1 

# Interagency Workgroup on Air Quality Modeling Phase 3 Summary Report: Near-Field Single Source Secondary Impacts2 

### EPA-454/P-15-002 July 2015

Interagency Workgroup on Air Quality Modeling Phase 3 Summary Report: Near-Field Single Source Secondary Impacts

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Analysis Division Air Quality Modeling Group Research Triangle Park, NC3



## Executive Summary

The Interagency Workgroup on Air Quality Modeling ( IWAQM) was originally formed in 1991 to provide a forum for development of technically sound region al air quality models for regulatory assessments of pollutant source impacts on Federal Class I areas. The IWAQM process largely concluded in 1998 with the publication of the Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long Range Transport Impacts (EPA-454/R-98-019) (U.S. Environmental Protection Agency, 1998). The IWAQM P hase 2 process provided a series of recommendations concerning the application of the C ALPUFF model for use in long range transport (LRT) modeling and informed the promulgation of tha t model for such regulatory purposes in 2003. The IWAQM process was reinitiated in June 2013 to infor m EPA’s commitment to update Appendix W to address chemically reactive pollutants in near fiel d and long range transport applications (U.S. Environmental Protection Agency, 2012a). This repor t provides information and recommendations from the “Phase 3” effort focused on near-field single s ource impacts of secondary pollutants.

This document describes chemical and physical proce sses important to the formation of ground-level O 3 and PM2.5 in the context of modeled near-field asse ssments to support permit review programs. Chemical transport models that characterize these p rocesses include both Lagrangian which typically only have a single source included in the model and photochemical grid models which include some representation of all anthropogenic, biogenic, and geogenic sources. Modeling systems appropriate for the purposes of estimating single source near-field secondary impacts are described and recommendations are made with respect to the use of certain types of modeling systems for this type of application. Model evaluation is important to ensur e that a particular system is fit for the purpose o f estimating near-field single source secondary impac ts. In addition to establishing a modeling system i s generally appropriate for this purpose, project spe cific evaluations that compare model estimated meteorology and chemical estimates with measurement s near the project source and key receptors is also an important model evaluation component.

An illustrative example is provided showing hypothe tical single source impacts in two different urban areas: Atlanta and Detroit. A photochemical grid mo del was applied with baseline emissions and subsequent additional simulations where a new hypot hetical source was included with a fixed precursor
emission rate. The simulations with the additional hypothetical source are compared with the baseline simulation where the hypothetical source is not inc luded (e.g. brute-force difference) and single sour ce impacts are estimated for O 3 and secondary PM2.5 sulfate and nitrate. Downwind impacts from these hypothetical sources tend to increase as precursor emissions increase. Impacts tend to be highest with in
15 km the source and decrease as distance from the source increases. However, there is variability in downwind impacts directionally from each of these s ources due to differences in meteorology and available oxidants and neutralizing agents.

Finally, a review of existing research published be tween 2005 and 2015 relating single source precurso r emissions and downwind impacts on O 3 and secondary PM2.5 is summarized. Downwind O 3 impacts from these studies show a general increase as NO X emissions increase. Downwind impacts for O 3 and secondary PM2.5 in the studies reviewed tend to be largest nearest the source and decrease as distance from the source increases. Approaches for developin g reduced-form or screening versions of more refined modeling tools such as photochemical transp ort models are also reviewed. At this time, it is n ot4

clear that a robust reduced form model exists for e ither O 3 or secondary PM2.5 for the purposes of assessing single source downwind impacts of these p ollutants.5



## Table of Contents

1 Background: IWAQM Phase 3 process ...............

................................................... ................................ 6

2 Regulatory Motivation ...........................

................................................... ............................................ 6

3 Model Selection .................................

................................................... ................................................ 7

3.1 Secondary Pollutant Formation: O 3 and PM2.5 ........................................ .................................... 7

3.2 Air Quality Models for Secondary Pollutants ...

................................................... .......................... 7

3.2.1 Lagrangian models ...........................

................................................... .................................. 8

3.2.2 Photochemical transport models ..............

................................................... ......................... 8

3.3 Recommendations for estimating single source O 3 and secondary PM impacts using photochemical grid models .........................

................................................... .......................................... 9

4 Model evaluation ................................

................................................... ............................................. 10

4.1 Fit for Purpose Evaluations ...................

................................................... ................................... 10

4.2 Model evaluation: meteorology .................

................................................... ............................. 11

4.3 Model evaluation: chemistry ...................

................................................... ................................ 11

4.4 Model performance evaluation data sources .....

................................................... ..................... 12

5 Illustrative example of near-field single source impacts on O 3 and PM2.5 ........................................ 14

5.1 Model application approach for estimating these illustrative example single source impacts . 14

5.2 Illustrative single source impact results for O 3 and PM2.5 ........................................ ................. 15

6 Review of published single source impacts on O 3 and PM2.5 ........................................ .................... 19

6.1 Single source ozone impacts ...................

................................................... ................................. 19

6.2 Single source PM impacts ......................

................................................... .................................. 21

7 Reduced form approaches for estimating single sou rce secondary impacts ............................. ........ 22

7.1 Review of reduced form approaches for estimatin g O 3 single source impacts .......................... 2 3

7.2 Review of reduced form approaches for estimatin g PM2.5 single source impacts ................... 2 3

8 Acknowledgements ................................

................................................... .......................................... 24

9 References ......................................

................................................... ................................................. 2 46



## 1 Background: IWAQM Phase 3 process

The Interagency Workgroup on Air Quality Modeling ( IWAQM) was originally formed in 1991 to provide a forum for development of technically sound region al air quality models for regulatory assessments of pollutant source impacts on Federal Class I areas. Meetings were held with personnel from participatin g Federal agencies: the Environmental Protection Agen cy (EPA), the U.S. Forest Service (USFS), the U.S. Fish and Wildlife Service (USFWS), and the National Park Service (NPS). The original purpose was to review respective modeling programs, develop an org anizational framework, and formulate reasonable objectives and plans for single source model applic ations. The IWAQM process largely concluded in 1998 with the publication of the Interagency Workgroup o n Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long Range Transport Impacts (EPA-454/R-98-019) (U.S. Environmental Protection Agency, 1998). The IWAQM P hase 2 report provided a series of recommendations concerning the application of the C ALPUFF model for use in long range transport (LRT) modeling and informed the promulgation of tha t model for such regulatory purposes in 2003. Draft updates to the IWAQM Phase 2 report were rele ased in 2009 to better reflect the state-of-thepractice of long range transport modeling technique s based on experience gained since the early 2000s.

The IWAQM process was reinitiated in June 2013 to i nform EPA’s commitment to update Appendix W to address chemically reactive pollutants in near fiel d and long range transport applications (U.S. Environmental Protection Agency, 2012a). Comments r eceived from the 10 th Modeling Conference (March 2012) from stakeholders support this interag ency collaborative effort to provide additional guidance for modeling single source impacts on seco ndarily formed pollutants in the near-field and for long range transport. Stakeholder comments also sup port the idea of this collaborative effort working in parallel with separate stakeholder efforts to furth er model development and evaluation.

This “Phase 3” effort includes the establishment of2 separate working groups, one focused on longrange transport of primary and secondary pollutants and the other on near-field single source impacts of secondary pollutants. While many of the objectives are similar for each of these groups, the focus and regulatory end-points are different. The primary ob jectives of this phase of IWAQM are shown below.

It is expected the “Phase 3” effort will continue w ith future efforts related to reviewing and respond ing to comments given on the 2015 proposed changes to A ppendix W related to single source impact
assessments for O 3 and secondary PM2.5. IWAQM3 near-field impacts tea m members (affiliated with U.S. Environmental Protection Agency unless noted o therwise) include James Kelly, George Bridgers, Andy Hawkins, Randall Robinson, Jaime Julian, Rebec ca Matichuk, Robert Kotchenruther, Rynda Kay, and Richard Monteith. Additional participation was provided by Erik Snyder, Robert Elleman, and Bret Anderson (U.S. Department of Agriculture).



## 2 Regulatory Motivation

Pursuant to 40 CFR part 51.166 and 52.21, subsectio ns (k)(1)(i) and (k)(1)(ii), new or modified source s emitting in significant amounts (see 40 CFR part 51 .166 and 52.21, subsection (b)(23)(i)) are required to demonstrate that the source under review does not c ause or contribute to a violation of any applicable National Ambient Air Quality Standards (NAAQS) ((k) (1)(i)) or maximum allowable increases over a baseline concentration ((k)(1)(ii)). The relevant p ermitting authority administers the National Ambien t Air Quality Standards and increments component of t he air quality analysis.7



## 3 Model Selection

This section describes the types of air quality imp acts that need to be assessed and the tools that ar e best suited for this purpose. For a variety of regu latory programs, impacts on secondary pollutants su ch

as O 3 and PM2.5 need to be assessed at two spatial scale s (near-source and long-range transport). It is important that modeling systems used for these asse ssments be fit for this purpose and be evaluated for skill in replicating meteorology and atmospheri c chemical and physical processes that result in secondary pollutants, and deposition.



### 3.1 Secondary Pollutant Formation: O 3 

### and PM2.5

PM and O 3 are closely related to each other in that they sha re common sources of emissions and are formed in the atmosphere from chemical reactions wi th similar precursors (U.S. Environmental Protection Agency, 2005). Air pollutants formed thr ough chemical reactions in the atmosphere are referred to as secondary pollutants. For example, g round-level ozone (O 3 ) is predominantly a secondary pollutant formed through nonlinear photochemical re actions driven by emissions of nitrogen oxides (NO
x ) and volatile organic compounds (VOCs) in the pres ence of sunlight. Warm temperatures, clear skies (abundant levels of solar radiation), and sta gnant air masses (low wind speeds) increase ozone formation potential (Seinfeld and Pandis, 2012).

In the case of particulate matter with aerodynamic diameter less than 2.5 μm (PM 2.5 or fine PM), PM 2.5 can be either primary (i.e. emitted directly from s ources) or secondary (formed in the atmosphere). Th e fraction of PM 2.5 which is primary versus secondary varies by locati on and season. In the United States,

PM 2.5 is dominated by a variety of chemical species: amm onium sulfate, ammonium nitrate, organic carbon (OC) mass, elemental carbon (EC), and other soil compounds and oxidized metals. PM 2.5 elemental (black) carbon and soil dust are both dir ectly emitted into the atmosphere from primary sources. Organic carbon particulate is directly emi tted from primary sources but also has a secondary component formed by atmospheric reactions of VOC em issions. PM 2.5 sulfate, nitrate, and ammonium ions are predominantly the result of chemical react ions of the oxidized products of sulfur dioxide (SO 2 )
and NO x emissions and direct ammonia (NH 3 ) emissions (Seinfeld and Pandis, 2012).



### 3.2 Air Quality Models for Secondary Pollutants

Single source impacts on secondary pollution includ ing ozone and PM2.5 are becoming increasingly important for facility permit reviews under the Pre vention of Significant Deterioration (PSD) program as well as New Source Review (NSR) and other regulator y programs. Gaussian dispersion models such as AERMOD have been used to quantify the near-field (l ess than 50 km) impacts of primary PM2.5 emissions from new or modified sources (Perry et al ., 2005; U.S. Environmental Protection Agency, 2005, 2014). However, these types of models cannot treat the important chemical and physical processes of ozone and secondary PM2.5.

Chemical transport models treat atmospheric chemica l and physical processes such as gas and particle chemistry, deposition, and transport. There are two types of chemical transport models which are
differentiated based on a fixed frame of reference (Eulerian grid based) or a frame of reference that moves with parcels of air between the source and re ceptor point (Lagrangian) (McMurry et al., 2004). Photochemical grid models are three-dimensional gri d-based models that treat chemical and physical8

processes in each grid cell and use Eulerian diffus ion and transport processes to move chemical specie s to other grid cells (McMurry et al., 2004). These t ypes of models are appropriate for assessment of ne arfield and regional scale reactive pollutant impacts from specific sources (Baker and Foley, 2011; Bake r and Kelly, 2014; Bergin et al., 2008; Zhou et al., 2 012) or all sources (Chen et al., 2014; Russell, 20 08; Tesche et al., 2006). Photochemical transport model s have been used extensively to support State Implementation Plans and explore relationships betw een inputs and air quality impacts in the United States and beyond (Cai et al., 2011; Civerolo et al ., 2010; Hogrefe et al., 2011).



### 3.2.1 Lagrangian models

Quantifying secondary pollutant formation requires simulating chemical reactions and thermodynamic partitioning in a realistic chemical and physical e nvironment. Some Lagrangian models treat in-plume gas and particulate chemistry. These models require as input background fields of time and space varying oxidant concentrations, and in the case of PM2.5 also neutralizing agents such as ammonia, because important secondary impacts happen when plu me edges start to interact with the surrounding chemical environment (Baker and Kelly, 2014; ENVIRO N, 2012b). These oxidant and neutralizing agents are not routinely measured, but can be generated wi th a three dimensional photochemical transport
model. Photochemical models simulate a more realist ic chemical and physical environment for plume growth and chemical transformation (Baker and Kelly , 2014; Zhou et al., 2012), but simulations may sometimes be more resource intensive than Lagrangia n or dispersion models.



### 3.2.2 Photochemical transport models

Publically available and documented Eulerian photoc hemical 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 emission s, chemical transformation, transport, and deposition using time and space variant meteorology . These modeling systems include primarily emitted species and secondarily formed pollutants such as o zone and PM2.5 (Chen et al., 2014; Civerolo et al., 2010; Russell, 2008; Tesche et al., 2006). Even tho ugh single source emissions are injected into a gri d volume, photochemical transport models have been sh own 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 asse ssing the contribution of single sources to primary and secondarily formed pollutants, photochemical gr id models could be used with a variety of approaches to estimate these impacts. These approac hes generally fall into the category of source sensitivity (how air quality changes due to changes in emissions) and source apportionment (how emissions contribute to air quality levels under mo deled atmospheric conditions).

The simplest source sensitivity approach (brute-for ce change to emissions) would be to simulate 2 sets of conditions, one with all emissions and one with the source of interest removed from the simulation (Cohan and Napelenok, 2011). The difference between these simulations provides an estimate of the air quality change related to the change in emissions f rom the project source. Another source sensitivity approach to identify the impacts of single sources on changes in model predicted air quality is the decoupled direct method (DDM), which tracks the sen sitivity of an emissions source through all chemical and physical processes in the modeling sys tem (Dunker et al., 2002). Sensitivity coefficients relating source emissions to air quality are estima ted during the model simulation and output at the
resolution of the host model.9

Some photochemical models have been instrumented wi th source apportionment, which tracks emissions from specific sources through chemical tr ansformation, 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 differ entiate the contribution from single sources on model predicted ozone and PM2.5 (Baker and Foley, 2 011; Baker and Kelly, 2014). DDM has also been
used to estimate O 3 and PM2.5 impacts from specific sources (Baker and Kelly, 2014; Bergin et al., 2008; Kelly et al., 2015) as well as the simpler brute-fo rce sensitivity approach (Baker and Kelly, 2014; Be rgin et al., 2008; Kelly et al., 2015; Zhou et al., 2012) . Limited comparison of single source impacts betwe en models (Baker et al., 2013) and approaches to ident ify single source impacts (Baker and Kelly, 2014; Baker et al., 2013) show generally similar downwind spatial gradients and impacts.

Near-source in-plume aircraft based measurement fie ld studies provide another approach for evaluating model estimates of (near-source) downwind transport and chemical impacts from single stationary point sources (ENVIRON, 2012b). Photochemical grid model source apportionment and source sensitivity simulation of a single source downwind impacts comp are well against field study primary and secondary ambient measurements (e.g. O 3 ) made in Tennessee and Texas (ENVIRON, 2012b). Thi s work indicates photochemical grid models and source apportionment and source sensitivity approaches provide meaningful estimates of single source impacts. Howe ver, additional evaluations are needed for longer time periods and more diverse environments, both ph ysical and chemical, to generate broader confidence in these approaches for this purpose. In particular, it is important to ensure that adequat e model performance is achieved in areas with complex terrain and meteorology.



### 3.3 Recommendations for estimating single source O 3 

### and secondary PM impacts using photochemical grid models

Photochemical transport models are suitable for est imating single source O 3 and secondary PM2.5 impacts since important physical and chemical proce sses related to the formation and transport of both are realistically treated. Source sensitivity and a pportionment techniques implemented in photochemical grid models have evolved sufficiently and provide the opportunity for estimating potential secondary pollutant impacts from one or a small group of emission sources. Photochemical grid models using meteorology output from prognosti c meteorological models have demonstrated skill in estimating source-receptor relationships in the near-field (Baker and Kelly, 2014; ENVIRON, 2012b) and over long distances (ENVIRON, 2012a).

In situations of close proximity between the source and receptor, a photochemical model instrumented with sub-grid plume treatment and sampling could po tentially represent these relationships. However, the simplest approach to better representing the sp atial gradient in source-receptor relationships whe n they are in close proximity would be to use smaller sized grid cells. Sub-grid plume treatment extensi ons in photochemical models typically solve for in-plum e chemistry and use a set of physical and chemical criteria for determination of when puff mass is mer ged back into the host model grid (Baker et al., 2014). Sub-grid plume (puff) sampling or sub-grid p uff merging with host grid cell estimates is necess ary because inherently in this type of system (sub-grid plume treatment in a photochemical grid model) some of the source’s impacts on air quality are res olved in puffs at the sub-grid scale and some has b een resolved in the 3-dimensional grid space. Just extr acting sub-grid plume information or just 3- dimensional model output would omit some of the sou rce’s contribution to air quality. In practice, som e type of source apportionment or source sensitivity (e.g. brute-force difference) would be necessary to10

track in the grid resolved source contribution in a ddition to sub-grid plume treatment to fully captur e source contribution when using sub-grid plume treat ment.

Given the complexities in fully determining project source impacts in both the grid model and sub-grid puffs (Baker et al., 2014) and the tendency for puf fs to stay aloft compared to grid-resolved mass (Ba ker et al., 2014; Kelly et al., 2015) the use of sub-gr id plume treatment for the purposes of estimating project source impacts for PSD/NSR would typically not be recommended. Additionally, these tools sometimes exacerbate numerical instability that inf requently might occur in inorganic and aqueous chemical reactions (Kelly et al., 2015).



## 4 Model evaluation

There are multiple components to model evaluation f or the purposes of assessing long range transport of secondary pollutants for AQRVs. First, an altern ative modeling system as defined in Appendix W must meet certain criteria for this purpose (Appendix W Section 3.2.2.e). One type of evaluation for this t ype of modeling system for this purpose is to show that the modeling system is theoretically fit for purpo se. A second evaluation component involves comparison t o ambient measurements to assess whether the modeling system and generated inputs are appropriat e for a specific project application.

Since PM2.5 and O3 impacts may be estimated for sin gle sources as part of a permit review process, it is important that a modeling system be able to capture single source primary (e.g. precursors) and secondary impacts. Near-source in-plume measurement s are useful to develop confidence that a modeling system captures secondarily formed polluta nts from specific sources. These types of assessments are typically only done occasionally wh en a modeling system has notably changed from previous testing or has never been evaluated for th is purpose. This type of assessment is discussed in
more detail in section 4.1.

A second type of evaluation fulfills the need to de termine whether inputs to the modeling system for a specific scenario are adequate for the specific con ditions of the project impact assessment (Appendix W Section 3.2.2.e). This type of evaluation usually c onsists of comparing model predictions with observation data that coincides with the episode be ing modeling for a permit review assessment. One of the most important questions in an evaluation conce rns whether the prognostic or diagnostic meteorological fields are adequate for their intend ed use in supporting the project model application demonstration. Sections 4.2 and 4.3 cover project s pecific evaluation approaches that develop confidence that a particular model application is a ppropriate for the project source and key downwind receptors. It is important to emphasize that a broa d evaluation of a model platform’s skill in estimat ing meteorology or chemical measurements may not suffic iently illustrate the appropriateness of that platform for specific projects that will be focused on a narrow subset of the larger set of model inpu ts and outputs. Therefore, broad model platform evalua tions should be supplemented with focused evaluation and discussion of the appropriateness of model inputs for specific project assessments.



### 4.1 Fit for Purpose Evaluations

Near-source in-plume aircraft based measurement fie ld studies provide another an opportunity for evaluating model estimates of (near-source) downwin d transport and chemical impacts from single stationary point sources (ENVIRON, 2012b). Since si ngle source impacts in the near field are assessed in each direction from a source at evenly distributed receptor locations model system skill in plume11

placement is not emphasized. Ideally, these modelin g systems will capture near-source plume placement to best match plume evolution with the su rrounding heterogeneous chemical environment. Model system skill in capturing secondary impacts i s important in near-field permit related assessment s and in-plume field measurements provide the best op portunity for evaluating model skill in capturing secondary impacts from a specific source. Often whe n comparing modeled and measured in-plume pollutants the model impacts are shifted spatially to match the location of the measured plume meaning the comparison is paired in time but not space and therefore emphasizing secondary pollutant formation over plume placement.

Photochemical grid model source apportionment and s ource sensitivity simulation of a single source downwind impacts compare well against field study p rimary and secondary ambient measurements made in Tennessee and Texas (Baker and Kelly, 2014; ENVIRON, 2012b). This work indicates photochemical grid models and source apportionment and source sensitivity approaches provide meaningful estimates of single source impacts. Howe ver, additional evaluations are needed for longer time periods and more diverse environments to gener ate broader confidence in these approaches for this purpose.



### 4.2 Model evaluation: meteorology

It is important to determine whether and to what ex tent confidence may be placed in a prognostic meteorological model’s output fields (e.g., wind, t emperature, mixing ratio, diffusivity, clouds/precipitation, and radiation) that will be u sed as input to models. Currently there is no brigh t line for meteorological model performance and acceptabil ity. There is valid concern that establishment of such criteria, unless accompanied with a careful ev aluation process might lead to the misuse of such goals as is occasionally the case with the accuracy , bias, and error statistics recommended for judgin g model performance. In spite of this concern, there remains nonetheless the need for some benchmarks against which to compare new prognostic and diagnos tic model simulations. A significant amount of information (e.g. model performance metrics) can be developed by following typical evaluation procedures that will enable quantitative comparison of the meteorological modeling to other contemporary applications and to judge its suitabil ity for use in modeling studies.

Development of the requisite meteorological databas es necessary for use of photochemical transport
models should conform to recommendations outlined i n Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Qualit y Goals for Ozone, PM2.5, and Regional Haze (EPA454/B-07-002) (U.S. Environmental Protection Agency , 2007). Demonstration of the adequacy of prognostic or diagnostic meteorological fields can be established through appropriate diagnostic and statistical performance evaluations consistent with recommendations provided in the appropriate model guidance (U.S. Environmental Protection Agenc y, 2007).



### 4.3 Model evaluation: chemistry

An operational evaluation is used to assess how acc urately the model predicts observed concentrations. Therefore, an operational evaluation can provide a benchmark for model performance and identify model limitations and uncertainties that require di agnostic evaluation for further model development/improvement. An operational evaluation for PM2.5 is similar to that for ozone. Some important differences are that PM2.5 consists of ma ny components and is typically measured with a 24- hour averaging time. The individual components of P M2.5 should be evaluated individually. In fact, it is12

more important to evaluate the components of PM2.5 than to evaluate total PM2.5 itself. Apparent “good performance” for total PM2.5 does not indicat e whether modeled PM2.5 is predicted for “the right reasons” (the proper mix of components). If p erformance of the major components is good, then performance for total PM2.5 should also be good. Da tabases that contain ambient O 3 , PM2.5, and key precursors are noted in section 4.4. Section 4.4 is not intended to provide an exhaustive review of al l ambient databases but provide an initial set of dat a that could be used for this purpose.

Regardless of the modeling system (e.g. photochemic al transport or Lagrangian puff model) used to estimate secondary impacts of ozone and/or PM2.5, m odel estimates should be compared to observation data to generate confidence that the mo deling system is representative of the local and regional air quality. For ozone related projects, m odel estimates of ozone should be compared with observations in both time and space. For PM2.5, mod el 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 mod el domain. Model performance metrics comparing observa tions 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., 2 012). There are no specific levels of any model performance metric that indicate “acceptable” model performance. Model performance metrics should be compared with similar contemporary applications to assess how well the model performs (Simon et al., 2012).

Accepted performance standards for speciated and to tal PM2.5 and ozone for photochemical models used in attainment demonstrations may not be applic able for single source assessments. Since the emissions and release parameters for the project so urce are well known, a direct connection between general photochemical model performance and the abi lity of the modeling system to characterize the impacts of the project source would be difficult to make. It is important that any potential approache s for photochemical model performance for the purpose s of single source assessments for PSD and NSR use an approach that would be universally applicabl e to any single source modeling system, which includes the Lagrangian models described above.



### 4.4 Model performance evaluation data sources

Provided below is an overview of some of the variou s ambient air monitoring networks currently available that provide relevant data for model eval uation purposes. Network methods and procedures are subject to change annually due to systematic re view and/or updates to the current monitoring network/program. Please note, there are other avail able monitoring networks which are not mentioned
here and more details on the networks and measureme nts should be obtained from other sources.

AQS: The Air Quality System (AQS) is not an air qua lity monitoring network. However it is a repository of ambient air pollution data and related meteorologic al data collected by EPA, state, local and tribal a ir pollution control agencies from tens of thousands o f monitors. AQS contains all the routine hourly gaseous pollutant data collected from State and Loc al Air Monitoring Stations (SLAMS) and National Air Monitoring Stations (NAMS) sites. SLAMS is a dynami c network of monitors for state and local directed monitoring objectives (e.g., control strategy devel opment). A subset of the SLAMS network, the NAMS has an emphasis on urban and multi-source areas (i. e, areas of maximum concentrations and high population density). The AQS database includes crit eria pollutant data (SO2, NO2, O3, and PM2.5) and speciation data of particulate matter (SO4, NO3, NH 4, EC, and OC), and meteorological data. The data13

are measured and reported on an hourly or daily ave rage basis. An overview of the AQS can be found at http://www.epa.gov/ttn/airs/airsaqs/index.htm.

IMPROVE: The Interagency Monitoring of PROtected Vi sual Environments (IMPROVE) network began in 1985 as a cooperative visibility monitoring effort between EPA, federal land management agencies, and state air agencies (IMPROVE, 2000). Data are collec ted at Class I areas across the United States mostl y at National Parks, National Wilderness Areas, and othe r protected pristine areas. Currently, there are approximately 160 IMPROVE rural/remote sites that h ave complete annual PM2.5 mass and/or PM2.5 species data. The website to obtain IMPROVE documen tation and/or data is http://vista.cira.colostate.edu/improve/.

STN: The Speciation Trends Network (STN) began oper ation in 1999 to provide nationally consistent speciated PM2.5 data for the assessment of trends a t representative sites in urban areas in the U.S. T he STN was established by regulation and is a companio n network to the mass-based Federal Reference Method (FRM) network implemented in support of the PM2.5 NAAQS. As part of a routine monitoring program, the STN quantifies mass concentrations and PM2.5 constituents, including numerous trace elements, ions (sulfate, nitrate, sodium, potassium , ammonium), elemental carbon, and organic carbon. In addition, there are approximately 181 supplement al speciation sites which are part of the STN
network and are SLAMS sites. The STN data at trends sites are collected 1 in every 3 days, whereas supplemental sites collect data either 1 in every 3 days or 1 in every 6 days. Comprehensive informati on on the STN and related speciation monitoring can be found at http://www.epa.gov/ttn/amtic/specgen.html and http: //www.epa.gov/aqspubl1/select.html.

CASTNet: Established in 1987, the Clean Air Status and Trends Network (CASTNet) is a dry deposition monitoring network where data are collected and rep orted as weekly average data (U.S. EPA, 2002b). Relevant CASTNet data includes weekly samples of in organic PM2.5 species and ground-level ozone. More information can be obtained through the CASTNe t website at http://www.epa.gov/castnet/.

SEARCH: The South Eastern Aerosol Research and CHar acterization (SEARCH) monitoring network was
established in 1998 and is a coordinated effort bet ween the public and private sector to characterize the chemical and physical composition as well as the ge ographical distribution and long-term trends of
PM2.5 in the Southeastern U.S. SEARCH data are coll ected and reported on an hourly/daily basis. Background information regarding standard measureme nt techniques/protocols and data retrieval can be found at http://www.atmospheric-research.com/stu dies/SEARCH/index.html.

NADP: Initiated in the late 1970s, the National Aci d Deposition Program (NADP) monitoring network began as a cooperative program between federal and state agencies, universities, electric utilities, a nd other industries to determine geographical patterns and trends in precipitation chemistry in the U.S. NADP collects and reports wet deposition measuremen ts as weekly average data (NADP, 2002). The network is now known as NADP/NTN (National Trends N etwork) and measures sulfate, nitrate, hydrogen ion (measure of acidity), ammonia, chlorid e, and base cations (calcium, magnesium, potassium). Detailed information regarding the NADP /NTN monitoring network can be found at http://nadp.sws.uiuc.edu/.14



## 5 Illustrative example of near-field single source i mpacts on O 3 

## and

PM2.5

Photochemical grid models have been used with a var iety of approaches to isolate secondary pollutant impacts from specific sources, including brute-forc e sensitivity (Baker and Kelly, 2014; Bergin et al.

, 2008; Kelly et al., 2015), direct decoupled method (DDM) (Baker and Kelly, 2014; Kelly et al., 2015), and source apportionment (Baker and Foley, 2011; Baker and Kelly, 2014). Here, downwind impacts of ozone and PM2.5 are described for hypothetical single sou rces placed in the Atlanta and Detroit metropolitan areas. These illustrative hypothetical single sourc e impacts are based on Community Multiscale Air Quality (CMAQ) model simulations done for two diffe rent urban areas using 4 km sized grid cells. Impacts are estimated on ozone and PM2.5 from a hyp othetical emissions source of SO 2 , NO X , and VOC using the brute-force sensitivity approach. The app roach taken for this assessment is not intended to be a prescriptive approach recommended by this process or EPA.



### 5.1 Model application approach for estimating these illustr ative example single source
impacts

The Community Multiscale Air Quality Model (CMAQ) v ersion 5.0.1 ( www.cmaq-model.org ) was applied for the entire year of 2007 to estimate PM2.5 and o zone. Aerosol chemistry is based on the AERO6 option that includes ISORROPIAII inorganic partitio ning and chemistry (Fountoukis and Nenes, 2007), aqueous phase chemistry that includes sulfur and me thylgyoxal oxidation (Sarwar et al., 2013), and organic aerosol partitioning (Carlton et al., 2010) . Gas phase chemistry is represented with the Carbo nBond 05 gas phase chemical mechanism with toluene u pdates (Sarwar et al., 2011).

Two separate model domains were used covering the D etroit and Atlanta metropolitan areas with 4 km sized grid cells (Figure 5-1; left panels). The ver tical domain extends to 50 mb using 25 layers (surf ace layer height is ~20 m) with most resolution in the boundary layer to capture important diurnal variati on in mixing height. The Weather Research and Forecast ing model (WRF), Advanced Research WRF core (ARW) model version 3.3 (Skamarock et al., 2008) wa s applied with a horizontal grid resolution of 4 km and 35 vertical layers. Additional details regardin g photochemical and meteorological model applicatio n and evaluation are provided elsewhere (U.S. Environ mental Protection Agency, 2013a, b).

Anthropogenic and biogenic emissions are processed for CMAQ input using the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system ( http://www.cmascenter.org/smoke/ ). Stationary point and
area sources are based on version 2 of the 2008 Nat ional Emissions Inventory (NEI) (U.S. Environmental Protection Agency, 2012b). Mobile source emissions are day specific for the model simulation period
based on data submitted to the National Emission In ventory and generated with SMOKE-MOVES (http://cmascenter.org/smoke/). Hourly WRF estimate d solar radiation and temperature are input to the Biogenic Emission Inventory System (BEIS) versi on 3.14 to generate emissions estimates of speciate d VOC and nitric oxide (Carlton and Baker, 2011). Hou rly boundary inflow from a coarser domain covering the continental United States with 12 km sized grid cells account for emissions from sources outside t he
4 km model domain.

In addition to the full set of anthropogenic and bi ogenic emissions for this model domain, new hypothetical emissions sources are added to illustr ate single source impacts on the Atlanta and Detroi t areas. VOC (Table 5-1) and NO X (90% NO and 10% NO 2 ) speciation are based on average speciation15

profiles for non-EGU point sources in these areas. The location of the fictitious sources are shown in Figure 5-1. The same set of stack parameters were u sed for each fictitious source location: stack heig ht 19 m, stack diameter 1.2 m, exit temperature 424 K, and exit velocity 14 m/s. Separate photochemical model simulations were done for each hypothetical s ource location and precursor emission rate: 100 tpy of VOC, 100 tpy of NO X , 100 tpy of SO 2 , 300 tpy of VOC, 300 tpy of NO X and 300 tpy of SO 2 . This resulted in a total of 6 simulations for 2 different hypothe tical source locations or 12 total simulations with hypothetical sources in addition to 2 baseline simu lations (1 for each area without any hypothetical source).

Table 5-1. Hypothetical source VOC speciation



### 5.2 Illustrative single source impact results for O 3 

### and PM2.5

Annual maximum 24-hr average PM2.5 sulfate ion impa cts are shown spatially in Figure 5-1 and by distance from the source in Figure 5-2 for hypothet ical sources emitting 100 and 300 tpy of SO 2 in the Detroit and Atlanta areas. Impacts are generally hi ghest nearest the source and decrease as distance from the source increases. However, visual examinat ion of the spatial extent of maximum concentrations for each source shows that peak seco ndary impacts are not always coincident with the
location of the emissions release. Occasional incre ases in downwind secondary impacts are generally related to single source precursor emissions reachi ng an area of increased oxidant availability, neutralizing agents, differences in terrain, mixing layer, or some combination of these influences.

The magnitudes and patterns of downwind impacts var y between these two areas and even within areas. This is likely due to differences in terrain features, available oxidants, and neutralizing che micals such as ammonia. Different magnitudes and spatial p atterns between these different source locations are more evident when looking at maximum 24-hr impa cts compared to annual average. Maximum impacts tend to be dominated by conducive meteorolo gy and nearby chemical environment on fewer days compared to the longer term average impacts, w hich is expected.



### Carbon Bond Specie Fraction Carbon Bond Specie Fraction

ALD2 0.0152 MEOH 0.0054

ALDX 0.0155 NVOL 0.0008

ETH 0.0324 OLE 0.1143

ETHA 0.0094 PAR 0.4057

ETOH 0.0090 TERP 0.0170

FORM 0.0757 TOL 0.1148

IOLE 0.0088 UNR 0.1080

ISOP 0.0007 XYL 0.067416

Figure 5

- 1. Annual maximum 24

- hr PM2.5 sulfate ion impacts from a hypothetical source emitting 100 and 300 tpy of SO 2 at a location in Atlanta (top row) and Detroit (bo ttom row).

Figure 5

- 2. Maximum 24

- hr PM2.5 sulfate ion impacts from a hypothetical source emitting 100 and 300 tpy of SO 2 at a location in Atlanta and Detroit by distance f rom the source. Results for Atlanta are shown on the top row and Detroit the bottom row.17

Daily maximum 8-hr O 3 impacts over all days in the ozone season are show n as a function of distance from the hypothetical source emitting NO X in Figure 5-3 and spatially in Figure 5-4. Similar information is provided in Figures 5-5 and 5-6 for hypothetical so urce emissions of VOC. Ozone impacts tend to be highest in grid cells adjacent to the hypothetical source and contributions generally decrease as dist ance from the source increases. Emissions of nitrogen ox ides are concentrated enough in the 4 km grid cell containing the source that titration often dominate s over production in that grid cell. For these particular urban areas and VOC emission mixtures, t he hypothetical sources emitting NO X emissions
tended to form more O 3 than when emitting similar amounts of VOC emission s. Ozone impacts from the hypothetical source placed in Detroit shows notable impacts over Lake Erie. This is more prominent for
the scenario of VOC emissions rather than NO X emissions. It is possible that VOC from this hypot hetical
source encounters a favorable combination of availa ble NO X emissions from commercial marine sources and low boundary layer heights resulting in larger downwind O 3 contribution than in other directions from the hypothetical source.

Figure 5

- 3 . Distribution of ozone season highest daily 8

- hr maximum O 3 by distance fr om the hypothetical source NO X emissions. Results for Atlanta are shown on the to p row and Detroit the
bottom row.18

Figure 5

- 4 . Spatial plot of ozone season highest daily 8

- hr maximum O 3 from the hypothetical source

NO X emissions.

Figure 5

- 5

. Distribution of ozone season highest daily 8

- hr maximum O 3 by distance from the hypothetical source VOC emissions. 

### Results for Atlanta on top row and Detroit the bot tom row.19

Figure 5

- 6

. Spatial plot of ozone season highest daily 8

- hr maximum O 3 from the hypothetical source VOC emissions.



## 6 Review of published single source impacts on O 3 

## and PM2.5

Published literature between 2005 and 2015 relating single source emissions with downwind O 3 and secondary PM2.5 impacts has been collected and summ arized. This information reflects currently understood relationships between precursors and sec ondary pollutants and provides context for future model applications. The assessments reviewed here m ay not reflect every study done between 2005 and 2015. The primary criteria for inclusion in thi s report is a published study that presents both th e emissions rate of a specific source and downwind im pacts on O 3 or secondary PM2.5 from that same source. While the published studies provide useful information, additional assessments of single sourc e impacts on secondary pollutants are still needed to provide a more comprehensive assessment of different source types and source environments.



### 6.1 Single source ozone impacts

Figure 6-1 presents the results from studies that q uantified the 1 hr average maximum downwind O 3 from a single source NO X emissions perturbation using photochemical grid mo dels. Figure 6-2 shows the results from studies that quantified single source downwind 8 hr average O 3 impacts. For the studies20

estimating downwind 1-hr O 3 contributions, there appears to be no clear patter n between NO X emissions and maximum downwind O 3 . A relationship is not evident even if results fro m studies with coarser model resolution (24 km, 36 km; N=2) are re moved. However, Figure 6-2 shows a clearer relationship between NO X emissions and 8-hr O 3 . This may be due to the longer averaging time smoothing out some of the conditions that cause hig h variability in the 1 hr results. Another explanat ion for the notable relationship between emissions and secondary impacts for 8-hr O 3 is that most of the elements in Figure 6-2 are from a single publicatio n that presented multiple source-impact relationshi ps, meaning the consistent relationships are likely due to consistent model application and post-processin g for each modeled source. Overall, peak 8-hr average O 3 impacts reported in literature range up to 33 ppb, which is a reported impact modeled from a larg e NO X source (~14,000 tpy) over multiple high ozone episodes in the Louisiana/eastern Texas area (ENVIRON, 2005) using 4 km grid resolution. The results for single source VOC releases show very hi gh variability in maximum downwind O 3 impacts due to large differences in ozone forming potential and OH reactivity of the VOC released.

Figure 6

- 1. Published single source NO X emissions impacts on downwind hourly O 3 estimated using photochemical grid modeling.

Publications reporting results from observational f ield studies provide additional context to the mode l based studies for ozone production from point sourc e emissions (Luria et al., 2003; Ryerson et al., 20 01; Springston et al., 2005; Zhou et al., 2012). In thes e publications, in-plume observations related to specific sources were collected with aircraft and t he sources examined were either power plants or petrochemical facilities. Many of these studies foc used on quantifying ozone production efficiency fro m precursor emissions and typically did not provide k ey information to discern a relationship between
source emissions and downwind O 3 impacts: a well-defined delta-O 3 (in plume O 3 minus an estimate of ambient background O 3 ) and facility precursor emissions. In some instanc es the study did not isolate impacts from a single facility plume but looked at some aggregate of nearby sources.21

Figure 6

- 2. Published single so urce NO X emissions impacts on downwind 8

- hr average O 3 estimated using photochemical grid modeling.

One observation based study analyzed data from a fi eld campaign that sampled plumes via helicopter downwind of the Tennessee Valley Authority Cumberla nd power plant on 4 summer days in 1998 and 1999 (Luria et al., 2003). These four days represen ted different NO X emissions rates and meteorology. Three of the four days were conducive to ozone prod uction, with one day having cooler temperatures where no positive delta-O 3 was observed at the farthest measured distance dow nwind. The three days with positive delta-O 3 reported the following maximum observed hourly del ta-O 3 and corresponding

NO X emissions: 36 ppb O 3 (140,643 TPY NO X ), 39 ppb O 3 (71,697 TPY NO X ), and 24 ppb O 3 (115,681 TPY

NO X ). While these NO X emissions rates are generally higher than those of most of the modeling studies reported in Figure 6-1 (1-hr O 3 ), the measured change in O 3 appears to be in reasonable agreement with modeled estimates compiled from studies published i n literature.



### 6.2 Single source PM impacts

A number of studies examine the effect of regional scale emissions on PM2.5 concentrations (Simon et al., 2012). Fewer studies have attempted to quantif y the effects of single sources on downwind PM2.5 concentrations, and fewer still that report estimat ed secondary PM2.5 enhancements from these single sources (Baker and Foley, 2011; Baker and Kelly, 20 14; ENVIRON, 2012a; National Association of Clean Air Agencies, 2011). Synthesizing the results of th ese limited studies is difficult due to differences in modeling (e.g., averaging timescales, PM2.5 species reported, etc), geographic area, emission profile and rates, stack height, near-source and regional N H 3 availability, and meteorology. Here, we summarize four studies which provide sufficient information o n precursor emissions and secondary PM2.5 enhancements from single sources.22

The NACAA Workgroup Final Report (National Associat ion of Clean Air Agencies, 2011) provides a test case where PM2.5 impacts from different sources are assessed. The Minnesota case study presents predicted PM2.5 ammonium sulfate and ammonium nitra te from a full photochemical grid model simulation (CAMx) applied at 12 km grid resolution with sub-grid plume treatment and 200 m sub-grid plume sampling (National Association of Clean Air A gencies, 2011). Four individual stacks over Minnesota were chosen to reflect varying emission s cenarios and background conditions. Emissions ranged from 400 to 13,000 tpy NO X and 500 to 15,000 tpy SO 2 , each with varying stack height. In these case study simulations all precursor emissions are co-emitted by each source (National Association of Clean Air Agencies, 2011). Over all cases, 24-hour maximum 98th percentile concentrations of secondary formation PM2.5 accounted for up to 1 μg/m 3 . These sources had stack release points well above ground level and had surface level peak impacts typ ically within 10 km of the source.

One published approach (Baker and Foley, 2011) use Particulate Matter Source Apportionment Technology (PSAT) applied with CAMx using 12 km gri d resolution to estimate annual average secondarily formed PM2.5 sulfate and nitrate from p recursor emissions. The study selects facilities representing the top 5% emitters of primary PM2.5, NO X , and SO X from all stationary point sources located east of -97 longitude over midwest, mid-Atl antic, and southeast modeling domains (NO X >7000 tpy, SO X >20,000 tpy, and PM2.5 > 1100 tpy based on 2005 in ventory). This study shows maximum annual average PM2.5 values of 0.385 μg/m 3 and 0.018 μg/m 3 for PM2.5 sulfate ion and PM2.5 nitrate ion respectively. Impacts were typically highest ne arest the source and decrease as distance from the source increases (Baker and Foley, 2011).

Secondary PM2.5 was estimated for the Hunter EGU em itting 18,800 tpy of NO X and 7,300 tpy of SO 2 in eastern Utah (ENVIRON, 2012). The CAMx model was ap plied with PSAT at 12 km resolution for an entire year. Predicted 24-hour (annual) maximum val ues were 3.44 μg/m 3 (0.11 μg/m 3 ) and 0.53 μg/m 3 (0.05 μg/m 3 ) of PM2.5 nitrate ion and PM2.5 sulfate ion respec tively. Again, the highest secondary PM2.5 impacts tended to be closest to the source (E NVIRON, 2012).

A case-study examined the impact of a single EGU on downwind O 3 and PM2.5 sulfate ion using several photochemical grid model based source impact assess ment approaches applied with the CMAQ model: brute force emission adjustments, DDM, and PSAT (Ba ker and Kelly, 2014). This case study examines emissions from the TVA Cumberland facility (48,000 tpy NO X , 8,300 tpy SO 2 ) during a high pollution episode focusing on July 6, 1999. Maximum enhanceme nts range up to 1.5 μg/m 3 of PM2.5 sulfate ion. The different approaches for estimating single sour ce impacts generally have similar spatial patterns.



## 7 Reduced form approaches for estimating single sourc e secondary
impacts

Predicting downwind secondary pollutant concentrati ons from point source emissions necessitates accounting for the interaction of the plume with am bient levels of oxidants, neutralizing agents, meteorology and potential nonlinear chemistry. Stat e of the science approaches involve employing photochemical transport models like CMAQ and CAMx. There have been recent efforts to develop
reduced form models that maintain the state of the science approach at their base, but provide a screening tool that is faster and cheaper to use. S creening tools that can provide rapid impact estima tes for a wide range of emission source types (e.g. dif ferent emission loadings, stack heights, geographic locations) and can indicate whether more sophistica ted approaches may provide credible information about the relationship between precursors and secon dary impacts in some situations. Ideally, screening23

level information would be an approximation of seco ndary impacts based on photochemical modeling of the single source, to take advantage of the state o f the science treatment of multi-phase chemistry an d deposition. This includes a realistic chemical and physical environment for single source impacts (Bak er and Kelly, 2014).



### 7.1 Review of reduced form approaches for estimating O 3 

### single source impacts

In reviewing the available literature for this repo rt, only one example of a reduced form O 3 modeling approach that incorporates a state of the science c hemical transport model at its foundation has been identified. This approach was developed for the New South Wales (NSW) Greater Metropolitan Region in Australia (Yarwood et al., 2011). Briefly descri bed, the Australian NSW approach involves a photochemical modeling analysis performed for sever al ozone seasons that includes hypothetical new emissions sources tracked using the higher order De coupled Direct Method (HDDM) (Dunker et al., 2002) to calculate sensitivity coefficients for O 3 to the additional of NO X and VOC emissions from the new hypothetical sources. The resulting O 3 sensitivity coefficients then allow O 3 impacts to be estimated
for other NO X and/or VOC sources within the same metropolitan ar ea (Yarwood et al., 2011). While new sources or sources making modifications may not mat ch the exact location, stack release characteristic s, or emissions rates of the hypothetical sources in t hat analysis, options exist to pick the modeled sou rce that best matches the new or modified source or eve n choose the highest impact from any of the modeled sources as a conservative approach.



### 7.2 Review of reduced form approaches for estimating PM 2.5 single source impacts

Approaches for estimating secondary PM2.5 from sing le source emissions range from simple screening techniques to Gaussian based dispersion models and grid-based Eulerian photochemical transport models. The National Association of Clean Air Agenc ies recommended a multi-tiered approach for conducting air quality analysis of secondary PM2.5 ranging from initial screening tools to more refine d analysis using complex photochemical modeling depen ding on the size and location of the source (National Association of Clean Air Agencies, 2011).

Screening techniques for secondary PM2.5 include th e emissions divided by distance metric (Q/D) and the 100% conversion assumption. For the Q/D metric, allowable total emissions (Q) in tons per year are divided by distance to key receptors (D). This appr oach allows for a relative comparison of a variety of sources, but does not directly relate emission to c oncentrations for comparison to regulatory impact levels nor does it account for the variability in s econdary PM2.5 formation. Under the 100% conversion assumption, NO X and SO 2 emissions are assumed to convert entirely to ammon ium nitrate and ammonium sulfate. A modeling study conducted with m ultiple case studies found that the 100% conversion approach was overly conservative, yieldi ng physically unrealistic estimates with (National Association of Clean Air Agencies, 2011).

One approach (Baker and Foley, 2011) developed a no nlinear regression model relating large single source emissions over the eastern United States (N= 99) to predicted annual average primary and secondary formed PM2.5 sulfate and nitrate as a fun ction of emissions and distance. Individual regression models are developed for primary PM2.5, PM2.5 sulfate ion, PM2.5 nitrate ion under favorable conditions, and PM2.5 nitrate ion under u nfavorable conditions (Baker and Foley, 2011). This approach, a more physically realistic improvement t o the Q/D approach, could be used to develop shorter-timescale (e.g., 24-hr), region and season specific regression models of PM2.5.This nonlinear24

regression approach has been applied to facilities in other countries to provide information about the downwind impacts of large point sources (Myllyvirta , 2014).

Inter-pollutant off-set ratios have been used to es timate secondary PM2.5 formation from single source

SO 2 and NO X emissions. In the case study of 4 EGUs in Minnesot a described above, (National Association of Clean Air Agencies, 2011) estimate primary PM2.5 concentrations using AERMOD, then secondary
concentrations were either added from CAMx results or using standard SO 2 and NO X to primary PM2.5 offset ratios in EPA’s New Source Review implementa tion rule for PM2.5 (73 FR 28321). The report concludes standard offset ratios lead to an over pr ediction of secondary PM2.5 and suggest a downward revision of offset ratios for the region. The repor t suggests new studies could be conducted to develo p region, and season-specific offsets for use in the regulatory permitting context.

The photochemical grid model CAMx was used to exami ne the effects of distance, season, grid resolution, emission rate and stack height on predi cted off-set ratios for the Port Washington coal-fi red power plant in Georgia (Boylan and Kim, 2012). The study suggests maximum ratios for this facility are found near the source and vary most strongly with s eason and distance from the source. More recent work (Boylan and Kim, 2014) expanded on that study to investigate the spatial variability in seasonal offset ratios by modeling Port Washington emissions at 8 different locations in Georgia. Off-set ratio s varied by up to a factor of 7 as a function of seas on or location, illustrating the sensitivity of sec ondary PM2.5 formation on meteorology and the local chemic al environment.

Several of the studies reviewed presented promising techniques for developing regional scale reducedform models to predict the impact of single sources on secondary pollutants. These approaches ranged from region specific ratios to more advanced regres sion models. Generalizing these techniques for area s not included in the original assessment is a diffic ult challenge but will be desired by many in the regulating and regulated communities.



## 8 Acknowledgements

The document includes contribution from Kirk Baker, Bob Kotchenruther, Rynda Kay, Bret Anderson, and Andy Hawkins. This document was reviewed by the IWA QM3-NFI group.



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