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Interagency Workgroup on Air Quality 

Modeling Phase 3 Summary Report: Long 

Range Transport and Air Quality Related 

Values 

 
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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, NC

 
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Executive Summary

The Interagency Workgroup on Air Quality Modeling (IWAQM) was originally formed in 1991 to provide 

a focus for development of technically sound regional air quality models for regulatory assessments of

pollutant source impacts on Federal Class I areas. The IWAQM process largely concluded in 1998 with 

the publication of the Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary 

Report and Recommendations for Modeling Long Range Transport Impacts (EPA-454/R-98-019) (U.S. 

Environmental Protection Agency, 1998). The IWAQM Phase 2 process provided a series of 

recommendations concerning the application of the CALPUFF model for use in long range transport 

(LRT) modeling and informed the promulgation of that model for such regulatory purposes in 2003. The 

IWAQM process was reinitiated in June 2013 to inform EPA’s commitment to update Appendix W to 

address chemically reactive pollutants in near field and long range transport applications (U.S. 

Environmental Protection Agency, 2012b). This report provides information and recommendations from 

the “Phase 3” effort focused on long-range transport of primary and secondary pollutants. The idea of 

applying photochemical grid models for these purposes 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 processes important to the formation of ground-level O3, 

PM2.5, visibility, and deposition in the context of modeled long range transport assessments for permit 

review programs. Chemical transport models that characterize these processes include both Lagrangian 

which typically only have a single source included in the model and photochemical grid models which 

include some representation of all anthropogenic, biogenic, and geogenic sources. Modeling systems 

appropriate for the purposes of estimating long-range 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 important to ensure that a particular system is fit for the 

purpose of estimating long-range single source secondary impacts. One aspect of this type of evaluation 

for long-range transport assessments would be demonstrating 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 meteorology and chemical estimates with 

measurements near the project source and key receptors is also an important model evaluation 

component. 

Regulatory context for estimating long-range transport of visibility and deposition is provided to present 

the range of purposes for single source impact assessments. In the case of visibility, single source impact 

assessment approaches are compared within the Regional Haze Rule context, Prevention of Significant 

Deterioration, and National Environmental Policy Act to better illustrate the similarities in these 

demonstrations and note where differences should be expected. 

 
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Table of Contents 

1 BACKGROUND: IWAQM Phase 3 process overview .............................................................................. 5 

2 REGULATORY MOTIVATION .................................................................................................................. 5 

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

2.2 Regional Haze Rule Visibility Impairment Modeling: BART program ........................................... 7 

2.3 Differences between single source assessments for 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 Neutralizing Capacity................................................ 12 

3 MODEL SELECTION .............................................................................................................................. 13 

3.1 Secondary Pollutant Formation: O3 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 Evaluations ..................................................... 17 

4.2 Long Range Transport Models – Meteorology Evaluation ......................................................... 18 

4.3 Long Range Transport Models – Chemistry Evaluation .............................................................. 18 

4.4 Model performance evaluation data sources ............................................................................. 19 

5 ACKNOWLEDGEMENTS ....................................................................................................................... 20 

6 REFERENCES ........................................................................................................................................ 20 
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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 regional 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 Agency (EPA), the U.S. Forest Service (USFS), the U.S. 

Fish and Wildlife Service (USFWS), and the National Park Service (NPS). The original purpose was to 

review respective modeling programs, develop an organizational framework, and formulate reasonable 

objectives and plans that could be presented to management for support and commitment. The IWAQM 

process largely concluded in 1998 with the publication of the Interagency Workgroup on Air Quality 

Modeling (IWAQM) Phase 2 Summary Report and Recommendations for Modeling Long Range 

Transport Impacts (EPA-454/R-98-019) (U.S. Environmental Protection Agency, 1998). The IWAQM 

Phase 2 report provided a series of recommendations concerning the application of the CALPUFF model 

for use in long range transport (LRT) modeling and informed the promulgation of that model for such 

regulatory purposes in 2003. Draft updates to the IWAQM Phase 2 report were released in 2009 to 

better reflect the state-of-the-practice of long range transport modeling techniques based on 

experience gained since the early 2000s. 

The IWAQM process was reinitiated in June 2013 to inform EPA’s commitment to update Appendix W to 

address chemically reactive pollutants in near field and long range transport applications (U.S. 

Environmental Protection Agency, 2012b). Comments received from the 10th Modeling Conference 

(March 2012) from stakeholders support this interagency collaborative effort to provide additional 

guidance for modeling single source impacts on secondarily formed pollutants in the near-field and for

long range transport. Stakeholder comments also support the idea of this collaborative effort working in 

parallel with stakeholders to further model development and evaluation. 

This “Phase 3” effort includes the establishment of 2 separate working groups, one focused on longrange transport of primary and secondary pollutants and the other on near-field single source impacts of 

secondary pollutants. While many of the objectives are similar for each of these groups, the focus and

regulatory end-points are different for each. 

It is expected the “Phase 3” effort will continue with future efforts related to reviewing and responding 

to comments given on the 2015 proposed changes to Appendix W related to single source impact 

assessments for air quality related values. IWAQM3 long-range transport team members include Rick 

Gilliam (US EPA), Kirk Baker (US EPA), Michael Feldman (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 provided 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 sets visibility goals for Class I areas. The 1999 Regional 

Haze Rule expands on Section 169 of the Clean Air Act and “Phase I” of the Visibility Protection Program. 

The Regional Haze Rule (RHR) has multiple provisions that may be supported by air quality modeling. 

The reasonable progress and BART determination components of the RHR and modeling requirements 

for the first planning period and future planning periods are shown in Table 1. Additional sections 
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contain requirements of other programs including Prevention of Significant Deterioration (PSD) and 

National Environmental Policy Act (NEPA) for visibility 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 Modeling: 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. Projected visibility is compared to the Uniform Rate of 

Progress, which is a linear interpolation between recent 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 reasonable 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 natural visibility conditions and thus supporting a 

demonstration of the reasonableness of the reasonable progress goal. 

A modeling system that treats emissions from all known anthropogenic and biogenic emissions sources 

with realistic chemical and physical transformations should be utilized to estimate future visibility 

conditions at a Class I area. The most appropriate tool that contains these qualities is a photochemical 

grid model. Commonly applied photochemical grid models for estimating visibility include the 

Comprehensive Air-Quality Model with Extensions (CAMx) 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 future year visibility impacts with photochemical grid 

models has been established and applied by States for their initial RH SIP demonstrations for 2018. This 

same type of photochemical model based assessment will need to be done for upcoming SIP 

demonstrations for subsequent planning periods (e.g. 2028, 2038, etc.) to determine if Class I areas will 

be on the glidepath to “natural” conditions. 

The estimates of “natural” conditions are critically important for the estimation of the uniform rate of 

progress. However, any future updates to the calculation of “natural” conditions will not substantively 

change the nature of the air quality model based assessments of projected visibility impairment. 

However, changes to the metrics (e.g. 20% worst days) used for demonstrating progress will provide for

more influential changes to projected visibility improvements. 
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2.2 Regional Haze Rule Visibility Impairment Modeling: BART program 

Dispersion modeling was recommended by US EPA to support decisions about which BART eligible 

sources “cause or contribute” to visibility impairment and may need enforceable emissions limits and/or 

emissions controls (U.S. Environmental Protection Agency, 2005b). If a BART eligible source 

“contributes” to visibility impairment then additional modeling was done to assess the improvement in 

visibility due to source control measures. BART-eligible sources meet specific criteria for source 

category, date of operation or existence, and potential to emit (see 70 FR 39158–39161; July 6, 2005).

BART is a program that only applies to the 1st regional haze planning period (the planning period ending 

2018). 

Single-source air quality modeling for BART assessments was typically done using the CALPUFF modeling 

system and daily maximum emission rates. However, it is important to note that other Lagrangian 

models or photochemical grid modeling systems can be used to isolate the primary and secondary 

impacts of single sources and thus be used for single 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 visibility impact assessments (U.S. Environmental 

Protection Agency, 2014a). 

The daily visibility metric for each Class I area is expressed as the change in deciviews compared to 

natural visibility conditions (U.S. Environmental Protection Agency, 1998). Natural visibility conditions 

are found in Appendix B of EPA’s Guidance for Estimating Natural Visibility Conditions under the 

Regional Haze Rule (U.S. Environmental Protection Agency, 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 threshold expressed in deciviews was commonly 

employed to determine whether a BART-eligible source “contributes” or “causes” visibility impairment 

as suggested in U.S. EPA guidance (U.S. Environmental Protection Agency, 2005b). BART assessments 

consider an estimate of maximum impacts over all modeled days, not the 20% best or 20% worst days 

which are considered for the reasonable progress assessment. 

The air quality impacts of BART controls are sometimes 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 state and/or Federal controls programs that may provide 

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 NOX, SO2, and primary PM2.5 emissions reductions from BART controls and BART 

alternatives. These “Better than BART” analyses have focused on changes in visibility due to emissions

changes on the 20% best and 20% worst visibility days. 

2.3 Differences between single source assessments for BART and RPG 

Fundamental differences in required approaches for evaluating source impacts for the purposes of BART 

determinations and RPG along with inherent differences in the models used for these purposes make 

directly comparing results for specific sources impossible. Single-source air quality modeling for BART 

assessments was typically done using the CALPUFF modeling system (69 FR 25,193-194), using maximum 
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emission rates (24-hr maximum emission rates during the baseline period) and consideration of the 

maximum visibility impact from the source. On the other hand, due to the need to quantify the future 

year visibility impairment from all types of emissions 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 realistic 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 of evaluating visibility impacts or benefits from 

emissions sources or emissions controls for reasonable progress and long-term strategy development 

may utilize photochemical grid models to estimate potential visibility benefits from controls on future 

year visibility conditions. 

Modeling for the purpose of establishing a reasonable progress goal (RPG) differs from the single source 

BART determination modeling for a variety of reasons. BART determinations are intended to provide 

information about current year impacts from a single 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 assessments are done using maximum 24-hr average 

emissions rates. The 8th highest model estimate of facility impacts at Class I areas from each year 

modeled are averaged and compared to that Class I area’s natural conditions to provide an estimate of a 

“worst case” scenario (70 FR 39124). The highest modeled impacts are not typically compared directly to 

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 reasonable progress and long-term strategy development 

use actual emission rates to provide a realistic estimate of current and future year visibility impacts 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 approach 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 emissions 

rather than maximum 24-hour average emissions. In addition, RPG assessments average impacts over 

the 20% worst days rather that selecting the 8th highest facility impact in a given year. RPG impacts are 

examined relative to the projected future year 20% worst days visibility estimate, while BART impacts 

are maximum source impacts compared to background natural conditions irrespective of the 

relationship to the 20% worst days. 

Finally, single source impacts estimated for RPG and BART will be different due to fundamental 

differences between photochemical grid models and puff dispersion models such as CALPUFF. 

Photochemical grid models include all emissions sources and provide a dynamic and realistic chemical 

and physical environment to estimate source emission 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; Karamchandani et al., 2008). CALPUFF’s representation 

of these important chemical species and PM2.5 chemistry 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 oxidants (e.g. ozone) and neutralizing agents (e.g. 

ammonia) while the same source in a photochemical model competes for oxidants and neutralizing 

agents which may result in different and possibly lower impacts. 
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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 assessments 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 requirements for a BART-like assessment but Lagrangian 

puff models are not ideal for reasonable progress demonstrations 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, subsections (k)(1)(i) and (k)(1)(ii), new or modified sources 

emitting in significant amounts (see 40 CFR part 51.166 and 52.21, subsection (b)(23)(i)) are required to 

demonstrate that the source under review does not cause or contribute to a violation of any applicable

national ambient air quality standards ((k)(1)(i)) or maximum allowable increases over a baseline 

concentration ((k)(1)(ii)). Additionally, 40 CFR parts 52.27, subsection (d)(1), requires that the permit 

reviewing authority must provide to all affected FLM’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 Class I area. The requirements of PSD potentially require 

the use of LRT models for both the maximum allowable increases (increments) and for air quality related 

values (AQRV’s) including visibility. Unique to this is the authorities under which each of these elements 

of air quality analyses is administered. The relevant permitting authority administers the NAAQS and 

increments component of the air quality analysis, while the Federal Land Manager is responsible for 

recommending models and analytical procedures for the air quality related values analysis (see 40 CFR 

part 51, Appendix W, subsection 6.1(b)). 

Single source impacts are typically compared to significant impact levels (SILs) and increments. For the 

purposes of long range transport it is expected based on an analysis of multiple hypothetical plants that 

O3 and secondary PM2.5 impacts would typically be below any significance threshold beyond 50 km 

(U.S. Environmental Protection Agency, 2015a). Analysis for primarily emitted pollutants indicates that 

in most situations significance thresholds are not exceeded beyond 50 km (U.S. Environmental 

Protection Agency, 2015b). Long-range transport assessments 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 screening and/or preferred model to determine the 

significance of ambient impact at or about 50 km from the new of modifying source. If this initial 

screening indicates there may be significant ambient impacts at that distance, then further screening is 

necessary. 

Where a long range transport assessment is still needed for primary pollutant impacts a Lagrangian (e.g. 

CALPUFF without chemistry) or photochemical grid modeling 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 should reach agreed upon approaches (models and 

modeling parameters) on a case-by-case basis in consultation with the appropriate reviewing authority,

Regional Office, and the affected Federal Land Manager(s) (FLM(s)). If a cumulative increment analysis is 

necessary, for these limited situations, the selection and use of an alternative model shall occur in 
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agreement with the appropriate reviewing authority (paragraph 3.0(b)) and approval by the EPA 

Regional Office based on the requirements of section 3.2.2(e). 

PSD analyses are also completed for NEPA air quality assessments using dispersion models or 

photochemical grid models. The typical PSD assessment description includes the following: 

The PSD demonstrations are for information and comparison 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, which is a useful metric for analyzing and comparing 

air quality impacts. The comparison is made to allowable 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 requirements 

for other programs such as PSD. PSD ensures the preservation 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 Work 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 visibility 

impairment in any of the 156 mandatory Federal Class I areas where visibility is considered an important 

attribute. In 1999, EPA issued revisions to the regulations to address visibility impairment in the form of 

regional haze, which is caused by numerous, diverse sources (e.g., stationary, mobile, and area sources) 

located across a broad region (40CFR 51.308–309). Section 169A of the Act requires states to develop 

SIPs containing long-term strategies for remedying existing and preventing future visibility impairment 

in the 156 mandatory Class I Federal areas, where visibility 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 and associated visibility impairment. 

The FLAG visibility modeling recommendations are divided into two distinct sections to address different 

requirements for 1) near field modeling where plumes 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 assess impacts of nitrogen and sulfur deposition at 

Class I areas. This guidance recognizes the importance of establishing critical deposition loading values 

(“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 essentially a 

level of atmospheric pollutant deposition below which negative ecosystem effects are not likely to 

occur. FLAG (2010) does not include any critical load levels for specific Class I areas and refers to site-
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specific critical load information on FLM websites for each area of concern. However, this guidance does 

recommend the use of deposition analysis thresholds (DATs) developed by the National Park Service and 

the Fish and Wildlife Service. The DATs represent screening level values for nitrogen and sulfur 

deposition. If the DAT is exceeded then the modeling results are considered significant and further 

AQRV analysis is required. If a source exceeds the DAT level then a comparison to Class I specific critical 

load values is necessary (U.S. Department of the Interior, 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 deposition to determine whether the Class I area specific 

screening level or critical load value would be exceeded. 

2.5 National Environmental Policy Act – Visibility Assessments 

NEPA air quality impact analyses assess potential air quality impacts that could occur from development 

within the project area and from other documented regional 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 the Federal Land Managers’ Air Quality Related 

Values Work Group (FLAG) Phase I Report – Revised 2010 (U.S. Department of the Interior, 2010). This 

method assesses project-specific visibility impacts at Class I and sensitive Class II areas by determining 

the incremental changes in light extinction relative to estimated natural background conditions and 

comparing the incremental changes to visibility thresholds. The visibility evaluation metric used in this 

analysis is the Haze Index, which is measured in deciview (dv) and used in EPA’s Regional Haze Rule. The 

change in visibility impacts of the proposed development is obtained by calculating the differences 

between the Haze Index with added project concentrations and the Haze Index based solely on 

background concentrations. Estimated visibility degradation at the Class I and sensitive Class II areas is 

presented in terms of the number of days that exceed a threshold percent change in extinction, or 

deciview (dv), relative to natural background conditions. The maximum and 98th percentile incremental 

changes in Haze Index (∆dv) at any receptor that intersects with the area of interest are compared to 0.5 

dv and 1.0 dv thresholds. A source whose 98th percentile value of the haze index is greater than 0.5 

deciview (dv) (approximately a 5% change in light extinction) is considered to contribute to regional 

haze visibility impairment. Similarly, a source that exceeds 1.0 dv (approximately a 10% change in light 

extinction) causes visibility impairment and corresponds 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 sensitive 

Class II areas (Silva and McCoy, 2012). The cumulative visibility assessments use the estimates of actual 

emissions that could occur from the proposed development 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 best and 20 percent worst days. 

Step 2: Estimate site-specific relative response factors (RRFs) 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 simulated 
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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 future-year concentrations corresponding 

to the 20 percent best and 20 percent worst visibility days. 

Step 4: Use the concentration estimates from Step 3 to calculate future-year visibility for the best 

and worst days. 

Step 5: Using the information from Step 4, calculate the future-year mean visibility for the 20 percent 

best and worst days. 

Steps 2 through 5 are applied for model scenarios with and without the proposed project emissions, and

then differences in visibility between the model scenarios are calculated and used to quantify the 

change in cumulative visibility resulting from project-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 future year is typically the maximum emission year 

projected for the proposed project, which in most cases 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-containing 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 cells 

that intersect a Class I area and identified grid cells for a sensitive Class II receptor area. Maximum and 

average predicted sulfur and nitrogen deposition impacts are estimated separately for each area and 

together across all areas. Nitrogen deposition impacts are calculated by taking the sum of the nitrogen 

contained in the fluxes of all nitrogen species modeled by the air quality model. If a photochemical grid 

model is used this includes reactive gaseous nitrate species, organic nitrates, particulate nitrate formed 

from primary emissions plus secondarily formed particulate nitrate, gaseous nitric acid, gaseous 

ammonia, and particulate ammonium. Sulfur deposition calculations are sulfur dioxide and particulate 

sulfate ion from primary emissions plus secondarily formed sulfate. 

2.7 National Environmental Policy Act – Acid Neutralizing Capacity 

Total annual sulfur and nitrogen deposition impacts from the project source is also used to assess the

change in water chemistry associated with atmospheric 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 bodies, or a threshold for a soil or lichen indicator.

Estimates of potential changes in ANC follow the procedure developed by the USFS Rocky Mountain 

Region (USFS, 2000). Region 2 of the U.S. Forest Service identifies water bodies with background ANC 

values less than 25 uel/l as being extremely sensitive to additional deposition. However, impacts to 

sensitive biota can occur below 100 ueq/L. The predicted changes in ANC are compared to threshold 

specified by the USFS, which include a 10 percent change 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 lakes 

with background ANC values equal to or less than 25 μeq/L (U.S. Department of Agriculture, 1985). 
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3 MODEL SELECTION 

This section describes the types of air quality impacts that need to be assessed and the tools that are 

best suited for this purpose. For a variety of regulatory programs secondary pollutant impacts such as O3

and PM2.5 need to be assessed at various spatial scales (near-source and long-range transport). It is 

important that modeling systems used for these assessments be fit for this purpose and be evaluated 

for skill in replicating meteorology and atmospheric chemical and physical processes that result in 

secondary pollutants, visibility degradation, and deposition. 

3.1 Secondary Pollutant Formation: O3 and PM2.5 

PM and O3 are closely related to each other in that they share common sources of emissions and are 

formed in the atmosphere from chemical reactions with similar precursors (U.S. Environmental 

Protection Agency, 2005a). Air pollutants formed through chemical reactions in the atmosphere are 

referenced as secondary pollutants. For example, ground-level ozone (O3) is predominantly a secondary 

pollutant formed through photochemical reactions driven by emissions of nitrogen oxides (NOx) and 

volatile organic compounds (VOCs). Ozone formation is a complicated nonlinear process that typically 

requires favorable meteorological conditions in addition to VOC and NOx emissions (Seinfeld and Pandis, 

2012). Warm temperatures, clear skies (abundant levels of solar radiation), and stagnant air masses (low 

wind speeds) increase ozone formation potential (Seinfeld and Pandis, 2012). 

In the case of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5 or fine PM), PM2.5

can be either primary (i.e. emitted directly from sources) or secondary in nature. The fraction of PM2.5

which is primary versus secondary varies by location and season. In the United States, PM2.5 is 

dominated by a variety of chemical species: ammonium sulfate, ammonium nitrate, organic carbon (OC) 

mass, elemental carbon (EC), and other soil compounds and oxidized metals. PM2.5 elemental (black) 

carbon and soil dust are both directly emitted into the atmosphere from primary sources. Organic 

carbon particulate is directly emitted from primary sources but also has a secondary component formed 

by atmospheric reactions of VOC emissions. PM2.5 sulfate, nitrate, and ammonium ions are 

predominantly the result of chemical reactions of the oxidized products of sulfur dioxide (SO2) and NOx

emissions and direct ammonia (NH3) emissions (Seinfeld and Pandis, 2012). 

3.2 Visibility and Deposition 

In most areas of the country, light scattering by PM2.5 is the most significant component of visibility 

impairment (U.S. Department of the Interior, 2010). The key components of PM2.5 contributing to 

visibility impairment include sulfates, nitrates, organic carbon, elemental carbon, and crustal material 

(U.S. Department of the Interior, 2010). Stream acidification is accompanied by decreasing pH levels, 

increasing aluminum concentrations, and decreasing acid-neutralizing capacity (ANC). As ANC decreases,

macroinvertebrate communities begin to decline, followed by fish species richness reductions, and 

eventually lethal and sub-lethal effects on brook trout populations and marked declines in aquatic insect 

families. At the same time, as sulfuric acid is deposited 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 amount of positively charged base cations adhere to 

the negatively charged sulfates and move into the soil water solution, acidifying the remaining soil and 
14 

fundamentally altering soil processes. The reduced availability of these base cations in the soils 

(specifically, calcium, magnesium and potassium) hinders the capacity for sensitive soils to recover from 

acidic deposition and compromises the health and continued growth of the plants dependent on these 

nutrients. Additionally, when soils become sufficiently acidic, aluminum may become mobile, eventually

entering plant roots more easily than other bases and displacing other nutrients during uptake, resulting 

in a nutrient deficiency. This deficiency is compounded 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 are available elsewhere (U.S. Department of the Interior, 

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 account chemical reactions and physical processes of 

various pollutants are needed for determining the current 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 pollutant 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 pollutants 

such as O3 and PM and appropriately estimating secondary PM necessitates realistic estimates of O3 and 

O3 precursors. 

Chemical transport models treat atmospheric chemical and physical processes such as deposition and 

motion. There are two types of chemical transport models which are differentiated based on a fixed 

frame of reference (Eulerian grid based) or a frame of reference that moves with parcels of air between 

the source and receptor point (Lagrangian) (McMurry et al., 2004). Photochemical grid models are 

three-dimensional grid-based models that treat chemical and physical processes in each grid cell and use 

Eulerian diffusion and transport processes move chemical species to other grid cells (McMurry et al., 

2004). These types of models are appropriate for assessment of near-field and regional scale impacts 

from specific sources (Baker and Foley, 2011; Baker and Kelly, 2014; Bergin et al., 2008; Zhou et al., 

2012) or all sources (Chen et al., 2014; Russell, 2008; Tesche et al., 2006). Photochemical transport 

models have been used extensively to support State Implementation Plans and to explore relationships 

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

2010; Hogrefe et al., 2011). 

3.3.1 Lagrangian models 

Quantifying secondary pollutant formation requires simulating chemical reactions and thermodynamic 

partitioning in a realistic chemical and physical environment. Some Lagrangian models treat in-plume 

gas and particulate chemistry. These models require as input background fields of time and space 

varying oxidant concentrations, and in the case of PM2.5 also neutralizing agents such as ammonia, 

because important secondary impacts happen when plume edges start to interact with the surrounding 

chemical environment (Baker and Kelly, 2014; ENVIRON, 2012c). These oxidant and neutralizing agents 

are not routinely measured, but can be generated with a three dimensional photochemical transport 

model. Photochemical models simulate a more realistic chemical and physical environment for plume 
15 

growth and chemical transformation (Baker and Kelly, 2014; Zhou et al., 2012), but simulations may 

sometimes be more resource intensive than Lagrangian or dispersion models. 

3.3.2 Photochemical grid models 

Publically available and documented Eulerian photochemical grid models such as the Comprehensive Air 

Quality Model with Extensions (CAMx) (ENVIRON, 2014) and the Community Multiscale Air Quality 

(CMAQ) (Byun and Schere, 2006) model treat emissions, chemical transformation, transport, and 

deposition using time and space variant meteorology. These modeling systems include primarily emitted 

species and secondarily formed pollutants such as ozone and PM2.5 (Chen et al., 2014; Civerolo et al.,

2010; Russell, 2008; Tesche et al., 2006). Even though single source emissions are injected into a grid 

volume, photochemical transport models have been shown to adequately capture single source impacts 

when compared with downwind in-plume measurements (Baker and Kelly, 2014; Zhou et al., 2012). 

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

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

approaches to estimate these impacts. These approaches generally fall into the category of source 

sensitivity (how air quality changes due to changes in emissions) and source apportionment (how 

emissions contribute to air quality levels under modeled atmospheric conditions). 

The simplest source sensitivity approach (brute-force change to emissions) would be to simulate 2 sets

of conditions, one with all emissions and one with the source of interest removed from the simulation 

(Cohan and Napelenok, 2011). The difference between these simulations provides an estimate of the air 

quality change related to the change in emissions from the project source. Another source sensitivity 

approach to identify the impacts of single sources on changes in model predicted air quality is the 

decoupled direct method (DDM), which tracks the sensitivity of an emissions source through all 

chemical and physical processes in the modeling system (Dunker et al., 2002). Sensitivity coefficients

relating source emissions to air quality are estimated during the model simulation and output at the 

resolution of the host model. 

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

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

to estimate a contribution to predicted air quality at downwind receptors (Kwok et al., 2015; Kwok et al., 

2013). Source apportionment has been used to differentiate the contribution from single sources on 

model predicted ozone and PM2.5 (Baker and Foley, 2011; Baker and Kelly, 2014). DDM has also been 

used to estimate O3 and PM2.5 impacts from specific sources (Baker and Kelly, 2014; Bergin et al., 2008; 

Kelly et al., 2015) as well as the simpler brute-force sensitivity approach (Baker and Kelly, 2014; Bergin 

et al., 2008; Kelly et al., 2015; Zhou et al., 2012). Limited comparison of single source impacts between 

models (Baker et al., 2013) and approaches to identify single source impacts (Baker and Kelly, 2014; 

Baker et al., 2013) show generally similar downwind spatial gradients and impacts. 

3.4 Recommendations 

Photochemical transport models are suitable for estimating visibility and deposition since important 

physical and chemical processes related to the formation and transport of PM are realistically treated. 

Source sensitivity and apportionment techniques implemented in photochemical grid models have 

evolved sufficiently and provide the opportunity for estimating potential visibility and deposition 

impacts from one or a small group of emission sources. Photochemical grid models using meteorology 
16 

output from prognostic meteorological models have demonstrated skill in estimating source-receptor 

relationships in the near-field (Baker and Kelly, 2014; ENVIRON, 2012c) and over long distances 

(ENVIRON, 2012b). In order to provide the user community 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 purposes of PSD and NSR programs should meet the 

general criteria for an “alternative model” outlined in 40 CFR 51.112 and 40 CFR part 51 (U.S. 

Environmental Protection Agency, 2005a). The acceptability of a particular model and approach for that

model application is an EPA Regional Office responsibility that could include consultation with EPA 

Headquarters if appropriate. The use of models incorporating complex chemical mechanisms should be 

considered on a case-by-case basis with proper demonstration 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 PM2.5. Use of photochemical grid models for AQRV analysis requirements, 

while not subject to specific EPA model approval requirements outlined in 40 CFR 51.166(l)(2) and 40 

CFR 52.21(l)(2), should be justified for each application and concurrence sought with the affected 

FLM(s). 

4 MODEL EVALUATION 

There are multiple components to model evaluation for the purposes of assessing long range transport 

of secondary pollutants for AQRVs. First, an alternative modeling system as defined in Appendix W must

meet certain criteria for this purpose (Appendix W Section 3.2.2.e). One type of evaluation for this type 

of modeling system for this purpose is to show that the modeling system is theoretically fit for purpose. 

A second evaluation component involves comparison to ambient measurements to assess whether the 

modeling system and generated inputs are appropriate for a specific project application. 

Visibility and deposition are estimated at receptors placed inside Class I areas. This means it is important 

that a long range transport modeling system be able to capture these types of source-receptor 

relationships. In addition, since visibility is largely PM2.5 and deposition a combination of primary 

emitted and secondarily formed pollutants, it is important that a modeling system be able to capture 

single source primary and secondary impacts. Both of 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 generate 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 assessments are typically only done occasionally when a 

modeling system has notably changed from previous testing or has never been evaluated for this 

purpose. This type of assessment is discussed in more detail in section 4.1. 

A second type of evaluation fulfills the need to determine whether inputs to the modeling system for a

specific scenario are adequate for the specific conditions of the project impact assessment (Appendix W 

Section 3.2.2.e). This type of evaluation usually consists of comparing model predictions with 

observation data that coincides with the episode being modeling for a permit review assessment. One of

the most important questions in an evaluation concerns whether the prognostic or diagnostic 

meteorological fields are adequate for their intended use in supporting the project model application 
17 

demonstration. Sections 4.2 and 4.3 cover project specific evaluation approaches that develop 

confidence that a particular model application is appropriate for the project source and key downwind 

receptors. It is important to emphasize that a broad evaluation of a model platform’s skill in estimating 

meteorology or chemical measurements may not sufficiently illustrate the appropriateness of that 

platform for specific projects that will be focused on a narrow subset of the larger set of model inputs 

and outputs. Therefore, broad model platform evaluations should be supplemented with focused 

evaluation and discussion of the appropriateness of model inputs for specific project assessments. 

4.1 Long Range Transport Models – Fit for Purpose Evaluations 

The typical regulatory application of an LRT modeling system is for Prevention of Significant 

Deterioration of Air Quality (PSD) Class I air quality related values (AQRVs) (visibility, deposition, etc.). 

When employed for these purposes, it is customary to 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 modeled 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 spatial 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 reasonably reliable in estimating the magnitude of the 

highest concentrations occurring sometime, somewhere within an area.” Based on this principle, the 

evaluation of near-source primary pollutant dispersion models focus on a model’s ability to replicate the 

highest end of the concentration distribution, regardless of temporal or spatial pairing. Since model skill 

in replicating transport in time and space is important for ARQV analysis, model evaluation should place 

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 proximity. 

For source-receptor distances greater than 50 km, regional scale models may be applied for the 

assessment of visibility impacts due to one or a small group of sources. Skill in estimating sourcereceptor relationships on this scale can be illustrated by evaluating modeling systems against regional 

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 designs 

most relevant for evaluating long range transport modeling systems include the 1980 Great Plains 

Mesoscale Tracer Field Experiment, the 1983 Cross-Appalachian Tracer Experiment (CAPTEX), the 1987 

Across North American Tracer Experiment (ANATEX), and 1994 European Tracer Experiment (ETEX) 

(ENVIRON, 2012b; Hegarty et al., 2013). Photochemical grid models have been shown to demonstrate 

similar skill to Lagrangian models for pollutant transport when compared to measurements made from 

multiple mesoscale field experiments (ENVIRON, 2012b). 

Near-source in-plume aircraft based measurement field studies are useful for evaluating model 

estimates of (near-source) downwind transport and chemical impacts from single stationary point 

sources (ENVIRON, 2012c). Photochemical grid model source apportionment and source sensitivity 

simulation of a single source downwind impacts compare well against field study primary and secondary 

ambient measurements made in Tennessee and Texas (Baker and Kelly, 2014; ENVIRON, 2012c). This 

work indicates photochemical grid models and source apportionment and source sensitivity approaches 
18 

provide meaningful estimates of single source impacts. However, additional evaluations are needed for 

longer time periods and more diverse environments to generate broader confidence in these 

approaches for this purpose. 

4.2 Long Range Transport Models – Meteorology Evaluation 

It is important to determine whether and to what extent confidence may be placed in a prognostic 

meteorological model’s output fields (e.g., wind, temperature, mixing ratio, diffusivity, 

clouds/precipitation, and radiation) that will be used as input to models. Currently there is no bright line 

for meteorological model performance and acceptability. There is valid concern that establishment of 

such criteria, unless accompanied with a careful evaluation process might lead to the misuse of such 

goals as is occasionally the case with the accuracy, bias, and error statistics recommended for judging 

model performance. In spite of this concern, there remains nonetheless the need for some benchmarks 

against which to compare new prognostic and diagnostic model simulations. A significant amount of 

information (e.g. model performance metrics) can be developed by following typical evaluation 

procedures that will enable quantitative comparison of the meteorological modeling to other 

contemporary applications and to judge its suitability for use in modeling studies. 

Development of the requisite meteorological databases necessary for use of photochemical transport 

models should conform to recommendations outlined in Guidance on the Use of Models and Other 

Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze (EPA454/B-07-002) (U.S. Environmental Protection Agency, 2007). Demonstration of the adequacy of 

prognostic or diagnostic meteorological fields can be established through appropriate diagnostic and 

statistical performance evaluations consistent with recommendations provided in the appropriate 

model guidance (U.S. Environmental Protection Agency, 2007). 

4.3 Long Range Transport Models – Chemistry Evaluation 

An operational evaluation is used to assess how accurately the model predicts observed concentrations.

Therefore, an operational evaluation can provide a benchmark for model performance and identify 

model limitations and uncertainties that require diagnostic evaluation for further model 

development/improvement. An operational evaluation for PM2.5 is similar to that for ozone. Some 

important differences are that PM2.5 consists of many components and is typically measured with a 24-

hour averaging time. The individual components of PM2.5 should be evaluated individually. In fact, it is 

more important to evaluate the components of PM2.5 than to evaluate total PM2.5 itself. Apparent 

“good performance” for total PM2.5 does not indicate whether modeled PM2.5 is predicted for “the 

right reasons” (the proper mix of components). If performance of the major components is good, then 

performance for total PM2.5 should also be good. Databases that contain ambient O3, PM2.5, and key 

precursors are noted in section 4.4. Section 4.4 is not intended to provide an exhaustive review of all 

ambient databases but provide an initial set of data that could be used for this purpose. 

Regardless of the modeling system (e.g. photochemical transport or Lagrangian puff model) used to 

estimate secondary impacts of ozone and/or PM2.5, model estimates should be compared to 

observation data to generate confidence that the modeling system is representative of the local and 

regional air quality. For ozone related projects, model estimates of ozone should be compared with 

observations in both time and space. For PM2.5, model estimates of speciated PM2.5 components (such 
19 

as sulfate ion, nitrate ion, etc) should be matched in time and space with observation data in the model 

domain. Model performance metrics comparing observations and predictions are often used to 

summarize model performance. These metrics include mean bias, mean error, fractional bias, fractional 

error, and correlation coefficient (Simon et al., 2012). There are no specific levels of any model 

performance metric that indicate “acceptable” model performance. Model performance metrics should 

be compared with similar contemporary applications to assess how well the model performs (Simon et 

al., 2012). 

Accepted performance standards for speciated and total PM2.5 and ozone for photochemical models 

used in attainment demonstrations may not be applicable for single source assessments. Since the 

emissions and release parameters for the project source are well known, a direct connection between 

general photochemical model performance and the ability of the modeling system to characterize the 

impacts of the project source would be difficult to make. It is important that any potential approaches 

for photochemical model performance for the purposes of single source assessments for PSD and NSR 

use an approach that would be universally applicable to any single source modeling system, which 

includes the Lagrangian models described above. 

4.4 Model performance evaluation data sources 

Provided below is an overview of some of the various ambient air monitoring networks currently 

available that provide relevant data for model evaluation purposes. Network methods and procedures 

are subject to change annually due systematic review and/or updates to the current monitoring 

network/program. Please note, there are other available monitoring networks which are not mentioned 

here and more details on the networks and measurements should be obtained from other sources. 

AQS: The Air Quality System (AQS) is not an air quality monitoring network. However it is a repository of 

ambient air pollution data and related meteorological data collected by EPA, state, local and tribal air 

pollution control agencies from tens of thousands of monitors. AQS contains all the routine hourly 

gaseous pollutant data collected from State and Local Air Monitoring Stations (SLAMS) and National Air

Monitoring Stations (NAMS) sites. SLAMS is a dynamic network of monitors for state and local directed 

monitoring objectives (e.g., control strategy development). A subset of the SLAMS network, the NAMS 

has an emphasis on urban and multi-source areas (i.e, areas of maximum concentrations and high 

population density). The AQS database includes criteria pollutant data (SO2, NO2, O3, and PM2.5) and 

speciation data of particulate matter (SO4, NO3, NH4, EC, and OC), and meteorological data. The data 

are measured and reported on an hourly or daily average basis. An overview of the AQS can be found at 

http://www.epa.gov/ttn/airs/airsaqs/index.htm. 

IMPROVE: The Interagency Monitoring of PROtected Visual Environments (IMPROVE) network began in 

1985 as a cooperative visibility monitoring effort between EPA, federal land management agencies, and 

state air agencies (IMPROVE, 2000). Data are collected at Class I areas across the United States mostly at 

National Parks, National Wilderness Areas, and other protected pristine areas. Currently, there are 

approximately 160 IMPROVE rural/remote sites that have complete annual PM2.5 mass and/or PM2.5 

species data. The website to obtain IMPROVE documentation and/or data is 

http://vista.cira.colostate.edu/improve/. 

STN: The Speciation Trends Network (STN) began operation in 1999 to provide nationally consistent 

speciated PM2.5 data for the assessment of trends at representative sites in urban areas in the U.S. The 
20 

STN was established by regulation and is a companion network to the mass-based Federal Reference 

Method (FRM) network implemented in support of the PM2.5 NAAQS. As part of a routine monitoring 

program, the STN quantifies mass concentrations and PM2.5 constituents, including numerous trace 

elements, ions (sulfate, nitrate, sodium, potassium, ammonium), elemental carbon, and organic carbon. 

In addition, there are approximately 181 supplemental speciation sites which are part of the STN 

network and are SLAMS sites. The STN data at trends sites are collected 1 in every 3 days, whereas 

supplemental sites collect data either 1 in every 3 days or 1 in every 6 days. Comprehensive information 

on the STN and related speciation monitoring can be found at 

http://www.epa.gov/ttn/amtic/specgen.html and http://www.epa.gov/aqspubl1/select.html. 

CASTNet: Established in 1987, the Clean Air Status and Trends Network (CASTNet) is a dry deposition 

monitoring network where data are collected and reported as weekly average data (U.S. EPA, 2002b). 

Relevant CASTNet data includes weekly samples of inorganic PM2.5 species and ground-level ozone. 

More information can be obtained through the CASTNet website at http://www.epa.gov/castnet/. 

SEARCH: The South Eastern Aerosol Research and CHaracterization (SEARCH) monitoring network was 

established in 1998 and is a coordinated effort between the public and private sector to characterize the 

chemical and physical composition as well as the geographical distribution and long-term trends of 

PM2.5 in the Southeastern U.S. SEARCH data are collected and reported on an hourly/daily basis. 

Background information regarding standard measurement techniques/protocols and data retrieval can 

be found at http://www.atmospheric-research.com/studies/SEARCH/index.html. 

NADP: Initiated in the late 1970s, the National Acid Deposition Program (NADP) monitoring network 

began as a cooperative program between federal and state agencies, universities, electric utilities, and 

other industries to determine geographical patterns and trends in precipitation chemistry in the U.S. 

NADP collects and reports wet deposition measurements as weekly average data (NADP, 2002). The 

network is now known as NADP/NTN (National Trends Network) and measures sulfate, nitrate, 

hydrogen ion (measure of acidity), ammonia, chloride, and base cations (calcium, magnesium, 

potassium). Detailed information regarding the NADP/NTN monitoring network can be found at 

http://nadp.sws.uiuc.edu/. 

5 ACKNOWLEDGEMENTS 

The document includes contributions from Bret Anderson, 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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Atmospheric Chemistry and Physics 12, 455-468. 

 
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United StatesEnvironmental Protection Agency 

Office of Air Quality Planning and Standards

Air Quality Analysis Division 

Research Triangle Park, NC 

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