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Interagency Workgroup on Air Quality
Modeling Phase 3 Summary Report: Near-Field
Single-source Secondary Impacts
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EPA-454/R-16-003
June 2016
Interagency Workgroup on Air Quality Modeling Phase 3 Summary Report: Near-Field
Single-source Secondary Impacts
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Air Quality Modeling Group
Research Triangle Park, NC
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Executive Summary
The Interagency Workgroup on Air Quality Modeling (IWAQM) was originally formed in 1991 to provide
a forum for development of technically sound regional air quality models for regulatory assessments of
pollutant source impacts on Federal Class I areas. The IWAQM process largely concluded in 1998 with
the publication of the Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary
Report and Recommendations for Modeling Long-range transport Impacts (EPA-454/R-98-019) (U.S.
Environmental Protection Agency, 1998). The IWAQM Phase 2 process provided a series of
recommendations concerning the application of the CALPUFF model for use in long-range transport
(LRT) modeling and informed the promulgation of that model for such regulatory purposes in 2003. The
IWAQM process was reinitiated in June 2013 to inform EPA's commitment to update the "Guideline on
Air Quality Models" (Appendix W to CFR Part 51), hereafter referred to as Appendix W, to address
chemically reactive pollutants in near-field and long-range transport applications (U.S. Environmental
Protection Agency, 2012a). This report provides information and recommendations from the "Phase 3"
effort focused on near-field single-source impacts of secondary pollutants. A separate report provides
information and recommentaitons on long-range transport for air quality related values (AQRVs) and
deposition (U.S. Environmental Protection Agency, 2015).
This document describes chemical and physical processes important to the formation of ground-level 03
and PM2.5 in the context of modeled near-field assessments to support permit review programs.
Chemical transport models that characterize these processes include both Lagrangian which typically
only have a single-source included in the model and photochemical grid models which include some
representation of all anthropogenic, biogenic, and geogenic sources. Modeling systems appropriate for
the purposes of estimating single-source near-field secondary impacts are described and
recommendations are made with respect to the use of certain types of modeling systems for this type of
application. Model evaluation is important to ensure that a particular system is fit for the purpose of
estimating near-field single-source secondary impacts. In addition to establishing a modeling system is
generally appropriate for this purpose, project-specific evaluations that compare model-estimated
meteorology and chemical estimates with measurements near the project source and key receptors is
also an important model evaluation component.
An illustrative example is provided showing hypothetical single-source impacts in two different urban
areas: Atlanta and Detroit. A photochemical grid model was applied with baseline emissions and
subsequent additional simulations where a new hypothetical source was included with a fixed precursor
emission rate. The rates used here are illustrative and not reflective of any specific policy or programs.
The simulations with the additional hypothetical source are compared with the baseline simulation
where the hypothetical source is not included (e.g. brute-force difference) and single-source impacts are
estimated for 03 and secondary PM2.5 sulfate and nitrate. Downwind impacts from these hypothetical
sources tend to increase as precursor emissions increase. Impacts tend to be highest near the source
and decrease as distance from the source increases. However, there is variability in downwind impacts
directionally from each of these sources due to differences in meteorology and available oxidants and
neutralizing agents.
Finally, a review of existing research published between 2005 and 2015 relating single-source precursor
emissions and downwind impacts on 03 and secondary PM2.5 is summarized. Downwind 03 impacts from
these studies show a general increase as NOx emissions increase. Downwind impacts for 03 and
secondary PM2.5 in the studies reviewed tend to be largest nearest the source and decrease as distance
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from the source increases. Approaches for developing reduced-form or screening versions of more
refined modeling tools such as photochemical transport models are also reviewed. Where appropriately
developed, relationships between hypothetical source precursor emissions and downwind 03 and
secondary PM2.5 impacts could be used to provide credible information about single-source secondary
pollutant impacts before applying more rigorous modeling techniques. This type of approach is more
commonly called a Tier 1 demonstration tool or Modeled Emission Rates for Precursors (MERPS).
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Table of Contents
1 Background: IWAQM Phase 3 process 6
2 Regulatory Motivation 6
3 Model Selection 7
3.1 Secondary Pollutant Formation: 03 and PM2.5 7
3.2 Air Quality Models for Secondary Pollutants 7
3.2.1 Lagrangian models 8
3.2.2 Photochemical transport models 8
3.3 Recommendations for estimating single-source 03 and secondary PM2.5 impacts using
photochemical grid models 9
4 Model evaluation 10
4.1 Fit for Purpose Evaluations 10
4.2 Model evaluation: meteorology 11
4.3 Model evaluation: chemistry 11
4.4 Model performance evaluation data sources 12
5 Illustrative example of near-field single-source impacts on 03 and PM2.5 14
5.1 Model application approach for estimating these illustrative example single-source impacts. 14
5.2 Illustrative single-source impact results for 03 and PM2.5 15
6 Review of published single-source impacts on 03 and PM2.5 19
6.1 Single-source ozone impacts 19
6.2 Single-source PM2.5 impacts 21
7 Reduced-form approaches for estimating single-source secondary impacts 22
7.1 Review of reduced-form approaches for estimating 03 single-source impacts 23
7.2 Review of reduced-form approaches for estimating PM2.5 single-source impacts 23
8 Acknowledgements 24
9 References 24
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1 Background: IWAQM Phase 3 process
The Interagency Workgroup on Air Quality Modeling (IWAQM) was originally formed in 1991 to provide
a forum for development of technically sound regional air quality models for regulatory assessments of
pollutant source impacts on Federal Class I areas. Meetings were held with personnel from participating
Federal agencies: the Environmental Protection Agency (EPA), the U.S. Forest Service (USFS), the U.S.
Fish and Wildlife Service (USFWS), and the National Park Service (NPS). The original purpose was to
review respective modeling programs, develop an organizational framework, and formulate reasonable
objectives and plans for single-source model applications. The IWAQM process largely concluded in
1998 with the publication of the Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2
Summary Report and Recommendations for Modeling Long-range transport Impacts (EPA-454/R-98-
019) (U.S. Environmental Protection Agency, 1998). The IWAQM Phase 2 report provided a series of
recommendations concerning the application of the CALPUFF model for use in long-range transport
(LRT) modeling and informed the promulgation of that model for such regulatory purposes in 2003.
Draft updates to the IWAQM Phase 2 report were released in 2009 to better reflect the state-of-the-
practice of long-range transport modeling techniques based on experience gained since the early 2000s.
The IWAQM process was reinitiated in June 2013 to inform EPA's commitment to update the "Guideline
on Air Quality Models" (Appendix W to CFR Part 51), hereafter referred to as Appendix W, to address
chemically reactive pollutants in near-field and long-range transport applications (U.S. Environmental
Protection Agency, 2012a). Comments received from the 10th Modeling Conference (March 2012) from
stakeholders support this interagency collaborative effort to provide additional guidance for modeling
single-source impacts on secondarily formed pollutants in the near-field and for long-range transport.
Stakeholder comments also support the idea of this collaborative effort working in parallel with
separate stakeholder efforts to further model development and evaluation.
This "Phase 3" effort includes the establishment of 2 separate working groups, one focused on long-
range transport of primary and secondary pollutants and the other on near-field single-source impacts
of secondary pollutants. A separate report provides information and recommentaitons on long-range
transport for air quality related values (AQRVs) and deposition (U.S. Environmental Protection Agency,
2015). While many of the objectives are similar for each of these groups, the focus and regulatory end-
points are different. It is expected the "Phase 3" effort will continue with future efforts related to
reviewing and responding to comments given on the 2015 proposed changes to Appendix W related to
single-source impact assessments for 03 and secondary PM2.5. IWAQM3 near-field impacts team
members (affiliated with U.S. Environmental Protection Agency unless noted otherwise) include James
Kelly, George Bridgers, Andy Hawkins, Randall Robinson, Jaime Julian, Rebecca Matichuk, Robert
Kotchenruther, Rynda Kay, and Richard Monteith. Additional participation was provided by Erik Snyder,
Robert Elleman, and Bret Anderson (U.S. Department of Agriculture).
2 Regulatory Motivation
Pursuant to 40 CFR part 51.166 and 52.21, subsections (k)(l)(i) and (k)(l)(ii), new or modified sources
emitting in significant amounts (see 40 CFR part 51.166 and 52.21, subsection (b)(23)(i)) are required to
demonstrate that the source under review does not cause or contribute to a violation of any applicable
National Ambient Air Quality Standards (NAAQS) ((k)(l)(i)) or maximum allowable increases over a
baseline concentration ((k)(l)(ii)). The relevant permitting authority administers the NAAQS and
increments component of the air quality analysis.
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3 Model Selection
This section describes the types of air quality impacts that need to be assessed and the tools that are
best suited for this purpose. For a variety of regulatory programs, impacts on secondary pollutants such
as 03 and PM2.5 need to be assessed at two spatial scales (near-source and long-range transport). It is
important that modeling systems used for these assessments be fit for this purpose and be evaluated
for skill in replicating meteorology and atmospheric chemical and physical processes that result in
secondary pollutants and deposition.
3.1 Secondary Pollutant Formation: O3 and PM2.5
PM2.5 and 03 are closely related to each other in that they are formed in the atmosphere from chemical
reactions with similar precursors (U.S. Environmental Protection Agency, 2005). Air pollutants formed
through chemical reactions in the atmosphere are referred to as secondary pollutants. For example,
ground-level ozone (03) is predominantly a secondary pollutant formed through nonlinear
photochemical reactions driven by emissions of nitrogen oxides (NOx) and volatile organic compounds
(VOCs) in the presence of sunlight. Warm temperatures, clear skies (abundant levels of solar radiation),
and stagnant air masses (low wind speeds) increase ozone formation potential (Seinfeld and Pandis,
2012). PM2.5 can be either primary (i.e. emitted directly from sources) or secondary (formed in the
atmosphere). The fraction of PM2.5 which is primary versus secondary varies by location and season. In
the United States, PM2.5 is dominated by a variety of chemical species: ammonium sulfate, ammonium
nitrate, organic carbon (OC) mass, elemental carbon (EC), and other soil compounds and oxidized
metals. PM2.5 elemental (black) carbon and soil dust are both directly emitted into the atmosphere from
primary sources. Organic carbon particulate is directly emitted from primary sources but also has a
secondary component formed by atmospheric reactions of VOC emissions. PM2.5 sulfate, nitrate, and
ammonium ions are predominantly the result of chemical reactions of the oxidized products of sulfur
dioxide (S02) and NOx emissions and direct ammonia (NH3) emissions (Seinfeld and Pandis, 2012).
3.2 Air Quality Models for Secondary Pollutants
Single-source impacts on secondary pollution including ozone and PM2.5 are becoming increasingly
important for facility permit reviews under the Prevention of Significant Deterioration (PSD) program as
well as New Source Review (NSR) and other regulatory programs. Gaussian dispersion models such as
AERMOD have been used to quantify the near-field (less than 50 km) impacts of primary PM2.5 emissions
from new or modified sources (Perry et al., 2005; U.S. Environmental Protection Agency, 2005, 2014).
However, these types of models cannot treat the important chemical and physical processes of O3 and
secondary PM2.5.
Chemical transport models treat atmospheric chemical and physical processes such as gas and particle
chemistry, 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).
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). These types of models are appropriate for assessment of near-
field and regional scale reactive pollutant impacts from specific sources (Baker and Foley, 2011; Baker
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and Kelly, 2014; Bergin et al., 2008; Zhou et al., 2012) or all sources (Chen et al., 2014; Russell, 2008;
Tesche et al., 2006). Photochemical transport models have been used extensively to support State
Implementation Plans and 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).
3.2.1 Lagrangian models
Quantifying secondary pollutant formation requires simulating chemical reactions and thermodynamic
partitioning in a realistic chemical and physical environment. Some Lagrangian models treat in-plume
gas and particulate chemistry. These models require as input background fields of time and space
varying oxidant concentrations, and in the case of PM2.5 also neutralizing agents such as ammonia,
because important secondary impacts happen when plume edges start to interact with the surrounding
chemical environment (Baker and Kelly, 2014; ENVIRON, 2012c). These oxidant and neutralizing agents
are not routinely measured, but can be generated with a three-dimensional photochemical transport
model and subsequently be input to a Lagrangian modeling system. Photochemical models simulate a
more realistic chemical and physical environment for plume growth and chemical transformation (Baker
and Kelly, 2014; Zhou et al., 2012), but simulations may sometimes be more resource intensive than
Lagrangian or dispersion models.
3.2.2 Photochemical transport models
Publically available and documented Eulerian photochemical grid models such as the Comprehensive Air
Quality Model with Extensions (CAMx) (ENVIRON, 2014) and the Community Multiscale Air Quality
(CMAQ) (Byun and Schere, 2006) model treat emissions, chemical transformation, transport, and
deposition using time and space variant meteorology. These modeling systems include primarily emitted
species and secondarily formed pollutants such as O3 and PM2.5 (Chen et al., 2014; Civerolo et al., 2010;
Russell, 2008; Tesche et al., 2006). Even though single-source emissions are injected into a grid volume,
photochemical transport models have been shown to adequately capture single-source impacts when
compared with downwind in-plume measurements (Baker and Kelly, 2014; Zhou et al., 2012). Where set
up appropriately for the purposes of assessing the contribution of single-sources to primary and
secondarily formed pollutants, photochemical grid models could be used with a variety of approaches to
estimate these impacts. These approaches generally fall into the category of source sensitivity (how air
quality changes due to changes in emissions from a specific source) and source apportionment (how
specific source emissions contribute to air quality levels under modeled atmospheric conditions).
The simplest source sensitivity approach (brute-force change to emissions) would be to simulate 2 sets
of conditions, one with all emissions and one with the source of interest changed from the baseline
simulation (e.g. post-construction conditions) (Cohan and Napelenok, 2011). The difference between
these simulations provides an estimate of the air quality change related to the change in emissions from
the project source. Another source sensitivity approach to identify the impacts of single-sources on
changes in model-predicted air quality is the decoupled direct method (DDM), which tracks the
sensitivity of an emissions source through all chemical and physical processes in the modeling system
(Dunker et al., 2002). Sensitivity coefficients relating source emissions to air quality are estimated during
the model simulation and output at the resolution of the host model.
Some photochemical models have been instrumented with source apportionment, which tracks
emissions from specific sources through chemical transformation, transport, and deposition processes
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to estimate a contribution to predicted air quality at downwind receptors (Kwok et al., 2015; Kwok et al.,
2013). Source apportionment has been used to differentiate the contribution from single-sources on
model predicted ozone and PM2.5 (Baker and Foley, 2011; Baker and Kelly, 2014). DDM has also been
used to estimate 03 and PM2.5 impacts from specific sources (Baker and Kelly, 2014; Bergin et al., 2008;
Kelly et al., 2015) as well as the simpler brute-force sensitivity approach (Baker and Kelly, 2014; Bergin
et al., 2008; Kelly et al., 2015; Zhou et al., 2012). Limited comparison of single-source impacts between
models (Baker et al., 2013) and approaches to identify single-source impacts (Baker and Kelly, 2014;
Baker et al., 2013) show generally similar downwind spatial gradients and impacts.
Near-source in-plume aircraft-based measurement field studies provide an opportunity for evaluating
model estimates of (near-source) downwind transport and chemical impacts from single stationary point
sources (ENVIRON, 2012c). Photochemical grid model source apportionment and source sensitivity
simulation of single-source downwind impacts compare well against field study primary and secondary
ambient measurements made in Tennessee and Texas (ENVIRON, 2012c). This work indicates
photochemical grid models and source apportionment and source sensitivity approaches provide
meaningful estimates of single-source impacts. However, additional evaluations are needed for longer
time periods and more diverse environments, both physical and chemical, to generate broader
confidence in these approaches for this purpose. In particular, it is important to ensure that adequate
model performance is achieved in areas with complex terrain and meteorology.
3.3 Recommendations for estimating single-source O3 and secondary PM2.5 impacts
using photochemical grid models
Photochemical transport models are suitable for estimating single-source 03 and secondary PM2.5
impacts since important physical and chemical processes related to the formation and transport of both
are realistically treated. Source sensitivity and apportionment techniques implemented in
photochemical grid models have evolved sufficiently and provide the opportunity for estimating
potential secondary pollutant impacts from one or a small group of emission sources. Photochemical
grid models using meteorology output from prognostic meteorological models have demonstrated skill
in estimating source-receptor relationships in the near-field (Baker and Kelly, 2014; ENVIRON, 2012c)
and over long distances (ENVIRON, 2012b).
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. However,
the simplest approach to better representing the spatial gradient in source-receptor relationships when
they are in close proximity would be to use smaller sized grid cells. Sub-grid plume treatment extensions
in photochemical models typically solve for in-plume chemistry and use a set of physical and chemical
criteria for determination of when puff mass is merged back into the host model grid (Baker et al.,
2014). Sub-grid plume (puff) sampling or sub-grid puff merging with host grid cell estimates is necessary
because 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 omit some of the source's contribution to air quality. In practice, some
type of source apportionment or source sensitivity (e.g. brute-force difference) would be necessary to
track in the grid resolved source contribution in addition to sub-grid plume treatment to fully capture
source contribution when using sub-grid plume treatment.
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Previous research has shown that photochemical grid models applied without "sub-grid plume
treatment" do capture the initial stage of plume chemistry (e.g. 03 titration) based on single-source
sensitivity simulations at multiple grid resolutions (Cohan et al., 2005) and also when comparing
modeled single-source impacts against near-source in-plume measurements (Baker and Kelly, 2014;
Zhou et al., 2012). Given the complexities in fully determining project source impacts in both the grid
model and sub-grid puffs (Baker et al., 2014) and the tendency for puffs to stay aloft compared to grid-
resolved mass (Baker et al., 2014; Kelly et al., 2015), the use of sub-grid plume treatment for the
purposes of estimating project source impacts for PSD/NSR would typically not be recommended.
Additionally, these tools sometimes exacerbate numerical instability that infrequently might occur in
inorganic and aqueous chemical reactions (Kelly et al., 2015).
4 Model evaluation
There are multiple components to model evaluation for the purposes of assessing single-source
secondary pollutant impacts. First, an alternative modeling system as defined in Appendix W must meet
certain criteria for this purpose (Appendix W Section 3.2.2.e). One type of evaluation is to show that the
modeling system is theoretically fit for purpose. A second evaluation component involves comparison to
ambient measurements to assess whether the modeling system and generated inputs are appropriate
for a specific project application.
Since PM2.s and 03 impacts may be estimated for single-sources as part of a permit review process, it is
important that a modeling system be able to capture single-source primary (e.g. precursors) and
secondary impacts. Near-source in-plume measurements are useful to develop confidence that a
modeling system captures secondarily formed pollutants from specific sources. These types of
assessments are typically only done occasionally when a modeling system has notably changed from
previous testing or has never been evaluated for this purpose. This type of assessment is discussed in
more detail in section 4.1.
A second type of evaluation fulfills the need to determine whether inputs to the modeling system for a
particular scenario are adequate for the specific conditions of the project impact assessment (Appendix
W Section 3.2.2.e). This type of evaluation usually consists of comparing model predictions with
observation data that coincides with the episode being modeling for a permit review assessment. One of
the most important questions in an evaluation concerns whether the prognostic or diagnostic
meteorological fields are adequate for their intended use in supporting the project model application
demonstration. Sections 4.2 and 4.3 cover project-specific evaluation approaches that develop
confidence that a particular model application is appropriate for the project source and key downwind
receptors. It is important to emphasize that a broad evaluation of a model platform's skill in estimating
meteorology or chemical measurements may not sufficiently illustrate the appropriateness of that
platform for specific projects that will be focused on a narrow subset of the larger set of model inputs
and outputs. Therefore, broad model platform evaluations should be supplemented with focused
evaluation and discussion of the appropriateness of model inputs for specific project assessments.
4.1 Fit for Purpose Evaluations
Near-source in-plume aircraft-based measurement field studies provide an opportunity for evaluating
model estimates of (near-source) downwind transport and chemical impacts from single stationary point
sources (ENVIRON, 2012c). Since single-source impacts in the near-field are assessed in each direction
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from a source at evenly distributed receptor locations, model system skill in plume placement is not
emphasized. Ideally, these modeling systems will capture near-source plume placement to best match
plume evolution with the surrounding heterogeneous chemical environment. Model system skill in
capturing secondary impacts is important in near-field permit related assessments and in-plume field
measurements provide the best opportunity for evaluating model skill in capturing secondary impacts
from a specific source. Often when comparing modeled and measured in-plume pollutants the model
impacts are shifted spatially to match the location of the measured plume, meaning the comparison is
paired in time but not space and therefore emphasizing secondary pollutant formation over plume
placement.
Photochemical grid model source apportionment and source sensitivity simulation of single-source
downwind impacts compare well against field study primary and secondary ambient measurements
made in Tennessee and Texas (Baker and Kelly, 2014; ENVIRON, 2012c). This work indicates
photochemical grid models and source apportionment and source sensitivity approaches provide
meaningful estimates of single-source impacts. However, additional evaluations are needed for longer
time periods and more diverse environments to generate broader confidence in these approaches for
this purpose.
4.2 Model evaluation: meteorology
It is important to determine whether and to what extent confidence may be placed in a prognostic
meteorological model's output fields (e.g., wind, temperature, mixing ratio, diffusivity,
clouds/precipitation, and radiation) that will be used as input to models. Currently there is no bright line
for meteorological model performance and acceptability. There is valid concern that establishment of
such criteria, unless accompanied with a careful evaluation process might lead to the misuse of such
goals as is occasionally the case with the accuracy, bias, and error statistics recommended for judging
model performance. In spite of this concern, there remains nonetheless the need for some statistical
performance metrics against which to compare new prognostic and diagnostic model simulations. A
significant amount of information (e.g. model performance metrics) can be developed by following
typical evaluation procedures that will enable quantitative comparison of the meteorological modeling
to other contemporary applications and to judge its suitability for use in modeling studies.
Development of the requisite meteorological databases necessary for use of photochemical transport
models should conform to recommendations outlined in Guidance on the Use of Models and Other
Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2 5, and Regional Haze (U.S.
Environmental Protection Agency, 2014). Demonstration of the adequacy of prognostic or diagnostic
meteorological fields can be established through appropriate diagnostic and statistical performance
evaluations consistent with recommendations provided in the appropriate model guidance (U.S.
Environmental Protection Agency, 2014).
4.3 Model evaluation: chemistry
An operational evaluation is used to assess how accurately the model predicts observed concentrations.
Therefore, an operational evaluation can provide information about model performance and identify
model limitations and uncertainties that require diagnostic evaluation for further model
development/improvement. An operational evaluation for PM2.5 is similar to that for ozone. Some
important differences are that PM2.5 consists of many components and is typically measured with a 24-
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hour averaging time. The individual components of PM2.5 should be evaluated individually. In fact, it is
more important to evaluate the components of PM2.5 than to evaluate total PM2.5 itself. Apparent "good
performance" for total PM2.5 does not indicate whether modeled PM2.5 is predicted for "the right
reasons" (the proper mix of components). If performance of the major components is good, then
performance for total PM2.5 should also be good. Databases that contain ambient 03, PM2.5, and key
precursors are noted in section 4.4. Section 4.4 is not intended to provide an exhaustive review of all
ambient databases but provide an initial set of data that could be used for this purpose.
Regardless of the modeling system (e.g. photochemical transport or Lagrangian puff model) used to
estimate secondary impacts of ozone and/or PM2.5, model estimates should be compared to observation
data to generate confidence that the modeling system is representative of the local and regional air
quality. For ozone related projects, model estimates of ozone should be compared with observations in
both time and space. For PM2.5, model estimates of speciated PM2.5 components (such as sulfate ion,
nitrate ion, etc.) should be matched in time and space with observation data in the model domain.
Model performance metrics comparing observations and predictions are often used to summarize
model performance. These metrics include mean bias, mean error, fractional bias, fractional error, and
correlation coefficient (Simon et al., 2012). There are no specific levels of any model performance metric
that indicate "acceptable" model performance. Model performance metrics should be compared with
similar contemporary applications to assess how well the model performs (Simon et al., 2012).
Accepted performance standards for speciated and total PM2.5 and ozone for photochemical models
used in attainment demonstrations may not be applicable for single-source assessments. Since the
emissions and release parameters for the project source are well known, a direct connection between
general photochemical model performance and the ability of the modeling system to characterize the
impacts of the project source would be difficult to make. It is important that any potential approaches
for photochemical model performance for the purposes of single-source assessments for PSD and NSR
use an approach that would be universally applicable to any single-source modeling system, which
includes the Lagrangian models described above.
4.4 Model performance evaluation data sources
Provided below is an overview of some of the various ambient air monitoring networks currently
available that provide relevant data for model evaluation purposes. Network methods and procedures
are subject to change annually due to systematic review and/or updates to the current monitoring
network/program. Please note, there are other available monitoring networks which are not mentioned
here and more details on the networks and measurements should be obtained from other sources.
AQS: The Air Quality System (AQS) is not an air quality monitoring network. However it is a repository of
ambient air pollution data and related meteorological data collected by EPA, state, local and tribal air
pollution control agencies from tens of thousands of monitors. AQS contains all the routine hourly
gaseous pollutant data collected from State and Local Air Monitoring Stations (SLAMS) and National Air
Monitoring Stations (NAMS) sites. SLAMS is a dynamic network of monitors for state and local directed
monitoring objectives (e.g., control strategy development). A subset of the SLAMS network, the NAMS
has an emphasis on urban and multi-source areas (i.e, areas of maximum concentrations and high
population density). The AQS database includes criteria pollutant data (S02, N02, 03, and PM2.5) and
speciation data of particulate matter (S04, N03, NH4, EC, and OC), and meteorological data. The data are
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measured and reported on an hourly or daily average basis. An overview of the AQS can be found at
https://www.epa.gov/aqs.
IMPROVE: The Interagency Monitoring of PROtected Visual Environments (IMPROVE) network began in
1985 as a cooperative visibility monitoring effort between EPA, federal land management agencies, and
state air agencies (IMPROVE, 2000). Data are collected at Class I areas across the United States mostly at
National Parks, National Wilderness Areas, and other protected pristine areas. Currently, there are
approximately 160 IMPROVE rural/remote sites that have complete annual PM2.5 mass and/or PM2.5
species data. The website to obtain IMPROVE documentation and/or data is
http://vista.cira.colostate.edu/improve/.
STN: The Speciation Trends Network (STN) began operation in 1999 to provide nationally consistent
speciated PM2.5 data for the assessment of trends at representative sites in urban areas in the U.S. The
STN was established by regulation and is a companion network to the mass-based Federal Reference
Method (FRM) network implemented in support of the PM2.5 NAAQS. As part of a routine monitoring
program, the STN quantifies mass concentrations and PM2.5 constituents, including numerous trace
elements, ions (sulfate, nitrate, sodium, potassium, ammonium), elemental carbon, and organic carbon.
In addition, there are approximately 181 supplemental speciation sites which are part of the STN
network and are SLAMS sites. The STN data at trends sites are collected 1 in every 3 days, whereas
supplemental sites collect data either 1 in every 3 days or 1 in every 6 days. Comprehensive information
on the STN and related speciation monitoring can be found at
https://www3.epa.gov/ttnamtil/speciepg.html.
CASTNet: Established in 1987, the Clean Air Status and Trends Network (CASTNet) is a dry deposition
monitoring network where data are collected and reported as weekly average data (U.S. EPA, 2002b).
Relevant CASTNet data includes weekly samples of inorganic PM2.5 species and ground-level ozone.
More information can be obtained through the CASTNet website at http://www.epa.gov/castnet/.
SEARCH: The South Eastern Aerosol Research and CHaracterization (SEARCH) monitoring network was
established in 1998 and is a coordinated effort between the public and private sector to characterize the
chemical and physical composition as well as the geographical distribution and long-term trends of PM2.5
in the Southeastern U.S. SEARCH data are collected and reported on an hourly/daily basis. Background
information regarding standard measurement techniques/protocols and data retrieval can be found at
http://www.atmospheric-research.com/studies/SEARCH/index.html.
NADP: Initiated in the late 1970s, the National Acid Deposition Program (NADP) monitoring network
began as a cooperative program between federal and state agencies, universities, electric utilities, and
other industries to determine geographical patterns and trends in precipitation chemistry in the U.S.
NADP collects and reports wet deposition measurements as weekly average data (NADP, 2002). The
network is now known as NADP/NTN (National Trends Network) and measures sulfate, nitrate,
hydrogen ion (measure of acidity), ammonia, chloride, and base cations (calcium, magnesium,
potassium). Detailed information regarding the NADP/NTN monitoring network can be found at
http://nadp.sws.uiuc.edu/.
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5 Illustrative example of near-field single-source impacts on O3 and
PM2.5
Photochemical grid models have been used with a variety of approaches to isolate secondary pollutant
impacts from specific sources, including brute-force sensitivity (Baker and Kelly, 2014; Bergin et al.,
2008; Kelly et al., 2015), direct decoupled method (DDM) (Baker and Kelly, 2014; Kelly et al., 2015), and
source apportionment (Baker and Foley, 2011; Baker and Kelly, 2014). Here, downwind impacts of ozone
and PM2.5 are described for hypothetical single-sources placed in the Atlanta and Detroit metropolitan
areas. These illustrative hypothetical single-source impacts are based on CMAQ model simulations done
for two different urban areas using 4 km sized grid cells. Impacts are estimated on ozone and PM2.5 from
a hypothetical emissions source of S02, NOx, and VOC using the brute-force sensitivity approach. The
approach taken for this assessment is not intended to be a prescriptive approach recommended by this
process or EPA. The emission rates used here are illustrative and do not have any policy or regulatory
implications.
5.1 Model application approach for estimating these illustrative example single-source
impacts
CMAQ version 5.0.1 (www.cmaq-model.org) was applied for the entire year of 2007 to estimate PM2.5
and ozone. Aerosol chemistry is based on the AER06 option that includes ISORROPIAII inorganic
partitioning and chemistry (Fountoukis and Nenes, 2007), aqueous phase chemistry that includes sulfur
and methylgyoxal oxidation (Sarwar et al., 2013), and organic aerosol partitioning (Carlton et al., 2010).
Gas phase chemistry is represented with the Carbon-Bond 05 gas phase chemical mechanism with
toluene updates (Sarwar et al., 2011).
Two separate model domains were used covering the Detroit and Atlanta metropolitan areas with 4 km
sized grid cells (Figure 5-1). The vertical domain extends to 50 mb using 25 layers (surface layer height is
~20 m) with most resolution in the boundary layer to capture important diurnal variation in mixing
height. The Weather Research and Forecasting model (WRF), Advanced Research WRF core (ARW)
model version 3.3 (Skamarock et al., 2008) was applied with a horizontal grid resolution of 4 km and 35
vertical layers. Additional details regarding photochemical and meteorological model application and
evaluation are provided elsewhere (U.S. Environmental Protection Agency, 2013a, b).
Anthropogenic and biogenic emissions are processed for CMAQ input using the Sparse Matrix Operator
Kernel Emissions (SMOKE) modeling system (http://www.cmascenter.org/smoke/). Stationary, point,
and area sources are based on version 2 of the 2008 National Emissions Inventory (NEI) (U.S.
Environmental Protection Agency, 2012b). Mobile source emissions are day-specific for the model
simulation period based on data submitted to the NEI and generated with SMOKE-MOVES
(http://cmascenter.org/smoke/). Hourly WRF estimated solar radiation and temperature are input to
the Biogenic Emission Inventory System (BEIS) version 3.14 to generate emissions estimates of speciated
VOC and nitric oxide (Carlton and Baker, 2011). Hourly boundary inflow from a coarser domain covering
the continental United States with 12 km sized grid cells account for emissions from sources outside the
4 km model domain.
In addition to the full set of anthropogenic and biogenic emissions for this model domain, new
hypothetical emissions sources are added to illustrate single-source impacts on the Atlanta and Detroit
14
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areas. VOC (Table 5-1) and NOx (90% NO and 10% N02) speciation are based on average speciation
profiles for non-EGU point sources in these areas. The location of the fictitious sources are shown in
Figure 5-1. The same set of stack parameters were used for each fictitious source location: stack height
19 m, stack diameter 1.2 m, exit temperature 424 K, and exit velocity 14 m/s. Separate photochemical
model simulations were done for each hypothetical source location and precursor emission rate: 100 tpy
of VOC, 100 tpy of NOx, 100 tpy of S02, 300 tpy of VOC, 300 tpy of NOx and 300 tpy of S02. This resulted
in a total of 6 simulations for 2 different hypothetical source locations or 12 total simulations with
hypothetical sources in addition to 2 baseline simulations (1 for each area without any hypothetical
source).
Table 5-1. Hypothetical source VOC speciation
Carbon Bond Specie
Fraction
Carbon Bond Specie
Fraction
ALD2
0.0152
MEOH
0.0054
ALDX
0.0155
NVOL
0.0008
ETH
0.0324
OLE
0.1143
ETHA
0.0094
PAR
0.4057
ETOH
0.0090
TERP
0.0170
FORM
0.0757
TOL
0.1148
IOLE
0.0088
UNR
0.1080
ISOP
0.0007
XYL
0.0674
5.2 Illustrative single-source impact results for O3 and PM2.5
Annual maximum 24-hr average PM25 sulfate ion impacts are shown spatially in Figure 5-1 and by
distance from the source in Figure 5-2 for hypothetical sources emitting 100 and 300 tpy of S02 in the
Detroit and Atlanta areas. Impacts are generally highest nearest the source and decrease as distance
from the source increases. However, visual examination of the spatial extent of maximum
concentrations for each source shows that peak secondary impacts are not always coincident with the
location of the emissions release. Occasional increases in downwind secondary impacts are generally
related to single-source precursor emissions reaching an area of increased oxidant availability,
neutralizing agents, differences in terrain, mixing layer, or some combination of these influences.
The magnitudes and patterns of downwind impacts vary between these two areas and even within
areas. This is likely due to differences in terrain features, available oxidants, and neutralizing chemicals
such as ammonia. Different magnitudes and spatial patterns between these different source locations
are more evident when looking at maximum 24-hr impacts compared to annual average. Maximum
impacts tend to be dominated by conducive meteorology and nearby chemical environment on fewer
days compared to the longer term average impacts, which is expected.
15
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Figure 5-1. Annual maximum 24-hr PM2.5 sulfate ion impacts from a hypothetical source emitting 100
and 300 tpy of S02 at a location in Atlanta (top row) and Detroit (bottom row).
Max. PM2.5 sulfate: 300 tpy S02
Max. PM2.5 sulfate: 100 tpy S02
5? .
A
"Tr
1150
1 i
4 i
1200
' (P9*n3>_
I
0 05 8 ,
"A
0.00 \
(r
rT^
1100 1150 1200 1250
Max. PM2.5 sulfate: 100 tpy S02
frigrtn3)
Max. PM2.5 sulfate: 300 tpy S02
Figure 5-2. Maximum 24-hr PM2.s sulfate ion impacts from a hypothetical source emitting 100 and 300
tpy of S02 at a location in Atlanta (top row) and Detroit (bottom row) by distance from the source.
Daily 24-hr PM2.5 (Atlanta)
Daily 24-hr PM2.5 (Atlanta)
300 tpy SOX surface release
0 20 40 60 80 100 120 140 160
e from the source (km)
Daily 24-hr PM2.5 (Detroit)
300 tpy SOX surface release
0 10 30 50 70 90 110 130
Distance from the source (km)
s °
o>
3 8
c P
52 o
to
i
8 „
5 8
100 tpy SOX surface release
"i—i—i—i—i—i—i—i—i—i—i—T
80 80 100 120 140 160
Distance from the source (km)
Daily 24-hr PM2.5 (Detroit)
100 tpy SOX surface release
0 10 30
70 90 110 130 150
Distance from the source (km)
16
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Daily maximum 8-hr 03 impacts over all days in the ozone season are shown as a function of distance
from the hypothetical source emitting NOx in Figure 5-3 and spatially in Figure 5-4. Similar information is
provided in Figures 5-5 and 5-6 for hypothetical source emissions of VOC. Ozone impacts tend to be
highest in grid cells adjacent to the hypothetical source and contributions generally decrease as distance
from the source increases. Emissions of nitrogen oxides are concentrated enough in the 4 km grid cell
containing the source that titration often dominates over production in that grid cell. For these
particular urban areas and VOC emission mixtures, the hypothetical sources emitting NOx emissions
tended to form more 03 compared to when emitting similar amounts of VOC emissions. Ozone from the
hypothetical source placed in Detroit shows notable impacts over Lake Erie. This is more prominent for
the scenario of VOC emissions rather than NOx emissions. It is possible that VOC from this hypothetical
source encounters a favorable combination of available NOx emissions from commercial marine sources
and low boundary layer heights resulting in larger downwind 03 contribution than in other directions
from the hypothetical source.
Figure 5-3. Distribution of ozone season highest daily 8-hr
hypothetical source NOx emissions at a location in Atlanta
Daily 8-hr max. 03 (Atlanta)
£
300 tpy NOX surface release
Iq:t
B
: 9 0 S
~l 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
0 20 40 00 80 1 DO 120 140 160
Distance from the source (km)
Daily 8-hr max. 03 (Detroit)
a
300 tpy NOX surface release
in
09 g
I
~l—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—
0 10 30 50 70 90 110 130 150
8
-------
Figure 5-4. Spatial plot of ozone season highest daily 8-hr maximum 03 from the hypothetical source
NOx emissions at a location in Atlanta (top row) and Detroit (bottom row).
Max. daily 8hr avg 03: 300 tpy NOX
Max. daily 8hr avg 03:100 tpy NOX
Max. daily 8hr avg 03: 300 tpy NOX
Max. daily 8hr avg 03:100 tpy NOX
Figure 5-5. Distribution of ozone season highest daily 8-hr maximum 03 by distance from the
hypothetical source VOC emissions at a location in Atlanta (top row) and Detroit (bottom row)
Daily 8-hr max. 03 (Atlanta)
Daily 8-hr max. 03 (Atlanta)
300 tpy VOC surface release
100 tpy VOC surface release
1 i11f I
1 DO 120 140 160
t—i—r~
100 120 140 160
Distance from the source (km)
Daily 8-hr max. 03 (Detroit)
0 20 40 80
Distance from the source (km)
Daily 8-hr max. 03 (Detroit)
300 tpy VOC surface release
100 tpy VOC surface release
0 10 30 50 70 90 110 130 150
Distance from tire source (km)
0 10 30 50 70 00 110 130 150
Distance from the source (km)
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Figure 5-6. Spatial plot of ozone season highest daily 8-hr maximum 03 from the hypothetical source
VOC emissions.
Max. daily 8-hr avg: 300 tpy VOC
i:
Max. daily 8-hr avg: 100 tpy VOC
0.05 S
i:
r
1100 1150 1200 1250
11DO 1150 1200 1250
Max. daily 8-hr avg: 300 tpy VOC
Max. daily 8-hr avg: 100 tpy VOC
it:
i
0.00
^ 0.00
6 Review of published single-source impacts on O3 and PM2.5
Published literature between 2005 and 2015 relating single-source emissions with downwind 03 and
secondary PM2 5 impacts has been collected and summarized. This information reflects currently
understood relationships between precursors and secondary pollutants and provides context for future
model applications. The assessments reviewed here may not reflect every study done between 2005
and 2015. The primary criteria for inclusion in this report is a published study that presents both the
emissions rate of a specific source and downwind impacts on 03 or secondary PM2.5 from that same
source. While these published studies provide useful information, additional assessments of single-
source impacts on secondary pollutants are still needed to provide a more comprehensive assessment
of different source types and source environments.
6.1 Single-source ozone impacts
Figure 6-1 presents the results from studies that quantified the 1-hr average maximum downwind 03
from a single-source NOx emissions perturbation using photochemical grid models (Baker et ai., 2015).
Figure 6-2 shows the results from studies that quantified single-source downwind 8-hr average 03
impacts (Baker et al.; 2015). For the studies estimating downwind 1-hr 03 contributions, there appears
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to be no clear pattern between NOx emissions and maximum downwind 03. A relationship is not evident
even if results from studies with coarser model resolution (24 km, 36 km; N=2) are removed. However,
Figure 6-2 shows a clearer relationship between NOx emissions and 8-hr 03. This may be due to the
longer averaging time smoothing out some of the conditions that cause high variability in the 1-hr
results. Another explanation for the notable relationship between emissions and secondary impacts for
8-hr 03 is that most of the elements in Figure 6-2 are from a single publication (ENVIRON, 2012a) that
presented multiple source-impact relationships, meaning the consistent relationships are likely due to
consistent model application and post-processing for each modeled source. Overall, peak 8-hr average
03 impacts reported in literature range up to 33 ppb, which is a reported impact modeled from a large
NOx source (~14,000 tpy) over multiple high ozone episodes in the Louisiana/eastern Texas area
(ENVIRON, 2005) using 4 km grid resolution. The results for single-source VOC releases show notable
variability in maximum downwind 03 impacts due to large differences in ozone forming potential and
OH reactivity of the VOC released (ENVIRON, 2005).
Figure 6-1. Published single-source NOx emissions impacts on downwind hourly 03 estimated using
photochemical grid modeling.
70-
60-
• Single Source Grid Modeling
ANOxvs. Max 1-HR A03
2-36 km grid resolution
>
JD
CL
CL
CO
o
<
a:
x
50-
40-
30-
X
CD
20-
10-
Coarse Modeling Grid
24 km & 36 km
20000
40000
60000
1
80000
100000
ANOx (tpy)
Publications reporting results from observational field studies provide additional context to the model-
based studies for ozone production from point source emissions (Luria et al., 2003; Ryerson et al., 2001;
Springston et al., 2005; Zhou et al., 2012). In these studies, in-plume observations related to specific
sources were collected with aircraft and the sources examined were either power plants or
petrochemical facilities publications (Luria et al., 2003; Ryerson et al., 2001; Springston et al., 2005; Zhou
et al., 2012). Many of these studies focused on quantifying ozone production efficiency from precursor
emissions and typically did not provide key information to discern a relationship between source
emissions and downwind 03 impacts: a well-defined delta-03 (in plume 03 minus an estimate of ambient
background 03) and facility precursor emissions. In some instances the study did not isolate impacts
from a single facility plume but looked at some aggregate of nearby sources.
20
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Figure 6-2. Published single-source NOx emissions impacts on downwind 8-hr average 03 estimated
using photochemical grid modeling.
35-
30-
>
.Q
Q.
Q.
CO
o
<1
a:
x
¦
00
X
CO
25-
20-
15-
10-
I'
10000
20000
30000
40000
ANO (tpy)
One observation-based study analyzed data from a field campaign that sampled plumes via helicopter
downwind of the Tennessee Valley Authority Cumberland power plant on four summer days in 1998 and
1999 (Luria et al., 2003). These four days represented different NOx emissions rates and meteorology.
Three of the four days were conducive to ozone production, with one day having cooler temperatures
where no positive delta-03 was observed at the farthest measured distance downwind. The three days
with positive delta-03 reported the following maximum observed hourly delta-03 and corresponding
NOx emissions: 36 ppb 03 (140,643 TPY NOx), 39 ppb 03 (71,697 TPY NOx), and 24 ppb 03 (115,681 TPY
NOx). While these NOx emissions rates are generally higher than those of most of the modeling studies
reported in Figure 6-1 (1-hr 03), the measured change in 03 appears to be in reasonable agreement with
modeled estimates compiled from studies published in literature. Also, these are likely not maximum
plume impacts since these measurements are transects that only sampled some portion of the plume in
space and time.
6.2 Single-source PM2.5 impacts
A number of studies examined the effect of regional scale emissions on PM2.5 concentrations (Simon et
al., 2012). Fewer studies have attempted to quantify the effects of single-sources on downwind PM2.5
concentrations, and fewer still that report estimated secondary PM2.5 enhancements from these single-
sources (Baker and Foley, 2011; Baker and Kelly, 2014; ENVIRON, 2012a; National Association of Clean
Air Agencies, 2011). Synthesizing the results of these limited studies is difficult due to differences in
modeling (e.g., averaging timescales, PM2.5 species reported, etc), geographic area, emission profile and
rates, stack height, near-source and regional NH3 availability, and meteorology. Here, we summarize
21
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four studies which provide sufficient information on precursor emissions and secondary PM2.5
enhancements from single-sources.
The NACAA Workgroup Final Report (National Association of Clean Air Agencies, 2011) provided a test
case where PM2.5 impacts from different sources are assessed. The Minnesota case study presented
predicted PM2.5 ammonium sulfate and ammonium nitrate from a full photochemical grid model
simulation (CAMx) applied at 12 km grid resolution with sub-grid plume treatment and 200 m sub-grid
plume sampling (National Association of Clean Air Agencies, 2011). Four individual stacks over
Minnesota were chosen to reflect varying emission scenarios and background conditions. Emissions
ranged from 400 to 13,000 tpy NOx and 500 to 15,000 tpy S02, each with varying stack height. In these
case study simulations all precursor emissions are co-emitted by each source (National Association of
Clean Air Agencies, 2011). Over all cases, 24-hour maximum 98th percentile concentrations of secondary
formation PM2.5 accounted for up to 1 ng/m3. These sources had stack release points well above ground
level and had surface level peak impacts typically within 10 km of the source.
One published approach (Baker and Foley, 2011) use Particulate Matter Source Apportionment
Technology (PSAT) applied with CAMx using 12 km grid resolution to estimate annual average
secondarily formed PM2.5 sulfate and nitrate from precursor emissions. The study selected facilities
representing the top 5% emitters of primary PM2.5, NOx, and SOx from all stationary point sources
located east of -97° longitude over midwest, mid-Atlantic, and southeast modeling domains (NOx >7000
tpy, SOx >20,000 tpy, and PM2.5 > 1100 tpy based on 2005 inventory). This study showed maximum
annual average PM2.5 values of 0.385 ng/m3 and 0.018 ng/m3 for PM2.5 sulfate ion and PM2.5 nitrate ion
respectively. Impacts were typically highest nearest the source and decrease as distance from the
source increases (Baker and Foley, 2011).
Secondary PM2.5 was estimated for the Hunter EGU emitting 18,800 tpy of NOx and 7,300 tpy of S02 in
eastern Utah (ENVIRON, 2012). The CAMx model was applied with PSAT at 12 km resolution for an
entire year. Predicted 24-hour (annual) maximum values were 3.44 ng/m3 (0.11 ng/m3) and 0.53 ng/m3
(0.05 ng/m3) of PM2.5 nitrate ion and PM2.5 sulfate ion respectively. Again, the highest secondary PM2.5
impacts tended to be closest to the source (ENVIRON, 2012).
A case-study examined the impact of a single EGU on downwind 03 and PM2.5 sulfate ion using several
photochemical grid model-based source impact assessment approaches applied with the CMAQ model:
brute force emission adjustments, DDM, and PSAT (Baker and Kelly, 2014). This case study examined
emissions from the TVA Cumberland facility located in western Tennessee (48,000 tpy NOx, 8,300 tpy
S02) during a high pollution episode focusing on July 6, 1999. Maximum modeled enhancements range
up to 1.5 ng/m3 of PM2.5 sulfate ion. The different approaches for estimating single-source impacts
generally have similar spatial patterns.
7 Reduced-form approaches for estimating single-source secondary
impacts
Predicting downwind secondary pollutant concentrations from point source emissions necessitates
accounting for the interaction of the plume with ambient levels of oxidants, neutralizing agents,
meteorology and potential nonlinear chemistry. State-of-the-science approaches involve employing
photochemical transport models like CMAQ and CAMx. There have been recent efforts to develop
reduced-form models that maintain the state of the science approach at their base, but provide a
22
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screening tool that is faster and cheaper to use. Ideally, screening level information would be an
approximation of secondary impacts based on photochemical modeling of the single-source, to take
advantage of the state of the science treatment of multi-phase chemistry and deposition. This includes a
realistic chemical and physical environment for single-source impacts (Baker and Kelly, 2014).
7.1 Review of reduced-form approaches for estimating O3 single-source impacts
One example of a reduced-form 03 modeling approach that incorporates a state of the science chemical
transport model at its foundation has been identified. This approach was developed for the New South
Wales (NSW) Greater Metropolitan Region in Australia (Yarwood et al., 2011). Briefly described, the
Australian NSW approach involves a photochemical modeling analysis performed for several ozone
seasons that includes hypothetical new emissions sources tracked using the higher order Decoupled
Direct Method (HDDM) (Dunker et al., 2002) to calculate sensitivity coefficients for 03 to the additional
of NOx and VOC emissions from the new hypothetical sources. The resulting 03 sensitivity coefficients
then allow 03 impacts to be estimated for other NOx and/or VOC sources within the same metropolitan
area (Yarwood et al., 2011). While new sources or sources making modifications may not match the
exact location, stack release characteristics, or emissions rates of the hypothetical sources in that
analysis, options exist to pick the modeled source that best matches the new or modified source or even
choose the highest impact from any of the modeled sources as a conservative approach. An existing set
of relationships between modeled impacts and precursor emissions are sometimes referred to as
Modeled Emission Rates for Precursors (MERPs). MERPs are a Tier 1 demonstration tool and provide a
simple way to relate maximum downwind impacts with a critical air quality threshold. MERPs are
intended to relate a specific precursor to regulated pollutant and are not intended to provide a single
demonstration for all NAAQS pollutants. For example, separate MERPs will relate VOC to 03, NOx to 03,
S02 to secondary PM2.5, and NOx to secondary PM2.5.
7.2 Review of reduced-form approaches for estimating PM2.5 single-source impacts
Simplified techniques for secondary PM2.5 include the emissions divided by distance metric (O/D) and
the 100% conversion assumption. For the O/D metric, allowable total emissions (Q) in tons per year are
divided by distance to key receptors (D). This approach allows for a relative comparison of a variety of
sources, but does not directly relate emission to concentrations for comparison to regulatory impact
levels nor does it account for the variability in secondary PM2.5 formation. Under the 100% conversion
assumption, NOx and S02 emissions are assumed to convert entirely to ammonium nitrate and
ammonium sulfate. A modeling study conducted with multiple case studies found that the 100%
conversion approach was overly conservative, yielding physically unrealistic estimates (National
Association of Clean Air Agencies, 2011).
One approach (Baker and Foley, 2011) developed a nonlinear regression model relating large single-
source emissions over the eastern United States (N=99) to predicted annual average primary and
secondarily formed PM2.5 sulfate and nitrate as a function of emissions and distance. Individual
regression models are developed for primary PM2.5, PM2.5 sulfate ion, PM2.5 nitrate ion under favorable
conditions, and PM2.5 nitrate ion under unfavorable conditions (Baker and Foley, 2011). This approach, a
more physically realistic improvement to the O/D approach, could be used to develop shorter-timescale
(e.g., 24-hr), region- and season-specific regression models of PM2.s.This nonlinear regression approach
has been applied to facilities in other countries to provide information about the downwind long term
impacts of large point sources (Myllyvirta, 2014).
23
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Inter-pollutant offset ratios have been used to estimate secondary PM2.5 formation from single-source
S02 and NOx emissions. In the case study of 4 EGUs in Minnesota described above, (National Association
of Clean Air Agencies, 2011) estimate primary PM2.5 concentrations using AERMOD, then secondary
concentrations were either added from CAMx results or using standard S02 and NOx to primary PM2.5
offset ratios in EPA's New Source Review implementation rule for PM2.5 (73 FR 28321). The report
concludes standard offset ratios lead to an over prediction of secondary PM2.5 and suggest a downward
revision of offset ratios for the region. The report suggests new studies could be conducted to develop
region, and season-specific offsets for use in the regulatory permitting context if that approach is used.
The photochemical grid model CAMx was used to examine the effects of distance, season, grid
resolution, emission rate and stack height on predicted offset ratios for the Port Washington coal-fired
power plant in Georgia (Boylan and Kim, 2012). The study suggests maximum ratios for this facility are
found near the source and vary most strongly with season and distance from the source. More recent
work (Boylan and Kim, 2014) expanded on that study to investigate the spatial variability in seasonal
offset ratios by modeling Port Washington emissions at 8 different locations in Georgia. Offset ratios
varied by up to a factor of 7 as a function of season or location, illustrating the sensitivity of secondary
PM2.5 formation on meteorology and the local chemical environment.
Several of the studies reviewed presented promising techniques for developing regional scale reduced-
form models to predict the impact of single-sources on secondary pollutants. These approaches ranged
from region-specific ratios to more advanced regression models. Generalizing these techniques for areas
not included in the original assessment is a difficult challenge but will be desired by many in the
regulating and regulated communities. A Tier 1 demonstration tool as described in the 2015 proposed
revision to the Guideline for Air Quality Modeling (Appendix W) consists of technically credible air
quality modeling done to relate precursor emissions and peak secondary pollutant impacts from specific
or hypothetical sources. Permit applicants should provide a narrative explanation describing how project
source post-construction emissions relate to the information provided as part of the Tier 1
demonstration tool. It should be made clear how the chemical and physical environments modeled as
part of an existing set of information included in the Tier 1 demonstration tool are relevant to the area
of the source and key receptors. The existing set of relationships between modeled impacts and
precursor emissions are also sometimes referred to as Modeled Emission Rates for Precursors (MERPs).
MERPs are a generic term for a Tier 1 demonstration tool based on existing credible air quality modeling
that meets the requirements in the 2015 revision to the Guideline for Air Quality Modeling (Appendix
W). MERPs provide a simple way to relate maximum downwind impacts with a critical air quality
thresholds.
8 Acknowledgements
The document includes contribution from Kirk Baker, Bob Kotchenruther, Rynda Kay, Bret Anderson,
Jennifer Liljegren, and Andy Hawkins. This document was reviewed by the IWAQM3-NFI group.
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-16-003
Environmental Protection Air Quality Assessment Division June 2016
Agency Research Triangle Park, NC
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