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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/P-15-002
July 2015
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 Analysis 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 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.
This document describes chemical and physical processes important to the formation of ground-level O3
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 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 O3 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 within
15 km 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 O3 and secondary PM2.5 is summarized. Downwind O3 impacts
from these studies show a general increase as NOX emissions increase. Downwind impacts for O3 and
secondary PM2.5 in the studies reviewed tend to be largest nearest the source and decrease as distance
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. At this time, it is not
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clear that a robust reduced form model exists for either O3 or secondary PM2.5 for the purposes of
assessing single source downwind impacts of these pollutants.
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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: O3 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 O3 and secondary PM 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 O3 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 O3 and PM2.5 15
6 Review of published single source impacts on O3 and PM2.5 19
6.1 Single source ozone impacts 19
6.2 Single source PM impacts 21
7 Reduced form approaches for estimating single source secondary impacts 22
7.1 Review of reduced form approaches for estimating O3 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 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. While many of the objectives are similar for each of these groups, the focus and
regulatory end-points are different. The primary objectives of this phase of IWAQM are shown below.
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 O3 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 National Ambient
Air Quality Standards 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 Oa 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: OB and PM2.5
PM and Oa are closely related to each other in that they share common sources of emissions and are
formed in the atmosphere from chemical reactions with similar precursors (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 (Oa) 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).
In the case of particulate matter with aerodynamic diameter less than 2.5 urn (PM2.5 or fine PM), 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 (SO2)
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 ozone 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
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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
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, 2012b). These oxidant and neutralizing agents
are not routinely measured, but can be generated with a three dimensional photochemical transport
model. 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 ozone 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) and source apportionment (how
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 removed from the simulation
(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.
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Some photochemical models have been instrumented with source apportionment, which tracks
emissions from specific sources through chemical transformation, transport, and deposition processes
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 Oa 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 another approach for evaluating
model estimates of (near-source) downwind transport and chemical impacts from single stationary point
sources (ENVIRON, 2012b). Photochemical grid model source apportionment and source sensitivity
simulation of a single source downwind impacts compare well against field study primary and secondary
ambient measurements (e.g. Oa) made in Tennessee and Texas (ENVIRON, 2012b). 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 OB and secondary PM impacts
using photochemical grid models
Photochemical transport models are suitable for estimating single source O3 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, 2012b)
and over long distances (ENVIRON, 2012a).
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 has 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
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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.
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 long range transport
of secondary pollutants for AQRVs. 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 for this type
of modeling system for this purpose 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 PM2.5 and O3 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
specific 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 another an opportunity for
evaluating model estimates of (near-source) downwind transport and chemical impacts from single
stationary point sources (ENVIRON, 2012b). Since single source impacts in the near field are assessed in
each direction from a source at evenly distributed receptor locations model system skill in plume
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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 a single source
downwind impacts compare well against field study primary and secondary ambient measurements
made in Tennessee and Texas (Baker and Kelly, 2014; ENVIRON, 2012b). 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 benchmarks
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 (EPA-
454/B-07-002) (U.S. Environmental Protection Agency, 2007). 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, 2007).
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 a benchmark for 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-
hour averaging time. The individual components of PM2.5 should be evaluated individually. In fact, it is
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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 O3, 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 (SO2, NO2, O3, and PM2.5) and
speciation data of particulate matter (SO4, NO3, NH4, EC, and OC), and meteorological data. The data
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are measured and reported on an hourly or daily average basis. An overview of the AQS can be found at
http://www.epa.gov/ttn/airs/airsaqs/index.htm.
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
http://www.epa.gov/ttn/amtic/specgen.html and http://www.epa.gov/aqspubll/select.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 Os 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 Community Multiscale Air
Quality (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 SCh, 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.
5.1 Model application approach for estimating these illustrative example single source
impacts
The Community Multiscale Air Quality Model (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 AERO6
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; left panels). 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 National Emission Inventory 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
areas. VOC (Table 5-1) and NOX (90% NO and 10% NO2) speciation are based on average speciation
14
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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 SO2, 300 tpy of VOC, 300 tpy of NOX and 300 tpy of SO2. 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
ALD2
ALDX
ETH
ETHA
ETON
FORM
IOLE
ISOP
Fraction
0.0152
0.0155
0.0324
0.0094
0.0090
0.0757
0.0088
0.0007
Carbon Bond Specie
MEOH
NVOL
OLE
1 PAR
TER"P~
1 TOL
UNR
XYL
Fraction
0.0054
0.0008
0.1143
0.4057
0.0170
0.1148
0.1080
0.0674
5.2 Illustrative single source impact results for OB and PM2.5
Annual maximum 24-hr average PM2.5 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 SO2 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 SO2 at a location in Atlanta (top row) and Detroit (bottom row).
Max. PM2.5 sulfate: 300 tpy SO2
Max. PM2.5 sulfate: 100 tpy SO2
1100 1150 1200 1250
Max. PM2.5 sulfate: 300 tpy SO2
1100 11S
1200 1250
Max. PM2.5 sulfate: 100 tpy SO2
1060 1100 1140 11BO
Figure 5-2. Maximum 24-hr PM2.5 sulfate ion impacts from a hypothetical source emitting 100 and
300 tpy of SCh at a location in Atlanta and Detroit by distance from the source. Results for Atlanta are
shown on the top row and Detroit the bottom row.
Daily 24-hr PM2.5 (Atlanta)
300 tpy SOX surface n
20 40 80 80 tDO 120 140 160
Distance from the source (km)
Daily 24-hr PM2.5 (Detroit)
300 tpy SOX surface release
0 10 30 50 TO 90 110 130 150
Distance from the source (km)
Daily 24-hr PM2.5 (Atlanta)
11-I
100 tpy SOX surface r
!»
•II
20 40 80 80 I DO 120 140 16
Distance from the source (km)
Daily 24-hr PM2.5 (Detroit)
•C a
100 tpy SOX surface release
0 10 30 50 70 90 110 130 150
Distance from tte source (km)
16
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Daily maximum 8-hr O3 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 Oa than when emitting similar amounts of VOC emissions. Ozone impacts 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 Oa contribution than in other directions
from the hypothetical source.
Figure 5-3. Distribution of ozone season highest daily 8-hr maximum Oa by distance from the
hypothetical source NOX emissions. Results for Atlanta are shown on the top row and Detroit the
bottom row.
Daily 8-hr max. O3 (Atlanta)
Daily 8-hr max. O3 (Atlanta)
ft
300 tpy NOX surface release
ft
100 tpy NOX surface release
20 40 60 30 100 120 140 16D
Distance from the source (km)
Daily 8-hr max. O3 (Detroit)
20 40 00 SO 100 120 140 160
Distance from the source (km)
Daily 8-hr max. O3 (Detroit)
8-
300 tpy NOX surface
§-
ft
i-
100 tpy NOX surface
0 10 30 50 70 90 110 130 150
Distance from the source (km)
3D 50 70 gfl 110 130 ISO
Distance from the source (km)
17
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Figure 5-4. Spatial plot of ozone season highest daily 8-hr maximum O3 from the hypothetical source
NOX emissions.
Max. dally Stir avg O3: 300 tpy NOX
Max. dally 8hr avg O3:100 tpy NOX
-0.2 a
1100 1150 1200 1250
Max. daily 8hr avg O3: 300 tpy NOX
1200 1250
Max. daily 8hr avg O3: 100 tpy NOX
1060 1100 1
1000 1100 1140 1181
Figure 5-5. Distribution of ozone season highest daily 8-hr maximum Oa by distance from the
hypothetical source VOC emissions. Results for Atlanta on top row and Detroit the bottom row.
Daily 8-hr max. O3 (Atlanta)
300 tpy VOC surface release
I •
1!!.
0 20 40 SO 80 1DO 120 140 18D
Distance from the source (km)
Daily 8-hr max. O3 (Detroit)
300 tpy VOC s '-rface retease
0 10 30 50 70 90 110 130 150
Distance from the source (km)
Daily 8-hr max. O3 (Atlanta)
8
O
i
ti
O
fi I
§
100 Tpy VOC surface release
0 20 40 60 80 100 120 140
Drstance from the source (km)
Daily 8-hr max. O3 (Detroit)
8 -
100 tpy VOC swfece retease
:BF
0 10 30 5C 70 90 110 130 150
Distance from the source (km)
18
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Figure 5-6. Spatial plot of ozone season highest daily 8-hr maximum O3 from the hypothetical source
VOC emissions.
Max. daily 8-hr avg: 300 tpy VOC
Max. daily 8-hr avg: 100 tpy VOC
Max. daily 8-hr avg: 300 tpy VOC
Max. daily 8-hr avg: 100 tpy VOC
'- 0.25
- B.2D
- D.15
1100 1140
1060 1100 1140 11BO
6 Review of published single source impacts on Os and PM2.5
Published literature between 2005 and 2015 relating single source emissions with downwind O3 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 O3 or secondary PM2.5 from that same
source. While the 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 O3
from a single source NOX emissions perturbation using photochemical grid models. Figure 6-2 shows the
results from studies that quantified single source downwind 8 hr average O3 impacts. For the studies
19
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estimating downwind 1-hr O3 contributions, there appears to be no clear pattern between NOX
emissions and maximum downwind O3. 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 O3. 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 O3 is that most of the
elements in Figure 6-2 are from a single publication 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 O3 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 very high variability in maximum downwind O3 impacts due
to large differences in ozone forming potential and OH reactivity of the VOC released.
Figure 6-1. Published single source NOX emissions impacts on downwind hourly O3 estimated using
photochemical grid modeling.
70
60-
> 50-
Q.
Q.
O
40-
CC
I 30
x
03
20-
10-
0
0
* Single Source Grid Modeling j
ANOxvs. Max1-HRAO3 !
2-36 km grid resolution i
Course Modeling Grid i
24 km & 36 km i
20000
40000
60000
(tpy)
80000
100000
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 publications, in-plume observations related to
specific sources were collected with aircraft and the sources examined were either power plants or
petrochemical facilities. 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 O3 impacts: a well-defined delta-O3 (in plume O3 minus an estimate of
ambient background O3) 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 O3 estimated
using photochemical grid modeling.
-
30-
.
->• 7 •
_Q 25 .
Q.
•3
0 20~
a: '.
lo
co -
X
.
c
•
9 *
i '
1
! •
•"
0000
Single Source Grid Modeling
ANOx vs. Max 8-HR AO3
2-12 km grid resolution
• Table 1 Data
• Table 3 Data
.
• . •
20000 30000 40000
ANOx (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 4 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-O3 was observed at the farthest measured distance downwind. The three days
with positive delta-O3 reported the following maximum observed hourly delta-O3 and corresponding
NOX emissions: 36 ppb O3 (140,643 TPY NOX), 39 ppb O3 (71,697 TPY NOX), and 24 ppb O3 (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 O3), the measured change in O3 appears to be in reasonable agreement with
modeled estimates compiled from studies published in literature.
6.2 Single source PM impacts
A number of studies examine 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
four studies which provide sufficient information on precursor emissions and secondary PM2.5
enhancements from single sources.
21
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The NACAA Workgroup Final Report (National Association of Clean Air Agencies, 2011) provides a test
case where PM2.5 impacts from different sources are assessed. The Minnesota case study presents
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 SO2, 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 u.g/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 selects 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 shows maximum
annual average PM2.5 values of 0.385 u.g/m3 and 0.018 u.g/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 SO2 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 u.g/m3 (0.11 u.g/m3) and 0.53 u.g/m3
(0.05 u.g/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 O3 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 examines
emissions from the TVA Cumberland facility (48,000 tpy NOX, 8,300 tpy SO2) during a high pollution
episode focusing on July 6, 1999. Maximum enhancements range up to 1.5 u.g/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
screening tool that is faster and cheaper to use. Screening tools that can provide rapid impact estimates
for a wide range of emission source types (e.g. different emission loadings, stack heights, geographic
locations) and can indicate whether more sophisticated approaches may provide credible information
about the relationship between precursors and secondary impacts in some situations. Ideally, screening
22
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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 OB single source impacts
In reviewing the available literature for this report, only one example of a reduced form Oa 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 Oa to the additional of NOX and VOC emissions from the
new hypothetical sources. The resulting Oa sensitivity coefficients then allow Oa 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.
7.2 Review of reduced form approaches for estimating PM2.5 single source impacts
Approaches for estimating secondary PM2.5 from single source emissions range from simple screening
techniques to Gaussian based dispersion models and grid-based Eulerian photochemical transport
models. The National Association of Clean Air Agencies recommended a multi-tiered approach for
conducting air quality analysis of secondary PM2.5 ranging from initial screening tools to more refined
analysis using complex photochemical modeling depending on the size and location of the source
(National Association of Clean Air Agencies, 2011).
Screening techniques for secondary PM2.5 include the emissions divided by distance metric (Q/D) and
the 100% conversion assumption. For the Q/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 SCh 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 with (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
secondary 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 Q/D approach, could be used to develop
shorter-timescale (e.g., 24-hr), region and season specific regression models of PM2.5.This nonlinear
23
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regression approach has been applied to facilities in other countries to provide information about the
downwind impacts of large point sources (Myllyvirta, 2014).
Inter-pollutant off-set ratios have been used to estimate secondary PM2.5 formation from single source
SO2 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 SCh 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.
The photochemical grid model CAMx was used to examine the effects of distance, season, grid
resolution, emission rate and stack height on predicted off-set 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. Off-set 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.
8 Acknowledgements
The document includes contribution from Kirk Baker, Bob Kotchenruther, Rynda Kay, Bret Anderson, and
Andy Hawkins. This document was reviewed by the IWAQM3-NFI group.
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