MEMORANDUM 

UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

RESEARCH TRIANGLE PARK, NC 27711 

DEC 03 21M 

OFFICE OF 

AIR QUALITY PLANNING 

AND STANDARDS 

SUBJECT: Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals for 

Ozone, PM2s, and Regional Haze 

FROM: Richard Wayland, Division Director~/l.tt/d...J/ Air Quality Assessment Division {j ----

TO: Regional Air Division Directors, Regions 1-10 

The Environmental Protection Agency (EPA) is providing a revised version of Modeling 

Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PMn. and Regional 

Haze to the state and local air agencies as well as the public for consideration, review, and 

comment. This document reflects the EPA's recommendations for how air agencies should 

conduct air quality modeling and related technical analyses to satisfy model attainment 

demonstration requirements for the 2008 ozone and 2012 PM2.s National Ambient Air Quality 

Standards (NAAQS), as well as for upcoming regional haze reasonable progress analyses. 1 This 

document updates the previous version of the modeling guidance which was released in April 

2007. 

This document does not substitute for provisions or regulations of the Clean Air Act (CAA), nor 

is it a regulation itself. As the term "guidance" suggests, it provides recommendations on how to 

implement the modeling requirements. Thus, it does not impose binding, enforceable 

requirements on any party, nor does it assure that the EPA will approve all instances of its 

application, as the guidance may not apply to a particular situation based upon the circumstances. 

Final decisions by the EPA regarding a particular State Implementation Plan (SIP) demonstration 

will only be made based on the statute and applicable regulations, and will only be made 

following a final submission by air agencies and after notice and opportunity for public review 

and comment. 

1 This draft guidance will be revised, as needed, once the review of the current ozone standard is 

completed in October 2015. 

Recycled/Recyclable •Printed wKh Vegetable Oil Based Inks on 100% Recycled Paper (40% Postconsumer) 
2

There are several modeling guidance issues that are most appropriate to be addressed in a notice 

and comment rulemaking such as the 2008 ozone and soon-to-be proposed 2012 PM2.5 

implementation rules. As such, there are several details that have either been removed from the 

draft guidance or are pending updates when the 2008 ozone and 2012 PM2.5implemenation rules 

are finalized. These are specifically noted in the guidance (in some cases with placeholders). 

The ozone implementation rule for the 2008 NAAQS was proposed in June 2013 and is expected 

to be finalized in early 2015. The PM2.5 implementation rule for the 2012 NAAQS is expected to 

be proposed in early 2015.

GUIDANCE UPDATES 

Like the original 2007 version, the revised guidance is divided into two main sections. The first 

part describes how to setup and apply a photochemical modeling platform (including 

meteorological, emissions, and air quality modeling), and the second part describes how to use 

the results of the air quality modeling to show whether future attainment of the ozone and/or 

PM2.5 NAAQS is likely. The guidance also describes how to use photochemical grid modeling to 

evaluate reasonable progress goals for regional haze. 

Many of the updates to the guidance are in the form of updated and reorganized text. In addition, 

numerous references have been updated and added. The attainment tests for the annual and 24-

hour average PM2.5 NAAQS are unchanged2, but the 8-hour ozone NAAQS attainment test has 

been updated to better reflect the level and form of the 2008 ozone NAAQS . More details are 

provided below.

Specific updates to the draft guidance include:

 A complete reorganization of the document including removal of outdated language and 

references.

 The ozone attainment test has been updated to address the 2008 NAAQS.

o The revised test eliminates the initial concentration threshold (which was formally 

85 ppb) and focuses the calculation of relative response factors (RRFs) to the ten 

highest modeled 8-hour average daily maximum ozone days.

 This potentially eliminates days in the RRF calculation that are well below 

the base year observed design values. 

o Elimination of the initial threshold concentration allows the test to be easily 

applied to any level of the NAAQS. The revised guidance reflects 

recommendations for the current 75 ppb NAAQS. Minimal (if any) changes to 

the test will be needed to address potential future ozone NAAQS.

 

2 The 24-hour PM2.5 attainment test is unchanged from the version which is detailed in a June 2011 

guidance memo. The attainment test from the June 2011 memo has been incorporated into this revised 

version of the guidance. 
3

 The emissions modeling section has been extensively updated to account for new and 

improved emissions models and input data. Additional information on SIP emissions 

inventory requirements is contained in the draft Emissions Inventory Guidance for 

Implementation of Ozone and Particulate Matter National Ambient Air Quality Standards 

(NAAQS) and Regional Haze Regulations available at:

http://www.epa.gov/ttn/chief/eidocs/eiguid/2014revisedeiguidance.pdf. The draft 

modeling guidance and the draft emissions inventory guidance have been carefully 

coordinated.

 The Weight of Evidence (WOE) section has been updated to include additional 

information that may be appropriate for use in a WOE demonstration. The WOE section 

includes discussions on emissions controls, modeling, and data analyses that have been 

recommended by stakeholders for inclusion in a WOE demonstration. 

The revised document is draft guidance for public review and comment. However, since the 

revised guidance replaces and updates the existing modeling guidance document without making 

major changes, EPA encourages states to follow the recommendations in this draft guidance until 

an updated version is released. These recommendations will be updated, as needed, based on 

comments received on this guidance and final decisions made in the implementation rules. States 

with upcoming attainment demonstration deadlines should consult with their EPA Regional 

Office to determine the appropriate course of action.

REVIEW AND COMMENT

The EPA will accept comments on the draft modeling guidance through the close of business on 

March 13, 2015. This allows at least 90 days for consideration, review, and comment on the 

material presented in the draft guidance. For convenience, the draft guidance document is 

available electronically on the EPA’s SCRAM website, 

http://www.epa.gov/ttn/scram/guidance/guide/Draft-O3-PM-RH-Modeling_Guidance-2014.pdf.

Comments should be electronically submitted to Brian Timin of EPA’s Air Quality Modeling 

Group at timin.brian@epa.gov. EPA will also schedule a conference call in Mid-December with 

state and local air agencies to summarize the guidance and allow for questions. 

In preparing the final version of the guidance, the EPA will take into consideration comments 

received on the draft modeling guidance, as well as decisions made in the final 2008 ozone 

and/or 2012 PM2.5 implementation rules. As there are a number of issues that need to be 

informed by these ozone and PM2.5 implementation rules, the timing for release of a final version 

of the guidance depends on the timing of the implementation rules. Depending on the timing of 

these final implementation rules (as well as any implementation rule that may be needed 

following review of the current ozone NAAQS), the EPA will consider releasing a 2nd draft 

version of the modeling guidance or clarification memos, as needed.
4

If there are any questions regarding the draft modeling guidance, please contact Brian Timin of 

EPA’s Air Quality Modeling Group at (919) 541-1850 or timin.brian@epa.gov.

cc: Steve Page

Mike Koerber

Anna Wood

Air Program Managers

Kristi Smith, OGC

Attachment
Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional 

Haze- December 2014 DRAFT

1 

DRAFT December, 2014 

Modeling Guidance for Demonstrating 

Attainment of Air Quality Goals for 

Ozone, PM2.5, and Regional Haze 

NOTE: Ozone NAAQS implementation requirements are currently being assessed as part of the 

Implementation of the 2008 National Ambient Air Quality Standards for Ozone: State 

Implementation Plan Requirements; Proposed Rule (78 FR 34178, June 6, 2013) (Hereinafter, 

“Proposed Ozone Implementation Rule”). Accordingly, the guidance provided in this document 

addresses several options that were addressed in the Proposed Ozone Implementation Rule, 

which may or may not be adopted in the final rule. To the extent that the Final Ozone 

Implementation Rule takes a different approach than contained in this guidance, the Final Rule 

will dictate what is required for air quality modeling. Once the Final Ozone Implementation 

Rule is published, EPA will (if necessary) revise this guidance to reflect the regulations and 

requirements adopted in the final rule. 

In addition, PM NAAQS implementation requirements are currently undergoing review as part 

of the planned proposed PM2.5 Implementation Rule. The guidance includes the use of “[PM 

Implementation Placeholder]” text where necessary. Once that rule is finalized, this guidance 

will be updated (if necessary) and the [PM Implementation Placeholder] text will be replaced 

with the PM-specific guidance associated with that rule. 
Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional 

Haze- December 2014 DRAFT

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Table of Contents 

1.0 Introduction ............................................................................................................................... 5

1.1 What Is The Purpose Of This Document? .............................................................. 6

1.2 Does The Guidance In This Document Apply To Me? ......................................... 6

1.3 Outline..................................................................................................................... 7

2.0 Building a Model Platform ....................................................................................................... 9

2.1 Conceptual Description ................................................................................................. 9

2.1.1 Example applications of conceptual models ................................................ 11

2.2 Modeling Protocol and Supporting Documentation ................................................... 12

2.2.1 Modeling protocol ........................................................................................ 12

2.2.2 Attainment Demonstration Documentation Package ................................... 13

2.3 Episode Selection ........................................................................................................ 14

2.3.1 Choosing Time Periods to Model ................................................................ 15

2.3.2 Future Year Selection ............................................................................... 17

2.4 Modeling Domain Selection ....................................................................................... 18

2.4.1 Domain Size .............................................................................................. 18

2.4.2 Vertical Layer Configuration .................................................................... 19

2.4.3 Horizontal Grid Cell Size .......................................................................... 20

2.5 Air Quality Model Selection ....................................................................................... 21

2.6 Meteorological Inputs ................................................................................................. 24

2.6.1 Developing base year meteorological fields ............................................. 24

2.6.2 Assessing Impacts of Future Year Meteorology ....................................... 28

2.6.3 Evaluation of base year meteorological fields ............................................. 29

2.7 How Are the Emission Inputs Developed? ................................................................. 31

2.7.1 What Emissions Inventory Years Should I use for Base and Future 

Modeling? ................................................................................................. 32

2.7.2 What Base Year Emission Inventory Data Are Needed to Support Air 

Quality Models? ........................................................................................ 32

2.7.3 What Pollutants Should Be Included in Base Year Inventories? ................. 33

2.7.4 What Temporal Resolution Should Be Used for Base Year Inventories? ... 33

2.7.5 What Spatial Coverage Is Need for Base Year Inventories? ....................... 34

2.7.6 How Can The National Emission Inventory by Air Agencies? ................... 35

2.7.7 How Should Base Year Emissions Be Created? .......................................... 36

2.7.8 What Other Data Are Needed to Support Emissions Modeling? ................ 44

2.7.9 How Are Inventory Data Converted Into Air Quality Model Input?........... 50

2.7.10 Other Emissions Modeling Issues .............................................................. 53

2.7.11 How Are Emissions Estimated for Future Years? ..................................... 56

2.8 Initial and Boundary Conditions ................................................................................. 58

3.0 Evaluating Model Performance .............................................................................................. 62

3.1 Overview of model performance evaluation............................................................... 62

3.2 Operational Evaluation ............................................................................................... 64

3.2.1 Metrics ......................................................................................................... 65

3.2.2 Plots.............................................................................................................. 68

3.2.3 Ozone Model Performance .......................................................................... 74
Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional 

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3.2.4 PM2.5 Model Performance .......................................................................... 74

3.3 Ambient Measurement Networks ............................................................................... 76

3.3.1 AQS........................................................................................................... 76

3.3.2 IMPROVE................................................................................................. 77

3.3.3 CASTNet................................................................................................... 77

3.3.4 CSN ........................................................................................................... 78

3.3.5 NADP ........................................................................................................ 78

3.3.6 SEARCH ................................................................................................... 79

3.3.7 Speciated PM2.5 Data Summary ................................................................... 79

3.4 Diagnostic Evaluation ................................................................................................. 85

3.4.1 Process Analysis .......................................................................................... 86

3.4.2 Indicator Ratios ............................................................................................ 88

3.4.3 Source Apportionment ................................................................................. 91

3.4.4 Sensitivity Studies ........................................................................................ 91

3.4.5 Dynamic Model Evaluation ......................................................................... 92

3.5 Evaluation Tools ......................................................................................................... 94

4.0 Assessing Modeled Attainment .............................................................................................. 95

4.1 Overview of model attainment test ............................................................................. 95

4.1.1 Establishing the base design value ............................................................... 97

4.1.2 Calculation of relative response factors .................................................... 99

4.2 Model attainment test for the primary ozone standard ....................................... 100

4.2.1 Model values to use in the RRF calculation ........................................... 100

4.2.2 Grid cells to use in the RRF calculation ................................................. 102

4.2.3 Sample model attainment test calculation............................................... 103

4.3 Model attainment test for the secondary ozone standard .......................................... 106

4.4 What is the Modeled Attainment Tests for the Annual Average PM2.5 NAAQS? ... 106

4.4.1 Ambient PM2.5 Data Used in the Annual PM2.5 Attainment Test ........... 109

4.4.2 FRM Monitors that Don’t Have Speciation Data ................................... 109

4.4.3 PM2.5 Species Calculations and Definitions ........................................... 110

4.4.4 Recommended Treatment of Species Data ............................................. 111

4.5 What Is The Recommended Modeled Attainment Test For The 24-Hour NAAQS? 124

4.6 Local Area Analyses- Special Considerations for Primary PM2.5 ............................ 130

4.6.1 Analysis of Primary PM2.5 Impacts at Monitors ..................................... 131

4.6.2 Analysis of Primary PM2.5 Impacts at Monitors using a Gaussian 

Dispersion Model .................................................................................... 132

4.7 Estimating design values at unmonitored locations .................................................. 144

4.7.1 Why does the unmonitored area analysis need to use both ambient data 

and model output? ................................................................................... 145

4.7.2 Implementation of Gradient Adjusted Spatial Fields.............................. 146

4.7.3 Using the Results of the Unmonitored Area Analysis ............................ 148

4.8 What Is The Recommended Modeling Analysis for Regional Haze? ...................... 149

4.8.1 Regional Haze Rule Background ............................................................ 149

4.8.2 How is Regional Haze Measured? .......................................................... 151

4.8.3 How Do I Apply A Model To Calculate Changes in Visibility? ............... 158

4.8.4 How Do I Select Appropriate Inputs For The Uniform Rate of Progress 

Analysis? ................................................................................................. 173
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4.9 “Weight of Evidence”- How Can Additional Analyses Be Used to Support the 

Attainment Demonstration? ................................................................................ 179

4.9.1 What Types of Additional Analyses Should Be Completed as Part of the 

Attainment Demonstration? .................................................................... 180

4.9.2 What Role Should Weight Of Evidence Play In Visibility-Related 

Analyses? ................................................................................................ 187

4.9.3 Weight of Evidence Summary ................................................................... 190

References ................................................................................................................................... 192

 

 
Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional 

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1.0 Introduction 

This document describes how to estimate if an emissions control strategy will lead to 

attainment of the annual average and 24-hour average national ambient air quality standards 

(NAAQS) for particles smaller than 2.5 μm in diameter (PM2.5) and the 8-hour NAAQS for ozone. 

We also describe how to use modeled and monitored data to estimate visibility improvement in 

Class I areas (e.g., national parks, wilderness areas) as part of a reasonable progress analysis.1

 

The document describes how to apply air quality models to generate the predictions used to 

evaluate attainment and/or uniform rate of progress assessments. Modeling to show 

attainment of the NAAQS primarily applies to nonattainment areas2

 for which modeling is 

required, or desired. Modeling to assess reasonable progress for regional haze applies to all 

states.3

 

This guidance is designed to implement national policy on air quality modeling requirements as 

embodied in the Clean Air Act (CAA), the Proposed Ozone SIP Requirements Rule (also called 

the Proposed Ozone Implementation Rule)4

, the PM2.5 Implementation Rule5, and the Regional 

Haze Rule. This guidance is intended for use by the United States (U.S.) Environmental 

Protection Agency (EPA) headquarters and regional offices; state, local and tribal air quality 

management authorities; and the general public. This document does not substitute for 

provisions or regulations of the CAA enumerated above, nor is it a regulation itself. As the term 

“guidance” suggests, it provides recommendations on how to implement the modeling 

 

1Modeling is not used to determine reasonable progress goals and does not determine whether 

reasonable progress has been met. Modeling is one part of the reasonable progress analysis. The 

modeling results are used to determine if future year visibility at Class I areas are estimated to be on a 

(glide) path towards reaching natural background. This is called a uniform rate of progress analysis or 

“glidepath” analysis. The uniform rate of progress analysis is described in more detail in section 4.8. 

2While this guidance document is primarily directed at modeling applications in nonattainment areas, it

may also be useful as a guide for modeling to support NEPA analyses, maintenance areas or to support 

other rules or provisions of the Clean Air Act. 

3The Regional Haze rule assumes that all 50 states either contain a Class I area or impact visibility within 

a Class I area. Therefore, a regional haze State Implementation Plan (SIP) is required for all states (40 

CFR 50.308(b)). 

4

 Implementation of the 2008 National Ambient Air Quality Standards for Ozone: State Implementation 

Plan Requirements; Proposed Rule (78 FR 34178, June 6, 2013) 

5

 Fine Particulate Matter National Ambient Air Quality Standards: State Implementation Plan 

Requirements, proposed rule anticipated in 2015. 
Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional 

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requirements. Thus, it does not impose binding, enforceable requirements on any party, nor 

does it assure that the EPA will approve all instances of its application, as the guidance may not 

apply to a particular situation based upon the circumstances. 

The EPA and state, local, and tribal air agencies (hereinafter, referred simply as “air agency” or 

“air agencies”) retain the discretion to adopt approaches on a case-by-case basis that differ 

from this guidance where appropriate. Final decisions by the EPA regarding a particular SIP 

demonstration will only be made based on the statute and applicable regulations, and will only 

be made following a final submission by air agencies and after notice and opportunity for public 

review and comment. Interested parties are free to raise questions and objections about the 

appropriateness of the application of this guidance to a particular situation; the EPA and air 

agencies should consider whether or not the recommendations in this guidance are appropriate 

in that situation. 

1.1 What Is The Purpose Of This Document? 

This document has two purposes. The first purpose is to describe how to apply an air quality 

model to produce results needed to support an attainment demonstration or a uniform rate of 

progress analysis in a scientifically appropriate manner. The second is to explain how to 

interpret whether results of modeling and other analyses support a conclusion that attainment 

of the ozone and/or PM2.5 NAAQS, and/or uniform rate of progress for regional haze will occur 

by the appropriate date for an area. 

1.2 Does The Guidance In This Document Apply To Me? 

This guidance applies to all locations required to submit a State Implementation Plan (SIP), or 

Tribal Implementation Plan (TIP) revision with an attainment demonstration designed to 

achieve attainment of the ozone and/or PM2.5 NAAQS. The guidance also applies to SIPs 

developed to address regional haze rule requirements. Areas required to submit an attainment 

demonstration and/or a reasonable progress demonstration are encouraged to follow the 

procedures described in this document. Details on when a State is required to submit a 

modeled attainment or reasonable progress demonstration can be found in the regional haze 
Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional 

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rule6, the 8-hour ozone implementation rule7 and the PM2.5 implementation rule.8

1.3 Outline 

Part 1 of the guidance provides an overview of the modeling and attainment demonstration 

process. Part 2 describes how to build a “modeling platform” and apply air quality models. A 

“modeling platform” consists of the building blocks of an air quality modeling demonstration. 

This includes emissions modeling9, meteorological modeling, and the development of all other 

inputs needed to run an air quality model. The model platform development process consists 

of the following steps as outlined in Section 2: 

Section 2.1 Development of a conceptual description of the problem to be addressed 

Section 2.2 Develop a modeling protocol 

Section 2.3 Select appropriate meteorological time periods to model 

Section 2.4 Choose an appropriate area to model with appropriate 

horizontal/vertical resolution 

Section 2.5 Select an appropriate model to support the demonstration 

Section 2.6 Generate meteorological inputs to the air quality model 

Section 2.7 Generate emissions inputs to the air quality model 

Section 2.8 Develop initial and boundary conditions that are suitable for the 

application 

Typically, the air quality model process starts with the development of base year emissions and 

meteorology for input to an air quality run to evaluate model performance. Section 3 describes 

the process for evaluating model performance and performing diagnostic analyses. After 

evaluating the model and making any necessary input changes or adjustments, the model is run 

for a future year, which corresponds to the appropriate attainment year for the area. The air 

quality model outputs are then used to apply the modeled attainment test to support an 

attainment demonstration, as described in Chapter 4 of the guidance. We explain what is 

 

640 CFR 51.308 

740 CFR 50.908 [to be updated when the ozone implementation rule update is finalized] 

840 CFR 50.1007 [to be updated when the PM2.5 implementation rule update is finalized] 

9

 Additional emissions inventory requirements and details on emissions modeling are contained in 

“Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter National Ambient 

Air Quality Standards (NAAQS) and Regional Haze Regulations” at: 

http://www.epa.gov/ttn/chief/eidocs/eiguid/2014revisedeiguidance.pdf 
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meant by a modeled attainment demonstration, a modeled attainment test, and a weight of 

evidence determination. Modeled attainment tests are described for the 8-hr ozone NAAQS, 

the annual and 24-hour PM2.5 NAAQS, and a visibility assessment for regional haze. 

Model applications require a substantial effort. Air agencies should work closely with the 

appropriate U.S. EPA Regional Office(s) in executing each step of the modeling and attainment 

demonstration process. This will increase the likelihood of approval of the demonstration at 

the end of the process. 

 
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2.0 Building a Model Platform 

2.1 Conceptual Description 

The first step in developing an attainment demonstration should be to construct a conceptual 

description of the problem that is being addressed. Conceptual descriptions, which are also 

referred to as conceptual models, are comprehensive summaries of the “state of the 

knowledge” regarding the influence of emissions, meteorology, transport, and other relevant 

atmospheric processes on air quality in the area (Vickery, 2004). For a conceptual description 

to be informative it should identify what processes and sources, in the generic sense, are most 

responsible for the air quality issue being simulated. Well-constructed conceptual models can 

substantially inform the design of the attainment demonstration modeling (e.g., episode 

selection, choice of domain, emissions priorities, evaluation focus) and should be conducted in 

advance of the development of a modeling protocol. It is worth noting that conceptual 

descriptions can be valuable in other air quality planning efforts besides attainment 

demonstrations. They can also be useful in determining nonattainment area boundaries, 

investigating emissions control program impacts, and monitoring network design, among 

others. 

The following bullets describe some of the key building blocks in developing a conceptual 

model. The process steps discussed below are meant to illustrate one possible template for 

building a conceptual model to inform an air quality modeling demonstration; there may be 

alternate approaches that better suit individual study areas or issues, which air agencies should 

discuss with the appropriate EPA Regional Office. 

• Introduce the general nature of the air quality problem addressed by the conceptual model: 

o What are the pollutants of concern in the area? 

o What are the current air quality levels in the area? 

o What is the attainment/nonattainment status of the area? 

o What is the geographical scope of poor air quality? 

o What is the temporal scope of poor air quality? 

o What are the air quality trends in the area? Is the problem getting better or worse? 

o What are the suspected mechanisms for formation of poor air quality levels? 

o What are the sources of emissions that may contribute to poor air quality? 

o Are there unique meteorological influences on local air quality levels? 
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• Describe the ambient monitoring network used for the conceptual model: 

o Develop a map of monitor sites and types (e.g., FRM, FEM, STN, IMPROVE, etc.) 

o Describe characteristics of individual monitoring sites (scale, frequency, etc.) 

• Describe the status and trends of air quality in the area: 

o Summarize the relevant monitoring data for the air quality problem being studied. 

o Identify locations that are in violation of the NAAQS 

o Describe the spatial pattern in pollutant and precursor pollutant levels 

o Describe the temporal pattern in pollutant and precursor pollutant levels 

o Describe the annual trends in concentrations over the period of interest 

o If appropriate, develop fused ambient/model surfaces to fill any gaps within the 

monitoring network. 

• Investigate possible relationships between emissions and air quality: 

o Examine emissions estimates for the main sector/source categories 

o Compare emission trends for annual and or seasonal/episodic periods to 

corresponding air quality trends 

o Identify key emission sources or source categories 

o Assess the historical effectiveness of control programs 

o Consider how future emissions growth or reductions may affect air quality 

o List control programs that are in place or that will soon be implemented that may 

impact emissions sources in the area 

• Investigate possible relationships between meteorology and air quality (AQ): 

o Describe meteorological characteristics on poor air quality days (e.g., wind 

speed/direction, temperatures, relative humidity levels, inversion indicators, etc.) 

o Identify any distinct meteorological phenomena that coincide with poor air quality 

days 

o Prepare pollution roses, HYSPLIT back-trajectories (Draxler, 1998) , or any other 

relevant analysis needed to link poor air quality days to specific wind/transport 

patterns 

o Assess the impact from pollutant transport into area based on meteorological data 

and ambient air quality data 

• Synthesize all of the relevant information into a detailed conceptual model: 

o Aggregate all elements of the conceptual model and provide the key findings 

resulting from the analyses completed 
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o Characterize the potential factors that influence air quality in the area and where 

possible the rank the importance of those influences 

o Identify areas that require further investigation, either in terms of data collection or 

chemical transport modeling. 

o Discuss how the subsequent SIP modeling and associated protocol is impacted by 

this conceptual description. 

2.1.1 Example applications of conceptual models 

 

The idea of developing a modeling protocol based upon a conceptual description of the sources 

and processes that lead to poor air quality is well-established (USEPA, 2007). Numerous 

excellent conceptual models of local and regional air quality issues have been developed over 

the past decade. Table 2-1 provides a non-comprehensive list of example conceptual models 

that have been developed since the last release of the guidance that may be useful in orienting 

future SIP modeling applications. 

Table 2.1: Examples of recently developed conceptual models 

Air quality issue Reference

PM2.5 / Regional Haze (Northeast U.S.) Downs et al. (2012) 

PM2.5 (Midwest U.S) LADCO (2009) 

Ozone (Austin TX) McGaughey et al. (2010) 

Ozone (Wyoming) Stoeckenius and Ma (2010) 

PM2.5 (Southeast Michigan) Turner (2008) 
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2.2 Modeling Protocol and Supporting Documentation 

As with any technical support document designed to inform air quality planning, an attainment 

demonstration should be supported by documentation that sufficiently describes the 

procedures used in the analysis. In order to facilitate the process of EPA Regional Office review 

and approval, we recommend the preparation of two separate supporting documents: one 

before the modeling analyses are initiated (modeling protocol) and one after the analyses have 

been completed (attainment demonstration package). 

2.2.1 Modeling protocol 

Developing and implementing a modeling protocol is an important part of a modeled 

attainment demonstration. The protocol should detail and formalize the procedures for 

conducting all phases of the modeling study, such as describing the background and objectives 

for the study, creating a schedule and organizational structure for the study, selection of 

appropriate period(s) for modeling, developing the input data, conducting model performance 

evaluations, interpreting modeling results, describing procedures for using the model to 

demonstrate whether proposed strategies are sufficient to attain the NAAQS and/or regional 

haze goals, and producing documentation to be submitted for EPA Regional Office review and 

approval. The most important function of the modeling protocol is to serve as a blueprint for 

planning how the modeled attainment demonstration will be performed. The protocol should 

be a valuable communication device by which air agencies, EPA, and other stakeholders can 

assess the applicability of default recommendations and develop area-specific alternatives, 

where needed prior to conducting the work to build the modeling system. A suitable protocol 

should lead to extensive participation by stakeholders in developing the demonstration. It 

should also reduce the risk of spending time and resources on efforts which are unproductive 

or inconsistent with EPA rules, policy, and guidance. 

Meaningful protocols should fully communicate the expected scope of the analysis and provide 

a blueprint for carrying out the needed analyses. There is no one-size-fits-all format for a 

sufficient modeling protocol as different individual areas may require specific points of 

emphasis. However, past attainment demonstrations have yielded several excellent protocol 

examples that could serve as templates for any groups developing new modeling protocols. 

While not exhaustive, potentially valuable protocol references include recent attainment 

demonstration modeling efforts completed in the Dallas/Fort Worth area (Environ, 2003), the 

Denver region (Morris, 2011), and the San Joaquin Valley (CARB, 2012). Based on past 
Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional 

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experience, EPA recommends that the following topics be core elements of any modeling 

protocol: 

• Overview of the air quality issue being considered including historical background 

• List of the planned participants in the analysis and their expected roles 

• Schedule for completion of key steps in the analysis and final documentation 

• Description of the conceptual model for the area 

• Description of periods to be modeled, how they comport with the conceptual 

model, and why they are sufficient 

• Models to be used in the demonstration and why they are appropriate 

• Description of model inputs and their expected sources (e.g., emissions, met, etc.) 

• Description of the domain to be modeled (expanse and resolution) 

• Process for evaluating base year model performance (meteorology, emissions, and 

air quality) and demonstrating that the model is an appropriate tool for the intended 

use 

• Description of the future years to be modeled and how projection inputs will be 

prepared 

• Description of the attainment test procedures and (if known) planned weight of 

evidence 

• Expected diagnostic or supplemental analyses needed to develop weight of evidence 

analyses 

• Commitment to specific deliverables fully documenting the completed analysis 

2.2.2 Attainment Demonstration Documentation Package 

Where the modeling protocol describes the planned scope of the analysis, the final attainment 

demonstration package summarizes the actual analysis conducted (including procedures used) 

to show that an area will meet the NAAQS under a specific set of future conditions. This 

document can follow the same basic outline as the modeling protocol, highlighting those 

aspects of the modeling that may have changed from the original plans. Additionally, the 

attainment demonstration package should have detailed information on any emission 

reduction strategies that will be implemented as part of an attainment SIP. Ultimately, the 

attainment demonstration package should provide a narrative fully describing the technical 

rationale behind the projection of attainment of a specific air quality goal in an area. Based on 

past experience, EPA recommends that the following topics be core elements of any attainment 

demonstration package: 
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• Executive summary that provides an overview of the analysis and the key 

conclusions 

• Reference to the modeling protocol noting any major deviations from the original 

plans 

• List of the institutional participants in the attainment demonstration and their roles 

• Description of air quality in the area and describe how that shaped the analysis 

• Justification for the model, episodes, domain, and grid(s) used in the analysis 

• Description of the development of the emissions inputs used in the base year 

modeling, including tabular summaries by state/county, as appropriate. 

• Description of the development of meteorological inputs used in the base year 

modeling 

• Description of all other base year modeling inputs 

• Evaluation of base year model performance (meteorology, emissions, and air 

quality), including a description of the observational database used in the evaluation 

and any diagnostic or sensitivity tests used to improve the model 

• Description of the strategy used to demonstrate attainment, including speciated 

emissions summaries for the future year and identification of authority for 

implementing these strategies 

• Description of the attainment test results 

• Description of any supplemental analyses designed to bolster the original 

attainment test results 

• Detailed summary of the entire analysis that leads to the conclusion that the 

selected attainment demonstration strategy is likely to produce attainment of the 

NAAQS and/or uniform rate of progress by the required date 

2.3 Episode Selection 

The modeled attainment test adjusts observed ambient concentrations during a base case 

period (e.g., 2009-2013) to a future period (e.g., 2018) using model-derived “relative response 

factors”. It is important that emissions used in the attainment modeling correspond with the 

period reflected by the chosen baseline design value period (e.g., 2009-2013). Deviations from 

this constraint will diminish the credibility of the relative response factors. The base year 

inventory typically corresponds to the middle year of the baseline average design value period 

(e.g. 2011 for a 2009-2013 average design value). The base emissions inventory is the inventory 

that is projected to a future year. Alternatively, the base year emissions can reflect average 

emissions from the base year period, but the emissions year or years should be representative 

of the 5 year design value window. 
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There is no recommended default base year for modeling. However, it is recommended to use 

a recent base year period. This ensures that the emissions projection period is as short as 

possible and the base year ambient data is as current as possible. For example, projecting 

emissions from 2011 to 2018 (using a 2009-2013 base year average design value) should be less 

uncertain than projecting emissions from 2005 to 2018 (using a 2003-2007 base year average 

design value). The most recent ambient design values reflect actual emissions changes that 

have occurred over time. It is therefore better to use recent design values, which are actual 

measurements, and modeled emissions changes to project future concentrations, than to use 

older ambient data to estimate the change in design values. When selecting a base modeling 

year, air agencies should review recent ambient data and consider the factors below. 

2.3.1 Choosing Time Periods to Model 

In the past, the choice of modeled episode days has been limited by the speed of computers 

and the ability to store model output files. With the advancement in computer technology over 

the past two decades, computer speed and storage issues are no longer an impediment to 

modeling long time periods. In fact, the majority of recent regulatory assessment modeling 

platforms have been inclusive of entire summers and/or full years (as appropriate) for ozone, 

PM2.5, and regional haze (Boylan and Russell, 2006; Morris et al., 2006; Rodriguez et al., 2009; 

Simon et al., 2012; Tesche et al., 2006; U.S. Environmental Protection Agency, 2011a, b). 

Ozone based research has shown that model performance evaluations and the response to 

emissions controls need to consider modeling results from relatively long time periods, in 

particular, full synoptic cycles or even full ozone seasons (Hogrefe et al., 2000; Vizuete et al., 

2011). In order to examine the response to ozone control strategies, it may not be necessary to 

model a full ozone season (or seasons), but, at a minimum, modeling “longer” episodes that 

encompass full synoptic cycles is advisable. Time periods which include a ramp-up to a high 

ozone period and a ramp-down to cleaner conditions allow for a more complete evaluation of 

model performance under a variety of meteorological conditions. 

Most model applications for the annual PM2.5 NAAQS have modeled a full year (Morris et al., 

2006; Tesche et al., 2006; U.S. Environmental Protection Agency, 2011a, b). This is a logical goal 

since every monitored day of the year is included in the calculation of the annual NAAQS. The 

annual PM2.5 NAAQS is unique because each and every ambient observation is included in the 

average. It is therefore likely that a full year of modeled data is needed to adequately 

represent the annual average PM2.5 standard. 
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Eight-hour ozone and 24-hour PM2.5 episode selection criteria are similar because both 

standards are based on short term peak concentration periods. Regional haze calculations are 

based on an average of 20 or more days per year (20% best and 20% worst days). Therefore, 

regional haze episode selection will likely include more days throughout the year. 

At a minimum, several criteria should be used to select time periods that are appropriate to 

model: 

• Model time periods that are close to the most recently compiled and quality assured 

National Emission Inventory (NEI). These comprehensive inventories are typically 

generated every 3 years (e.g. 2005, 2008, 2011, etc.) but often contain year specific data 

for the intermediate years. Since NEI years will have a nationwide complete and 

comprehensive inventory, selecting a base modeling year that is also an NEI year may 

save resources. However, other factors should be considered when selecting a base 

modeling year, such as observed ambient data, meteorology, and availability of special 

study data. After consideration of all factors, the most appropriate base year may or 

may not be an NEI year. See Section 2.7 for more information on base year inventory 

selection issues. 

• Model time periods in which observed concentrations are close to the appropriate base 

year design value or level of visibility impairment and ensure there are a sufficient 

number of days so that the modeled attainment test applied at each monitor violating 

the NAAQS is based on multiple days (see sections 4.2 and 4.5). 

• Model time periods both before and following elevated pollution concentration 

episodes to ensure the modeling system appropriately characterizes low pollution 

periods, development of elevated periods, and transition back to low pollution periods 

through synoptic cycles. 

• Simulate a variety of meteorological conditions conducive to elevated air quality. 

Primary ozone (8-Hour Ozone) - Choose time periods which reflect a variety of 

meteorological conditions that frequently correspond with observed 8-hour daily maxima 

concentrations greater than the level of the NAAQS at monitoring sites in the nonattainment 

area. 

24-Hour PM2.5 - Choose time periods which reflect a variety of meteorological conditions 

which frequently correspond with observed 24-hour average concentrations greater than the 

level of the NAAQS at monitoring sites in the nonattainment area. 
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Annual PM2.5 - The best way to represent the meteorological variability within season and 

over an entire year is to model an entire year that has meteorology generally conducive to 

elevated PM2.5 concentrations. 

Regional Haze - Choose time periods which reflect the variety of meteorological conditions 

which represent visibility impairment on the 20% best and 20% worst days in the Class I areas 

being modeled (high and low concentrations necessary). This is best accomplished by 

modeling a full year. 

Long-term or seasonal ozone – Since long-term (e.g. W126) ozone assessments are based on 

accumulated ozone over 3 month periods during the ozone season the most appropriate way 

to represent the variability in high and low ozone is to model an entire ozone season that is 

representative of long term design values. [Note: This analysis is only applicable if the 

upcoming final ozone NAAQS rule includes a seasonal secondary ozone NAAQS, such as 

W126] 

2.3.2 Future Year Selection 

Future emissions should be projected to the attainment year or appropriate time period, based 

on the area’s classification. The “Final Rule to Implement the 8-Hour Ozone National Ambient 

Air Quality Standard, Phase 1" provides a schedule for implementing emission reductions 

needed to ensure attainment by the area’s attainment date (40 CFR 51.908) [Note: when the 

ozone implementation rule for the 2008 NAAQS is finalized, the draft guidance will be updated 

to reflect the latest information]. Specifically, it states that emission reductions needed for 

attainment must be implemented no later than the beginning of the ozone season immediately 

preceding the area’s attainment date. The PM2.5 implementation rule contains similar 

provisions. It states that emissions reductions should be in place no later than the beginning of 

the year preceding the attainment date (40 CFR 51.1007) [Note: when the PM2.5 

implementation rule for the 2012 NAAQS is finalized, this draft guidance will be updated to 

reflect the latest information]. 

In addition to demonstrating attainment, air agencies are required to conduct a Reasonably 

Available Control Measures (RACM) analysis to determine if they can advance their attainment 

date by at least a year. A RACM analysis is also required for PM2.5 nonattainment areas. Since 

areas are required to attain as expeditiously as practicable, results of the RACM analysis may 

indicate attainment can be achieved earlier. 

There are varying requirements related to attainment year and RACM analyses for ozone and 

PM2.5 nonattainment areas, depending on classifications and other factors. Please see the 
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appropriate ozone and/or PM2.5 implementation rule for more details and information. [Note:

additional details on the RACM analysis will be added to this draft guidance when the ozone and 

PM2.5 implementation rules are finalized.] 

For regional haze assessments, the first progress goal review is for 2018, with subsequent 

reviews every 10 years. The 2028 review will likely be based on monitored data from the 2023-

2027 period. Therefore, a logical future period to model for the 2018 review is the middle of 

the five year review period (2025). However, the uniform rate of progress level can be 

calculated for any year in the future. Unless there are reasons to the contrary, any of the years 

between 2025 and 2028 can be considered to be representative of the regional haze future 

planning period. The calculation of the uniform rate of progress should be consistent with the 

emissions projection period. For example, if a future year of 2025 is modeled, the uniform rate 

of progress calculation should be based on the number of years between the base period and 

2025. Similarly, if a future year of 2028 is modeled, then the uniform rate of progress should be 

based on the number of years between the base period and 2028. 

2.4 Modeling Domain Selection 

A modeling domain identifies the geographical bounds of the area to be modeled. Horizontal 

resolution is the geographic size of individual grid cells within the modeling domain. Vertical 

resolution is specified in terms of multiple layers of the atmosphere between the surface and 

top of the model (usually near the tropopause). 

2.4.1 Domain Size 

The principal determinants of model domain size are the nature of the ozone, PM2.5 and/or 

regional haze problem being modeled, and the spatial scale of the emissions which impact the 

nonattainment or Class I area. The influence of boundary inflow concentrations should be 

minimized to the extent possible by allowing for adequate space between the monitors, key 

emissions sources, and the model boundaries. Minimizing boundary influence can be achieved 

by using a large model domain and using the hourly output from a larger regional or global 

modeling simulation to feed hourly boundary conditions (see section 2.7 for more information 

on boundary conditions). The grid containing the key emissions sources and receptors needs to 

be large enough to capture recirculation due to shifting wind directions. An alternative to using 

a large domain to capture recirculation is to apply a chemical transport model with a smaller 

fine grid nested inside a coarser grid with feedback between the grids (i.e., 2-way nesting). This 
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option allows pollutants from the fine grid to move into the coarser domain and back into the 

fine grid depending on wind patterns. 

Global models are routinely being used to supply regional chemical transport models 

temporally and spatially variant boundary conditions. Since regional and urban model 

applications for attainment demonstration purposes are typically less than or equal to 12 km 

sized grid cells it may be worthwhile to apply the regional scale model at a coarser grid 

resolution (e.g. 36 km) to downscale global model estimates to the regional model. The coarser 

simulation results provide a better transition from the global simulation to the nested urban or 

urban/region of interest both at the boundaries near the surface and in the free troposphere. 

See section 2.8 for more information on using global and/or regional models to derive initial 

and boundary conditions. 

Isolated nonattainment areas that have limited impacts from regional transport of ozone 

and/or PM2.5 and its precursors may be able to use a relatively small domain. The modeling 

domain should be designed so that all major upwind source areas that influence the downwind 

nonattainment area are included in the modeling domain. This is important to be able to 

directly capture the impact of future year emissions increases or decreases from upwind areas. 

In addition, all monitors that are currently or recently (within any of the years included in the 

base year period) violating the NAAQS or close to violating the NAAQS in the nonattainment 

area should be contained in the modeling domain far enough from the edge to minimize 

complications associated with air mass recirculation and to minimize boundary influence. 

Similarly, all Class I areas to be evaluated in a regional haze modeling application should be 

sufficiently distant from the edge of the modeling domain. 

2.4.2 Vertical Layer Configuration 

There is no correct maximum or minimum number of vertical layers needed in attainment 

demonstration modeling. However, the specification of the air quality model vertical layer 

structure should closely match the vertical layer structure of the meteorological model used to 

generate inputs for the air quality model. It is best to have an air quality model’s vertical layers 

align with the layers in the meteorological model matching one-to-one. However, resource 

constraints typically preclude direct vertical layer mapping. When vertical layer collapsing is 

necessary, the highest resolution is most needed to capture the diurnal variability in mixing 

heights. Recent air quality model applications considering the entire troposphere have used 

anywhere from 14 to 35 vertical layers (U.S. Environmental Protection Agency, 2011a, b). 
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The top of the modeling domain should typically be set near the tropopause at the 50 or 100 

millibar level. The lowest layer (surface layer) in the air quality model should be no more than 

~40 meters thick. The vertical resolution between the surface layer and model top will vary 

depending on the application. It is important that layer thickness monotonically increases as 

altitude increases. 

 

In view of the importance of carefully specifying the temporal variation in mixing height, high 

precision below and near the anticipated maximum afternoon mixing height is ideal. In 

addition, near-surface vertical resolution is important to capture overnight stable conditions. 

Layers above the boundary layer are important for characterizing clouds and precipitation and 

layers near the tropopause are important to create a realistic stratification of strongly variant 

pollutant concentrations in the stratosphere and free troposphere (Emery et al., 2011). 

There are some model applications which may not need to consider the full extent of the 

troposphere. These applications typically use vertical domains which extend up to 4 or 5 km. 

These types of applications may cover episodes of elevated pollutant concentrations associated 

with strong surface-based inversions that occur in isolated areas. However, in almost all cases, 

EPA encourages the use of full-scale one-atmosphere models which account for all atmospheric 

processes throughout the extent of the troposphere. 

2.4.3 Horizontal Grid Cell Size 

Recent chemical transport modeling applications for ozone, PM2.5, and regional haze have 

commonly used grid cell sizes ranging from 4 km to 12 km resolution (Morris et al., 2006; 

Rodriguez et al., 2009; Simon et al., 2012; U.S. Environmental Protection Agency, 2011b; 

Vizuete et al., 2010). Urban scale assessments have used chemical transport grid models at 1 to 

2 km resolution (Vizuete et al., 2011). Intuitively, one would expect to get more accurate results 

in urban applications with smaller grid cells (e.g., ~1 or 4 km) provided the spatial details in the 

emissions and meteorological inputs support making such predictions. However, model 

performance at 4 km resolution is not always better than 12 km resolution (Simon et al., 2012). 

For coarse portions of the regional grids a grid cell size of 12km is generally recommended. For 

urban areas it may be desirable to use grid cells sized ~1 to 4 km, but not larger than 12 km. The 

relative importance of using a domain with grid cells as small as ~1 km should be weighed on a 

case-by-case basis. It is likely that spatial gradients in concentrations are higher for primary 

particulate matter than for secondary particulate matter because physical processes dominate 

concentration rather than chemical processes. Although it is clear that spatial resolution of 

primary PM2.5 will impact the predicted concentrations, it is not clear how it will impact the 
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relative change in concentrations due to controls. Areas that have large gradients in primary 

PM2.5 may need to use finer resolution (approximately ~1 to 4 km) or may need to supplement 

the grid modeling with dispersion modeling. This is particularly true if violating monitors are 

strongly impacted by local sources of primary PM2.5 emissions. 

The most important factor to consider when establishing grid cell size is model response to 

emissions controls. Analysis of ambient data, sensitivity modeling, and past modeling results 

can be used to evaluate the expected response to emissions controls at various horizontal 

resolutions for both ozone and PM2.5 and regional haze. If model response is expected to be 

different (and presumably more accurate) at higher resolution, then higher resolution modeling 

should be considered. If model response is expected to be similar at both high and low(er) 

resolution, then high resolution modeling may not be necessary. The use of grid resolution 

finer than 12 km would generally be more appropriate for areas with a combination of complex 

meteorology, strong gradients in emissions sources, and/or land-water interfaces in or near the 

nonattainment area(s). 

Sensitivity tests comparing relative response factors in predicted 8-hour daily maximum ozone 

at sites in the eastern United States indicate relatively small unbiased differences (< .04, in 95% 

of the comparisons) using a grid with 12 km vs. 4 km sized grid cells (Arunachalam, 2006). The 

largest difference in the relative response of secondary pollutants at varying resolution is likely 

to occur in oxidant limited areas (areas that are more sensitive to VOC reductions than NOx 

reductions). In such areas, horizontal resolution may have a large impact on the spatial 

distribution and magnitude of NOx “disbenefits” (i.e., ozone increases in oxidant limited areas 

when NOx emissions are reduced). 

2.5 Air Quality Model Selection 

Chemical transport models address the physical processes and chemistry that form ozone and 

PM2.5. Air quality models continue to evolve and each has their own strengths and weakness 

(Galmarini et al., 2012; Pirovano et al., 2012; Russell, 2008; Simon et al., 2012). The most 

commonly used chemical transport models for attainment demonstrations are the Community 

Multiscale Air Quality Model (CMAQ; www.cmascenter.org ) (Byun and Schere, 2006; Foley et 

al., 2010) and the Comprehensive Air Quality Model with Extensions (CAMx; www.camx.com) 

(Baker and Scheff, 2007; Vizuete et al., 2011). The mention of CMAQ and CAMx is not intended 

to be a comprehensive list of available chemical transport models and exclusion from this 

discussion does not imply that an alternative model cannot be used to support a modeled 

attainment demonstration or reasonable progress assessment. In the same way, inclusion in 

this discussion does not imply that a model is “preferred” for a particular type of application. 
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A modeling based demonstration of the impacts of an emissions control scenario for 

attainment of the ozone or PM2.5 NAAQS or as part of a regional haze assessment usually 

necessitates the application of a chemical transport grid model. However, it is not necessary to 

use a model which considers atmospheric chemistry in addressing changes in primary PM2.5 

components. Either a numerical grid or a Lagrangian (such as a Gaussian dispersion) model or 

other empirical techniques could potentially be used for this purpose. In general, modeling 

primary PM2.5 components with a grid model is acceptable, but dispersion modeling may be 

necessary in areas with large spatial gradients of primary PM2.5. Depending on the nature of 

the problem, an air agency may choose to use a regional chemical transport grid model to 

address both primary and secondary components of particulate matter or they may need to use 

a chemical transport model to address secondary PM2.5 and regional primary PM and an inert 

model applied over a more limited domain to address fine scale primary components of PM. 

A model should meet several general criteria in order to be considered in an attainment 

demonstration or reasonable progress assessment. These general criteria are consistent with 

requirements in 40 CFR 51.112 and 40 CFR part 51, Appendix W (U.S. EPA, 2005a). 40 CFR Part 

51 Appendix W does not identify a “preferred model” for use in attainment demonstrations of 

the NAAQS for ozone or PM2.5 or reasonable progress assessments for regional haze. 

Therefore, at this time, EPA is not recommending a specific model for use in ozone or PM2.5 

attainment demonstrations or regional haze uniform rate of progress assessments. Models 

used for these purposes should meet requirements for “alternative models” in 40CFR Part 51 

Appendix W. Air agencies should use a non-proprietary model, which is a model whose source 

code is available for free (or for a reasonable cost). Furthermore, the user should be able to 

revise the source code to perform diagnostic analyses and/or to improve the model’s ability to 

describe observations in a credible manner. Several additional prerequisites should be met for 

a model to be used to support an attainment demonstration or reasonable progress 

assessment (see 40 CFR Part 51 Appendix W §3.2.2(e)): 

1) It should have received a scientific peer review. 

An air quality model may be considered to have undergone “scientific peer 

review” if each of the major components of the modeling system has been 

described and tested, and the results have been documented and reviewed 

by one or more disinterested third parties, and the model has been revised in 

response to the review. This review is not the responsibility of US EPA. Air 

agencies should reference this documentation to gain acceptance of an air 

quality model for use in a modeled attainment demonstration. 
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2) It should be appropriate for the specific application on a theoretical basis. 

o Prior to use of a selected model’s results in an attainment demonstration, 

the model should be shown to perform adequately for the specific 

application. The model should be scientifically appropriate and applicable 

for the intended purpose. 

3) It should be used with data bases which are available and adequate to support its 

application. 

4) It should be shown to have performed well in past modeling applications. 

o If the application is the first for a particular model, then the air agency should 

demonstrate that the new model is expected to perform sufficiently. For a 

model to be used in an attainment demonstration, evidence should be 

presented that it has been found acceptable for estimating hourly and eighthourly ozone concentrations and/or hourly and 24-hour average PM2.5 and 

PM2.5 component concentrations. In addition to ozone and/or PM2.5, the 

model should be acceptable for estimating precursor species, important 

chemical intermediates, and in the case of PM2.5, the main chemical 

constituents. Preference should be given to models exhibiting satisfactory 

past performance under a variety of conditions. 

5) It should be applied consistently with an established protocol on methods and 

procedures. 

In addition to the criteria in Appendix W, EPA also recommends that the following prerequisites 

be met: 

1) A user’s guide (including a benchmark example and outputs) and technical 

description of the model should be available. 

2) The advanced technical features available in a model could be a consideration when 

other criteria are satisfied. Models are often differentiated by their available 

advanced science features and tools. For example, some models include advanced 

probing tools that allow tracking of downwind impacts from upwind emissions 

sources. Availability of probing tools and/or science algorithms is a legitimate 

reason to choose one equally capable model over another. 
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3) When other criteria are satisfied, resource considerations may be important. This is 

a legitimate criterion provided the other listed criteria are met. 

2.6 Meteorological Inputs 

In order to solve for the pollutant concentrations over time and space, air quality models 

require accurate meteorological inputs to properly simulate the formation, transport, and 

removal of pollutant material. The required meteorological inputs can vary by air quality 

model, but all models require parameters such as wind, vertical mixing, temperature, humidity, 

and solar radiation. While model inputs can be created strictly from ambient measurements, a 

more credible technical approach is to use off-line, dynamic, meteorological grid models to 

provide the necessary inputs. When these models are applied retrospectively (i.e., for historical 

time periods) they are able to blend ambient data with model predictions via four-dimensional 

data assimilation (FDDA), thereby providing temporally and spatially complete data sets that 

are grounded by actual observations. 

This section provides recommendations for generating the meteorological data sets needed for 

regional air quality modeling purposes. In many ways, the development of meteorological 

inputs parallels the steps needed to conduct the air quality modeling. A meteorological model 

platform (episodes, domain, model selection, model configuration, input data, etc.) must be 

established and then evaluated. Because of the strong sensitivity of the eventual air quality 

results to the input meteorology (Appel, 2007, Appel, 2010), it is recommended that air 

agencies spend extensive effort in developing and evaluating the meteorological inputs. 

2.6.1 Developing base year meteorological fields 

The recommended approach for generating the base year meteorological data needed to 

conduct the attainment demonstration is to apply off-line, dynamic meteorological models with 

FDDA. These models use the fundamental equations of momentum, thermodynamics, and 

moisture to determine the evolution of specific meteorological variables from a given initial 

state. When modeling historic episodes, the use of data assimilation helps to "nudge" solutions 

so that they do not diverge greatly from the actual observed meteorological fields. A major 

benefit of using dynamic meteorological models is that they provide a way of consistently 

characterizing conditions at times and locations where observations do not exist. Examples of 

frequently used meteorological models are listed below: 
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• The Weather Research and Forecasting Model (WRF) (Skamarock, 2008), 

• The Penn State University / National Center for Atmospheric Research mesoscale 

meteorological model v5 (MM5) (Grell, 1994). 

The WRF model is a community supported mesoscale numerical prediction model which 

replaced the previous community supported MM5 model. The MM5 model is no longer being 

updated or supported. Other meteorological models can be used as long as they are 

appropriate for the situation being modeled and are properly evaluated. A number of recent 

studies (Appel et al., 2010; de Meij et al., 2009; Gilliam et al., 2010) have compared the ability 

of the WRF and MM5 models to reproduce past meteorological conditions and their suitability 

for use in air quality modeling. Generally speaking, comparative evaluation efforts have shown 

that the WRF model outputs represent equivalent or better statistical skill over a variety of 

meteorological conditions. An additional advantage of the WRF model is that there is a large 

community of WRF users and developers. As a result, the code is both maintained and 

frequently updated as new science emerges. EPA recommends the use of a well-supported 

gridded mesoscale meteorological model for generating meteorological inputs to regional air 

quality model attainment demonstrations. 

In some cases, however, there may be legitimate reasons for using an approach other than 

well-supported gridded mesoscale meteorological models to provide the requisite 

meteorological data. For instance, there may be long-standing local experience for a given 

domain and/or episode with a different meteorological model. In other cases, dynamic 

meteorological models may not adequately capture key meteorological elements of an 

airshed's conceptual model (e.g., source-receptor transport vectors to key monitoring 

locations). In rare cases such as these, it may be appropriate to blend the dynamic model data 

with wind data from an objective analysis of observed wind fields. The guiding factor in 

determining which meteorological model or approach to use should be an assessment of which 

set of inputs will best capture the key meteorological conditions that led to poor air quality. It 

is recommended that a description of the methods used to generate the meteorological fields 

be included in the modeling protocol. For those cases in which an off-line prognostic 

meteorological model is not used, it is recommended that a detailed description of the 

alternate technique that will be used to generate the three-dimensional meteorological fields 

be shared with the appropriate EPA regional office(s) prior to conducting the meteorological 

analysis and air quality modeling analysis. 

As with other parts of the air quality modeling system, choices made regarding how to 

configure the meteorological modeling can affect the quality and suitability of the air quality 

model predictions. Decisions regarding the configuration of complex dynamic meteorological 
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models can be particularly challenging because of the amount of flexibility available to the user. 

The goal in establishing the proper configuration for meteorological modeling should be to 

obtain the best possible meteorological model performance, especially for those parameters 

identified as most important in the conceptual description. As part of the overall chemical 

transport modeling system evaluation process, performance issues in the chemical transport 

model can sometimes be explained by options chosen in the meteorological simulation. 

Therefore, a feedback between the meteorological model evaluation and the air quality model 

evaluation can be highly beneficial. 

2.6.1.1 Selecting a Model Domain 

The selection of the meteorological modeling domain should closely match the air quality 

domain. The outermost grid should capture all upwind areas that can reasonably be expected 

to influence local concentrations of ozone, and/or PM2.5. In terms of selecting an appropriate 

meteorological modeling domain, one should extend the grid at least 3 to 6 cells beyond the 

domains of each air quality modeling grid to avoid boundary effects. It is recommended that 

the vertical and horizontal grid structures be generally consistent within the meteorological and 

air quality models to minimize interpolation and aggregation issues associated with the postprocessing of meteorological model outputs into air quality model inputs. 

2.6.1.2 Selecting Physics Options 

Meteorological models have a suite of physics options that allow users to configure how a given 

meteorological effect will be simulated. For example, there may be several options for 

specifying the planetary boundary layer scheme or how sub-grid cumulus clouds will be 

handled. In many situations, the "optimal" configuration cannot be determined without 

performing an initial series of sensitivity tests which consider various combinations of physics 

options over specific time periods and regions. While these tests may not ultimately conclude 

that any one configuration is clearly superior at all times and in all areas, it is recommended 

that these sensitivity tests be completed, as they should lead to a modeling analysis that is bestsuited for the domain and period being simulated. An optimal starting point of physics options 

may be selected from another modeling application that has a similar set of meteorological 

conditions that lead to elevated pollutant levels. Typically, the model configuration which 

yields predictions that provide the best statistical match with observed data over the most 

cases (episodes, regions, etc.) is the one that should be chosen, although other more qualitative 

information can also be considered. Additionally, model configurations should be designed to 

account for the pollutants and time periods that are of most interest, per the conceptual 

description. 
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2.6.1.3 Use of Data Assimilation 

As noted above, the use of FDDA helps to keep the model predictions from widely diverging 

from what was actually observed to occur at a particular point in time/space. Studies have 

shown that the incorporation of FDDA into atmospheric models improves meteorological 

simulations (Otte, 2008a,b; Pleim and Gilliam, 2009; Gilliam and Pleim, 2010). In particular, 

these studies have shown that better representation of the atmospheric fields aloft through 

assimilation of data from nationwide atmospheric profiler networks can aid in reproducing 

certain meteorological features (e.g., low-level jets) (Godowitch et al., 2011; Gilliam et al., 

2012). However, if used improperly, FDDA can significantly degrade overall model performance 

and introduce computational artifacts (Tesche and McNally, 2001). Inappropriately strong 

nudging coefficients can distort the magnitude of the physical terms in the underlying 

atmospheric thermodynamic equations and result in "patchwork" meteorological fields with 

strong gradients between near-site grid cells and the remainder of the grid. Additionally, if 

specific meteorological features are expected to be important for predicting the location and 

amount of pollution formed, based on an area's conceptual model, then the meteorological 

modeling should be set up to ensure that FDDA does not prevent the model from forming these 

features (e.g. lake/sea breeze circulations). 

In general, analysis nudging strengths should be no greater than 3.0 x 10-4 for winds and 

temperatures and 1.0 x 10-5 for water vapor mixing ratio. In the case of observation nudging 

(i.e., FDDA based on individual observations as opposed to analysis fields), it is recommended 

that the resultant meteorological fields be examined to ensure that the results over the entire 

domain are still consistent. Further, based on past experience, we recommend against using 

FDDA within the boundary layer for thermodynamic variables like temperature and water vapor 

mixing ratio because of the potential for spurious convection. If the dynamic model is applied 

without FDDA, it is suggested that the simulation durations be no longer than 24 hours. As with 

selecting physics options, it is often valuable to conduct sensitivity testing of various FDDA 

approaches to help determine the optimal nudging configuration for any specific case. 

Along with application of the meteorological modeling, users should be careful when 

translating the meteorological model outputs into air quality modeling inputs. A number of air 

quality model pre-processors (which are meteorological model post-processors) exist to 

conduct this off-line coupling, including the Meteorology-Chemistry Interface Processor (MCIP) 

(Otte and Pleim, 2010) and WRF2CAMX (ENVIRON, 2011). These meteorological pre-processors 

generate the complete set of meteorological data needed for the air quality simulation by 

accounting for issues related to: 

1) Data format translation 

2) Conversion of parameter units 
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3) Extraction of data for appropriate window domains 

4) Reconstruction of the meteorological data on different grid and layer structures 

5) Calculation of additional variables 

As noted above, most meteorological modeling applications should be set up to minimize the 

influence of the pre-processing software. For instance, it is recommended that the horizontal 

grid structures be consistent within the meteorological and air quality models to minimize 

issues associated with interpolation and aggregation. While it is often not feasible to run the 

air quality model with the same vertical layer structure as the meteorological model, it is 

recommended that vertical layer collapsing between the models be minimized, to the extent 

possible. 

While the traditional approach for attainment demonstration modeling is to convert “off-line” 

meteorological model outputs into air quality model inputs via specific pre-processing tools, 

there have been recent efforts to couple “on-line” meteorological models within air quality 

model applications (Grell et al., 2005; Davidson et al., 2007; Wilczak et al., 2009). The 

advantage of an on-line meteorological model is that one can simulate impacts that air quality 

changes might have on meteorological conditions (and vice versa) through direct and indirect 

radiative feedback mechanisms. In most cases, EPA does not expect emissions reductions from 

local SIP reductions to appreciably affect future meteorological conditions. Therefore, off-line 

meteorological approaches, where the same meteorological fields are used for multiple 

emissions scenarios, are expected to be sufficient for the majority of SIP modeling exercises. 

2.6.2 Assessing Impacts of Future Year Meteorology 

Traditionally, SIP meteorology simulations have not accounted for the impact of global 

emissions of carbon dioxide and other climate forcers on future meteorological conditions. 

Ozone concentrations have a high correlation with daily maximum temperatures over many 

areas in the U.S. (Camalier et al., 2007), suggestive of a potential climate connection. Recent 

research, taking into account temperature and other meteorological variables, indicates that in 

substantial, populated regions of the country, climate change could lead to higher future ozone 

concentrations (often called a “climate penalty”) (Jacob and Winner, 2009; Bloomer et al., 

2009; EPA, 2009; CARB 2010, Fiore, 2012). Assuming climate change does lead to higher ozone 

concentrations, there could potentially be a need for more stringent emissions reductions to 

counteract the higher ozone potential from warmer conditions. However, there are significant 

uncertainties regarding the precise location and timing of climate change impacts on air quality. 

Generally, climate projections are more robust for periods at least several decades in the future 

because the forcing mechanisms that drive near-term natural variability in climate patterns 
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(e.g., El Nino, North American Oscillation) have substantially larger signals over short time spans 

than the driving forces related to long-term climate change. In contrast, attainment 

demonstration projections are generally for time spans of less than 20 years. Given the 

relatively short time span between base and future year meteorology in most SIP 

demonstrations, EPA does not recommend that air agencies explicitly account for long-term 

climate change in attainment demonstrations. However, air agencies may want to consider 

potential climate impacts in their specific areas, especially where and when these impacts are 

believed to be, or are expected to become, potentially important. 

2.6.3 Evaluation of base year meteorological fields

While the air quality models used in attainment demonstrations have consistently been 

subjected to a rigorous performance assessment, in many cases the meteorological inputs to 

these models have received less rigorous evaluation, even though this component of the 

modeling is quite complex and has the potential to substantially affect air quality predictions 

(Tesche, 2002). EPA recommends that air agencies devote appropriate effort to the process of 

evaluating the meteorological inputs to the air quality model as we believe good meteorological 

model performance will yield more confidence in predictions from the air quality model. One 

of the objectives of this evaluation should be to determine if the meteorological model output 

fields represent a reasonable approximation of the actual meteorology that occurred during the 

modeling period. Further, because it will never be possible to exactly simulate the actual 

meteorological fields at all points in space/time, a second objective of the evaluation should be 

to identify and quantify the existing biases and errors in the meteorological predictions in order 

to allow for an downstream assessment of how the air quality modeling results are affected by 

issues associated with the meteorological data. To address both objectives, it will be necessary 

to complete both an operational evaluation (i.e., quantitative, statistical, and graphical 

comparisons) as well as a more phenomenological assessment (i.e., generally qualitative 

comparisons of observed features vs. their depiction in the model data). 

2.6.3.1 Operational Evaluation 

The operational evaluation results should focus on the values and distributions of specific 

meteorological parameters as paired with and compared to observed data. It is recommended 

that the observation - model matching be paired as closely as possible in space and time. 

Typical statistical comparisons of the key meteorological parameters will include: comparisons 

of the means, mean bias, normalized mean bias, mean absolute error, normalized mean error, 

and root mean square error. For modeling exercises over large domains and entire ozone 

seasons or years, it is recommended that the operational evaluation be broken down into 
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individual segments such as geographic subregions and/or months/seasons to allow for a more 

comprehensive assessment of the meteorological strengths and weaknesses. It may also be 

useful to breakout model performance aloft, at the surface, during individual episodes (e.g., 

high ozone / PM2.5 days), over the diurnal cycle, and as a function of synoptic regime. 

Modelers are also encouraged to set aside a portion of the ambient data strictly for evaluation 

purposes (i.e., data not used in the FDDA). Examples of observed meteorological data sets 

available for operational evaluations include (some of these data sets are not typically used in 

FDDA and therefore may be serve as an “independent” source of evaluation data): 

1. TDL U.S and Canada Surface Hourly Observations 

(http://rda.ucar.edu/datasets/ds472.0) 

2. Cooperative Agency Profilers (https://madis-data.noaa.gov/cap/) 

3. NOAA Profiler Network (http://profiler.noaa.gov/npn/) 

4. Individual State Climate Office Observation Networks 

While these data are readily available, it is up to the air agency to determine if the data have 

undergone appropriate QA/QC procedures through the provider. 

It may be helpful when calculating domainwide and/or regional summary statistics to compare 

the results against previously generated meteorological model performance "benchmarks" 

(Emery et al., 2001). However, because of concerns about potentially misleading comparisons 

of model performance findings across different analyses with differing model configurations 

and FDDA strengths, EPA does not recommend using these benchmarks as a "pass/fail" 

indicator of the acceptability of a model simulation. The benchmarks should only be used as a 

means of assessing general confidence in the meteorological model data. Statistical results that 

are outside the range of the compiled benchmarks may indicate a performance issue that 

should be given further examination. In some cases, adjustment of input data and/or model 

settings may be needed in order to improve model performance. The Atmospheric Model 

Evaluation Tool (AMET) was designed for use in the assessment of meteorological and chemical 

transport models. AMET provides a method of pairing observed and simulated and performing 

common analysis techniques to evaluate the performance and suitability of atmospheric 

models (CMAS, 2008). 

2.6.3.2 Phenomenological Evaluation

As discussed in Section 2.1, it is recommended that a conceptual description of the area's air 

quality problem be developed prior to the initiation of any air modeling study. Within the 

conceptual description, it is recommended that the specific meteorological parameters that 

influence air quality be identified and qualitatively ranked in importance. When evaluating 

meteorological models or any other source of meteorological data, the focus of the 

phenomenological evaluation should be on those specific meteorological phenomena that are 
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thought to strongly affect air pollution formation and transport within the geographic area of 

interest. This event-oriented evaluation should summarize model performance in terms of 

statistical metrics such as probability of detection and false alarm rate. As an example of a 

potential phenomenological analysis, many regional air quality modeling exercises attempt to 

assess the effects of transport of pollutants from one area to a downwind area with the intent 

of establishing source-receptor relationships. For these types of analyses, accurate transport 

wind trajectories are needed to properly establish these source-receptor linkages. A useful 

event-based meteorological evaluation would be to compare model-derived trajectories versus 

those based on ambient data to determine what error distance can be associated with the 

model fields. Other examples of phenomenological evaluations would be the replication by the 

model of low level nocturnal jets, cold pool conditions, fog events, or land/sea breezes. 

2.7 How Are the Emission Inputs Developed? 

Air quality modeling for 8-hour ozone, PM2.5, and regional haze requires emissions data for a 

base year and future modeling years. While not explicitly a requirement of the implementation 

rules, the emissions data for modeling are a necessary input to the modeling that is required by 

the implementation rules as part of the modeled attainment demonstration. 

A separate EPA guidance document describes the planning inventories that are specifically 

required for SIPs and also provides an overview of all emission inventory requirements, 

resources, and techniques. That guidance is the "Emissions Inventory Guidance for 

Implementation of Ozone and Particulate Matter National Ambient Air Quality Standards 

(NAAQS) and Regional Haze Regulations" (draft; U.S. EPA, 2014a), and it is used as a key 

reference throughout this section. Many of the issues associated with the planning inventories 

are also relevant for modeling inventories. We will refer to that guidance here as the “EI SIP 

Guidance”. 

Table 1 of the EI SIP Guidance provides a list of emissions estimation resources for emissions 

inventories. In addition to the resources listed there, EPA also provides emissions data used in 

modeling analyses in support of rulemakings. The current available "emissions platforms" are 

based on 2007, 2008, and 2011 base years. These platforms are available publically on the 

Emissions Modeling Clearinghouse at http://www.epa.gov/ttn/chief/emch/index.html. The 

emissions modeling data and other data included in these platforms can be a starting point for 

air agencies in developing their own capabilities for preparing emissions for use in air quality 

models. 
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2.7.1 What Emissions Inventory Years Should I use for Base and Future 

Modeling? 

For the base year, the EPA does not require a particular year for 8-hour ozone or PM2.5 SIPs and 

regional haze plans. However, for several reasons, it is technically appropriate to use a recent 

year as the base year. Currently, the most recent base National Emissions Inventory (NEI) is 

2011 (U.S. EPA 2014b) and NEI data are developed every three years (the next NEI year will be 

2014). In most cases, the most recent NEI year will be the most appropriate year to use for 

base case modeling. In some cases, other years (either before or after the most recent NEI 

year) may be more appropriate for modeling in view of unusual meteorology, transport 

patterns, exceptional events (i.e. large wildfires), lack of air pollution events, or other factors 

that may vary from year to year. See Section 2.3.1 for more information on episode selection. 

The choice of the base year should be discussed with the appropriate EPA regional office as part 

of the SIP planning process for the attainment demonstration. 

The choice of a future year for an inventory depends on the nonattainment classification of the 

nonattainment area, as described in Section 2.3.2. For ozone and PM2.5, in most cases the 

future modeling year will be the expected attainment year. Requirements for selecting 

appropriate future years for modeling are provided in the ozone and PM2.5 NAAQS 

implementation rules. For regional haze, the future year will depend on the date of the 

planning period being examined. 

2.7.2 What Base Year Emission Inventory Data Are Needed to Support Air 

Quality Models? 

Air quality models require hourly emissions of modeled species for each grid cell and model 

layer in the modeling domain. For the base year, the inventories are called the base year 

inventory for modeling. To meet the input requirements of models, the inventory must have 

pollutants that are the scientific precursors (pollutants that impact the relevant chemistry) to 

the ozone, PM2.5, and/or regional haze being addressed in the modeling demonstration. The 

time frame of the emissions inventory must be consistent with the period being modeled. The 

inventory must include states, counties, and tribes that are covered by the spatial extent of the 

modeling domain. Additionally, the emissions sources included must be comprehensive, 

including emissions from all source categories. As explained with additional detail below, this 

includes point sources, nonpoint stationary sources, on-road and nonroad mobile sources, fires, 

and biogenic sources. 
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2.7.3 What Pollutants Should Be Included in Base Year Inventories? 

Because many sources emit more than one of the precursor pollutants, and because the 

precursor pollutants have the potential to be transported across state boundaries, EPA 

encourages air agencies to develop inventories for all pollutants where possible to support 

integrated, regional-scale modeling, and control strategy development for ozone, PM2.5, and 

regional haze. However, there are some considerations for which pollutants to include in 

inventories for the purpose of supporting air quality modeling. 

For 8-hour ozone NAAQS modeling, the pollutants to be inventoried are VOC, NOX, and CO. For 

PM2.5 NAAQS modeling, the pollutants to be inventoried are primary emissions of PM2.5 and 

PM10 (including both the filterable and condensable portions) and emissions of SO2, NH3, VOC, 

NOX, and CO. While certain PM2.5 precursors may not be considered precursors for planning 

purposes in some nonattainment areas10, inclusion of all precursors is a prerequisite for

appropriate application of air quality models for modeled attainment demonstrations. For 

regional haze, the pollutants to be inventoried include all of the pollutants and precursor 

pollutants identified for ozone and PM2.5. While CO does not need to be included in the 

planning inventories associated with the SIP submission (as described in the EI SIP Guidance), it 

should be included for modeling purposes because CO plays an important role in the modeled 

atmospheric chemistry in air quality models. 

2.7.4 What Temporal Resolution Should Be Used for Base Year Inventories? 

The emissions inventories should cover the time periods associated with the modeled time 

period. Different approaches to accomplishing this goal arise because of the nature of ozone, 

PM2.5, and regional haze problems. 

For ozone, the episodic nature of 8-hour ozone exceedances lead to inventories that reflect a 

specific period being modeled. For this reason, the planning inventories should be provided as 

an ozone season day inventory11 (as described in the EI SIP Guidance). For the nonattainment 

area, the planning ozone season day inventories are also often used for modeling inventories. 

However, modeling inventories also include areas outside the nonattainment area, for which 

 

10 See the PM2.5 implementation rule for detailed information on the definition of “planning” precursors 

for PM2.5 nonattainment areas. 

11 The definition of “ozone season day” emissions specifies weekday emissions and is intended to reflect

the periods of emissions associated with monitored ambient ozone exceedances. 
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ozone season day emissions may not be readily available. In these instances, air agencies may 

elect to start with an annual inventory and use an emissions model to adjust the annual 

emissions to reflect the modeling period. This may not be advisable in every case; in particular, 

mobile source emissions are highly variable with meteorology and day of the week, and are 

often available on a monthly or even daily basis that reflects changes in temperature, traffic 

patterns, and other factors. In addition, more detailed temporal data from electric generating 

units (EGUs), biogenic sources, or other sources should be used when available to enhance the 

technical quality of the modeled attainment demonstration. 

For PM2.5, both annual and daily standards result in different inventory temporal possibilities 

for the modeling episode, depending on which standard(s) a particular nonattainment area 

must address in their SIP. [Note: This guidance will be updated (if necessary) when the PM2.5 

implementation rule is finalized]. 

For regional haze, the use of annual emissions inventories is appropriate given the nature of the 

regional haze problem. However, the same considerations apply for the inventory’s temporal 

aspects as they do in the cases above: more detailed temporal data should be used as the 

inventory starting point when it is available for the pollutants included in the inventory. 

2.7.5 What Spatial Coverage Is Need for Base Year Inventories? 

Modeling inventories must cover all areas of the modeling domain, which will include areas 

outside of a nonattainment area and most often includes areas in neighboring states or tribes. 

This subsection addresses areas within and outside the nonattainment area separately. 

2.7.5.1 Areas within the nonattainment area 

As mentioned above, the planning inventories include emissions within the nonattainment 

area. The EPA expects that modeling inventories will be consistent with those planning 

inventories; however, some exceptions may exist. Where possible, the planning inventories can 

be a sum (for annual data) or average (for ozone season day data) of day-specific or hourspecific data used for modeling. In some cases, however, this approach may not be sufficient 

for modeling purposes. For example, greater spatial and temporal detail are needed for on-road 

mobile modeling inventories as compared to the base year (planning) inventory for the 

nonattainment area. For the planning inventory, one goal is to allow for the repeatability of the 

approach in order to create average, seasonal, or annual inventories for use in rule 

requirements, such as reasonable further progress or conformity demonstrations. That goal is 

not necessarily compatible with the modeling need for spatial and temporal detail. In cases 
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where some differences are unavoidable, air agencies should attempt to promote consistency 

where feasible. 

2.7.5.2 Areas outside of the nonattainment area 

For air quality modeling, emissions inventories are often needed for areas outside the 

nonattainment area. Developing modeling inventories for these areas should be relatively 

straightforward and can be included as part of the SIP planning inventories. For areas outside 

of the air agency’s jurisdiction, however, it can be more difficult for air agencies to develop the 

inventories themselves. In these cases, the EPA encourages participation in regional modeling 

efforts which are designed to allow sharing of data and help promote consistent approaches 

across state boundaries. When those organizations lack the needed inventories, EPA 

recommends using the EPA’s publicly available emissions, as described in the next section. 

2.7.6 How Can The National Emission Inventory by Air Agencies? 

The EPA recommends that state, local, and tribal agencies start with available inventories 

suitable for air quality modeling of the selected time period(s). If no such inventories are 

available from a prior state or tribal effort, from regional modeling group, or from some other 

source, air agencies may derive an inventory suitable for use with models from EPA-provided 

information. Two sources of information are available, as described below. In both cases, the 

EPA expects that air agencies will only use such data after additional review to ensure the 

appropriate quality of the data for SIP planning purposes. 

First, the National Emission inventory (NEI) provides criteria air pollutant and hazardous air 

pollutant emissions as annual emissions totals for most (if not all) sources of emissions relevant 

for ozone, PM2.5, and regional haze implementation purposes. Air agencies may use the most 

recent public release of the NEI as a starting point for inventory development. The data and 

documentation are available from the latest inventory page linked at 

http://www.epa.gov/ttn/chief/eiinformation.html. 

Additionally, the EPA provides emissions modeling "platforms" that are based on the NEI and 

can be a starting point for air agencies. The modeling platform data provide additional 

information not available from the NEI, such as daily EGU and fire emissions, monthly nonroad 

emissions, biogenic emissions and/or input to biogenic emissions models, and inputs to mobile 

source models. Furthermore, these modeling platforms usually include future-year emissions 

projections. Both base and future-year data are provided in a format used by emissions models 

and can therefore be more readily adopted by air agencies that need to process emissions for 

use in air quality models. The data and documentation are available from the latest platform 
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release at the Emissions Modeling Clearinghouse 

http://www.epa.gov/ttn/chief/emch/index.html). 

The detail and accuracy of the EPA emissions may or may not be sufficient for use in SIPs. Air 

agencies should review the emissions and associated documentation and make improvements 

where deficiencies exist. Such improvements will often be necessary to demonstrate 

appropriate model performance, as described in Section 3. Additional degrees of review are 

needed for areas closer to the nonattainment area. For example, EPA data may be sufficient 

for estimating emissions in states that are hundreds of kilometers upwind or downwind of the 

nonattainment area, but may not be sufficient for estimating emissions closer to the 

nonattainment area because those emissions can have a greater impact on the final results of 

the attainment demonstration. 

2.7.7 How Should Base Year Emissions Be Created? 

Emission inventory data from at least five general categories are needed to support air quality 

modeling: stationary point-source emissions, stationary area-source emissions (also called nonpoint), emissions for on-road mobile sources, emissions for nonroad mobile sources (including 

railroad and marine vessels), and biogenic/geogenic emissions. A sixth category of wildland fires 

also exists and includes day-specific emissions from wildfires and prescribed burns. These fire 

emissions are included as an “event” source in the NEI. Emissions inventories are expected to 

include “all” sources of emissions; so if an air agency wishes to exclude a particular source (e.g., 

because it is a very small contributor), then the air agency should document the reasons such 

omissions are acceptable. In addition, some “traditional” nonroad mobile sources (e.g., airports 

and rail yards) can also be included in inventories as a point source. For example in the NEI, 

airports and rail yards are included as point sources and railroad and marine vessel emissions as 

part of the nonpoint data category. 

The EI SIP Guidance (EPA, 2014a) addresses numerous details about emissions inventory 

development. Readers are encouraged to refer to information in that document as a resource 

for building base year emissions inventories. That guidance contains additional information on 

the definitions of the data categories (point sources, nonpoint sources, etc.), provides more 

information about emissions estimation for each data category, includes a process for 

prioritization of inventory development, and includes quality assurance recommendations. All 

of this information is highly relevant to inventories developed for air quality modeling. In the 

remainder of this section, we provide a basic description of the data categories and their base 

year emissions and additional information needed for their use in modeling applications where 

appropriate. 
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2.7.7.1 Point Sources 

Point sources are emissions sources that are attributed to specific facilities and emissions 

release points within those facilities. The Air Emissions Reporting Rule (AERR) 

(http://www.epa.gov/ttnchie1/aerr/) provides information on how to define which sources to 

include as specific point sources in the inventory. More information on the relationship 

between SIP inventories and the AERR and how that affects SIP inventories is available in the EI 

SIP Guidance. For modeling inventories, data developers may include sources with emissions 

smaller than the required reporting threshold, but should be careful to prevent double counting 

with nonpoint source categories that may overlap with some of these smaller sources. This is 

especially a concern when compiling point and nonpoint inventories from different sources, 

because a nonpoint inventory is compiled using certain assumptions about what an associated 

point source inventory includes. This issue is described as “nonpoint reconciliation” and is 

discussed further in the EI SIP Guidance. 

Point source inventories for modeling should be compiled at a minimum by country, 

state/tribe, county, facility, unit, process or source category code (SCC) and by “release point” 

or stack (see references below to point-source inventory development). The point source data 

should include information on the location of the sources (e.g., latitude/longitude coordinates); 

their stack parameters (e.g., stack diameter and height, exit gas temperature and velocity); and 

operating schedules (e.g., monthly, day-of-week, and diurnal). Information on existing control 

device types, measures and associated emissions reductions are also useful to identify sources 

that might be further controlled and to prepare future year inventories. 

For modeling needs, many point sources will also be post-processed for the estimation of 

plume rise of the emissions vertically through layers of the atmosphere as modeled by the air 

quality model. This plume rise depends on the stack parameters (listed above) and on 

meteorological conditions. Plume rise can be calculated by emissions models but is more often 

calculated as part of air quality models. In this latter case, inventory developers are responsible 

only for identifying for which sources plume rise will be computed using tools available in 

emissions models. 

Also for modeling needs, hourly emissions data are usually a starting point for inventories of 

electric generating units (EGUs). This is because such data are readily available from Continuous 

Emissions Monitoring Systems (CEMS) installed as part of EPA trading programs12 and include 

hourly emissions of NOX, SO2, CO2, and heat input. The heat input data can be used to estimate 

 

12 CEM data are provided by EPA’s Clean Air Markets Division in a format suitable for use in emissions 

processing at http://ampd.epa.gov/ampd/. From this page, click the “Prepackaged data” tab and scroll 

to the bottom to download the data in a form that can be input into SMOKE. 
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hourly emissions of other pollutants (such of primary PM2.5 emissions) using facility-specific 

emission factors or other emission factors. The EPA’s modeling platform also includes these 

hourly data. 

2.7.7.2 Nonpoint Sources 

Nonpoint sources are also called “area sources”. These sources collectively represent individual 

sources of emissions that have not been inventoried as specific point sources. The individual 

sources within the nonpoint emissions county totals are typically too small, numerous, or 

difficult to inventory using the methods for point sources. The nonpoint emissions data should 

be compiled by country, state/tribe, county, and SCC, and are sometimes further specified by 

emission type (e.g., hoteling and cruising for commercial marine emissions in the NEI). 

2.7.7.3 On-Road Mobile 

Emissions from on-road vehicles are the result of several emission processes, including the 

combustion of fuel while vehicles are starting, idling, and moving, evaporation of fuel from the 

fuel system and during refueling, and from brake wear and tire wear. 

On-road mobile source emissions, including refueling, should be estimated with the latest EPA 

on-road mobile model, which is currently the Motor Vehicle Emission Simulator (MOVES), 

(http://www.epa.gov/otaq/models/moves/index.htm). For on-road emissions in California, the 

most recent EPA-approved version of the EMission FACtors model (EMFAC) should be used 

(http://www.arb.ca.gov/msei/modeling.htm). The Sparse Matrix Operator Kernel Emissions 

(SMOKE) emissions modeling system (http://www.smoke-model.org/index.cfm) includes tools 

that support the use of MOVES emission factors with SMOKE, and thereby allows modelers to 

account for the sensitivity of on-road emissions to temperature. SMOKE uses county-specific 

inputs and gridded hourly temperature and other meteorological information, which is typically 

based on the same meteorological data used for air quality modeling. 

The SMOKE-MOVES approach first generates emissions rates from MOVES by process (running, 

start, vapor venting, refueling, etc.), vehicle type, road type, temperature, speed, and hour of 

day, according to specified fuel parameters and other inputs. Then, subsequent steps apply the 

appropriate MOVES emissions rates along with vehicle miles traveled (VMT) and vehicle 

population data for the counties and grid cells in the modeling domain for each hour of the 

modeling episode. Modelers should consider using the SMOKE-MOVES system or some 

equivalent or additionally refined approach for use in emissions modeling needed for modeled 

attainment demonstrations. 
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 The MOVES model allows modelers to override default database settings to obtain a localspecific on-road inventory, and the EPA strongly encourages data developers to develop and 

use MOVES inputs databases that are specific to the states and counties within the modeling 

domain. The MOVES Technical Guidance discusses when local data should be used in place of 

model default data, as well as potential sources of local data. 

For some input parameters, there is overlap with the inputs needed for other models, such as 

the nonroad mobile models. For example, meteorology and fuels information is needed to drive 

both the mobile and non-road emissions models. Efforts should be made to use the same 

source of data across multiple categories when the inputs are shared by multiple models. Not 

doing so calls into question the validity of one or both approaches. Air agencies should explain 

the use of shared inputs, or reasons for not using them, as part of the documentation prepared 

with their modeled attainment demonstration. 

On-road emissions and VMT should be compiled at least at the country, state/tribe, county, and 

SCC level. Vehicle population data by county and vehicle type should also be compiled as well 

as idling hours, if available. For some inputs such as VMT, modelers may optionally compile and 

use data for individual road segments (called “links”). Link approaches require starting and 

ending coordinates for each road link. Link-based modeling is typically performed for fine scale 

(i.e. 4-km or finer) modeling studies. Travel Demand Models (TDMs) can be a source of data 

needed to develop link-based approaches. In regional scale modeling, county-level on-road 

emissions can be allocated onto road links using spatial surrogates that are generated based on 

link data and mapped into the modeling grid cells using appropriate weights. 

2.7.7.4 Nonroad Mobile Equipment 

Nonroad mobile equipment emissions result from the use of fuel in a diverse collection of 

vehicles and equipment, and the EPA’s NONROAD emissions model estimates these emissions. 

These nonroad emissions should be compiled as country, state/tribe, county and SCC totals. 

The NONROAD model includes more than 80 basic and 260 specific types of nonroad 

equipment and further stratifies equipment types by horsepower rating. Fuel types include 

gasoline, diesel, compressed natural gas (CNG), and Liquified Petroleum Gas (LPG). NONROAD 

supports many SIP-related inventory development needs, including support for partial counties 

and seasonal emissions. The EPA provides the latest version of the NONROAD model and all 

information related to its use for inventories at http://www.epa.gov/otaq/nonrdmdl.htm. Air 

agencies should use NONROAD as a resource for estimating nonroad mobile emissions and 

refer to the documentation on the NONROAD website for more information. The EPA is 

currently in the process of incorporating NONROAD model capabilities into MOVES. When this 
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update is available, guidance on the transition to using MOVES for estimating nonroad 

emissions will be included on the EPA’s MOVES and NONROAD web pages. NONROAD can 

optionally be run using the National Mobile Inventory Model (NMIM) (see 

http://www.epa.gov/otaq/nmim.htm). 

Air agencies have the option of replacing default model inputs with more representative data. 

Common input adjustments include equipment population, geographic allocations, and local 

growth rates. If agencies make changes to default model values, the agency should submit the 

input files to the EPA as well as a description of why the defaults were changed. 

In some cases, air agencies have developed their own nonroad mobile modeling approaches. 

For SIPs including these alternative approaches, air agencies should make available the details 

and models for the EPA to consider and explain why their approaches are better than the EPA’s 

NONROAD approach using custom inputs. 

2.7.7.5 Nonroad Mobile: Commercial Marine Vessels, Locomotives, and Aircraft

Emissions from commercial marine vessels, locomotives, and aircraft are estimated in other 

ways as described in detail in the EI SIP Guidance. These sources can be particularly important 

for urban areas (especially in coastal areas) and therefore can be more important for 

attainment demonstration modeling than for regional modeling. Thus, air agencies should 

consider whether the contributions from these sources merit additional effort. The EI SIP 

Guidance contains information about methods available to estimate emissions from these 

sources. 

Commercial marine vessel emissions can be a particular challenge for nonattainment areas that 

include ports or are adjacent to major shipping lanes. While the level of emissions from these 

vessels can be outside of the control of state air agencies (coming under national rather than 

state jurisdiction), it is still important to accurately represent these emissions when they are 

significant contributors in the vicinity of a nonattainment area. Vessels are divided into the 

largest, ocean-going “category 3” (C3) vessels and the smaller “category 1” (C1) and “category 

2” (C2) vessels such as tug boats, ferries, research vessels, fishing vessels, and others. 

Commercial marine emissions include both in-port emissions and underway emissions, with 

most (~90% of nationwide emissions) emissions associated with underway activity. For the 

planning inventories covered by the EI SIP Guidance, air agencies need to include only those 

emissions within state waters. For the modeling inventories, however, air agencies may also 

need to include emissions in Federal waters when the modeling domains extend more than a 

few miles offshore. This is especially important for coastal areas where offshore commercial 
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marine emissions may be an important contributor to ozone and/or PM in the nonattainment 

area. 

The current EPA methods for estimating commercial marine emissions uses top-down 

approaches for C1, C2, and C3 vessels, with a different approach being used for C3 vessels since 

the emissions are affected by international fuel agreements and more emissions occur far 

offshore. The methods apportion national activities and emissions to counties and geographic 

information system “shapes” that define waterways and offshore shipping lanes. These data 

are available for use from the EPA’s modeling platform data if no other data are available. 

Since such a top-down approach has high uncertainties, the EPA strongly encourages air 

agencies preparing modeling inventories to seek out, develop, and use local-specific 

information about their ports and waterway vessels to better compute emissions. 

2.7.7.5 Biogenic Emissions

Biogenic sources are a subset of natural emissions sources that may be an important 

component of the emissions inventory. Vegetation (i.e., forests and agriculture) is the 

predominant biogenic source of VOC and is typically the only source that is included in a 

biogenic VOC emissions inventory. Microbial activity in the soil contributes to natural biogenic 

NOx and CO emissions. 

Biogenic emissions from vegetation and soils are computed using a model which utilizes spatial 

information on vegetation and land use and environmental conditions of temperature and solar 

radiation13. The model inputs are typically horizontally allocated (gridded) data, and the outputs 

are gridded biogenic emissions which can then be speciated and utilized as input to chemical 

transport grid models. Several models exist, and are described more fully in section 2.7.9.2 and 

the EI SIP Guidance. 

2.7.7.6 Geogenic and Other Natural Sources

Geogenic emissions are primarily the result of oil or natural gas seeps, soil wind erosion, and 

sea salt spray. In addition, lightning may also be an important contributor to natural NOX

emissions in an inventory area. Volcanoes and fumaroles (i.e., vapor or gas vents in a volcanic 

region) can be additional sources of geogenic emissions. More information on these sources is 

provided in the EI SIP Guidance. Of particular relevance for modeling inventories are soil wind 

erosion, sea salt, and lighting emissions. 

 

13 In most cases, ozone, PM, and regional haze modeling includes the emissions from vegetation and 

soils, but not other potentially relevant sources such as volcanic emissions, lightning, and sea salt.
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As a source of ozone precursor emissions, geogenic sources are usually less significant than 

biogenic sources. However, geogenic wind erosion may contribute substantially to PM 

emissions in an area. In 2012, the EPA released CMAQ v5.0 that includes a module that 

dynamically estimates hourly natural emissions of fine and coarse dust particles due to wind 

action over arid and agricultural land. This allows the contribution of this source to be included 

in a modeled attainment demonstration without the need to prepare an emissions inventory 

first. The windblown dust approach is based on the work of Tong et al. (2011) (see also the 

CMAQ wiki at http://www.airqualitymodeling.org/cmaqwiki/ 

index.php?title=CMAQv5.0_Windblown_Dust). Air agencies using this feature should evaluate 

the results of the CMAQ-based approach to determine if it provides a reasonable 

representation of dust emissions in their modeling domain. Often times, CMAQ overestimates 

ambient crustal matter concentrations without the addition of windblown dust and so adding 

emissions may not be advisable. 

The interaction of sea-salt particles in coastal environments with air chemistry can impact 

concentrations of PM2.5 and gas-phase species in the atmosphere. The CMAQ model has been 

updated to account for these emissions, so that emission inventory developers are able to rely 

primarily on the air quality model to internally calculate sea-salt emissions. Additional 

information for CMAQ is available in Kelly et al., (2010). Additional information on sea salt in 

CAMx simulations is available in Athanasopoulou et al. (2008). If other models are used, 

modelers should consider the possible need for including sea salt emissions data as an input to 

the air quality model. 

Lightning produces NO, which is oxidized to NO2 in the presence of ozone or in a 

photochemically reactive atmosphere. The NO and NO2 is formed in the mid-upper 

troposphere and can impact the vertical distribution of reactive nitrogen as well as ozone. 

Because lightning is not a direct source of NO2, accounting for this source category is more 

important for air quality modeling purposes than for SIP planning inventory purposes. The EPA’s 

CMAQ v5.0 includes a parameterized approach to estimate NOX emissions and its vertical 

distribution that is generated from lightning. The peer reviewed literature contains both a 

description of the approach (Allen, 2012) and a model evaluation (Appel, 2010). More 

information is available from the CMAQ v5.0 wiki (see 

http://www.airqualitymodeling.org/cmaqwiki/index.php?title=CMAQv5.0_Lightning_NO). 
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2.7.7.7 Wildland Fires (Events)

Inventories used for ozone, PM2.5, and regional haze modeling purposes should include 

emissions for fires. Thus, wildfire and prescribed fire emissions should be included in the 

nonattainment area inventories when theese emissions occur within the modeling domain 

during time periods being modeled. Detailed information about how the EPA develops 

wildland fire inventories can be found in the latest version of an NEI Technical Support 

Document, such as that provided with the 2011 NEI. EPA’s approach relies on a combination of 

satellite detection of fires merged with on-the-ground observational data where available. The 

EPA encourages the use of ground-based observations and local fuel information whenever 

possible as these factors can have a large impact on the emissions. 

Wildland fires (WLFs) are generally defined as any non-structure fire that occurs in the wild land 

(an area in which development is essentially non-existent, except for roads, railroads, power 

lines, and similar transportation facilities). The National Wildfire Coordinating Group has 

identified the following two types of WLFs : 

• Prescribed fire: Any fire ignited by management actions to meet specific objectives 

(generally related to the reduction of the biomass potentially available for wildfires). 

A written, approved prescribed fire plan must exist, and National Environmental 

Policy Act requirements must be met (where applicable) prior to ignition. 

• Wildfire: An unplanned, unwanted WLF including unauthorized human-caused fires, 

escaped WLF use events, escaped prescribed fire projects, and all other WLFs where 

the objective is to put the fire out. 

Wildland fires are a unique source of emissions with respect to NAAQS attainment planning. For 

ozone, PM2.5, and regional haze planning purposes, these fires are major sources of primary 

PM2.5 and VOC and a moderate source of NOX. They can consequently impact the ambient PM2.5

and ozone; however, they have a number of unique properties: 

• Depending on the fuel source and acreage burned, both prescribed and wildfires can 

emit significant emissions, but emissions from prescribed fires are generally lower 

due to the burning characteristics and the relative amounts of flaming and 

smoldering emissions. In addition, prescribed fires can be used to prevent future 

wildfires, which are generally larger, hotter, more dangerous, and more polluting. 

• Wildfires are considered to be naturally occurring events for which the emissions are 

generally not reasonable to prevent or control. As such, air agencies may request 

that the EPA exclude from regulatory determinations ambient data influenced by 

the wildfire if the request meets the criteria, schedule, and procedural requirements 
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for an exceptional event set forth in 40 CFR 50.14. In some cases, prescribed fires 

can also be considered to be exceptional events (also subject to the criteria of 40 

CFR 50.14). 

• Fire emissions can rise very high in the atmosphere on plumes, and the pollutants 

can be transported far beyond state boundaries. Fires occurring outside NAAs can 

have impacts within NAAs both on days that have been determined to be 

exceptional events and days that may not be determined to be exceptional events. 

• Since wildfires and prescribed fires generally occur in forests and rangelands, they 

are less likely to be within the boundaries of NAAs, which are usually urbanized 

areas. 

• Fires can also reduce ozone formation because their smoke can block sunlight that 

otherwise would result in more ozone. 

Since fire emissions can be a large source of emissions, the treatment of fires should be 

discussed with the relevant EPA Regional Office(s) as part of the modeling protocol 

development process. 

2.7.8 What Other Data Are Needed to Support Emissions Modeling? 

The emission inventories should be converted, through emissions modeling, from their original 

resolution (e.g., annual emissions by point source or count) to input files for air quality models. 

Air quality models generally require emissions inputs to be specified by model grid cell, hour, 

and model chemical species. Modeling inventories also identify which point sources should 

have their plume rise computed. This section describes the ancillary data that modelers should 

collect and prepare to allow emissions models to convert the emission inventory data into air 

quality model inputs. 

2.7.8.1 Temporal allocation

Ancillary data for temporal allocation are necessary for stationary point, stationary area, and all 

mobile sources. To facilitate temporal allocation of the emissions, factors called “temporal 

profiles” should be created to convert annual emissions to specific months (i.e., monthly 

profiles), average-day emissions to a specific day of the week (i.e., weekly profiles), and daily 

emissions to hours of the day (i.e., hourly profiles). Additionally, a cross-reference file is 

needed to assign the temporal profiles to the inventory records by SCC, facility, geographic area 

such as state or county, or some other inventory characteristics. Where available, the 

operating information that may be available from the point-source inventory should be used to 

create inventory-specific temporal factors. The EPA provides a starting point for the temporal 
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profiles and cross-reference files as part of the latest modeling platforms (previously 

described), available at http://www.epa.gov/ttn/chief/emch/index.html. 

The EPA has developed a SMOKE utility program (Gentpro) that estimates temporal profiles for 

residential wood combustion (RWC), agricultural NH3 from animals, and other generic (userdefined) area sources by relating meteorology to air emission fluxes. Gentpro reads in hourly 

gridded meteorological data (temperature and wind) from the Meteorology-Chemistry 

Interface Processor (MCIP)14 and a gridded spatial surrogate to produce temporal profiles and 

cross-reference data. Annual MCIP input data are required to calculate temporal profiles with 

Gentpro and the spatial surrogates are needed to identify the grid cells for computing countyaveraged meteorology variables. The Gentpro program is described in the latest SMOKE User’s 

Manual (http://www.smoke-model.org). 

For RWC, Gentpro allocates emissions to specific days from annual totals based on a regression 

equation that was developed by relating daily minimum temperatures and chemical tracers of 

RWC. This approach results in more RWC emissions assigned to periods of colder temperatures 

using a user-defined temperature threshold that restricts RWC emissions to days with morning 

lows below a specific value. Air agencies that use EPA's emissions data for this category should 

note that this approach has been used to temporally allocate the RWC emissions. 

For agricultural NH3 from animals (i.e. from concentrated animal feeding operations- CAFOs), 

Gentpro allocates annual, monthly or daily emissions to hourly based on empirical equations 

derived from investigations of emissions from animal waste decomposition as a function of 

temperature and wind speed. The resolution of emissions input to Gentpro for this sector 

depends on the available data. Air agencies that use EPA’s emissions data for this category 

should note that this approach has been used to temporally allocate agricultural NH3 emissions 

from CAFOs. 

For point sources, hourly CEMS data are recommended for use in base year emissions modeling 

rather than temporal profiles, where such CEMS data are available. CEMS data are collected as 

part of emissions trading programs. Because these programs require complete hourly data, 

alternative emissions values are assigned to hours when CEMS devices are not functioning. 

These alternative values can be extremely high values (compared to actual emissions rates), in 

order to motivate CEMS operators to provide a complete data record to the extent possible. As 

a result, CEMS data may have some anomalies and should be reviewed prior to use in a 

 

14 MCIP is a meteorology postprocessing program. It is typically used to convert WRF meteorological 

model output data to CMAQ input format. The latest version of MCIP can be found here: 

http://www.cmascenter.org/index.cfm. 
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modeling study. The CEMS data fields include an emissions method code, which identifies the 

hours with alternative values. Furthermore, CEMS data may be available for only part of a year; 

therefore, for annual modeling, users should be careful to ensure CEMS data are used for the 

appropriate periods and total annual emissions are temporally allocated to fill in the other 

hours of the year. 

For wildland fire emissions, the EPA recommends using daily, location-specific fire data such as 

the NEI “events” sources. These sources can have a large impact on ambient levels of ozone, 

PM2.5, and regional haze. Although emissions from these sources are largely uncontrollable, the 

inclusion of all emissions sources (including biogenic and uncontrollable sources) is needed to 

accurately predict pollutant concentrations and derive relative response factors for both PM 

and ozone. 

For future-year modeling there are two choices for representing EGU emissions derived from 

CEMs data and wildfire emissions. Modelers can use day specific emissions for both the base 

and future years, or a “typical year” temporal allocation (averaging) approach can be used to 

create representative emissions. If a typical year approach is used, it should include similar 

daily temporal variability as could be expected for any given year (in most cases, the daily 

temporal variability also needs to be consistent with the base year meteorology). Care should 

be taken to preserve the day-to-day variability in either the day specific or averaging approach, 

with the exception of eliminating year-specific outages or other year-specific anomalies15 using 

an averaging approach. Air agencies should discuss the temporal approach to fires and point 

sources with the appropriate EPA regional office as part of the modeling protocol development 

process. 

2.7.8.2 Chemical speciation

The emissions models also need information about the chemical species of the VOC and PM2.5

emissions for all emissions sources. These data are used to disaggregate the total VOC and 

PM2.5 emissions to the chemical species expected by the air quality model and are called 

speciation “factors” or “profiles”. The EPA provides a starting point for the VOC and PM2.5

speciation data, which are available with the modeling platforms (previously described), at 

http://www.epa.gov/ttn/chief/emch/. The speciation profiles are based on the SPECIATE 

 

15The baseline inventory should contain emissions estimates that are consistent with emissions patterns which 

might be expected in the future year(s). If unplanned outages or shutdowns at point sources occurred in the base 

year, then it may not be appropriate to project those emissions to the future. Using a typical or average year 

baseline inventory can provide an appropriate platform for comparisons between the base year and future years. 

However, certain emissions strategies may depend on capturing temporal emissions patterns on days with peak 

ambient concentrations. This should be considered when creating typical or average year inventories. 
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database, which is available at http://www.epa.gov/ttn/chief/software/speciate/. In addition 

to the speciation profiles, a speciation cross-reference file assigns speciation profiles. The 

default EPA speciation cross-reference assigns profiles to sources using SCCs. 

For large or critical VOC and PM2.5 sources in the modeling domain, air agencies should consider 

determining the individual chemical compounds contributing to the total VOC and PM2.5. If 

collected, this information should then be used to compile speciation profiles for the critical 

facilities or source categories. These speciation profiles should be assigned to the facilities by 

updating the speciation cross-reference file to support a facility-specific or facility-SCC-specific 

assignment. 

Additionally, the EPA encourages the use of HAP emissions data when available to augment 

VOC speciation; therefore, we suggest using the inventory HAPs where criteria air pollutants 

(CAPs) and HAPs were provided by the same data source. When possible, the EPA’s modeling 

platforms generally process the following HAPs rather than relying on VOC speciation: 

benzene, acetaldehyde, formaldehyde and methanol. These 4 HAPs are collectively known as 

“BAFM” and are removed from VOC speciation calculations for the remaining VOC HAPs 

needed for the air quality models. Ethanol can also be added to this “VOC integration” process 

where ethanol emissions are available. It is entirely up to the user to determine the 

“integration” status of specific sources in each inventory. A simple test for possible integration 

of VOC HAPs is the following. For each inventory unique source (i.e., a stack or 

nonpoint/mobile FIPS+SCC): 1) confirm that VOC HAPs and VOC were reported by the same 

data provider, and 2) compute VOC HAPs to ensure they are non-zero and less than inventory 

VOC. A detailed methodology is provided in Section 3.2.1 of the 2011 emissions modeling 

platform technical support document available at: 

http://www.epa.gov/ttn/chief/emch/2011v6/outreach/ 

2011v6_2018base_EmisMod_TSD_26feb2014.pdf. 

County-specific speciation profile combinations are also available for cases where user-defined 

mixtures of two or more speciation profiles are needed. The “GSPRO_COMBO” feature in 

SMOKE (http://www.smoke-model.org/version3.1/html/ch08s05s03.html) can be applied by 

state and county and time period (e.g., month) and is currently used for on-road and nonroad 

mobile and gasoline-related stationary sources where the emissions sources use fuels with 

varying ethanol content (e.g., E0 and E10 profiles). Ethanol content varies spatially by state and 

county via nonattainment areas or local programs, temporally (e.g., during ozone season 

months), and by modeling year because future years have more ethanol. Certain source 

categories will require more careful review of speciation assignments. For example, the oil and 

gas sector (point, area and nonroad) inventories are generally constructed for specific drilling 
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areas (i.e. drilling basins) which may include multiple counties with unique production gas 

characteristics. Knowledge of the type of gas (e.g., dry, wet, coal bed methane) or oil produced, 

flared and vented in these basin inventories is helpful for developing basin specific speciation 

assignments. 

2.7.8.3 Spatial allocation

For all source sectors that are compiled at a county resolution, the emissions models also need 

information about allocating the countywide emissions to individual grid cells that intersect the 

county boundaries. Such sectors include stationary area, nonroad mobile, and (non-link) onroad mobile sources. The spatial allocation process assigns fractions of county-total emissions 

to the model grid cells intersecting the county based on a “surrogate” data type (e.g., 

population, housing data). The appropriate types of surrogate data to use for each SCC in the 

inventories should be identified for this processing step. Spatial surrogates can be created 

using Geographic Information Systems (GISs) to calculate the fraction of countywide emissions 

to allocate to each grid cell based on the surrogate type. These calculations can also be made 

using EPA’s Surrogate Tool (http://www.ie.unc.edu/cempd/projects/mims/spatial/), which is 

based on the Multimedia Integrated Modeling System (MIMS) Spatial Allocator. In addition, all 

SCCs needing spatial surrogates should be assigned a surrogate in a cross-reference file. Point 

sources do not need spatial surrogates, since the emissions models assign the grid location 

based on the latitude and longitude of the point sources. EPA provides spatial surrogates and 

cross-references at http://www.epa.gov/ttn/chief/emch/index.html. The same spatial 

surrogate data is normally used in both the base and future year modeling. However, emissions 

developers can choose to alter the spatial surrogate data based on predicted changes in land 

use patterns, population growth, and demographics, although the impact and utility of such 

approaches are not well characterized39, so their use needs to be well documented and 

explained. 

2.7.8.3 Biogenic ancillary inputs

For biogenic emissions modeling, a biogenic emissions model (as described in Section 2.7.9.2) 

must be used. These models come with some ancillary data, with exception of the meteorology 

data and (in some cases) the gridded land-use data. 

 

39At the time this document was written, tools to readily predict future-year land use patterns are not 

readily available in a form for use in emissions modeling. 
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For biogenic emissions model inputs, the meteorological data should be consistent with the 

data used for the air quality model. Biogenic emissions models are generally set up to use 

gridded hourly meteorological data, so inventory developers should be able to convert their 

meteorological inputs from air quality modeling inputs for use in their selected biogenics 

emissions model. 

Land use data can be created with the MIMS Spatial Allocator 

(http://www.ie.unc.edu/cempd/projects/mims/spatial/), using raw Biogenic Emissions 

Landcover Data (BELD), version 3 (http://www.epa.gov/ttn/chief/emch/biogenic/). Based on 

inputs from the emissions developer, the Spatial Allocator will select the 1-km grid cells from 

the BELD data that intersect the modeling domain and (if necessary) aggregate to the grid 

resolution of the modeling domain. For future-year modeling, land use data is typically held 

constant (same as the base case). Emissions developers can choose to change their land cover 

data based on predicted changes in land use patterns, however, the impact and utility of such 

approaches is not well characterized, so their use needs to be well documented and explained. 

2.7.8.4 Other ancillary inputs

On-road emissions for fine-scale model grids (e.g., 4-km grid cells or smaller) may be based on a 

link-based approach as mentioned in Section 2.7.7.3. The VMT and speed data needed for a 

link-based approach can be provided using a Travel Demand Model (TDM). These models 

require their own sets of inputs, which depend on the specific TDM used. Details on using 

TDMs for link-based on-road mobile emissions are available from the EIIP document “Use of 

Locality-Specific Transportation Data for the Development of Source Emission Inventories” 

(http://www.epa.gov/ttn/chief/eiip/techreport/volume04/iv02.pdf). Additional information on 

the use of TDMs can be found in “Use of Travel Demand Model Data to Improve Inventories in 

Philadelphia” (http://www.epa.gov/ttnchie1/conference/ei15/session1/cook.pdf), 

Emissions models have other input files that must be created. Previously we noted that for 

point sources, emissions developers will have to identify which sources should be treated as 

elevated sources by an air quality model. To do this, data developers will select criteria such as 

stack height or a screening plume height value to allow an emissions model to flag point 

sources as elevated sources. In the SMOKE modeling system for example, the Elevpoint 

program can be used for this purpose. 

The emissions models have a large number of files and settings which work together in fairly 

complex ways; therefore users should be careful to select the appropriate files needed for the 

emissions model, and to prepare all input files in a way that will support using the emissions 
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model for the specific air quality model application. A possible shortcut can be starting with the 

emissions modeling scripts included with the EPA’s modeling platforms. 

2.7.9 How Are Inventory Data Converted Into Air Quality Model Input? 

Emissions models are used to convert inventory data to inputs for air quality modeling. As 

described in Section 2.7.8, as part of the emissions modeling process, additional ancillary data 

are needed to augment the raw emissions inventories. The emissions data for each of the six 

emissions sectors (point, area, on-road mobile, nonroad mobile, biogenics, and wildland fires) 

are temporally allocated, chemically speciated, and spatially allocated by the emissions model. 

The resulting hourly, gridded, and speciated emissions from all sectors are then combined 

before being used by an air quality model. In this section, we will provide information on 

several emissions models and summarize some key issues with the application of emissions 

models. 

2.7.9.1 Emissions models

Several emissions models are available for use in developing SIPs. While no single model has 

been specifically created for all situations, each model is generally capable of performing the 

necessary emissions processing steps, including temporal, chemical, and spatial allocation. 

Users of such models are responsible for ensuring that the emissions processing steps 

transform the emission inventories as intended and are not changing the emissions in any 

unexpected way. Each model has different capabilities, limitations, and nuances. Therefore, 

when choosing an emissions model, it is worthwhile to discuss the choice with the developers 

of these systems and/or with EPA to determine which model is best suited for a particular 

application. Note that there are a number of programs that process emissions for individual 

source sectors. These are discussed below in the sector-specific subsections. Currently, SMOKE 

is the primary emissions model used to develop emissions data for input into chemical 

transport grid models. Other emissions models can also be used, such as the Consolidated 

Community Emissions Processing Tool (CONCEPT)). 

SMOKE supports processing of criteria, mercury, and toxics inventories for stationary point, 

stationary area, mobile, and biogenic emissions. This emissions model can create input files for 

the CMAQ and CAMX air quality models. Applications of SMOKE have been presented at several 

of the International Emissions Inventory Workshops (Houyoux, 2000; Strum, 2003, Zubrow, 

2012, Baek, 2010, Adelman, 2008, Baek, 2007a, Baek, 2007b, Baek, 2006, Eyth, 2006, Pouliot, 

2005). SMOKE is available for UNIX and Linux operating systems, but is not recommended for 

use on PCs running Windows. It does not require third party software. The SMOKE software 
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and User’s Guide are available at http://www.smoke-model.org/index.cfm. Support for the 

SMOKE system is available through the Community Modeling and Analysis System (CMAS) help 

desk (http://www.cmascenter.org/html/help.html), hosted by the University of North Carolina 

at Chapel Hill’s Institute for the Environment (http://www.ie.unc.edu). 

Utilities for creating speciation profiles, biogenic land use, or spatial surrogates are not included 

within SMOKE. However, the Speciation Tool can create speciation profile inputs for SMOKE 

(Eyth, 2006), and biogenic land use and spatial surrogates can be built using the Multimedia 

Integrated Modeling System (MIMS) Spatial Allocator Tool 

(http://www.ie.unc.edu/cempd/projects/mims/spatial/) along with the related Surrogate Tools. 

The CONCEPT emissions model was developed as an open source model written primarily in 

PostgreSQL. The database structure of the model makes the system easy to understand, and 

the modeling codes are documented to encourage user participation in customizing the system 

for specific modeling requirements. The CONCEPT model structure and implementation allows 

for multiple levels of QA analysis during every step of the emissions calculation process. Using 

the database structures, an emissions modeler can trace a process or facility and review the 

calculation procedures and assumptions for any emissions value. CONCEPT includes modules 

for the major emissions source categories: area source, point source, on-road motor vehicles, 

non-road motor vehicles and biogenic emissions, as well as a number of supporting modules, 

including spatial allocation factor development, speciation profile development, growth and 

control for point and area sources, and CEM point source emissions handling. Additional tools 

include CEM preprocessing software, graphical QA tools, and an interface to the travel demand 

models for estimating on-road motor vehicle emissions. Note that the CONCEPT website 

(http://www.conceptmodel.org) has not been updated since 2005. 

2.7.9.2 Biogenic emissions models

There are several available biogenic emissions models that can be used to develop biogenic 

emissions for modeled attainment demonstrations. The most commonly used model by EPA is 

the Biogenic Emission Inventory System (BEIS), version 3.14 (BEIS3.14) (Geron, et al., 1994 and 

(http://www.cmascenter.org/conference/2008/slides/pouliot_tale_two_cmas08.ppt). BEIS 

comes with all needed ancillary data, except the gridded land-use data and meteorology data 

for a specific air quality modeling domain and grid resolution (as described previously in Section 

2.6.1). 

The BEIS3 model estimates CO, NOX, and VOC emissions from vegetation and soils in the 

gridded, hourly, and model-species forms needed for air quality modeling. Guenther, et al., 
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(2000) contains the technical development and review of BEIS3. Vukovich, et al., (2002) 

summarizes input data and algorithms as implemented within SMOKE. Arunachalam, et al., 

(2002) presents the impact of BEIS3 emissions on ozone. For more detailed local estimates, air 

agencies should review the biogenic emissions on a county basis and update the land use data 

as needed. BEIS3.14 was the latest version of BEIS3 in use by EPA at the time this document 

was written and is integrated directly into SMOKE v3.0 and later versions. 

Other biogenic models such as Model of Emissions of Gases and Aerosols from Nature (MEGAN) 

(http://acd.ucar.edu/~guenther/MEGAN/MEGAN.htm) can be used as well. In all cases, users 

should demonstrate the suitability of the biogenic emissions model for the particular modeling 

application (Baker, 2011). 

2.7.9.3 Agricultural ammonia emissions

Ammonia emissions from agricultural operations can be estimated using standalone models. A 

commonly used model to estimate ammonia from animals and fertilizer application is the 

Carnegie Mellon University Ammonia Model (http://www.cmu.edu/ammonia/). Another 

model is a process-based model developed by LADCO and other collaborators, most recently 

summarized by: 

http://www.ladco.org/about/general/2014%20Midwest%20Air%20Quality%20Workshop/Prese

ntations_files/B_Ammonia/McCourtney_Ammonia_Model.pdf. 

In addition, recent improvements to the CMAQ model (v5.0) have included bi-directional NH3

flux computations directly from hourly meteorology and land use information.16 The CMAQ bidirectional NH3 flux option only calculates emissions for fertilizer ammonia. Using this CMAQ 

option requires the user to remove all anthropogenic NH3 fertilizer application emissions from 

the emissions input to CMAQ when running the model with bi-directional flux. An evaluation of 

the bidirectional NH3 exchange can be found here: 

http://www.cmascenter.org/conference/2011/slides/bash_evaluation_bidirectional_2011.pdf. 

2.7.9.4 Other methods 

The Industrial Sector Integrated Solutions (ISIS) (EPA, 2010) model is under development by EPA 

for specific industrial source categories. Currently, ISIS can be used to generate base and future 

year emissions data for the Portland Cement industry. ISIS is also being developed for the Iron 

and Steel sector as well as the Pulp and Paper sector. ISIS baseline and policy case emissions 

 

16 The CMAQ ammonia bi-directional flux option calculates emissions only for fertilizer ammonia, 

although the flux calculations consider all sources of ammonia. 
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are currently not provided in a SMOKE-ready format and therefore require some formatting 

and data conversion preprocessing. 

2.7.10 Other Emissions Modeling Issues 

In addition to the emissions modeling process and tools described above, there are several other 

important issues to consider. In the remainder of this section, we briefly address each of these 

issues. 

2.7.10.1 Elevated Point Sources 

Point sources need to be assigned to an appropriate model layer17 (the vertical modeling 

dimension). Depending on the air quality model that is being used, different emissions modeling 

steps can be taken. Models such as CMAQ and CAMX expect input emissions files separately for 

layer one emissions versus the elevated point-source emissions. Additionally, elevated point 

sources may be flagged for treatment with a plume-in-grid (PinG) approach. Emissions modelers 

must supply criteria for specifying which point sources will be treated as elevated and/or as PinG 

sources18. For elevated sources, the air quality model calculates the plume rise of the point 

source emissions when the model is run. For PinG sources, the model does additional 

calculation for modeling the plume at a scale smaller than a grid cell, releasing the resulting 

computed concentrations to the neighboring cells only after accounting for in-plume chemistry 

and other sub-scale factors. 

2.7.10.2 Treatment of Fires 

For the purpose of model evaluation, the EPA recommends that detailed point-format dayspecific wildfire and prescribed burning emissions be used. These data are available using the 

latest version of the Satellite Mapping Automated Reanalysis Tool for Fire Incident 

Reconciliation (SMARTFIRE) system (Du et al., 2013; Sullivan, et al., 2008). Detailed information 

on the SMARTFIRE system is available in Raffuse et al, 2007. Additional references and 

 

17 Point sources generally comprise most of the elevated emissions (above layer 1), although other area 

sources, such as fires and aircraft, may also have emissions assigned to elevated layers. 

18 Plume rise is calculated based on point source stack parameters provided as inputs to the air quality

model. Stack parameter information (especially for large sources) should be reviewed in order to ensure 

that emissions are placed in the correct vertical layer. In addition, stack parameters (such as stack

height, diameter, and exit velocity) can change over time due to re-configuration of the stack(s) after 

installation of control devices such as scrubbers. 
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information are available as part of the 2011 NEI Technical Support Document, 

http://www.epa.gov/ttn/chief/net/2011inventory.html#inventorydoc. 

Due to the potential large local and regional impact from fires, for some modeling applications 

it may be appropriate to reduce the acute temporal and spatial impacts of wildfires and 

prescribed burning emissions by averaging them spatially and temporally. The EPA Fire 

Averaging Tool (FAT) has been developed to convert one or more years of SMARTFIRE point and 

day-specific fire inventories into daily nonpoint (county-level) inventories. The FAT user also 

has the option to apply an averaging technique to further smooth wildfire and prescribed 

burning emissions to a selected averaging period in days. For example, in the 2007v5 platform, 

EPA created a 29-day averaging period with 7 years of available wildfire data 

(http://epa.gov/ttn/chief/emch/2007v5/2007v5_2020base_EmisMod_TSD_13dec2012.pdf). 

The techniques referenced above represent EPA’s treatment of “actual” and “average” fire 

emissions in modeling to support EPA rules, but other techniques can be used to represent fire 

emissions. Since fire emissions can be a large source of PM2.5 and VOC emissions, the 

treatment of fires should be discussed with the appropriate EPA Regional Office(s) as part of 

the modeling protocol development process. 

2.7.10.3 Treatment of ‘atypical’ Sources of Emissions 

In the NEI, there are several sources of emissions that are reported by only one state or a small 

number of states. These sources typically have very low emissions, so their removal typically 

has little or no impact on air quality modeling. Examples of these sources include domestic and 

wild animal waste emissions (primarily NH3), human perspiration and cigarette smoke, and 

swimming pools. In some inventories, large catastrophic/accidental releases are reported (e.g., 

car accidents, tire fires). It is generally considered inappropriate to include these sources in 

emissions processing for air quality models. However, some source categories may be 

important or unique to a particular area. Before removing seemingly unimportant source 

categories, the relative importance (e.g. as a percentage of emissions in the area) of each 

category should be evaluated. 

In addition, air agencies should review and consider the EI SIP Guidance related to startup and 

shutdown emissions from individual facilities. This guidance provides information on when 

such emissions should be included in planning inventories. Since modeling inventories are 

expected to be consistent with the planning inventories where possible, air agencies should 

attempt to include any such emissions included in the planning inventories in their modeling 
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inventories as well. Such emissions may need a detailed temporal treatment to account for the 

variable nature of the startups and shutdowns. 

Similarly, air agencies should review and consider the draft EI SIP Guidance and the ozone 

implementation rule regarding EGU emissions during High Electric Demand Day (HEDD) periods. 

Such emissions can be crucial for having a complete understanding of relevant emissions 

sources within a modeling domain. Where these emissions are important for assessing a 

nonattainment problem, air agencies should consider the potential impact of HEDD emissions 

when selecting time periods (e.g. episodes, season, year) to model. 

2.7.10.4 Transport and Meteorology-based Reductions in Fugitive Dust Emissions

Fugitive dust emissions are another large source of primary PM2.5 emissions. Past modeling 

experience has shown large modeled PM2.5 concentration over-predictions from these 

emissions (Pouliot et al, 2010). Therefore EPA recommends reducing area-source fugitive dust 

emissions prior to air quality modeling to account for “capture” by the terrain and removal by 

meteorology (such as precipitation or frozen ground). These two components should be 

accounted for when adjusting the inventory PM emissions from PM sources such as paved and 

unpaved roads, construction, mining, agricultural activities, and industrial dust and other lowlevel fugitive dust source categories. The “captured” portion of the reduction can be based on 

gridded land use data such as vegetation and building characteristics. The EPA has developed a 

methodology within SMOKE to adjust the fugitive dust emissions. The “meteorological” 

component of the reduction requires intermediate SMOKE processing scripts to merge hourly, 

gridded precipitation data and snow cover values to zero-out all fugitive dust emissions where 

appropriate. To avoid double-counting the precipitation-based reduction, the user should 

make sure that the inventory fugitive dust inventory does not already include a “MET-adjusted” 

reduction prior to these manipulations. This combined transport and met-based reduction 

approach is documented in Pouliot et al., 2010. 

2.7.10.5 Quality Assurance 

Quality assurance (QA) is a key component of any successful emissions modeling effort. A brief 

synopsis of appropriate quality assurance (QA) approaches for emissions modeling is available 

in Section 2.20 of the SMOKE manual (http://www.smokemodel.org/version3.1/html/ch02s20.html). The purpose of QA for emissions modeling is to 

ensure that the inventories are correctly processed using the information the modeler 

intended. (It is assumed here that the inventory itself has already been QA’d and erroneous 

outliers removed through inventory QA procedures, as described in the EI SIP Guidance .) 
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Emissions modeling QA includes such activities as: 

• Reviewing log files for errors and warnings and addressing problems; 

• Comparing emissions between each of the processing stages (e.g., data import, 

speciation, temporal allocation) to ensure mass is conserved; 

• Checking that the correct speciation, temporal allocation, and spatial allocation 

factors were applied; 

• Ensuring that emissions going into the air quality model are consistent with 

expected results; and 

• Reviewing the modeling-specific parameters such as stack parameters. 

It is also useful to compare air quality modeling-ready summaries to previous emissions 

modeling efforts to verify expected emissions changes by source category(s) and geographic 

area(s) of interest. In addition, the process of running emissions inventories through emissions 

models and air quality models often provides insights into the emission inventories themselves. 

These insights can lead to inventory changes that improve the quality of inventories for 

additional modeling iterations. 

In general, this guidance also encourages the use of graphical analysis and Geographic 

Information Systems (GIS) for improved QA of emissions data and processing. A commonly 

used analysis tool for model-ready emissions data is the EPA sponsored Visualization 

Environment for Rich Data Interpretation (VERDI) tool. The VERDI software package provides 

for efficient, flexible and modular capabilities of overlaying meteorology, emissions and air 

quality modeling data with GIS shapefiles. More information on VERDI is available here: 

http://www.verdi-tool.org/index.cfm. VERDI can be downloaded from here: 

https://www.cmascenter.org/verdi/. In addition to VERDI, the older Package for Analysis and 

Visualization of Environmental Data (PAVE) tool is also available at 

http://www.ie.unc.edu/cempd/EDSS/pave_doc/index.shtml. 

2.7.11 How Are Emissions Estimated for Future Years? 

Emissions estimates for future years are called “emissions projections”. The inventory 

developed for modeled attainment demonstrations is called the attainment projected inventory 

for modeling. These emissions projections include emissions changes (due to increased or 

decreased activities), facility and/or unit-level shutdowns due to settlements (which can be due 

to settlements, consent decrees, or ownership decisions), and emissions controls (which can be 

due to regulations, settlements or fuel-switchings that reduce emissions in specific ways in the 
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future). The goal in making projections is to obtain a reasonable estimate of future-year 

emissions that accounts for the key variables that will affect future emissions. Each air agency 

is encouraged to incorporate in its analysis the variables that have historically been shown to 

drive its economy and emissions, as well as the changes in growth patterns and regulations that 

are expected to take place between the time of the base year and future years. Complete 

guidance on emissions projections is provided in the EI SIP Guidance. In addition, several 

additional issues are listed here for consideration for modeling inventories. 

Emissions models (e.g., SMOKE) provide the capability to create future-year inventories using 

base year inventories and projection information. Emissions modelers will need to convert the 

projection data into specific formats for input to these models. Inventory developers should 

determine which emissions model will be used to perform the calculations and make sure that 

the type(s) of information needed by the model is being collected. 

For EGUs, models are available that can not only estimate annual emissions, but also can 

estimate summer/winter or even day-specific emissions. The EPA uses the Integrated Planning 

Model (IPM) to model future electricity demand and emissions trading programs that now 

dominate the prediction of future-year emissions from EGUs. More information on IPM is 

available at (http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html). Additionally, IPMbased emissions are posted by CAMD on the EPA’s website (http://ampd.epa.gov/ampd/). 

Other future-year EGU emissions models can be used, and should include explanations 

regarding the approaches taken and the expected improvements over the EPA-developed 

approaches. 

The mobile sources models (e.g., MOVES and NONROAD/NMIM) are designed to give different 

results based on the output year selected. MOVES account for expected changes in vehicles 

over time, with newer (more controlled) vehicles gradually replacing older vehicles and newer 

nonroad engines replacing older ones. These models must be rerun for the future year 

emissions using the same resolution as was used for the base year modeling inventories. It is 

generally not appropriate to simply apply projection factors to these sources. For example, if 

SMOKE-MOVES is used in the base year (with base year emission factors generated by MOVES), 

then SMOKE-MOVES would need to be also run for the future year (using future-year emission 

factors). 

Ancillary input files for inputs to emissions models, such as spatial surrogates, temporal 

allocation factors and especially speciation profiles may also need to be adjusted to reflect 

conditions expected in the future. The EPA modeling platforms, for example, consider the 
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impact on speciation of fuels used in the future, which are expected to change over time due to 

regulations. 

Once a future-year inventory and other data have been created, it should undergo the same 

steps as for the base-year modeling, including temporal allocation, speciation, spatial 

allocation, elevated source selection, special on-road mobile processing, any other custom 

processing steps (e.g. applying meteorology-based information to fugitive dust, agricultural NH3

and residential wood combustion), and quality assurance. Except where intentional, every 

attempt should be made to use consistent approaches between the future year and the base 

year for all of these modeling steps. Inconsistencies in approaches between the future-year 

modeling and the base-year modeling can lead to artificial differences in air quality modeling 

results that can affect conclusions19. Therefore, it is critical to avoid such differences whenever 

possible. If inconsistent base and future year approaches are necessary, they should be well 

documented and explained.

2.8 Initial and Boundary Conditions 

The behavior of a dynamic system is strongly dependent upon its initial state. In order to solve 

the requisite chemical and physical equations in space and time, air quality models require 

specification of the initial state of the chemical conditions within the domain at the first time 

step of the modeling period. This model input is typically referred to as the initial condition file. 

Additionally, air quality models also require information regarding the time-varying chemical 

state of the atmosphere just outside the edges of the modeling domain in order to properly 

account for transport of material into the area of interest. This model input is typically referred 

to as the boundary condition file. As with other model inputs, the establishment of the initial 

and boundary condition (IC/BC) data should be guided by the conceptual model of the air 

quality issue being simulated. For instance, more attention will be needed in developing 

accurate boundary conditions for cases in which ambient data analyses (or previous modeling 

scenarios) have indicated the potential for long-range transport of pollutant material, 

compared to cases in which the air quality problem has been identified as largely local in 

nature. Numerous studies have highlighted the importance of accurately representing 

boundary conditions in optimizing model performance and identifying the most effective path 

to attaining the NAAQS (Jacob et al., 1999; Tang et al., 2009; Hogrefe et al., 2011). 

 

19 This is especially important due to the use of the “relative” change in modeled concentrations as part 

of the modeled attainment test. An inconsistency in base and future year emissions may lead to a very

large modeled percent change in ozone and/or PM2.5 concentrations. 
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Given limitations in available ambient data, it is impossible to exactly specify the complex threedimensional chemical characteristics of the initial or boundary conditions. As a result, two basic 

approaches to establishing these fields have been used within the modeling community. The 

first approach is to attempt to minimize the impacts of the initial and boundary conditions by 

configuring the domain and modeling period such that the air quality concentrations in the 

locations and times of interest are largely unaffected by the IC/BC inputs. The second approach 

is to characterize the IC/BC data as accurately as possible given the existing state of knowledge 

about ambient conditions at those times and in those locations. 

In most cases, it will be possible to minimize the impacts of the initial conditions data via the 

use of a model “spin up” period. A spin up period is defined as some number of modeling days 

that precede the time period of actual interest. The model outputs for this spin up period are 

discarded from any analytical post-processing, as they are potentially affected by the uncertain 

initial conditions. The length of the needed model spin up period will vary as a function of: the 

domain size, prevailing meteorological conditions, and the chemical lifetimes of the pollutants 

being simulated. It is a simple modeling exercise to test the sensitivity of the key time periods 

to the initial conditions. Previous sensitivity modeling has determined that the initial 

conditions are typically “washed out” of the system in as few as 3-5 days for a local-scale 

modeling analysis; approximately 10 days for a regional-scale analysis; and on the order of six 

months for a hemispheric-scale analysis (Berge et al., 2001; USEPA 2010; Anenberg et al., 

2011). If there is any question as to the appropriate length of the spin-up period, then 

sensitivity modeling tests should be conducted. Otherwise, the above guidelines should be 

sufficient for most ozone and PM2.5 attainment demonstrations and reasonable progress 

assessments. 

For the determination of base year boundary conditions, it will typically be a case-by-case 

determination, guided by the conceptual model, as to whether “impact minimization” or 

“accurate characterization” should the preferred approach. In some situations, it may be 

possible to construct a modeling platform in which the boundary conditions will not 

substantially influence the air quality issue under consideration. For instance, areas for which 

there is no evidence of pollutant transport from the far edges of the model domain to the local 

area may be able to demonstrate that the determination of boundary condition data will not 

impact the eventual attainment demonstration. As with the initial conditions data, this 

conclusion can be proven via simple sensitivity tests in which the boundary conditions are 

modified from a base state and the results between the two runs are compared (e.g., Appel et 

al, 2007). For situations in which alterations to the boundary condition data will not impact key 

findings, it is acceptable to use simple generic profiles of species data in the boundary condition 

file. 
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In other cases, it will not be feasible to build a large enough domain to minimize the impacts 

from emissions outside the domain. For these cases, in which accurate characterizations of 

boundary conditions are important, it will be necessary to downscale the requisite species 

concentration data from well-performing, coarser-grid, larger domain modeling runs. (In rare 

cases, it may be possible to assign boundary conditions from detailed field study species data, 

but most domains will not have complete chemical profiles in space and time along the domain 

edges.). These larger “parent” grids may be generated by the same regional scale model being 

used for the attainment demonstration at coarser resolution; or they can be generated via 

global or hemispheric models. In recent years, several global/hemispheric air quality models 

have been used to generate regional boundary condition inputs. A list of these models include: 

GEOS-Chem (Bey et al., 2001), the AM3 atmospheric component of the GFDL global coupled 

model (Donner, et al., 2011), the Model for Ozone And Related Tracers (MOZART, Emmons et 

al, 2010), the WRF-Chem model (Zhang, et al., 2012a), and the hemispheric CMAQ model (Byun 

and Schere, 2006; Fu et al., 2012). EPA does not specifically recommend any one particular 

global/hemispheric model. Each regional modeling exercise should individually assess whether 

the chosen global/hemispheric or coarse regional model accurately captures inflow boundary 

conditions. 

For cases in which the boundary conditions will be extracted from larger-scale 

regional/hemispheric modeling, EPA recommends two efforts to optimize the BC 

characterizations. First, modelers should try to minimize any disconnects in the downscaling 

process. Wherever possible, one should attempt to utilize the same species, chemical 

mechanism, layer depths, and meteorological inputs in the regional/hemispheric model as are 

being used in the finer-scale attainment demonstration modeling (Lam and Fu, 2009). This will 

minimize the number of uncertain translations needed to convert coarse-scale outputs to finerscale BC inputs. In particular, careful consideration of the stratosphere in the two modeling 

systems will be needed to ensure that regional/hemispheric ozone that is stratospheric in origin 

is not excessively mixed down to the surface in the finer-scale modeling due to disconnects 

between the two systems20. Second, there should be a comprehensive evaluation of the 

regional/hemispheric model performance (Bey et al., 2001). While there may not be a detailed 

 

20 In establishing the layer structure for both global and regional models, it is advisable to avoid selecting 

upper model layers that span both the upper troposphere and lower stratosphere. It has been seen that

“hybrid” layers can allow for inappropriate diffusive mixing between those two generally decoupled 

sections of the atmosphere. In cases where stratospheric intrusions are not expected to influence 

ozone in the area, it may be appropriate to artificially cap stratospheric ozone concentrations to avoid 

ozone performance issues. This should be a “last resort” fix, only applied after detailed assessments of 

the vertical diffusion and vertical advection schemes within the model. 
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data set available for model evaluation along the domain boundaries in most cases, 

consideration of model performance across the domain will provide a first-order indication of 

the ability of the coarser-scale model to characterize air quality at the boundaries. Additionally, 

it may also be possible to infer information about the quality of the coarse-model estimates via 

comparisons with satellite data (Liu et al., 2006, Henderson et al., 2013). 

Due to the potential impact of boundary conditions on model response to emissions controls, it 

is important to accurately characterize the relative influence of boundary conditions versus 

local emissions. For example, if the impacts of boundary conditions on local air quality are 

overestimated, it will be more difficult to reduce design values from local controls. Conversely, 

if the modeling system underestimates the role of inflow air quality, local emissions reductions 

may appear more effective in the model than they will be in practice. 

The selection of future year boundary conditions also has potentially important ramifications 

on the relative response factors derived from the modeling for the various control scenarios. 

If boundary conditions are known to impact local air quality, then attainment demonstration 

modelers may need to consider how these contributions will change between the base year 

and the attainment year (Adelman et al., 2011). Recent ambient trends analyses have 

indicated that free tropospheric ozone levels above the western U.S. have increased by 

approximately 0.5 ppb/year over the past two decades (Cooper et al., 2012; Cooper et al., 

2010). If these trends continue into the next decade, or were they to be reversed, they have 

the potential to impact attainment of the NAAQS in some areas. It is left to the discretion of 

the air agency to determine whether varying future boundary conditions are needed as part of 

an attainment demonstration. This decision should consider both the length of the projection 

period and the potential for significant air quality changes outside the modeling domain. 

Additionally, careful consideration of upstream emissions will be needed as part of any 

additional future-year regional/hemispheric modeling conducted to develop future boundary 

conditions. 
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3.0 Evaluating Model Performance 

3.1 Overview of model performance evaluation 

The results of a model performance evaluation (MPE) should be considered prior to using 

modeling to support an attainment demonstration or regional haze assessment. The objective 

of the MPE is to demonstrate that the base case model can simulate observed pollution 

concentrations during historical pollution episodes, and to develop confidence that the model 

can reliably predict how future pollution levels will change in response to changes in emissions. 

A particular concern in photochemical models is that compensating errors in the model can 

cause the model to reproduce observed pollution concentrations in the base case while 

incorrectly representing the emissions, dispersion and chemistry processes that control 

pollution formation. Models that achieve good performance through compensating errors will 

generally not be reliable for predicting how pollution levels will respond to future emissions 

reductions. Thus, a key goal of the MPE is to demonstrate that the model is getting good results 

for the right reason (Russell and Dennis, 2000). In addition, the MPE provides air agencies with 

tools to identify and fix causes of poor model performance such as problems in the 

meteorological, emissions, or boundary condition input files. 

Dennis et al. (2010) describe an MPE framework that includes four different approaches that 

can be used to evaluate air quality models: 

1. Operational evaluation techniques include statistical and graphical analyses aimed at 

determining whether the modeled simulated variables are comparable to 

measurements. 

2. Diagnostic evaluation focuses on process-oriented analyses that determine whether the 

individual processes and components of the model system are working correctly, both 

independently and in combination. 

3. Dynamic evaluation assesses the ability of the air quality model to predict changes in air 

quality given changes in source emissions or meteorology, the principal forces that drive 

the air quality model. 

4. Probabilistic evaluation attempts to assess the level of confidence in the model 

predictions through techniques such as ensemble model simulations. 

The goal of the operational evaluation is to determine how well the model replicates observed 

concentrations of PM2.5 components, ozone and their precursors. Diagnostic and dynamic 

evaluations are used to determine whether the model accurately represents the processes that 

determine pollutant concentrations, including emissions, dispersion, chemical reactions, and 
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deposition, i.e., to determine if the model is getting good results for the right reason. For cases 

in which the model performs poorly in the operational evaluation, the diagnostic and dynamic 

evaluation can provide insights into the causes of the poor performance and to improve model 

performance. Ultimately, the goal of these evaluation methods is to assess the degree of 

confidence in the use of the model as a tool to inform the planning process, and more 

specifically, to determine how reliably the model predicts the response of ozone and/or PM2.5 

to changes in emissions. The modeled attainment tests use models to predict the response of 

ozone and PM2.5 to changes in emissions and then applies the resulting relative response 

factors (RRFs) to observed (rather than modeled) ambient data. Thus, while historically, most 

of the effort has focused on the operational evaluation, the relative attainment test makes the 

diagnostic and dynamic evaluation also important. 

At a minimum, a model used in an attainment demonstration should include a complete 

operational MPE using all available ambient monitoring data for the base case model simulation 

period. Section 3.2 describes the types of metrics and plots that are typically developed as part 

of an operational MPE, and Section 3.3 describes ambient data that is available through 

continuously operating monitoring networks. If available, monitoring data from special field 

studies and other research data should also be used in the operational MPE. 

Where practical, the MPE should also include some level of diagnostic evaluation. The use of 

sensitivity studies, process analysis, indicator ratios and source apportionment approaches for 

diagnostic evaluation, including dynamic and probabilistic evaluations are described in Section 

3.4. Given that air agencies might have limited resources and time to perform diagnostic and 

dynamic evaluation, the use of these methods may be limited in scope in a typical SIP modeling 

application. However, more comprehensive diagnostic testing of the model can be a valuable 

component of the weight of evidence analysis used to support a SIP model attainment 

demonstration. 

 It is clear that there is no single definitive test for evaluating model performance. All of the 

tests mentioned here have strengths and weaknesses. Further, even with a single performance 

test, it is not appropriate to assign “bright line” criteria that distinguish between adequate and 

inadequate model performance. In this regard, EPA recommends that a “weight of evidence” 

approach be used to determine whether a particular modeling application is valid for assessing 

the future attainment status of an area. EPA recommends that air agencies conduct a variety of 

performance tests and weigh them qualitatively to assess model performance. Provided 

suitable databases are available, greater weight should be given to those tests which assess the 

model capabilities most closely related to how the model is used in the modeled attainment 

test (i.e. tests that provide insight into the accuracy of the model’s relative response to 

emissions reductions). Generally, additional confidence should be attributed to model 
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applications in which a variety of the tests described here are applied and the results indicate 

that the model is performing well. From an operational standpoint, EPA recommends that air 

agencies compare their evaluation results against similar modeling exercises to ensure that the 

model performance approximates the quality of other applications. A recent literature review 

(Simon et al, 2012) summarized photochemical model performance for applications published 

in the peer-reviewed literature between 2006 and 2012. This review may serve as a resource 

for identifying typical model performance for state of the science modeling applications. 

3.2 Operational Evaluation 

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

concentrations. The underlying rationale is that if we are able to correctly characterize 

historical concentrations of ozone, PM and their precursors for a variety of meteorological 

conditions, this gives us some confidence that we can correctly characterize future 

concentrations under alternative emissions levels. Typically, this type of evaluation is 

comprised of statistical assessments of modeled versus observed data. Operational evaluations 

are generally accompanied by graphical and other qualitative descriptions of the model's ability 

to replicate historical air quality patterns. An operational evaluation provides a benchmark for 

model performance and can therefore identify model limitations and uncertainties that may 

require further model development/improvement and/or diagnostic evaluation 

The robustness of an operational evaluation is directly related to the amount and quality of the 

ambient data available for comparison. An operational evaluation for ozone should include 

collocated measurements of ozone precursors NOx, CO and VOC, and ideally, vertical profile 

measurements that can be used to determine the extent of vertical mixing of pollutants and the 

concentration of O3 and precursors above the boundary layer. 

An operational evaluation for PM2.5 and regional haze is similar to that for ozone, however, an 

important difference is that PM2.5 consists of many component species and is typically 

measured with a 24-hour averaging time. The individual components of PM2.5 should be 

evaluated individually. In fact, in undertaking an operation assessment, it is more important to 

evaluate the components of PM2.5 than to evaluate total PM2.5. Apparent “good performance” 

for total PM2.5 does not indicate whether the model is predicting the proper mix of 

components, which is important for the modeled attainment test. If performance of the major 

components is good, then it follows that performance for total PM2.5 will also be good. In 

addition to measurements of VOC and NOx, which are precursors for organic aerosols and 

nitrate, a PM2.5 model evaluation should also include measurements of gaseous precursors SO2
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and (where available) NH3. Due to the influence of initial conditions, model predictions from 

the spin-up days should be excluded from the analysis of model performance. 

3.2.1 Metrics 

Operational evaluations quantify model performance through a variety of statistical metrics. 

Recommended metrics are described below and shown in Table 3.1. It is important to include 

multiple statistical measures in any operational evaluation to fully characterize model 

performance. At a minimum, we recommend evaluating: Mean obs, Mean Model, Mean Bias, 

Mean Error and/or Root Mean Square Error, Normalized Mean Bias and/or Fractional Bias, 

Normalized Mean Error and/or Fractional Error, and the correlation coefficient. The equations 

for calculating each of these metrics are given in Table 3.1. 

Mean Obs: The time-averaged mean observed value (in ppb or μg/m3). 

Mean Model: The time-averaged mean predicted value (in ppb or μg/m3) paired in time and 

space with the observations.

Mean Bias (MB): This performance statistic averages the model/observation residual paired in 

time and space. A value of zero would indicate that the model over-predictions and model 

under predictions exactly cancel each other out. An advantage of this metric is the bias is 

reported in the unit of measure (ppb or μg/m3) making interpretation simpler. 

Mean (Gross) Error (ME/MGE): This performance statistic averages the absolute value of the 

model/observation residual paired in time and space. A value of zero would indicate that the 

model exactly matches the observed values at all points in space/time. An advantage of this 

metric is the bias is reported in the unit of measure (ppb or μg/m3) making interpretation 

simpler. 

Root Mean Square error (RMSE): This performance statistic (in units of ppb or ug/m3) is a 

measure of the average distance between predicted and observed values. It is calculated as the 

standard deviation of the difference between modeled and observed values. 

Normalized Mean Bias (NMB): This statistic (given in units of percent) normalized MB to the 

average observed value. NMB values range from -100% to +infinity. Consequently, negative and 

positive bias values using this metric are not symmetrical around 0. NMB is a useful model 

performance indicator because it avoids over inflating the observed range of values 
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Normalized Mean Error (NME): This performance statistic (given in units of percent) is used to 

normalize the mean error relative to the average observation. This statistic averages the 

absolute value of the difference (model - observed) over the sum of observed values. NME 

values range from 0 to +infinity. NME is a useful model performance indicator because it avoids 

over inflating the observed range of values. 

(Mean) Fractional Bias (MFB/FB): Fractional bias is determined by normalizing the MB by the 

average of observed and modeled concentrations. Since normalized bias can become very 

large when a minimum threshold is not used, fractional bias may be used as a substitute. The 

range of FB is -200% to +200%. The fractional bias for cases with factors of 2 under- and overprediction are -67 and + 67 percent, respectively (as opposed to -50 and +100 percent, when 

using normalized bias). Fractional bias is a useful indicator because it has the advantage of 

equally weighting positive and negative bias estimates (underestimates and overestimates are 

symmetrical around 0). The single largest disadvantage is that the predicted concentration is 

found in both the numerator and denominator. 

(Mean) Fractional Error (MFE/FE): Fractional error is determined by normalizing the ME by the 

average of observed and modeled concentrations. Since normalized error can become very 

large when a minimum threshold is not used, fractional error may be used as a substitute. The 

range of values for FE is 0 to 200%. It is similar to the fractional bias except the absolute value 

of the difference is used so that the error is always positive. 

Correlation Coefficient (R2

): This performance statistic measures the degree to which two 

variables are linearly related. A correlation coefficient of 1 indicates a perfect linear 

relationship; whereas a correlation coefficient of 0 means that there is no linear relationship 

between the variables.

 

Table 3.1: Definitions of recommended statistical metrics

Abbreviation Term Definition

MB mean bias 1



( − )

ME mean error 1



| − 

|
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RMSE root mean squared error 

∑( − )






FB fractional bias 100% ×

2





( − )

( + )

FE fractional error

100% ×

2





| − |

( + )

NMB normalized mean bias

100% ×

∑( − )

∑ 

NME normalized mean error

100% ×

∑| − |

∑ 

r

2

coefficient of determination 

∑ ( − ) × ( − )





∑ ( − )

 


 ∑ ( − )

 









Mi = modeled concentration i. Oi = observed concentration i. N = number of paired obs/model 

concentrations. M = mean modeled concentration. O = mean observed concentration.

Observations and model predictions should initially be paired in time and space, matching the 

temporal resolution of the raw observation data. The units of time associated with model and 

observed concentrations can be 24-hour (usually for particulate matter and its species), hourly 

(usually for species with continuous measurements, like ozone) or sometimes weekly (such as 

for CASTNet filter pack measurements). Temporal averaging is appropriate when used to 

match a relevant regulatory metric such as maximum 8-hr daily average ozone. As appropriate, 

air agencies should then aggregate the raw statistical results into meaningful groups of subregions or sub-periods. For example, examining model performance within and near the nonattainment area(s) of interest may be more important than performance in other parts of the 
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modeling domain. For larger areas with more spatial variation, it may also be informative to 

evaluate sub-regions within the non-attainment area. In addition to any spatially aggregated 

statistics, model performance should also be evaluated at individual monitors within the 

nonattainment area. Similarly, priority may be placed on examination of the days that are 

potentially used in the attainment test (i.e. base period days with 8-hour ozone > 60 ppb). That 

is not to say that model performance evaluations should ignore performance on lower ozone 

days or in areas outside of the nonattainment areas. Model performance on lower 

concentration days and in areas outside of the nonattainment area are still important, but it is 

appropriate to give more attention to the model outputs that most directly impact the outcome 

of the attainment test. 

In terms of pairing model predictions with monitored observations, EPA recommends that the 

grid cell value in which the monitor resides be used for the calculations. It would also be 

acceptable to consider bi-linear interpolation of model predictions to specific monitoring 

locations21. Air agencies should recognize that, even in the case of perfect model performance, 

model-observed residuals are unlikely to result in exact matches due to measurement 

uncertainty and differences between the model predictions which are volume averages and the 

observations which are point values. 

3.2.2 Plots 

In addition to statistical summaries, graphical displays of data allow for a fuller characterization 

of model performance. Therefore, plots play a key role in any model performance evaluation. 

Below are examples of some types of plots which have been useful in past evaluations. 

Time series plots of model and predicted hourly and/or daily (1-hour or 8-hour) maximum 

ozone for each monitoring location in the nonattainment area, as well as key sites outside of 

the nonattainment area. These plots can indicate if there are particular times of day or days of 

the week when the model performs especially poorly. 

 

21In certain instances, air agencies may also want to conduct performance evaluations using a “near the 

monitor” grid cell array (i.e. the best match within a 3X3 grid cell array centered on the monitor). A 

“near the monitor” analysis may be useful when strong ozone gradients are observed, such as in the 

presence of a sea breeze or in strongly oxidant limited conditions. 
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Figure 3.2.1 Example time series plot of daily 1-hour maximum ozone concentrations 

Scatter plots of predicted and observed concentrations at each site. These plots are useful for 

indicating if there is a particular part of the distribution of observations that is poorly 

represented by the model. It may also be useful to develop separate plots for individual time 

periods or key sub-regions. 

Figure 3.2.2 Example scatter plot of 8-hour daily maximum ozone concentrations 
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Density plots are a variation on scatter plots which are especially useful with large amounts of 

data when individual points overlap on a scatter plot. Density plots color code each point on 

the scatter plot based on the number of points falling in that location. 

Figure 3.2.3 Example density ply of 8-hour daily maximum ozone concentrations 

Box plots can be developed for model performance evaluation. These types of plots show the 

distribution of observations, model estimates, or performance metrics, which can be grouped 

in a variety of ways, most commonly by observed ozone concentration, month, or hour of the 

day. Box plots show several quantities: the 25% to 75% percentiles, the median values, and 

outliers. The monthly box plot can be used to quickly visualize model performance across the 

entire year, highlighting the seasonal change in model performance. The hourly or “diurnal” 

box plot is constructed using hourly data, and shows how the model predictions compare 

against observations throughout an entire day. 
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Figure 3.2.4 Example box plots of (a) monthly average sulfate concentrations, (b) daily diurnal 

average ozone concentrations, and (c) seasonal ozone bias binned by concentration range 

(a) (b) 

(c) 
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Daily gridded tile plots of predicted concentrations across the modeling domain with the actual 

observations as an overlay. Plots should be prepared for relevant averaging time (i.e. 1-hour 

maxima, daily 8-hour maxima, 24-hour average, etc.). These plots can reveal locations where 

the model performs poorly. Superimposing observed concentrations on the predicted isopleths 

reveals useful information on the spatial alignment of predicted and observed plumes. 

Figure 3.2.5 Example gridded tile plot of daily maximum 1-hour ozone concentrations (with 

overlaid observations) 

Animations of predicted hourly concentrations for all episode days or for certain periods of 

interest. Animations are useful for examining the timing and location of pollutant formation. 

Animations may also reveal transport patterns (especially when looking at aloft layers). 

Animations can also be used to qualitatively compare model outputs with the conceptual 

model of particular pollution episodes. These plots may also reveal whether an important 

feature was simulated but was spatially or temporally displaced. 

The soccer plot (Tesche et al, 2006) is so named because the dotted lines illustrating 

performance goals resemble a soccer goal. The plot is a convenient way to visualize model 

performance of both bias and error on a single plot. As bias and error approach zero, the points 

are plotted closer to or within the “goal”, represented by the dashed boxes. 
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Figure 3.2.6 Example soccer plot of sulfate normalized mean bias and error. 

The bugle plot (Boylan and Russell, 2006; Tesche et al., 2006), named for the shape formed by 

the criteria and goal lines, is another plot available for model performance evaluation. The 

bugle plots are shaped as such because the goal and criteria lines are adjusted based on the 

average concentration of the observed species. As the average concentration becomes smaller, 

the criteria and goal lines become larger to adjust for the model’s ability to predict at low 

concentrations. 

Figure 3.2.7 Example bugle plot of sulfate normalized mean bias as a function of modeled 

concentration. 
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3.2.3 Ozone Model Performance 

It is recommended that, at a minimum, statistical performance metrics be calculated for hourly 

ozone and 8-hourly maxima ozone for each day of the model simulation used to support the 

attainment demonstration. EPA recommends that the metrics be calculated both for all pairs of 

modeled and observed data and for pairs in which the hourly or 8-hr daily max values are above 

60 ppb. Another acceptable approach would be to estimate averaged metrics binned by 

observed ozone concentration. This would allow for a thorough evaluation of the model 

tendencies to estimate ambient concentrations in different observed ranges of interest. 

Wherever possible, these types of performance measures should also be calculated for ozone 

precursors and related gas phase oxidants. 

Ozone precursors and related gas-phase oxidants (partial list) are listed below. 

• Total Volatile Organic Compounds (VOCs) 

• Speciated VOC 

• Nitric acid (HNO3) 

• Nitric oxide (NO) 

• Nitrogen dioxide (NO2) 

• Peroxyacetyl nitrate (PAN) 

• NOy (sum of NOx and other oxidized compounds) 

• Carbon monoxide (CO) 

3.2.4 PM2.5 Model Performance 

Performance metrics for PM and regional haze are similar to those calculated for ozone. 

Metrics should be estimated for components of PM2.5, and PM precursors. Since modeling for 

PM2.5 and regional haze will likely require modeling different times of year, season-specific 

statistics and graphical displays are helpful for evaluating and diagnosing model performance. 

Statistics and graphics can be averaged for various time scales depending on the model 

application. For example, statistical metrics and scatterplots can show daily averaged ambientmodeled pairs or annual average pairs for attainment demonstration of the daily and annual 

PM2.5 standards respectively. In addition, EPA recommends that daily pairs be grouped by 

season or month and statistics be given for different times of the year since different processes 

dominated PM2.5 formation and fate in different seasons. Note that in this case, pairings should 

be performed on 24-hour average concentrations and statistics may then be reported for 

subgroups (seasons, regions etc.) of this data. This is different than pairing monthly or 

seasonally averaged data since statistics and plots tend to look “better” as the averaging time 
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increases from daily to monthly to quarterly to annual. As such, daily pairs should always be 

examined to ensure a detailed look at model performance on the time scale of the FRM and 

CSN measurements (24-hour average). Finally, air agencies may also want to subset the 20% 

best and worst visibility days or the “high” modeled PM2.5 days (e.g. the 10% highest modeled 

days in each quarter) to examine model performance on the days which will potentially be used 

in the attainment test. 

Because PM2.5 is a mixture, a meaningful performance evaluation should include an assessment 

of how well the model is able to predict individual chemical components that constitute PM2.5. 

Components of PM2.5 of interest include: 

• Sulfate ion (SO4) 

• Nitrate ion (NO3) 

• Ammonium ion (NH4) 

• Elemental Carbon (EC) 

• Organic Carbon (OC) and/or Organic Carbon Mass22

• Crustal (weighted average of the most abundant trace elements in ambient air) 

• Sea salt constituents (Na and Cl)23

There are a number of gas phase species that can be used to evaluate PM2.5. Their presence 

may affect the formation of secondary PM and the response of secondary PM components to 

emissions strategies. Also, the model's performance in predicting certain gaseous species may 

provide diagnostic clues which help explain poor performance. Gas phase species of most 

interest for performance evaluation due to being a direct precursor to secondarily formed PM2.5

are listed below. 

• Nitric acid (HNO3), 

• Ammonia (NH3) 

• Sulfur dioxide (SO2) 

• Volatile Organic Compounds (VOCs) 

Other related gas-phase oxidants (partial list) that indirectly influence PM2.5 formation and 

destruction are listed below. 

 

22 For predicted/observed comparisons, organic carbon is preferred over organic carbon mass to allow 

for transparency between model predictions and observed measurements. Ambient measurements will 

generally only account for the carbon portion of organic carbon. Organic carbon mass may include 

hydrogen, oxygen, and other components. 

23 White (2008) recommends the use of 1.8xCl-

 to characterize ambient sea salt concentrations at coastal 

IMPROVE sites due to uncertainty in the XRF Na measurements. 
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• Ozone (O3) 

• Nitric oxide (NO) 

• Nitrogen dioxide (NO2) 

• Peroxyacetyl nitrate (PAN) 

• NOy (sum of NOx and other oxidized compounds) 

• Carbon monoxide (CO) 

• Hydrogen peroxide (H2O2) 

Some of the species listed above are currently available from either existing monitoring 

network systems and/or special studies. However, it is important to note that many of the 

species listed above are not routinely measured (e.g. H2O2, HNO3, and NH3) or co-located with 

speciated PM measurements. Model performance can best be assessed using extensive data 

bases, such as those obtained in major field studies. Where possible, model time periods can 

be prioritized to correspond to periods when field study or special study data was collected. 

However, it is recognized that many of the precursor species will not be available for every 

model application. 

3.3 Ambient Measurement Networks 

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

currently available. Network methods and procedures are subject to change annually due to 

systematic review and/or updates to the current monitoring network/program. Please note, 

there are other available monitoring networks which are not mentioned here and more details 

on the networks and measurements can be obtained from other sources, if necessary. 

3.3.1 AQS 

The Air Quality System (AQS) 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 (it is not an actual monitoring network). The monitoring data in 

AQS are the result of the various Clean Air Act requirements to provide a national database of 

ambient air pollution data. This information management system contains over 1000 

pollutants from 1957 through the present day. 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) which consists of 
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approximately 4,000 monitoring stations. 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, CO, PM10, PM2.5 and Pb) and 

speciation data of particulate matter (SO4, NO3, NH4, EC, OC, and crustal Material), air toxic 

data, photochemical assessment data, and meteorological data. The data are measured and 

reported on an hourly or daily average basis. The AQS system continues to expand to include 

more ambient air pollutants. An overview of the AQS can be found at 

http://www.epa.gov/ttn/airs/airsaqs/index.htm . For querying the database or viewing the 

data one can use EPA's AirData website (http://www.epa.gov/airdata/ ) which is a collection of 

user-friendly visualization tools for air quality analysts. The tools generate maps, graphs, and 

data tables dynamically. An AQS User’s Guide is available at: 

http://www.epa.gov/ttn/airs/airsaqs/manuals/docs/users_guide.html 

3.3.2 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, 2011). 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 181 IMPROVE sites that have complete annual PM2.5

mass and/or PM2.5 species data. There are 110 IMPROVE monitoring sites which represent air 

quality at the 156 designated Class I areas. The 71 additional IMPROVE sites are “IMPROVE 

protocol” sites which are generally located in rural areas throughout the U.S. These sites use 

the IMPROVE monitoring samplers and collection routines, but are not located at Class I areas. 

The website to obtain IMPROVE documentation and/or data is 

http://vista.cira.colostate.edu/improve/. Data advisories are posted at: 

http://vista.cira.colostate.edu/improve/Data/QA_QC/Advisory.htm. 

3.3.3 CASTNet 

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

monitoring network where PM data are collected and reported as weekly average data (U.S. 

EPA, 2012a). In addition, this network measures and reports hourly ozone concentrations. 

CASTNet provides atmospheric data on the dry deposition component of total acid deposition, 

ground-level ozone and other forms of atmospheric pollution. The data (except for ozone) are 

collected in filter packs that sample the ambient air continuously during the week. CASTNet 
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now comprises 90 monitoring stations across the United States. The longest data records are 

primarily at eastern U.S. sites. More information can be obtained through the CASTNet website 

at http://www.epa.gov/castnet/. 

3.3.4 CSN 

The Chemical Speciation Network (formerly known as STN: The Speciation Trends Network) 

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 CSN 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 CSN quantifies mass concentrations and PM2.5 constituents, including numerous 

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

and organic carbon. There are currently 52 trends sites in the CSN nationally in operation. CSN 

sites are largely static urban monitoring stations with protocols for sampling methods; 

dedicated to characterizing aerosol mass components in urban areas of the U.S. to discern longterm trends and provide an accountability mechanism to access effectiveness of control 

programs. In addition, there are approximately 150 supplemental speciation sites which are 

part of the CSN and are SLAMS sites. The CSN 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 CSN and related speciation monitoring can be found at 

http://www.epa.gov/ttn/amtic/specgen.html . 

3.3.5 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, 2013). The network is now known as NADP/NTN (National Trends 

Network) with nearly 200 sites in operation. The NADP analyzes the constituents important in 

precipitation chemistry, including those affecting rainfall acidity and those that may have 

ecological effects. The NTN 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/ . 
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3.3.6 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 (Edgerton, 2005; Hansen, 2003). SEARCH 

data are collected and reported on an hourly/daily basis. A total of eight sites have historically 

been operated by SEARCH, although only six are currently operating: Birmingham, Alabama 

(urban), Centreville, Alabama (rural), Gulfport, Mississippi (urban), Jefferson Street, Atlanta, 

Georgia (urban), Oak Grove, Mississippi (rural) (not operational as of 2010), Yorkville, Georgia 

(rural) (not operational as of 2009), suburban Pensacola, Florida (suburban), and downtown 

Pensacola, Florida (urban). Background information regarding standard measurement 

techniques/protocols and data retrieval can be found at http://www.atmosphericresearch.com/studies/SEARCH/index.html . 

3.3.7 Speciated PM2.5 Data Summary 

Speciated PM2.5 data play an especially important role in performance evaluation, as the 

deterministic models predict exact chemical components which can be compared to some of 

the corresponding measured analytes. There are known positive and negative sampling 

artifacts as well as analysis artifacts associated with FRM and speciation monitors (Frank, 2006). 

Due to the complex nature of the measurements, a basic understanding of the various PM2.5

and speciation monitoring technology and measurements are needed before undertaking a 

model evaluation.

Table 3.2: Known measurement artifacts for PM2.5 and speciated PM for different monitoring 

networks. 

Network Measurement Notes Sampling Time 

FRM PM2.5 - Negative artifacts: ammonium nitrate, 

organic carbon 

- Positive artifacts: organic carbon, 

water

24-hour average 1/3 

days or 1/6 days (some 

daily sites) 

PM2.5
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Network Measurement Notes Sampling Time 

CSN (formally 

STN) 

- Negative artifacts: ammonium nitrate, 

organic carbon 

- Positive artifacts: organic carbon, 

water 

-Note that positive and negative 

artifacts for PM2.5 may be different 

from artifacts for individual PM 

components because the speciated 

PM2.5 samples are collected on 

different types of filters than are used 

for total PM2.5 gravimetric 

measurements 

-CSN PM2.5 mass may be different from 

FRM measurements due to differences 

in handling procedures

24-hour average 1/3 

days or 1/6 days 

SO4 

NO3 

NH4 

Organic Carbon - Blank correction is needed to account 

for positive artifact.24

- Before 2007, OC/EC split was 

operationally defined using TOT 

 

24 CSN OC measurements reported in AQS do not currently have any artifact correction applied, although 

ambient data provided in MATS does already include a blank correction. Simultaneous with the switch 

from TOT to TOR, CSN OC samplers were also updated so that the same artifact correction techniques 

could be applied to measurements from CSN and IMPROVE networks. The OC artifact from CSN 

monitors after the change in samplers is generally smaller than OC artifact from CSN monitors before 

the change in samplers, although this difference has not been fully characterized at this point. Work by 

Malm et al (2011) suggests that CSN OC from 2005 and 2006 (before the switch in samplers) likely had a

positive multiplicative bias of ~1.2 and a positive additive bias between 1.0 and 1.9 depending on the

time of year. Frank (2012) recommended updating artifact correction methodologies for both CSN and 

IMPROVE. These recommendations have not yet been implemented. Until further network-wide 

artifact policy has been put in place, we recommend adjusting uncorrected AQS OC data by subtracting 

1.4 for measurement before the switch to IMPROVE-like measurements and by subtracting 0.12 from 

measurements after the switch based on Frank (2012). 
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Network Measurement Notes Sampling Time 

CSN (formerly 

STN) 

(con’t.) 

method (NIOSH). CSN monitors 

switched to using the TOR method 

(consistent with IMPROVE monitors) 

between 2007and 2009.25

Elemental Carbon - Before 2007, OC/EC split was 

operationally defined using TOT 

method (NIOSH). CSN monitors 

switched to using the TOR method 

(consistent with IMPROVE monitors) 

between 2007 and 2009(see footnote 

23) 

CASTNet 

SO4 

weekly average 

NO3 Split between NO3 and HNO3 may not 

be reliable in warm weather due to 

conversion from NO3 to HNO3. 

Recommend comparing total nitrate 

(NO3 + HNO3) between model and 

observations. 

NH4 

SO2 

HNO3 Split between NO3 and HNO3 may not 

be reliable in warm weather due to 

conversion from NO3 to HNO3. 

Recommend comparing total nitrate 

(NO3 + HNO3) between model and 

observations. 

PM2.5 - Negative artifacts: ammonium nitrate, 

organic carbon 

 

25 The transition from TOT to TOR for CSN monitors occurred in shifts: 56 sites switched in May 2007, 63 

sites switched in April 2009, and 78 sites switched in October 2009. 
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Network Measurement Notes Sampling Time 

IMPROVE 

- Positive artifacts: organic carbon, 

water 

-Note that positive and negative 

artifacts for PM2.5 may be different 

from artifacts for individual PM 

components because the speciated 

PM2.5 samples are collected on 

different types of filters than are used 

for total PM2.5 gravimetric 

measurements 

24-hour average 1/3 

days 

SO4 and S -IMPROVE monitors measure both 

elemental S (by XRF) and ionic SO4

-

 by 

ion chromatography. Generally either 

measurement is reliable and may be 

used for model evaluation purposes 

although some caveats should be 

noted.26

NO3 

NH4 -Only available at a limited number of 

sites for a limited number of years 

Organic Carbon - OC/EC split is operationally defined 

using Thermal Optical Reflectance 

(TOR) analysis. 

- Blank corrections based on a median 

measured positive OC artifact from 

back-up filters at six sites are applied 

on a month-specific basis to all data 

before they are reported (Chow et al, 

 

26 Hyslop et al (2012) report that XRF elemental sulfur measurements are somewhat affected by changes 

in XRF methodologies over time. Also, prior to 2000, filters used to collect sulfate for ion 

chromatography became clogged during high sulfate episodes. Changes in filter collection techniques 

eliminated this problem after 2000. In addition, recent literature by Tolocka and Turpin (2012) suggests 

that differences between XRF sulfur and ion chromatography sulfate are due to the presence of organic 

sulfur in aerosol (which would be measured by XRF but not IC). If this is the case, than the IC sulfate 

measurement is more appropriate for comparison with modeled sulfate which generally does not 

include formation of organic sulfur compounds. 
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Network Measurement Notes Sampling Time 

IMPROVE

(con’t.) 

2010; Watson et al., 2009). Frank 

(2012) recommended changing blank 

correction procedures for both 

IMPROVE and STN OC measurements. 

These recommendations have not yet 

been implemented. 

Elemental Carbon - OC/EC split is operationally defined 

using Thermal Optical Reflectance 

(TOR) analysis. 

Sodium 

XRF analysis is not as sensitive to 

sodium as other methods and 

generally under-reports sodium 

concentrations. 

Silicon 

XRF silicon measurements are 

unreliable at high sulfur conditions 

(when S/Si > about 7) 

Aluminum 

Elemental concentrations above the 

MDL can go undetected. IMPROVE 

has published an alternate soil 

calculation which estimates Al 

based on Si concentrations.27

In general, the speciated PM2.5 data can be directly compared to the model outputs. Of note, 

care should be taken in examining issues with blank corrections for organic carbon (especially 

when measured OC is low) as well as the EC/OC split. Prior to 2006, the CSN and IMPROVE 

networks used different analysis techniques when estimating EC and OC (CSN monitor locations 

switched to a similar method to IMPROVE between 2007 and 2009, depending on the monitor). 

The EC/OC analysis method will generally have a larger impact on EC concentrations than OC 

concentrations. The difference in OC/EC splits between the two techniques has been 

investigated in depth in the literature and by IMPROVE staff (Chow, 2001, Chow, 2010, White, 

 

27 Due to difficulties in detecting Al, IMPROVE sometimes uses an alternative SOIL calculation which does 

not include Al: SOIL = 3.48*Si + 1.63*Ca + 2.42*Fe + 1.94*Ti 

(http://vista.cira.colostate.edu/improve/publications/SOPs/ucdavis_sops/sop351_V2.pdf) 
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2007). It may not be appropriate to compare EC concentrations between networks for years 

prior to 2009 (although the data can still be used to evaluate model performance). 

Total PM2.5 mass measured gravimetrically at CSN and IMPROVE monitors is known to have 

several measurement artifacts (both positive and negative) (Frank, 2006, Simon, 2011, Malm, 

2011). Consequently, summing the mass of all PM2.5 species components is not likely to give 

the same answer as the total measured PM2.5 mass from a Teflon filter. Therefore, model 

evaluations should concentrate on comparisons with speciated PM2.5 components at these 

sites, rather than total PM2.5 mass. 

FRM monitors do not measure all of the PM2.5 in the air, and the speciation samplers don't 

measure PM2.5 in the same way as the FRM monitors (Frank, 2006). Due to these positive and 

negative artifacts, the FRM data may not be directly comparable with model outputs without 

adjustment. The models predict PM species as they might be measured in the ambient air, but 

the FRM measurements may not always measure what is in the air (due to known positive and 

negative artifacts). As part of the PM2.5 attainment test we recommended default 

methodologies to adjust speciation (CSN and IMPROVE) data to better approximate the FRM 

data (see sections 4.4 and 4.5). A similar adjustment could be made to the model output data 

to allow the direct comparison to FRM measurements. However, as noted earlier, there may 

be limited value of information gained by evaluating daily average total measured PM2.5 mass 

(as compared to PM2.5 components). More meaningful information may be gleaned from 

continuous PM2.5 ambient data. 

Another issue to consider when comparing modeled and observed PM concentrations is the 

size definitions for PM. IMPROVE and CSN ambient monitors measure PM2.5 using inlets which 

screen out any particles with aerodynamic diameters greater than 2.5 μm. Air quality models 

use varying schemes to characterize particulate matter size distributions. The two most 

comment modeling schemes are the sectional treatment and the modal treatment. The default 

configuration for CAMx includes a sectional aerosol treatment while the default configuration 

for CMAQ includes a modal aerosol treatment, although other versions of these models exist 

with alternate aerosol treatments. Sectional aerosol modules split particles into bins defined 

using discrete size cutpoints based on aerodynamic diameter (e.g. particles with aerodynamic 

diameters between 1 μm and 2.5 μm). One common sectional set-up includes 2 bins, one 

containing particles with aerodynamic diameters < 2.5 μm (fine PM) and one containing 

particles with aerodynamic diameters > 2.5 μm (coarse particles). Other models have split the 

fine PM into more than two bins (Nolte et al., 2008; Pandis et al., 1993). With sectional models, 

the comparison to measured PM2.5 is straightforward so long as one of the PM bins has a cutoff 

at an aerodynamic diameter of 2.5 μm (i.e. add up PM mass in all bins less than 2.5 μm). The 

modal approach treats PM as occurring in distinct “modes”, each one having a lognormal 
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distribution. Each PM mode is characterized by the geometric mean diameter and the 

geometric standard deviation of the diameter (the geometric mean and standard deviation can 

be tracked for particle number, surface area, mass or all three). With modal aerosol treatment, 

PM2.5 can be estimated in several ways. One method is to simply sum the mass in the modes 

that generally contain fine PM. Another method would be to use the geometric mean and 

standard deviation to calculate the fraction of PM mass in each mode which is associated with 

particles less than 2.5 μm. However there are several complicating factors with taking the 

second approach. First, the EPA’s National Emissions Inventory and many other inventories 

define PM2.5 as being entirely less than 2.5 μm in diameter. If these primary emissions are 

injected into a modal model (generally into a fine PM mode), they are instantaneously mixed 

with existing aerosol mass in that mode. The tail of the fine PM mode will include particles 

greater than 2.5 μm. Therefore, a fraction of the PM that was operationally defined as 

primarily emitted PM2.5 (diameter < 2.5 μm) would be incorrectly distributed into the > 2.5 μm 

size range based on the mode distribution. If primary emissions were quantified based on 

modes versus a discrete cutpoint, then this would not be a problem. Secondly, some modal 

models have been shown to over-predict the peak-concentration diameter and distribution 

width of the fine PM mode (Kelly et al., 2011; Kelly et al., 2010). This would lead to underpredictions of the PM2.5 fraction by increasing the mass fraction in the >2.5 micron diameter 

range. Based on these two complications, at this time we do not recommend calculating the 

fraction of PM mass in each mode below the 2.5 μm cutpoint unless air agencies have specific 

analysis for their application to show that method to be more accurate than simply summing 

mass over the fine PM mode(s). 

Finally, although we recommend weighing performance of speciated PM components more 

heavily than total PM, some additional insight can be gained by looking at FEM continuous 

PM2.5 measurements. Since speciated measurements are generally derived from filters, these 

measurements are usually taken as 24-hour averages. The FEM continuous measurements 

provide a diurnal profile of PM2.5 concentrations that can be compared against hourly model 

outputs to determine whether the model is replicating the measured temporal pattern. 

3.4 Diagnostic Evaluation 

The goal of a diagnostic model evaluation is to determine whether the model correctly 

represents the physical and chemical processes that control the ambient concentrations of 

precursors and secondary pollutants. It is possible that a model can incorrectly represent the 

relative amount of primary emissions versus secondary production or that a model can 
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incorrectly represent the importance of dispersion, deposition or chemical reaction as a sink. 

Thus, the ability of a model to accurately reproduce the observed concentration of a particular 

pollutant does not guarantee that the model correctly simulated the processes that determine 

the concentration of the pollutant. The goal of a diagnostic evaluation is to investigate the 

processes that determine the ambient concentrations of ozone, PM2.5 and their precursors, and 

to develop confidence that the model can accurately predict how these concentrations will 

change in the future (e.g., Arnold et al., 2006). It is likely that research grade measurements 

needed to perform a diagnostic evaluation will not be available in many SIP modeling studies, 

however, it can still be valuable to perform diagnostic analyses and compare the results with 

previous modeling research studies. 

Several different tools and analysis methods are available for use in diagnostic model 

evaluation including process analysis, indicator ratios, source apportionment methods, model 

sensitivity studies, and retrospective studies. 

3.4.1 Process Analysis 

Both the CMAQ and CAMx models include a process analysis tool (Jeffries and Tonnesen, 1994) 

that provides additional diagnostic information on the chemical reactions and physical 

processes (e.g., emissions, deposition, advection, vertical mixing, and net change from 

chemistry) that determine species concentrations. Chemical reaction data can be output either 

as the hourly integrated reaction rate (IRR) for each reaction or as a summary of key reactions 

in the chemical process analysis (CPA). Physical processes can be output as hourly 

concentration changes in the integrated process rate (IPR) file. Detailed analysis off IRR and IPR 

outputs have been demonstrated in research studies (Jang et al., 1995; Vizuete et al., 2008; 

Henderson et al., 2011), however, significant time and effort are required to complete these 

analyses. The CPA output provides a more limited set of chemical process information that is 

more accessible and that can be visualized using the same tools used to analyze model 

concentration output files. Minimal computational cost is required to produce the CPA files 

and they are in the same format as the model concentration files and can be view using the 

same visualization tools. Below are examples of these plots comparing modeled ozone 

concentration, daily total Ox production, and net O3 production in NOx limited and oxidant 

limited chemical regimes. The CPA tool can also provide information on the chemical reactions 

of VOC, NOy and radical species. 
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Figure 3.4.1 Chemical process analysis plots showing modeled ozone concentration, daily 

total Ox production, and net ozone production under VOC sensitive and NOx sensitive 

conditions. 

 
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3.4.2 Indicator Ratios 

One way to evaluate the response of the model is to examine predicted and observed ratios of 

indicator species. Indicator species techniques have been developed for both ozone and 

secondary PM species (in particular nitrate) (Sillman, 1995, 1998; Ansari and Pandis, 1998; 

Blanchard et al., 2000; Tonnesen and Dennis, 2000a,b). If ratios of observed indicator species 

are very high or very low, they provide a sense of whether further ozone or secondary PM2.5 

production at the monitored location is likely to be limited by availability of NOx or VOC (or NH3

in the case of particulate nitrate). Agreement between paired observed and predicted ratios 

suggests a model may correctly predict the sensitivity of ozone or secondary PM2.5 at the 

monitored locations to emission control strategies. Thus, the use of indicator species aims to 

validate the modeled chemical regime and may help to build confidence in the RRFs predicted 

from the specific model application. 

For ozone, measurements of certain “indicator species ratios” are a potentially useful way to 

assess whether local ozone formation is VOC or NOx limited at any particular point in space and 

time as well as, help reveal whether the model is correctly predicting the sensitivity of ozone to 

VOC and/or NOx controls (e.g., comparisons between modeled and observed ratios: O3/NOy, 

O3/HNO3) (Millford et al., 1994; Sillman, 1995, 1997, 1998, 2002; Lu and Chang, 1998). 

Tonnesen and Dennis (2000a,b) distinguished between short lived indicators of instantaneous 

rates of production of O3 (PO3) or odd oxygen (POx) versus long lived indicators of peak O3

concentration and found that the ratio of production of H2O2 to production of HNO3 

(PH2O2/PHNO3) is strongly associated with POx sensitivity to VOC and NOx. Tonnesen and 

Dennis also found that the ratio of formaldehyde to NO2 was useful as an indicator of O3

sensitivity, and this ratio can be evaluated using satellite data (Martin et al., 2004; Duncan et 

al., 2010). For PM, such a comparison may reveal whether the model is predicting sensitivity of 

secondary components of PM2.5 to changes in SO2, NH3, VOC and/or NOx controls correctly 

(Ansari and Pandis, 1998; Blanchard et al., 2000; Pun and Seigneur, 1999, 2001). If a model 

accurately predicts observed indicator species relationships, then one can conclude with 

additional confidence that the predicted change in future year ozone or PM may be accurate. 

Although ratios of indicator species have the potential to be quite useful, they should be used 

with caution since different studies have identified different ranges of values to characterize 

VOC and NOx limited regimes in different locations. Table 3.3 summarizes the range of values 

determined for 10 indicator species or ratios in various studies. In cases where the NOx and 

VOC limited ranges are broad or overlap, air agencies should attempt to use values from studies 

in the local area. At a minimum, air agencies should be careful with respect to the use of 

indicator species when the range of observed values for which the preferred direction of 
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control is not clear. When this occurs, agreement between predictions and observations does 

not necessarily imply that the response to controls, as predicted by the model is correct 

(especially for secondary particulate matter to changes in precursors). If a model predicts 

observed values of indicator species relationships such that observed and predicted value fall 

within the same range, this provides some confidence that the predicted change in ozone or 

secondary particulate matter may be accurate. 

A second precaution is that this method often requires more types of measurements than are 

commonly available. In some cases, it may be difficult to calculate meaningful indicator ratios 

from relatively imprecise measurements from routine monitoring networks. Finally, much of 

the work done to date with indicator species has focused on peak concentrations of ozone. 

Limited research has been conducted on development of indicator ratios for secondary PM. 

Table 3.3: Ranges of indicator species/ratios that have been reported for VOC and NOx limited 

regimes. 

indicator median VOC-sensitive transition median NOx-sensitive 

O3/NOy 1.6 - 8.8a,b,d,f-m 6 – 15c,d,m,n,q 8.8 – 26a,b,d,f-m

O3/NOz 5.8 – 16a,d,f,j-m 7 – 100c-f,n,q 10.1 – 35a,d,f,j-m

O3/NOx < 15 15 – 60n,o,q,t > 40

O3/HNO3 7 - 33.9a,d,j,k,m 12 – 25c,d 17.2 - 62.9a,d,j,k,m

H2O2/HNO3 0.3 - 1.5d,j-m 0.2 - 2.4c,e,h,n-q 0.4 - 3.6d,j-m

H2O2/NOz 0.2 - 1.1a,j 0.2 - 0.25c 0.6 - 1.3a,j

H2O2/NOy 0.2 - 0.4a,j 0.12 - 0.17c 0.6 - 1.0a,j

HCHO/NO2 < 1 1-2

n,o,u > 2

NOz/NOy < .5 0.5-0.6f > 0.6

NOy 7.7 - 12.2j 5 -2 0c,e,f,n,r,s 3.2 - 5.7j

aCastell et al., 2009; bXie et al., 2011; cTorres-Jardon and Keener, 2006; d

Sillman and He, 2002; eSillman, 

1995; f

 Lu and Chang, 1998; gStein et al, 2005; hSillman et al., 1997; iMartilli et al., 2002; jJimenez and 

Baldasano, 2004; kTorres-Jardon et al, 2009; lChock et al, 1999; mSillman et al., 2003; nZhang et al, 2009; 
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oTonnesen and Dennis, 2000a; pHammer et al., 2002; q

Liang et al., 2006; rMilford et al, 1994; sVogel et al, 

1995; tTonnesen and Dennis, 2000b; uMartin et al., 2004 

Blanchard et al., (2000) have examined how indicator species might be used to assess whether 

particulate nitrate concentrations are limited by NOx or by ammonia emissions using 

mechanisms which incorporate reactions dealing with secondary particulate matter. These 

authors identify several indicators which appear potentially useful for identifying limiting 

precursors for secondary nitrate particulate matter. The indicators include: 

(1) excess NH3 defined in Equation 3-1 (values greater than zero indicate NOx limited regimes 

and values less than 0 indicate ammonia limited regimes); 

	!" = 	 [NH3(g)]	+	 [NH4 + (a)]	– 	2 ∗ [SO42 − (a)]	− 	 [NO3 − (a)]	–	[HNO3(g)]		

− 	 [HCl(g)]	+ 	2 ∗ [Ca2+]	+ 	2 ∗ [Mg2+]	+	 [Na+]	+ 	 [K+]	+ 	 [Cl−]

 [3-1]28

(2) The ratio of particulate to total nitrate. Values above 0.9 at high temperatures indicate NOx 

limited regimes (values below 0.3 to 0.5 were indicative of ammonia limited regimes.) 

Ansari and Pandis (1998) also have suggested the use of the gas ratio (GS), defined in equation 

3-2, as an indicator of PM formation regimes. 

45 = 67

8

697

:

 [3-2]29

Despite these precautions, comparing predicted and observed relationships of indicator species 

provides a means of assessing a model's ability to accurately characterize the sensitivity of 

predicted ozone and predicted secondary components of PM2.5 to changes in precursors. 

Where observations are available, it is recommended that SIP modeling demonstrations show 

reasonable agreement between observations and ratios of indicator species. 

 

28Concentrations are in μmol/m3

, (g) represent gas-phase and (a) represents aerosol phase. 

29 NH3

F

 is free ammonia (total ammonia – 2*sulfate). HNO3

T

 is total nitrate (gas + PM). 
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3.4.3 Source Apportionment 

Source apportionment methods and model sensitivity studies are two complementary 

approaches that can be used to identify emissions sources that contribute to high pollutant 

concentrations. The CMAQ and CAMx models both include options to use source 

apportionment tools to identify sources that contribute to the production of ozone (ENVIRON, 

2014; Kwok et al, 2014) and some components of PM2.5 including sulfate, nitrate, ammonium 

and primary emissions of PM2.5 (ENVIRON, 2014; Kwok et al., 2013). Source apportionment 

tools employ a set of model tracers to attribute pollutant concentrations to user selected 

emissions source sectors and geographic areas. This approach can be useful both as a 

diagnostic tool to identify sources that contribute to ozone and PM2.5 and also as an aid in 

regulatory analysis to identify emissions control measures that will be effective for attaining air 

quality standards. An advantage of source apportionment approaches is that tracers attribute 

the mass contribution from a source to a receptor, and therefore this approach is less 

susceptible to the effects of non-linear chemistry that can complicate the interpretation of 

model sensitivity simulations. 

3.4.4 Sensitivity Studies 

Model sensitivity simulations can be used to identify model inputs (IC/BC, meteorological data, 

chemical reactions, and emissions) that have the greatest impact on model predicted 

concentrations. The objective of sensitivity tests is to identify whether the inputs have a large 

enough influence that errors in these inputs could be a driving influence on the model 

predictions. In “brute-force” sensitivity simulations, the model input data is modified directly, 

e.g., increasing or decreasing emissions in the model input file. Brute force simulations are 

relatively easy to implement but are resource intensive when many different inputs must be 

evaluated. The Direct Decoupled Method (DDM) is an approach that can be used to evaluate 

the first order sensitivity to multiple model inputs in a single model simulation (e.g., Cohan et 

al., 2005; Napelenok et al., 2006). Because of non-linearity in chemical reactions, first order 

sensitivity is not always sufficient, and high-order DDM (HDDM) can be used to evaluate 

second order sensitivities to inputs that are subject to non-linear chemistry (Zhang et al., 

2012b). 

In cases where the model performs poorly in the base case, sensitivity simulation may be useful 

to identify potential causes for poor model performance. However, caution should be used in 

modifying inputs to achieve good model performance because arbitrarily “tuning” of model 
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input data to fit the observations can introduce compensating errors. For example, if a model 

underestimates observed ozone because the meteorological data is overly dispersive, the 

model performance could be improved by increasing emissions or BC concentrations, but this 

model would not be reliable for predicting future concentrations because its good performance 

in the base case was achieved through compensating errors in the input. An appropriate use of 

sensitivity approaches is to identify each of the processes or inputs that has a large effect on 

model predictions, and then perform additional analysis of those inputs to determine if they 

are accurately represented in the base case model 

3.4.5 Dynamic Model Evaluation 

A second method for assessing a model's performance in predicting the sensitivity of ozone or 

PM2.5 species to changes in emissions is to perform a retrospective dynamic analysis. This 

involves comparing model predicted historical trends with observed trends. Retrospective 

analyses provide potentially useful means for diagnosing why a strategy did or did not work as 

expected. They also provide an important opportunity to evaluate model performance in a way 

which is closely related to how models are used to support an attainment demonstration. 

A dynamic evaluation uses various analyses to assess the accuracy of the model in 

characterizing the sensitivity of ozone and/or PM2.5 to changes in emissions. Although dynamic 

evaluation is always recommended, the measurements and resources needed for a dynamic 

evaluation may not be readily available to many air agencies performing SIP modeling. 

Additional challenges for retrospective analyses include identifying appropriate case studies for 

which responses to emissions changes are large enough to be distinguished in ambient data, 

developing or obtaining equivalent and appropriate emissions for multiple historical years, and 

disentangling the confounding influences of year to year changes in meteorology and 

emissions. Consequently, retrospective dynamic evaluations may be beyond the scope of many 

SIP modeling demonstrations. Major challenges in evaluating indicator species include 

variability in the characteristic ratios for VOC and NOx limited regimes in different locations and 

the availability of ambient data. Where appropriate observational data are available, modelers 

are encouraged to evaluate these indicators. 

A retrospective analysis is intended to examine the ability of the model to respond to emissions 

changes by comparing recent trends in observed ozone or PM2.5 concentrations to the modelpredicted trend over the same time period. The approach is a direct assessment of what is 
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most important in an attainment demonstration: does the model accurately predict changes in 

air quality as a result of changes in emissions? As part of a retrospective the model is run for 

(a) current episodes or time periods and (b) episodes in one or more historical time periods 

using the emissions and meteorological inputs appropriate for each time period modeled. 

While retrospective analyses may be useful, it may be difficult to obtain meteorological and 

emissions inputs for the historical time period(s) that are calculated using techniques and 

assumptions which are consistent with the calculation of these same inputs for the current time 

period. Using inconsistent inputs will confound the interpretation of the predicted trend. One 

method for ensuring consistent methodology for emissions estimates is to backcast emissions 

from the most recent year to the earliest year. The model should respond in a predictable way 

if the emissions changes are large enough. Hence, modeling an emissions change of only a few 

percent would not be viable. However, if NOx or SO2 emissions have been reduced by a large 

percentage (e.g. 30% or more) over a relatively short time period, then that may be a good time 

period to test the response of the model. 

Because differences in meteorology between years can confound the apparent change in 

pollutants, a complete retrospective analysis could include multiple sensitivity runs using both 

year-specific meteorology and constant meteorology. The year-specific meteorology would 

allow the modeler to compare modeled and observed trends most accurately, and the constant 

meteorology would help inform the analysis on what portion of the change in pollutant 

concentrations were due to emissions versus meteorological differences. 

Retrospective analyses are one of the few tools that can be used to determine if the model is 

responding “adequately” to control measures. Retrospective analyses may be an important 

tool to show that the models are responding “correctly” to emissions changes. Recent efforts 

to perform this type of analysis have been undertaken by Zhou et al (2013), Gilliland et al 

(2008), Godowitch et al (2010), Pierce et al. (2010) and (Foley et al, 2014). These published 

analyses may be used to better understand the ability of certain models to properly capture 

relationships between ambient pollution levels and emissions. However, since model response 

is highly dependent on the meteorology and emissions inputs, past studies will not necessarily 

provide insight on specific SIP demonstration modeling episodes which rely on independently 

derived meteorology and emissions. 

Another dynamic evaluation approach is to look at operational performance under varying 

conditions, e.g., by day of the week, by season, and regionally (Marr and Harley, 2002; Murphy 

et al, 2007; Harley et al, 2005). The mix of pollutants vary by day of the week and from city to 

city so when a model shows good operational performance across these different chemical 

environments, this supports the assertion that it will respond appropriately to changes in 
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emissions. Two recent studies have performed model evaluations that focused on the model’s 

ability to capture weekend/weekday changes (CRC, 2011; Pierce et al, 2010) 

3.5 Evaluation Tools 

One available software tool to assist in model performance evaluations is the Atmospheric 

Model Evaluation Tool (AMET) (Appel, 2011) (www.cmascenter.org). AMET is built on several 

open-source software packages and uses the MySQL database software to store paired 

observation and model output data. There are modules in AMET for evaluating both 

meteorological and air quality model output. AMET provides a useful set of scripts for creating 

various types of plots and statistics. 
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4.0 Assessing Modeled Attainment 

A model attainment demonstration usually consists of two key components: a) analyses which 

estimate whether a set of simulated emissions reductions will result in ambient concentrations 

that meet the National Ambient Air Quality Standards (NAAQS) and b) an identified set of 

control measures which will result in the required emissions reductions. This guidance focuses 

on the first component of a model attainment demonstration, that is, the completion and 

interpretation of analyses to estimate the amount of emission reduction needed to attain the 

NAAQS. 

Air agencies should estimate the amount of emission reductions needed to demonstrate 

attainment of the NAAQS using the model attainment test. The model attainment test is a 

technical procedure in which an air quality model is used to simulate base year and future air 

pollutant concentrations for the purpose of demonstrating attainment of the relevant NAAQS. 

The recommended test uses model estimates in a “relative” rather than “absolute” sense to 

estimate future year design values. As explained in more detail in subsequent sections, the 

fractional changes in air pollutant concentrations between the model future year and model 

base year are calculated for all valid monitors. These ratios are called relative response factors 

(RRF). Future ozone and/or PM2.5 design values are estimated at existing monitoring sites by 

multiplying the modeled relative response factor for each monitor by the monitor-specific base 

year design value. The resulting estimates of future concentrations are then compared to the 

NAAQS. If the future estimates of ozone and/or PM2.5 design values do not exceed the NAAQS, 

then this provides evidence that attainment will be reached. 

In addition to the model attainment test, air agencies should also consider performing a set of 

corroboratory analyses to further assess whether a proposed set of emission reductions will 

lead to attainment of the NAAQS (or uniform rate of progress for regional haze). As discussed 

later in this section (see section 4.9), the model attainment test and these supplemental 

analyses should be aggregated into a weight of evidence determination to evaluate whether the 

selected emissions reductions will yield attainment. 

4.1 Overview of model attainment test 

There are four reasons why the recommended model attainment test is based on application of 

the model in a relative sense. First and foremost, this approach has the effect of anchoring the 

future concentrations to a “real” measured ambient value, which is important given model bias 
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and error in the base year simulation(s). It is reasoned that factors causing bias (either under or 

over-predictions) in the base case will also affect the future case. While good model 

performance remains a prerequisite for use of a model in an attainment demonstration, 

problems posed by imperfect model performance on individual days are expected to be 

reduced when using the relative approach. An internal EPA analysis (USEPA, 2014b) considered 

whether daily ratios of model future/current maximum daily 8-hour ozone averages (MDA8) 

varied strongly as a function of site-specific base case model performance. This analysis was 

completed using a national model simulation that projected 2020 ozone concentrations from a 

2007 base case. The analysis determined that when modeled MDA8 ozone bias was relatively 

small (e.g., less than +/- 20 ppb), the average response ratios were not a strong function of the 

model MDA8 bias. This provides confidence that the model can detect the air quality response 

in the midst of reasonable levels of absolute bias and error. Second, the relative model 

attainment test allows for a future projection of 3-year average ozone and PM2.5 design values 

without explicitly having to model a three-year period. Third, because PM2.5 concentrations 

typically consist of a diverse mix of primary and secondary components, the relative model 

attainment test decreases the possibility of choosing ineffective control strategies based on 

inaccurate model estimations of PM2.5 composition by assessing component specific RRFs. 

Fourth and finally, there is evidence, based on a retrospective modeling analysis of ozone 

changes between 2002 and 2005 (Foley et al, 2014b) and 2002 and 2010 (Hogrefe et al, 2014), 

that using the model in a relative sense provides better estimates of future ozone design values 

than using the absolute future year simulation. The correlation between model-projected and 

actual design values, as well as the accuracy of model projections of attaining a 75 ppb NAAQS 

both appear to be slightly improved when a relative model attainment test is utilized. 

Equation (4.1) describes the recommended model attainment test in its simplest form, as 

applied for monitoring site i: 

(DVF)> = (??@) ∗ (ABC) [4.1] 

where DVF> is the estimated design value for the future year in which attainment is required at 

monitoring site i; RRF> is the relative response factor at monitoring site i; and DVB> is the base 

design value monitored at site i. Each of these terms is discussed in more detail below. 

EPA has developed the Model Attainment Test Software (MATS) tool to enable completion of 

the model attainment tests for PM2.5 and ozone, as well as for calculating changes in visibility in 

Class I areas as part of the uniform rate of progress analysis for regional haze (Abt, 2014). This 

tool can be found on EPA’s Support Center for Regulatory Atmospheric Modeling (SCRAM) 

website in the section on “Modeling Guidance and Support” 

http://www.epa.gov/scram001/modelingapps_mats.htm 
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4.1.1 Establishing the base design value

The base design value for each monitoring site (ABC) is the anchor point for estimating future 

year projected concentrations. Because the modeling is being used in a relative sense to 

determine how the modeled emissions changes will affect air quality design values in an area, it 

is important to match the base design value as closely as possible to the base year for which 

future/base ratios will be assessed. That is, if one is estimating the expected air quality change 

from emissions reductions between 2010 and 2020, it is important to establish an air quality 

anchor that represents 2010 emissions levels as closely as possible. However, it is wellestablished that inter-annual variability in meteorological conditions often leads to year-to-year 

differences in design values, even with static emissions levels (USEPA, 2012). In addition, there 

is also year to year variability in emissions due to economic factors (such as recessions) and 

compliance with regulations. We therefore recommend using the base design value approach 

described below. 

For the model attainment tests for ozone and PM2.5, we recommend using the average of the 

three design value periods which include the base emissions inventory year.30 This average is 

expected to best represent the air quality resulting from base year emissions with some 

consideration of meteorological and emissions variability. For example, if the model 

attainment test uses base year emissions from 2011, then the base design value would be 

calculated as shown in equation 4.2 for each site i: 

 

30 EPA recommends using ambient design values that are consistent with the official design values as 

calculated according to 40 CFR part 50 Appendix N (PM2.5 NAAQS) and Appendix P (8-hour ozone 

NAAQS). This includes flagging and removing event-influenced data that meet the requirements set 

forth in the Exceptional Events Rule (40 CFR 50.14). In general, air agencies flag data that they believe 

may qualify for removal as an exceptional event and are then responsible for developing and providing 

documentation to EPA to support these requests for exclusion. EPA Regional Offices review exceptional

events claims and decide whether to concur with each individual claim. Once EPA concurs with an air 

agency’s request, the event-influenced data are officially noted and removed from the data set used to

calculate official design values. In some cases, historical ambient data may meet the requirements of

the Exceptional Events Rule, but remain in the data set used to calculate official design values. Air

agencies may or may not have flagged these data as being potentially influenced by exceptional events 

or may have flagged these data, but not submitted the required documentation. Air agencies may not 

closely examine potential event-influenced data that do not affect attainment/nonattainment 

decisions. However, the influence of potential event-influenced data may affect future year 

projections that are part of the modeled attainment demonstration. If potential exceptional eventinfluenced data from the historical record may affect the outcome of the modeled attainment 

demonstration, we encourage air agencies to consult with their EPA regional office to determine how 

best to handle this situation. 
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(ABC)F = ((GH	
I)JK(GH	
I
)JK(GH	
I")J

)

"

 [4.2]31

This has the desired effect of weighting the projected ozone or PM2.5 base design values 

towards the middle year of a five year period (2009-2013), in this example for a 2011 base 

emissions inventory year. Additionally, an average of 3 design values will be more stable (less 

year-to-year variability) than any single design value period. An EPA analysis of recent design 

values data at 761 ozone monitors over the period between 2002 and 2011 concluded that the 

median standard deviation of individual 3-year ozone design values was 5.3 ppb, whereas the 

standard deviation of the average of multiple 3-year ozone design values was only 4.0 ppb. 

In cases in which there are less than five years of valid monitoring data at a site, we recommend 

that base design values be calculated only when there are at least three years of consecutive 

valid data (i.e., at least one complete design value). If a location has less than three consecutive 

years of data, then that site should not ordinarily be used in the attainment test32. An example 

calculation for DVBi is shown in Table 4.1, and includes the number of significant digits to 

include for ozone and each form of the PM2.5 standard. 

 Table 4.1: Example illustrating the calculation of base design values.

 2011 DV 2012 DV 2013 DV DVB 

O3 NAAQS (ppb)33 76 76 78 76.7 

Annual PM2.5 NAAQS (μg/m3) 12.9 13.1 12.6 12.87 

24-hour PM2.5 NAAQS (μg/m3) 32 33 32 32.3 

The measured 3-year average design values use the rounding/truncation rules established in 40 

CFR Part 50 Appendix P (8-hour ozone) and Appendix N (PM2.5). The resultant 5-year weighted 

average base design value should carry one digit to the right of the decimal point for ozone and 

24-hour PM2.5 and two digits to the right of the decimal for annual PM2.5. There are several 

 

31 Note: for nomenclature purposes, the three-year design value is referred to by the last year of the 

averaging period. That is, DV 2011 represents air quality data averaged over 2009, 2010, and 2011 for

ozone and PM2.5. 

32 Monitoring sites with less than 3 years of data cannot ordinarily be used to determine attainment 

status. Therefore, in most cases, those sites should not be used in the attainment demonstration. 

However, there may be cases where a monitoring site with incomplete data indicates an area of high 

ozone or PM concentration. Further examination of the monitoring data and modeled response (RRF) in 

the area is recommended as part of either the monitor based attainment test or an unmonitored area 

analysis, as appropriate. 

33 The current ozone NAAQS is defined in units of ppm. For ease of use, this document will use ppb 

units. 
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calculations in the modeled attainment test which carry the tenths digit or hundredths digit. 

Prematurely rounding and/or truncating concentrations and RRFs can lead to an overestimate 

or underestimate of the modeled impact of emissions controls. In some cases, a few tenths of 

a ppb (or μg/m3) change (or a few tenths of a percent reduction) can affect the outcome of the 

model attainment test. Rounding or truncating can make the change appear to be equal to a 

full ppb (or 1%) or equal to zero change. For 8-hour ozone, it is recommended to round to the 

tenths digit until the last step in the calculation when the final future design value is truncated. 

For annual PM2.5 it is recommended to round to the hundredths digit until the last step when 

the final future design value is rounded to the nearest tenths place. For daily PM2.5 it is 

recommended to round to the tenths digit until the last step when the final future design value 

is rounded to the nearest whole number. 

In practice, the choice of the base design value can be critical to the determination of the 

estimated future year design values (Cohan, 2006) and careful consideration should be given to 

the calculation of base year values. There is no single methodology that can derive a “correct” 

base design value, but the 5 year weighted average value establishes a relatively stable value 

that is weighted towards the emissions and meteorological modeling year. In most cases, this 

provides an appropriate anchor point in which to base the future year design value calculations. 

Alternate, equally plausible, calculations of base design values may be considered as part of the 

corroborating analyses that comprise the aggregate weight of evidence determination. For 

instance, one may want to consider establishing the base design value on the highest of the 

relevant design values to ensure that the emissions measures will yield attainment even in 

periods with meteorological conditions especially conducive to poor air quality. 

4.1.2 Calculation of relative response factors 

The relative response factor for each monitoring site (??@) is the fractional change in air 

quality concentrations that is simulated due to emissions changes between a base and a future 

year emissions scenario. The specific RRF calculation techniques vary by pollutant and are 

described in more detail in the remainder of section 4 below. The most important 

consideration associated with the RRF calculation is determining which model values are most 

representative of the expected air quality change for a given location. This requires 

consideration of all of the varying changes in hourly pollutant concentrations between the base 

and future cases and a determination of the most appropriate summary (average) value to 

apply to the base design value. As with the selection of a base design value, there may be 

plausible alternative means of calculating the relative response factors that can differ from the 

approaches recommended below (e.g., Kulkarni et al., 2014, Porter et al., 2014). Where 
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justified, alternate RRF calculation techniques can be included as corroborative analyses as part 

of a weight of evidence determination. 

4.2 Model attainment test for the primary ozone standard 

The 8-hour ozone design value is currently calculated as the three-year average of the fourth 

highest monitored daily 8-hour maximum value at each monitoring site. The standard is 

considered to be attained if the observed design value does not exceed the level of the ozone 

NAAQS.34 Similarly, the model attainment test is considered satisfied when the (DVF)> is less 

than or equal to the current NAAQS level. The recommended test is described below. 

4.2.1 Model values to use in the RRF calculation 

Given the formulation of the model attainment test, it is important to identify which model 

days are best suited for determining the expected air quality response resulting from a set of 

emissions changes. On any given modeled day, meteorological conditions may or may not be 

conducive to high concentrations at a particular monitor. If ozone predicted near a monitor on 

a particular day is much less than the design value, the model predictions for that day could be 

unresponsive to controls (e.g., the location could be upwind from most of the emissions in the 

nonattainment area on that day). If these days are included in the RRF calculation, they will 

likely lead to a higher RRF (closer to one) and a potential overestimation of the future design 

value. 

Internal EPA analyses (USEPA, 2014b) have shown that model response to decreasing emissions 

is generally most stable when the base ozone predictions are highest. The greater model 

response at higher concentrations is likely due to more “controllable” ozone at higher 

concentrations. In addition, the high model days are selected individually for each monitor. 

Meteorological conditions that lead to high ozone may differ between monitors in an area. For 

example, monitors that are typically “upwind” or “downwind” of an area may experience high 

ozone on different days, depending on the prevailing wind direction. In most urban areas, on 

days with high ozone concentrations, there is a relatively high percentage of locally generated 

ozone compared to days with low base case concentrations (U.S. EPA 2014c). Days with low 

 

34 At the time of publication of this guidance, the ozone NAAQS was 0.075 ppm per 40 CFR Part 50.15. 

Design values for the .075 ppm NAAQS are truncated to the 3rd decimal digit. Therefore, 0.0759 ppm 

(75.9 ppb) is considered attainment and 0.0760 (76.0 ppb) is considered nonattainment. The same 

rounding/truncation procedures should be applied to the modeled attainment test. A detailed 

description of the methodology to calculate ambient ozone design values is provided in 40 CFR Appendix

P to Part 50. 
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ozone concentrations are more likely to have a high percentage of ozone due to background 

and boundary conditions. Since the form of the standard is also focused on the highest days of 

an ozone season (i.e., the fourth highest MDA8), the RRF calculation should also focus on days 

when the model predicts the highest ozone concentrations. 

As the ozone NAAQS has been lowered, a key question is whether the attainment test should 

use an average RRF based on all modeled days that are greater than the NAAQS, as in the 

previous guidance approach. In some areas of the country, this could lead to RRF calculations 

that might include upwards of 100 days per ozone season (for the 75 ppb NAAQS). In these 

cases, similar to RRFs based on low modeled concentrations, too many days in the RRF 

calculation may lead to higher (closer to 1) RRFs. Using the highest modeled days at each 

monitor is most likely to represent the response of the observed design value at a monitor. 

Since the design value is based on the seasonal 4th high observed values, we recommend 

selecting a set of modeled days that are likely to encompass a range of values that are 

somewhat higher than and somewhat lower than the 4th high value. We therefore recommend 

calculating the RRF based on the highest 10 modeled days in the simulated period (at each 

monitoring site). We believe this balances the desire to have enough days in the RRF to 

generate a robust calculation, but not so many days that the RRF does not represent days with 

concentrations near the observed design values. In addition, use of the highest 10 days 

(without an initial threshold) allows the attainment test to be easily adapted to any level of the 

NAAQS with little or no change in the methodology. In support of the revised recommendation, 

a recent assessment of modeled ozone changes between two ozone seasons (Foley et al., 2014) 

suggests that the use of the highest 10 days in the mean RRF calculation yields a slightly better 

estimate of the actual observed ozone change than the previous guidance approach. 

EPA recommends that the ozone RRF calculations be based on the 10 highest days in the base 

year modeling at the monitor location, as long as the base MDA8 values are greater than or 

equal to 60 ppb for that day. In cases for which the base model simulation does not have 10 

days with MDA8 values greater than or equal to 60 ppb at a site, then EPA recommends using 

all days where MDA8 >= 60 ppb, as long as there are at least 5 days that meet that criteria. If 

there are less than 5 days with MDA8 >= 60 ppb, EPA recommends that air agencies do not 

calculate a DVF for the site.35 If the model attainment test is based on less than 10 days, then 

other assessments in the aggregate weight of evidence demonstration will need to be more 

rigorous to counter the reduced robustness of the model attainment test. 

 

35 A scenario in which the 5th highest base model MDA8 values is less than 60 ppb either indicates an 

extremely low ozone site or poor model performance. In either case, projections of DVF values are likely 

not reliable. 
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In addition, as discussed by Vizuete et al. (2011), there can be days in which the modeled 

source-receptor relationships may not yield a representative response for a particular cell or 

array of grid cells. This can result from small inconsistencies between the model representation 

of transport patterns (or chemical formation) and what actually occurred on that day. As part 

of the weight of evidence demonstration, if there is compelling evidence to determine that a 

particular day, while being among the 10 highest MDA8 values at a location, is not 

representative of the expected source-receptor relationship at that location; then that day can 

be considered for removal from the RRF calculation. Air agencies should document the 

evidence that argues for the exclusion of any otherwise-eligible days. As noted earlier, poor 

model performance on individual days can lead to ratios of future to base ozone that may be 

biased on those days. For example, underprediction of ozone concentrations may lead to ratios 

that are unresponsive to emissions controls. Air agencies may want to examine the day- and 

site-specific model performance for days that are part of the RRF calculation. Where feasible, it 

is recommended that days with model biases greater than +/- 20 percent be examined to make 

sure that they can be appropriately used in calculating the expected response. 

4.2.2 Grid cells to use in the RRF calculation 

It is recommended that ozone RRF calculations consider model response in grid cells 

immediately surrounding the monitoring site along with the grid cell in which the monitor is 

located. There are two primary reasons why we believe it is appropriate to include predictions 

from grid cells near a monitor rather than just the cell containing the monitor. First, limitations 

in the inputs and model physics can affect model precision at the grid cell level. Allowing some 

leeway in the precision of the predicted location of daily maximum ozone concentrations can 

help assure that possibly artificial, fine scale variations do not inadvertently impact an 

assessment of modeled ozone response. Second, some ozone monitors and important 

emission sources may be located very close to the border of a grid cell. Considering multiple 

cells near a monitor rather than the single cell containing the monitor diminishes the likelihood 

of inappropriate results which may occur from the artificial geometry of the superimposed grid 

system. 

Based on the above considerations, it is recommended that the RRF be based on a 3 x 3 array of 

cells centered on the location of the grid cell containing the monitor. This is a change from the 

2007 guidance (USEPA, 2007) which pre-identified 15 km as an appropriate distance for grid cell 

proximity to a monitor and therefore recommended the selection of RRF grid cells to be 

contingent upon the chosen model grid resolution (i.e., a finer grid resolution considered a 

larger array of grid cells to maintain the pre-identified 15 km distance). The revised approach 

allows for small spatial differences in the response to be considered in the RRF calculation and 
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presumes that the chosen model grid resolution is able to appropriately capture model 

response at the location of the monitoring site. Additionally, as described in section 4.2.3, the 

guidance now recommends that the grid cell with the highest base year MDA8 ozone value in 

the 3 x 3 array be used for both the base and future components of the RRF calculation, as 

opposed to the 2007 guidance approach which recommended selecting the peak value in the 

base and future cases, even if they were in different grid cells. 

The selection of grid cells to use in the RRF calculation is especially important in “oxidant 

limited” areas (i.e., areas where incremental NOx decreases may lead to ozone increases) 

because these areas are likely to have grid cells that respond to VOC controls, with NOx 

sensitive grid cells nearby (perhaps even in the same 3 x 3 array). The 3 x 3 array methodology 

could lead to unrealistically small or large RRFs in these locations, depending on the specific 

case. Internal EPA analyses with a 12 km grid resolution have shown that on average, modelestimated future design values vary by less than 1 ppb when a single cell is used as opposed to 

the recommended array, although higher variability can occur. (USEPA, 2014b). 

While the use of a 3 x 3 array of cells is the recommended approach for the model attainment 

test, air agencies are encouraged to consider the actual spatial scales of the monitors at which 

the model attainment test is being applied. Most ozone monitors are generally sited to be 

representative of an urban or neighborhood scale. In cases in which the spatial 

representativeness of a monitoring location is much smaller or larger than the area covered by 

the 3 x 3 array of cells, air agencies may consider assessing site-specific model response over an 

alternative grid cell array as part of corroborative analyses that inform the aggregate weight of 

evidence determination. 

4.2.3 Sample model attainment test calculation 

Once the appropriate RRF days are determined at each site, the calculation is straightforward. 

Relative response factors are not calculated on a daily basis, but instead are based on the mean 

8-hour daily maximum values on all eligible calculation days. At the last step in the process, the 

final DVF should be calculated in accordance with the data handling conventions associated 

with the NAAQS (e.g., truncated to the third decimal in ppm units as per current Appendix P). 

An example is provided below for a 2011 base year and 2020 future year simulation based on 

an annual model simulation. This hypothetical model attainment test considers an area with 

three ozone monitors. 

Step 1: Compute site-specific base year design values (DVBs) from observed data at all eligible 

sites in the area by calculating the average of the design value periods which include the base 
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year inventory year. All averaged values should be rounded to the tenth of a ppb. See Table 

4.2 for the calculation in this sample case. The base design value is the average of the 3-year 

design values for 2011, 2012, and 2013. 

Table 4.2: Ambient ozone base design values in example calculation.

Monitoring site 2011 DV (ppb) 2012 DV (ppb) 2013 DV (ppb) Base design value (ppb)

A 84 79 73 78.7

B N/A 67 83 75.0

C 86 76 80 80.7

Step 2: Compute site-specific relative response factors (RRFs) from modeled data at all eligible 

sites (i.e., A, B, and C). This is done by first identifying the 10 days with the highest daily 8-hour 

maximum ozone concentrations greater than 60 ppb (or subset of days if there are 5-9 days 

greater than 60 ppb) in the base year. For each of these days, identify the grid cell with the 

highest base year MDA8 ozone in the 3 x 3 cell array surrounding the monitor cell location and 

use that value in the calculation of a multi-day average. 10-day averages (where possible) 

should be calculated for both the base and future simulations. The future case average should 

be based on MDA8 values paired to the same cell as used in the base average. Figure 4.1 below 

shows an abbreviated subset of the RRF calculation for three days at a single sample site. 

Model estimates of MDA8 (in ppb) should be calculated to at least two places to the right of the 

decimal, with the last digit rounded. 
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Figure 4.1. Example demonstration of daily selections of 8-hour maxima to be used in RRF 

calculation

Base 

6/21/2011

60.24 68.15 66.20

73.41 76.26 71.27 

67.62 74.02 73.24

6/26/2011

76.20 75.35 77.41

78.51 76.79 75.92 

76.66 75.95 77.21

7/17/2011

50.22 58.55 56.22

53.47 56.22 51.28

57.76 59.10 53.29 

Future 

6/21/2011

56.89 59.22 58.05

63.69 68.46 63.39 

61.86 67.16 66.80

6/26/2011

69.84 67.71 68.14

68.30 66.27 67.95 

64.28 65.15 66.58

7/17/2011

46.80 49.27 48.55

51.91 50.41 50.34

53.62 54.10 50.62 

Day 1 is straightforward. The daily 

maximum 8-hour average is > 60 

ppb and is in the same cell in both 

the base and future case. 

For day 2, the peak location shifts 

from the base to another cell in the 

array in the future (underlined cell). 

However, the center left cell is used 

for base and future values (paired 

in space). 

Day 3 would not be eligible for the 

RRF calculation because there are 

no base values > 60 ppb. 

Step 3: Once the MDA8 values are selected for each appropriate day for each monitoring site, 

they should be averaged together over the days while maintaining at least two places to the 

right of the decimal (round to the last decimal digit). The RRF is the ratio of the average future 

MDA8 values to the average base MDA8 values. Table 4.3 shows an example RRF calculation, 

using the results from days 1 and 2 from Figure 4.1, plus the other eight (top 10) days in which 

the daily model predictions were deemed appropriate for RRF calculation. RRF values should 

be estimated to four decimal places (rounded to the 4th decimal digit).

Table 4.3. Relative response factor (RRF) calculation in example calculation.

Date Base (ppb) Future (ppb) 

6/21/2011 76.26 68.46 

6/26/2011 78.51 68.30 

6/27/2011 75.22 65.43 

6/28/2011 70.05 62.76 

7/11/2011 76.33 68.18 

7/12/2011 81.67 72.62 

7/13/2011 78.31 64.52 

8/02/2011 73.69 69.96 

8/03/2011 75.83 65.05 

8/21/2011 71.44 59.36 

Average 75.73 66.46 

RRFi 0.8776 
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The last step of the process is to multiply all of the site-specific RRFs by the corresponding site-specific 

DVBs and compare the results against the current level of the ozone NAAQS. In the final step, as noted 

in footnote 34, the design values are truncated to the integer ppb unit. Table 4.4 shows those results, 

assuming that the sample RRF shown above is for site C in the example. The final DVF for this sample 

site would be 70 ppb or 0.070 ppm. This would be considered a passing model attainment test for an 

ozone NAAQS of 0.075 ppm. 

Table 4.4. Calculation of DVFi for the three sites in the sample exercise. 

Monitoring site Base design 

value (ppb) RRF Future design value, pre-truncation (ppb) 

Final Future design 

value (ppb) 

A 78.7 0.8933 70.3 70

B 75.0 0.9138 68.5 68

C 80.7 0.8776 70.8 70

Note: The “final” future year design value represents the truncated value to compare to the NAAQS. 

However, it is recommended to retain the tenths (of a ppb) digit in all documentation, so that small 

ozone changes can be gleaned from control strategy and sensitivity analyses. 

4.3 Model attainment test for the secondary ozone standard 

At the time this guidance document was written, the secondary ozone NAAQS is the same as 

the primary NAAQS. However, in the past, EPA has considered setting a secondary ozone 

NAAQS that is based on a cumulative measure of ozone concentrations, as opposed to the 

three-year average of the annual fourth-highest daily maxima. If EPA were to establish a 

distinct secondary standard, it is possible that air agencies would need to separately show that 

the planned emissions reductions would be sufficient to meet both the primary and secondary 

NAAQS. If a new secondary standard is subsequently developed, EPA will provide 

supplemental guidance on how to assess attainment of that new standard at the time of its 

promulgation. 

4.4 What is the Modeled Attainment Tests for the Annual Average PM2.5

NAAQS?

Since PM2.5 consists of multiple PM species, the modeled attainment test for PM2.5 utilizes both 

PM2.5 and individual PM2.5 component species. A separate RRF is calculated for each PM2.5

species. In order to perform the recommended modeled attainment test, air agencies should 

divide observed mass concentrations of PM2.5 into 8 components (plus passive mass): 
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• Sulfates 

• Nitrates 

• Ammonium 

• Organic carbon 

• Elemental carbon 

• Particle bound water 

• Salt 

• “Other” primary inorganic particulate matter 

• Passively collected mass (blank mass) 

To apply the attainment test, air agencies must first have outputs from air quality model 

simulations for a base year and a future year emissions scenario. We recommend a modeled 

attainment test which has 4 basic steps, as described in this section. 

Step 1. Compute observed quarterly mean PM2.5 and quarterly mean composition for each 

monitor. 

Derive base year quarterly mean concentrations36 for each of the major components of PM2.5. 

This is done by multiplying the monitored quarterly mean concentration of Federal Reference 

Method (FRM) derived PM2.5 (40 CFR Part 53) by the monitored fractional composition of PM2.5

species for each quarter. (e.g., 20% sulfate x 15.0 μg/m3

 PM2.5 = 3.0 μg/m3 sulfate). 

Step 2. Using air quality model results, derive component-specific relative response factors 

(RRF) at each monitor for each quarter. 

Use the air quality model predictions to calculate the quarterly mean concentration for each 

PM2.5 component for the base year and future year scenario. Take the ratio of future to base 

year predictions for each component. The result is a component-specific relative response 

 

36Concentrations should be calculated based on calendar quarters for two reasons. First, the NAAQS is 

calculated for a calendar year, so the quarters need to fit evenly within a year. Second, the monitored 

data used to calculate design values is averaged on a calendar quarter basis before calculating annual

averages. 
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factor (RRF).

 The relative response factor for component j at a site i is given by the following expression: 

(RRF)ij = [Cij, projected]/[Cij, base year] [4.3]

where Cij, base year is the quarterly mean concentration predicted at or near the 

monitoring site37 with emissions characteristic of the period used to calculate the base 

year design value for annual PM2.5.

Cij, projected is the future year quarterly mean concentration predicted at or near the 

monitoring site. 

 (e.g., given model predicted base year sulfate of 10.12 μg/m3 and future year concentration of 

8.23 μg/m3, then RRF for sulfate is 0.8132). 

Step 3. Apply the component specific RRFs to observed air quality to obtain projected 

quarterly species estimates.

For each quarter, multiply the base year quarterly mean component concentration (step 1) 

times the component-specific RRF obtained in step 2. This leads to an estimated future 

quarterly mean concentration for each component. (e.g., 3.0 μg/m3 sulfate x 0.8132 = future 

sulfate of 2.44 μg/m3

).

Step 4. Calculate a future year annual average PM2.5 estimate. 

Sum the quarterly mean components to get quarterly mean PM2.5 values. Then average the 

quarterly mean PM2.5 concentrations to get a future year annual average PM2.5 estimate for 

each FRM site. 

 

37 For greater than 12km grid resolution, use the single grid cell. For 12km or less grid resolution, use 

the mean of the 3 x 3 array of the grid cells surrounding the monitor. 
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Compare the projected average annual arithmetic mean PM2.5 concentration obtained in Step 4 

with 12.0 μg/m3. If all values are < 12.0 μg/m3, the test is passed. 

4.4.1 Ambient PM2.5 Data Used in the Annual PM2.5 Attainment Test 

PM2.5 data collected at FRM and FEM sites are used for nonattainment designations. Therefore 

we recommend using FRM and FEM data as the base data for projecting future PM2.5

concentrations. As can be seen from the list of steps, the modeled attainment test is critically 

dependent on the availability of species component mass at FRM sites. This raises several 

issues. First, the majority of FRM sites do not have co-located chemical speciation network 

(CSN38) samplers.39 And second,the FRM filter measurements and PM2.5 speciation 

measurements do not always measure the same mass (Frank, 2006). There are numerous 

known issues with positive and negative sampling artifacts. Both of these issues are addressed 

below. 

4.4.2 FRM Monitors that Don’t Have Speciation Data 

Species concentration data and/or species fractions are needed in order to apply the PM2.5 

attainment test. There are approximately 1200 FRM and FEM PM2.5 monitoring sites, but only 

~200 urban speciation monitoring sites. This makes it difficult to apply the attainment test at 

the majority of FRM/FEM sites. There are several possible ways to estimate species 

concentrations at FRM monitors that lack speciation data. Among them are: 

1) Use concurrent data from a nearby speciation monitor to estimate species 

concentrations and/or fractions at one or more FRM sites. 

2) Use representative speciation data (from a different time period) collected in an area to 

estimates species data at FRM sites. 

3) Use interpolation techniques to create spatial fields using ambient speciation data. 

4) Use interpolation techniques to create spatial fields using ambient speciation data and

 

38References to CSN monitors in this document refers to the overall speciation network which includes 

both trends sites and SLAMS sites. 

39There are ~1100 FRM measurement sites and ~200-250 urban speciation sites (trends and SLAMS). 
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gridded model outputs to adjust the species concentrations based on the modeled 

spatial gradients. 

In general, we recommend using spatial interpolation techniques to estimate species 

concentrations at FRM sites that do not have speciation data (numbers 3 and 4 above). But in 

some cases, interpolating data from nearby sites may not be appropriate, may not be feasible, 

or simply may not be necessary. In the EPA’s Model Attainment Test Software (MATS), a 

relatively simple interpolation technique is used to estimate species concentrations at all FRM 

sites in the country. The analysis uses a Voronoi Neighbor Averaging (VNA) technique. Other 

interpolations have been done using Kriging and other more complicated methodologies such 

as “Downscaler” (Berrocal, 2010a and 2010b). Air agencies are encouraged to explore 

techniques that are most appropriate for their area and situation. EPA’s MATS software is 

available for air agencies to use as a default technique. 

For areas which contain one or more speciation sites, and where FRM sites exhibit very little 

spatial gradients, it may be appropriate to assume that the speciation site is representative of 

the entire area. Areas which exhibit strong spatial gradients in PM2.5 concentrations should 

strongly consider using more sophisticated techniques to estimate species concentrations. 

Combining ambient data with model output concentrations (number 4 above) may help adjust 

concentrations in areas with strong gradients, but limited speciation data. This technique has 

been used extensively to create spatial fields of PM2.5 concentrations for the purpose of 

generating health benefits calculations, but it has generally not been used to generate species 

fractions at FRM sites. The technique is further limited by uncertainties in the 

representativeness of the model outputs and emissions inventories. 

4.4.3 PM2.5 Species Calculations and Definitions 

Data analyses (Frank, 2006) have noted that the FRM monitors do not measure the same 

components and do not retain all of the PM2.5 that is measured by routine speciation samplers 

and therefore cannot be directly compared to speciation measurements from the Chemical 

Speciation Network (CSN).40 The FRM mass measurement does not retain all ammonium 

 

40The information in this section applies to the most common samplers in the CSN. Some networks use 

alternative special purpose samplers to collect both PM2.5 and PM2.5 speciation data. The characteristics 

of the sampler and the analytical procedures used to produce chemical speciation data should be 
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nitrate and other semi-volatile materials (negative sampling artifacts) and includes particle 

bound water associated with sulfates, nitrates and other hygroscopic species (positive sampling 

artifacts). This results in FRM measured concentrations (and percent contributions to PM2.5

mass) which may be different than the ambient levels of some PM2.5 chemical constituents. 

Because the FRM data are used to determine compliance with the NAAQS, it is critical to 

estimate the species composition as measured on the FRM filters. In addition, for the purposes 

of predicting changes in PM2.5 components, constructed PM2.5 mass should match the 

composition of mass retained by the FRM. 

The goal is to reconstruct the measured species components so that they add up to the 

measured FRM mass. This concept can generally be represented by equation 4.4: 

RCFMFRM = [Ammoniated Sulfate Mass] + [Retained Ammoniated Nitrate Mass] + [Retained 

Carbonaceous Mass] + [Other Primary PM2.5] + [Other Components] [4.4] 

In the above characterization, RCFMFRM equals reconstructed fine mass and all of the listed 

chemical components reflect those retained during sampling and equilibration on the FRM’s 

Teflon filter. Sulfate and nitrate mass include associated ammonium which may be different 

than assumed ammonium sulfate and ammonium nitrate compounds. Carbonaceous mass 

includes elemental carbon (EC), organic carbon (OC), and hundreds of different carbon 

compounds which are not directly measured, and which may not match the carbon mass in 

speciated measurement data. Also, sulfates, nitrates and carbonaceous mass includes particle 

bound water associated with these hygroscopic aerosols. In this characterization, other primary 

PM2.5 mass is intended to be a more general term that includes fine soil, and oxides that result 

from other PM emissions. 

4.4.4 Recommended Treatment of Species Data 

We recommend an adjustment technique based on an FRM mass construction methodology 

which results in reduced nitrates (relative to the amount measured by routine speciation 

 

considered in determining which, if any adjustments are appropriate to make the data useful for 

comparison to FRM data. 
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networks), higher mass associated with sulfates and nitrates (reflecting water included in 

gravimetric FRM measurements) and a measure of organic carbonaceous mass which is derived 

from the difference between measured PM2.5 and its non-organic carbon components (Frank, 

2006). This characterization of PM2.5 mass also reflects “other” primary inorganic PM2.5 and 

other minor constituents. Frank (2006) terms this approach “sulfate, adjusted nitrate, derived 

water, inferred carbonaceous material balance approach (SANDWICH)”. The resulting 

characterization provides a complete mass balance. It does not have any unknown mass which 

is sometimes presented as the difference between measured PM2.5 mass and the characterized 

chemical components derived from routine speciation measurements. The recommended 

SANDWICH based characterizations should yield more accurate assessments of future PM2.5

concentrations as measured by FRM monitors, compared to using unadjusted CSN data. 

4.4.4.1 Retained Nitrate Mass

The first step in the procedure for identifying FRM mass components is to estimate the retained 

nitrate mass on the FRM filters. The FRM does not capture all of the semi-volatile components 

of the ambient air, such as ammonium nitrate. The retained amount of nitrate ion, however, 

can be estimated by a simple thermodynamic model that involves 24-hr ambient nitrate 

speciation concentrations (as measured by a standard speciation sampler using a nylon filter 

preceded by a HNO3 denuder) together with hourly ambient temperature and humidity. 

Atmospheric nitrates are highest during the cooler months. Retention on the FRM is also 

higher during the cooler months and essentially all the nitrates are lost during the summer. The 

retention does not appear to depend on ambient NH3 or HNO3 concentrations. More NO3 is 

retained at low temperatures and high humidity, which varies by sampling location and time of 

year. 

Because nitrate retention varies by site and season, a simple ammonium nitrate equilibrium 

model can be used to predict the amount of nitrates retained on the FRM Teflon filter. As used 

by Hering (Hering, 1999; Zhang, 1992), 

 

 

 24 

delta NO3 (μg/m3)= 745.7/TR* 1/24* Σ (Ki

 1⁄2 ) [4.5] 

 i=1 

where delta NO3 is the amount of volatilized nitrate mass, TR is the reference temperature for 
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the sampled air volume in degrees Kelvin and Ki is the dissociation constant for ammonium 

nitrate evaluated at the ambient temperature and relative humidity for hour i. The nitrate loss 

can be predicted for each day, based on hourly temperature and relative humidity data. It must 

be subtracted from the NO3 measured at speciation sites to estimate the FRM retained nitrate. 

The MATS software is populated with a speciation ambient data file with daily average retained 

nitrate already calculated for all CSN and IMPROVE sites. The analysis uses National Weather 

Service temperature and relative humidity data from the closest station. Other sources of 

meteorological data may also be appropriate. Further details on the nitrate loss calculations 

can be found in (Frank, 2006). 

4.4.4.2 Ammonium Associated with Sulfates and Retained Nitrates

There are several ways to estimate ammonium mass for use in attainment test. The most 

direct way is to use measured ammonium concentrations from the CSN network (IMPROVE 

does not measure ammonium ion). A second, more indirect method is to estimate the 

ammonium associated with nitrate, and to calculate the ammonium associated with sulfate and 

the degree of neutralization of sulfate (DON), and then use the resulting information to 

calculate ammonium mass. Due to uncertainties associated with the ammonium 

measurements and the lack of ammonium measurements in rural areas, this indirect method is 

recommended as the default attainment test methodology. 

To determine the mass associated with nitrates, we assume retained nitrate is all ammonium 

nitrate. Thus, using the ratio of the molecular weight of ammonium to that of nitrate, the 

ammonium associated with nitrates (NH4NO3) can be derived directly from the estimated 

retained nitrate (NO3FRM) as: 

NH4NO3 = 0.29 * NO3FRM [4.6] 

The difference between total FRM NH4 (amount associated with nitrates and sulfates), termed 

NH4FRM, and the measured NH4, termed NH4CSN, is needed to determine the ammoniated form 

of sulfates as described by equation 4.4. A measurement study by Collett (Collett, 2004) shows 

that NH4 may not be completely retained during collection on nylon filters preceded by a nitric 

acid denuder, such as are used at CSN sites. At several FRM study sites, the CSN NH4 which is 

adjusted for evaporated NH4NO3 tends to more closely correspond to the measured NH4 from 

the FRM Teflon filter. However, for other sites, the measured CSN NH4 appears to agree with 
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FRM NH4. 

The available information suggests that using measured ammonium (assuming none is 

volatilized) may be the best assumption. We therefore recommend using unadjusted 

ammonium, but further analysis of this issue is warranted. 

To calculate the ammonium associated with sulfate, we recommend using the degree of 

neutralization of sulfate (DON). This is defined as the ammonium associated with sulfate (i.e., 

total ammonium less the ammonium associated with nitrate), divided by sulfate. This reflects 

the degree to which sulfate has been neutralized by ammonium. The ambient data is such that 

almost all of the ammonium data is from urban sites (CSN), but the sulfate and nitrate data is 

from both urban (CSN) and rural (IMPROVE) sites. This may lead to an overestimation of 

ammonium concentration in rural areas when ammonium is directly interpolated. Therefore, 

we recommend using calculated DON with SO4 and NO3FRM, and interpolating to each FRM site 

to get quarterly average concentrations. The interpolated DON and species concentrations are 

used to calculate NH4FRM using the following equation: 

NH4FRM = DON * SO4 + 0.29*NO3FRM [4.7] 

The indirect calculation of ammonium mass from interpolated fields tends to smooth out the 

gradients in mass. This is deemed to be beneficial, due to the uncertainty in the 

measurements. 

4.4.4.3 Particle Bound Water 

Because ammoniated sulfate and ammonium nitrate are hygroscopic, the retained sulfate and 

nitrate mass will include water. Particle bound water (PBW) can be estimated using an 

equilibrium model. The recommended approach is to use the Aerosol Inorganic Model (AIM) 

(Clegg, 1998) to calculate PBW. PBW is derived from quarterly average FRM concentrations of 

sulfate, ammonium, and nitrate as described above. Estimated hydronium ion, H+, needed to 

achieve ionic balance is derived from the latter values. The model enables the distribution of 

water and ions to be calculated between liquid, solid and vapor phases for specific temperature 

and relative humidity conditions. Typical FRM filter equilibration conditions of 35% RH and 22 

deg C (295 deg K) temperature are used to estimate water concentrations at those conditions. 
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Application of AIM at the specified FRM filter equilibration conditions show that PBW is much 

more dependent on sulfate concentration compared to nitrate and that the relationship varies 

somewhat by season. There is proportionally less estimated PBW water for wintertime aerosol 

which has higher NO3 and lower SO4. The PBW concentrations are also sensitive to the degree 

of neutralization of the sulfate particles (determined by the relative concentration of NH4FRM). 

We recommend calculating PBW as a component of PM2.5 mass. The AIM model (or other 

equilibrium models) can be used, or a regression equation can be developed to simplify the 

process. For computational convenience, EPA derived a polynomial regression equation fit to 

the calculated water mass from AIM and the three input values to AIM (sulfate, nitrate and 

ammonium). The polynomial equation is used in the MATS software to calculate PBW. See the 

MATS User’s Guide for more details on the default PBW equation. 

4.4.4.4 Salt

In the PM2.5 attainment test methodology, salt is represented as a species which accounts 

primarily for sea salt at sites near the oceans. The salt species is estimated using the CSN 

measured Cl+

 ion concentration multiplied by 0.56. The multiplier accounts for the sodium 

contribution to the species. In areas distant from oceans, measured Cl+ may represent PM mass 

that is not associated with salt water. For instance, there may be chloride emissions resulting 

from various industrial processes. Therefore, the use of measured salt in the attainment test 

calculations should be evaluated on an individual area/monitor basis. 

Measured salt is included by default in the MATS species ambient data file. The inclusion of 

modeled salt (and hence an RRF for salt) is optional and can be included if there are accurate 

modeled salt concentrations. If modeled salt is not included in the MATS input files, an RRF of 

1.0 used in the MATS calculations. 

4.4.4.5 Other Primary PM2.5

The terms “crustal”, “fine soil”, “major metal oxides”, “inorganic particulates”, and “other PM” 

are sometimes used interchangeably. For PM2.5 design value calculations we will refer to this 

material as “other primary PM2.5"(OPP). For regional haze calculations, we will continue to 

refer to this material as “fine soil”. 
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For the purposes of estimating OPP for the PM2.5 attainment test, all measured non-carbon 

mass that is not organic in nature (not associated with sulfate and/or nitrate) should be 

counted. As with the other PM2.5 components measured on the FRM filter, there is uncertainty 

associated with this estimate. The “crustal” or “fine soil” definition from IMPROVE can be used 

to estimate OPP41 or an alternative formula can be defined which better estimates the urban 

nature of the FRM measurements. The IMPROVE definition of “fine soil” accounts for the 

typical crustal components (and attached mass) that would be expected in remote Class I areas. 

Fine soil is represented as five elements (Al, Si, Fe, Ti, and Ca) with coefficients to represent 

various oxides and material which may be attached to or associated with the major elements. 

In urban areas, inorganic PM which is not elemental carbon, or associated with sulfate or 

nitrate, may come from many sources such as re-suspended dust or industrial sources (stack or 

fugitives). It is generally “crustal” in nature (dominated by silicon), but urban PM is more likely 

to contain heavy metals and industrial components. 

Although the composition of inorganic PM may differ between urban and rural (remote) areas, 

we recommend using an equation similar to the IMPROVE fine soil equation to estimate OPP 

for the PM2.5 attainment test. The recommended equation is suggested by (Frank, 2006) and 

uses only four elements. The equation removes aluminum (and accounts for associated mass 

by increasing the coefficient for Si), due to the fact that aluminum is often missing from the 

speciation measurements. This allows for more complete data. 

The recommended equation is as follows: 

Other primary PM2.5 mass = 3.73 × [Si] + 1.63 × [Ca] + 2.42 × [Fe] + 1.94 × [Ti] [4.8] 

4.4.4.6 Blank mass 

The other quantifiable components of PM2.5 mass include passively collected mass, represented

by a field blank concentration of 0.3-0.5μg/m3 (U.S. EPA, 2002). This appears to constitute a 

contamination of the filter resulting from handling or contact with the FRM cassette. This value 

is deemed to be an important constituent of PM2.5 mass (it is assumed to not be dependent on 

pollutant emissions). We recommend using a default nominal blank mass value of 0.5 μg/m3. 

 

41IMPROVE estimates fine soil as: 2.2 × [Al] + 2.49 × [Si] + 1.63 × [Ca] + 2.42 × [Fe] + 1.94 × [Ti] 
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This value can be modified based on local FRM blank mass measurements. The blank mass is 

assumed to remain constant through time (RRF=1.0). 

4.4.4.7 Calculation of Carbonaceous Mass

Organic carbon mass is typically estimated from blank corrected speciation data, where organic 

carbonaceous mass is first estimated by multiplying the organic carbon concentrations by 1.4 or 

alternative factors to account for the oxygen, hydrogen and other elements associated with 

ambient carbon particles. 

There are many uncertainties in estimating carbonaceous mass from carbon measurements 

(Turpin, 2001; Chow, 2004). Uncertainties include differences in carbon measurement protocol 

between urban and rural monitoring locations; a relatively “bumpy” surface of urban carbon 

concentrations as derived from urban and rural organic carbon measurements; and lack of 

carbon measurements at all FRM locations. We therefore recommend an alternative approach 

to estimate the organic carbon contribution to PM2.5 mass. 

The recommended attainment test approach estimates organic carbon by mass balance. 

Precisely measured FRM PM2.5 mass (U.S. EPA, 2003) is compared to the sum of its non-organic 

carbon components. The latter are sulfates, ammonium, nitrates, estimated particle bound 

water, elemental carbon, estimated other primary PM2.5 material, salt, plus 0.5 μg/m3 blank 

mass as discussed earlier. 

This approach estimates retained organic carbon FRM mass and explicitly accounts for the 

following important and difficult to estimate carbon mass properties: (1) regional and urbanrural differences in the mix of carbonaceous aerosols, i.e. the amount of oxygen, hydrogen, etc; 

(2) retained water associated with hygroscopic carbon compounds (Saxena, 1996; Yua, 2004); 

(3) volatile carbonaceous material measured by speciation samplers, but not retained in FRM 

mass; and (4) uncertainties associated with blank corrections of measured organic carbon. 

Organic Carbon Mass by mass balance (OCMmb) is defined as, 

OCMmb = PM2.5 - { [SO4] + [NO3FRM] + [NH4FRM] + [water] + [EC] + [OPP] + [salt] + [0.5] } [4.9] 

In this expression, all of the above components represent the mass retained on FRM Teflon 

filters. 
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This approach completely accounts for FRM mass42 and OCMmb is often greater than the 

amount that would be derived directly from speciation measurements. Because of 

uncertainties in speciation measurements and their estimates from interpolated surfaces, 

setting a lower limit (floor) may be necessary so that the OCMmb is not unreasonably low. In 

some cases, the OCMmb could be lower than the measured OC or OCM. Since measured OC 

(without accounting for mass associated with OC) is a conservative minimum estimate of OC 

mass, we recommend setting the OCMmb “floor” to be equal to measured OC. 

 There may also be situations where an OCMmb “ceiling” is needed. In remote urban areas 

with relatively high FRM concentrations that may be surrounded by rural background 

concentrations, the OC by mass balance technique may apportion 95%+ of the PM2.5 mass to 

OCM. If this is not a reasonable assumption, then a ceiling may be needed to cap the OCM as a 

percentage of PM2.5 mass. Based on measured data (FRM sites with co-located speciation 

data), it appears that on a quarterly average basis, OCM is rarely more than 80% of total PM2.5

mass. This may be a reasonable default ceiling, but a lower value (or in rare circumstances a 

higher value) may be more appropriate in some regions of the country. 

4.4.4.8 Summary of PM2.5 Composition Calculations

The terms of equation 4.10 reflect the final estimated composition of the particles measured by 

the FRM (for each quarter). Quarterly average FRM mass is equal to the sum of the eight 

species plus blank mass. 

PM2.5FRM = { [OCMmb] + [EC] + [SO4] + [NO3FRM] + [NH4FRM] + [water] + [OPP] + [salt] + [0.5] } [4.10] 

 

 

42The OC by mass balance technique assumes that all other mass is accounted for and therefore all 

remaining mass is OCM. This may not always be a good assumption. The results of the technique 

should be carefully evaluated to ensure that OC mass is not overestimated (and therefore other mass 

components are underestimated). This may be an issue in areas that do not have nearby speciation 

data and have relatively large concentrations of primary PM2.5. The OC by mass balance technique may 

inadvertently apportion mass to organic carbon which may actually be EC or “other” primary PM2.5 mass 

components (such as heavy metals). All available ambient data and modeling data should be used to 

evaluate the species apportionment results. 
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The recommended order to generate the data is as follows: 

1) Calculate adjusted nitrate using hourly meteorology and 24-hour average nitrate 

measurements. 

2) Calculate quarterly averages for adjusted nitrate, sulfate, elemental carbon, 

ammonium (or degree of sulfate neutralization (DON)), OPP, salt, and measured OCM.43

3) Quarterly average ammonium is calculated from the adjusted nitrate, sulfate, and 

DON values (if measured ammonium is not used directly). 

4) Ammonium, sulfate, and nitrate concentrations are input into the polynomial water 

equation to derive particle bound water concentrations. 

5) Carbon mass by difference (OMCmb) is calculated from the PM2.5 mass, adjusted 

nitrate, ammonium, sulfate, water, elemental carbon, salt, other primary PM2.5, and 

blank mass values. The sum of the 8 species plus blank mass is equal to the FRM mass. 

We illustrate application of the recommended test in example 4.4.1. 

Example 4.4.1

Given: (1) Area “C” has 2 monitoring sites. 

(2) Monitored FRM air quality data show the following average quarterly mean PM2.5

concentrations based on a 5 year weighted average of observations from 2005-2009 at each 

site (values are in μg/m3

 ). 

 

43The measured OCM is only used to calculate the “floor” for OCMmb 
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Table 4.4.1 Quarterly average PM2.5 FRM mass at 2 example sites 

Site Quarter 1 Quarter 2 Quarter 3 Quarter 4 

1 17.21 μg/m3 16.34 20.30 14.76 

2 15.39 17.98 18.23 13.76 

(3) The area has 1 urban speciation site which is co-located with FRM site 2. The speciation 

data is available for the entire time period, but the species concentrations for the 2006-2008 

time period have been deemed to be most representative of the 2005-2009 5 year weighted 

average. Therefore, the species data for the average three year period (2006-2008) has been 

interpolated to derive estimated species concentrations at each site. The species 

concentrations are matched up with FRM data for the same 2006-2008 period to derive 

representative species fractions. The average monitored air quality for the 3rd quarter of 2006-

2008 at site 1 is as follows44: 

Table 4.4.2 Average monitored 3rd quarter ambient data at site 1 

FRM 

Mass 

(μg/m3)

Blank 

Mass 

(μg/m3)

Nonblank 

Mass 

(μg/m3)

 Sulfate 

(μg/m3)

Nitrate 

(μg/m3)

Organic 

Carbon 

Mass 

(μg/m3)

Elemental 

Carbon 

(μg/m3) 

Water 

(μg/m3)

Ammonium

(μg/m3) 

Salt 

(μg/m3)

OPP 

(μg/m3)

22.92 0 .5 22.42 8.51 1.11 5.21 0.91 2.31 3.31 0.35 0.71 

The blank mass is subtracted before species fractions are calculated because the blank mass is 

held constant at 0.5 μg/m3 throughout the analysis. In the example, the measured FRM mass 

for quarter 3 is 22.92 μg/m3. The non-blank FRM mass is 22.42 μg/m3. The mass of the eight 

species add up to the non-blank mass. 

 

44The species concentrations can be derived from co-located speciation data, nearby speciation data or 

from interpolated spatial fields. FRM mass is not interpolated. 
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(4) Species fractions are calculated for each quarter for each species. In the example below, a 

fraction of non-blank mass is calculated for each of the eight species. Blank mass remains fixed 

at 0.5 μg/m3. 

Table 4.4.3 3

rd quarter average species fractions for 2006-2008 at site 1 

FRM 

Mass 

(μg/m3) 

Blank 

Mass 

(μg/m3) 

Nonblank 

Mass 

(μg/m3) 

 % 

Sulfate 

% 

Nitrate

% 

Organic 

aerosol 

% 

Elemental 

Carbon 

% 

Water

% 

Ammonium 

% 

Salt 

% 

OPP 

22.92 0 .5 22.42 37.96 4.95 23.24 4.06 10.30 14.76 1.56 3.17 

The percentages in table 4.4.3 above are the relative composition for the 3rd quarter of 2006-

2008. It is assumed that these species fractions are representative of the 2005-2009 time 

period.45 

(5) The weighted quarterly average FRM design values are used as the base year FRM value for 

each monitoring site (2005-2009). The species fractions from the 2006-2008 speciation data 

were used to estimate the species concentrations for the base year FRM PM2.5 data. The 

percentage compositions for 2006-2008 are applied to the quarterly weighted average design 

values as shown in table 4.4.4. In the example below, the weighted average design value for 

the 3rd quarter for the site from table 4.4.1 is 20.30 μg/m3. This leads to the following 

concentrations of PM2.5 species: 

 

45The default assumption is that the average of the middle three years of speciation data is 

representative of the 5 year weighted average FRM data. However, other assumptions (number of 

years of speciation data) can be used. It is generally assumed that the inter-annual variability of the 

species fractions is small compared to the variability of species concentrations. 
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Table 4.4.4 Calculation of the “base case” species concentrations at site 1 

Weighted 

Avg. 

FRM Mass

(μg/m3) 

Blank 

Mass 

(μg/m3)

Nonblank 

Mass 

(μg/m3)

 Sulfate

(μg/m3)

Nitrate 

(μg/m3) 

Organic 

aerosol 

(μg/m3) 

Elemental 

Carbon 

(μg/m3) 

Water 

(μg/m3) 

Ammoni

um 

(μg/m3) 

Salt OPP 

(μg/m3) 

20.30 0 .5 19.80 7.52 0.98 4.60 0.80 2.04 2.92 0.31 0.63 

This procedure is repeated for each PM2.5 site and quarter to complete the calculation of base 

year ambient concentrations used as the basis for future estimates of PM2.5 mass and its 

components. 

(6) Modeled results show the following relative response factors (RRF) for predicted mass of 5 

components of PM2.5 for the 3rd quarter:46

Table 4.4.5 3

rd quarter modeled RRFs for site 1

RRF 

Sulfate

RRF 

Nitrate

RRF 

Organic 

aerosol

RRF 

Elemental 

Carbon 

RRF 

Salt 

RRF 

OPP 

0.876 0.943 0.971 0.932 1.0 1.042 

(7) The quarterly mean RRFs from table 4.4.5 are multiplied by the weighted quarterly average 

species concentrations from table 4.4.4 to derive future year concentrations. 

 

46 The default assumption for the annual average PM2.5 NAAQS test is to calculate RRFs from the mean of the 3 X 3 

matrix of grid cells surrounding each FRM monitoring site. 
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From the example above, the future year 3rd quarter concentrations are: 

 

SulfateFuture = 7.52 * 0.876 = 6.58 μg/m3 

NitrateFuture= 0.98 * 0.943 = 0.92 μg/m3 

Organic carbon massFuture= 4.60 * 0.971 = 4.47 μg/m3 

Elemental CarbonFuture= 0.80 * 0.932 = 0.75 μg/m3 

SaltFuture=0.31*1.0 = 0.31 μg/m3 

OPPFuture= 0.63 * 1.042 = 0.65 μg/m3 

(8) The future year concentrations derived above are used to calculate the future year 

concentration of ammonium (if the direct ammonium RRF is not used) and particle bound 

water. 

The future year ammonium concentrations are calculated from the sulfate, nitrate, and (base 

year) DON values. Assuming that the DON is unchanged from the base year47, the ammonium 

is calculated using the following formula: 

NH4future = DON * SO4future + 0.29*NO3future, [4.7] 

In the example above, assuming the base year DON is 0.336, 

AmmoniumFuture= 0.336 * 6.58 + 0.29 * 0.92 = 2.48 μg/m3 

The NH4future, SO4future, and NO3future concentrations can then be input into an equilibrium 

 

47Due to the uncertainty in the ammonium measurements, by default, the DON is assumed to stay 

constant through time. The water calculation is sensitive to the ammonium (and therefore the DON 

value) concentrations. Holding the DON constant allows for the future year ammonium and water 

values to be solely a function of the change in sulfate and nitrate concentrations. Otherwise, the water 

concentration can go up when the sulfate and nitrate concentrations go down (and vice versa). This 

may occur if sulfate becomes more neutralized in the future. It is a somewhat illogical outcome 

(although scientifically possible) and is highly dependent on an uncertain measurement (ammonium). 

Therefore, use of a constant DON creates a more stable set of calculations. If the measured and 

modeled ammonium concentrations are believed to be accurate and respond in a reasonable way to 

emissions controls, then it would be more scientifically credible to use the model predicted change in

ammonium. Otherwise, it is a reasonable assumption to keep the DON constant over time. 
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model (AIM or another alternative model) or through a polynomial regression equation to 

predict future year particle bound water concentration (based on the default equation, the 

future year water concentration in this case is 1.73 μg/m3). 

(9) The future species concentrations at each FRM site are then summed over the eight species 

plus blank mass to estimate the future quarterly average PM2.5 concentration. 

In the example above, the total PM2.5Future= 

6.58 + 0.92 + 4.47 + 0.75 + 0.65 + 2.48 + 1.73 + 0.5 = 18.08 μg/m3 

(10) The same calculations are completed for the other 3 quarters to get a future year PM2.5

concentration for each quarter. The 4 quarters are then averaged to get a final future year 

annual average PM2.5 concentration for each FRM site. 

(11) The future year annual average concentration is compared to 12.0 μg/m348. 

4.5 What Is The Recommended Modeled Attainment Test For The 24-

Hour NAAQS? 

Our recommended modeled attainment test for the 24-hour NAAQS for PM2.5 is similar to the 

previously described test for the annual NAAQS in that it uses model predictions in a relative 

sense to reduce site-specific observations (averaged over 5 years). In the test, we are 

interested in reducing the base year design values49 at each site to < 35 μg/m3 (the 2006 24 

hour PM2.5 NAAQS). 

Ideally, the modeled attainment test should reflect model results obtained for days in each 

season having observed PM2.5 concentrations above the design value. Even though the 24-hour 

 

48In the final step, the future year concentration should be rounded to the tenths digit. A (rounded) 

value of 12.0 μg/m3 meets the NAAQS. A value of 12.1 μg/m3 or greater violates the NAAQS. 

49The PM2.5 24-hour standard was lowered to 35 μg/m3 in 2006 (71 FR 61224) (40 CFR 50.13). 
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NAAQS is based on the single 98th percentile value of all days in the year, it is important to 

perform the test on a seasonal basis, since PM2.5 consists of a mixture of pollutants whose 

composition can vary substantially from season to season. There could be a substantial amount 

of uncertainty associated with predictions on any single day. Thus, our test is most likely to be 

reliable when relative response factors (RRFs) reflect composite responses from multiple days. 

Therefore, we recommend modeling as many days as feasible where observed PM2.5 is greater 

than 35 μg/m3. Alternatively, the test can focus on the high end of the distribution of days in 

each quarter50 (e.g. the top 10% of PM2.5 days51) assuming that the high days are representative 

of days that violate the NAAQS. As with the annual NAAQS (and for the same reasons), the 

preferred approach is to develop RRFs which are season (i.e., quarter) specific.52

 

The 24-hour PM2.5 attainment test should be based on the same 5 year weighted average 

methodology that was used for the annual standard, with some slight modifications. The 24-

hour design values are calculated from the 98th percentile value for each year. In the 24-hour 

PM2.5 calculations, the 98th percentile value from each year is used in the final calculations. 

Since the 98th percentile value can come from any day in the year, all quarters and years should 

be carried through to near the end of the calculations. To calculate final future year design 

 

50 Similar to the annual PM2.5 NAAQS, the default recommended procedures for the 24-hr. NAAQS 

assume handling of the ambient data and model data on a calendar quarter basis. However, in some 

nonattainment areas, 24-hr PM2.5 NAAQS exceedances may occur during a very limited time of the year 

or season. Processing the data (both ambient and model) on a sub-quarterly basis may be appropriate 

in certain circumstances. 

 

51The top 10% of days may seem like a large number of days per quarter to use for an annual 98th

percentile based standard, but for sites with a 1 in 3 day sampling schedule, the top 10% of days is only 

3 days per quarter. For most sites, the top 10% of monitored days per quarter will represent between 3 

and 8 ambient measurement days. 

52In some areas it may not be necessary to model and evaluate the 24-hour NAAQS for all quarters. For 

example, if PM2.5 concentrations only exceed the NAAQS in the 1st and 4th quarters, and concentrations 

in the 2nd and 3rd quarters are very low, then it may not be necessary to model the full year. But for 

areas that have monitored violations (or high values that are close to the NAAQS) in all 4 seasons, the 

entire year should be evaluated. 
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values, the 98th percentile day for each year is identified and then a five year weighted average 

of the 98th percentile values for each site is calculated to derive the future year design value53. 

In nonattainment areas that measure violations of the daily PM2.5 NAAQS in both the summer 

and winter, the highest PM days can sometimes shift between seasons when days are projected 

to the future. A consequence of this shift is that a base year 98th percentile winter day may 

have a higher future year concentration than a summer day that started with a higher 

concentration in the base year. Therefore, the recommended attainment test methodology 

does not assume that the temporal distribution of high days in the base and future periods will 

remain the same. 

In the 24-hour attainment test methodology (described below), the eight highest ambient PM2.5

days in each quarter at each site are projected to the future (32 days per year) using species 

specific quarterly relative response factors. After all 32 days are projected to the future, the 

days are re-ranked to determine the future year 98th percentile day. 54 The rank of the future 

year 98th percentile day is selected based on the rank of the 98th percentile day in the base year 

ambient data. For example, at monitoring site A, if there are 120 observations in year 1 then 

the 98th percentile day is the 3rd high observation day for the year. In that case, the future year 

98th percentile day is selected as the 3rd high future day for the year (out of the 32 highest 

days). 

Similar to the annual PM2.5 attainment test, we recommend interpolation techniques for FRM 

monitors that do not have co-located speciation data. Because the 24-hour standard is a 98th

percentile based value, the species composition on high concentration days may be highly 

variable from day to day and from site to site. Therefore, while interpolation techniques may 

be needed, we strongly recommend collecting speciation data at all FRM sites that violate the 

24-hour NAAQS. A precise estimate of the PM2.5 components at violating sites will help reduce 

uncertainty in projecting the future year concentration estimates. 

 

53Similar to the annual average PM2.5 attainment test, design values are calculated for consecutive three 

year periods. From the five year base period, three design values are calculated and then averaged 

together to create a five year weighted average. 

54 The observed 98th percentile day is always between the 1st and 8th high for the year, depending on the 

sampling schedule. Therefore, projecting the 8 highest days in each quarter ensures that the observed

98th percentile day is always captured. More days could be projected, but a maximum of 32 days per 

year is needed to guarantee that the 98th percentile observation day is projected to the future. 
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We recommend a modeled attainment test for the 24-hour PM2.5 NAAQS with the following 

steps. 

Step 1. Identify the high observed PM2.5 days at each FRM monitoring site for each year. 

The first step in projecting the daily design value is to identify at each FRM site, the eight 

highest observed 24-hour PM2.5 concentration days in each quarter for each year of the base 

period (up to 5 years), and identify the day rank of the observed 98th percentile value for each 

year based on the number of collected ambient samples (i.e. 3rd highest day of the year). This 

results in a data set containing 32 days of data for each year (for up to 5 years) for each site.55

The test should be performed for each monitoring site that meets the data completeness 

criteria for calculating a design value for the 24-hour NAAQS. There may not always be data 

available for all four quarters and all five years. We recommend using eight days per quarter 

because the 98th percentile day for a year is always one of the 8 highest days of the year.56 If all 

of the high days occur in a single quarter, then using the 8 highest days from each quarter will 

ensure that the actual 98th percentile day is always captured. This may result in processing 

more days than necessary, but effectively limits the design value calculations to a reasonably 

small number of days. 

Step 2. Calculate “high days” species fractions for each quarter for each FRM monitor. 

In this step, quarterly ambient species fractions on “high” days are calculated for each of the 

major component species of PM2.5 (i.e. sulfate, nitrate, ammonium, elemental carbon, organic 

carbon mass, particle bound water, salt, and blank mass). This calculation is performed by 

multiplying the average monitored concentrations of FRM-derived total PM2.5 mass on the high 

PM2.5 days at each site for each quarter, by the average monitored species composition on the 

 

55 The methodology does not assume that the temporal distribution of high days in the base and future 

periods will remain the same. We recommend projecting a larger set of ambient days from the base 

period to the future and then re-rank the entire set of days to find the new future year 98th percentile 

value (for each year). 

56 If there are 365 samples in a year, then the 98th percentile is the eighth high day. 
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high PM2.5 days for each quarter.57 The default recommendation for identification of “high” 

days is the top 10% of days in each quarter. This results in a relatively robust calculation which 

typically uses between 3 and 9 days per quarter (depending on the sampling frequency). The 

end result is a set of quarterly “high days” species fractions for each FRM site. 

Step 3. Calculate species concentrations for each of the high ambient days.

Multiply the quarterly “high day” species fractions from step 2 by the PM2.5 mass concentration 

for the 8 high days per quarter identified in step 1. This results in a set of species 

concentrations for each of the 32 days per year identified in step 1. 

Step 4. Calculate component specific RRFs for the high days in each quarter. 

For each quarter, calculate the ratio of future year to base year modeled predictions for sulfate, 

nitrate, elemental carbon, organic carbon, salt, and OPP for the top 10 percent of modeled 

PM2.5 days based on modeled concentrations of 24-hour average PM2.5. The result is a set of 

species-specific “high day” relative response factors (RRF) for each quarter. 

The relative response factor for component j at a site i is given by the following expression: 

(RRF)ij = ([Cj, projected]/[Cj, base year])i 

where Cj, base year is the base year mean species concentration (for the high modeled 

PM2.5 days for each quarter) predicted at the (single) grid cell which contains monitoring 

sitei. 

Cj, projected is the future year mean species concentration (for the high modeled days for 

each quarter) predicted at the (single) grid cell which contains monitoring sitei. 

 

57 Similar to the annual average calculations, for FRM sites that do not have co-located species data, we

recommend calculating the quarterly species fractions using interpolated species data. For the 24-hour 

species interpolations, we recommend interpolating the average of the highest 10% of monitor days in 

each quarter. 
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For example, assume that base year predicted sulfate mass on the 10 percent highest PM2.5

days for quarter 3 for a particular location is 20.0 μg/m3 and the future year modeled sulfate 

concentration is 16.0 μg/m3

, then the RRF for sulfate for quarter 3 is 16.0/20.0 or 0.80. Due to 

potentially large spatial and temporal gradients, we recommend 24-hour PM2.5 NAAQS RRFs to 

be calculated based on the modeled concentrations at the single grid cell where the monitor is 

located. 

Step 5. Apply the component specific RRFs to observed air quality by quarter.

For each of the 8 days in each quarter, multiply the daily species concentrations from step 3 by 

the quarterly species-specific RRFs obtained in step 4. If there is one modeled base year, then 

there will be four quarterly RRFs at each monitor. The modeled quarterly RRF for quarter 1 is 

multiplied by the ambient data for quarter 1 (8 days in each year) for each of the 5 years of 

ambient data. For example, for day A, 21.0 μg/m3 nitrate x 0.75 = future nitrate of 15.75 

μg/m3. The same procedure is applied for the 8 high days per quarter in the other 3 quarters. 

This leads to an estimated future concentration for each component for each day. 

Step 6. Calculate remaining future year PM2.5 species. 

The future year concentrations for the remaining species are then calculated for each of the 

days.58 Similar to the annual PM2.5 attainment test, we recommend that the future year 

ammonium is calculated based on the future year sulfate and nitrate concentrations, using a 

constant value for the degree of neutralization of sulfate (from the ambient data). The future 

year particle bound water concentration is then calculated from an empirical formula derived 

from the AIM model. The inputs to the formula are the future year concentrations of sulfate, 

nitrate, and ammonium calculated in step 5. 

Step 7. Sum the species components to get total PM2.5 concentrations for each day. 

Sum the species concentrations for each day to obtain total PM2.5 values for the 32 days per 

year per site. 

 

58 If salt is not explicitly modeled, then the salt RRF should be held constant. Blank mass is assumed to 

be constant between the base and future year. 
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Step 8. Determine future year 98th percentile concentrations for each site year. 

Sort the 32 days for each site for each year by total PM2.5 concentration. For each site year, the 

monitored 98th percentile rank (for each year) is used to identify the 98th percentile rank for 

each year. For example, if the base year 98th percentile value for year 1 was the 3rd high 

concentration, then the future year 98th percentile concentration is identified as the 3rd high 

future year PM2.5 concentration (out of the 32 days). 

Step 9. Calculate future 5 year weighted average 24-hour design values and compare to the 

NAAQS. 

The estimated 98th percentile values for each of the 5 years are averaged over 3 year intervals 

to create 3 year average design values (e.g. the 98th percentile values from year 1, year 2, and 

year 3 are averaged. Then the 98th percentile values from year 2, year 3, and year 4 are 

averaged, etc.). These design values (up to 3) are then averaged to create a future year 5 year 

weighted average design value for each monitoring site. 

The preceding steps for determining future year 24-hour PM2.5 concentrations are applied for 

each FRM site. The 24-hour PM2.5 design values are truncated after the first decimal place. This 

approach is consistent with the truncation and rounding procedures for the 24-hour PM2.5

NAAQS. Any value that is greater than or equal to 35.5 μg/m3 is rounded to 36 μg/m3 and is 

considered to be violating the 24-hour NAAQS. Similarly, a value of 35.4 μg/m3 is rounded down 

to 34 μg/m3

 and is considered to be attaining the 24-hour NAAQS.

4.6 Local Area Analyses- Special Considerations for Primary PM2.5

Primary particulate matter does not undergo chemical transformation between being emitted 

and its arrival at a receptor location. Thus, high concentrations of primary particulate matter 

can occur without relatively lengthy travel times from source to receptor. Unlike secondary 

particulate matter or ozone, we would expect concentrations of primary particulate matter to 

increase the closer one gets to its source(s) of emissions. Therefore, if a designated 

nonattainment area contains concentrated sources of primary particulate matter, we would 

expect there to be relatively large spatial gradients near sources of the primary portion of the 

organic carbon, elemental carbon and other particulate matter components of ambient PM2.5 
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(these localized areas of high concentrations are often called “hotspots”). 

Note that the PM2.5 NAAQS ambient monitoring rule language59 indicates that some monitoring 

locations may not be comparable to the annual PM2.5 NAAQS. PM2.5 measurement data from 

monitors that are not representative of “area-wide” air quality but rather of relatively unique 

micro-scale, or localized hot spot, or unique middle-scale impact sites are not eligible for 

comparison to the annual PM2.5 NAAQS. The ambient air quality monitor siting rules define the 

appropriate scales of influence for the PM2.5 monitoring network.60

4.6.1 Analysis of Primary PM2.5 Impacts at Monitors 

The majority of FRM and FEM PM2.5 monitors are located at “urban scale” sites. These sites 

tend to represent relatively large spatial scales and do not have large gradients compared to 

other monitors in the area. Some sites are classified as “neighborhood” scale and may be 

influenced by more local sources. For reasons stated above, local influences creating large 

spatial gradients are likely to consist mostly of primary PM2.5 (OC, EC, and OPP). These sources 

may be point sources, or they may be nearby roads or other mobile or area sources. 

As part of the attainment demonstration modeling, it may be necessary to evaluate the localscale impacts of primary PM2.5 sources for contributions to the 24-hour and/or the annual 

NAAQS. As stated earlier, for the purposes of determining attainment with the NAAQS, it may 

not always be appropriate to compare ambient data from PM2.5 monitoring sites that are 

dominated by point sources to the annual NAAQS. But there are numerous cases where local 

source contributions may not be dominant, but are a sizable contributor to total PM2.5 (~10-

30% of total annual average PM2.5). In these cases, a more refined analysis of the contribution 

of local primary PM2.5 sources to PM2.5 at the monitor(s) will help explain the causes of 

nonattainment at and near the monitor and may lead to the more efficient ways to attain the 

NAAQS by controlling emissions from local sources which may be important contributors to the 

violating area. 

There are several modeling tools that can be used to evaluate contributions of local PM2.5

 

5940 CFR part 58 subpart D 58.30 

60 40 CFR part 58 Appendix E 
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sources. A grid model can be run at very high horizontal resolution (1 or 2 km or finer) or a 

Gaussian dispersion model can be used. Grid based models simulate chemical transformation 

and complex meteorological conditions, while dispersion models are generally more simplistic; 

being limited to a local-scale, using Gaussian approximations with little or no chemistry. 

Therefore, while dispersion models may not be an appropriate tool for determining secondary 

PM2.5 or ozone concentrations, they work well for use in determining local primary PM2.5

impacts. The model(s) and model setup should be evaluated to determine the most 

appropriate tools for a specific situation. 

Regardless of which type of models are used to evaluate changes in primary PM2.5 at 

monitoring locations, we recommend that the model results be used in a relative manner. This 

is consistent with the already described attainment tests. If a grid model is used at very high 

resolution, the attainment test as described in Sections 4.4 and 4.5 should be followed. If a 

Gaussian dispersion model is used then the application of the attainment test will vary slightly. 

The test will need to combine results from the chemical transport grid based modeled 

attainment test and the results from local-scale dispersion modeling. If such an approach is 

taken, the suggested variations on the attainment test are discussed in the following section. 

4.6.2 Analysis of Primary PM2.5 Impacts at Monitors using a Gaussian 

Dispersion Model 

To apply a dispersion model in an attainment test, it is important to determine the local 

component of primary PM2.5 at the monitor and the sources that are contributing to this 

component. There is no single, simple method for quantifying this contribution, but detailed 

analysis of ambient data and advanced data analysis techniques such as receptor modeling may 

help quantify the contribution. EPA provides software and information on the CMB, UNMIX, 

and PMF receptor models at: http://www.epa.gov/scram001/receptorindex.htm 

The simplest method for identifying the local component of PM2.5 is to examine local ambient 

PM2.5 concentrations. For this type of analysis, it is important to identify the local contributions 

from as small a source area as possible. This will make it easier to identify contributing sources. 

One aspect of this analysis is to examine monitored concentration differences between urban 

monitors (with the highest concentrations) and more suburban measurements. This is likely to 

provide a representative indication of the excess contribution from a relatively local area. 
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“Urban excess” calculations which pair an urban monitor with an appropriate rural background 

monitor (U.S. EPA, 2004b) are likely to indicate “local” contributions that may be representative 

of a metropolitan area. In most cases, the local component will include contributions from 

more than one source. 

Sources identified as contributing to the monitor through emissions analysis or data analysis 

(such as receptor modeling) can then be modeled with a dispersion model (or alternatively, a 

very high resolution grid model). 61 It is common practice to run dispersion models for a limited 

number of major point sources in relatively small areas (out to a maximum distance of ~50 km). 

Dispersion models can also be run for all sources of primary PM2.5 in a limited area (including 

mobile, non-road, and area sources). 

When applying a model to evaluate the impacts from emissions of primary PM2.5, one should 

determine the PM2.5 species that make up the local primary particulate matter.62 We 

recommend that the individual components of primary PM2.5 be tracked separately in the 

dispersion model. This will allow for a more thorough evaluation of the dispersion modeling 

results (comparing model output to speciated ambient data) and may aid in the development of 

primary PM2.5 control strategies. 

The majority of the primary PM2.5 will consist of the primary portion of the organic carbon 

component, elemental carbon (EC), and the general category of “other primary particulate 

matter” (OPP). Speciated PM2.5 measurements may be able to identify some of the more 

important “other” inorganic components such as heavy metals or salt. In some cases, directly 

emitted sulfate (and in rare cases nitrate) may also be a significant component of the local 

primary PM2.5. 

The dispersion modeling results should be evaluated to ensure adequate model performance. 

Similar to grid modeling, the dispersion model results should be compared to ambient data to 

 

61 This guidance only applies to demonstrating attainment of the PM2.5 NAAQS to satisfy requirements in 

CAA Section 189. Other guidance documents and procedures are applicable for SO2, NO2, CO, and/or Pb 

attainment demonstrations and PM2.5 permitting applications. 

62The PM2.5 emissions should be broken out into individual species using the best information available. 

Typically, SCC specific speciation profiles are used to “speciate” the PM into individual components. 

Local, source specific information should be used whenever possible. 
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ensure that the model is providing concentration representative of observed values. Although 

section 3 of this guidance is geared towards evaluating grid models, many of the same 

statistical calculations can be made for primary PM2.5 and PM2.5 components predicted by a 

Gaussian dispersion model. Since secondary PM2.5 is often a large component of total PM2.5

concentrations, it may be difficult to separate the primary and secondary components of 

ambient PM2.5. EC and OPP should be considered to be primary PM2.5. Much of the rest of the 

primary PM2.5 concentration will likely be primary OC. 

As part of the local-source impact analysis, an estimated concentration of primary OC is 

needed. There are several methods available for estimating the primary vs. secondary portion 

of ambient OC. Among these are the EC tracer method and receptor modeling. The EC tracer 

method is the most common method used to estimate secondary and primary OC 

concentrations (Turpin, 1995), (Strader, 1999), (Cabada, 2004), (Chu, 2005), (Saylor, 2006). The 

method uses measurements of OC and EC and calculated OC to EC ratios to identify periods 

when OC is likely to be mostly primary. This information is then used to calculate the secondary 

contribution to OC. Receptor models such as CMB and PMF have also been used to estimate 

secondary organic concentrations (Na, 2004), (Yuan, 2006). 

 

The following sections describe a suggested methodology for quantifying the impacts on PM 

concentrations from local primary sources of PM for the Annual and 24-hour PM2.5 NAAQS. 

Because each nonattainment area has unique emissions sources and source-receptor 

relationships, air agencies should work closely with their EPA Regional Office in developing local 

area analysis applications. 

4.6.2.1 Methodology for the Annual PM2.5 NAAQS 

The suggested steps outlined below assume that the attainment test has been performed using 

chemical transport modeling and that the annual DV has been computed as discussed in 

Section 4.4. 

(1) Identify local primary PM2.5 sources that are thought to be contributing to the violating 

monitor(s) and causing non-attainment of the annual NAAQS. 

(2) By quarter, identify at the monitor, primary versus secondary components of 

PM2.5. 
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(3) Identify the percentage of total primary PM2.5 at the monitor due to the local sources 

identified in step (1). Convert the percentage to an estimated concentration in μg/m3. 

(4) Run a dispersion model to estimate base year and future concentrations from the identified 

local sources. 

-Place receptors at and near the monitor location(s).63 

-The receptor spacing should be fine enough to get a representative picture of the 

concentration at the monitor. 

-Model the base year and future year annual average 

-For both base and future year use the same 1 year of meteorological data – preferably 

the same year as used for the chemical transport modeling (if available, use more than 1 

year of meteorological data). 

(5) From the modeling in step (4), calculate quarterly RRFs64 for total primary PM2.5 modeled 

for the selected sources (using concentration values for receptors located at monitors). 

(6) Multiply the primary PM2.5 identified in step (3) by the RRFs from step (5). 

(7) Subtract the result in step (6) from the primary PM2.5 identified in step (3) 

(8) For each quarter, subtract the amount in step (7) from the future year quarterly DV 

obtained from the chemical transport modeling. 

(9) Average the four quarters to get an annual average DV. 

 

63Additional receptors should be placed near monitors to examine predicted concentration gradients. 

Receptors should only be located in areas that are appropriate for placing FRM monitors for comparison

to the annual NAAQS (see EPA, 1997). 

64RRFs should be calculated as a ratio of the base and future mean concentrations (a ratio of the means,

not a mean of the daily ratios). 
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Annual NAAQS Example: 

Based on ambient monitoring data, for quarter 4, a monitor has a 5 year weighted mean 

concentration of 17.0 μg/m3. 

(1) Based on emissions and ambient data analysis, it has been determined that several steel 

mills in the area are contributing to the local component of PM2.5 at a violating monitor. 

(2) Based on comparisons to other monitors in the area, it is believed that the local PM2.5

component at the monitor for quarter 4 is 2 μg/m3. 

(3) Using receptor modeling, it is determined that, on average, the steel mills are contributing a 

local PM2.5 component in quarter 4 of approximately 1 μg/m3. 

(4) Modeling with a dispersion model shows the following contribution at the monitor from the 

identified local sources (steel mills): 

Base year: 2.0 μg/m3 

Future year: 1.5 μg/m3 

(5) Quarter 4 RRF local = 1.5/2.0 = 0.750. 

(6) 1 μg/m3 * 0.750 = 0.75 μg/m3. 

(7) 0.75 μg/m3 - 1 μg/m3 = -0.25 μg/m3 (total Q4 reduction from identified local sources is 0.25 

μg/m3) 

(8) Quarter 4 New DV = 17.0 – 0.25 μg/m3 = 16.75 μg/m3 

(9) Steps 1-8 are repeated for each quarter with similar results. The annual DV = (Q1 New DV + 

Q2 New DV + Q3 New DV + Q4 New DV)/4 

4.6.2.2 Methodology for the 24-Hour NAAQS 

The suggested steps outlined below assume that the attainment test has been performed using 

chemical transport modeling and that the quarterly DV’s have been computed as discussed in 
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Section 4.5. As with the annual design value, monitoring data for 5 years are used as the basis 

for the projection. 

(1) Using emissions and/or data analyses described above, determine the local primary PM2.5

sources that are thought to be contributing to the monitor for each of the 4 quarters.65 

(2) For each of the four quarters, determine the primary versus secondary components of PM2.5

at the monitor on the high observed PM2.5 days (e.g. the high 10% of monitored days in each 

quarter) 

(3) Identify the percentage of total primary PM2.5 at the monitor due to the local sources 

identified in step (1). Convert the percentage to an estimated concentration in μg/m3. 

(4) Model with a dispersion model the base year and future concentrations of these local 

sources using emissions that are representative of emissions on high PM2.5 days. 

-Place receptors at and near the monitor location(s).66

 

-The receptor spacing should be fine enough to get an accurate picture of the 

concentration at the monitor. 

-Model the base and future year. 

-For both base and future year use the same 1 year of meteorological data used for the 

chemical transport modeling (if available, use more than 1 year of met data). 

-Calculate the 24-hour average concentration for each day. Average the high days67

within each quarter to determine a modeled high concentration for each quarter in the 

 

65The selection of these sources should be representative of the 5-year base period used to calculate the 

5 year weighted average design values. If large emissions changes have occurred over the 5 year period 

then the identification of the sources contributing to the monitor should be most influenced by the base 

modeling year (the middle year of the 5 year period) since it is the year being modeled and it has the

strongest weighting in the design value calculations. 

66Additional receptors should be placed near monitors to examine predicted concentration gradients. 

Receptors should only be located in areas that are appropriate for placing FRM monitors for comparison

to the 24-hour NAAQS (see EPA, 1997). 

67High days may be all days > 35 μg/m3 or the high end of the distribution of modeled days (i.e. top 10%

days). 
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base and future year. 

(5) From the modeling in step (4), calculate quarterly “high days” RRFs68 for total primary PM2.5

modeled for the selected sources. 

(6) Multiply the primary PM2.5 identified in step (3) by the RRFs from step (5). 

(7) Subtract the result in step (6) from the primary PM2.5 identified in step (3) 

(8) For each of the 5 years used to calculate the 5 year weighted average DV, subtract the result 

from step (7) from each of the 8 high days per quarter, for each year. Re-sort the 32 high days 

per year to get the new 98th percentile value for each year. Repeat for all 5 years of ambient 

data. 

(9) Average the annual 98th percentile concentrations to get 3 DVs and then average the 3 DVs 

to get the final future year 5 year weighted average DV. 

24 Hour NAAQS Example: 

The base year design values are calculated from the 5 year base period centered around the 

base emissions year. The dispersion model is run with emissions and meteorology for year 3 

(the center year of the 5 year period). 

(1) It is believed that several steel mills in the area are contributing to the local component of 

PM2.5 at the monitor. 

(2) Based on a comparison to other monitors in the area, it is believed that the local primary 

component of PM2.5 at the monitor on high PM days is the following for each of the quarters in 

Year 3: 

Quarter 1: 15 μg/m3 

Quarter 2: 13 μg/m3 

 

68RRFs should be calculated as a ratio of the base and future mean concentrations (a ratio of the means,

not a mean of the daily ratios). 
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Quarter 3: 8 μg/m3 

Quarter 4: 20 μg/m3 

It is believed that this is representative of the other four years within the 5-year period. 

(3) Using receptor modeling, it is determined that the steel mills are contributing a local 

PM2.5 component (on high PM days) of the following for each of the quarters in Year 3: 

Quarter 1: 8 μg/m3 

Quarter 2: 4 μg/m3 

Quarter 3: 4 μg/m3 

Quarter 4: 5 μg/m3 

It is believed that this is representative of the other four years within the 5-year period. 

(4) A dispersion model was run for a base year and a future year. For each quarter, compute 

the 24-hr average modeled concentration for each day and compute the mean of the high days 

for each quarter to get the base and future year concentrations. The dispersion modeling 

shows the following contribution at the monitor (on high days) from the identified local sources 

(steel mills) for total primary PM2.5 in each of the quarters in Year 3: 

Quarter 1: Base: 11.7 μg/m3 Future: 8.3 μg/m3 

Quarter 2: Base: 6.3 μg/m3 Future: 4.4 μg/m3 

Quarter 3: Base: 5.3 μg/m3 Future: 3.1 μg/m3 

Quarter 4: Base: 8.3 μg/m3 Future: 6.2 μg/m3 

(5) Based on the results from the modeling in (4), the RRF values are: 

Quarter 1: 8.3/11.7 = 0.709 

Quarter 2: 4.4/6.3 = 0.698 

Quarter 3: 3.1/5.3 = 0.585 

Quarter 4: 6.2/8.3 = 0.747 

(6) Multiplying the primary PM2.5 identified in (3) by the RRFs from (5). 

Quarter 1: 8 μg/m3 * 0.709 = 5.7 μg/m3 

Quarter 2: 4 μg/m3 * 0.698 = 2.8 μg/m3 

Quarter 3: 4 μg/m3 * 0.585 = 2.3 μg/m3 

Quarter 4: 5 μg/m3 * 0.747 = 3.7 μg/m3 
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(7) Subtracting the result in (6) from the primary PM2.5 identified in (3) 

Quarter 1: 5.7 μg/m3 - 8 μg/m3 = -2.3 μg/m3 

Quarter 2: 2.8 μg/m3 - 4 μg/m3 = -1.2 μg/m3 

Quarter 3: 2.3 μg/m3 - 4 μg/m3 = -1.7 μg/m3 

Quarter 4: 3.7 μg/m3 - 5 μg/m3 = -1.3 μg/m3 

(8) The results from (7) are subtracted from the future year concentration for each of the 8 days 

per quarter (32 days per year) for each of the 5 years. Re-sort the 32 high days per year to get 

the new 98th percentile value for each year. Repeat for all 5 years of ambient data. 

Below is an example for the year 1 ambient data: 

 

Quarter

Future year Concentration

(based on ambient data 

projected to future year) 

(μg/m3)

Modeled reduction 

from dispersion 

model (μg/m3)

Adjusted future year 

concentration 

(μg/m3) 

1 45.57 -2.3 43.27 

1 44.3 -2.3 42 

1 39.58 -2.3 37.28 

1 35.86 -2.3 33.56 

1 34.87 -2.3 32.57 

1 32.87 -2.3 30.57 

1 31.87 -2.3 29.57 

1 31.6 -2.3 29.3 

Quarter

Future year 

Concentration (μg/m3) 

Modeled reduction 

from dispersion 

model (μg/m3) 

Adjusted future year 

concentration 

(μg/m3) 

2 14.06 -1.2 12.86 

2 12.8 -1.2 11.6 

2 12.51 -1.2 11.31 

2 11.93 -1.2 10.73 

2 10.86 -1.2 9.66 

2 10.67 -1.2 9.47 

2 10.18 -1.2 8.98 

2 10.09 -1.2 8.89 
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Quarter

Future year 

Concentration (μg/m3) 

Modeled reduction 

from dispersion 

model (μg/m3)

Adjusted future year 

concentration 

(μg/m3)

3 37.92 -1.7 36.22

3 22.39 -1.7 20.69

3 21.92 -1.7 20.22

3 18.93 -1.7 17.23

3 16.12 -1.7 14.42

3 15.93 -1.7 14.23

3 15.28 -1.7 13.58

3 15 -1.7 13.3

Quarter

Future year 

Concentration (μg/m3) 

Modeled reduction 

from dispersion 

model (μg/m3) 

Adjusted future year 

concentration 

(μg/m3) 

4 52.79 -1.3 51.49 

4 50.8 -1.3 49.5 

4 45.73 -1.3 44.43 

4 44.92 -1.3 43.62 

4 44.65 -1.3 43.35 

4 42.66 -1.3 41.36 

4 42.21 -1.3 40.91 

4 41.57 -1.3 40.27 

The year one 98th percentile value for the monitoring site is the 3rd high value. Therefore the 

adjusted “new” 98th percentile value for year 1 is 44.43 μg/m3 (the 3rd highest adjusted future 

high day out of the 32 days for year 1). 

The same calculations are completed for the other 4 years of data with the following result: 

Year 1: 44.43 

Year 2: 47.34 

Year 3: 50.22 

Year 4: 41.68 

Year 5: 49.77 
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(9) Averaging the 3 design values: 

DV1 = (44.43 + 47.34 + 50.22)/3 = 47.3 

DV2 = (47.34 + 50.22 + 41.68)/3 = 46.4 

DV3 = (50.22 + 41.68 + 49.77)/3 = 47.2 

Averaging the 3 DV to get the final future year 5 year weighted average DV: 

(47.3 + 46.4 + 47.2)/3 = 46.9 = 47 μg/m3

In the above examples, emissions reductions from steel mills were modeled with a dispersion 

model, which provided additional reductions to the future 5 year weighted average annual and 

24-hour design values. Changes in DVs derived from chemical transport modeling should be 

examined to quantify the impact of emissions changes from the flagged steel mills. The change 

in concentration in the chemical transport model from steel mill emissions controls may need 

to be adjusted in order to ensure that there is no double counting of emission reductions (see 

below). 

4.6.2.3 Double Counting

The methodology discussed above may result in double counting of local emissions sources and 

reductions. The change in emissions from local sources is accounted for in the chemical 

transport modeling and again in the dispersion modeling. There are several ways to examine 

this issue. 

1) Run the chemical transport model with the primary PM2.5 emissions from the flagged 

source(s) in the base case and hold the emissions from the flagged source(s) constant in 

the future case(s). 

2) Run the grid model with a source tagging approach. The flagged sources can be tagged 

so that their contribution in the grid model can be explicitly tracked (Environ, 2006), 

(Environ, 2014). 

3) Employ a methodology to combine the results of the grid and dispersion models so that 

double counting is eliminated or minimized. Several techniques have been developed 

to do this (Isakov, 2007). 

The potential for double counting can be evaluated by considering the grid resolution of the 

chemical transport model and the number of flagged sources in the dispersion model. The finer 
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the resolution of the grid model, the more double counting will be a problem (because the local 

sources will be more finely resolved). Additionally, the more sources that are flagged, the more 

double counting will be a problem. If the grid cell size is relatively large (e.g. 12km) and the 

primary emissions are relatively small, it may be possible to assume that double counting is 

small enough to be ignored. But the nature of the issue should be evaluated on a case by case 

basis. 

The simplest way to evaluate the magnitude of the problem is to run the chemical transport 

model with and without the flagged sources from the dispersion model. This will indicate the 

maximum impact that the sources can have on the chemical transport modeling results. A very 

small impact means that double counting is not a problem. A large impact indicates that a 

more explicit analysis is needed in order to resolve double counting issues. 

4.6.2.4 Analysis of Primary PM2.5 at Monitors- Quality Assurance

As with any modeling exercise, it is important to quality assure the model inputs and outputs. 

In particular, we recommend focused quality assurance checks on emissions from sources 

flagged for a dispersion modeling analysis. Prior to applying a model, air agencies should 

review available information to ensure that there are no major discrepancies between modeled 

estimates and nearby monitored data for particulate matter. The emission factors, activity 

data, and speciation profiles of the PM2.5 emissions should also be analyzed for accuracy. If a 

speciation monitor exists, the speciated data from the monitor can be compared to the 

speciation profiles of the flagged sources. Receptor models can also be used as a QA tool. 

Discrepancies between receptor modeling results (which are based on analyzing ambient data) 

and speciated emissions may indicate a problem with the magnitude and/or the speciation of 

the emissions sources. If discrepancies are found, those implementing the modeling protocol 

(see section 2.1) should consult with the appropriate EPA regional office to reach agreement on 

what, if any, adjustments should be made to the emissions estimates for the identified sources. 

It is also important to quality assure the model outputs. Modeling each species of primary 

PM2.5 separately within the dispersion model should aid in this analysis, especially if the 

selected control strategies applied in the future year do not control each primary species to the 

same degree. If a speciation monitor exists, the speciated data from the monitor may also help 

quality assure the dispersion model results. However, the application of the dispersion model 
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results in a relative sense will help to reduce the impact of possible over- or under-estimations 

by the dispersion model due to emissions, meteorology, or general selection of other model 

input parameters. 

4.7 Estimating design values at unmonitored locations 

High ozone and/or PM2.5 concentrations can occur at (or near) existing monitors or in 

unmonitored areas. The modeled attainment test is primarily a monitor based test. As such, 

the focus of the attainment test is whether attainment can be reached at existing monitors. 

An additional “unmonitored area analysis” can also be performed to examine ozone and/or 

PM2.5 concentrations in unmonitored areas. The unmonitored area analysis is intended to be a 

means for identifying high ozone and/or PM2.5 concentrations outside of traditionally 

monitored locations, particularly in nonattainment areas where modeling or other data 

analyses have indicated potential high concentration areas of ozone and/or PM2.5 outside of the 

existing monitoring network. An unmonitored area analysis may also be more important in 

areas where the ozone or PM2.5 monitoring networks just meet or minimally exceed the 

minimum required network in a nonattainment area. 

This review can help ensure that a control strategy leads to reductions in ozone or PM2.5 at 

other locations which could have base case (and future) design values exceeding the NAAQS 

were a monitor deployed there. This document describes how an unmonitored area analysis 

can be performed for a particular area. See the respective ozone and PM2.5 implementation 

rules (including the preamble and response to comments) for more information on when an 

unmonitored area analysis is recommended or required. 

We use the term “unmonitored area analysis” to describe an analysis that uses a combination 

of model output and ambient data to identify areas that might exceed the NAAQS in areas that 

not currently monitored. The unmonitored area analysis for a particular nonattainment area is 

intended to address the potential for air quality problems within or near that nonattainment 

area. The analysis should include, at a minimum, all nonattainment counties and counties 

surrounding the nonattainment area, as appropriate. It is possible that unmonitored area 

violations may appear in counties far upwind or downwind of the local area of interest. In 

those cases, the distance to the nonattainment area and ability of the modeling to represent far 
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downwind areas should be evaluated on a case-by-case basis. 

The spatial resolution of the unmonitored area analysis should be consistent with the 

appropriate model resolution for the attainment demonstration. For example, if the 

attainment demonstration for ozone or PM2.5 is conducted at 12km grid resolution, the 

unmonitored area analysis should also be performed at the same horizontal resolution. [Note: 

When the PM2.5 implementation rule for the 2012 NAAQS is finalized, this draft guidance will be 

updated to reflect additional recommendations and/or requirements for near-road PM2.5 

modeling]. 

4.7.1 Why does the unmonitored area analysis need to use both ambient data 

and model output? 

When performing an unmonitored area analysis, it is recommended to use interpolated spatial 

fields of ambient data in combination with gridded modeled outputs (i.e. fused surfaces). 

Ambient data can be interpolated to provide a set of spatial fields. The resultant spatial fields 

will provide an indication of pollutant concentrations in monitored and unmonitored areas. But 

interpolated ambient data cannot identify unmonitored areas with higher concentrations than 

those measured at monitors. The interpolated concentration between monitors will generally 

be the same or lower than the measured concentration at the monitors (assuming that more 

sophisticated statistical techniques are not used). The interpolation technique does not 

account for emissions or chemistry information that may be needed to identify high 

concentrations in unmonitored areas. 

In addition to interpolated surfaces, the gridded model output (absolute) concentrations can be 

used to examine unmonitored area concentrations. The model provides an hourly 

concentration for every grid cell. The concentrations can be analyzed to determine 

unmonitored areas where the model predicts high values. But the absolute predictions from 

the model may not be entirely accurate. But unlike the interpolated ambient data, the model 

explicitly accounts for emissions, chemistry, and meteorology over the entire domain. 

When considered together, interpolated ambient data and absolute model outputs both have 

strengths and weaknesses. We can take advantage of the strengths of each dataset by 

combining the two types of data. Interpolated spatial fields of ambient data provide a strong 
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basis for estimating accurate pollutant concentrations at and near monitor locations. The 

model outputs can be used to adjust the interpolated spatial fields (either up or down) so that 

more accurate estimates can be derived in the unmonitored areas.69 A recommended way to 

use the modeling in the creation of fused surfaces is to adjust the ambient spatial fields using 

the concentration gradients derived from the model predictions. It is preferable to assume that 

the model is accurately predicting areas of generally high or low ozone or PM, rather than to 

assume that the absolute predictions from the model are correct. For example, in unmonitored 

areas where the model predicts relatively high ozone or PM concentrations, the spatial fields 

can be adjusted upward. In unmonitored areas where the model predicts relatively low ozone 

or PM concentrations, the spatial fields can be adjusted downward. In this way, it is be 

possible to predict unmonitored areas that may have high concentrations. At the same time, 

concentrations in rural areas, (which may be overly influenced by high monitored 

concentrations near urban areas), may be adjusted downward. We refer to this combination 

of interpolated spatial fields and modeled output as “gradient adjusted spatial fields”

4.7.2 Implementation of Gradient Adjusted Spatial Fields 

Gradient adjusted spatial fields are first created for the base year. Future year estimates can 

then be created by applying gridded RRFs to the gradient adjusted spatial fields. The basic 

steps are as follows: 

1) Interpolate base year ambient data to create a set of spatial fields. 

2) Adjust the spatial fields using gridded model-based spatial gradients (base year values). 

3) Apply gridded model-based RRFs to the gradient adjusted spatial fields. 

4) Determine if any unmonitored areas are predicted to exceed the NAAQS in the future. 

EPA’s “Modeled Attainment Test Software” (MATS) will spatially interpolate data, adjust the 

spatial fields based on model output gradients, and multiply the fields by model calculated RRFs 

(steps 1-3 above) (ABT, 2014). Air agencies can use the EPA-provided software or are free to 

develop alternative techniques that may be appropriate for their areas or situations. Air 

Agencies should consult with the appropriate EPA regional office to determine how an 

 

69The accuracy of interpolated fields can be evaluated by removing data from monitors (either in groups 

or one at a time) to see how well the interpolation scheme estimates known concentrations at ambient 

monitoring sites. This is known as a cross validation. 
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unmonitored area analysis should be conducted (in cases where an unmonitored area analysis 

is recommended or required). 

Step 1

The first step in developing an unmonitored area analysis is to interpolate ambient data. 

Ideally, design values should be interpolated. Either the 5 year weighted average design values 

that are used in the monitor based model attainment test or a single design value period can be 

used in the development of ambient spatial fields. Care should be taken so that the 

interpolated fields are not unduly influenced by monitoring sites that do not have complete 

data. Since the design values can vary significantly from year to year, it is important to use a 

consistent set of data. 

We are not recommending a single interpolation technique. EPA’s MATS uses the Voronoi 

Neighbor Averaging (VNA) technique. Past analyses have used a Kriging interpolation 

technique (U.S.EPA, 2004). 

For ozone analyses, a single spatial field of ozone values is needed. But for the PM2.5 NAAQS, 

we recommend creating interpolated spatial fields for PM2.5 (FRM) and for each component 

species of PM2.5. For the annual PM2.5 NAAQS, a set of quarterly average spatial fields can be 

created. The 4 quarters are averaged to get an annual average set of fields. 

For the 24-hour PM2.5 standard, a spatial field can be created using the high end of the 

distribution of 24-hour PM2.5 concentrations. This is best represented by interpolating the 

measured high values from each quarter. For the PM2.5 component species, we recommend 

interpolating the high PM2.5 days in each quarter. This can be based on the highest monitored 

days in each quarter. 

Step 2

The second step in the process involves the use of gridded model output to adjust the spatial 

fields derived from ambient data. MATS uses the eVNA technique, where the basic VNA 

interpolation is “enhanced” by adjusting the interpolated values based on the modeled ozone 

or PM gradient. See the MATS user’s guide for more details (Abt, 2014). Other “model 
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adjusted” spatial fields techniques include Hierarchical-Bayesian (McMillan, 2010) and 

“Downscaler” (Berrocal, 2010a and 2010b). 

A specific metric is needed to determine the model predicted gradient in concentrations for 

each of the NAAQS. For ozone, a logical metric is the 4th highest ozone prediction in each grid 

cell.70 For the annual PM2.5 NAAQS, the model predicted quarterly mean concentrations for 

PM2.5 and PM2.5 species can be used to adjust the ambient spatial fields. For the 24-hour PM2.5

NAAQS, the gradient adjusted fields can be derived from the high end of the distribution of 

daily averages in each quarter. This could be for the top 10% of modeled days in each quarter 

or for all days with 24-hour average concentration > 35 μg/m3 (or some other relatively high 

value). 

Step 3

The next step is to create future year fields by multiplying the base year gradient adjusted 

spatial fields by model derived gridded RRFs. The RRFs for the unmonitored area analysis are 

calculated in the same way as the monitored based attainment test (except that the grid cell 

array is not used in the spatial fields based analysis). The future year concentrations are equal 

to the base year concentration multiplied by the RRF in each grid cell. For ozone, a single 

spatial field is multiplied by a single set of model derived RRFs. For PM2.5, the RRFs for each of 

the species, for each quarter, are multiplied by the spatial fields for each species, for each year. 

The future year gradient adjusted spatial fields are then analyzed to determine if any grid cells 

are predicted to remain above the NAAQS. 

4.7.3 Using the Results of the Unmonitored Area Analysis 

States should consult with their EPA regional office to determine if an unmonitored area 

analysis should be conducted for a particular nonattainment area, and how the results of an 

unmonitored area analysis should be used. Note: The guidance will be updated to include 

 

70The metric should approximate the measured design values at monitoring sites. Depending on the 

days modeled, other metrics, such as the average of the top 4 modeled days may be a better proxy for 

the design value. 
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additional information on the appropriate use of unmonitored area analysis results when final 

decisions are made in the ozone and PM2.5 implementation rules. 

4.8 What Is The Recommended Modeling Analysis for Regional Haze?

This document focuses on the modeling analysis required to assess future visibility 

improvement relative to the uniform rate of progress or “glidepath” (for each Class I area) as 

part of a reasonable progress analysis. In this section, we describe the recommended modeled 

analysis to assess visibility improvement. The visibility analysis has many similarities to the 

PM2.5 attainment tests described in sections 4.4 and 4.5. The recommended visibility analysis 

and the attainment tests both use monitored data to define base year air quality. They divide 

PM2.5 into major species, and component-specific relative response factors are multiplied by 

base year monitored values to estimate future concentrations. 

Section 4.8.1 provides background information on the requirements of the Regional Haze rule 

and defines the components of a reasonable progress analysis. The rest of the section 

describes the recommended modeling analysis that air agencies should perform as part of their 

reasonable progress analysis. 

4.8.1 Regional Haze Rule Background 

Section 169A(a)(1) of the Clean Air Act states “Congress hereby declares as a national goal the 

prevention of any future, and the remedying of any existing, impairment of visibility in 

mandatory class I Federal areas which impairment results from manmade air pollution.” 

Section 169B calls for EPA to “carry out the Administrator's regulatory responsibilities under 

[section 169A], including criteria for measuring ‘reasonable progress’ toward the national goal.” 

In response to these mandates, EPA promulgated the regional haze rule (RHR) on July 1, 1999.71

 

Under section 51.308(d)(1) of this rule, states must “establish goals (expressed in deciviews) 

that provide for reasonable progress towards achieving natural visibility conditions for each 

 

71 64 FR 35714 (codified at 40 CFR 51.300-309). 
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Class I area within a state.” These reasonable progress goals (RPGs) must provide for an 

improvement in visibility for the most impaired days over the period of the implementation 

plan and ensure no degradation in visibility for the least impaired days over the same period.72

 

RPGs, measured in deciviews, are interim goals that represent incremental visibility 

improvement over time toward Congress’s goal of natural background conditions and are 

developed in consultation with other affected air agencies and Federal Land Managers.73

 In determining what would constitute reasonable progress, section 169A(g)(1) of the Clean Air 

Act requires air agencies to consider the following four factors: 

• The costs of compliance; 

• The time necessary for compliance; 

• The energy and non-air quality environmental impacts of compliance; and 

• The remaining useful life of existing sources74

States must demonstrate in their SIPs how these factors are taken into consideration in 

selecting the RPG for each Class I area in the state. More details on the setting of RPGs and the 

appropriate use of the four factors is contained in “Guidance for Setting Reasonable Progress 

Goals Under the Regional Haze Program” (U.S. EPA, 2007b). 

As explained in the RHR, the baseline for each Class I area is the average visibility (in deciviews) 

on the 20% most impaired days, or “worst days”, and on the 20% least impaired days, or “best 

days,” for the years 2000 through 2004.75

 

The regional haze rule also establishes an additional analytical requirement in the process of 

establishing RPG. States are required to determine the rate of improvement in visibility needed 

 

72 40 CFR 51.308(d)(1). 

73 40 CFR 51.308(d)(1)(iv) and 51.308(i). 

74 42 U.S.C. § 7491(g)(1); 40 CFR 51.308(d)(1)(i)(A). 

75 64 FR at 35730. 
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to reach natural conditions by 2064, and to set each RPG taking this “glidepath” into account.76 

The glidepath is the amount of visibility improvement needed in each review period to stay on a 

linear path towards visibility improvement to natural conditions by 2064. The glidepath 

represents a linear or uniform rate of progress. Therefore, the amount of visibility 

improvement needed to stay on the glidepath is also referred to as “uniform rate of progress”. 

The terms “glidepath” and “uniform rate of progress” are used interchangeably in this 

document. 

This section does not address the process of calculating the glidepath or setting and evaluating 

RPGs.77 The determination of reasonable progress goals is a multi-step process that assesses 

the uniform rate of progress, the predicted rate of progress, and the four factor test. The RPGs 

are determined by air agencies for each Class I area by considering the factors and the modeling 

analysis. The results of the modeling analyses will be one of several components in the 

determination of RPGs. 

4.8.2 How is Regional Haze Measured? 

Regional haze is measured by an extinction coefficient (bext) which represents light attenuation 

resulting from scattering and absorption of light from ambient PM plus scattering of light due 

to gas molecules in the air (i.e., Rayleigh scattering). Although bext can be estimated by several 

different methodologies, the RHR requires that it be estimated using measured ambient PM. 

This follows since, for a given set of meteorological conditions, visibility can be improved by 

reducing concentrations of ambient PM. Thus, deriving bext in this manner provides a direct link 

between regional haze and related pollutant concentrations. Equation (4.11) is applied for each 

Class I area to estimate bext (IMPROVE, 2000). The equation reflects empirical relationships 

derived between measured mass of PM components and transmissometer measurements of 

bext at monitoring sites in Class I areas within the IMPROVE monitoring network. 

bext = 3(f(rh))[SO4] + 3(f(rh))[NO3] + 4(f’(rh))[OC] + 10[EC] + 1[Fine Soil] + 0.6[CM] + brayleigh 

[4.11]

 

76 40 CFR 51.308(d)(1)(i)(B). 

77 For more details on calculating the glidepath and setting reasonable progress goals see (U.S. EPA 

2007b, U.S. EPA 2003b and U.S. EPA 2003c). 
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where the numerical coefficients on the right hand side of the equation represent the light 

scattering or absorption efficiency, m2/gm of the corresponding component of particulate 

matter, 

f(rh), f’(rh) are relative humidity adjustment factors applied to the light scattering efficiency (to 

be described in greater detail shortly), dimensionless, 

SO4 is the mass associated with sulfates, μg/m3,

NO3 is the mass associated with nitrates, μg/m3

, 

OC is the mass associated with organic carbon, μg/m3, 

EC is the mass associated with elemental carbon, μg/m3, 

Fine Soil is inorganic primary particulate matter (excluding primary sulfate and nitrate particles) 

associated with soil components with aerodynamic diameter < 2.5 um, μg/m3, 

CM is coarse PM with aerodynamic diameter > 2.5 um, but < 10 um, μg/m3, 

brayleigh is light-scattering attributable to Rayleigh scattering, Mm-1 (i.e., inverse “megameters”), assumed to be 10 Mm-1

. 

bext is the estimated extinction coefficient, Mm-1

. 

4.8.2.1 “Revised” IMPROVE Algorithm

The IMPROVE program revised the IMPROVE algorithm in 2006 (Hand, 2006); (Pitchford, 2007). 

The revised algorithm is intended to reduce biases in light extinction estimates compared to the 

old algorithm. The revised algorithm, which is somewhat more complicated than the old 

algorithm, is as follows: 

bext ≈ 2.2 x fs(RH) x [Small Sulfate] + 4.8 x fL(RH) x [Large Sulfate] 

+ 2.4 x fs(RH) x [Small Nitrate] + 5.1 x fL(RH) x [Large Nitrate] 
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+ 2.8 x {Small Organic Mass] + 6.1 x [Large Organic Mass] 

+ 10 x [Elemental Carbon] 

+ 1 x [Fine Soil] 

+ 1.7 x fss(RH) x [Sea Salt] 

+ 0.6 x [Coarse Mass] 

+ Rayleigh Scattering (site specific) 

+ 0.33 x [NO2 (ppb)] [4.12]

The total sulfate, nitrate and organic carbon compound concentrations are each split into two 

fractions, representing small and large size distributions of those components. The organic 

mass concentration used in the new algorithm is 1.8 times the organic carbon mass 

concentration (changed from 1.4 times carbon mass concentration used for input into the old 

IMPROVE algorithm). New terms have been added for sea salt (important for coastal locations) 

and for absorption by NO2 (only used where NO2 data are available). Site-specific Rayleigh 

scattering is calculated based on the elevation and annual average temperature of each 

IMPROVE monitoring site. 

The apportionment of the total concentration of sulfate compounds into the concentrations of 

the small and large size fractions is accomplished using the following equations. 

[Large Sulfate] = [Total Sulfate]/20 μg/m3 x [Total Sulfate], for [Total Sulfate] < 20 μg/m3 

[Large Sulfate] = [Total Sulfate], for [Total Sulfate] ≥ 20 μg/m3 

[Small Sulfate] = [Total Sulfate] - [Large Sulfate]

The same equations are used to apportion total nitrate and total organic mass concentrations 

into the small and large size fractions. 

Sea salt is calculated as 1.8 x [Chloride] or 1.8 x [Chlorine] if the chloride measurement is below 

detection limits, missing or invalid. The algorithm also uses three new water growth 

adjustment terms. They are for use with the small size distribution and the large size 

distribution sulfate and nitrate compounds and for sea salt (fS(RH), fL(RH) and fSS(RH) 

respectively). 
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Testing of the revised algorithm reveals that, in most regions of the country, the revised 

equation gives somewhat higher light extinction, especially on the worst visibility days. But 

compared to the old equation, the relative percentage of extinction attributed to the various 

PM components is nearly unchanged (Hand, 206). Therefore, the revised equation is not 

expected to make a large difference in the rate of visibility changes resulting from potential 

control strategy simulations. However, the revised equation could lead to differences in the 

relative change in light extinction predicted by the models. As of 2014, the IMPROVE program 

has stopped reporting visibility data using the old IMPROVE equation. Therefore, due to the 

improvements associated with the revised equation and unavailability of routine calculations 

using the old equation, we recommend using the revised IMPROVE equation for all reasonable 

progress related modeling calculations. If states want to use the old equation or a variation of 

the revised equation, they should provide documentation concerning the reasons why an 

alternative is more appropriate than the “revised” IMPROVE calculation. 

4.8.2.2 Deciview Haze Index

The RHR stipulates that reasonable progress is to be measured in terms of changes in a 

deciview haze index or simply “deciviews”(dv). Deciviews are defined as the natural logarithm 

of the ratio of the extinction coefficient to Rayleigh scattering (Pitchford and Malm, 1994) 

Deciview = 10 ln(bext/10) [4.13] 

Where the units of bext and light scattering due to Rayleigh scattering78 (i.e., the “10" in the 

denominator of the logarithmic expression) are both expressed in Mm-1. 

A change in deciviews, which is how progress is tracked is given by Equation (4.14). A one 

deciview change is equivalent to ~10% change in bext. 

 

78Even though the “revised” IMPROVE equation uses a site-specific Rayleigh scattering value, the 

denominator in the deciview equation should always be 10. In this way, the deciview calculation will be 

consistent across all Class I areas. Under very clean conditions, when the site-specific Ralyeigh 

scattering is less than 10, the deciview value can be negative, but this should not be considered a 

problem. 
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Δdv = dvfuture - dvbaseline [4.14] 

A negative number indicates a reduction in deciviews, which is an improvement in visibility. 

4.8.2.3 Estimating Mass Associated With Components of Particulate Matter

All Class I areas either have on-site speciated PM2.5 measurements or have been assigned a 

representative IMPROVE monitoring site with measurements.79 Therefore, it is generally not 

necessary to interpolate measured PM2.5 species data to a site. The existing IMPROVE database 

of PM2.5 measurements should be adequate to provide data for all Class I areas.80

 

FRM data is not used in the regional haze analysis. Therefore, it is not necessary for the sum of 

species components to equal gravimetric measurements obtained with a Federal Reference or 

equivalent method for measuring PM2.5. Therefore, for regional haze calculations, it is not 

necessary to use the speciated data adjustment procedures introduced in Section 4.4 (the 

“SANDWICH” adjustments). 

Hand (2006) has developed a set of default assumptions for mass associated with each of the 

components of PM for Equation (4.12). These are presented in Table 4.8.1. The components in 

Table 4.8.1 are similar to those used in the modeled attainment demonstrations for the PM2.5

NAAQS. Notice however, that sulfate and nitrate mass are assumed to be fully neutralized 

(therefore, ammonium is not needed as a separate component); organic carbon is assumed to 

be equal to 1.8 times measured OC mass (1.4 times OC in the old IMPROVE equation); there is 

no water component; and there is a term for coarse PM. 

 

79See U.S. EPA 2003b, Appendix A, Table A-2. 

80There are some IMPROVE sites that don’t have enough complete data to provide baseline condition 

information for the 2000-2004 period. It may be necessary to substitute data from other monitoring 

networks or to interpolate data to the site(s). Data substitution is discussed in (U.S. EPA 2007b). 
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Table 4.8.1. Default Assumptions Used To Derive Aerosol Species Concentrations for 

Estimating Extinction Coefficients 

(1) 

Species 

(2) 

Formula 

(3) 

Assumptions 

Sulfate 1.375 *measured SO4 Sulfate is assumed to be fully 

neutralized (ammonium sulfate) 

Nitrate 1.29 * measured nitrate Denuder efficiency is ~100% & all 

nitrate is from ammonium nitrate 

EC high + low temperature EC All high temperature carbon is 

elemental 

OC 1.8 * organic carbon (or 1.4 * OC 

for the old IMPROVE equation) 

Average organic molecule is 56% 

carbon 

Fine Soil 2.2*Al + 2.49*Si + 1.63*Ca + 

2.42*Fe + 1.94*Ti 

(Soil K)=0.6(Fe), FeO & Fe2O3 are 

equally abundant, a factor of 1.16 is 

used for MgO, Na2O, H2O & CO3

Sea Salt 1.8*ClSea salt is 55% chloride ion by 

weight 

PM2.5

-Measured gravimetrically 

-Represents dry ambient fine 

aerosol mass for continental sites 

CM 

 (coarse mass) 

(PM10) - (PM2.5) Consists only of insoluble soil 

particles 

Throughout this section we make every attempt to be consistent with the “Guidance for 

Tracking Progress under the Regional Haze Program” (hereafter referred to as the “Tracking 
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Guidance”) (U.S. EPA, 2003b). It will be easier to interpret the modeling results and compare 

them to future measured values if the data are calculated in a similar manner. 

4.8.2.4 Normalizing trends in regional haze- f(rh) factors

It is clear from equations 4.11 and 4.12 that relative humidity can have a substantial effect on 

estimated extinction coefficients, as well as the relative importance of changes in different 

components of PM on trends in regional haze. Because of the importance of relative humidity 

as a determinant of regional haze, it is necessary to normalize any apparent trend in the 

estimated extinction coefficient for differences in relative humidity. This enables an 

assessment of whether an emissions control strategy will reduce regional haze, without 

confounding effects of different relative humidity during the base and future periods. 

There are two possible approaches to normalize trends in visibility for changes in relative 

humidity. The first is to assume that the same day to day changes in relative humidity which 

were observed in the base period calculations will occur in future years. Thus, one would use 

the relative humidity observations made on a specific day together with measured components 

of particulate matter on that day to compute the day specific visibility extinction coefficient on 

that day. However, the approach could lead to misleading conclusions if humidity observations 

were missing for some days or if the humidity observations are atypical in some way. Further, if 

an air agency wanted to perform visibility calculations in a number of Class I areas, they would 

need to obtain hourly relative humidity data for each area. 

The second approach is to review relative humidity data over a long period of record to derive 

climatological estimates for relative humidity adjustment factors. These climatological 

estimates would then be used in Equations 4.11 and/or 4.12 to estimate visibility extinction 

coefficients. These estimates are more likely to reflect “typical” relative humidity at different 

times of year and, thus, expected future visibility extinction. 

The need to use climatological f(rh) data for tracking progress of measured data is obvious. The 

measured relative humidity on the 20% worst days in a baseline period will be different than 

the measured relative humidity in a future period. Therefore the only way to normalize for 

relative humidity between the base years and the future is to use a single climatological value. 

However, in the context of modeling, there is a choice. Either climatological data or modeled 

(or measured) relative humidity data from a meteorological model can be used. Since the 
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meteorological data is held constant when predicting future year air quality, the modeled 

relative humidity values would also be constant. This would be one way to normalize the data. 

But since the Tracking Guidance recommends tracking visibility using climatological f(rh) values, 

it will be easier to interpret the modeling results and compare them to future measured values 

if the data are calculated in a similar manner. Therefore, we recommend using climatological 

f(rh) values to calculate baseline and future visibility. Appendix A (Table A-1) from the Tracking 

Guidance displays the relationship between relative humidity and f(rh) values. The f(rh) values 

were calculated from data reported by Tang (1996). The f(rh) values are 1.00 up to 36% relative 

humidity and grow to a maximum value of 7.40 at a relative humidity of 95%.81 The Tracking 

Guidance contains monthly climatological average f(rh) values for each Class I area. These 

values were calculated using 10 years of meteorological data. 

For calculations with the revised IMPROVE equation, three separate f(rh) curves are needed. 

These can be found in (Hand, 2006). The monthly f(rh) values for both the old and revised 

IMPROVE equations have been incorporated into pre-calculated daily extinction and deciview 

data that can be found on the IMPROVE website at: 

http://vista.cira.colostate.edu/improve/Data/IMPROVE/summary_data.htm . The use of precalculated f(rh) and visibility data makes it more simple for air agencies and regional 

organizations to calculate visibility changes and will also provide for more consistency. 

4.8.3 How Do I Apply A Model To Calculate Changes in Visibility? 

The purpose of a regional haze modeling assessment is to project future visibility to determine 

progress relative to the glidepath. In the procedures below, the observed visibility data should 

be linked to the base modeling year. Similar to the ozone and PM2.5 attainment tests, the 5 

year ambient data period should be centered about the base modeling year. For example, for a 

base modeling year of 2011, the ambient IMPROVE data should be from the 2009-2013 

period.82 However, unlike the ozone and PM2.5 attainment tests, the calculation is a 5 year 

 

81For the regional haze calculations, the f(rh) factors have been capped at the f(rh) value associated with 

95% relative humidity. Relative humidity value above 95% use the same value. 

82 The base period for the regional haze program continues to be 2000-2004, and the uniform rate of 

progress is calculated using that historical data. However, the modeled reasonable progress visibility 

projections should use ambient data that corresponds to the modeled base year meteorological and 

emissions data. 
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mean where each year counts equally (unlike the 5-year weighted average values for the ozone 

and PM2.5 attainment test). This is consistent with the regional haze rule ambient data 

calculations for the 20% best and worst days. The analysis we recommend has 6 steps. 

1) For each Class I area, rank observed visibility (in deciviews) on each day using observed 

speciated PM2.5 data plus PM10 data for each of the 5 years comprising the base period. 

2) For each of the 5 years comprising the base period, calculate the mean deciviews for the 

20% of days with worst and 20% of days with best visibility. For each Class I area, 

calculate the 5 year mean deciviews for worst and best days from the 5 year-specific 

values. 

3) Use an air quality model to simulate base period emissions and future emissions. Use 

the resulting information to develop relative response factors for each component of 

PM identified on the right hand side of Equation (4.12). 

4) Multiply the relative response factors by the measured species concentration data 

during the base period (for the measured 20% best and worst days). This results in daily 

future year species concentrations data. 

5) Using the results in Step 4 and the IMPROVE algorithm (equation 4.12), calculate the 

future daily extinction coefficients for the 20% best and worst visibility days in each of 

the five base years. 

6) Calculate daily deciview values (from total daily extinction) and then compute the future 

average mean deciviews for the worst and best days for each year. Average the five 

years together to get the final future mean deciview values for the worst and best days. 

We describe each of these steps more fully below. The methodology follows the same basic 

procedures outlined in the Tracking Guidance. Further details (for steps 1 and 2) can be found 

in that document. We conclude this subsection with an example illustrating the recommended 

modeled uniform rate of progress analysis. 
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Step 1. Using monitored data, rank baseline visibility for each day with PM10, PM2.5 and 

speciated PM2.5 measurements within a Class I area 

Ranking should be performed separately for each of the 5 years comprising the base period.83 

The deciview (dv) should serve as the basis for ranking. Day-specific observations for mass 

associated with SO4, NO3, OC, EC, soil, and CM, as defined in Table 4.7 or 4.8, should be used to 

calculate bext for each day. The appropriate month- and area-specific climatological relative 

humidity adjustment factor(s) (f(rh)) should be used. Total bext for all components should be 

converted to deciviews for each day to get a daily deciview value. 

Step 2. Calculate the average baseline deciviews for the 20% of days with worst and the 20% 

days with best visibility. 

For each of the 5 years in the base period, order all days considered in Step 1 from worst 

(highest deciview value) to best (lowest deciview value) visibility. For each year, note the 20% 

of days with worst and the 20% of days with best visibility. Calculate the arithmetic mean 

deciview value for the identified 20% worst- and best visibility days in each year. Average the 

resulting 5 yearly mean deciview values reflecting worst visibility. This represents the value 

subject to improvement (i.e., reduction) to meet the glidepath for regional haze. Average the 5 

yearly mean deciview values reflecting mean visibility on the days with best visibility. 

Step 3. Estimate relative response factors (RRF) for each component of PM2.5 and for CM. 

The RRFs should be determined using results from air quality modeling of base year and future 

year emissions. These model simulations should be performed for a large number of days.84 

The (temporal) arithmetic mean concentration for each PM2.5 component (and coarse mass) 

computed near the Class I monitoring site from the future year modeling is divided by the 

 

83Pre-calculated and ranked extinction and deciview calculations for all Class I areas is available on the 

IMPROVE website at 

http://vista.cira.colostate.edu/improve/Data/IMPROVE/summary_data.htm. This data can be 

used to satisfy steps 1 and 2. 

84How many and which days to simulate is discussed in Section 2.3.1. 
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corresponding arithmetic mean concentration for each component from the base year 

modeling. The resulting ratios are the component-specific RRF’s. A separate set of RRF values 

are calculated for the worst and best visibility days identified in step 2. The RRFs are calculated 

using the identified 20% best and 20% worst visibility monitored days at each Class I area. This 

will likely be a different set of days at each monitor. 

Step 4. Using the RRFs and ambient data, calculate future year daily concentration data for the 

best and worst days. 

Multiply the relative response factors derived in Step 3 by measured daily concentration data 

for each component of PM2.5 and CM to get future daily estimates of species concentrations for 

PM2.5 components and CM on worst visibility and best visibility days. These multiplications 

produce future concentration estimates for SO4, NO3, OC, EC, Soil and CM for each of the 

previously selected worst and best visibility days. This calculation is performed for each best 

and worst day for the five year period using an RRF for each PM component (a separate set of 

RRFs for the best days and the worst days). 

Step 5. Use the information developed in Step 4 to compute future year daily bext values for the 

best and worst days. 

Use the future year concentration data calculated in step 4 to calculate future year daily bext

values for each PM component for each of the best and worst days for the five year period. 

This is accomplished by applying the IMPROVE visibility algorithm (equations 4.11 and 4.12). 

Step 6. Use the daily total bext values from step 5 to calculate future mean deciview values for 

the best and worst days. 

The total daily bext for each day is converted to deciviews. This gives a future year daily 

deciview value for each of the best and worst days. 

Next, compute the arithmetic mean future deciview value for the worst and best visibility days 

for each year. This leads to 5 future estimated mean deciview values for the worst and 5 future 

estimated mean deciview values for the best visibility days. Compute the arithmetic mean of 

the 5 mean values for deciviews on the worst days, and the arithmetic mean of the 5 mean 
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deciview values estimated for the best visibility days. 

The resulting 5 year average mean values for deciviews on worst and best visibility days can be 

compared to the baseline mean deciview values calculated in step 2. If the resulting change in 

deciviews is a negative number (future - base), this represents an improvement in visibility. The 

future year visibility values can also be compared to the glidepath to determine if the Class I 

area is expected to be on, above, or below the glidepath in the future planning period. 

Example 4.8.1 

We use example 4.8.1 to illustrate the modeled uniform rate of progress assessment. For ease 

of presentation, we assume there are only 10 speciated samples for PM in the 5 years 

comprising the base period and the example uses the old IMPROVE equation.85 Since sampling 

occurs every third day, a typical sample size is about 120 days per year. We illustrate the 

calculations for the first base year and then furnish information regarding mean deciviews for 

the other four base years to illustrate subsequent steps in the test. For simplicity sake, the 

example uses the old IMPROVE algorithm. The procedure is the same for the revised IMPROVE 

algorithm. 

Given: 

Ten days have measured components of PM in a Class I area during the first year of a 5-year 

base period. Table 4.8.2 below shows the measurements (in μg/m3

) for each of the 10 days. 

Table 4.8.2 also shows the date of each measurement and the corresponding climatological 

relative humidity adjustment factor (made up for this example) for the appropriate month and 

area. 

 

85 We recommend use of the revised IMPROVE equation. However, the old IMPROVE equation is used in the 

example for simplicity. 
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Table 4.8.2 Example observed IMPROVE data at a Class I area 

Day Date f(rh) SO4

(μg/m3) 

NO3

(μg/m3) 

OC 

(μg/m3) 

EC 

(μg/m3) 

Soil 

(μg/m3) 

CM 

(μg/m3) 

Rayleigh 

Scattering

(Mm-1) 

1 2/15 1.7 4.53 2.23 3.37 0.89 0.32 7.33 10 

2 3/15 2.9 5.12 1.78 2.92 0.78 0.44 9.17 10 

3 4/15 3.5 5.67 1.25 2.44 0.54 0.43 10.25 10 

4 5/15 3.7 6.59 1.12 2.86 0.50 0.66 9.80 10 

5 6/15 4.3 7.47 0.93 3.49 0.61 0.98 10.99 10 

6 7/15 4.6 8.33 0.79 3.50 0.69 1.08 11.38 10 

7 8/15 4.4 9.22 0.89 3.24 0.67 0.89 10.78 10 

8 9/15 4.1 8.05 0.97 3.12 0.73 0.71 8.25 10 

9 9/30 4.1 6.84 1.15 3.21 0.85 0.42 8.82 10 

10 10/30 2.7 5.32 1.41 3.26 0.76 0.64 8.11 10 

Using the IMPROVE data for the Class I area and the estimated natural background values, the 

uniform rate of progress on the 20% worst days for the future year has been calculated as 26.7 

dv. In addition, the mean visibility on the 20% best visibility days should not deteriorate. 

Find: Does the control strategy simulated in this model analysis lead to future year visibility 

that is on, or below the glidepath in this Class I area? 
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Solution:

Step 1. Using monitored data, rank baseline visibility for each day with PM10, PM2.5 and 

speciated PM2.5 measurements within a Class I area. 

First, estimate the extinction coefficient for each day with the needed PM measurements. 

This is done using the information in table 4.8.2 with Equation (4.11). For day 1 in year 1, the 

current extinction coefficient is: 

bext = (3)(1.7)[4.53] + (3)(1.7)[2.23] + (4)(1)[3.37] + (10)[0.89] + (1)[0.32] + (0.6)[7.33] + 10 

bext = 71.6 Mm-1 

Then convert extinction into deciviews: 

dv = 10 * ln(71.6/10) = 19.7 dv 

Base year extinction coefficients and deciviews for the remaining 9 days with monitored data in 

year 1 are calculated in a similar manner. The days are then ranked. The day with the highest 

deciviews (i.e., worst visibility) is given a rank of “1". The results of these calculations are 

displayed in the table 4.8.3 below. Based on these rankings, days 6 and 7 comprise the 20% of 

days with worst visibility. Days 1 and 10 comprise the 20% of days with best visibility 

Table 4.8.3 Example observed IMPROVE extinction, deciviews, and rank at a Class I area 

Day Date f(rh) SO4

(Mm-1) 

NO3

(Mm-1) 

OC 

(Mm-1) 

EC 

(Mm-1) 

Soil 

(Mm-1) 

CM 

(Mm-1) 

bext

baseline

(Mm-1) 

Decivi

ews 

Rank 

1 2/15 1.7 23.1 11.4 13.5 8.9 0.3 4.4 71.6 19.7 10 

2 3/15 2.9 44.5 15.5 11.7 7.8 0.4 5.5 95.5 22.6 8 
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3 4/15 3.5 59.5 13.1 9.8 5.4 0.4 6.2 104.4 23.5 7 

4 5/15 3.7 73.1 12.4 11.4 5.0 0.7 5.9 118.6 24.7 6 

5 6/15 4.3 96.4 12.0 14.0 6.1 1.0 6.6 146.0 26.8 4 

6 7/15 4.6 115.0 10.9 14.0 6.9 1.1 6.8 164.7 28.0 2 

7 8/15 4.4 121.7 11.7 13.0 6.7 0.9 6.5 170.5 28.4 1 

8 9/15 4.1 99.0 11.9 12.5 7.3 0.7 5.0 146.4 26.8 3 

9 9/30 4.1 84.1 14.1 12.8 8.5 0.4 5.3 135.3 26.1 5 

10 10/30 2.7 43.1 11.4 13.0 7.6 0.6 4.9 90.7 22.0 9 

Step 2. Calculate the average base year deciviews for the 20% of days with worst visibility 

and the 20% days with best visibility.

For year 1, mean worst visibility = (28.0 + 28.4) / 2 = 28.2 dv, and 

mean best visibility = (19.7 + 22.0) / 2 = 20.9 dv 

Mean worst and best visibility for years 2-5 is provided in table 4.8.4. Table 4.8.4 below 

summarizes mean worst and best visibility for each of the 5 years in the base period. 
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Table 4.8.4 Example mean worst days visibility and best days visibility at a Class I area

Year Mean dvcurrent, 

Worst Visibility Days 

Mean dvcurrent, 

Best Visibility Days 

1 28.2 20.9 

2 29.1 20.4 

3 30.2 18.7 

4 27.8 19.1 

5 28.5 19.5 

The average mean base period worst and best visibility is obtained by taking the arithmetic 

mean of the average worst and best visibility for the 5 years. Thus, the 5 year mean of the 

average worst visibility is given by 

dvbaseline = (28.2 + 29.1 + 30.2 + 27.8 + 28.5) / 5 = 28.8 dv 

The average mean best visibility is 

dvbaseline = (20.9 + 20.4 + 18.7 + 19.1 + 19.5) / 5 = 19.7 dv

Step 3. Apply a model to develop component specific RRF’s for SO4, NO3, OC, EC, Soil and CM. 

Tables 4.8.5 and 4.8.6 show the procedure for calculating component-specific relative response 

factors using an air quality model. The example shows the calculation of RRFs for the worst 

days in the year modeled. The same calculation is repeated for the best days. 
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Table 4.8.5 Base year modeled species concentrations on worst days (in μg/m3) 

Modeled 

Output 

SO4 NO3 OC EC Soil CM 

Day 1 2.12 6.54 3.56 0.87 1.23 5.43 

Day 2 8.33 2.11 4.67 1.23 0.34 7.32 

Day 3 9.33 1.23 6.32 1.45 0.87 8.21 

Day 4 6.43 1.67 4.56 0.54 1.07 4.67 

Day 5 10.53 0.57 4.12 0.69 1.54 10.32 

Mean Base 

Concentration 

7.35 2.42 4.65 0.96 1.01 7.19 

Table 4.8.6 Future year modeled species concentrations on worst days (in μg/m3) 

Modeled 

Output 

SO4 NO3 OC EC Soil CM 

Day 1 2.01 4.23 3.45 0.77 1.21 5.75 

Day 2 7.56 1.89 4.55 1.01 0.32 7.54 

Day 3 6.54 1.11 5.99 1.09 1.02 8.71 

Day 4 5.32 1.45 4.23 0.35 1.1 5.02 

 
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Day 5 7.23 0.43 3.99 0.45 1.51 10.47 

Mean Future 

Concentration 

5.73 1.82 4.44 0.73 1.03 7.50 

Table 4.8.7 Component specific RRFs for the 20% worst days 

Worst days 

RRF 

SO4 NO3 OC EC Soil CM 

RRF (mean 

future/mean 

base) 

0.780 0.752 0.956 0.768 1.022 1.043 

Step 4. Multiply the relative response factors times the measured daily species concentration 

data during the base period to compute future daily species concentrations. 

In year 1, we previously identified days 6 and 7 as those included in the 20% of days with worst 

visibility (i.e., see Step 1). Similarly, days 1 and 10 are the 20% of days with best visibility. In

this step, we estimate future concentrations for components of PM2.5 and for CM for these two 

sets of days. This is done using information shown in tables presented in Steps 1 and 3 as well 

as the best days RRFs given in table 4.8.8 below: 

Table 4.8.8 Component specific RRFs for the20% best days 

Best days 

RRF 

SO4 NO3 OC EC Soil CM 

RRF (mean 

future/mean 

base) 

0.870 0.823 0.976 0.784 1.112 1.058 
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Worst Days

Day 6: [SO4]future = (RRF)SO4 [SO4]baseline = (0.780) [8.33] = 6.50 μg/m3

[NO3]future = (0.752) [0.79] = 0.59 μg/m3

[OC]future = (0.956) [3.50] = 3.35 μg/m3

[EC]future = (0.768) [0.69] = 0.53 μg/m3

(Soil)future = (1.022) [1.08] = 1.10 μg/m3

[CM]future = (1.043) [11.38] = 11.87 μg/m3

Day 7: [SO4]future = (0.780) [9.22] = 7.19 μg/m3

[NO3]future = (0.752) [0.89] = 0.67 μg/m3

[OC]future = (0.956) [3.24] = 3.10 μg/m3

[EC]future = (0.768) [0.67] = 0.51 μg/m3

(Soil)future = (1.022) [0.89] = 0.91 μg/m3

[CM]future = (1.043) [10.78] = 11.24 μg/m3

Best Days

Day 1: [SO4]future = (0.870) [4.53] = 3.94 μg/m3

[NO3]future = (0.823) [2.23] = 1.84 μg/m3

[OC]future = (0.976) [3.37] = 3.29 μg/m3

[EC]future = (0.784) [0.89] = 0.70 μg/m3

(Soil)future = (1.112) [0.32] = 0.36 μg/m3

[CM]future = (1.058) [7.33] = 7.76 μg/m3

Day 10: [SO4]future = (0.870) [5.32] = 4.63 μg/m3

[NO3]future = (0.823) [1.41] = 1.16 μg/m3

[OC]future = (0.976) [3.26] = 3.18 μg/m3

[EC]future = (0.784) [0.76] = 0.60 μg/m3

(Soil)future = (1.112) [0.64] = 0.71 μg/m3

[CM]future = (1.058) [8.11] = 8.58 μg/m3
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Similar calculations (using the same model derived component specific RRFs) are performed for 

each of the worst and best days in each of the other 4 years in the base period.86

 

Step 5. Using the results in Step 4, calculate the future year extinction coefficients for the 20% 

best and 20% worst visibility days in each of the five base years. 

Using future PM components obtained in Step 4, we can estimate future daily total bext. 

For year 1

Worst Days

Day 6: bext = (3)(4.6)[6.50] + (3)(4.6)[0.59] + (4)[3.35] + (10)[0.53] + 1.10 + (0.6)[11.87] + 10 = 

134.8 Mm-1 

Day 7: bext = (3)(4.4)[7.19] + (3)(4.4)[0.67] + (4)[3.10] + (10)[0.51] + 0.91 + (0.6)[11.24] + 10 = 

139.0 Mm-1

Best Days

 

Day 1: bext = (3)(1.7)[3.94] + (3)(1.7)[1.84] + (4)[3.29] + (10)[0.70] + 0.36 + (0.6)[7.76] + 10 = 

64.6 Mm-1

Day 10: bext = (3)(2.7)[4.63] + (3)(2.7)[1.16] + (4)[3.18] + (10)[0.60] + 0.71 + (0.6)[8.58] + 10 = 

81.4 Mm-1

 

86Unless multiple meteorological years are being modeled, the same mean RRFs (for each component) 

are applied to the concentrations on all of the worst days for each of the 5 years of data. The mean 

RRFs are derived from the modeled days. A separate set of RRFs are derived (in the same manner) for 

the best days. 
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Step 6. Use the daily total bext values from step 5 to calculate future mean deciview values for 

the best and worst days. 

Calculate daily deciview values (from total daily extinction) and then compute the future 

average mean deciviews for the worst and best days for each year. Then average the 5 years 

together to get the final future mean deciview value for the worst and best days. 

For Year 1

Worst days

Day 6: 134.8 Mm-1 = 10* ln(134.8/10) = 26.0 dv 

Day 7: 139.0 Mm-1 = 10* ln(139.0/10) = 26.3 dv 

Future mean visibility on worst days = (26.0 + 26.3) / 2 = 26.2 dv

Best Days

Day 1: 64.6 Mm-1 = 10* ln(64.6/10) = 18.7 dv 

Day 10: 81.4 Mm-1 = 10* ln(81.4/10) = 21.0 dv 

Future mean visibility on best days = (18.7 + 21.0) / 2 = 19.8 dv

Similar calculations are performed for previously selected worst and best days in each of years 

2-5. To illustrate the uniform rate of progress assessment, assume these other calculations 

yield the following estimates for future mean dv on worst and best visibility days. 
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Table 4.8.9 Future year mean deciviews on the worst and best days for each of the 5 years 

Year Future Mean dv On Worst 

Visibility Days 

Future Mean dv On Best 

Visibility Days 

1 26.2 19.8 

2 27.3 19.3 

3 27.6 17.6 

4 25.9 18.3 

5 26.1 18.6 

 

Using results in table 4.8.9, we see that the estimated future average mean visibility for the 

20% days with worst visibility is 

dvfuture = (26.2 + 27.3 + 27.6 + 25.9 + 26.1) / 5 = 26.6 dv

The estimated future average mean visibility for the 20% days with best visibility is 

dvfuture = (19.8 + 19.3 + 17.6 + 18.3 + 18.6) / 5 = 18.7 dv

The worst days results generated in step 6 can then be compared to the uniform rate of 

progress for the particular future year for the Class I area. The future year best days are 

compared to the base year best days to ensure that visibility on the best days is not forecast to 

degrade. 

The future year strategy leads to an estimated visibility impairment of 26.6 dv, which is lower 

than the calculated uniform rate of progress value of 26.7 dv. Also visibility is not predicted to 

deteriorate on the best days, and is in fact estimated to improve by 1.0 dv (from 19.7 dv in the 

base year to 18.7 in the future year). 
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This information is used in the process of setting reasonable progress goals, in conjunction with 

other analyses, as described in “Guidance for Setting Reasonable Progress Goals Under the 

Regional Haze Program” (U.S. EPA, 2007). The other steps in the process are beyond this 

modeling guidance. The modeling can be used to determine the predicted improvement in 

visibility and whether the visibility levels are on, above, or below the glidepath. It cannot by 

itself determine the reasonable progress goals or determine whether the reasonable progress 

goal is met, and it does not satisfy the requirements for the statutory four factor analysis. See 

the Regional Haze Rule and related guidance documents for more information on the four 

factor analysis, including control strategy analysis for single sources. 

4.8.4 How Do I Select Appropriate Inputs For The Uniform Rate of Progress 

Analysis? 

In Section 4.8.3, we described the recommended regional modeling analysis to evaluate future 

year visibility relative to the glidepath. An important part of the analysis requires using 

component-specific RRFs, obtained with models, to estimate future concentrations of these 

components and, subsequently, future visibility. In this subsection, we address more details 

concerning the calculation of visibility RRFs. Second, there are several assumptions inherent in 

the recommended modeled uniform rate of progress analysis. We identify these in this 

subsection and comment on their underlying rationale. More specifically, we address seven 

issues: 

1. How to estimate base period air quality in a Class I area without monitored data; 

2. How to handle a base year without data or with a small sample size; 

3. Use of the same days to estimate changes in visibility for the worst days and a different 

set of same days to estimate changes in visibility for the best days; 

4. Which predictions to use to derive relative response factors; 

5. How many and what kind of days to use to develop RRF values; 

6. RRFs when modeled species concentration are very low; and 

7. Alternative RRF calculations 
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Estimating baseline worst and best visibility in a Class I area without monitors. 

There are 156 officially designated Class I areas and 110 IMPROVE sites in or near Class I areas. 

Therefore, approximately 46 Class I areas do not have co-located IMPROVE monitors. EPA’s 

Tracking Guidance recommends using IMPROVE data from a nearby site to represent the 

visibility at each Class I area that does not have ambient data. Table A-2 in Appendix A of the 

Tracking Guidance lists the recommended monthly f(rh) values87 for each Class I area as well as 

the representative site for each Class area. The representative IMPROVE site data will be used 

to track regional haze progress for the Class I areas. Therefore, it follows that visibility 

improvement for tracking purposes should be predicted at the location of the IMPROVE 

monitor, not at the actual Class I area. For the purposes of deriving ambient data for modeling, 

we recommend following the same representative site assignments contained in the Tracking 

Guidance. In this way, the 20% worst and best days can be derived for each Class I area from 

the network of 110 IMPROVE sites.88 Similarly, the modeling results should be extracted for the 

location of the representative monitor, not the actual location of the Class I area.89 

Considering a base year with little or no monitored particulate matter or missing data 

The Tracking Guidance recommends calculating baseline visibility values for sites with at least 3 

out of 5 complete years of data. It further contains recommendations for determining if a year 

has complete data. In general, a site should have 50% data completeness in all quarters and 

meet a 75% completeness criteria for the full year. There should be no periods with more than 

30 consecutive days without data. The guidance assumes that all IMPROVE sites will have at 

 

87The f(rh) values in the Tracking Guidance were developed for the “old” IMPROVE algorithm and should 

not be used to calculate visibility using the “new” IMPROVE algorithm. 

88Bering Sea Wilderness (Alaska) is the only Class I area that has no IMPROVE monitor and no 

representative IMPROVE site. On-site IMPROVE or representative IMPROVE data can be found for the 

other 155 sites. 

89There may be other reasons to calculate visibility improvement at the actual Class I area. The MATS 

provides options for calculating visibility changes at both the Class I area grid cell center and at the 

representative IMPROVE monitoring site. It may be informative to compare model response at the 

representative IMPROVE site versus the actual location of the Class I area. Large differences in the 

model response may indicate that the “representative” site may not adequately represent visibility at 

the Class I area. 
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least 3 complete years in the base period (which may or may not be true). The guidance also 

contains procedures for filling in missing data as part of the calculation of the 20% best and 

worst days. 

There are several data completeness issues that may cause problems within the visibility 

modeling analysis. First, a site may have less than 3 years of complete data. It is possible to 

calculate visibility improvement based on as little as 1 year of ambient data. States should work 

with their EPA Regional Office and Federal Land Managers to determine how to estimate 

baseline visibility for these area(s). 

Another issue that is a more specific problem for modeling analyses occurs when data is missing 

during the meteorological time period that was modeled. It is likely that most air agencies will 

only be modeling a single year of meteorology. Therefore it is possible that the ambient data at 

one or more Class I areas is incomplete during that year. Without ambient data, it is impossible 

to identify the 20% best and worst days which are used to calculate modeled RRFs. 

Again, if this occurs, states should work with their Regional Office and FLMs to determine the 

best way to calculate visibility on the best and worst days. Potential options are to use data 

from another nearby IMPROVE site, use nearby data from a different ambient data network, or 

interpolate ambient data to the site. 

Using a constant sample of days to estimate baseline and future visibility 

For a typical Class I area, there will be about 120 days per year having measurements needed to 

estimate (dv)baseline with Equation (4.11) or (4.12). Thus, there should be about 24 worst and 24 

best visibility days for each of the 5 years in the base period. It is conceivable that the identity 

of these worst and best days could change if emissions were altered to reflect net effects of 

controls and growth. The recommended test described in Section 4.8 assumes that the identity 

of the worst and best days remains unchanged. This is done primarily to avoid having to 

perform iterative analyses to identify future worst and best visibility days and to keep the test 

relatively simple and more readily understood. This assumption could cause improvement in 

visibility to be overestimated for the worst days and could also cause the test to overestimate 

the difficulty in preventing deterioration of visibility on the best days. However, for the 

reasons described below, we do not believe the effects of this assumption are substantial. 
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It is unlikely that there would be any wholesale change in the identity of worst or best days with 

future vs. base year emissions. Analyses performed by Meyer, et al. (1997) have shown that 

the predicted ranked severity of high ozone days is largely unaffected by simulated controls and 

growth (i.e., highest days tend to remain the highest days after the effects of growth and 

controls are simulated). There is no reason to expect a different outcome for other secondary 

pollutants. If there are differences, we would expect these to occur near the borderline 

between the worst days and more moderate days. 

Because the uniform rate of progress analysis relies on mean visibility values on 20 or more 

worst visibility days and most of these days are unlikely to change, we would expect little 

difference in the outcome of the analysis. Further, because of the shape of the distribution of 

extinction coefficients, the mean of the worst days is more heavily influenced by extreme days 

rather than those on the borderline between worst and more moderate light extinction. There 

could be differences in some best visibility days corresponding with pre- and post-control 

emissions. However, because the differences in concentrations of particulate matter on such 

days are likely to be relatively low, differences in the computed mean visibility for best days are 

likely to be small. Further, any resulting difference in the progress analysis for best days is likely 

to be protective of the environment. If our recommended procedure leads to suspected 

problems in the outcome of a test, an air agency may perform a more rigorous version of the 

analysis (in which the identity of pre-control and post-control days changes) as part of a weight 

of evidence determination. 

Selecting predictions to use in deriving RRF

Relative response factors should be developed for each Class I area. When a Class I area 

contains a monitoring site, the RRF estimates should be derived using predictions which are 

made “near” that site. For each day, daily average surface predictions of each component of 

PM made near a monitor should be estimated. Similar to the annual PM2.5 NAAQS attainment 

test, nearby grid cells should be averaged. For 12km or finer resolution, we recommend 

averaging the modeled concentrations for a 3 x 3 array of grid cells surrounding the monitor. 

Note that for cells larger than 12 km on a side, no spatial averaging is necessary—states should 

just use the prediction in the cell containing the monitor. These nearby estimates should then 

be spatially averaged to estimate a spatially representative daily concentration. Spatially 

representative daily concentrations obtained for each modeled day with monitored data should 

then be averaged. This final average should be used to compute the RRF. Thus, component-
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specific RRF values for a Class I area with a monitor are the ratio of the temporally averaged 

spatial mean of nearby concentrations predicted with future emissions to that predicted with 

base year emissions. 

Selecting days to derive RRF values 

RRF values should be estimated by taking the ratio of future predictions averaged over at least 

several days to base year predictions averaged over the same several days. It may often 

happen that a planning organization or a group of states decides to model the visibility impacts 

of a strategy for numerous Class I areas simultaneously. As we note in Section 2.3.1, this may 

make it advisable to simulate (at least) a full year so that relative response factor (RRF) values 

for each Class I area is based on a substantial number of observed best and worst days. For the 

worst days in the chosen year, the RRF for a component of PM should be estimated as the ratio 

of its arithmetic mean predicted value on the 20% worst days with future emissions to that with 

base year emissions. Thus, the RRF should reflect values averaged over ~ 24 worst days in that 

year. The same procedure is followed to derive RRFs over the ~24 best days in the year. 

Since meteorological conditions and/or emissions may be markedly different on best visibility 

vs. worst visibility days, we recommend calculation of a separate set of RRF values for best 

visibility days. As with worst days, the preferred approach is to model an entire year and select 

an RRF value for concentrations averaged over the 20% best visibility days for each Class I area. 

The RRF values are the ratios of the future to base year modeled averages. The appropriate 

RRF values should then be used in concert with each observed best day to estimate future 

concentrations for each component on each identified best day. 

RRFs when modeled species concentration are very low 

In most cases, the RRFs derived for visibility components are either less than 1.0 or slightly 

more than 1.0. However, large RRFs (much greater than 1.0) can occur when the mean 

modeled concentration of a PM component on the 20% best or worst days is very small and the 

concentration of that component increases in the future year case. This most often occurs with 

the nitrate component. On both the 20% best and worst days, the modeled nitrate 

concentrations at Class I areas can be very small (i.e. < .01 μg/m3). These low modeled 

concentrations can sometimes increase in future year scenarios by orders of magnitude. This 

occurs most often in ammonia limited areas when sulfate concentrations are reduced in the 

future (due to SO2 emissions reductions) causing nitrate increases due to an increase in 
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available ammonia. Large RRFs should be closely examined to identify the cause of the issue. 

In many cases, large RRFs are caused by poor model performance (i.e. underpredicted base 

case nitrate concentrations) or instability in one or more model components (i.e. the nitrate 

partitioning model). One solution is to cap the RRF at 1.0 or close to 1 in cases where nitrate 

(or any other PM2.5 component) increases in the future by an unrealistic amount due to very 

low modeled concentrations. Any adjustments to the default calculations should be 

documented and discussed with the appropriate EPA regional office and the FLMs. 

Alternative RRF calculations 

The default analysis is relatively simple in that a single mean RRF is calculated for each PM 

component (separate RRFs on worst and best days). A series of tests with more complicated 

methods has shown that 1) the difference between various versions of the test are usually 

small and 2) each of the alternative tests has limitations in its applicability (Environ, 2005). 

Possible variations include the use of day specific RRFs, season (or quarter) specific RRFs, or 

climatological based RRFs. In some cases, these more complicated techniques may provide 

different answers, but sometimes not. There are specific limitations noted with each of these 

alternatives. We have chosen to keep the single mean RRF test as the default 

recommendation. States are encouraged to explore other methods for estimating RRFs if it is 

thought that the default recommendation is too simplistic to accurately capture the change in 

future visibility at any particular Class I area. The SIP demonstration should use the most 

appropriate method of projecting future concentrations for the characteristics of each Class I 

area.90 Alternative methods should be discussed in the modeling protocol and discussed with 

the appropriate EPA Regional Office and FLMs. 

 

90In particular, issues may arise when dealing with large visibility contributions from fires, coarse mass 

and fine soil (from wind-blown dust), and international transport (and possibly other issues). Each of 

these issues should be addressed in the modeling protocol and solutions should be discussed with the 

appropriate EPA regional office(s) and FLMs on a case by case basis. 
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4.9 “Weight of Evidence”- How Can Additional Analyses Be Used to 

Support the Attainment Demonstration? 

By definition, models are simplistic approximations of complex phenomena. The modeling 

analyses used to assess whether emission reduction measures will bring an individual area into 

attainment for the NAAQS contain many elements that are uncertain (e.g., emission 

projections, meteorological inputs, science formulations, etc.). These uncertain aspects of the 

analyses can sometimes prevent definitive assessments of future attainment status. The 

confidence in the representativeness of the quantitative results from a modeled attainment 

test should be a function of the degree to which the uncertainties in the analysis were 

minimized. In general, by following the recommendations contained within this guidance 

document, EPA expects that the attainment demonstrations will mitigate the uncertainty as 

much as is possible given the current state of modeling inputs, procedures, and science. 

However, while air quality models represent the best tools for integrating emissions and 

meteorological information with atmospheric chemistry, and no single additional analysis can 

match the expected reliability of these models’ results, EPA believes that all attainment 

demonstrations and reasonable progress assessments will be strengthened by additional 

analyses that can supplement the modeling to enhance the assessment of whether the planned 

emissions reductions are likely to result in attainment (or expected visibility progress). 

Supplemental evidence should accompany all model attainment demonstrations. Generally, 

those modeling analyses that show that attainment will be reached in the future with some 

margin of safety will need more limited supporting material. For other attainment cases, in 

which the projected future design value is closer to the NAAQS, more rigorous supporting 

analyses should be completed. There may be some areas for which the supplemental evidence 

is persuasive enough to support a conclusion that the area can expect to achieve timely 

attainment despite failing the model attainment test, and other areas for which the modeled 

attainment test demonstrates attainment but the supplemental evidence casts significant 

doubt on that result. This section of the guidance will discuss some specific information and 

additional analyses that can be used to supplement the model projections. Of particular 

interest are analyses that help determine whether the modeling-based projections are likely to 

provide a prediction of the air quality improvement that is likely to occur by the attainment 

date. Air agencies should review these supplemental analyses, in combination with the 

modeling analysis, in a “weight of evidence” assessment of whether each area is likely to 
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achieve timely attainment. Again, it should be noted that no single supplemental analysis can 

serve as an adequate substitute for the air quality model; however, in aggregate, supplemental 

analyses may provide information which may indicate a different outcome than the modeled 

test. 

In considering the use of supplemental analyses, an important factor is the time (number of 

years) until the attainment date. In general, modeling and related analyses are the most useful 

corroborative analyses for areas with attainment dates which are more than several years in 

the future. In contrast, ambient data and emissions trends become more important (and hence 

model results become less important) the closer the area is to their attainment date. For 

example, if an area is only one or two years away from their attainment date, ambient data is in 

most cases the best predictor of likely air quality levels in the near future. However, this may 

not be true if there are large emissions reductions that are expected to occur in the near term 

period. In that case, modeling is needed to estimate additional ozone and/or PM reductions 

that are likely to occur due to the emissions controls in the short term. Similarly, if the 

attainment date is 5 or 10 years or more in the future, modeling will likely be the most reliable 

indicator of future year ozone, PM, and/or regional haze levels. Emission changes over a 

relatively long time period are likely to overwhelm the influence of ambient data trends and 

meteorological variability, making ambient data and emissions trends analyses relatively less 

important. 

4.9.1 What Types of Additional Analyses Should Be Completed as Part of the 

Attainment Demonstration? 

There are three basic types of analyses that are recommended to supplement the primary 

modeling analysis. They are: 

1) Additional modeling analyses 

2) Analyses of trends in ambient air quality and emissions 

3) Additional emissions controls/reductions 
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4.9.1.1 Modeling Analyses 

The relative attainment tests described in sections 4.2, 4.4, and 4.5 are the primary modeling 

tools used in an attainment demonstration. The application of a chemical transport grid model, 

applied on a regional or local scale is the best tool available to judge the impacts of changes in 

future year emissions on concentrations. In addition to this “primary” modeling analysis, there 

are various other models, model applications, and tools that can be used to supplement the 

results of the modeled attainment test. These include, but are not limited to: 

• Available regional or national scale modeling applications that are suitable91 for the local 

area. For example, modeling in support of EPA rulemakings or regional multi-jurisdictional 

organization modeling that may be available for the appropriate future year of interest. 

Modeling analyses may be available that used different models and/or inputs. 

• Use of other appropriate local modeling that includes the nonattainment area of interest. 

This may include applications using alternative models and/or inputs or research oriented 

analyses. 

• Use of photochemical source apportionment, DDM, and/or process analysis modeling tools 

to help explain why attainment is (or is not) demonstrated. 

• Use of multiple air quality models / model input data sets (e.g., multiple meteorological 

data sets, alternative chemical mechanisms or emissions inventories, etc.). Multiple model 

configurations can be used to estimate sensitivity and uncertainty of future year design 

value predictions. 

• For results to be most relevant to the way we recommend models be applied in 

attainment demonstrations, it is preferable that such procedures focus on the 

sensitivity of estimated relative response factors (RRF) and resulting projected 

design values to the variations inputs and/or model formulations. 

 

91 The resolution, emissions, meteorology, and other model inputs should be evaluated for applicability 

to the local nonattainment area. Additionally, model performance of the regional modeling for the local 

nonattainment area should be examined before determining whether the regional model results are 

suitable for use in the local attainment demonstration. 
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• Application of the attainment test with alternative procedures compared to the default 

recommendations in Sections 4.2, 4.4, and 4.5 of this guidance. Any alternate approaches 

should be accompanied with a technical justification as to why the approach is appropriate 

for the area in question and should be discussed with the appropriate EPA regional office. 

 Alternative RRF calculations using non-default assumptions or more detailed temporal 

and spatial analysis of RRFs at one or more monitoring locations. If a sound technical 

argument can be made for why atypically high RRFs at any particular location are not 

reasonable, then these types of supplemental analyses would suggest that attainment is 

more likely to be achieved than the default modeling analysis alone would indicate. 

 Alternate base year design values that may differ from the 5-year weighted average 

value. Alternative values could include different methodologies for determining base 

year design values or removal of potential exceptional events data (see footnote 30). 

• Use of dispersion models to address primary PM2.5 contributions to PM2.5 concentrations. In 

areas with large spatial gradients of primary PM2.5, dispersion models are best suited to 

characterizing the change in primary PM2.5 in the future. A local area analysis may be useful 

as a supplemental analysis (in either monitored or unmonitored areas, as appropriate) for 

areas that at least partially rely on local primary PM controls to reach attainment and did 

not otherwise perform and submit a local area analysis of part of the attainment 

demonstration. 

EPA has determined that the best approach to using models to demonstrate attainment of the 

NAAQS is to use a model in a relative mode. However, some types of “absolute” model results 

may be used to assess general progress towards attainment from the baseline inventory to the 

projected future inventory.92 Example metrics include: 

 

92 Care should be taken in interpreting absolute metrics if the model evaluation shows a large 

underprediction or overprediction of ozone or PM2.5 concentrations. An underprediction of observed

concentrations will make it artificially easy to show progress towards absolute attainment levels and an 

overprediction will make it artificially difficult to show progress towards attainment.
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• Percent change in total amount of ozone or PM2.5 >= NAAQS93 within the nonattainment 

area 

• Percent change in number of grid cells >= NAAQS within the nonattainment area 

• Percent change in grid cell-hours (days) >= NAAQS within the nonattainment area 

• Percent change in maximum modeled 8-hour ozone within the nonattainment area 

While these metrics can be used to estimate the magnitude, frequency, and relative amount of 

ozone or PM2.5 reductions from any given future emissions scenario, there are no threshold 

quantities of these metrics that can directly translate to an attainment determination. 

Generally, a large reduction in the frequency, magnitude, and relative amount of 8-hour ozone 

nonattainment (i.e., >= 76 ppb) or PM2.5 nonattainment (24-hour and/or annual) is consistent 

with a conclusion that a proposed strategy would meet the NAAQS. In the context of a weight 

of evidence determination, these metrics could be used to suggest that a particular location 

may be “stiff” or relatively unresponsive to emissions controls, while the rest of the modeling 

domain/nonattainment area is projected to experience widespread reductions. 

4.9.1.2 Analyses and Trends in Ambient Air Quality and Emissions 

Generally, air quality models are regarded as the most appropriate tools for assessing the 

expected impacts of a change in emissions. However, it may also be possible to evaluate 

progress towards attainment of the ozone or PM2.5 NAAQS based on measured historical trends 

of air quality and emissions. It may be possible to develop a relationship between past 

emissions changes and historical and current air quality. Once the relationship between 

past/present emissions and air quality is established, a response to the expected emissions 

reductions from a particular control strategy can be estimated. There are several elements to 

this analysis that are difficult to quantify. First, in most cases, the ambient data trends are best 

assessed by normalizing to account for year-to-year meteorological variations. Second, one 

must have an accurate accounting of the year-to-year changes in actual emissions (NOx, VOC, 

and/or SO2 and NH3) for the given area and any surrounding areas whose emissions may impact 

local concentrations. Third, one must have a solid conceptual model of how ozone or PM2.5 is 

 

93For each of these metrics, the appropriate comparison to the level of the NAAQS is 75 ppb for 8-hour 

ozone; 35 μg/m3 for 24-hour PM2.5; and 12 μg/m3 for annual PM2.5. 
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formed in the local area (e.g., influence of meteorology, NOx-limited, ammonia limited, 

transport-influenced, etc.). 

 If available, meteorologically adjusted ozone and PM2.5 concentrations can be used to establish 

air quality trends. There are several techniques that have been used to examine the influence 

of meteorology on air quality. Among them are (a) statistical modeling (U.S. EPA, 2005b ); (b) 

filtering techniques (Rao, 1995; Flaum, 1996; Milanchus, 1998; and Hogrefe, 2000), (c) using a 

probability distribution of meteorological severity based on climatological data (Cox and Chu, 

1993, 1996), (d) and using CART analysis to identify meteorological classes and selecting days 

from each year so that the underlying frequency of the identified meteorological classes 

remains the same (Deuel and Douglas, 1996). Most of this work has examined the relationship 

between ozone and meteorology. Only recently have analyses examined the relationship 

between meteorology and PM2.5. Additionally, compared to PM2.5, the established relationship 

between ozone and meteorological variables is generally stronger (higher r-square values). In 

the case of PM2.5, the relationship between concentration and meteorology is complicated by 

the fact that PM2.5 components experience high concentrations at different times of the year 

and for different reasons. This makes it more difficult to meteorologically adjust PM2.5

concentrations. 

 If a meteorologically adjusted trend in ozone or PM2.5 can be estimated, then the information 

can be used to establish a link between emissions and air quality trends. This is not always 

straightforward due to the multitude of emissions precursors that may lead to high ozone and 

PM2.5 concentrations. A careful analysis of (meteorologically adjusted) air quality trends and 

emissions trends of each of the ozone and PM precursors (as well as primary PM) is needed to 

fully establish relationships. Detailed emissions information as well as a solid understanding of 

the conceptual model of ozone or PM2.5 formation is needed. If a trend can be established 

based on past emissions changes and air quality changes, then future year predicted emissions 

levels can be used to extrapolate future air quality. 

Meteorologically adjusted trends in ozone and/or PM2.5 attempt to adjust observed 

concentrations to levels which would have been observed during average meteorological 

conditions (Camalier, 2007). This makes it easier to observe trends primarily due to emissions 

changes and also allows identification of years with above average or below average 

meteorologically conducive conditions. Identification of “extreme” meteorological conditions 
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which are not likely to re-occur in a particular design value period can be used as part of a 

weight of evidence analysis. For example, the presence of one or more “extreme” (high 

concentration) meteorological years in either the base year period or the most recent design 

value period may make it appear that an area is farther away from attaining the NAAQS than 

would otherwise be the case if more typical meteorological conditions had occurred. It is 

important to note that one or more years of meteorological conditions which are not conducive 

to ozone formation will have the opposite effect. An area may appear to be on track to attain 

the NAAQS (or close to attaining), but in reality may need substantial additional emissions 

reductions in order to attain under average or above average meteorological conditions. 

Meteorological adjusted concentration and/or design value calculations can help explain both 

of these situations. More information on meteorologically adjusted trends can be found here: 

http://www.epa.gov/airtrends/weather.html . 

A simpler (and more uncertain) way to qualitatively assess progress toward attainment is to 

examine recently observed air quality and emissions trends. Downward trends in observed air 

quality and in emissions (past and projected) are consistent with progress towards attainment. 

Strength of the evidence produced by emissions and air quality trends is increased if an 

extensive monitoring network exists and if there is a good correlation between past emissions 

reductions and current trends in ozone or PM2.5. Until recently, EPA prepared annual air quality 

trends reports for all criteria pollutants. The latest trends report analyzes ambient data 

through 2010 and can be found here: 

http://www.epa.gov/airtrends/2011/report/fullreport.pdf. The formal trends reports have 

been replaced by less formal annual updates which are contained on the Air Trends website. 

Pollutant specific trends updates can be found here: http://www.epa.gov/airtrends/index.html 

Weight given to trend analyses depends on several factors. Analyses that use more air quality 

data and apply a greater variety of trend parameters provide more credible results. More 

weight can be placed on the results if the procedure used to normalize the trend for 

meteorological differences explains much of the variability attributable to these differences. In 

addition, trend analysis is more believable if the extrapolation (as applicable) does not extend 

very far into the future. Finally, trend analysis is most credible if the contemplated strategy is 

similar to a past strategy (e.g., both strategies focus on reducing sulfates for PM or NOx for 

ozone). For example, if a past strategy focused on reducing sulfates, but a future one envisions 
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controlling OC, there is no guarantee that ambient OC will respond similarly to changes in past 

emissions. 

In addition to ambient data trends, further analysis of ambient data can provide information on 

ozone production efficiency, evidence of transport and assist in the QA of emissions inputs. In 

particular, photochemical assessment monitoring station (PAMS) data, special study data, 

research and non-routine ambient data (i.e. NOy, and VOC data) can provide insights into the 

ozone and/or PM2.5 concentrations observed in the nonattainment area and can help assess the 

accuracy of the modeling results and control strategies that are being relied upon for 

attainment. 

4.9.1.3 Additional Emissions Controls/reductions

Models are used to predict the future expected concentrations of ozone and/or PM2.5, based on 

modeled emissions changes between a base year and future year. It is expected that emission 

inventories are as accurate as possible and represent emissions changes (including growth and 

controls) that are expected to occur. However, there may be various emissions sources and/or 

controls that are difficult to represent in the modeling analysis and the effects of some 

emissions controls may be difficult to quantify. Additionally, there may be uncertainty in how 

emissions controls may be implemented either in the home state or in upwind regions which 

may contribute ozone to the nonattainment area. 

The following are some of the types of control measures that may be appropriate to include in 

a weight of evidence demonstration: 

• Measures that are difficult to quantify or may not be enforceable in the SIP. Examples 

include: 

o Energy efficiency or renewable energy (EE/RE) programs may be implemented 

statewide (and in fact may be enforceable). But emissions reductions from such 

programs are hard to quantify and it may be difficult to assign reductions to 

particular utility sources. EPA has put together an “EE/RE Roadmap Manual” 

which can be found here: http://epa.gov/airquality/eere/manual.html . The 

roadmap includes a weight of evidence “pathway” for air agencies that want to 

acknowledge the emissions benefits and potential reductions from EE/RE 

measures, but are unable to accurately quantify the emissions reductions in their 
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SIP. 

o Smart growth initiatives such as land use planning and transportation planning. 

o Truck stop electrification. 

o Emissions reductions from idling regulations which may not be quantified in the 

mobile emissions modeling. 

• Voluntary measures 

o Air agencies may have numerous voluntary measures that are not enforceable in 

the SIP. These could include measures such as “ozone action days”, voluntary no 

burn days, telework programs, idling reduction initiatives, etc. Even though 

these programs may be voluntary, they can still lead to positive actions in the 

nonattainment area that can lead to lower emissions. Voluntary measures 

should be documented to the fullest extent possible, including estimates of 

emissions benefits, where possible. 

• Regional/super-regional and/or national programs that may not have been accounted 

for in the attainment demonstration. 

o Federal measures which were not accounted for in the modeling because they 

were proposed and/or finalized after the state modeling was completed. 

o Upwind regional, state, and/or local measures which were not known or not able 

to be quantified in the attainment demonstration due to timing or lack of 

information. 

4.9.2 What Role Should Weight Of Evidence Play In Visibility-Related 

Analyses? 

We believe a weight of evidence analysis is an appropriate option for air agencies to use when 

examining visibility goals. In this subsection, we note some potential supplemental analyses 

that may be used to examine visibility trends. 

4.9.2.1 Additional air quality modeling 

Sensitivity tests can be performed to see if conclusions about trends in the 20% worst and best 

visibility days are robust. One example of such an analysis is applying a model with and without 

a more finely resolved nested grid near source areas or near Class I areas. A purpose of this 
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would be to see whether conclusions are affected by the degree of detail in which nearby 

sources are considered. A second example of an analysis would be to consider alternative 

future emissions and/or differing growth rate assumptions. This may be a particular concern 

for regional haze analyses because the emissions projection period is generally longer than for 

most ozone and PM2.5 attainment demonstrations. Uncertainty in emissions and growth rates 

become more important as the projection period is lengthened. 

If trends in visibility for worst and/or best days are similar using sensitivity tests, alternative 

models and/or alternative modeling approaches, this finding supports conclusions reached in 

the uniform rate of progress analysis. 

4.9.2.2 Review of trends 

A review of trends generally involves a comparison, sometimes qualitative, between past 

trends in reconstructed visibility and estimated changes in emissions (e.g., early ‘00’s to early 

‘10's). This information could be used to confirm that more control measures on previously 

reduced emissions of a component or its precursors is likely to be useful. It may also be used to 

see whether certain PM components are becoming increasingly important sources of light 

extinction. 

4.9.2.3 Other models and tools 

Trajectory models may be useful for identifying the types of meteorological conditions most 

often corresponding to observed worst and best visibility in various Class I areas. This, in turn, 

may enable air agencies to draw inferences about the orientation of areas containing sources 

most likely to influence visibility in a Class I area on days with “poor” and “good” visibility. Grid

model based techniques such as DDM or source apportionment may also be useful in 

identifying areas and emissions sources most responsible for visibility impairment on the worst 

or best days. 

4.9.2.4 Refinements to the recommended uniform rate of progress analysis 

If a modeled control strategy appears generally successful, but the glidepath is not met in 

certain Class I areas, states may consider refining the recommended modeling analysis in some 

manner. Refinements are best made if they are based on local observations/analyses which 

suggest that some of the underlying assumptions in the recommended assessment may not be 
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applicable. We list some potential refinements that could be considered. The list is intended to 

illustrate types of additional analyses that could be performed. 

• Use an alternative light extinction equation or an area-specific version. 

• Available speciated data and other information may be reviewed to see whether the 

outcome of the test is being influenced by including one or more days with 

extraordinary events (e.g., a major wildfire lasting a number of days or transported dust 

events). If convincing arguments can be made that the event is a “natural” one, 

excluding these days from the calculations should be discussed with the appropriate 

EPA regional office and FLMs. 

• Daily component specific RRFs can be examined to determine if one or more days are 

responding in a different way compared to the majority of the best or worst days. 

Determining why days may be more or less responsive to emissions controls may lead to 

conclusions regarding the suitability of particular days to be represented in the mean 

response. It may be appropriate, in some cases, to re-calculate mean RRFs with suspect 

days removed. 

• Re-rank future estimated light extinction (i.e., bext values) for all days with current 

measurements and re-compute mean future 20% best and worst visibility (i.e., do not 

assume that the identity of baseline 20% best and worst days remains the same). 

Concerns about modeling days with best visibility 

In some parts of the United States, concentrations of the components of PM used in visibility 

calculations may be very close to background levels on days with best visibility. Measurements 

and model estimates may be subject to more relative uncertainty (i.e., lower signal to noise 

ratio) on days where observed concentrations of PM are very low (and light extinction is also 

low). The utility of a WOE determination is heightened in such cases. A state should see 

whether a model’s inability to accurately predict one or more individual components of PM has 

a substantial effect on the extinction coefficient calculated with Equation (4.12). If it does, and 

diagnostic tests are unable to resolve a performance problem, a state may need to address the 

goal for best visibility days in the particular Class I area(s) without using results from a grid 

model. 

 
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4.9.3 Weight of Evidence Summary 

A WOE determination examines results from a diverse set of analyses, including the outcome of 

the primary attainment test, and attempts to summarize the results into an aggregate 

conclusion with respect to whether a chosen set of control strategies will result in an area 

attaining the NAAQS by the appropriate year. The supplemental analyses discussed above are 

intended to be part of a WOE determination, although the level of detail required in a WOE 

submittal will vary as a function of many elements of the model application (e.g., model 

performance, degree of residual nonattainment in the modeled attainment test, amount of 

uncertainty in the model and its inputs, etc.). Each WOE determination will be subject to areaspecific conditions and data availability. Area-specific factors may also affect the types of 

analyses that are feasible for a nonattainment area, as well as the significance of each. Thus, 

decisions concerning which analyses to perform and how much credence to give each need to 

be made on a case by case basis by those implementing the modeling/analysis protocol. Air 

agencies are encouraged to consult with their EPA Regional office (and FLMs, when 

appropriate) in advance of initiating supplemental analyses to determine which additional 

analyses may be most appropriate for their particular area. 

The most useful supplemental analyses are those providing the best evidence as to how much 

air quality improvement can be expected as compared to the improvement projected by the air 

quality modeling analysis. Each analysis is weighed qualitatively, depending on: 1) the capacity 

of the analysis to address the adequacy of a strategy and 2) the technical credibility of the 

analysis. If the overall WOE produced by the combination of the primary modeling analysis and 

the various supplemental analyses supports the attainment hypothesis, then attainment of the 

NAAQS is demonstrated with the proposed strategy. The end product of a weight of evidence 

determination is a document which describes analyses performed, data bases used, key 

assumptions and outcomes of each analysis, and why an air agency believes that the evidence, 

viewed as a whole, supports a conclusion that the area will, or will not, attain the NAAQS. In 

conclusion, the basic criteria required for an attainment demonstration based on weight of 

evidence are as follows: 

1) A fully-evaluated, high-quality modeling analysis that projects future values that are 

close to the NAAQS. 
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2) A description of each of the individual supplemental analyses, preferably from multiple 

categories. Analyses that utilize well-established analytical procedures and are 

grounded with sufficient data should be weighted accordingly higher. 

3) A written description as to why the full set of evidence leads to a conclusive 

determination regarding the future attainment status of the area that differs from the 

results of the modeled attainment test alone. 
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