nearby areas). The EPA
recommends an examination of total PM2.5 data (in the form of Federal Reference
Method (FRM) measurements, Federal Equivalent Methods (FEM) measurements,
Interagency Monitoring of Protected Visual Environments (IMPROVE) data, and/or other
special study or research data), and ambient PM2.5 speciation data, which characterizes
the composition of total mass. PM2.5 species data are critical for this analysis, since they
allow for an accounting of ambient secondary PM2.5 concentrations and provide a way
to link precursor emissions to secondary PM2.5 components.25 See details on PM2.5
species components and accounting for the various measured species in section 3.1,
below. Additional analyses and information, including emissions inventory data and
modeling can also be used to support a concentration-based analysis. This may be
particularly important for major stationary source analyses since in most cases it is
difficult to estimate major stationary source impacts solely from ambient data. See
sections 3.1.7 and 3.2 for more details.
3.1	Ambient Data Analysis of Secondarily-Formed PM2.5
PM2.5 is a complex and highly variable mixture, but the majority of PM2.5 mass is
comprised of five constituents: (i) organic matter (OM); (ii) elemental carbon (EC); (iii)
crustal material; (iv) ammonium sulfate ((NhUhSO^; and (v) ammonium nitrate
(NH4NO3) (Hand, 2012) (Seinfeld, 2006). In general, EC and crustal PM2.sare considered
"primary" components (i.e., they are emitted directly from sources and are not the
product of chemical reactions of precursor gases in the atmosphere). (NhUhSC^ and
NH4NO3 are considered "secondarily formed" PM2.5 components because they are the
24	See 40 CFR 51.1006(a)(l)-(2).
25	Ambient data, including PM speciation data can be found at EPA's AQS Data Mart:
https://aqs.epa.gov/aqsweb/documents/data mart welcome.html.
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product of chemical reactions of precursor gases in the atmosphere.26 OM can have
both primary and secondary components. Since this guidance addresses precursors to
secondary PM2.5, we will focus the discussion on the most common secondary PM2.5
components.
A large number of possible chemical reactions, often non-linear in nature, can convert
the gases SO2, NOx, VOC and NH3 to PM2.5. Thus, these gases are precursors to PM2.5.
OM is the fraction of ambient PM2.5 with the most diverse chemical composition,
containing potentially thousands of different organic compounds (i.e., those compounds
containing carbon) composed primarily of carbon, hydrogen, oxygen and nitrogen. Both
primary particles and secondary particles contribute to ambient OM concentrations.
Secondary OM particle formation involves oxidation of both anthropogenic and biogenic
(plant-derived) VOC, and can involve other, more complex chemical reactions. Sulfate
(SO4), nitrate (NO3) and ammonium (NH4), react in the ambient air to form ammonium
sulfate ((NH4)2S04) and ammonium nitrate (NH4NO3). If there is not enough NH3 in the
ambient air to neutralize fully the available sulfate, ammonium bi-sulfate (NH4HSO4) or
sulfuric acid (H2SO4) may also form. In addition, particle-bound water is often also
associated with measured sulfate and nitrate PM2.5. A brief discussion of SO4, NO3 and
Secondary Organic Aerosol (SOA) formation, as well as the role of NH3 in their
formation, follows.
3.1.1	Ammonium Sulfate
SO2 is a gas-phase species emitted mostly from the combustion of fossil fuels (the
largest source is coal combustion from electric utility boilers). When SO2 oxidizes, it
forms aerosol sulfuric acid. In the presence of NH3, however, sulfuric acid will react to
form ammonium sulfate [(NH4)2S04], a less acidic compound and one of the five major
components of PM2.5. If there is not enough NH3 present to fully neutralize the sulfuric
acid, part of it may convert to ammonium bi-sulfate (NH4HSO4), which is more acidic
than ammonium sulfate [(NH4)2S04], but less so than sulfuric acid. All three products
[H2SO4, NH4HSO4, and (NH4)2S04] solely reside as particle-phase (or aqueous-phase)
species in the atmosphere. There is a large amount of emerging scientific evidence that
SO2 may also contribute to the formation of SOA from biogenic VOC emissions (see
section later on SOA). Sulfate levels in the ambient air peak in summer months due to
increased SO2 emissions, generally from electric generating units (EGUs), and from
meteorological conditions that are conducive to sulfate formation.
3.1.2	Ammonium Nitrate
The main sources of NOx emissions are combustion of fossil fuel in boilers (e.g., electric
utility boilers) and internal combustion engines (e.g., cars and trucks). NOx reacts in the
26 There is a small primary component to both sulfate and nitrate ions, but the vast majority of
measured sulfate and nitrate is secondary in nature.
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atmosphere to form nitric acid. In the presence of NH3, nitric acid converts to
ammonium nitrate, one of the five main components of PM2.5. Low temperatures and
high relative humidity create ideal conditions for the formation of ammonium nitrate,
typically leading to higher atmospheric levels in winter months and lower levels in
summer months (Hand, 2012). At high temperatures and low relative humidity,
particulate nitrate (most commonly in the form of ammonium nitrate) converts back
into its component species of nitric acid (HNO3) and ammonium ion (NH4). Therefore,
nitrate ion (NO3) cannot exist in particulate form without being neutralized by NH3 or
another neutralizing cation.27 Similarly, NH3 would not exist in particle form if not for
the presence of acidic species (sulfate or nitrate) with which it can combine to form a
particle.
3.1.3	Secondary Organic Aerosols
VOCs (both anthropogenic and biogenic) are key precursors to the SOA component of
PM2.5. The relative importance of these compounds in the formation of organic particles
varies between geographic areas, depending upon local emission sources, atmospheric
chemistry and season of the year. It should be further noted that not all inventoried
VOC might be contributing to the formation of organic particles. For example, chemical
reactions involving VOC are generally accelerated in warmer temperatures, and, for this
reason, studies show that SOA typically comprises a higher percentage of PM2.5 in the
summer than in the winter (Pandis, 1992).
Anthropogenic sources of VOC include mobile sources, petrochemical manufacturing, oil
and gas emissions and solvents (USEPA, 2016a). In addition, some biogenic VOC,
emitted by vegetation such as trees, can contribute significantly to SOA formation,
especially in heavily forested areas, such as the Southeast U.S. It should be noted,
however, that anthropogenic impacts on SOA are likely highest in the wintertime when
biogenic SOA levels are lower; conversely, in the summertime, the influence of biogenic
emissions on SOA is likely higher (Carlton, 2010a). Despite significant progress in
understanding the origins and properties of SOA, it remains the least understood
component of PM2.5 and continues to be a significant topic of research and
investigation.
3.1.4	Role of NOx and SO2 in Secondary PM Chemistry
In addition to influencing secondary particulate nitrate formation, NOx also reacts with
anthropogenic and biogenic VOC to enhance the secondary formation of sulfate and
organic compounds that make up SOA (Carlton, 2010b). NOx is thus involved in all
secondary PM chemistry, not just in particulate nitrate formation. Similarly, recent
27 If ammonia is not available, nitric acid can also be neutralized by calcium (Ca) or sodium (Na)
(if available) to form calcium nitrate [Ca(N03)2] and sodium nitrate (NaN03), respectively. Unlike
ammonium nitrate, Ca(N03)2 and NaN03 do not convert back to the gas phase at higher
temperatures.
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research has indicated that SO2 can impact SOA formation (Surrat, 2010). One recent
study found that chemical reactions involving SO2 and NOx combined may be
responsible for up to 70 percent of the total measured organic aerosol in the Southeast
U.S. in the summer (Xu, 2015). Consequently, when NOx or SO2 emissions are decreased
or increased in the atmosphere, there can be impacts on all secondary PM2.5 species,
including ammonium ion, nitrate ion, sulfate ion, and SOA.
3.1.5 Assigning PM2.5 Species to Precursors - Summary
Ambient PM2.5 species data are generally measured and reported as OM, EC, crustal,
nitrate, sulfate, and ammonium. For the purpose of precursor demonstrations,
elemental carbon and crustal PM2.5 can be ignored (since they are primary species). One
basic way of developing a concentration-based analysis for a particular precursor is to
calculate the portion of the total PM2.5 mass measured at the relevant location that is
associated with the precursor. The EPA's default recommendation for "assigning" the
measured secondary PM2.5 species to their respective precursors is shown in Table 1
below:
Table 1. Default Recommended Assignment of PIVh.s Precursors to PlVk.s Species
PM2.5
Recommended Assignment to
Comment
Precursor
PM2.5 Species

NOx
Nitrate ion + portion of
Include all measured nitrate ion plus

ammonium associated with
the ammonium that is in the form of

nitrate
ammonium nitrate (do not include
the ammonium attached to sulfate).
S02
Sulfate ion
All measured sulfate ion.
nh3
Ammonium + nitrate ion
Include all measured ammonium
(including ammonium attached to
sulfate and nitrate), and nitrate ion.
VOC
SOA
Estimate the secondary component
of OM. This can be further
disaggregated into the impact on
SOA from anthropogenic VOC
sources.
Further explanation of the recommended assignments outlined in Table 1 is provided as
follows:
• NOx- the default recommendation assigns measured nitrate to NOx as well as the
portion of ammonium that is attached to nitrate in the form of ammonium
nitrate. When considering the impact of NOx on PM2.5, NOx directly influences
the formation of ammonium nitrate. However, nitrate ion cannot exist in the
atmosphere as a particle without being neutralized by NH3 (it would exist as a
gas in the form of nitric acid). Therefore, the ammonium portion of ammonium
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nitrate should also be counted when evaluating whether NOx contributes to
PM2.5 mass.
•	SO2- The default recommendation assigns measured sulfate to SO2. Note that
the ammonium attached to sulfate (mostly in the form of ammonium sulfate) is
not counted toward the SO2 impact on PM2.5 mass because sulfate can exist in
the atmosphere as a particle in the form of sulfuric acid even if it is not
neutralized by NH3.
•	NH3- The default recommendation assigns all measured ammonium to NH3 as
well as the entire nitrate ion mass. This is for the same reason that part of
ammonium is assigned to NOx. Ammonium nitrate cannot exist in the
atmosphere as a particle without being neutralized by NH3. Therefore, if no NH3
were present, nitrate would exist only as a gas (in the form of nitric acid). As a
result, all of the mass of ammonium nitrate should also be counted towards
ammonia's impact on PM2.5 mass.28
•	VOC- The default recommendation assigns measured SOA to VOC. The most
conservative assumption is to assume that all of the measured organic aerosol
mass is SOA.29 However, SOA is only a portion of measured organic mass and is
not directly measured. Therefore, in some cases, the SOA portion can be
estimated through data analysis techniques (Cabada, 2004; Saylor, 2006;
Lewandowski, 2008; and Rutter, 2014). In some areas, a high percentage of SOA
originates from biogenic sources (especially in the summer). Therefore, if SOA is
estimated as a percentage of total organic mass, the SOA concentration can be
further refined by estimating the portion of SOA that is a result of anthropogenic
VOC emissions.30
The default recommendations above are the simplest and most straightforward
assignment of precursors to species. However, other methods may be used to estimate
alternative PM2.5 concentration apportionment. For example, the PM2.5 attainment
demonstration modeling guidance recommends the use of the "sulfate, adjusted
nitrate, derived water, inferred carbonaceous material balance approach" (SANDWICH)
(Frank, 2006) to adjust measured PM2.5 species data to match better the total PM2.5
28	If an air agency submits precursor demonstrations for both ammonia and NOx, the nitrate
component should be counted towards the contribution of both precursors to ambient PM2.5
levels. This is appropriate since particulate ammonium nitrate formation is dependent on having
both nitric acid (from NOx) and ammonia available.
29	The measured organic carbon should be multiplied by an appropriate factor (typically 1.4 to
1.8) to convert from organic carbon to organic mass (which includes additional mass attached to
the carbon).
30	Due to the difficulty in calculating SOA and the contribution of VOC to ambient PM2.5 data, air
quality modeling may be the most straightforward way to determine the VOC contributions (see
section 5).
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mass, which is measured on FRM filters. The FRM mass, which is compared to the
NAAQS to determine attainment/nonattainment, suffers from various artifacts, which
can affect the concentration of some PM2.5 species collected on Teflon filters. For
example, organic mass experiences both positive and negative artifacts, and nitrate
mass is generally lower (negative artifact) on FRM filters compared to species
measurements, due to temperature and humidity influences. In addition, the
SANDWICH technique estimates particle bound water mass, which is attached to both
sulfate and nitrate particles. The water mass should be counted in assessing a
contribution to PM2.5 because it is collected on the filter and counted as measured PM
mass that is part of total PM2.5. In addition to SANDWICH, there may be other
technically credible adjustments that can be applied to measured species data,
depending on the nature of the species, the area of the country, and the season in
which the measurement occurs. Although prior guidance recommends these methods,
all adjustments to ambient data should be discussed with the EPA Regional office and
carefully documented and explained.
3.1.6	Evaluating Concentration Based Analysis Results
The estimated impact on PM2.5 mass from a specific precursor should be compared to
the recommended "contribution" thresholds for the annual and/or 24-hour NAAQS that
were identified in section 2.2.
3.1.7	Additional Information
In addition to ambient PM2.5 species data, other information can be used to support the
concentration-based analyses. Emissions inventory data (i.e., such as data from the
National Emissions Inventory31 or from the inventory developed for the nonattainment
area plan by the state or local air agency) can help support claims that a precursor does
not contribute significantly to PM2.5 concentrations in the nonattainment area,
particularly when emissions of the precursor are small. Other considerations in the
demonstration can be the size of the nonattainment area, the population of the
nonattainment area, geographical considerations (such as an isolated mountain valley
area), meteorological considerations, etc. The default recommendation is to compare
the measured ambient PM2.5 species data to the relevant air quality "contribution"
threshold. However, there are other techniques that can be used to attempt to further
account for the impact of sources in the nonattainment area on ambient data
concentrations. Analyses to support the disaggregation of ambient data into the local
nonattainment area impact should be as detailed as possible, focused on the
precursor(s) of interest in the demonstration, and discussed with the appropriate EPA
Regional office. Note also that air quality modeling is the most technically credible way
31 NEI data can be found at: https://www.epa.gov/air-emissions-inventories/national-emissionS"
inventory-nei.
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to calculate the concentration of PM2.5 mass due to emissions sources from within the
nonattainment area. See section 3.2 below for more details.
3.2 Air Quality Modeling
Air quality modeling can also be used to quantify the impact of precursors on PM2.5
concentrations in a nonattainment area. In general, air quality modeling is resource
intensive, but it is the most direct method to capture the non-linear and complicated
associations between PM2.5 precursor emissions and PM2.5 concentrations. For example,
in the ambient data analysis section above, we delineated many caveats and
assumptions that need to be considered when estimating the impact of precursor
emissions on measurements of specific PM2.5 species. Many of those assumptions are
not necessary when evaluating air quality modeling outputs (although there are
different considerations and assumptions that are involved). A photochemical grid
model takes into account the complicated chemical interactions among precursors and
tracks the individual species concentrations, including species like SOA,32 which cannot
be directly quantified from measurements. Photochemical modeling also allows a
potentially more precise accounting of impacts from precursor emissions in the
nonattainment area. In addition, since air quality modeling is both a statutory and
regulatory requirement for Moderate and Serious PM2.5 attainment demonstrations,33
most nonattainment areas will have photochemical air quality modeling available to
support their modeled attainment demonstration.
Air agencies have several choices to analyze modeled air quality impacts of precursor
emissions on PM2.5 as part of a concentration-based analysis. The simplest approach
would be to perform brute force "zero-out" model runs, which involves at least two
model runs: one "baseline" run with all emissions, and one with anthropogenic
emissions of the precursor of interest removed from the nonattainment area in the
original baseline simulation (Cohan et al., 2005). The difference between these
simulations provides an estimate of the air quality change due to the precursor
emissions.
32	Photochemical modeling of SOA is generally more uncertain than the other PM2.5 components.
SOA formation is not yet fully understood mechanistically and, therefore, cannot yet be reliably
modeled. Because we lack reliable tools for distinguishing between primary and secondary
organic aerosol in the ambient air and have even less confidence that models can reliably
simulate SOA formation, it is difficult to validate a modeled conclusion that VOC precursor
emissions have an insignificant contribution to PM2.5- Therefore, especially in the case of VOC as
a precursor, additional evidence should be submitted to help validate modeling results.
Additional information could include ambient data analyses, special study data and research,
and detailed emissions information {e.g., VOC speciation data showing that the makeup of the
nonattainment area VOC emissions are not likely to form SOA).
33	See CAA section 189(a)(1)(B), CAA section 189(b)(1)(A), 40 CFR 51.1009(a)(4) and 40 CFR
51.1010(a)(5).
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An alternative approach to isolating precursor impacts in photochemical grid models is
"photochemical source apportionment." Some photochemical models have been
developed with a photochemical source apportionment capability, which tracks
emissions from specific sources or groups of sources and/or source regions through
chemical transformation, transport, and deposition processes to estimate the
apportionment of predicted PM2.5 species concentrations (Kwok et al., 2015; Kwok et
al., 2013). Source apportionment (ENVIRON, 2016; Kwok et al., 2015; Kwok et al., 2013;
Wang et al., 2009) has been implemented in modeling systems such as the
Comprehensive Air Quality Model with Extensions (CAMx) (ENVIRON, 2016) and the
Community Multiscale Air Quality (CMAQ) (Byun and Schere, 2006).
3.2.1 Evaluating Modeling Results
The calculated impact of the precursor on total PM2.5 concentrations should be
compared to the "contribution" thresholds for annual and 24-hour PM2.5 identified in
section 2.2. See section 5 for more details on the choice of models, model setup, and
post-processing of model results.
4.0	ivity Based Analysis
The PM2.5 SIP Requirements Rule also allows for an optional "sensitivity-based" analysis
for attainment plan demonstrations.34 This modeling analysis examines the sensitivity of
ambient PM2.5 concentrations in the nonattainment area to decreases in precursor
emissions in the area. This type of optional analysis is only necessary if the
concentration-based analysis described above fails to demonstrate that a precursor
does not contribute significantly to PM2.5 concentrations in the nonattainment area. By
performing a sensitivity analysis, it may still be possible for an air agency to demonstrate
adequately that a precursor contribution is insignificant. Where decreases in emissions
of the precursor result in insignificant air quality impacts (i.e., the area is "not sensitive"
to decreases), such a small degree of impact can be considered to not "contribute" to
PM2.5 concentrations for the purposes of determining whether control requirements
should apply. Accordingly, the EPA expects that it will approve a precursor
demonstration if it can adequately be shown that the area is not sensitive to precursor
emissions reductions. Note that the sensitivity analysis described in this section is only
applicable to evaluating emissions reductions as part of the attainment plan part of the
SIP. A similar but distinct sensitivity analysis is applicable to NNSR precursor
demonstrations, but addresses sensitivity to precursor emissions increases rather than
decreases (see section 6 for more details on NNSR precursor demonstrations).
A sensitivity-based analysis demonstrates the degree to which PM2.5 concentrations in
the nonattainment area are sensitive to decreases of one or more precursors. Changes
in PM2.5 concentrations at a particular location often will not be linear with respect to
34 See 40 CFR 51.1006(a)(l)(ii) and 40 CFR 51.1006(a)(2)(ii).
27

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changes in PM2.5 precursor emissions. As previously discussed, several PM2.5
components are secondarily formed in the atmosphere as the result of chemical
reactions between various PM2.5 precursors. In some nonattainment areas, one
precursor may be abundant while a second precursor, with which it primarily reacts,
may be less abundant. In such cases, a modeled sensitivity analysis may find that PM2.5
concentrations in the area are relatively insensitive to emissions reductions of the more
abundant precursor.
4.1 ModeIIiing for Seinsitiiviity IDeinnoinstiratiioins
Precursor demonstrations analyze the relationship between precursor emissions and
the formation of secondary PM2.5 components. Air quality models are the most
appropriate tool to be able to predict the impact of precursor emissions reductions on
PM2.5 concentrations. Since PM2.5 precursors form secondary PM2.5 through chemical
reactions, a chemical transport model (CTM) is best able to examine the sensitivity of
precursor emissions to secondary PM2.5 concentrations. See section 5 for more details
on CTMs.
As part of performing a sensitivity analysis, there are two additional questions that need
to be addressed:
1)	What amount of emissions reduction should be examined as part of a sensitivity
analysis?
2)	What air quality concentration threshold should be used to determine if the
modeled air quality change from the precursor is insignificant?
4.1.1 Emissions Reductions for Sensitivity Analyses
When deciding on the appropriate emissions reduction to model in a sensitivity analysis,
it is important to consider the nature of the question being asked. In this case, the CAA
and the PM2.5 SIP Requirements Rule allow a demonstration to show that emissions of a
precursor in the area do not contribute significantly to PM2.5 levelsthat exceed the
standard in the area.35 Given the emissions makeup and resultant interactions between
precursors in the area, the pertinent question is whether PM2.5 concentrations in the
nonattainment area are "insensitive" to certain amounts of emissions reductions of the
precursor. This question should not be confused with whether there are known
available emissions reductions of a certain size within the nonattainment area. For
example, an air agency may identify only a very small percentage precursor reduction
from available controls. However, modeling the sensitivity of the area to that very small
percentage reduction and then comparing it to EPA's recommended thresholds does
not effectively answer whether the area is sensitive to the precursor. The analysis
should use a percentage emissions reduction (or a series of different percentage
35 See CAA section 189(e) and 40 CFR 51.1006(a).
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reductions model runs) that is large enough to provide a robust answer (given non-
linearities due to complex secondary PM2.5 chemistry).
The EPA recommends modeling a range of percentage emissions reductions for all
sensitivity analyses. For attainment plan analyses of existing emissions sources, a fixed
tonnage reduction of a precursor would not be appropriate since the number of tons of
precursors in each nonattainment area may vary by orders of magnitude. Therefore, a
percentage reduction is appropriate for this type of analysis because it allows for
consistency between nonattainment areas and takes into consideration the amount of
existing emissions of the particular precursor in each area.
The definition of the range of percentage emissions reductions to model should
consider two basic factors:
1)	The reduction should be large enough to test the interaction and non-linearity of
the secondary PM2.5 components, such as those considered in the published
literature.
2)	The reduction should not be so large that it alters the chemistry in such a way
that gives an unrealistic PM2.5 concentration response, especially given emissions
reductions that could possibly occur within the 6-10 year timeframe of Moderate
and Serious area attainment demonstrations.
The percentage reduction should not be solely based on an analysis of potential
emissions reductions over the next 6-10 year period. This approach could lead to claims
of very small emissions reductions, which may not be large enough to truly test whether
the area is sensitive to precursor emissions reductions. Therefore, the EPA is
recommending a range of percentage precursor emissions reductions that is applicable
to all sensitivity demonstrations.
Based on the information available at this time, the EPA recommends application of
multiple percentage emissions reductions sensitivities, which span what has typically
been seen in the published literature.36 The EPA recommends a range of 30-70 percent
reductions in precursor emissions in the nonattainment area to test the PM2.5
concentration sensitivity of an area. Multiple model runs can be conducted which test
the PM2.5 sensitivity within the recommended range. For example, model runs of 30
percent, 50 percent, and 70 percent precursor reductions would test the entire
36 The EPA examined examples in the published literature of general sensitivity modeling studies
that look at the impact of across-the-board percentage reductions in precursor emissions on
secondary pollutants (including PM2.5, PM10, and ozone) (Vieno, 2016; Megaritis, 2013; Harrison,
2013; Derwent, 2014; Liu, 2010; Pun, 2001). The majority of studies have used across-the-board
percentage precursor emissions reductions of between 30 and 60 percent, with the most
common reduction percentages being 30 and 50 percent.
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sensitivity range to see whether the contribution threshold is exceeded within the range
of reductions. Air agencies can perform multiple model runs to test various sensitivity
levels and provide a range of impacts. However, modeling the highest end of the
percent reduction range as the initial model run will potentially limit the resources
involved in the analysis. If the modeled PM2.5 concentration change at the highest end
of the percent reduction range is below the recommended threshold, then additional
lower percentage model sensitivity runs will likely not be needed. If, however, the
modeled concentration change is above the threshold, then additional lower percentage
sensitivity model runs would help identify the point where the threshold is exceeded.
For the reasons stated above, in most cases, the EPA recommends that air agencies do
not use percent reductions of less than 30 percent for sensitivity analyses.
Review of recent projections of expected emission changes suggests that EPA's
recommended range is reasonable. For example, the EPA compiled the estimated state
level percent change in precursor emissions between 2011 and 2017 from the Cross-
State Air Pollution Rule Update rulemaking documentation (USEPA, 2016a). This
represents an emissions change, which occurred over a 6-year period, which is the same
amount of time allowed for a Moderate PM2.5 area to attain the NAAQS. Table 2 shows a
summary of the emissions analysis.
Table 2. Nationwide State Level37 Total Percent Change38 in Anthropogenic39 PM2.5
Precursors Between 2011 and 2017
PM2.5 Precursor
Median emissions
Range of emissions

change (%)
changes(%)
NOx
-31.8%
-7.7% to -39.9%
S02
-63.6%
-15.2% to -89.0%
VOC
-18.8%
57.5 % to -26.9%
nh3
0.8%
6.1% to -9.3%
The percent change in emissions in Table 2 show a wide variation by precursor. In
general, the largest reductions were seen in SO2 emissions (median value of -64
percent), with NOx having the second largest reductions (median value of -32 percent).
VOC had a larger range of changes (including some increases) and more than half of the
states had estimated increases in NH3 over the example 6-year period. The emissions
data show that at least half of the states achieve more than a 30-percent reduction in
NOx and SO2 in the 6-year period.
37	The percent change in precursor emissions was calculated for each of the lower 48 states. The
2011 data are historical emissions, derived primarily from the National Emissions Inventory. The
2017 emissions were projected from the 2011 data.
38	Negative percent changes reflect a decrease in emissions. Positive percent changes reflect an
increase in emissions.
39	Emissions totals do not include biogenic (NOx or VOC) emissions or fires.
30

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In addition, it can be seen in Table 2 that certain PM2.5 precursors (e.g., SO2) have been
reduced by as much as 60-90 percent over the 2011-2017 period. This does not indicate
an additional 60 plus percentage reduction in SO2 (or any other precursor) will occur in
any future 6 or 10-year period. However, depending on the circumstances in the area, it
may be appropriate to consider emissions sensitivities that are much larger than a 30
percent reduction (e.g, up to 70 percent, as referenced above). This is especially true in
nonattainment areas which can expect large additional emissions reductions of certain
precursors from on-the-books controls and in areas that are dominated by one or a few
stationary sources or categories of sources that are largely uncontrolled.
Consistent with the PM2.5SIP Requirements Rule, the EPA may in some cases require air
agencies to evaluate available emissions controls in support of a precursor
demonstration that relies on a sensitivity analysis.40 It is particularly important for states
to evaluate available controls where the recommended contribution threshold - that is,
the threshold used for identifying an impact that is "insignificant" - is close to being
exceeded at the low end of the recommended sensitivity range (e.g., 30 percent). In
these cases, the EPA may determine that to sufficiently evaluate whether the area is
sensitive to reductions, the state must determine the potential precursor emission
reductions achievable through the implementation of available and reasonable controls
for a Moderate area (or best controls for a Serious area). For example, an area that
determines it is close to exceeding the contribution threshold with a 30-percent
precursor emissions reduction may need to evaluate the impact (i.e., the percent
reduction in the precursor) of the application of reasonably available controls of the
relevant precursor. An evaluation of potential controls is less likely to be needed for
areas that do not exceed the contribution threshold at a higher modeled percent
reduction (e.g., 50-70 percent). The air agency should consult the appropriate EPA
Regional office to determine whether an emissions control analysis is needed to support
a particular precursor demonstration.
In summary, for a comprehensive sensitivity-based analysis, the EPA recommends
modeling reductions of 30-70 percent of all existing anthropogenic emissions of the
precursor (including stationary, area, and mobile sources) in the nonattainment area.
For a major stationary source sensitivity-based analysis, the EPA recommends modeling
reductions of 30-70 percent of anthropogenic emissions of the precursor from existing
major stationary sources in the nonattainment area. In addition, the EPA may in some
cases require air agencies to evaluate available emissions controls in support of a
precursor demonstration.
40 See 40 CFR 51.1009(a)(2) and 51.1010(a)(2).
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4.1.2 Evaluating Sensitivity Modeling Results
As noted previously, the EPA recommends comparing the estimated impacts of
precursor emissions on PM2.5 mass from sensitivity modeling to the contribution
thresholds for the annual average and 24-hour NAAQS, as appropriate, identified in
section 2.2.41 The EPA generally expects that if modeling demonstrates that reductions
in the 30-70 percent range produce an air quality impact below these thresholds, then it
would approve such a demonstration as adequate to show that the precursor is
insignificant. However, the higher the modeled percentage reduction, the stronger the
demonstration. Therefore, modeling the high end of the range is encouraged. The EPA
recommends submittal of supporting information for all sensitivity demonstrations,
especially for demonstrations that can only pass the recommended threshold(s) at the
low end of the range.
"¦ < » ' U '.! ill 1 , ! 11 I ! ! 1 l'» 1 !l )r ! v ! 11 >i I tf .il' >1 1
Quantifying secondary pollutant formation requires simulating chemical reactions and
thermodynamic gas-particle partitioning in a realistic chemical and physical
environment. Chemical transport models treat atmospheric chemical and physical
processes such as deposition and transport. There are two types of chemical transport
models which are differentiated based on a fixed frame of reference (Eulerian grid
based), or a frame of reference that moves with parcels of air between the source and
receptor point (Lagrangian) (McMurry et al., 2004).
A variety of Lagrangian and Eulerian modeling systems exist that could potentially be
used to estimate impacts on secondarily-formed PM2.5. These modeling systems
represent varying levels of complexity in the treatment of chemistry and the chemical
and physical environment in which precursors exist. Photochemical grid models are
three-dimensional grid-based models that treat chemical and physical processes in each
grid cell and use Eulerian diffusion and transport processes to move chemical species to
other grid cells (McMurry et al., 2004). Photochemical models are advantageous by
providing a spatially and temporally dynamic realistic chemical and physical
environment for plume growth and chemical transformation (Baker and Kelly, 2014;
Zhou et al., 2012). Publicly available and documented Eulerian photochemical grid
models such as CMAQ (Byun and Schere, 2006) and CAMx (Environ, 2016) 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). These models have been used extensively to
41 Note that when calculating the PM2.5 impact of the precursor sensitivity, all components of
modeled PM2.5 mass should be added together to get the total PM2.5 impact from the individual
precursor emissions (see section 3.1.4).
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support SIPs and to explore relationships between inputs and air quality impacts in the
United States and beyond (Cai et al., 2011; Civerolo et al., 2010; Hogrefe et al., 2011).
5.1 Mod or Attainment Plan Precursor Demonstrations
In general, attainment plan precursor demonstration modeling should follow the
recommendations in the PM2.5 photochemical modeling guidance for attainment
demonstrations [Modeling Guidance for Demonstrating Air Quality Goals for Ozone,
PM2.5, and Regional Haze (USEPA, 2018c)]. As noted above, since air quality modeling is
a required element of PM2.5 attainment demonstrations42, most air agencies will already
have a photochemical grid modeling platform available for precursor demonstrations.
Where a grid modeling platform is available for an attainment demonstration, the
process of setting up and running the model will generally be the same for a precursor
demonstration. If a photochemical modeling platform is not available, the air agency
should consult with the appropriate EPA Regional office to discuss options. Possible
alternative options include the use of a simplified box model, regional or national
photochemical grid modeling that may separately be available, or other conservative
techniques for estimating the impact of precursor emissions on PM2.5 concentrations in
the particular area.
5.1.1 Air Quality Modeling Process
Typically, the air quality modeling process starts with the development of base year
emissions and meteorology for input to an air quality run to evaluate model
performance. The photochemical PM2.5 modeling guidance describes the process for
evaluating model performance and performing diagnostic analyses. After evaluating the
model and making any necessary input changes or adjustments, the model is run for a
future year, which corresponds to the appropriate attainment year for the area. The air
quality model outputs are then used to apply the modeled attainment test to support
an attainment demonstration.
At the beginning of the modeling process, the EPA recommends a modeling protocol be
developed to support the modeling exercise. A modeling protocol is intended to
communicate the scope of the analysis and generally includes the types of analysis
performed, the specific steps taken in each type of analysis, the rationale for the choice
of modeling system, names of organizations participating in preparing and
implementing the protocol, and a complete list of model configuration options. The
protocol should detail and formalize the procedures for conducting all phases of the
modeling study, such as describing the background and objectives for the study,
creating a schedule and organizational structure for the study, developing the input
data, conducting model performance evaluations, interpreting modeling results,
42 See 40 CFR 51.1011(a)(2) and 51.1011(b)(2) and CAA section 189(a)(1)(B) and 189(b)(1)(A).
33

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describing procedures for using the model to demonstrate whether regulatory levels are
met, and producing documentation to be submitted for review and approval.
If a modeling protocol is already available in support of an attainment demonstration,
then it may not be necessary to develop a separate protocol to document a precursor
demonstration. In that case, the details of the modeling and analyses to support a
precursor demonstration can be incorporated into the existing structure of the
modeling protocol. If a modeling protocol is not otherwise available, the EPA
recommends developing a separate protocol that outlines the elements of the precursor
demonstration. A modeling protocol should include the following elements at a
minimum.
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 review authority
2.	Model and Modeling Inputs
•	Rationale for the selection of air quality, meteorological, and emissions models
•	Modeling domain specifications
•	Horizontal resolution, vertical resolution and vertical structure
•	Episode selection and rationale for episode selection
•	Description of meteorological model setup
•	Description of emissions inputs
•	Specification of initial and boundary conditions
•	Methods used to quality assure emissions, meteorological, and other model
inputs
3.	Model Performance Evaluation
•	Identify relevant ambient data and provide relevant model performance in the
modeling domain with a focus on the nonattainment area
•	List evaluation procedures
•	Identify possible diagnostic testing that could be used to improve model
performance
4.	Model Outputs
•	Describe the process for calculating precursor impacts to annual average and/or
24-hour average PM2.5 concentrations in the nonattainment area.
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The existing attainment demonstration modeling guidance provides recommendations
on all of the protocol elements above (USEPA, 2018c). This includes selecting air quality
models, meteorological modeling, episode selection, the size of the modeling domain,
the grid size and number of vertical layers, and model performance. Precursor
demonstrations for attainment plans should generally follow the recommendations in
the attainment demonstration modeling guidance.
5.1.2 Modeling Approaches
The simplest sensitivity modeling approach (brute force change to emissions) would be
to simulate two sets of conditions, one with all emissions and one with an across-the-
board anthropogenic emissions reduction (or zero precursor emissions in the case of a
"zero-out" model run). The difference between these simulations provides an estimate
of the air quality change related to the change in emissions from the precursor.
Additionally, some photochemical models have been instrumented with source
apportionment, which tracks emissions from specific sources, source sectors, and/or
source regions through chemical transformation, transport, and deposition processes to
estimate the apportionment of predicted PM2.5 species concentrations (Kwok et al.,
2015; Kwok et al., 2013). Source apportionment has been used to calculate the
contribution from multiple states on model predicted ozone and PM2.5 as part of several
transport related rulemakings (USEPA, 2011; USEPA, 2016b). Air agencies can choose
the most efficient modeling technique for their particular situation and should discuss
the options with the appropriate EPA Regional office.
5.2 Base Ye	:ure Year Model Assessments
Modeled attainment demonstrations typically include modeling for a base year (used to
evaluate model performance), and modeling using future year emissions to simulate the
impact of emissions changes (both emission reduction programs and emissions growth)
on future air quality concentrations. Attainment demonstrations (and impracticability
demonstrations) use the future year modeled air quality concentrations to determine if
attainment is likely to be reached by the nonattainment area attainment deadline.
Where air agencies have both base year and future year modeling in support of an
attainment demonstration or an impracticability demonstration, precursor
demonstration modeling to demonstrate that precursor emissions do not contribute
significantly to PM2.5 concentrations in the nonattainment area could be done in either a
base year or a future year. The base year modeling has less uncertainty compared to the
future year since model performance is known for the base year and the modeling does
not depend on projections of emissions to a future year. In addition, some control
requirements (e.g., RACT) may apply before the maximum statutory future year
attainment date. However, there may be situations, such as with the NNSR precursor
demonstration (see section 6), where it could be more appropriate to model future
conditions that provide a more representative sensitivity analysis based on the period of
time when a new source will begin to operate.
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In most cases, the modeled base year is the best representation of current conditions.
Note, however, that the modeled base year is not necessarily the same as modeling a
"current" year. In some cases, the base year used for modeling purposes may be several
years or more in the past. Therefore, future year baseline modeling may in some cases
be more appropriate for attainment plan-modeled precursor demonstrations. Given the
multitude of considerations, air agencies should consult the appropriate EPA Regional
office to determine the appropriate analysis year(s). In addition, air agencies should
provide an explanation of how the choice of analysis year(s) and associated assumptions
are appropriate for the particular precursor demonstration.
5.3 Calculating the Modeled Impact from Precursors
The modeled precursor impact on PM2.5 levels can be calculated as either the absolute
modeled concentration change or as the relative concentration change, based on the
percent modeled change in PM2.5 species, applied to ambient data. The photochemical
modeling guidance (USEPA, 2018c) recommends performing a "relative" attainment test
for modeled attainment demonstrations. The recommended test uses model estimates
in a "relative" rather than "absolute" sense to estimate future year design values. The
fractional changes in air pollutant concentrations between the model future year and
model base year are calculated for all valid monitor locations. These ratios are called
relative response factors (RRF). Future PM2.5 design values are estimated at existing
monitoring sites by multiplying the modeled RRFs for each monitor by the monitor-
specific base year design value. The resulting estimates of future concentrations are
then compared to the NAAQS. If the model is over-or-under-predicting PM species
concentrations, the absolute modeled response to emissions precursor changes may be
biased (high or low). However, the relative attainment test has the benefit of anchoring
the projected PM2.5 concentrations to measured ambient data, which helps mitigate
modeled over-or under-predictions, relative to the level of the NAAQS.
In contrast to an attainment demonstration, precursor demonstrations do not examine
changes in emissions between a base year and a future year. Instead, the calculation of
changes in PM2.5 concentrations occur between a modeled case with all emissions and a
modeled case with reduced precursor emissions.
Even though it may be appropriate to calculate absolute modeled PM2.5 concentration
changes, there are advantages to calculating relative concentration changes, using the
relative attainment test procedures for the modeled attainment test in the modeling
guidance. The relative attainment test procedure involves applying adjustments to the
ambient data to reconstruct the measured species components so that they add up to
the measured FRM mass. Data analyses (Frank, 2006) have noted that the FRM monitors
do not measure the same components and do not retain all of the PM2.5 that is
measured by routine speciation samplers and, therefore, cannot be directly compared
36

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to speciation measurements from the Chemical Speciation Network (CSN).43 It is
possible to reconstruct PM2.5 species so that they more closely match the composition
of mass retained by the FRM. This adjustment can be applied to the modeled change in
PM2.5 species components. This will result in calculated PM2.5 species mass, which is
anchored to the measured mass, and more closely reflects the species concentrations
that are retained on the FRM filters, including an estimate of particle bound water. See
the photochemical modeling guidance (USEPA, 2018c; Frank, 2006) for more details on
the recommended calculations.
The EPA provides a software package called the Software for the Modeled Attainment
Test (SMAT) (USEPA 2018d), which provides default ambient data and performs the
relative attainment test calculations. Assuming that the precursor impacts are
calculated using base year modeling, a single SMAT run is needed to calculate precursor
impacts. SMAT can be run with the base case concentrations as the "base year" and the
zero-out/source apportionment (see page 26) or sensitivity model run (see page 27)
case(s) as the "future year" (even though the model run does not actually represent a
future year).44 The "future year" PM2.5 concentration values are subtracted from the
base year values to get the total PM2.5 contribution from the precursor. The precursor
impact is then compared to the threshold(s) identified in section 2.2. If the precursor
impacts are calculated using future year modeling, two SMAT runs are needed to
calculate precursor impacts. The first SMAT run will calculate future year base case
PM2.5 concentrations using the base case and future year model outputs. The second
SMAT run will calculate future year PM2.5 concentrations from the zero-out/source
apportionment or sensitivity model run(s). The two future year PM2.5 concentration
values are subtracted from each other to get the total PM2.5 impact from the precursor.
The precursor impact is then compared to the threshold(s) identified in section 2.2.
When calculating modeled precursor impacts to PM2.5, it is important to consider model
performance. This is especially true in cases where air agencies choose to use absolute
model results. If the model over-predicts PM2.5 species concentrations, the absolute
modeled concentration changes may be biased high. Similarly, if the model under-
predicts PM2.5 species concentrations, the absolute modeled concentration changes may
be biased low. Therefore, model under-predictions are a particular concern (especially
43	The information in this section applies to the most common samplers in the CSN. Some
networks use alternative special purpose samplers to collect both PM2.5 and PM2.5 speciation
data. The characteristics of the sampler and the analytical procedures used to produce chemical
speciation data should be considered in determining which, if any adjustments are appropriate
to make the data useful for comparison to FRM data.
44	In a typical attainment demonstration, base year model outputs and future year model
outputs are used to calculate RRFs. Therefore, the SMAT attainment test software graphical user
interface uses the terms "base year" and "future year" when referring to the input files needed
to calculate RRFs. The text here is referring to the SMAT software terms "base year" and "future
year", even though in this case the year is the same in both model runs used to calculate RRFs.
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when considering absolute modeled impacts) since this could lead to modeled precursor
impacts that may be biased low.
5.3.1	Estimating the Annual PM2.5 Impact from Precursors
The first step for estimating annual PM2.5 impacts from a precursor is to estimate the
annual average PM2.5 at each monitor location (the grid cell where the monitor is
located) for the baseline scenario. Second, calculate the annual average PM2.5 at each
monitor for the zero-out/source apportionment or sensitivity scenario. Calculate the
difference between the zero-out/source apportionment or sensitivity scenario annual
average PM2.5 and baseline scenario annual average PM2.5 for each monitor location.
This difference is the impact from the PM2.5 precursor. Based on the recommendation in
section 2.4, the impacts are calculated at monitor locations. When using the relative
attainment test, the default recommendation is to average the concentrations at the
nine (9) surrounding grid cells (a 3 x 3 matrix of grid cells, with the monitor in the center
grid cell).45
5.3.2	Estimating the Daily PM2.5 Impact from Precursors
The first step for estimating 24-hour PM2.5 impacts from a precursor is to estimate the
24-hour average PM2.5 mass at each monitor location (the grid cell where the monitor is
located) for the baseline scenario. Second, calculate the 24-hour average PM2.5 at each
monitor for the zero-out/source apportionment or sensitivity scenario. Calculate the
difference between the zero-out/source apportionment or sensitivity scenario 24-hr
average PM2.5 and baseline scenario 24-hour average PM2.5 for each day for each
monitor location. This difference is the contribution from the PM2.5 precursor. Based on
the recommendation in section 2.4, the contributions are calculated at monitor
locations. When using the relative attainment test, the default recommendation is to
use the single grid cell where the monitor is located to represent the location of the
monitor.
When calculating absolute daily impacts, the highest 24-hour average PM2.5 impact from
the modeled time period should be compared to the daily PM2.5 "contribution"
threshold at each monitor location. If the highest daily average secondarily-formed
PM2.5 impact is greater than the level of the threshold, then a 2nd tier analysis may be
appropriate to further examine the precursor impacts on the high modeled and/or
observed PM2.5 days. Air agencies should consult with the appropriate EPA Regional
office to discuss the details of the calculations.
Application of the relative attainment test (using SMAT) for the 24-hour NAAQS already
takes into consideration the high measured PM2.5 days. Therefore, no further (2nd tier)
45 See the photochemical modeling guidance section 4.2.2 for more details (USEPA, 2018c).
38

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analysis is necessary to calculate the impact on exceedance days. The SMAT 24-hr air
quality impact is calculated on high measured PM2.5 days in the area.
' m Nonattainment New '"h ! ^ i. > ¦ NFvl h ¦ nrsor
ration
The PM2.5 SIP Requirements Rule identifies a specific type of precursor demonstration
that air agencies may use to demonstrate that sources of a particular precursor do not
need to be controlled with respect to that precursor under the NNSR program for a
particular PM2.5 nonattainment area.46 As detailed in the PM2.5 SIP Requirements Rule,
the NNSR precursor demonstration is based on the premise that the sensitivity of a
particular nonattainment area to precursor emissions from future new major stationary
sources and major modifications is best indicated by an emissions increase test. The
sensitivity of an area to precursor increases may be different from the sensitivity of that
same area to decreases (e.g., where there are low emissions of the precursor from only
a few sources). Therefore, for NNSR, the rule allows an air agency to undertake a
sensitivity-based test in order to demonstrate that increases in emissions of a particular
precursor would not contribute significantly to PM2.5 levels that exceed the standard,
and that sources of such precursor may in some cases be exempted from PM2.5 controls
for that precursor(s) under the NNSR permitting requirements.
Note that the NNSR precursor demonstration is optional and an air agency may
satisfactorily demonstrate that a precursor is insignificant for all other control
requirements other than NNSR, using the analyses previously described in this guidance
or other appropriate analyses, without analyzing whether the precursor contributes
significantly to PM2.5 levels for the purposes of NNSR. In such cases, the nonattainment
planning requirements would not apply to existing sources of that precursor, but the
NNSR requirements would apply in the event that a new major stationary source or
major modification in that area triggers NNSR permitting. Such an approach may be
efficient for air agencies who do not want to expend the resources necessary to
complete an NNSR precursor demonstration because they expect few (or no) new or
modified major stationary sources of the precursor in question.
The NNSR precursor demonstration differs from the other two demonstrations
(comprehensive and major stationary source precursor demonstrations), which are
attainment plan tests, in that the latter two demonstrations examine air quality changes
resulting from emissions reductions from existing sources. An attainment
demonstration deals with existing emissions sources and how emissions reductions
from those sources can help a nonattainment area reach attainment of the NAAQS. In
contrast, the NNSR program addresses the management of major stationary source
growth (new major stationary sources and major modifications) in the nonattainment
area. Thus, by its nature, NNSR deals with increases of emissions in the nonattainment
area. Even in an area that currently has no existing major stationary sources, PM2.5
46 See 40 CFR 51.1006(a)(3).
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precursors from new major stationary source growth occurring in the nonattainment
area could still contribute significantly to PM2.5 levels in the area. Therefore, the PM2.5
SIP Requirements Rule mandates that, if performed, NNSR precursor demonstrations be
based on a sensitivity analysis which examines potential increases of emissions in the
nonattainment area.47
Similar to the questions described in section 4.1, in performing a sensitivity analysis for
NNSR, there are several questions that need to be addressed:
1)	What amount of emissions increase should be examined as part of the NNSR
sensitivity analysis?
2)	What location(s) should be used to model the precursor emissions increases
resulting from potential major stationary source growth?
3)	What air quality concentration threshold should be used to determine if the
modeled air quality change from the precursor is insignificant?
The EPA recognizes that there may be a number of factors inherent to a particular
nonattainment area that could influence the potential emissions increase from new
major stationary sources and major modifications. The following section addresses
these factors and sets forth guidance for air agencies to consider in completing the
NNSR precursor demonstration.
6.1 NNSR Demonstrations
The purpose of the NNSR precursor demonstration is to determine if the nonattainment
area is sensitive to PM2.5 precursor emissions increases that may occur in a particular
area from new major stationary sources and/or major stationary source modifications. It
would be appropriate for the air agency to base estimates of any potential emissions
increases in part on the types and size of new major stationary sources that are most
likely to locate within the nonattainment area and/or existing sources most likely to
undergo a major modification. To help determine the size and types of potential
sources, the EPA also recommends an examination of recent (e.g., the last 5 years)
major stationary source permits in the region. In order to gather enough information on
recently permitted emissions sources, it may be necessary to examine a broad region
encompassing the nonattainment area. For example, an air agency may want to
examine permits issued within the entire Northeast, Southeast, or Midwest region.
Gathering information concerning permitted major stationary sources that have located
elsewhere (magnitude of emissions, stack parameters, etc.) can help inform the process
and make the modeling of precursor emissions more realistic.48
47	See 40 CFR 51.1005(a)(3)(i).
48	This recommendation is not meant to direct or limit the information that an air agency can
use to develop appropriate model inputs for an NNSR precursor demonstration. Available
information on the nature (i.e.,. type, size, location) of existing (and potential) major stationary
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The identified range of emissions from recently permitted major stationary sources may
vary widely among PM2.5 precursors. SO2, NOx and VOC are PM2.5 precursors that are
also regulated as pollutants associated with other NAAQS (while NH3 is not nationally
regulated under any other NAAQS). All new major stationary sources and major
modifications of SO2, NOx and VOC must already meet Best Available Control
Technology (BACT) level controls (or Lowest Achievable Emissions Rate controls if they
are located in nonattainment areas) and all other NSR program requirements. The
treatment of SO2, NOx and VOC under the NSR program for other NAAQS pollutants
(besides PM2.5) thus serves to limit the potential increase of PM2.5 precursor emissions
from new sources, even absent controls as a PM2.5 precursor. Therefore, how the
particular precursor is treated as a result of regulation pursuant to other NAAQS is an
important consideration when determining the potential emissions increases that
should be modeled for a PM2.5 NNSR precursor demonstration.
Other important considerations for determining the amount of emissions increase that
should be analyzed in the NNSR precursor demonstration include but are not limited to:
the size of the nonattainment area; the size, location, number, and types of existing
major stationary sources (from which major modifications could occur); and current and
future land use and zoning.
Upon the EPA's approval of an NNSR precursor demonstration, the air agency would not
need to apply the PM2.5 control requirements to new major stationary sources and
major modifications with respect to that precursor under the NNSR program for PM2.5
(for the current SIP). Therefore, the NNSR demonstration should include a conservative
representation of potential emissions increases from new and modified major stationary
sources. For example, the modeled size of sources (in tons per year of emissions) and
the number and location of sources should be adequately conservative to analyze more
than what is merely "likely" to occur in the area. The goal of the NNSR demonstration is
not simply to determine the PIVh.sair quality impact of likely new sources. Instead, it is
to examine whether the nonattainment area is sensitive to increases of precursor
emissions and whether the resulting PM2.5 air quality change that could result from
potential major stationary source growth would represent a significant contribution to
PM2.5 levels that exceed the NAAQS in a PM2.5 nonattainment area. It is, however,
important to consider the potential size and number of new sources of PM2.5 precursors
that may possibly locate in the nonattainment area (using conservative assumptions)
when planning the analysis.
sources will vary considerably from area to area. Each air agency should consider the facts and
circumstances most relevant to their situation.
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6.2 Modeling for NNSR Demonstrations
The fundamental approach for analyzing changes in emissions pursuant to the NNSR
precursor demonstration involves the use of a photochemical model to project the air
quality changes associated with potential emissions increases from hypothetical new
major stationary sources and/or major modifications. In most cases, it will not be
sufficient to model potential emissions increases from existing major stationary sources
in the area. Some nonattainment areas may only have one or, in some cases, no existing
major stationary sources. Moreover, it is important to examine the area's sensitivity to
emissions increases from potential source locations across the entire nonattainment
area because a new source may locate in any part of the nonattainment area
(notwithstanding relevant land use and/or zoning restrictions). New and/or modified
sources could contribute significantly to existing monitored locations within the
nonattainment area or cause new exceedances of the standard in other parts of the
nonattainment area. Therefore, as discussed in section 2.4, in most cases it will be
necessary to model a number of hypothetical new sources, placed in various locations
across the nonattainment area. EPA expects the air agency to analyze the air quality
sensitivity of multiple locations of potential new sources that may locate anywhere
within the nonattainment area. A reasonable number of sources (depending on the size
of the nonattainment area) should be modeled in different parts of the area to ensure
that the spatial variability of chemistry response to additional precursor emissions is
well represented in the demonstration. (Section 6.3 provides additional considerations
for the location of potential major source growth.) The location of existing major
stationary sources and the stack parameters of those sources can be used to help design
the NNSR modeling demonstration. The existing major stationary source information
can be the starting point of the analysis, with additional hypothetical new sources (that
may or may not resemble existing sources) placed in other parts of the area, as
necessary.
The EPA also recommends modeling multiple hypothetical sources with emission rates
and stack release characteristics typical of existing sources in the area or region. The
overall approach for hypothetical source impact assessment would be generally similar
to the analysis documented in "Estimating ozone and secondary PM2.5 impacts from
hypothetical single source emissions in the central and eastern United States" (Baker,
2016). Choices made for these hypothetical sources should be done in consultation with
the appropriate EPA Regional office.
Due to the unique sensitivity levels of nonattainment areas to air quality impacts from
individual PM2.5 precursors, the EPA is not making default recommendations on the size
and number of hypothetical new and/or existing sources to model in an NNSR
demonstration. The details of the analysis, including a modeling protocol, should be
discussed in advance with the appropriate EPA Regional office.
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6.2.1 Types of Models
Quantifying secondary pollutant formation requires simulating chemical reactions and
thermodynamic gas-particle partitioning in a realistic chemical and physical
environment. Therefore, in most cases, the EPA believes it will be necessary to employ a
CTM for NNSR precursor demonstrations. CTMs treat atmospheric, chemical, and
physical processes such as deposition and transport. In some limited cases, a simplified
box model49 that employs chemistry may be sufficient. Below, we describe additional
details for the purposes of estimating the magnitude of secondarily-formed PM2.5 from
PM2.5 precursor emissions associated from major stationary source growth.
6.2.2 Modeling for Major Stationary Sources
As previously described in section 5.0, a variety of modeling systems exist that could
potentially be used to estimate stationary source impacts from new and/or modified
major stationary sources on secondarily-formed pollution such as PM2.5. These modeling
systems represent varying levels of complexity in the treatment of plume chemistry and
the chemical and physical environment in which the plume exists. It is important that
any modeling system be appropriately applied for assessing the effects of major
stationary sources on secondarily-formed pollutants such as PM2.5 for the purposes of a
precursor demonstration (USEPA, 2005).
Puff or dispersion (Lagrangian) modeling systems that have been used to assess single
source impacts in North America include CALPUFF, HYSPLIT, FLEXPART, SCIPUFF, and
SCICHEM. Some Lagrangian models treat in-plume gas and particulate chemistry. These
models require time and space varying oxidant concentrations and, in the case of PM2.5,
also neutralizing agents (such as NH3) as important secondary impacts happen when
plume edges start to interact with the surrounding chemical environment (Baker and
Kelly, 2014; ENVIRON, 2012). These oxidant and neutralizing agents are not routinely
measured, but can be generated with a three dimensional photochemical transport
model and subsequently input to a Lagrangian modeling system.
Because a NNSR demonstration adds hypothetical point sources, it is, therefore,
possible to use a Lagrangian model to support an NNSR precursor demonstration.
However, since it is likely that multiple hypothetical sources will need to be modeled
and the Lagrangian model requires realistic background oxidant information (which can
be supplied from a photochemical model), it will be easier in most cases to use a
photochemical grid model for the demonstration. See section 5 for more details on
photochemical grid models.
It is important that modeling systems used for these assessments be fit for this purpose
and evaluated for skill in replicating meteorology and atmospheric chemical and
49 A box model is a simplistic single cell model that can represent photochemistry in a small
isolated area.
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physical processes that result in secondary pollutant formation and deposition. A
candidate model for use in estimating the effects of precursors emitted from potential
major stationary sources on secondarily-formed PM2.5 for the purposes of an NNSR
precursor demonstration should meet the general criteria for an "alternative model"
outlined in 40 CFR part 51, Appendix W, Section 3.2 (USEPA, 2017). Whether an air
agency chooses to use a photochemical grid model such as CAMx or CMAQ, or a plume
model such as SCICHEM, they should consult the appropriate EPA Regional office to
determine the appropriate model and modeling approach for an NNSR precursor
demonstration.
6.2.3	Modeling Approaches
The simplest modeling approach to calculate impacts in a photochemical grid model for
an NNSR precursor demonstration is to model a brute force change in emissions by
simulating two sets of conditions: one with all existing emissions, and one that includes
an increase in emissions of the precursor that could result from major stationary source
growth (new major stationary sources and major modifications) (Baker and Kelly, 2014;
Bergin et al., 2008; Kelly et al., 2015; Zhou et al., 2012). The difference between these
simulations provides an estimate of the air quality change related to the increase in
emissions from the precursor. Additionally, some photochemical grid models have been
instrumented with source apportionment, which tracks emissions from specific sources,
source sectors, and/or source regions through chemical transformation, transport and
deposition processes to estimate an impact to predicted air quality (Kwok et al., 2015;
Kwok et al., 2013). Source apportionment has been used to differentiate the impact
from single sources on model predicted ozone and PM2.5 (Baker and Foley, 2011; Baker
and Kelly, 2014; Baker et al., 2016).
Alternatively, the Direct Decoupled Method (DDM) source sensitivity technique has also
been used to estimate O3 and PM2.5 impacts from specific sources (Baker and Kelly,
2014; Bergin et al., 2008; Cohan et al., 2005; Cohan et al., 2006; Kelly et al., 2015).
Since an NNSR precursor demonstration may require modeling multiple sources in
multiple locations, an advanced technique such as source apportionment may save
resources compared to numerous brute force photochemical grid model runs. Air
agencies can choose the most efficient modeling technique for their particular situation;
discussing the options in advance with the appropriate EPA Regional office is strongly
advised.
6.2.4	Horizontal Grid Resolution
NNSR precursor demonstrations for nonattainment areas should be conducted at
horizontal grid resolutions between ~1 kilometer (km) up to ~12 km. Photochemical grid
model application with grid cells up to 12 km has been shown to estimate similar urban
area changes in air quality due to changes in emissions from a specific source on
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secondary pollutants when estimated with finer grid resolution (Cohan et al., 2006). In
instances where the air agency is considering modeling sources either at coarser
resolutions or at resolutions finer than 1 km, consultation with the appropriate EPA
Regional office is advised.
Even though single source emissions are averaged into a grid volume, photochemical
transport models have been shown to adequately capture single source impacts when
compared with downwind in-plume measurements (Baker and Kelly, 2014; Zhou et al.,
2012). Where set up appropriately for the purposes of assessing the impact of single
sources on secondarily-formed pollutants, photochemical grid models could be used
with a variety of approaches to estimate these impacts (see section 6.2.3, above).
In some instances, where the source and key receptors are in very close proximity, the
source and receptor may be located in the same photochemical grid model cell. Since
physical and chemical processes represent a volume average, this may not adequately
represent the gradients of pollution possible between the source and receptor when
they are located in such proximity. The preferred approach to better represent the
spatial gradient in source-receptor relationships when they are in close proximity is to
use smaller sized grid cells. In such cases, grid resolution should be defined such that the
source and receptor are no longer in the same grid cell. Ideally, there should also be
several grid cells between the source and receptor to resolve best near-source pollution
gradients.
In situations of close proximity between the source and receptor, a photochemical
model instrumented with sub-grid plume treatment and sampling could potentially
represent these relationships. Sub-grid plume treatment extensions in photochemical
models typically solve for in-plume chemistry and use a set of physical and chemical
criteria for a determination of when puff mass is merged back into the host model grid.
A notable limitation of sub-grid plume treatments is that these implementations do not
have more refined information related to meteorology or terrain than the host grid cell.
In addition to tracking puffs at sub-grid scale, the host modeling systems must be able to
track and output surface layer sub-grid puff concentrations, "sub-grid plume sampling,"
to best represent receptor concentrations that are in close proximity to the source
(Baker et al., 2014). Another important reason sub-grid plume sampling is necessary is
that inherently in this type of system (sub-grid plume treatment in a photochemical grid
model) some of the source's impacts on air quality are resolved in puffs at the sub-grid
scale and some have been resolved in the 3-dimensional grid space. Just extracting sub-
grid plume information or just 3-dimensional model output would miss some of the
source's impacts on air quality, which means that accounting for both is necessary
either with sub-grid sampling or options that integrate puffs within a grid cell with grid
cell concentrations. Sub-grid plume treatments in photochemical grid models do not
track grid resolved source impacts separately from other sources in the model
simulation. When either sub-grid treatment is applied for an NNSR precursor
demonstration, source apportionment or source sensitivity is necessary to track the grid
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resolved source impact in addition to sub-grid plume treatment to fully capture source
impact.
6.3	Location and Source Characteristics of Potential Major Stationary Source
Growth
As discussed in section 2.4 above, the EPA believes that the analysis should evaluate the
projected air quality change from potential future major stationary sources in all parts
of the nonattainment area. Air agencies should consult with the appropriate EPA
Regional office to determine the appropriate number, and location, of potential major
stationary sources in an NNSR precursor demonstration. Enough locations should be
included in the demonstration such that new sources are placed in a variety of chemical
regimes to provide full coverage over the nonattainment area. The journal article
"Estimating ozone and secondary PM2.5 impacts from hypothetical single source
emissions in the central and eastern United States" (Baker, 2016) provides examples of
different types of hypothetical sources, modeled to examine secondary PM2.5 impacts.
This article examined the PM2.5 concentration impacts from several different size
sources with different stack parameters. For example, hypothetical sources were
modeled in different areas across the country using stack parameters that represented
both elevated sources and near ground level sources. The techniques applied in that
study may be useful for designing future major stationary sources for sensitivity
modeling in NNSR precursor demonstrations. In addition, the air agency may
demonstrate that certain locations are clearly unsuitable for major stationary source
growth (e.g., agricultural, residential and resort areas) so that they can be eliminated as
potential growth sites for the modeling analysis.
6.4	Base Ye	:ure Year Model Assessments
Modeled attainment demonstrations typically include modeling for a base year (used to
evaluate model performance), and future year emissions are used to simulate the
impact of emissions changes (due to emission reduction programs or any emissions
growth) on future air quality concentrations. Attainment demonstrations (and
impracticability demonstrations) use the future year modeled air quality concentrations
to determine if attainment is likely to be reached by the applicable attainment deadline.
Where air agencies have both base year and future year modeling in support of an
attainment demonstration or an impracticability demonstration, modeling emissions
increases to support an NNSR precursor demonstration could be done in either a base
year or a future year. A demonstration based on future year modeling may be
appropriate because air agencies should evaluate emissions controls in the context of
achieving needed air quality improvements in the attainment year. On the other hand,
air agencies should account for the fact that new major stationary sources could locate
in the nonattainment area at any time between the nonattainment designation date
and the date when the area is eventually redesignated to attainment. Since NNSR
provisions are effective immediately after the area is designated as nonattainment,
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there is some basis for using base year modeling for an NNSR precursor demonstration.
However, in some situations, particularly where no new major stationary source permit
applications have yet been filed and any new major stationary sources therefore would
not be in operation for a number of years, air agencies may find that future year
modeling may more accurately reflect atmospheric conditions for secondary PM2.5
formation when precursor emissions increases from potential major stationary source
growth may occur. Given the multitude of considerations, air agencies should consult
the appropriate EPA Regional office to determine the appropriate analysis year.
6.5 Calculating the Modelled Impact from Precursors
The modeled precursor impacts on PM2.5 concentrations can be calculated either as the
absolute modeled concentration changes or as relative concentration changes, based on
the percent modeled change in PM2.5 species, applied to ambient data. The
photochemical modeling guidance recommends performing a "relative" attainment test
for modeled attainment demonstrations. However, modeling for PSD analyses of single
stationary sources typically uses absolute model results (USEPA, 2017 and USEPA, 2014).
Since the modeled emissions and stack parameters from existing major stationary
sources are well characterized and known, the use of absolute concentration change
estimates from those sources in a photochemical model is, in most cases, appropriate.
Adjusting the single source impacts up or down based on overall modeled
concentrations of species (using the relative attainment test procedures) may, in some
cases, inappropriately adjust the absolute modeled concentration change. Therefore,
the EPA recommends using absolute model outputs to calculate major stationary source
impacts for NNSR precursor demonstrations. However, there may be some cases where
relative impacts for an NNSR precursor demonstration may be appropriate. Air agencies
should consult with the appropriate EPA Regional office to determine the most
appropriate post-processing procedures for the particular demonstration.
6.5.1	Estimating the Annual PM2.5 Impact from Precursors
The first step for estimating annual PM2.5 impacts from a precursor is to estimate the
annual average PM2.5 at each receptor in the nonattainment area (if using a grid model,
each grid cell is a receptor) for the baseline scenario. The second step is to calculate the
annual average PM2.5 at each receptor for the sensitivity scenario. The final step is to
calculate the difference between the sensitivity scenario annual average PM2.5 and
baseline scenario annual average PM2.5 for each receptor. This difference yields the
impact from the PM2.5 precursor. Based on the recommendation in section 2.4, the
impacts are calculated for all locations (grid cells) within the nonattainment area and
should be compared to the thresholds recommended in section 2.2.
6.5.2	Estimating the Daily PM2.5 Impact from Precursors
The first step for estimating 24-hour PM2.5 impacts from a precursor is to estimate the
24-hour average PM2.5 at each receptor in the nonattainment area (if using a grid model,
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each grid cell is a receptor) for the baseline scenario. The second step is to calculate the
24-hour average PM2.5 at each receptor for the sensitivity scenario. The final step is to
calculate the difference between the sensitivity scenario 24-hour average PM2.5 and
baseline scenario 24-hour average PM2.5 for each day for each receptor. This difference
yields the impact from the PM2.5 precursor. Based on the recommendation in section
2.4, the contributions are calculated for all locations (grid cells) within the
nonattainment area and should be compared to the thresholds recommended in section
2.2.
When calculating absolute daily impacts, the highest 24-hour average PM2.5 impact from
the modeled time period should be compared to the daily PM2.5 threshold at each grid
cell. If the highest daily average secondarily-formed PM2.5 contribution is greater than
the level of the threshold, then a 2nd tier analysis may be appropriate to further examine
the precursor impacts on the highest modeled PM2.5 days. Air agencies should consult
with the appropriate EPA Regional office to discuss the details of the calculations.
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United States	Office of Air Quality Planning and Standards	Publication No. EPA-454/R-19-004
Environmental Protection	Air Quality Assessment Division / Air Quality Policy Division May 2019
Agency	Research Triangle Park, NC

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