1 

Guidance on the use of models for assessing the 

impacts of emissions from single sources on the 

secondarily formed pollutants ozone and PM2.5 

 
2 

EPA-454/P-15-001 

July 2015 

Guidance on the use of models for assessing the impacts of emissions from single sources on 

the secondarily formed pollutants ozone and PM2.5 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Analysis Division 

Air Quality Modeling Group 

Research Triangle Park, NC

 
3 

Executive Summary 

The purpose of this document is to provide guidance on how to assess the air quality impact on ozone 

and secondary particulate matter from individual sources (either new sources or modifications to 

existing sources) as part of the New Source Review (NSR), Prevention of Significant Deterioration (PSD), 

or other programs. Sources are required to estimate both the impacts of primarily emitted and 

secondarily formed pollutants. AERMOD, which is a steady-state gaussian plume dispersion model, is 

EPA’s preferred model for estimating project source impacts for primarily emitted pollutants, including 

primarily emitted PM2.5 (U.S. Environmental Protection Agency, 2005). This guidance outlines 

procedures for estimating O3 or secondarily formed PM2.5 impacts from project sources for the 

purposes of permit review programs. 

To date, the EPA has issued some guidance to assist sources needing to demonstrate compliance with 

the PM2.5 NAAQS and PSD increments, including consideration of the secondarily formed components 

of PM2.5. In Guidance for PM2.5 Permit Modeling (U.S. Environmental Protection Agency, 2014a), the

EPA identified three different approaches for assessing the secondary PM2.5 impacts: a qualitative 

assessment, a hybrid qualitative/quantitative assessment that utilizes existing technical work, and a full 

quantitative photochemical grid modeling exercise. Previously for PM2.5 (and currently for ozone), the

appropriate analytical technique for assessing the air quality impact of project source emissions on 

secondarily formed pollutants was determined on a case-by-case basis in consultation with the 

permitting authority. 

The degree of complexity required to assess potential secondary pollutant impacts from single sources 

varies depending upon the nature of the source, its emissions, and the background environment. A twotiered approach for addressing single-source impacts on ozone and secondary PM2.5 has been proposed 

by EPA (Preamble, Appendix W NPRM, 2015) to provide permit applicants flexibility for these 

assessments. The first tier involves use of appropriate and technically credible relationships between

emissions and impacts developed from existing information that is deemed sufficient for evaluating a 

source’s impacts. The second tier involves more sophisticated case-specific chemical transport modeling 

(e.g. with an Eulerian grid or Lagrangian puff photochemical model). The appropriate tier for a given 

permit application should be selected in consultation with the appropriate permitting authority. This 

document is intended to provide more detail for applicants seeking to estimate single source impacts on 

secondary pollutants for purposes of comparison to a SIL or level of the NAAQS under the first (Sections 

5.3.2.b and 5.4.2.b) and second (Sections 5.3.2.c and 5.4.2.c) tiers outlined in Appendix W. 

The first tier screening-level approaches for estimating single source secondary impacts described in the 

2015 revision to Appendix W are intended to reduce burden on sources that can rely on existing 

information to characterize their impacts. Additional information is provided here about the types of 

analysis and options that could satisfy the requirements of a first tier (Sections 5.3.2.b and 5.4.2.b) 

assessment under Appendix W. For first tier assessments, it is generally expected that applicants would 

use existing empirical relationships between precursors and secondary impacts based on modeling 

systems appropriate for this purpose as detailed in this guidance. It is also possible screening 

approaches based on full science chemical transport modeling systems (e.g. reduced form models) could 

provide information to satisfy the first tier in some situations. The use of pre-existing credible technical 

information or a screening model for the purposes of estimating single source secondary impacts will be 

considered on a case-by-case basis and should be done in consultation with the appropriate permitting 

authority. 
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Examples of existing relevant technical information include air quality modeling for sources with similar 

or larger emissions in similar atmospheric environments for appropriate time periods that are conducive 

to the formation of O3 or secondary PM2.5. EPA has extensively reviewed existing published technical 

reports and peer-reviewed literature that provide single source impact estimates for O3 and secondary 

PM2.5 (U.S. Environmental Protection Agency, 2015). However, it is important to note that published 

research studies were not designed specifically for the purposes of estimating single source secondary

impacts for regulatory review. Therefore these studies may not have applied models for relevant time 

periods or post-processed results in a way consistent with methods preferred for this purpose. 

For second tier assessments (Sections 5.3.2.c and 5.4.2.c) when necessary, guidance is provided on the

air quality models, inputs, run time options, receptor placement, and application approach for the 

purposes of estimating the impacts on ozone and secondarily formed PM2.5 from single project sources. 

Within the second tier described in Appendix W, applicants are provided flexibility in terms of the 

complexity of model application for comparison to both the SIL and NAAQS. The sub-tiers of the 

Appendix W second tier allow for simpler approaches to be compared conservatively to the SIL and 

NAAQS and more sophisticated approaches could be applied to provide a more representative impact 

for a source. 

Estimating single source impacts can be done using a Lagrangian modeling system that includes O3 and 

PM2.5 chemistry or photochemical grid models. In the simplest case, a model simulation containing the 

project source emitting at post-construction conditions is compared to a baseline simulation where the

source is operating at pre-construction conditions or not included if the project source is new 

construction (See Figure 1). More details about these scenarios are provided in section 4.7. 

Figure 1. Simplified representation of modeling single source secondary impacts using Lagrangian puff or 

photochemical grid models. This does not represent the process when a source emits both primary 

PM2.5 and PM2.5 precursor emissions (see section 6.1 for this case). 

Baseline Conditions 

Scenario (1)

Project Source 

Scenario (2)

Project Source 

Contribution Estimate 

(2-1)

•Model simulation with 

baseline conditions and the 

project source operating at preconstruction conditions (or not 

in the simulation if a new 

source)

•Model simulation with 

baseline conditions and the 

appropriate project source 

emissions adjustments

•Estimated by difference 

between the simulation with 

baseline conditions and the 

appropriate project source 

adjustments (2) and model 

simulation with baseline 

conditions only (1) 
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Once project source contributions have been estimated as detailed through procedures outlined in this 

guidance document those impacts are compared to the appropriate significant impact level (SIL) and the

level of the appropriate NAAQS if a cumulative assessment is necessary. A simplified schematic of this

process and some illustrative examples of the flexibility afforded with each sub-tier of the significant 

impact analysis and cumulative impact analysis are shown in Figure 2. 

Figure 2. Simplified schematic of the “second tier” process for assessing secondary pollutant impacts 

from source emissions with a significant impact analysis and cumulative impact analysis (if needed). This 

does not represent the process when a source emits both primary PM2.5 and PM2.5 precursor 

emissions (see section 6.1 for this case). 

Project Source 

Contribution Estimate 

 Estimated by difference between the simulation with baseline conditions with the appropriate 

project source adjustments and model simulation with baseline conditions only. See sections 

5.3, 5.4, and 5.5 for NAAQS specific estimation approaches. 

 

Significant Impact 

Analysis 

 Project source contribution estimate compared to NAAQS appropriate Significant Impact Level 

(SIL). Sections 6.2.1, 6.3.1, and 6.4.1 provide more details about NAAQS specific impact 

comparison to the SIL. 

Significant Impact 

Analysis Level 1 

 The modeled highest impact over all receptors should be compared to the SIL. Model options 

include Lagrangian or photochemical transport model. 

 

Significant Impact 

Analysis Level 2 

 A more refined approach than level 1. This may include the tool used for impact estimation 

(photochemical grid rather than Lagrangian model), configuration options for such tool (e.g. 

more refined grid resolution), or inclusion of additional representative episodes. After 

consultation with the permitting authority, a value less than the maximum impact could be 

used for comparison to the SIL. 

 

Cumulative Impact 

Analysis 

 Project source contribution estimate compared to NAAQS appropriate Significant Impact Level 

(SIL) and also combined with an estimate of “background” for comparison to the level of the 

appropriate NAAQS. Sections 6.2.2, 6.3.2, and 6.4.2 provide more details about NAAQS specific 

cumulative impact assessments. 

Cumulative Impact 

Analysis Level 1 

 The highest contribution from the project source over all receptors should be added to the 

highest monitored design value in the same area. Model options include Lagrangian or 

photochemical transport model. 

 

Cumulative Impact 

Analysis Level 2 

 A more refined approach than level 1. This may include the tool used for impact estimation, 

configuration options for such tool, or inclusion of additional representative episodes. Here, 

the highest contribution from the project source on high modeled days at each receptor should 

be added to the monitored design value at that same receptor or an approved interpolated 

field of monitored design values and compared to the level of the NAAQS. After consultation 

with the permitting authority, a value less than the maximum impact could be used for 

addition to background estimates and comparison to the NAAQS. 
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The schematic shown in Figure 2 is relevant for O3 impacts and sources that do not emit primary PM2.5

emissions. For project sources that emit both primary PM2.5 and PM2.5 precursors an alternative 

construct is provided. For those situations, the methods and approaches described here for estimating 

single source secondary PM2.5 impacts are used as an input to AERMOD as an addition to the 

“BACKGROUND” concentration input variable and established procedures are followed for the 

application of AERMOD for permit source applications as described in Appendix W. This approach takes 

advantage of existing well known procedures for AERMOD application and community expertise with 

the AERMOD modeling system. It is not expected that all situations would necessitate rigorous chemical

transport modeling to provide AERMOD with an estimate of secondary PM2.5 from the project source. 

The selection of project-specific secondary PM2.5 impacts should be done in consultation with the 

appropriate permitting authority and be consistent with EPA guidance. 

This document is consistent with the recommendations for air quality modeling in the “Guideline on Air

Quality Models” (Appendix W to 40 CFR Part 51), hereafter referred to as Appendix W (U.S. 

Environmental Protection Agency, 2005). Hereafter, references to “Appendix W” refer to both the 

proposed 2015 revisions and existing form. Information presented here is intended to expand upon the 

principles set forth in Appendix W for estimating single source impacts for permit review purposes. 

 
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Table of Contents 

1 Background ........................................................................................................................................... 9 

2 Modeling systems for estimating secondary impacts .......................................................................... 9 

2.1 Lagrangian modeling systems ..................................................................................................... 10 

2.2 Eulerian photochemical grid models .......................................................................................... 11 

2.3 Elements of a model protocol ..................................................................................................... 12 

3 Relevant existing information or “First Tier” assessment of single source impacts on O3 and 

secondary PM2.5 ........................................................................................................................................ 13 

4 Refined or “Second Tier” assessment of single source impacts on O3 and secondary PM2.5 ........... 14 

4.1 Model inputs ............................................................................................................................... 14 

4.1.1 Project source emissions..................................................................................................... 14 

4.1.2 Nearby source emissions .................................................................................................... 14 

4.1.3 Meteorology inputs............................................................................................................. 14 

4.2 Episode selection ........................................................................................................................ 15 

4.3 Receptor placement and domain extent .................................................................................... 16 

4.4 Vertical domain resolution ......................................................................................................... 16 

4.5 Horizontal domain resolution ..................................................................................................... 17 

4.6 Use of photochemical grid models for single source impact assessments ................................ 17 

4.7 Use of lagrangian models for single source impacts .................................................................. 18 

4.8 Model evaluation ........................................................................................................................ 19 

4.9 Project-specific modeling ............................................................................................................ 20 

5 Appropriate processing of modeled estimates & background ........................................................... 21 

5.1 Operational definition of particulate matter .............................................................................. 21 

5.2 “Absolute” and “Relative” modeling approaches ....................................................................... 21 

5.3 Estimating the O3 impact from a project source ........................................................................ 21 

5.4 Estimating the annual PM2.5 impact from a project source ...................................................... 22 

5.5 Estimating the daily PM2.5 impact from a project source ......................................................... 22 

5.6 Background concentrations ........................................................................................................ 22 

6 Regulatory assessment: PSD ............................................................................................................... 23 

6.1 Sources emitting both primarily emitted PM2.5 and PM2.5 precursors .................................... 23 

6.2 Assessments of 8-hr Ozone impacts ........................................................................................... 23 
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6.2.1 Assessments of 8-hr Ozone impacts – significant impact analysis ..................................... 24 

6.2.2 Assessments of 8-hr Ozone impacts – cumulative impact analysis .................................... 24 

6.3 Assessment of sources only emitting PM2.5 precursors: annual PM2.5 .................................... 25 

6.3.1 Assessments of annual average PM2.5 impacts – significant impact analysis ................... 25

6.3.2 Assessments of annual average PM2.5 impacts – cumulative impact analysis .................. 25 

6.4 Assessment of sources only emitting PM2.5 precursors: daily PM2.5 ....................................... 26 

6.4.1 Assessments of daily average PM2.5 impacts – significant impact analysis ...................... 26 

6.4.2 Assessments of daily average PM2.5 impacts – cumulative impact analysis ..................... 26

7 Regulatory assessment: nonattainment NSR ..................................................................................... 27 

7.1 Assessments of 8-hr ozone impacts ............................................................................................ 27 

7.2 Assessments of PM2.5 impacts ................................................................................................... 28 

8 Regulatory assessment: Economic Development Zones .................................................................... 28 

9 References .......................................................................................................................................... 29 
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1 Background 

Sources with proposed modifications or new sources may be required to estimate the air quality impacts

of these emissions as part of the New Source Review (NSR), Prevention of Significant Deterioration 

(PSD), or other programs. Sources are required to estimate both the impacts of primarily emitted 

pollutants and secondarily formed pollutants. Steady-state Gaussian plume dispersion models such as 

AERMOD are used to estimate the impacts of chemically inert (primarily emitted) compounds (U.S. 

Environmental Protection Agency, 2005). 

To date, the EPA has issued some guidance to assist sources needing to demonstrate compliance with 

the PM2.5 NAAQS and PSD increments, including consideration of the secondarily formed components 

of PM2.5. In Guidance for PM2.5 Permit Modeling (U.S. Environmental Protection Agency, 2014a), the

EPA identified three different approaches for assessing secondary PM2.5 impacts: a qualitative 

assessment, a hybrid qualitative/quantitative assessment utilizing existing technical work, and a full

quantitative photochemical grid modeling exercise. Previously for PM2.5 (and currently for ozone), the

appropriate analytical technique for assessing the air quality impact of project source emissions on 

secondarily formed pollutants was determined on a case-by-case basis in consultation with the 

permitting authority. 

For assessing secondary pollutant impacts from single sources, the degree of complexity required to 

assess potential impacts varies depending upon the nature of the source, its emissions, and the 

background environment. A two-tiered approach for addressing single-source impacts on ozone and 

secondary PM2.5 has been proposed by EPA (preamble for 2015 Appendix W NPRM) to provide the user 

community flexibility in estimating these impacts. The first tier involves use of technically credible and 

appropriate relationships between emissions and impacts developed from previous modeling that is 

deemed sufficient for evaluating a source’s impacts. The second tier involves application of more 

sophisticated case-specific photochemical modeling analyses, which is the focus of this document. The 

appropriate tier for a given permit application should be selected in consultation with the appropriate 

permitting authority and be consistent with EPA guidance. The EPA’s expectation is that the first tier 

should be appropriate for most permit applicants; the second tier may only be necessary in special 

situations. Also, to further assist permit applicants, the EPA is considering undertaking a rulemaking to 

provide screening tools for this purpose. 

This document describes the air quality models, inputs, run time options, receptor placement, and 

application approach for the purposes of estimating the impacts on ozone and secondarily formed 

PM2.5 from single project sources. This document is consistent with the recommendations for air 

quality modeling in the “Guideline on Air Quality Models” (Appendix W to 40 CFR Part 51), hereafter 

referred to as Appendix W (U.S. Environmental Protection Agency, 2005). These recommendations are 

considered relevant for both the 2015 proposed revisions to Appendix W and existing form. 

2 Modeling systems for estimating secondary impacts

Quantifying secondary pollutant formation requires simulating chemical reactions and thermodynamic 

gas-particle partitioning in a realistic chemical and physical environment. Chemical transport models 

treat atmospheric chemical and physical processes such as deposition and transport. There are two 

types of chemical transport models which are differentiated based on a fixed frame of reference 
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(Eulerian grid based) or a frame of reference that moves with parcels of air between the source and 

receptor point (Lagrangian) (McMurry et al., 2004). 

A variety of Lagrangian and Eulerian modeling systems exist that could potentially be used to estimate

single source impacts on secondarily formed pollution such as ozone and PM2.5. These modeling 

systems represent varying levels of complexity in the treatment of plume chemistry and the chemical 

and physical environment in which the plume exists. It is important that any Lagrangian or Eulerian 

modeling system be appropriately applied for assessing the impacts of single sources on secondarily 

formed pollutants such as ozone and PM2.5 for the purposes of permit review. This means that existing 

guidance for dispersion models and photochemical models developed for other purposes may not be 

totally applicable for this type of assessment. Sound science and appropriate purpose are the 

fundamental basis of the approaches described for assessing single source impacts on secondarily 

formed pollutants. 

This section describes tools that are best suited for the purpose of estimating single source secondary 

pollutant impacts. For a variety of regulatory programs secondary pollutant impacts such as O3 and 

PM2.5 need to be assessed near the emitting source and sometimes long-range transport to key 

downwind receptors. It is important that modeling systems used for these assessments be fit for this 

purpose and have been evaluated for skill in replicating meteorology and atmospheric chemical and 

physical processes that result in secondary pollutant formation and deposition. 

A candidate model for use in estimating single source impacts on secondarily formed pollutants such as

ozone and PM2.5 for the purposes of permit review programs should meet the general criteria for an 

“alternative model” outlined in 40 CFR part 51, Appendix W, section 3.2 (U.S. Environmental Protection

Agency, 2005). The acceptability of a particular model and approach for that model application is an EPA 

Regional Office responsibility that should include consultation with EPA’s Model Clearinghouse, if 

appropriate. 

2.1 Lagrangian modeling systems 

Lagrangian modeling systems that have been used to assess single source impacts in North America 

include CALPUFF, HYSPLIT, FLEXPART, SCIPUFF, and SCICHEM. Some Lagrangian models treat in-plume 

gas and particulate chemistry. These models require time and space varying oxidant concentrations, and

in the case of PM2.5 also neutralizing agents such as ammonia, as important secondary impacts happen 

when plume edges start to interact with the surrounding chemical environment (Baker and Kelly, 2014; 

ENVIRON, 2012). These oxidant and neutralizing agents are not routinely measured, but can be 

generated with a three dimensional photochemical transport model and subsequently input to a 

Lagrangian modeling system. 

The Second-order Closure Integrated PUFF model with chemistry (SCICHEM) is an extension of the 

Second-order Closure Integrated PUFF model (SCIPUFF) and simulates in-plume chemistry and 

subsequent transport and dispersion using second order closure to solve turbulent diffusion equations 

using spatially and temporally variant meteorological input (Sykes et al., 1998). SCICHEM is a non-steady 

state puff dispersion model treating both O3 and PM2.5 formation and their fate in the atmosphere 

(Chowdhury et al., 2010). CALPUFF is a multi-specie non-steady state puff dispersion model that treats

pollutant emissions, transport, represents some chemical processes, and deposition using temporally 

and spatially variant meteorological inputs (Scire et al., 2000). The CALPUFF system estimates primary

and secondary PM2.5 but O3 is input to the model and not estimated (Scire et al., 2000). 
11 

2.2 Eulerian photochemical grid models 

Photochemical grid models are three-dimensional grid-based models that treat chemical and physical 

processes in each grid cell and use Eulerian diffusion and transport processes to move chemical species 

to other grid cells (McMurry et al., 2004). Photochemical models are advantageous by providing a 

spatially and temporally dynamic realistic chemical and physical environment for plume growth and 

chemical transformation (Baker and Kelly, 2014; Zhou et al., 2012). 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). These models have been used extensively to support State Implementation Plans and to explore 

relationships between inputs and air quality impacts in the United States and beyond (Cai et al., 2011; 

Civerolo et al., 2010; Hogrefe et al., 2011). 

Even though single source emissions are averaged into a grid volume, photochemical transport models 

have been shown to adequately capture single source impacts when compared with downwind in-plume 

measurements (Baker and Kelly, 2014; Zhou et al., 2012). Where set up appropriately for the purposes 

of assessing the 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 (the contribution of a specific source emissions to a receptor 

under existing ambient conditions). 

The simplest source sensitivity approach (brute-force change to emissions) would be to simulate two 

sets of conditions, one with all emissions and one with a new source or a source of interest modified to 

reflect changes in operation (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 differentiate the impacts of single sources on changes in 

model predicted air quality is the decoupled direct method (DDM), which tracks the sensitivity of an 

emissions source through all chemical and physical processes in the modeling system (Dunker et al., 

2002). Sensitivity coefficients relating source emissions to air quality are estimated during the model 

simulation and output at the resolution of the host model. 

Some photochemical models have been instrumented with source apportionment, which tracks 

emissions from specific sources through chemical transformation, transport, and deposition processes 

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; 

Cohan et al., 2005; Cohan et al., 2006; 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 

differentiate single source impacts (Baker and Kelly, 2014; Cohan et al., 2006; Kelly et al., 2015) show 

generally similar downwind spatial gradients and impacts. 
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2.3 Elements of a model protocol 

A model protocol is intended to communicate the scope of the analysis. This document generally 

includes the types of analysis performed, the specific steps taken in each type of analysis, the rationale 

for the choice of modeling system, names of organizations participating in preparing and implementing 

the protocol, and a complete list of model configuration options. The protocol should detail and 

formalize the procedures for conducting all phases of the modeling study, such as describing the 

background and objectives for the study, creating a schedule and organizational structure for the study, 

developing the input data, conducting model performance evaluations, interpreting modeling results, 

describing procedures for using the model to demonstrate whether regulatory levels are met, and 

producing documentation to be submitted for review and approval. Protocols should include the 

following elements at a minimum. 

1. Overview of Modeling/Analysis Project 

• Participating organizations 

• Schedule for completion of the project 

• Description of the conceptual model for the project source/receptor area 

• Identify how modeling and other analyses will be archived and documented 

• Identify specific deliverables to the review authority 

2. Model and Modeling Inputs 

• Rationale for the selection of air quality, meteorological, and emissions models 

• Modeling domain specifications 

• Horizontal resolution, vertical resolution and vertical structure 

• Episode selection and rationale for episode selection 

• Description of meteorological model setup 

• Description of emissions inputs 

• Specification of initial and boundary conditions 

• Methods used to quality assure emissions, meteorological, and other model inputs 

3. Model Performance Evaluation 

• Identify relevant ambient data near the project source and key receptors; provide relevant 

performance near the project source and key receptor locations 

• List evaluation procedures 

• Identify possible diagnostic testing that could be used to improve model performance 

4. Model Outputs 

• Describe the process for extracting project source impacts including temporal aggregation and 

in the case of PM2.5 chemical species aggregation 
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3 Relevant existing information or “First Tier” assessment of 

single source impacts on O3 and secondary PM2.5 

This section is intended to provide more detail for applicants seeking to estimate single source impacts 

on secondary pollutants for purposes of comparison to a SIL or level of the NAAQS under the first tier

outlined in Appendix W (Sections 5.3.2.b and 5.4.2.b). More refined approaches that would fall under 

the second tier are described in the sections subsequent to this chapter. 

Under the first tier, existing technical information is used in combination with other supportive 

information and analysis for the purposes of estimating secondary impacts from a particular source. The 

existing technical information should provide a credible and representative estimate of the secondary 

impacts from the project source. In these situations, a more refined approach for estimating secondary

pollutant impacts from project sources may not be necessary where agreement is reached with the 

permitting authority. 

EPA has been compiling and reviewing screening models, screening approaches, and reduced form 

models that are based on technically credible tools (e.g. photochemical grid models) that relate source 

precursor emissions to secondary impacts. A review of existing approaches detailed in peer reviewed 

journal articles and non-peer reviewed forms (e.g. technical reports, conference presentations) indicates 

a very limited number of screening approaches have been developed and fewer still have been fully 

documented and tested for robust application (U.S. Environmental Protection Agency, 2015). One 

example of a reduced form model is the development of a tool for the New South Wales (NSW) Greater 

Metropolitan Region in Australia (Yarwood et al., 2011). High O3 impact days are modeled using a 

photochemical grid model with the higher order Decoupled Direct Method (HDDM) (Dunker et al., 2002) 

to calculate sensitivity coefficients for O3 to additional NOX and VOC emissions from 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. The relevancy and applicability of a given 

screening technique should be discussed with the permitting authority for a determination about 

whether that approach would fulfill or partially fulfill a first tier assessment. 

A demonstration tool could include the use of existing credible photochemical model impacts for 

sources deemed to be similar in terms of emission rates, release parameters, and background 

environment. A review has been done examining published relationships between single source 

precursor emissions and downwind O3 and secondary PM2.5 impacts (U.S. Environmental Protection 

Agency, 2015). Secondary impacts typically increase as precursor emissions increase. Single source 

secondary impacts are not always highest at the facility fence-line, but are usually highest in proximity 

to the source and tend to decrease as distance from the source increases (U.S. Environmental Protection 

Agency, 2015). Empirical relationships based on existing technical work may be relevant where the 

modeling system used conforms to those for estimating single source secondary impacts in Appendix W 

and described in this guidance for alternative models. The project source must generate a modeling 

protocol and describe how the existing modeling conforms to the O3 or PM2.5 that is conceptually 

thought to form in that particular area. Where the existing technical information is based on chemical

and physical conditions less similar to the project source and key receptors, a more conservative 

estimate of impacts using demonstration tools may be adequate. 
14 

An example of using existing empirical relationships would be a hypothetical source with an additional

600 tpy of SO2 emissions resulting from new construction at a facility in the Atlanta metropolitan area. 

Empirical relationships between single sources of SO2 emissions in the Atlanta area and downwind 

impacts have been published (U.S. Environmental Protection Agency, 2015). However, the published 

impacts are the result of 100 and 300 tpy emissions in that area. Impacts could be extrapolated by 

increasing the downwind PM2.5 sulfate ion impacts from the published 300 tpy hypothetical source by a 

factor of 2 to estimate the post-construction impacts of source seeking a permit. If those impacts are

well below the PM2.5 SIL, then no further technical analysis may be necessary. If a source was locating 

in an area where existing information does not exist, that source could present the most conservative 

estimate of impacts from sources previously modeled in areas with generally similar meteorology and 

air quality. However, in this case additional conservatism may need to be introduced to the previously

estimated downwind impacts given the additional incongruity between the existing information and 

actual conditions. 

In all cases, additional information is needed from the project source with the existing information to 

corroborate the appropriateness and relevancy of the existing information for the anticipated conditions 

at the project source and key receptors. 

4 Refined or “Second Tier” assessment of single source impacts 

on O3 and secondary PM2.5 

4.1 Model inputs 

4.1.1 Project source emissions 

Compliance with PSD should be demonstrated using emissions input data for the project source 

consistent with Appendix W (U.S. Environmental Protection Agency, 2005). Appendix W states that 

project sources should be “modeled using the design capacity (100 percent load).” Emissions inputs for

the project source should be consistent with Tables 8.1 and 8.2 in Appendix W, meaning project source 

emissions should be the maximum allowable emissions or federally enforceable permit level limits (U.S.

Environmental Protection Agency, 2005). 

4.1.2 Nearby source emissions 

A realistic characterization of chemistry surrounding the project source is important for estimating 

secondary impacts. Therefore, unlike the project source that is modeled at maximum allowable 

emissions, other sources in proximity should be modeled or characterized with a typical emission profile 

and stack characteristics for the purposes of estimating single source impacts on secondary pollutant 

formation. 

4.1.3 Meteorology inputs 

The importance of meteorology coupled with the spatial heterogeneity of chemical reactants in a 

project area necessitate meteorological inputs to the air quality model that capture differences in 
15 

meteorology (i.e. temperature and relative humidity) over the entire spatial extent of the project area 

both horizontally and vertically. Prognostic meteorological model output should be used to support air

quality modeling of secondary impacts of PM and ozone. Candidate prognostic meteorological models 

should be considered state of the science by the air quality modeling community, be routinely used as 

input for regulatory air quality modeling applications, be peer-reviewed, fully documented, freely 

available on the internet, and actively supported by the model developer. Currently, one of the more 

widely used prognostic meteorological models in the United States is the Weather Research and 

Forecasting (WRF) model (Skamarock et al., 2008) available from the National Center for Atmospheric 

Research (http://www.wrf-model.org/index.php). 

The prognostic meteorological model application should be configured to match the grid projection and 

size of the air quality model domain used to assess the single source impacts on secondary pollutants.

Prognostic meteorological model applications should include the entire troposphere with the finest 

vertical resolution in the planetary boundary layer to appropriately capture the dynamic processes 

related to vertical mixing. Prognostic meteorological model output should be translated for input to the 

selected air quality model by making any needed adjustments to match grid structure. None of the 

prognostic model output variables should be re-diagnosed or changed before input to the air quality 

model. It is important to maintain the integrity of the meteorological field to minimize dynamic 

inconsistencies between the air quality and meteorological models. 

Where project source site-specific meteorology is available, the project sponsors are encouraged to 

incorporate that data into the prognostic meteorological model simulation. This can be done through 

inclusion with other observation data as the input analysis field and through observation nudging during 

the model simulation. 

4.2 Episode selection 

Meteorology is an important factor in the formation of many secondarily formed pollutants, both 

directly (i.e. ammonium nitrate formation under cool, humid conditions) and indirectly (i.e. warm 

temperatures and sunlight increase photochemistry and the availability of oxidants). Since secondary 

pollutant impacts are being estimated, the year(s) of meteorology selected for use in the assessment is 

important. A time period with generally conducive meteorology to the formation of secondary PM2.5 

and/or ozone is necessary. This means that time periods with elevated PM2.5 and/or ozone at the 

source and receptors must be used in the analysis. 

At a minimum, modeling systems applied for the purposes of characterizing secondary annual PM2.5 

should be applied with at least one year of meteorological inputs that vary in time and space since some 

components of PM2.5 are highest in the different seasons. An entire year should be modeled to capture 

different formation regimes and to capture the variety of wind flows at the sources and receptors being 

analyzed. It may not always be necessary that the period used for estimating secondary impacts match a

period used to estimate impacts of primarily emitted PM2.5 when those impacts are estimated with a 

dispersion model such as AERMOD. 

When daily PM2.5 impacts from single project sources are being modeled, it is preferential to model 

those impacts over at least one entire time period that has been shown to be generally conducive to 

elevated PM2.5 formation. Since PM2.5 formation varies in a given area, multiple elevated PM2.5 

episodes would be appropriate for modeling the impacts of a single source on ambient PM2.5 
16 

concentrations to capture the variety of wind flows and formation regimes in a given area. Where 

multiple episodes simulations are necessary for a single source assessment, it is not necessary they be 

consecutive. 

When ozone impacts from single project sources are being modeled, it is preferential to model those 

impacts over at least one entire ozone season that has been shown to be generally conducive to 

elevated ozone formation. Since ozone formation varies in a given area, multiple ozone seasons or 

multiple well characterized ozone episodes would be appropriate for modeling the impacts of single 

source on ambient ozone concentrations to capture the variety of wind flows and ozone formation 

regimes in a given area. Where multiple ozone episode/season simulations are necessary for a single 

source assessment, it is not necessary they be consecutive. 

4.3 Receptor placement and domain extent 

Receptors are locations in the project area where an air quality model estimates pollutant 

concentrations. A receptor network design should emphasize resolution and location rather than match 

a minimum number of receptors. Receptors should be placed at all locations where high concentrations 

may occur, not just where high concentrations may be anticipated prior to the analysis. Receptor 

spacing near the source should be sufficient to capture expected concentration gradients around the 

locations of maximum modeled concentrations. Where grid models are applied, the receptor location is 

the center of the surface layer grid cell. 

For primarily emitted PM, the peak impacts are more likely to be near the emissions source. Secondarily 

formed pollutants including PM and ozone may have maximum impacts near the source or further 

downwind depending on meteorology, stack release characteristics, and availability of important 

chemical reactants. Receptors should be placed in all directions surrounding a project source to capture 

meteorological and chemical variability. 

Receptor placement should extend from the fence-line of the project source out to a sufficient distance 

from the project source to account for the impacts of downwind chemical transformations and changing 

availability of important chemical species that may enhance secondary pollutant formation. Receptors 

should be placed to capture maximum concentrations of secondary impacts which may extend out to 50 

km (U.S. Environmental Protection Agency, 2015) from the project source (Appendix W). Receptor 

placement for the purposes of estimating air quality impacts at downwind Class I areas for air quality

related values should follow guidance developed by Federal land managers (U.S. Department of the 

Interior, 2010). 

4.4 Vertical domain resolution 

The best approach to representing the vertical atmosphere in an air quality model is to match the 

vertical layer structure of the input prognostic meteorological model. However, it may not always be 

necessary in the air quality model to use the full vertical extent applied in the prognostic meteorological 

model and resource considerations may make vertical layer collapsing necessary at times. When vertical

layer collapsing is employed, it is important to match most closely with the prognostic meteorological

model the layers closest to the surface to best resolve the diurnal and seasonal variability in the mixing 

height. Consultation with the permitting authority is recommended for instances when modeling the 
17 

entire troposphere may not be necessary and when vertical layers are not matched one to one between 

the air quality and meteorological models. 

4.5 Horizontal domain resolution 

Photochemical grid based models have been applied for long periods using domains covered by grid 

cells ranging in size from <1 to 15 square km (Couzo et al., 2012; Hogrefe et al., 2011; Jin et al., 2010; 

Rodriguez et al., 2011; Stroud et al., 2011). Lagrangian models have been applied using similar horizontal 

grid spacings (Dresser and Huizer, 2011; Levy et al., 2002). Horizontal grid spacing is important to 

appropriately represent the heterogeneity in pollutant concentrations between a source and receptor. 

This concentration gradient varies depending on a variety of factors including chemistry, available 

reactants, size of the particle, and terrain features among other influencing conditions. 

Single source impact assessments for urban areas, where the source and receptors are in the same 

urban area, should be conducted at grid resolutions between ~1 km up to ~12 km. Photochemical grid 

model application up to 12 km has been shown to capture similar changes in air quality due to changes 

in emissions from a specific source on secondary pollutants in an urban area estimated with finer grid

resolution (Cohan et al., 2006). In instances where sources may be modeled at coarser resolution or at

resolutions finer than 1 km consultation with the permitting authority is appropriate. 

Single source impact assessments at regional scales, where the source and receptors are hundreds of km

apart should be conducted at grid resolutions no larger than ~12-15 km. Where resources are an 

important consideration, options such as 2-way nesting may be useful to reduce computation runtime. 

In these situations, the source and receptors would be included in 2-way nests using finer grid 

resolution. In regional scale assessments, using too fine grid spacing may not be appropriate as chemical 

and meteorological data may be insufficient leading to unrealistic results. 

If a project source is modeled using a horizontal grid resolution finer than what is typically applied for 

such purpose, the project source should also be modeled using a horizontal grid spacing typical of 

contemporary applications for similar purpose and the contribution estimates using the coarser model 

domain should be considered along with the finer domain impacts. 

4.6 Use of photochemical grid models for single source impact assessments 

Where set up appropriately for the purposes of assessing the contribution of single sources to 

secondarily formed pollutants, photochemical grid models could be used with a variety of approaches to

estimate these impacts. The simplest approach would be to simulate 2 sets of conditions, one with all 

emissions and one with the source of interest modified from the original “baseline” simulation (Cohan et 

al., 2005). The difference between these simulations provides an estimate of the air quality change due 

to the adjusted emissions. 

Source apportionment has been implemented in modeling systems such as CMAQ and CAMx in the past 

(ENVIRON, 2014; Kwok et al., 2015; Kwok et al., 2013; Wang et al., 2009). CAMx currently includes 

multiple approaches to estimating ozone source contribution (ENVIRON, 2014). The standard OSAT 

approach apportions contribution based on estimated NOX/VOC sensitivity while an alternative 

approach (APCA) diverts ozone contribution to the anthropogenic source when ozone is formed from a 

combination of anthropogenic and biogenic sources (most typically anthropogenic NOX and biogenic 
18 

VOC). For the purposes of estimating project source impacts for permit review, the APCA approach is 

preferred. 

In some instances where the source and key receptors are in very close proximity, the source and 

receptor may be located in the same photochemical grid model cell. Since physical and chemical 

processes represent a volume average, this may not represent the gradients of pollution possible 

between the source and receptor when they are located in such proximity. The preferred 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. Grid resolution would be defined such that the source 

and receptor are no longer in the same grid cell. Ideally, there would also be several grid cells between 

the source and receptor to best resolve near-source pollution gradients. 

In these situations of close proximity between the source and receptor, a photochemical model 

instrumented with sub-grid plume treatment and sampling could potentially represent these 

relationships. Sub-grid plume treatment extensions in photochemical models typically solve for in-plume 

chemistry and use a set of physical and chemical criteria for determination of when puff mass is merged 

back into the host model grid. A notable limitation of sub-grid plume treatments is that these 

implementations do not have more refined information related to meteorology or terrain than the host 

grid cell. In addition to tracking puffs at sub-grid scale, the host modeling systems must be able to track 

and output surface layer sub-grid puff concentrations, “sub-grid plume sampling”, to best represent 

receptor concentrations that are in close proximity to the source (Baker et al., 2014). Another important 

reason sub-grid plume sampling is necessary is that inherently in this type of system (sub-grid plume 

treatment in a photochemical grid model) some of the source’s impacts on air quality are resolved in 

puffs at the sub-grid scale and some has been resolved in the 3-dimensional grid space. Just extracting 

sub-grid plume information or just 3-dimensional model output would miss some of the source’s 

contribution to air quality meaning accounting for both is necessary either with sub-grid sampling or 

options that integrate puffs within a grid cell with grid cell concentrations. Sub-grid plume treatments in 

photochemical grid models do not track source impacts separately from other sources in the model 

simulation. When sub-grid treatment is applied for a project source under permit review, either source

apportionment or source sensitivity is necessary to track the grid resolved source contribution in 

addition to sub-grid plume treatment to fully capture source contribution. 

4.7 Use of lagrangian models for single source impacts 

Given the complex nature of chemical reactions and spatial and temporal variability in chemical 

reactants it is of critical importance that when secondary impacts are estimated from single sources that 

they exist in a dynamic and realistic chemical and physical environment. Lagrangian models may provide

adequate representation of in-plume gas, aqueous, and aerosol chemistry, but without realistic 

concentrations of oxidants and reacting pollutants the impacts from single sources may not be 

appropriately characterized. Many important oxidants are not routinely measured. Variability from the 

surface vertically through the troposphere is also critically important given that many sources will have 

plumes that do not solely exist at the surface. The use of ambient measurements is unlikely to provide

the spatial (at the surface and vertically) and temporal variability in oxidants and reactants in an area. 

This data need typically necessitates the use of photochemical model concentration estimates to be 

used as input to a Lagrangian model. 
19 

Due to the existence of overlapping puffs in many Lagrangian puff models, multiple puffs can occupy the 

same location at a given time. These overlapping puffs interact with background concentrations 

independently. Under certain conditions, this modeling approach can lead to artifacts associated with 

double-counting background concentrations. For instance, (Karamchandani et al., 2008) found that 

under certain conditions the concentration of nitrate in particles can greatly exceed the theoretical 

maximum value based on the availability of gas-phase ammonia (NH3) for ammonium nitrate formation. 

Since each overlapping puff has access to the entire background amount of NH3, nitrate in each puff will 

condense according to that amount. Under conditions where NH3 is the limiting species for ammonium 

nitrate formation, each overlapping puff can independently deplete the gas-phase NH3 concentration 

and cause over-prediction of particle nitrate when puffs overlap. 

In an attempt to counteract the nitrate errors just mentioned, a post-processing step known as the 

Ammonia Limiting Method (ALM) is sometimes applied (Escoffier-Czaja and Scire, 2002). This method 

repartitions total nitrate between nitric acid and particle nitrate with the total amount of NH3 at a given 

receptor. The approach is not necessary in 3D photochemical grid model simulations because Eulerian 

grid models do not allow for overlap of different air parcels. Use of the ALM approach is especially 

problematic in long-range transport applications because the deposition velocities of nitric acid and fineparticle nitrate differ greatly, and so the extent of transport of the pollutants depends on whether they 

exist in the gas or particle phase. The ALM post-processing step does not account for the differences in 

transport of nitric acid and particle nitrate due to their different atmospheric lifetimes, and so ALM does 

not correct for the flaws in the approach to modeling overlapping puffs and likely introduces new biases 

to the air quality model estimates. 

Errors similar to those just described for nitrate could potentially occur in gas-phase chemistry 

calculations in situations where overlapping puffs are interacting with background concentrations of 

oxidants that are in limited supply. All chemical reactions should happen dynamically and continually at 

run-time during model application when assessing the impacts of single sources on secondary 

pollutants. Post-processing changes to chemical phase or other similar techniques that occur after the

model simulation has completed are not appropriate for assessing project source impacts on secondary 

pollutants. 

4.8 Model evaluation 

There are multiple components to model evaluation for the purposes of assessing single source 

secondary pollutant impacts for permit review programs. According to Appendix W (Section 3.2.2.b), an 

alternative model should be evaluated from both a theoretical and a performance perspective before it 

is selected for use. Comparing modeling estimates against regional tracer experiments and against nearsource in-plume measurements are examples of evaluations to satisfy the theoretical fit for use 

evaluation requirements and are typically only done when a modeling system has notably changed from 

previous testing or has never been evaluated for this purpose. The tracer experiments are useful for 

assessing whether a modeling system correctly captures long-range source-receptor relationships. Nearfield plume transects are useful for evaluating the model system’s skill in capturing primary and 

secondarily formed pollutant concentrations. 

Also, it is necessary to determine whether the inputs to the modeling system for a specific scenario are 

adequate (Appendix W Section 3.2.2.e). This type of evaluation usually consists of operationally 

comparing model predictions with observation data that coincides with the episode being modeling for 
20 

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 a

variety of air quality modeling exercises. 

It is important that any potential approaches for model performance for the purposes of single source 

assessments for PSD and NSR use a model evaluation approach that is universally applicable to any 

single source modeling system, which includes both photochemical grid and Lagrangian modeling 

systems. Regardless of the modeling system 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 model applications of similar 

geographic areas and time of year to assess how well the model performs (Simon et al., 2012). 

4.9 Project-specific modeling 

The different types of model simulations that are needed for different types of permit review 

assessments are described in this section. The necessary modeling scenarios depend on the purpose of 

the modeling and the type of model tool used for the assessment. A photochemical model used for 

estimating impacts for a PSD permit review would typically require both a baseline and project source 

scenario but a Lagrangian modeling system may only require a project source scenario. The credit 

source scenario is only needed where emissions offsets are being compared to project source impacts. 

Baseline conditions scenario. This scenario includes all sources in an area operating under typical 

(actual) conditions during the selected modeling period. Where impacts of a new project source will be

estimated then the new source should not have any emissions in this simulation. Where the impacts of a

project source operating modification will be estimated then the project source should be modeled 

using conditions representing pre-construction. This step may not be necessary where modeling the 

impacts of a new source using a Lagrangian modeling system because those modeling systems typically 

only output source impacts. 

Project source scenario. This scenario is the same as the baseline scenario except it includes either 1) a 

new project source or 2) a modified project source as part of the simulation. Where project source 

impacts are estimated using photochemical model source attribution techniques such as DDM or source 

apportionment this step may be necessary and the baseline conditions scenario would not be necessary. 

Credit source scenario. This scenario is only needed for situations where a new or modified source is 

seeking some type of emissions offset. This scenario is the same as the baseline scenario except the 

facility or facilities identified for emissions credit offsets are modeled with appropriate changes to

operations reflective of the target emission offsets (only the targeted offset emissions are adjusted in 

this scenario not the entire facilities). The location of the facilities from which offsets are desired should 
21 

be modeled at their actual locations (or last operating location) unless directed otherwise after 

consultation with the permitting authority. 

5 Appropriate processing of modeled estimates & background 

5.1 Operational definition of particulate matter 

An important consideration when using any modeling system for the purposes of assessing single source 

impacts on total PM2.5 is the operational definition of PM2.5. Since PM2.5 is the sum of all particulate 

matter species with aerodynamic diameters less than 2.5 microns, it is important to understand how the

modeling system used defines the size of PM2.5. Some modeling systems use a size sectional approach 

and others use a modal approach to approximate the size distribution of PM2.5. A straightforward and 

conservative way to estimate secondary PM2.5 in modal models (e.g. CMAQ) is to sum the secondary 

components of the fine particle modes (the Aitken and accumulation, i.e., “i” and “j”). The fine modes 

largely contain particle mass in the PM2.5 size range that includes particles with aerodynamic diameters 

less than 2.5 microns. This approach produces an estimate consistent with modeling systems that use a 

sectional representation of particle size distributions (e.g. CAMx) but internally assume all secondary PM 

is in the PM2.5 size range. 

5.2 “Absolute” and “Relative” modeling approaches 

For the purposes of single source impact assessments for permit review programs, the absolute 

modeled concentrations are compared to significance thresholds. Photochemical models used for the 

purposes of projecting future year design values for ozone and PM2.5 attainment demonstrations 

estimate relative response factors at key monitors with the change in model response on the highest 

modeled days in the baseline period. One reason for using relative response factors is to minimize 

uncertainty in the different components of the emission inventory. Since project source emissions are 

well characterized and known, the use of the absolute contribution estimate by a photochemical grid 

model is appropriate in single source permit applications. Additionally, it is necessary to estimate project 

source impacts throughout the area impacted by a source not just at locations where monitors exist. 

5.3 Estimating the O3 impact from a project source 

The first step for estimating 8-hr O3 impacts from a project source is to estimate the maximum daily 8-hr 

O3 (MDA8) at each receptor for each modeled simulation day of the baseline scenario. This step may not 

be necessary when using a single source Lagrangian model. 

Second, calculate the MDA8 at each receptor for each modeled simulation day of the project scenario 

using the same hours used to estimate MDA8 in the baseline scenario. Estimate the difference between 

the project scenario MDA8 and baseline scenario MDA8 for each receptor and model simulation day. 

This difference is the contribution from the project source. When a Lagrangian single source simulation 

has been completed, the absolute air quality impacts from the project scenario represent the project 

source impacts. 
22 

If a credit scenario was modeled, calculate the MDA8 at each receptor for each modeled simulation day 

of the credit scenario using the same hours used to estimate MDA8 in the baseline scenario. Estimate 

the difference between the credit scenario MDA8 and baseline scenario MDA8 for each receptor and 

model simulation day. This difference is the impact from the credit source(s). 

5.4 Estimating the annual PM2.5 impact from a project source 

The first step for estimating annual PM2.5 impacts from a project source is to estimate the annual 

average PM2.5 at each receptor for the baseline scenario. This step may not be necessary when using a 

single source Lagrangian model. 

Second, calculate the annual average PM2.5 at each receptor for the project scenario. Estimate the 

difference between the project scenario annual average PM2.5 and baseline scenario annual average 

PM2.5 for each receptor. This difference is the contribution from the project source. When a Lagrangian 

single source simulation has been completed, the absolute air quality impacts from this project scenario 

represent the project source impacts. 

If a credit scenario was modeled, calculate the annual average PM2.5 at each receptor for the credit 

scenario. Estimate the difference between the credit scenario annual average PM2.5 and baseline 

scenario annual average PM2.5 for each receptor. This difference is the impact from the credit source(s). 

5.5 Estimating the daily PM2.5 impact from a project source 

The first step for estimating daily PM2.5 impacts from a project source is to estimate the daily 24-hour 

average PM2.5 at each receptor for each modeled simulation day for the baseline scenario. This step 

may not be necessary when using a single source Lagrangian model. 

Second, calculate the daily average PM2.5 at each receptor for each modeled simulation day for the 

project scenario. Estimate the difference between the project scenario daily average PM2.5 and baseline 

scenario daily average PM2.5 for each receptor and model simulation day. This difference is the 

contribution from the project source. When a Lagrangian single source simulation has been completed, 

the absolute air quality impacts from this project scenario represent the project source impacts. 

If a credit scenario was modeled, calculate the daily average PM2.5 at each receptor for each modeled 

day for the credit scenario. Estimate the difference between the credit scenario daily average PM2.5 and 

baseline scenario daily average PM2.5 for each receptor and model simulation day. This difference is the 

impact from the credit source(s). 

5.6 Background concentrations 

Appendix W (section 8.2.1) states background concentrations are essential for determining source 

impacts. Concentrations are spatially and temporally variable throughout a project area due in part to

differences in meteorology, terrain, landuse, and emissions. For a cumulative assessment (multi-source

areas), Appendix W states that two components of background should be determined and include 

contributions from nearby sources and contributions from other sources. There is no single prescribed 

approach for characterizing the background concentration that must be added to the project source 
23 

contribution for comparison to the level of the appropriate NAAQS. An appropriate methodology for 

characterizing background concentrations must be chosen in consultation with the permitting authority.

Background concentrations could be based on monitored concentrations in the project area or 

combined observed/modeled estimates at monitored locations in the project area. Given the greater 

potential of secondary pollution to form downwind of a project source compared to primarily emitted 

PM, monitored values are an appropriate representation of “background” and “nearby” sources. A 

variety of approaches have been used to combine model surfaces with observation data, these 

techniques are generally referred to as “fused surfaces” (Fann et al., 2013). Any air quality modeling 

that includes future emissions reductions from a proposed rule or hypothetical emissions reductions 

that are not associated with Federally enforceable State Implementation Plan (SIP) commitments should 

not be used to represent “background” or fused with observation data to represent “background”. This 

situation could occur when a projected future year used for a NAAQS nonattainment demonstration has 

past (e.g. a 2009 simulation projected from 2002) and may be thought to better reflect current air 

quality conditions. 

6 Regulatory assessment: PSD 

A simplified schematic of this process and the flexibility afforded each sub-tier of the significant impact 

analysis and cumulative impact analysis are shown in Figure 2. The schematic shown in Figure 2 is 

relevant for O3 impacts and sources that do not emit primary PM2.5 emissions. For project sources that 

emit both primary PM2.5 and PM2.5 precursors an alternative construct is provided. For these types of 

PM2.5 assessments, the project source impacts may be estimated using different models; one for 

secondary and another for primary impacts. In this situation primary PM2.5 emissions would be 

modeled using AERMOD and the secondary impacts would be estimated with a more complex modeling 

system that includes chemistry. The approach for estimating single source impacts described in this 

section are intended to be relevant for PSD permit assessments. However, these approaches may be 

relevant for other programs and purposes. 

6.1 Sources emitting both primarily emitted PM2.5 and PM2.5 precursors 

The analytical technique for estimating daily or annual average secondarily formed PM2.5 impact could 

be from an approved reduced form (screening) approach or a refined approach. The secondary 

contribution impact could be input to AERMOD as an addition to the "BACKGROUND" concentration. 

Consequently, the impact compared to the SIL would include the AERMOD modeled primary impact and 

the estimate of secondary impact. In this approach of using AERMOD plus the “BACKGROUND” 

component including an estimate of secondary PM2.5 contribution, the AERMOD output should be used 

according to procedures outlined in Appendix W. 

6.2 Assessments of 8-hr Ozone impacts 

The modeled daily 8-hr maximum ozone impact, for this case representing the change in emissions from 

the project source should be calculated for each receptor and day of the simulation as described in 

Section 5.3. A tiered approach for comparison of project source impacts to the Significant Impact Level

1

 

1

 Although there is currently no SIL for ozone, one of the screening tools suggested earlier would be to establish an 

ozone SIL. The EPA is considering undertaking a rulemaking to do this. 
24 

(note: currently there is no ozone SIL) and for a Cumulative Impact Assessment follows. The cumulative

impact modeled ozone contribution should include the change in emissions from the project source 

combined with the appropriate “background” 8-hr ozone value that represents ozone impacts from all 

sources. This combined ozone impact should be compared to the level of the 8-hr ozone NAAQS. 

6.2.1 Assessments of 8-hr Ozone impacts – significant impact analysis 

8-hr Ozone Significant Impact Analysis - First tier: 

The modeled highest daily 8-hr maximum ozone impact over all receptors should be compared to the 

ozone SIL. If the highest daily 8-hr maximum ozone contribution is greater than the level of the SIL then 

a second tier assessment is necessary. The analytical technique for estimating the ozone impact could 

be an approved Lagrangian or photochemical model based approach. 

8-hr Ozone Significant Impact Analysis - Second tier: 

The second tier differs from the first tier in that the analytical technique for estimating ozone impact 

must be estimated with a refined approach. The refined approach may include the tool used for 

estimating impacts, configuration options for such tool (e.g. more refined grid resolution), or inclusion of 

additional representative episodes. After consultation with the permitting authority, a value less than 

the maximum impact may be used for comparison to the SIL. 

6.2.2 Assessments of 8-hr Ozone impacts – cumulative impact analysis 

8-hr Ozone Cumulative Impact Analysis - First tier: 

The highest daily 8-hr maximum ozone contribution from the project source over all modeled days and 

all receptors should be added to the highest monitored design value in the same area. If this value is

below the level of the NAAQS or the source impact is below the SIL then this test has been met. If this 

value is greater than the level of the NAAQS and the project source contribution is greater than the SIL 

then a second tier assessment is necessary. The analytical technique for estimating the ozone impact 

could be an approved Lagrangian or photochemical model based approach. 

 

8-hr Ozone Cumulative Impact Analysis - Second tier: 

Identify each receptor on each model day that is greater or equal to the value representing a “high 

modeled day” for the baseline scenario only. High modeled days include days at each receptor where 

modeled 8-hr daily maximum ozone exceeds 60 ppb. If less than 5 days are greater than 60 ppb then the 

test is not valid for that receptor (U.S. Environmental Protection Agency, 2014b). If the receptor where 

there are less than 5 high modeled days is considered likely to have large impacts from the project 

source, additional episode days may be needed to adequately represent the project source impacts at 

that particular receptor. 

The highest daily 8-hr maximum ozone contribution from the project source on high modeled days (or 

all modeled days if a single source Lagrangian model is applied) at each receptor should be added to the 

monitored design value at that same receptor or an approved interpolated field of monitored design 

values and compared to the level of the NAAQS. If this value is below the level of the NAAQS or the 
25 

source impact is below the SIL then this test has been met. The refined approach may include the tool 

used for estimating impacts, configuration options for such tool (e.g. more refined grid resolution), or 

inclusion of additional representative episodes. After consultation with the permitting authority, a value 

less than the maximum impact may be used for addition to background estimates and comparison to 

the NAAQS. 

6.3 Assessment of sources only emitting PM2.5 precursors: annual PM2.5 

The modeled annual average PM2.5 impact, for this case representing the change in emissions from the 

project source, should be calculated for each receptor. A tiered approach for comparison of the project 

source impacts to the Significant Impact Level and a Cumulative Impact Assessment follows. The 

cumulative impact modeled PM2.5 contribution should include the change in emissions from the project 

source in addition to the rest of the facility and all other “nearby” emissions sources. This combined

annual PM2.5 impact should be compared to the level of the annual average PM2.5 NAAQS. 

6.3.1 Assessments of annual average PM2.5 impacts – significant impact analysis 

Annual Average PM2.5 Significant Impact Analysis - First tier secondary impacts only: 

The highest annual average PM2.5 impact over all receptors should be compared to the annual PM2.5 

SIL. If the highest annual average secondarily formed PM2.5 impact is greater than the level of the SIL 

then a second tier assessment is necessary. The analytical technique for estimating the annual 

secondarily formed PM2.5 impact could be from an approved Lagrangian or photochemical model based 

approach. 

Annual Average PM2.5 Significant Impact Analysis - Second tier secondary impacts only: 

The second tier differs from the first tier in that the analytical technique for estimating the secondarily 

formed PM2.5 impact must be estimated with a refined approach. The refined approach may include 

the tool used for estimating impacts, configuration options for such tool (e.g. more refined grid 

resolution), or inclusion of additional representative years. After consultation with the permitting 

authority, a value less than the maximum impact may be used for comparison to the SIL. 

6.3.2 Assessments of annual average PM2.5 impacts – cumulative impact analysis 

Annual Average PM2.5 Cumulative Impact Analysis - First tier secondary impacts only: 

The highest annual average PM2.5 impact over all receptors should be added to the highest monitored 

design value in the same area. If this value is below the level of the NAAQS or the project source 

contribution is below the SIL then this test has been met. If this value is greater than the level of the 

NAAQS and the project source contribution is greater than the SIL then a second tier assessment is 

necessary. The analytical technique for estimating annual average secondary PM2.5 impact could be an 

approved Lagrangian or photochemical model based approach. 

Annual Average PM2.5 Cumulative Impact Analysis - Second tier secondary impacts only: 
26 

The annual average PM2.5 impact at each receptor should be added to a monitored design value 

representing the receptor location. This means the monitored design value should be paired in space 

but not necessarily in time with receptors. If this value is below the level of the NAAQS or the project 

source contribution is below the SIL then this test has been met. The analytical technique for estimating 

the secondarily formed PM2.5 impact must be estimated with a refined approach. The refined approach 

may include the tool used for estimating impacts, configuration options for such tool (e.g. more refined 

grid resolution), or inclusion of additional representative years. After consultation with the permitting 

authority, a value less than the maximum impact may be used for comparison to the SIL. 

6.4 Assessment of sources only emitting PM2.5 precursors: daily PM2.5 

The modeled daily average PM2.5 impact, for this case representing the change in emissions from the 

project source, should be calculated for each receptor. A tiered approach for comparison of the project 

source impacts to the Significant Impact Level and a Cumulative Impact Assessment follows. The 

cumulative impact modeled PM2.5 contribution should include the change in emissions from the project 

source in addition to the rest of the facility and all other “nearby” emissions sources. This combined

daily PM2.5 impact should be compared to the level of the daily average PM2.5 NAAQS. 

6.4.1 Assessments of daily average PM2.5 impacts – significant impact analysis 

Daily Average PM2.5 Significant Impact Analysis - First tier secondary impacts only: 

The highest daily average PM2.5 impact over all receptors should be compared to the daily PM2.5 SIL. If 

the highest daily average secondarily formed PM2.5 impact is greater than the level of the SIL then the 

second tier assessment is necessary. The analytical technique for estimating the daily secondarily 

formed PM2.5 impact could be from an approved Lagrangian or photochemical model based approach. 

Daily Average PM2.5 Significant Impact Analysis - Second tier secondary impacts only: 

The second tier differs from the first tier in that the analytical technique for estimating the secondarily 

formed PM2.5 impact must be estimated with a refined approach. The refined approach may include 

the tool used for estimating impacts, configuration options for such tool (e.g. more refined grid 

resolution), or inclusion of additional representative years. After consultation with the permitting 

authority, a value less than the maximum impact may be used for comparison to the SIL. 

6.4.2 Assessments of daily average PM2.5 impacts – cumulative impact analysis 

Daily Average PM2.5 Cumulative Impact Analysis - First tier secondary impacts only: 

The daily average PM2.5 impact over all receptors should be added to the highest monitored design 

value in the same area. If this value is below the level of the NAAQS or the project source contribution is 

below than the SIL then this test has been met. If this value is greater than the level of the NAAQS and 

the project source contribution is greater than the SIL then a second tier assessment is necessary. The 

analytical technique for estimating daily average secondary PM2.5 impact could be approved Lagrangian 

or photochemical model based approach. 

Daily Average PM2.5 Cumulative Impact Analysis - Second tier secondary impacts only: 
27 

Identify each receptor on each model day that is greater or equal to the value representing a “high 

modeled day” for the baseline scenario only. High modeled days include the top 10% of modeled total 

PM2.5 in each quarter of the simulation. (U.S. Environmental Protection Agency, 2014b). 

The highest daily average PM2.5 contribution from the project source on high modeled days (or all 

modeled days if a single source Lagrangian model is applied) at each receptor should be added to the 

monitored design value at that same receptor or an approved interpolated field of monitored design 

values and compared to the level of the NAAQS. The monitored design value should be paired in space 

but not necessarily in time with receptors. If this value is below the level of the NAAQS or the project 

source contribution is below than the SIL then this test has been met. The analytical technique for 

estimating the secondarily formed PM2.5 impact must be estimated with a refined approach. The 

refined approach may include the tool used for estimating impacts, configuration options for such tool

(e.g. more refined grid resolution), or inclusion of additional representative years. After consultation 

with the permitting authority, a value less than the maximum impact may be used for comparison to the 

SIL. 

7 Regulatory assessment: nonattainment NSR 

The approach for estimating single source impacts described in this section are intended to be relevant 

for nonattainment NSR permit assessments related to O3 and PM2.5 precursor emissions trading. 

However, these approaches may be relevant for other programs and purposes. 

7.1 Assessments of 8-hr ozone impacts

The modeled daily 8-hr maximum ozone impact, for this case representing the change in emissions from 

the project source should be calculated for each receptor and day of the simulation as described in 

Section 5.3. Also, the modeled daily 8-hr maximum ozone impact should be similarly calculated from the

credit source for each receptor and day of the simulation as described in Section 5.3. 

8-hr Ozone Precursor Emissions Offset Trading Analysis - First tier: 

The modeled daily 8-hr maximum ozone project source and credit source impacts should be paired in 

time (by episode day) for each receptor along with baseline modeled estimates (e.g. no project source 

or credit source modification). The analytical technique for estimating the ozone impact could be an 

approved Lagrangian or photochemical model based approach. Applicants should consult with the 

appropriate permitting authority to determine the most appropriate approach for interpreting modeled 

results for the purposes of establishing an emissions trade. 

8-hr Ozone Precursor Emissions Offset Trading Analysis - Second tier: 

The second tier differs from the first tier in that the analytical technique for estimating ozone impacts 

must be estimated with a refined approach. The refined approach may include the tool used for 

estimating impacts, configuration options for such tool (e.g. more refined grid resolution), or inclusion of 

additional representative episodes. After consultation with the permitting authority, an alternative 

trade ratio may be established compared to the first tier analysis. 
28 

7.2 Assessments of PM2.5 impacts 

For project sources that emit both primary PM2.5 and PM2.5, the project source impacts may be 

estimated using different models; one for secondary and another for primary impacts. In this situation

primary PM2.5 emissions would be modeled using AERMOD and the secondary impacts would be 

estimated with a more complex modeling system that includes chemistry and accounted for in the 

AERMOD simulation. Where only secondary impacts from PM2.5 precursors are included in the 

assessment an appropriate chemical transport model could be used without a primary component 

estimated with AERMOD. 

Annual average PM2.5 Precursor Emissions Offset Trading Analysis - First tier: 

The modeled annual average PM2.5 project source and credit source impacts should be estimated as 

described in Section 5.4 and paired for each receptor along with baseline modeled estimates (e.g. no 

project source or credit source modification). The analytical technique for estimating the PM2.5 impact 

could be an approved Lagrangian or photochemical model based approach. Applicants should consult 

with the appropriate permitting authority to determine the most appropriate approach for interpreting 

modeled results for the purposes of establishing an emissions trade. 

24-hr PM2.5 Precursor Emissions Offset Trading Analysis - First tier: 

The modeled daily 24-hr average PM2.5 project source and credit source impacts should be estimated 

as described in Section 5.5 and paired in time (by episode day) for each receptor along with baseline 

modeled estimates (e.g. no project source or credit source modification). The analytical technique for

estimating the PM2.5 impact could be an approved Lagrangian or photochemical model based 

approach. Applicants should consult with the appropriate permitting authority to determine the most 

appropriate approach for interpreting modeled results for the purposes of establishing an emissions 

trade. 

Annual and 24-hr PM2.5 Precursor Emissions Offset Trading Analysis - Second tier: 

The second tier differs from the first tier in that the analytical technique for estimating PM2.5 impacts 

must be estimated with a refined approach. The refined approach may include the tool used for 

estimating impacts, configuration options for such tool (e.g. more refined grid resolution), or inclusion of 

additional representative episodes. After consultation with the permitting authority, an alternative 

trade ratio may be established compared to the first tier analysis. 

8 Regulatory assessment: Economic Development Zones

The approach for estimating single source impacts described in this section are intended to be relevant 

for the purposes of air quality assessments intended to support the establishment of an Economic 

Development Zone (EDZ). These designations may be given to parts of areas designated as nonattainment for a NAAQS to allow for industrial growth in those areas without the administrative 

requirements of acquiring nonattainment NSR permits up to a certain limit of precursor emissions. 

Section 173(a)(1)(B) of the Clean Air Act authorizes EPA to identify, in consultation with the Secretary of 

Housing and Urban Development, zones within non-attainment areas which should be targeted for 

economic development. A new or modified major stationary source located in an EDZ is relieved of the 
29 

New Source Review requirements to obtain offsets if the emissions from the new or modified stationary 

source do not exceed the emissions growth allowance that is identified for that EDZ in the State 

Implementation Plan for that nonattainment area. 

Since an EDZ is essentially an “a priori” air quality credit for some part of a nonattainment area, refined 

modeling done to support an EDZ demonstration should generally follow the approach outlined for a 

nonattainment NSR credit demonstration in chapter 7 of this guidance. Choices made for these 

hypothetical sources should be done in consultation with the permitting authority. The approach 

described here for assessing air quality impacts from potential future emissions in a proposed EDZ 

constitutes one aspect of a multi-component analysis for this purpose. 

9 References 

Baker, K.R., Foley, K.M., 2011. A nonlinear regression model estimating single source concentrations of 

primary and secondarily formed PM2.5. Atmospheric Environment 45, 3758-3767. 

Baker, K.R., Hawkins, A., Kelly, J.T., 2014. Photochemical grid model performance with varying horizontal 

grid resolution and sub-grid plume treatment for the Martins Creek near-field SO 2 study. Atmospheric 

Environment 99, 148-158. 

Baker, K.R., Kelly, J.T., 2014. Single source impacts estimated with photochemical model source 

sensitivity and apportionment approaches. Atmospheric Environment 96, 266-274. 

Baker, K.R., Kelly, J.T., Fox, T., 2013. Estimating second pollutant impacts from single sources (control 

#27). http://aqmodels.awma.org/conference-proceedings/. 

Bergin, M.S., Russell, A.G., Odman, M.T., Cohan, D.S., Chameldes, W.L., 2008. Single-Source Impact 

Analysis Using Three-Dimensional Air Quality Models. Journal of the Air & Waste Management 

Association 58, 1351-1359. 

Byun, D., Schere, K.L., 2006. Review of the governing equations, computational algorithms, and other 

components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system. Applied 

Mechanics Reviews 59, 51-77. 

Cai, C., Kelly, J.T., Avise, J.C., Kaduwela, A.P., Stockwell, W.R., 2011. Photochemical modeling in 

California with two chemical mechanisms: model intercomparison and response to emission reductions. 

Journal of the Air & Waste Management Association 61, 559-572. 

Chen, J., Lu, J., Avise, J.C., DaMassa, J.A., Kleeman, M.J., Kaduwela, A.P., 2014. Seasonal modeling of PM 

2.5 in California's San Joaquin Valley. Atmospheric Environment 92, 182-190. 

Chowdhury, B., Sykes, R.I., Parker, S.F., Henn, D.S., 2010. SCICHEM User's Guide. Sage Management, 

Princeton, NJ. 

Civerolo, K., Hogrefe, C., Zalewsky, E., Hao, W., Sistla, G., Lynn, B., Rosenzweig, C., Kinney, P.L., 2010. 

Evaluation of an 18-year CMAQ simulation: Seasonal variations and long-term temporal changes in 

sulfate and nitrate. Atmospheric environment 44, 3745-3752. 

Cohan, D.S., Hakami, A., Hu, Y., Russell, A.G., 2005. Nonlinear response of ozone to emissions: Source

apportionment and sensitivity analysis. Environmental Science & Technology 39, 6739-6748. 
30 

Cohan, D.S., Hu, Y., Russell, A.G., 2006. Dependence of ozone sensitivity analysis on grid resolution.

Atmospheric Environment 40, 126-135. 

Cohan, D.S., Napelenok, S.L., 2011. Air quality response modeling for decision support. Atmosphere 2, 

407-425. 

Couzo, E., Olatosi, A., Jeffries, H.E., Vizuete, W., 2012. Assessment of a regulatory model's performance 

relative to large spatial heterogeneity in observed ozone in Houston, Texas. Journal of the Air & Waste 

Management Association 62, 696-706. 

Dresser, A.L., Huizer, R.D., 2011. CALPUFF and AERMOD Model Validation Study in the Near Field: 

Martins Creek Revisited. Journal of the Air & Waste Management Association 61, 647-659. 

Dunker, A.M., Yarwood, G., Ortmann, J.P., Wilson, G.M., 2002. The decoupled direct method for 

sensitivity analysis in a three-dimensional air quality model - Implementation, accuracy, and efficiency. 

Environmental Science & Technology 36, 2965-2976. 

ENVIRON, 2012. Evaluation of chemical dispersion models using atmospheric plume measurements 

from field experiments, EPA Contract No: EP-D-07-102. September 2012. 06-20443M6. 

ENVIRON, 2014. User's Guide Comprehensive Air Quality Model with Extensions version 6, 

www.camx.com. ENVIRON International Corporation, Novato. 

Escoffier-Czaja, C., Scire, J.S., 2002. The effects of ammonia limitation on nitrate aerosol formation and 

visibility impacts in Class I areas. https://ams.confex.com/ams/pdfpapers/44058.pdf. 12th Joint 

Conference on the Applications of Air Pollution Meteorology with A&WMA, May 2002. 

Fann, N., Fulcher, C.M., Baker, K., 2013. The Recent and Future Health Burden of Air Pollution 

Apportioned Across US Sectors. Environmental Science & Technology 47, 3580-3589. 

Hogrefe, C., Hao, W., Zalewsky, E., Ku, J.-Y., Lynn, B., Rosenzweig, C., Schultz, M., Rast, S., Newchurch, 

M., Wang, L., 2011. An analysis of long-term regional-scale ozone simulations over the Northeastern 

United States: variability and trends. Atmospheric Chemistry and Physics 11, 567-582. 

Jin, L., Brown, N.J., Harley, R.A., Bao, J.-W., Michelson, S.A., Wilczak, J.M., 2010. Seasonal versus episodic 

performance evaluation for an Eulerian photochemical air quality model. Journal of Geophysical 

Research-Atmospheres 115. 

Karamchandani, P., Chen, S.-Y., Seigneur, C., 2008. CALPUFF Chemistry Upgade. Prepared for American 

Petroleum Institute, 1220 L Street NW, Washington, DC 20005. Document CP277-07-01. 

Kelly, J.T., Baker, K.R., Napelenok, S.L., Roselle, S.J., 2015. Examining single-source secondary impacts 

estimated from brute-force, decoupled direct method, and advanced plume treatment approaches. 

Atmospheric Environment 111, 10-19. 

Kwok, R., Baker, K., Napelenok, S., Tonnesen, G., 2015. Photochemical grid model implementation of 

VOC, NO x, and O 3 source apportionment. Geoscientific Model Development 8, 99-114. 

Kwok, R., Napelenok, S., Baker, K., 2013. Implementation and evaluation of PM2.5 source contribution 

analysis in a photochemical model. Atmospheric Environment 80, 398-407. 
31 

Levy, J.I., Spengler, J.D., Hlinka, D., Sullivan, D., Moon, D., 2002. Using CALPUFF to evaluate the impacts 

of power plant emissions in Illinois: model sensitivity and implications. Atmospheric Environment 36, 

1063-1075. 

McMurry, P.H., Shepherd, M.F., Vickery, J.S., 2004. Particulate matter science for policy makers: A 

NARSTO assessment. Cambridge University Press. 

Rodriguez, M.A., Barna, M.G., Gebhart, K.A., Hand, J.L., Adelman, Z.E., Schichtel, B.A., Collett, J.L., Jr., 

Malm, W.C., 2011. Modeling the fate of atmospheric reduced nitrogen during the Rocky Mountain 

Atmospheric Nitrogen and Sulfur Study (RoMANS): Performance evaluation and diagnosis using 

integrated processes rate analysis. Atmospheric Environment 45, 223-234. 

Russell, A.G., 2008. EPA Supersites program-related emissions-based particulate matter modeling: initial 

applications and advances. Journal of the Air & Waste Management Association 58, 289-302. 

Scire, J.S., Strimaitis, D.G., Yamartino, R.J., 2000. A User's Guide for the CALPUFF Dispersion Model 

(Version 5). Technical report. Earth Tech, Inc. 

http://www.src.com/calpuff/download/CALPUFF_UsersGuide.pdf. 

Simon, H., Baker, K.R., Phillips, S., 2012. Compilation and interpretation of photochemical model 

performance statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139. 

Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W., 

Powers, J.G., 2008. A description of the Advanced Reserch WRF version 3. NCAR Technical Note 

NCAR/TN-475+STR. 

Stroud, C.A., Makar, P.A., Moran, M.D., Gong, W., Gong, S., Zhang, J., Hayden, K., Mihele, C., Brook, J.R., 

Abbatt, J.P.D., Slowik, J.G., 2011. Impact of model grid spacing on regional- and urban- scale air quality 

predictions of organic aerosol. Atmospheric Chemistry and Physics 11, 3107-3118. 

Sykes, R.I., Parker, D.S., Henn, C.P., Cerasoli, C.P., Santos, L.P., 1998. PC-SCIPUFF Version 1.2PD Technical 

Documentation. ARAP Report No. 718. Titan Corporation, Titan Research & Technology Division, 

Princeton, NJ. 

Tesche, T., Morris, R., Tonnesen, G., McNally, D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx annual 2002

performance evaluation over the eastern US. Atmospheric Environment 40, 4906-4919. 

U.S. Department of the Interior, 2010. Federal Land Managers' Air Quality Related Values Work Group 

(FLAG) Phase I Report - Revised 2010. http://www.nature.nps.gov/air/pubs/pdf/flag/FLAG_2010.pdf, 

Natural Resource Report NPS/NPRC/NRR-2010/232. 

U.S. Environmental Protection Agency, 2005. 40 CFR, Part 51, Appendix W. Revision to the Guideline on 

Air Quality Models, 68 FR 68235-68236, November 9, 2005. 

U.S. Environmental Protection Agency, 2014a. Guidance for PM2.5 Permit Modeling, EPA-454/B-14-001. 

http://www.epa.gov/scram001/guidance/guide/Guidance_for_PM25_Permit_Modeling.pdf. 

U.S. Environmental Protection Agency, 2014b. Modeling Guidance for Demonstrating Attainment of Air 

Quality Goals for Ozone, PM2.5, and Regional Haze. 

http://www.epa.gov/ttn/scram/guidance/guide/Draft_O3-PM-RH_Modeling_Guidance-2014.pdf. 
32 

U.S. Environmental Protection Agency, 2015. Interagency Workgroup on Air Quality Modeling (IWAQM) 

Phase 3 Summary Report: Near-Field Single Source Secondary Impacts. EPA-454/P-15-002. 

Wang, Z.S., Chien, C.J., Tonnesen, G.S., 2009. Development of a tagged species source apportionment 

algorithm to characterize three-dimensional transport and transformation of precursors and secondary 

pollutants. Journal of Geophysical Research-Atmospheres 114. 

Yarwood, G., Scorgie, Y., Agapides, N., Tai, E., Karamchandani, P., Duc, H., Trieu, T., Bawden, K., 2011. 

Ozone impact screening method for new sources based on high-order sensitivity analysis of CAMx 

simulations for NSW metropolitan areas. 

Zhou, W., Cohan, D.S., Pinder, R.W., Neuman, J.A., Holloway, J.S., Peischl, J., Ryerson, T.B., Nowak, J.B., 

Flocke, F., Zheng, W.G., 2012. Observation and modeling of the evolution of Texas power plant plumes. 

Atmospheric Chemistry and Physics 12, 455-468. 

 
33 

United StatesEnvironmental Protection Agency 

Office of Air Quality Planning and Standards

Air Quality Analysis Division 

Research Triangle Park, NC 

Publicat