1 

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-thepractice 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 longrange 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)(1)(i) and (k)(1)(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)(1)(i)) or maximum allowable increases over a 

baseline concentration ((k)(1)(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 O3 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 

PM and O3 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 (O3) 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 μm (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 nearfield 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 O3 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. O3) 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 O3 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 (EPA454/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 
13 

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/aqspubl1/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/. 
14 

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 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 SO2, 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 CarbonBond 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 
15 

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 

5.2 Illustrative single source impact results for O3 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. 

 

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
16 

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). 

Figure 5-2. Maximum 24-hr PM2.5 sulfate ion impacts from a hypothetical source emitting 100 and

300 tpy of SO2 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. 
17 

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 O3 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 O3 contribution than in other directions 

from the hypothetical source. 

Figure 5-3. Distribution of ozone season highest daily 8-hr maximum O3 by distance from the 

hypothetical source NOX emissions. Results for Atlanta are shown on the top row and Detroit the 

bottom row. 
18 

Figure 5-4. Spatial plot of ozone season highest daily 8-hr maximum O3 from the hypothetical source 

NOX emissions. 

Figure 5-5. Distribution of ozone season highest daily 8-hr maximum O3 by distance from the

hypothetical source VOC emissions. Results for Atlanta on top row and Detroit the bottom row.
19 

Figure 5-6. Spatial plot of ozone season highest daily 8-hr maximum O3 from the hypothetical source

VOC emissions. 

6 Review of published single source impacts on O3 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 
20 

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. 

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. 
21 

Figure 6-2. Published single source NOX emissions impacts on downwind 8-hr average O3 estimated 

using photochemical grid modeling. 

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. 
22 

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 μg/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 μg/m3 and 0.018 μg/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 μg/m3

 (0.11 μg/m3) and 0.53 μg/m3

(0.05 μg/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 μg/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
23 

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 

In reviewing the available literature for this report, only one example of a reduced form O3 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 O3 to the additional of NOX and VOC emissions from the 

new hypothetical sources. The resulting O3 sensitivity coefficients then allow O3 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 SO2 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 
24 

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 SO2 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 reducedform 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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Atmospheric Chemistry and Physics 12, 455-468. 

 
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United StatesEnvironmental Protection Agency 

Office of Air Quality Planning and Standards

Air Quality Analysis Division 

Research Triangle Park, NC 

Publication