1 

# Interagency Workgroup on Air Quality Modeling Phase 3 Summary Report: Long Range Transport and Air Quality Related
Values2 

### EPA-454/P-15-003 July 2015

Interagency Workgroup on Air Quality Modeling Phase 3 Summary Report: Long Range Transport and Air Quality Related Values

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 focus 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, 2012b). This repor t provides information and recommendations from the “Phase 3” effort focused on long-range transpor t of primary and secondary pollutants. The idea of applying photochemical grid models for these purpos es is explored in more detail in response to a growing community interest in using these types of models for estimating single source secondary pollutant impacts over long distances.

This document describes chemical and physical proce sses important to the formation of ground-level O 3 , PM2.5, visibility, and deposition in the context of modeled long range transport assessments for permi t review programs. Chemical transport models that cha racterize these processes include both Lagrangian which typically only have a single source included in the model and photochemical grid models which include some representation of all anthropogenic, b iogenic, and geogenic sources. Modeling systems appropriate for the purposes of estimating long-ran ge transported single source 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 i mportant to ensure that a particular system is fit for the purpose of estimating long-range single source seco ndary impacts. One aspect of this type of evaluatio n for long-range transport assessments would be demon strating model skill in meteorological processes important for long distant transport by replicating appropriate mesoscale tracer release experiments. In addition to establishing whether a modeling system is generally appropriate for this purpose, project specific evaluations that compare model estimated m eteorology and chemical estimates with measurements near the project source and key recept ors is also an important model evaluation
component.

Regulatory context for estimating long-range transp ort of visibility and deposition is provided to pre sent the range of purposes for single source impact asse ssments. In the case of visibility, single source i mpact assessment approaches are compared within the Regio nal Haze Rule context, Prevention of Significant Deterioration, and National Environmental Policy Ac t to better illustrate the similarities in these
demonstrations and note where differences should be expected.4



## Table of Contents

1 BACKGROUND: IWAQM Phase 3 process overview ......

................................................... ..................... 5

2 REGULATORY MOTIVATION ...........................

................................................... .................................... 5

2.1 Regional Haze Rule Visibility Impairment Modeli ng: Reasonable Progress Goals (RPG) ............. 6

2.2 Regional Haze Rule Visibility Impairment Modeli ng: BART program .................................. ......... 7

2.3 Differences between single source assessments f or BART and RPG ................................... ......... 7

2.4 Prevention of Significant Deterioration .......

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

2.5 National Environmental Policy Act – Visibility Assessments ....................................... ............... 11

2.6 National Environmental Policy Act – Sulfur and Nitrogen Deposition Assessments .................. 12

2.7 National Environmental Policy Act – Acid Neutra lizing Capacity.................................... ............ 12

3 MODEL SELECTION .................................

................................................... .......................................... 13

3.1 Secondary Pollutant Formation: O 3 and PM2.5 ........................................ .................................. 13

3.2 Visibility and Deposition .....................

................................................... ..................................... 13

3.3 Air Quality Models for Secondary Pollutants ...

................................................... ........................ 14

3.4 Recommendations ...............................

................................................... .................................... 15

4 MODEL EVALUATION ................................

................................................... ....................................... 16

4.1 Long Range Transport Models – Fit for Purpose E valuations ........................................ ............. 17

4.2 Long Range Transport Models – Meteorology Evalu ation ............................................. ............ 18

4.3 Long Range Transport Models – Chemistry Evaluat ion ............................................... ............... 18

4.4 Model performance evaluation data sources .....

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

5 ACKNOWLEDGEMENTS ................................

................................................... .................................... 20

6 REFERENCES ......................................

................................................... ............................................... 205



## 1 BACKGROUND: IWAQM Phase 3 process overview

The Interagency Workgroup on Air Quality Modeling ( IWAQM) was originally formed in 1991 to provide a focus 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 interested 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 that could be presented to man agement for support and commitment. The IWAQM process largely concluded in 1998 with the publicat ion of the Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report and Recomme ndations for Modeling Long Range Transport Impacts (EPA-454/R-98-019) (U.S. Environm ental Protection Agency, 1998). The IWAQM Phase 2 report provided a series of recommendations concerning the application of the CALPUFF model for use in long range transport (LRT) modeling and informed the promulgation of that model for such regulatory purposes in 2003. Draft updates to the I WAQM Phase 2 report were released in 2009 to better reflect the state-of-the-practice of long ra nge transport modeling techniques 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, 2012b). 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 stakeholders to further model develop ment 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 for each.

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 air quality related values. IWAQM3 long-range transport team members include Rick Gilliam (US EPA), Kirk Baker (US EPA), Michael Feld man (US EPA), Gail Tonnesen (US EPA), Chris Owen (US EPA), Bret Anderson (USFS), Tim Allen (US FWS), John Notar (NPS), John Vimont (NPS), and Craig Nicholls (BLM). Additional participation was provid ed by Erik Snyder (US EPA), Rebecca Matichuk (US EPA), and Robert Elleman (US EPA).



## 2 REGULATORY MOTIVATION

Sections 165, 169A, and 169B of the Clean Air Act s ets visibility goals for Class I areas. The 1999 Re gional Haze Rule expands on Section 169 of the Clean Air A ct and “Phase I” of the Visibility Protection Progr am. The Regional Haze Rule (RHR) has multiple provision s that may be supported by air quality modeling. The reasonable progress and BART determination comp onents of the RHR and modeling requirements for the first planning period and future planning p eriods are shown in Table 1. Additional sections6

contain requirements of other programs including Pr evention of Significant Deterioration (PSD) and National Environmental Policy Act (NEPA) for visibi lity modeling.

Table 1. Modeling requirements for multisource and single source assessments to support various regulatory programs. **Program Multisource** 

**Assessment Requirement**  **Single Source**

**Assessment Requirement**  Regional Haze Rule Reasonable Progress Yes No Regional Haze Rule BART No Yes (initial planning cycle only)

PSD No Yes

NEPA Yes (method 2) Yes (method 1)



### 2.1 Regional Haze Rule Visibility Impairment Modeli ng: Reasonable Progress Goals (RPG)

Modeling may be used to assess Reasonable Progress by projecting future year visibility impairment at Class I areas due to all emissions sources. Project ed visibility is compared to the Uniform Rate of Progress, which is a linear interpolation between r ecent air quality measurements and the 2064 “natural” visibility goal for each Class I area (U. S. Environmental Protection Agency, 2005b). Single source modeling is not a requirement in setting rea sonable progress goals. However, single source modeling can be used to evaluate visibility impacts or benefits from emissions sources or emissions
controls to inform decisions on emission reduction measures that may be necessary to meet long-term strategy requirements toward meeting the goal of na tural visibility conditions and thus supporting a
demonstration of the reasonableness of the reasonab le progress goal.

A modeling system that treats emissions from all kn own anthropogenic and biogenic emissions sources with realistic chemical and physical transformation s should be utilized to estimate future visibility conditions at a Class I area. The most appropriate tool that contains these qualities is a photochemic al grid model. Commonly applied photochemical grid mod els for estimating visibility include the Comprehensive Air-Quality Model with Extensions (CA Mx) and the Community Multiscale Air Quality Model (CMAQ). EPA has issued SIP modeling guidance for Regional Haze (U.S. Environmental Protection Agency, 2014b) in which an approach for assessing f uture year visibility impacts with photochemical gr id models has been established and applied by States f or their initial RH SIP demonstrations for 2018. Th is same type of photochemical model based assessment w ill need to be done for upcoming SIP demonstrations for subsequent planning periods (e.g . 2028, 2038, etc.) to determine if Class I areas w ill be on the glidepath to “natural” conditions.

The estimates of “natural” conditions are criticall y important for the estimation of the uniform rate of progress. However, any future updates to the calcul ation of “natural” conditions will not substantivel y change the nature of the air quality model based as sessments of projected visibility impairment. However, changes to the metrics (e.g. 20% worst day s) used for demonstrating progress will provide for more influential changes to projected visibility im provements.7



### 2.2 Regional Haze Rule Visibility Impairment Modeli ng: BART program

Dispersion modeling was recommended by US EPA to su pport decisions about which BART eligible sources “cause or contribute” to visibility impairm ent and may need enforceable emissions limits and/o r emissions controls (U.S. Environmental Protection A gency, 2005b). If a BART eligible source “contributes” to visibility impairment then additio nal modeling was done to assess the improvement in visibility due to source control measures. BART-eli gible sources meet specific criteria for source category, date of operation or existence, and poten tial to emit (see 70 FR 39158–39161; July 6, 2005). BART is a program that only applies to the 1 st regional haze planning period (the planning period ending 2018).

Single-source air quality modeling for BART assessm ents was typically done using the CALPUFF modeling system and daily maximum emission rates. However, i t is important to note that other Lagrangian models or photochemical grid modeling systems can b e used to isolate the primary and secondary impacts of single sources and thus be used for sing le source visibility assessments (Baker and Foley, 2011; Baker and Kelly, 2014; ENVIRON, 2012a, c; Zhou et al., 2012). Photochemical grid models have been used to support regulatory single source visib ility impact assessments (U.S. Environmental Protection Agency, 2014a).

The daily visibility metric for each Class I area i s expressed as the change in deciviews compared to natural visibility conditions (U.S. Environmental P rotection Agency, 1998). Natural visibility conditi ons are found in Appendix B of EPA’s Guidance for Estim ating Natural Visibility Conditions under the Regional Haze Rule (U.S. Environmental Protection A gency, 2003). The daily average visibility degradation beyond natural conditions expressed in deciviews is kept for each Class I area and ranked over the length of the modeling simulation. A thres hold expressed in deciviews was commonly employed to determine whether a BART-eligible sourc e “contributes” or “causes” visibility impairment as suggested in U.S. EPA guidance (U.S. Environment al Protection Agency, 2005b). BART assessments consider an estimate of maximum impacts over all mo deled days, not the 20% best or 20% worst days which are considered for the reasonable progress as sessment.

The air quality impacts of BART controls are someti mes estimated in aggregate rather than on a sourceby-source basis. A cumulative “BART alternative” or “Better than BART” analysis can also be completed to examine the visibility benefits of alternative s tate and/or Federal controls programs that may prov ide more reasonable progress benefits compared to BART (U.S. Environmental Protection Agency, 2012a). Most “Better than BART” analyses to date have used photochemical modeling to examine the regional visibility benefits of NO X , SO 2 , and primary PM2.5 emissions reductions from BART controls and BART alternatives. These “Better than BART” analyses hav e focused on changes in visibility due to emissions changes on the 20% best and 20% worst visibility da ys.



### 2.3 Differences between single source assessments f or BART and RPG

Fundamental differences in required approaches for evaluating source impacts for the purposes of BART determinations and RPG along with inherent differen ces in the models used for these purposes make directly comparing results for specific sources imp ossible. Single-source air quality modeling for BAR T assessments was typically done using the CALPUFF mo deling system (69 FR 25,193-194), using maximum8

emission rates (24-hr maximum emission rates during the baseline period) and consideration of the maximum visibility impact from the source. On the o ther hand, due to the need to quantify the future year visibility impairment from all types of emissi ons sources on the 20% best and 20% worst days, reasonable progress assessments require the use of photochemical grid models. Photochemical grid
models include all emissions sources and have reali stic representation of formation, transport, and removal processes of particulate matter less than 2 .5 microns that causes visibility degradation. Similarly, single source modeling for the purpose o f evaluating visibility impacts or benefits from
emissions sources or emissions controls for reasona ble progress and long-term strategy development may utilize photochemical grid models to estimate p otential visibility benefits from controls on futur e year visibility conditions.

Modeling for the purpose of establishing a reasonab le progress goal (RPG) differs from the single sour ce BART determination modeling for a variety of reason s. BART determinations are intended to provide information about current year impacts from a singl e facility at Class I areas to supplement other
relevant emissions control information. Since BART controls need to be assessed against “worst case” emissions from specific sources, these model assess ments are done using maximum 24-hr average
emissions rates. The 8 th highest model estimate of facility impacts at Clas s I areas from each year modeled are averaged and compared to that Class I a rea’s natural conditions to provide an estimate of a “worst case” scenario (70 FR 39124). The highest mo deled impacts are not typically compared directly t o visibility thresholds, recognizing some uncertainty exists in the modeling system and abnormal meteorology may result in an unusually high source contribution. In contrast, RPG assessments and single-source assessments for the purposes of reaso nable progress and long-term strategy development use actual emission rates to provide a realistic es timate of current and future year visibility impact s on the 20% best and 20% worst days at a Class I area.

Given differences in emissions and modeled impacts for these different assessment approaches, visibility impacts will be lower using the RPG appr oach compared to a BART assessment. BART determinations are current year “worst case” single source impact scenarios and RPG assessments are intended to provide realistic projections of future visibility. RPG necessitates using actual emission s rather than maximum 24-hour average emissions. In a ddition, RPG assessments average impacts over the 20% worst days rather that selecting the 8 th highest facility impact in a given year. RPG impac ts are examined relative to the projected future year 20% worst days visibility estimate, while BART impacts are maximum source impacts compared to background n atural conditions irrespective of the relationship to the 20% worst days.

Finally, single source impacts estimated for RPG an d BART will be different due to fundamental differences between photochemical grid models and p uff dispersion models such as CALPUFF. Photochemical grid models include all emissions sou rces and provide a dynamic and realistic chemical and physical environment to estimate source emissio n impacts. The CALPUFF model uses fixed uniform concentrations of important oxidants such as ozone and neutralizing agents such as ammonia and does not perform key thermodynamic transformations that strongly influence atmospheric residence time and thus transport (Karamchandani et al., 2009; Kar amchandani et al., 2008). CALPUFF’s representation of these important chemical species and PM2.5 chemi stry will result in different estimated source impacts than a photochemical grid model even if the exact same source emissions and release characteristics are used in both modeling systems. Additionally, Lagrangian puff models such as CALPUFF allow the project source full access to oxi dants (e.g. ozone) and neutralizing agents (e.g. ammonia) while the same source in a photochemical m odel competes for oxidants and neutralizing agents which may result in different and possibly l ower impacts.9

In sum, the differences in the types of models, the inputs to the models, and how the models and model
results are used means that the results from a BART determination or similar modeling using CALPUFF cannot be directly compared to estimated impacts of emissions controls from a single source on a reasonable progress goal. If recommended procedures change for either BART determination impact assessments or reasonable progress goal impact asse ssments the comparability between approaches would also change. Photochemical grid models could be applied to estimate single source impacts and post-processed in a manner consistent with requirem ents for a BART-like assessment but Lagrangian puff models are not ideal for reasonable progress d emonstrations since they typically characterize one or a small group of sources.



### 2.4 Prevention of Significant Deterioration

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 ((k)(1)(i)) or maximum allowable increases over a baseline concentration ((k)(1)(ii)). Additionally, 40 CFR pa rts 52.27, subsection (d)(1), requires that the per mit reviewing authority must provide to all affected FL M’s written notification for any permit application which may affect visibility in any Federal Class I area. Notification must include a proposed source’s anticipated impact on visibility on any Federal Cla ss I area. The requirements of PSD potentially requ ire
the use of LRT models for both the maximum allowabl e increases (increments) and for air quality relate d values (AQRV’s) including visibility. Unique to thi s is the authorities under which each of these elem ents of air quality analyses is administered. The releva nt permitting authority administers the NAAQS and increments component of the air quality analysis, w hile the Federal Land Manager is responsible for recommending models and analytical procedures for t he air quality related values analysis (see 40 CFR part 51, Appendix W, subsection 6.1(b)).

Single source impacts are typically compared to sig nificant impact levels (SILs) and increments. For t he purposes of long range transport it is expected bas ed on an analysis of multiple hypothetical plants t hat

O 3 and secondary PM2.5 impacts would typically be bel ow any significance threshold beyond 50 km (U.S. Environmental Protection Agency, 2015a). Anal ysis for primarily emitted pollutants indicates tha t in most situations significance thresholds are not exceeded beyond 50 km (U.S. Environmental Protection Agency, 2015b). Long-range transport ass essments may be necessary in certain limited situations for PSD increment. In these situations, a screening approach could be used that relies upon the near-field application of the appropriate scree ning and/or preferred model to determine the significance of ambient impact at or about 50 km fr om the new of modifying source. If this initial screening indicates there may be significant ambien t impacts at that distance, then further screening is

necessary.

Where a long range transport assessment is still ne eded for primary pollutant impacts a Lagrangian (e. g. CALPUFF without chemistry) or photochemical grid mo deling system (e.g. CAMx, CMAQ) could be used to estimate those impacts. Typically, a Lagrangian model is the type of model appropriate to use for these screening assessments; however, applicants sh ould reach agreed upon approaches (models and modeling parameters) on a case-by-case basis in con sultation with the appropriate reviewing authority, Regional Office, and the affected Federal Land Mana ger(s) (FLM(s)). If a cumulative increment analysis is necessary, for these limited situations, the select ion and use of an alternative model shall occur in10

agreement with the appropriate reviewing authority (paragraph 3.0(b)) and approval by the EPA Regional Office based on the requirements of sectio n 3.2.2(e).

PSD analyses are also completed for NEPA air qualit y assessments using dispersion models or photochemical grid models. The typical PSD assessme nt description includes the following:

The PSD demonstrations are for information and comp arison purposes only and do not constitute a regulatory PSD increment consumption analysis. This PSD comparison analysis is used as an indicator of the relative change of air quality, wh ich is a useful metric for analyzing and comparing air quality impacts. The comparison is made to allo wable PSD increments for Class I and Class II areas for project-specific and cumulative impacts.

2.4.1 Visibility

Visibility impairment due to single sources may be assessed for the purposes of satisfying requirement s for other programs such as PSD. PSD ensures the pre servation of certain levels of air quality related values (AQRVs), including visibility, at designated Class I areas. Model assessments for AQRVs follow the Federal Land Managers’ Air Quality Related Values W ork Group (FLAG) Phase I Report (revised 2010) (U.S. Department of the Interior, 2010). Visibility in important natural areas (e.g., Federal Class I areas) is protected under a number of provisions of the Clean Air Act, including Sections 169A and 169B (addressing impacts primarily from existing sources ) and Section 165 (new source review).

Visibility regulations (40 CFR 51.300–309) require States to mitigate current and prevent future visib ility impairment in any of the 156 mandatory Federal Clas s I areas where visibility is considered an importa nt attribute. In 1999, EPA issued revisions to the reg ulations to address visibility impairment in the fo rm of regional haze, which is caused by numerous, diverse sources (e.g., stationary, mobile, and area source s) located across a broad region (40CFR 51.308–309). S ection 169A of the Act requires states to develop SIPs containing long-term strategies for remedying existing and preventing future visibility impairmen t in the 156 mandatory Class I Federal areas, where v isibility is considered an important attribute. In order to develop long-term strategies to address regional haze, many States will need to conduct regionalscale modeling of fine particulate concentrations a nd associated visibility impairment.

The FLAG visibility modeling recommendations are di vided into two distinct sections to address differe nt requirements for 1) near field modeling where plume s or layers are compared against a viewing background and 2) distant/multi-source modeling for plumes and aggregations of plumes that affect the general appearance of a scene. The recommendations separately address visibility assessments for sources proposing to locate relatively near and at farther distances from these areas (U.S. Department of the Interior, 2010).

2.4.2 Deposition

FLAG (2010) recommends that applicable sources asse ss impacts of nitrogen and sulfur deposition at Class I areas. This guidance recognizes the importa nce of establishing critical deposition loading val ues (“critical loads”) for each specific Class I area, as these critical loads are completely dependent on local atmospheric, aquatic and terrestrial conditions and chemistry. Critical load thresholds are essentiall y a level of atmospheric pollutant deposition below whi ch negative ecosystem effects are not likely to occur. FLAG (2010) does not include any critical lo ad levels for specific Class I areas and refers to site-11

specific critical load information on FLM websites for each area of concern. However, this guidance do es recommend the use of deposition analysis thresholds (DATs) developed by the National Park Service and the Fish and Wildlife Service. The DATs represent s creening level values for nitrogen and sulfur deposition. If the DAT is exceeded then the modelin g results are considered significant and further AQRV analysis is required. If a source exceeds the DAT level then a comparison to Class I specific cri tical load values is necessary (U.S. Department of the In terior, 2011). Project source annual total sulfur deposition and annual total nitrogen deposition are added to Class I area specific measured or estimated total sulfur and total nitrogen depositio n to determine whether the Class I area specific screening level or critical load value would be exc eeded.



### 2.5 National Environmental Policy Act – Visibility Assessments

NEPA air quality impact analyses assess potential a ir quality impacts that could occur from developmen t within the project area and from other documented r egional emissions sources within a defined study area. Visibility impairment due to project-specific sources and groups of sources are quantified and compared to applicable state and federal standards and thresholds for AQRV impacts (e.g., visibility) (U.S. Department of Agriculture et al., 2011). Two methodologies are typically used to process model results and evaluate visibility impacts.

The first methodology follows recommendations in th e Federal Land Managers’ Air Quality Related Values Work Group (FLAG) Phase I Report – Revised 2 010 (U.S. Department of the Interior, 2010). This method assesses project-specific visibility impacts at Class I and sensitive Class II areas by determi ning the incremental changes in light extinction relativ e to estimated natural background conditions and comparing the incremental changes to visibility thr esholds. The visibility evaluation metric used in t his analysis is the Haze Index, which is measured in de ciview (dv) and used in EPA’s Regional Haze Rule. T he change in visibility impacts of the proposed develo pment is obtained by calculating the differences between the Haze Index with added project concentra tions and the Haze Index based solely on background concentrations. Estimated visibility deg radation at the Class I and sensitive Class II area s is presented in terms of the number of days that excee d a threshold percent change in extinction, or deciview (dv), relative to natural background condi tions. The maximum and 98 th percentile incremental changes in Haze Index ( ∆ dv) at any receptor that intersects with the area o f interest are compared to 0.5 dv and 1.0 dv thresholds. A source whose 98th perce ntile value of the haze index is greater than 0.5 deciview (dv) (approximately a 5% change in light e xtinction) is considered to contribute to regional haze visibility impairment. Similarly, a source tha t exceeds 1.0 dv (approximately a 10% change in lig ht extinction) causes visibility impairment and corres ponds to a change in visibility impairment that is just perceptible to the human eye.

The second methodology examines the cumulative (all sources) visibility impacts at Class I and sensiti ve Class II areas (Silva and McCoy, 2012). The cumulat ive visibility assessments use the estimates of act ual emissions that could occur from the proposed develo pment and all sources within a defined study area. This approach consists of five steps, as follows:

**Step 1:** Calculate the average baseline visibility for each Class I and sensitive Class II area based on five years of monitoring data for the 20 percent be st and 20 percent worst days. **Step 2:** Estimate site-specific relative response factors (R RFs) for each visibility component (as specified in the new IMPROVE equation) based on the future-year and base-year modeling
results. Note that the RRF is defined as the ratio of the future-year to base-year simulated12 concentration in the vicinity of a monitoring site. The “future year” may simply be a scenario including the new project source(s). **Step 3:** Apply the RRFs to the monitoring data to estimate f uture-year concentrations corresponding to the 20 percent best and 20 percent worst visibil ity days. **Step 4:** Use the concentration estimates from Step 3 to calc ulate future-year visibility for the best and worst days. **Step 5:** Using the information from Step 4, calculate the fu ture-year mean visibility for the 20 percent best and worst days.

Steps 2 through 5 are applied for model scenarios w ith and without the proposed project emissions, and then differences in visibility between the model sc enarios are calculated and used to quantify the change in cumulative visibility resulting from proj ect-specific emissions. The cumulative multisource (method 2) visibility assessments (Silva and McCoy, 2012) are similar to the multi-source RPG assessments for Regional Haze except that the futur e year is typically the maximum emission year projected for the proposed project, which in most c ases is much closer to the baseline period than a projected future year for reasonable progress goals or 2064 natural conditions.



### 2.6 National Environmental Policy Act – Sulfur and Nitrogen Deposition Assessments

Wet and dry fluxes of sulfur- and nitrogen-containi ng species are processed to estimate total annual sulfur and nitrogen deposition values at each Class I and sensitive Class II area. The maximum annual sulfur and nitrogen deposition values from any grid cell that intersects the area of interest are used to represent deposition for that area, in addition to the average annual deposition values of all grid ce lls that intersect a Class I area and identified grid c ells for a sensitive Class II receptor area. Maximu m and average predicted sulfur and nitrogen deposition im pacts are estimated separately for each area and together across all areas. Nitrogen deposition impa cts are calculated by taking the sum of the nitroge n contained in the fluxes of all nitrogen species mod eled by the air quality model. If a photochemical g rid model is used this includes reactive gaseous nitrat e species, organic nitrates, particulate nitrate fo rmed from primary emissions plus secondarily formed part iculate nitrate, gaseous nitric acid, gaseous ammonia, and particulate ammonium. Sulfur depositio n calculations are sulfur dioxide and particulate sulfate ion from primary emissions plus secondarily formed sulfate.



### 2.7 National Environmental Policy Act – Acid Neutra lizing Capacity

Total annual sulfur and nitrogen deposition impacts from the project source is also used to assess the change in water chemistry associated with atmospher ic deposition from project activities and
cumulative sources for each of the sensitive lakes. This analysis assesses the change in the acid neutralizing capacity (ANC) for sensitive water bod ies, or a threshold for a soil or lichen indicator. Estimates of potential changes in ANC follow the pr ocedure developed by the USFS Rocky Mountain Region (USFS, 2000). Region 2 of the U.S. Forest Se rvice identifies water bodies with background ANC values less than 25 uel/l as being extremely sensit ive to additional deposition. However, impacts to sensitive biota can occur below 100 ueq/L. The pred icted changes in ANC are compared to threshold specified by the USFS, which include a 10 percent c hange in ANC for lakes with background ANC values greater than 25 micro equivalents per liter [μeq/L] , and no more than a 1 μeq/L change in ANC for lake s with background ANC values equal to or less than 25 μeq/L (U.S. Department of Agriculture, 1985).13



## 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 secondary pollutant impacts such as O 3 and PM2.5 need to be assessed at various spatial sc ales (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, visibility degradation, and d eposition.



### 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, 2005a). Air pollutants formed th rough chemical reactions in the atmosphere are referenced as secondary pollutants. For example, gr ound-level ozone (O 3 ) is predominantly a secondary pollutant formed through photochemical reactions dr iven by emissions of nitrogen oxides (NO
x ) and volatile organic compounds (VOCs). Ozone formation is a complicated nonlinear process that typically requires favorable meteorological conditions in add ition to VOC and NO x emissions (Seinfeld and Pandis, 2012). Warm temperatures, clear skies (abundant lev els of solar radiation), and stagnant air masses (l ow wind speeds) increase ozone formation potential (Se infeld 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 in nature. The fraction of PM 2.5 which is primary versus secondary varies by locatio n and season. In the United States, PM 2.5 is dominated by a variety of chemical species: ammoniu m sulfate, ammonium nitrate, organic carbon (OC) mass, elemental carbon (EC), and other soil compoun ds and oxidized metals. PM 2.5 elemental (black) carbon and soil dust are both directly emitted into the atmosphere from primary sources. Organic carbon particulate is directly emitted from primary sources but also has a secondary component formed by atmospheric reactions of VOC emissions. PM 2.5 sulfate, nitrate, and ammonium ions are predominantly the result of chemical reactions of t he oxidized products of sulfur dioxide (SO 2 ) and NO
x emissions and direct ammonia (NH 3 ) emissions (Seinfeld and Pandis, 2012).



### 3.2 Visibility and Deposition

In most areas of the country, light scattering by P M2.5 is the most significant component of visibilit y impairment (U.S. Department of the Interior, 2010). The key components of PM2.5 contributing to visibility impairment include sulfates, nitrates, o rganic carbon, elemental carbon, and crustal materi al (U.S. Department of the Interior, 2010). Stream aci dification is accompanied by decreasing pH levels, increasing aluminum concentrations, and decreasing acid-neutralizing capacity (ANC). As ANC decreases, macroinvertebrate communities begin to decline, fol lowed by fish species richness reductions, and eventually lethal and sub-lethal effects on brook t rout populations and marked declines in aquatic ins ect families. At the same time, as sulfuric acid is dep osited from the atmosphere onto the landscape, molecules separate into positively charged hydrogen ions and a negatively charged sulfate molecule. In order to maintain an ionic balance, an equivalent a mount of positively charged base cations adhere to the negatively charged sulfates and move into the s oil water solution, acidifying the remaining soil a nd14

fundamentally altering soil processes. The reduced availability of these base cations in the soils (specifically, calcium, magnesium and potassium) hi nders the capacity for sensitive soils to recover f rom acidic deposition and compromises the health and co ntinued growth of the plants dependent on these nutrients. Additionally, when soils become sufficie ntly acidic, aluminum may become mobile, eventually entering plant roots more easily than other bases a nd displacing other nutrients during uptake, result ing in a nutrient deficiency. This deficiency is compou nded by the toxic effect of aluminum on fine roots, further reducing the potential uptake of nutrients and water by plants. More information about acidification effects associated with deposition ar e available elsewhere (U.S. Department of the Inter ior, 2010).



### 3.3 Air Quality Models for Secondary Pollutants

Chemical transformations can play an important role in defining the concentrations and properties of certain air pollutants. Models that take into accou nt chemical reactions and physical processes of various pollutants are needed for determining the c urrent state of air quality, as well as predicting and projecting the future evolution of these issues (U. S. Environmental Protection Agency, 2005a). The chemical and physical processes discussed above are interrelated in a complex system. It is often not possible to predict the response of a certain pollu tant to emissions reductions without the aid of models. Models can simultaneously account for these various chemical reactions and physical processes or the chemical coupling of multiple pollutants. A regulatory need exists to model secondary pollutant s such as O 3 and PM and appropriately estimating secondary PM n ecessitates realistic estimates of O 3 and

O 3 precursors.

Chemical transport models treat atmospheric chemica l and physical processes such as deposition and motion. There are two types of chemical transport m odels which are differentiated based on a fixed frame of reference (Eulerian grid based) or a frame of reference that moves with parcels of air betwee n the source and receptor point (Lagrangian) (McMurry et al., 2004). Photochemical grid models are three-dimensional grid-based models that treat chem ical and physical processes in each grid cell and u se Eulerian diffusion and transport processes move che mical species to other grid cells (McMurry et al., 2004). These types of models are appropriate for as sessment of near-field and regional scale impacts from specific sources (Baker and Foley, 2011; Baker and Kelly, 2014; Bergin et al., 2008; Zhou et al., 2012) or all sources (Chen et al., 2014; Russell, 2 008; Tesche et al., 2006). Photochemical transport models have been used extensively to support State Implementation Plans and to explore relationships between inputs and air quality impacts in the Unite d States and beyond (Cai et al., 2011; Civerolo et al., 2010; Hogrefe et al., 2011).

3.3.1 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, 2012c). 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 plume15

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.

3.3.2 Photochemical grid 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.

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.



### 3.4 Recommendations

Photochemical transport models are suitable for est imating visibility and deposition since important physical and chemical processes related to the form ation and transport of PM are realistically treated

. Source sensitivity and apportionment techniques imp lemented in photochemical grid models have evolved sufficiently and provide the opportunity fo r estimating potential visibility and deposition impacts from one or a small group of emission sourc es. Photochemical grid models using meteorology16

output from prognostic meteorological models have d emonstrated skill in estimating source-receptor relationships in the near-field (Baker and Kelly, 2 014; ENVIRON, 2012c) and over long distances (ENVIRON, 2012b). In order to provide the user comm unity flexibility in estimating single source secondary pollutant impacts and given the emphasis on the use of photochemical transport models for these purposes, Appendix W should no longer contain language that requires the use of a Lagrangian puff model (CALPUFF). A candidate model for use in estimating single source impacts on secondarily formed pollutants such as ozone and PM2.5 for the p urposes of PSD and NSR programs should meet the general criteria for an “alternative model” outline d in 40 CFR 51.112 and 40 CFR part 51 (U.S. Environmental Protection Agency, 2005a). The accept ability of a particular model and approach for that model application is an EPA Regional Office respons ibility that could include consultation with EPA Headquarters if appropriate. The use of models inco rporating complex chemical mechanisms should be considered on a case-by-case basis with proper demo nstration of applicability (U.S. Environmental Protection Agency, 2005a). It is important that the application of the wide range of modeling systems be appropriately applied for the purposes of assessing the impacts of sources on secondarily formed pollutants, such as ozone and PM 2.5 . Use of photochemical grid models for AQRV analysi s requirements, while not subject to specific EPA model approval re quirements outlined in 40 CFR 51.166(l)(2) and 40 CFR 52.21(l)(2), should be justified for each appli cation and concurrence sought with the affected FLM(s).



## 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.

Visibility and deposition are estimated at receptor s placed inside Class I areas. This means it is imp ortant that a long range transport modeling system be able to capture these types of source-receptor relationships. In addition, since visibility is lar gely PM2.5 and deposition a combination of primary emitted and secondarily formed pollutants, it is im portant that a modeling system be able to capture single source primary and secondary impacts. Both o f these components are important for generating confidence that a modeling system is theoretically fit for this purpose. Comparing model estimates against regional tracer experiments is one way to g enerate confidence that a modeling system can replicate long-range transport between a source and downwind receptors. Near-source in-plume measurements are useful to develop confidence that a modeling system captures secondarily formed pollutants from specific sources. These types of as sessments are typically only done occasionally when a modeling system has notably changed from previous t esting or has never been evaluated for this purpose. This type of assessment is discussed in mo re 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 application17

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 Long Range Transport Models – Fit for Purpose E valuations

The typical regulatory application of an LRT modeli ng system is for Prevention of Significant Deterioration of Air Quality (PSD) Class I air qual ity related values (AQRVs) (visibility, deposition, etc.). When employed for these purposes, it is customary t o only model discrete receptors defined within the boundaries of national parks and wilderness areas ( federal mandatory Class I areas with specially protected air quality related values) and compare m odeled concentrations against short-term averaging periods with few exceedance periods. Given the need to capture impacts at specific locations and times

, some emphasis is needed on the evaluation of the sp atial and temporal metrics. This implies a fundamentally different evaluation philosophy than typically used for dispersion models such as AERMOD that are applied within 50 kilometers, which is noted in the Guideline on Air Quality Models (EPA, 2005) with the statement “the models are reas onably reliable in estimating the magnitude of the highest concentrations occurring sometime, somewher e within an area.” Based on this principle, the evaluation of near-source primary pollutant dispers ion models focus on a model’s ability to replicate the highest end of the concentration distribution, rega rdless of temporal or spatial pairing. Since model skill in replicating transport in time and space is impor tant for ARQV analysis, model evaluation should pla ce a similar level of emphasis upon a model’s ability to simulate spatial and temporal pairing.

It is important that modeling tools used for single source long-range transport impacts assessments demonstrate skill in adequately replicating source- receptor relationships that are not in close proxim ity. For source-receptor distances greater than 50 km, r egional scale models may be applied for the assessment of visibility impacts due to one or a sm all group of sources. Skill in estimating sourcereceptor relationships on this scale can be illustr ated by evaluating modeling systems against regiona l scale inert tracer release experiments. These field study releases of inert tracers with downwind receptors typically arranged in arcs or distributed over a given area are designed for assessing model skill in long-range transport (Hegarty et al., 2013 ). The regional tracer release experiments with des igns most relevant for evaluating long range transport m odeling systems include the 1980 Great Plains Mesoscale Tracer Field Experiment, the 1983 Cross-A ppalachian Tracer Experiment (CAPTEX), the 1987 Across North American Tracer Experiment (ANATEX), a nd 1994 European Tracer Experiment (ETEX) (ENVIRON, 2012b; Hegarty et al., 2013). Photochemic al grid models have been shown to demonstrate similar skill to Lagrangian models for pollutant tr ansport when compared to measurements made from multiple mesoscale field experiments (ENVIRON, 2012 b).

Near-source in-plume aircraft based measurement fie ld studies are useful for evaluating model estimates of (near-source) downwind transport and c hemical impacts from single stationary point sources (ENVIRON, 2012c). 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 made in Tennessee and Texas (B aker and Kelly, 2014; ENVIRON, 2012c). This work indicates photochemical grid models and source apportionment and source sensitivity approaches18

provide meaningful estimates of single source impac ts. However, additional evaluations are needed for longer time periods and more diverse environments t o generate broader confidence in these approaches for this purpose.



### 4.2 Long Range Transport Models – Meteorology Evalu ation

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 Long Range Transport Models – Chemistry Evaluat ion

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 is more important to evaluate the components of PM2.5 than to evaluate total PM2.5 itself. Apparent “good performance” for total PM2.5 does not 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 (such19

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 systematic revie w 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 data 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 he20

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/.



## 5 ACKNOWLEDGEMENTS

The document includes contributions from Bret Ander son, Kirk Baker, Bill Jackson, Rebecca Matichuk,
and Michael Feldman. The document has been reviewed by the members of the IWAQM3-LRT group.



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. U.S. Environmental Protection Agency, 2014a. 40 CFR , Part 52, Approval and Promulgation of Implementation Plans; Texas and Oklahoma; Regional Haze State Implementation Plans; Interstate Transport State Implementation Plan To Address Poll ution Affecting Visibility and Regional Haze; Feder al Implementation Plan for Regional Haze and Interstat e Transport of Pollution Affecting Visibility; Proposed Rule, Vol. 79, No. 241 FR 74818-74892, Dec ember 16, 2014.

U.S. Environmental Protection Agency, 2014b. Modeli ng Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. http://www.epa.gov/ttn/scram/guidance/guide/Draft_O 3-PM-RH_Modeling_Guidance-2014.pdf

. U.S. Environmental Protection Agency, 2015a. Intera gency Workgroup on Air Quality Modeling (IWAQM) Phase 3 Summary Report: Near-Field Single S ource Secondary Impacts. EPA-454/P-15-002.

U.S. Environmental Protection Agency, 2015b. Techni cal Support Document (TSD) for AERMOD-Based Assessments of Long-Range Transport Impacts for Pri mary Pollutants, Document Number: EPA-454/B15-003.

Zhou, W., Cohan, D.S., Pinder, R.W., Neuman, J.A., H olloway, J.S., Peischl, J., Ryerson, T.B., Nowak, J .B., Flocke, F., Zheng, W.G., 2012. Observation and model ing of the evolution of Texas power plant plumes. Atmospheric Chemistry and Physics 12, 455-468.24United StatesEnvironmental ProtectionAgencyOffice of AirQuality Planning and StandardsAir Quality Analysis DivisionResearch Triangle Park, NCPublication No. EPA-454/P-15-003July 2015