Draft 3.2- September 2006

Guidance on the Use of Models and

Other Analyses for Demonstrating

Attainment of Air Quality Goals for

Ozone, PM2.5, and Regional Haze 

Draft 3.2- September 2006
Draft 3.2- September 2006

Guidance on the Use of Models and Other 

Analyses for Demonstrating Attainment of

Air Quality Goals for Ozone, PM2.5, and

Regional Haze

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Emissions, Monitoring, and Analysis Division

Air Quality Modeling Group

Research Triangle Park, North Carolina
Draft 3.2- September 2006 iii

Note to Reviewers 9/12/06

This revised draft final version of the modeling guidance combines the ozone, PM2.5, and

regional haze modeling guidance. The contents from the final ozone guidance (October, 2005)

are essentially unchanged, except for minor editorial updates. We consolidated the two guidance

documents to try to eliminate overlap. We were able to reduce the original 2 guidance

documents from ~400 total pages to a single document with ~200 pages.

Due to the desire to quickly issue the final version of this guidance, we are providing a

short comment period of ~3 weeks. We plan to issue the final guidance as soon as possible, after

receiving comments. Please try to limit comments to “major” issues. Here are some questions

to consider and some information that may help in the review process:

1) Are there any major issues associated with any of the suggested analyses, algorithms,

definitions, etc.?

2) Are there any contradictory recommendations compared to the previous draft version or

compared to informal guidance that has been previously given?

2) Is there anything missing? Are there any issues that are not covered or are too vague to fully

understand?

3) This is not the final version. Some language still needs to be “cleaned up”.

4) Many references still need to be added to the reference section. Please do not comment on

missing citations from the reference section. Please do suggest additional references which are

not in the guidance and may be appropriate.

6) The relative test for primary PM2.5 is new (section 5.3). Comments on this section are

welcome.

7) The guidance mentions new EPA model attainment test software (MATS). The software is

still under development and will be released after testing is completed.

Please send comments to Brian Timin at timin.brian@epa.gov by September 29th, 2006.
Draft 3.2- September 2006 iv

ACKNOWLEDGMENTS

We would like to acknowledge contributions from members of an external review group,

the STAPPA/ALAPCO emissions/modeling committee and U.S. EPA Regional Office modeling

staffs in providing detailed comments and suggestions regarding the final version of this

guidance. In particular, we would like to thank staff members of the Lake Michigan Air

Directors Consortium (LADCO), Carolina Environmental Program (CEP), South Coast Air

Quality Management District (SCAQMD), California Air Resources Board (CARB), Texas

Commission on Environmental Quality (TCEQ), North Carolina Division of Air Quality

(NCDAQ), New York State Department of Environmental Conservation (NYDEC) and

Computer Sciences Corporation (CSC) for testing our ideas for a modeled attainment test and

sharing the results with us. 

We would also like to acknowledge the contributions and accomplishments of Ned

Meyer. Ned wrote the original drafts of the ozone and PM2.5 modeling guidance documents. He

also developed the relative attainment tests and put his vision on paper. The final version of this

guidance is shaped by Ned’s words and thoughts. 
Draft 3.2- September 2006 v

ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

FOREWORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

1.0 Introduction ...........................................................1

1.1 What Is The Purpose Of This Document? .............................2

1.2 Does The Guidance In This Document Apply To Me? ..................2

1.3 How Does The Perceived Nature Of Ozone, PM, and Regional Haze Affect

My Attainment Demonstration? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3.1 Ozone, PM, and Regional Haze ...............................3

1.3.2 How is Measuring and Modeling Particulate Matter Different (and

Often More Complicated) than Ozone? . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 What Topics Are Covered In This Guidance? . . . . . . . . . . . . . . . . . . . . . . . . 10

Part I. How Do I Use Results Of Models And Other Analyses To Help Demonstrate

Attainment? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.0 What Is A Modeled Attainment Demonstration?--An Overview . . . . . . . . . . . . . . . 13

2.1 What Is The Recommended Modeled Attainment Test for Ozone and PM2.5?-

An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.2 What Is The Recommended Modeled Test For Reasonable Progress?-

An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.3 What Does A Recommended Supplemental Analysis/Weight Of Evidence

Determination Consist Of? --An Overview

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

2.4 Why Should A Model Be Used In A “Relative” Sense And Why May

Corroboratory Analyses Be Used In A Weight Of Evidence Determination?16

3.0 What Is The Recommended Modeled Attainment Test? . . . . . . . . . . . . . . . . . . . . . . 19

3.1 Calculating site-specific baseline concentrations. . . . . . . . . . . . . . . . . . . . . . . 20

3.2 Identifying surface grid cells near a monitoring site. . . . . . . . . . . . . . 24

3.3 Choosing model predictions to calculate a relative response factor

(RRF)I near a monitor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4 Estimating design values at unmonitored locations: what is an unmonitored

area analysis and why is it needed? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.4.1 Why does the unmonitored area analysis need to use both ambient

data and model output? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.4.2 Implementation of Gradient adjusted Spatial Fields . . . . . . . . . . . . . 30

3.4.3 Using the Results of the Unmonitored Area Analysis . . . . . . . . . . . . 32

3.5 Which base year emissions inventory should be projected to the future for

the purpose of calculating RRFs? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.6 Choosing a year to project future emissions. . . . . . . . . . . . . . . . . . . . . . . . . 34
Draft 3.2- September 2006 vi

4.0 Ozone Attainment Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.1 Limiting modeled 8-hour daily maxima chosen to calculate RRF. . . . . . . . 36

4.2 How Do I Apply The Recommended 8-Hour Ozone Modeled Attainment Test?

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.0 What Are The Modeled Attainment Tests For The Two PM2.5 NAAQS? . . . . . . . . 41

5.1 What Is The Recommended Modeled Attainment Test For The Annual

 NAAQS? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.1.1 What Ambient PM2.5 Data is Used in the Attainment Test? . . . . . . . 43

5.1.2 FRM Monitors that Don’t Have Speciation Data . . . . . . . . . . . . . . . . 43

5.1.3 New Species Calculations and Definitions . . . . . . . . . . . . . . . . . . . . . 44

5.1.4 Recommended Treatment of Species Data . . . . . . . . . . . . . . . . . . . . . 45

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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.3 Special Considerations for Primary PM2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5.3.1 Analysis of Primary PM2.5 Impacts at Monitors . . . . . . . . . . . . . . . . . . 60

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

Dispersion Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

6.0 What Is The Recommended Modeled Test For Reasonable Progress Reducing

Regional Haze? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

6.1 How are Regional Haze Trends Measured? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

6.2 How Do I Apply A Modeled Test To Demonstrate Uniform Rate of Progress? 75

6.3 How Do I Select Appropriate Inputs To The Reasonable Progress Test? . . . . . 85

7.0 How Can Additional Analyses Can Be Used to Support the Attainment

Demonstration? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

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

Attainment Demonstration? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

7.2 If I Use A Weight Of Evidence Determination, What Does This Entail? . . . 97

7.3 What Role Should Weight Of Evidence Play In Visibility-Related Analyses? . 98

8.0 What Additional Analyses and Data Collection Can Be Completed to Assess

Progress towards Attainment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

9.0 What Documentation Do I Need To Support My Attainment Demonstration?

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Part II. How Should I Apply Air Quality Models To Produce Results Needed To

Help Demonstrate Attainment? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

10.0 How Do I Apply Air Quality Models?-- An Overview . . . . . . . . . . . . . . . . . . . . . . . 111

11.0 How Do I Get Started?- A “Conceptual Description” . . . . . . . . . . . . . . . . . . . . . . . 116
Draft 3.2- September 2006 vii

11.1 What Is A “Conceptual Description”? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

11.1.1 8-Hour Ozone NAAQS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

11.1.2 Annual PM2.5 NAAQS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

12.0 What Does A Modeling/Analysis Protocol Do, And What Does Developing One

Entail? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

12.1 What Is The Protocol’s Function? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

12.2 What Subjects Should Be Addressed In The Protocol? . . . . . . . . . . . . . . . . 124

13.0 What Should I Consider In Choosing An Air Quality Model? . . . . . . . . . . . . . . . . 125

13.1 Types of Air Quality Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

13.2 What Prerequisites Should An Air Quality Model Meet To Qualify For Use

In An Attainment Demonstration? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

13.3 What Factors Affect My Choice of A Model For A Specific Application?

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

13.4 What Are Some Examples Of Air Quality Models Which May Be Considered?

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

14.0 How are the Meteorological Time Periods (Episodes) Selected? . . . . . . . . . . . . . . 131

14.1 What Are The Most Important Criteria For Choosing Episodes? . . . . . . . 132

14.1.1 8-Hour Ozone NAAQS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

14.1.2 Recommendations for the 24-Hour PM2.5 NAAQS . . . . . . . . . . . . . . 138

14.1.3 Annual PM2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

14.1.4 Regional Haze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

14.2 What Additional, Secondary Criteria May Be Useful For Selecting Time

Periods to Model? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

15.0 What Should Be Considered When Selecting The Size And Horizontal/Vertical

Resolution Of The Modeling Domain? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

15.1 How is the Size of the Modeling Domain Chosen? . . . . . . . . . . . . . . . . . . . . 143

15.2 How are the Initial and Boundary Conditions Specified? . . . . . . . . . . . . . . 144

15.3 What Horizontal Grid Cell Size Is Necessary? . . . . . . . . . . . . . . . . . 146

15.3.1 Horizontal Resolution Recommendations for Ozone . . . . . . . . . . . . 147

15.3.2 Horizontal Resolution Recommendations for the PM2.5 NAAQS . . 148

15.3.3 Horizontal Resolution Recommendations for Regional Haze . . . . . 148

15.4 How Should the Vertical Layers Be Selected? . . . . . . . . . . . . . . . . . . . . . . 149

16.0 How are the Meteorological Inputs Prepared for Air Quality Modeling? . . . . . . . 151

16.1 What Issues are Involved in the Generation and Acquisition of

Meteorological Modeling Data? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

16.2 How Should the Meteorological Modeling Analysis be Configured? . . . . . . 152

16.3 How Should the Performance of the Meteorological Modeling Be Evaluated?154

17.0 How Are the Emission Inputs Developed? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
Draft 3.2- September 2006 viii

17.1 What Emission Inventory Data are Needed to Support Air Quality Models? 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

17.2 Can The National Emission Inventory Be Used As a Starting Point? . . . . 162

17.3 What Other Data are Needed to Support Emissions Modeling? . . . . . . . . . 162

17.4 How Are Inventory Data Converted Into Air Quality Model Input? . . . . . 164

17.5 Are there Other Emissions Modeling Issues? . . . . . . . . . . . . . . . . . . . . . . . . 167

17.6 How Are Emissions Estimated for Future Years? . . . . . . . . . . . . . . . . . . . . 168

17.6.1 Identify and prioritize key sectors for projections . . . . . . . . . . . . . . 169

17.6.2 Collect available data and models for each sector needing projections

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

17.6.3 Evaluate and refine data for projections in modeling region . . . . . 176

17.6.4 Create future-year inventories and air quality model inputs . . . . . 177

17.7 Emissions Preparation for a Local Area Analysis . . . . . . . . . . . . . . . . . . . . 177

18.0 What are the Procedures for Evaluating Model Performance and What is the Role

of Diagnostic Analyses? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

18.1 How Do I Assess Model Performance And Make Use of Operational and

Diagnostic Evaluations? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179

18.2 What Pollutants Are of Interest for Model Performance? . . . . . . . . . . . . . 180

18.3 What Are the PM2.5 Measurement versus Model Concentration Issues? . 181

18.4 How Should the Operational Evaluation of Performance Be Completed? 186

18.4.1 Ozone Operational Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

18.4.2 PM/RH Operational Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

18.5 How Do I Assess Model Performance and What Types of Analyses Can be

Done to Evaluate the Accuracy of the Model Response: Diagnostic

Evaluations? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

18.5.1 Observational models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

18.5.2 Probing Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

18.5.3 Receptor Models (Multi-variate Source Apportionment) . . . . . . . . . 200

18.5.4 Retrospective Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

18.5.5 Alternative Base Cases (Sensitivity tests) . . . . . . . . . . . . . . . . . . . . 202

18.6 How Should the Results of the Model Evaluation be Assessed (Interpreted)? 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

APPENDIX A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

APPENDIX B: Summary of recent model performance evaluations conducted by other

modeling groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
Draft 3.2- September 2006 ix

FOREWORD

The purpose of this document is to provide guidance to EPA Regional, State, and Tribal

air quality management authorities and the general public, on how to prepare 8-hour ozone and

PM2.5 attainment demonstrations and regional haze reasonable progress demonstrations using air

quality models and other relevant technical analyses. This guidance is designed to implement

national policy on these issues. This document does not substitute for any Clean Air Act

provision or EPA regulation, nor is it a regulation itself. Thus, it does not impose binding,

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

its application. The guidance may not apply to a particular situation, depending upon the

circumstances. The EPA and State decision makers retain the discretion to adopt approaches on

a case-by-case basis that differ from this guidance where appropriate. Any decisions by EPA

regarding a particular State Implementation Plan (SIP) demonstration will only be made based

on the statute and regulations, and will only be made following notice and opportunity for public

review and comment. Therefore, interested parties will be able to raise questions and objections

about the contents of this guidance and the appropriateness of its application for any particular

situation. 

This guidance is a living document and may be revised periodically. Updates, revisions,

and additional documentation will be provided at http://www.epa.gov/ttn/scram/. Any mention

of trade names or commercial products in this document is not intended to constitute

endorsement or recommendation for use. Users are cautioned not to regard statements

recommending the use of certain procedures or defaults as either precluding other procedures or

information, or providing guarantees that using these procedures or defaults will result in actions

that are fully approvable. As noted above, EPA cannot assure that actions based upon this

guidance will be fully approvable in all instances, and all final actions will only be taken

following notice and opportunity for public comment. The EPA welcomes public comments on

this document and will consider those comments in any future revisions of this guidance

document, providing such approaches comply with all applicable statutory and regulatory

requirements.
1

While this guidance document is primarily directed at modeling applications in

nonattainment areas, it may also be useful as a guide for modeling in maintenance areas or to

support other rules or sections of the Clean Air Act. 

2

The 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 (reference).

Draft 3.2- September 2006 1

1.0 Introduction

 

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

attainment of annual and 24-hour national ambient air quality standards (NAAQS) for particles

smaller than 2.5 :m in diameter (PM2.5) and the 8-hour NAAQS for ozone. We also describe

how to use modeled and monitored data to estimate whether a control strategy will result in

meeting goals to reduce regional haze in Class I areas (e.g., national parks, wilderness areas). 

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

used to evaluate attainment and/or reasonable progress goals. Modeling to show attainment of

the NAAQS primarily applies to nonattainment areas1

 for which modeling is needed, or desired. 

Modeling to show reasonable progress for regional haze applies to all States2

The guidance consists of two major parts. Part I addresses the question, “how should I

use the results of models and other analyses to help demonstrate attainment and/or reasonable

progress?” We begin by describing a modeled attainment test for the 8-hour ozone NAAQS, the

annual PM2.5 NAAQS, and the 24-hour PM2.5 NAAQS. We also recommend a modeled test for

reasonable progress goals to reduce regional haze. We explain what is meant by a modeled

attainment demonstration, a modeled attainment test, and a weight of evidence determination. 

We also identify additional data which, if available, can enhance the credibility of model results. 

Part I concludes by identifying what documentation States/Tribes should include as part of an

attainment demonstration. 

Part II of the guidance describes how to apply air quality models. The recommended

procedure for applying a model has nine steps. The results of this process are then used to apply

the modeled attainment test to support an attainment demonstration, as described in Part I of the

guidance. 

1. Develop a conceptual description of the problem to be addressed.

2. Develop a modeling/analysis protocol.

3. Select an appropriate model to support the demonstration.

4. Select appropriate meteorological time periods to model.

5. Choose an appropriate area to model with appropriate horizontal/vertical resolution

and establish the initial and boundary conditions that are suitable for the application.

6. Generate meteorological inputs to the air quality model.
3

O3/PM/RH rules Ozone Implementation Rule- Phase 2 

Draft 3.2- September 2006 2

7. Generate emissions inputs to the air quality model.

8. Evaluate the performance of the air quality model and perform diagnostic tests to

improve the model, as necessary.

9. Perform future year modeling (including additional control strategies, if necessary) and

apply the attainment test.

Model applications require a substantial effort. States/Tribes should work closely with

the appropriate U.S. EPA Regional Office(s) in executing each step. This will increase the

likelihood of approval of the demonstration at the end of the process. 

1.1 What Is The Purpose Of This Document?

This document has two purposes. The first 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 reasonable progress goals for regional haze will occur by the appropriate date

for an area. The second purpose is to describe how to apply an air quality model to produce

results needed to support an attainment demonstration or reasonable progress demonstration. 

The guidance herein should be viewed as recommendations rather than requirements. 

Although this guidance attempts to address issues that may arise in attainment demonstrations,

situations which we have failed to anticipate may occur. These should be resolved on a case by

case basis in concert with the appropriate U.S. EPA Regional Office.

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 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 rule, the 8-

hour implementation rule and preamble3

 and the PM2.5 implementation rule and preamble.

Implementation plan revisions for ozone and PM2.5 are due three years after an area is

designated “nonattainment” (e.g., June 15, 2007 for areas whose effective designation dates are

June 15, 2004). Regional haze SIPs are due 3 years from .....? Attainment and reasonable

progress demonstrations supporting these revisions should be completed in time to allow

sufficient time to complete the rulemaking process by the SIP due date. 

 
4

 Ambient design values are based on observations made at monitor locations.

Draft 3.2- September 2006 3

1.3 How Does The Perceived Nature Of Ozone, PM, and Regional Haze Affect

My Attainment Demonstration?

Guidance for performing attainment demonstrations needs to be consistent with the

perceived nature of the pollutant. In this section, we identify several premises regarding ozone,

PM2.5, and regional haze. We then describe how the guidance accommodates each.

1.3.1 Ozone, PM, and Regional Haze

Premise 1. There is uncertainty accompanying model predictions. “Uncertainty” is the

notion that model estimates will not perfectly predict observed air quality at any given location,

neither at the present time nor in the future. Uncertainty arises for a variety of reasons, for

example, limitations in the model’s formulation which may be due to an incomplete

representation in the model of physiochemical processes and/or meteorological and other input 

data base limitations, and uncertainty in forecasting future levels of emissions. States/Tribes

should recognize these limitations when preparing their modeled attainment demonstrations.

We recommend several qualitative means for recognizing model limitations and resulting

uncertainties when preparing an attainment demonstration. First, we recommend using models

in a relative sense in concert with observed air quality data (i.e., taking the ratio of future to

present predicted air quality and multiplying it times an “ambient” design value)4

. As described

later, we believe this approach should reduce some of the uncertainty attendant with using

absolute model predictions alone. Second, we recommend that a modeling analysis be preceded

by analyses of available air quality, meteorological, and emissions data to gain a qualitative

understanding of an area’s nonattainment problem. Such a description should be used to help

guide a model application and may provide a reality check on the model’s predictions. Third, we

recommend that States/Tribes use several model outputs, as well as other supporting analyses, to

provide corroborative evidence concerning the adequacy of a proposed strategy for meeting the

NAAQS. Modeling results and other supporting analyses can be weighed to determine whether

or not the resulting evidence suggests a proposed control strategy is adequate to meet the

NAAQS. Finally, we identify several activities/analyses which States/Tribes could undertake, if

they so choose, to apply models and corroborative approaches in subsequent reviews and

analyses of a control strategy, such as mid-course reviews. These subsequent reviews are useful

for determining whether a SIP is achieving progress as expected. 

Premise 2. For many areas, nested regional/urban model applications will be needed to

support the attainment demonstration. Available air quality data suggest ozone and PM2.5

concentrations approach levels specified in the NAAQS throughout much of the eastern U.S. and

in large parts of California (U.S. EPA 2004c, U.S. EPA 2004d). A number of analyses

(U.S.EPA, 1998b and U.S. EPA, 2005a) show that regional ozone and PM transport can impact
5

A modeling system includes a chemical model, an emissions model and a meteorological

model. Terms, such as this one, which are introduced using italics are defined more fully in a

glossary at the back of this guidance. “Modeling system” and “air quality model” are used

interchangeably. “Air quality model” means “modeling system” in this guidance.

6

EPA provides funding to five regional planning organizations to address regional haze

and related issues. http://www.epa.gov/air/visibility/regional.html

Draft 3.2- September 2006 4

areas several hundred miles or more downwind. The regional extent of ozone and PM transport

patterns and distances in some areas will likely necessitate nested regional model applications. 

This guidance identifies several modeling systems5

 with nesting capabilities to resolve

meteorological parameters, emissions, chemistry, and transport. We believe it is not beneficial

to identify any modeling system as the preferred, or “guideline model” for ozone, PM2.5 or

regional haze modeling. States/Tribes may use any appropriate modeling system provided that

the requirements of 40 CFR 51.112 are met. In this guidance, we provide certain criteria to

assist States/Tribes in justifying the use of such modeling systems. These criteria apply equally

to U.S.EPA models and alternative air quality model(s). The guidance also provides

recommendations for developing meteorological, air quality and emissions inputs used in nested

regional model applications, and makes suggestions for quality assuring inputs and evaluating

performance of emissions, meteorological and air quality models. 

Premise 3. Resource intensive approaches may often be needed to support an adequate

attainment demonstration. This follows from the regional nature of ozone and PM2.5

concentrations in the Eastern U.S. While we believe that existing and future regional reductions

in NOx and SO2 emissions will reduce ozone and PM2.5 over much of the eastern U.S., elevated

regional ozone and PM2.5 concentrations will continue to affect local strategies needed to attain

the NAAQS in the remaining nonattainment areas.

 This guidance recommends using regional modeling domains. Regional modeling

applications require coordination, quality assurance and management of data bases covering

large areas of the country. Resources used to run recommended models for generating

meteorological and emissions inputs and the air quality model itself can be substantial. 

States/Tribes facing the need to develop an attainment demonstration requiring resource

intensive techniques may wish to consider pooling resources in some manner. Examples might

include delegating responsibilities for certain parts of the analyses to a single State/Tribe which

can “specialize” in that kind of analysis. Another example might be formation of a regional

technical center to perform analyses as directed by its client group of States/Tribes (e.g., multistate and tribal organizations such as the Regional Planning Organizations (RPO) 6

, LADCO,

and the Ozone Transport Commission (OTC)). 
Draft 3.2- September 2006 5

Premise 4. High concentrations of ozone, PM2.5 and regional haze often have a common

origin. Ozone formation and formation of secondary particulates result from several common

reactions and reactants. Secondary particulates are a major part of PM2.5. Often similar sources

contribute precursors to both ozone and PM2.5. In some regions of the U.S., high regional ozone

and secondary particulates are observed under common types of meteorological conditions. 

Reducing PM2.5 is the principal controllable means for improving regional haze. Reducing PM2.5

precursors can also lead to reductions in ozone. Models intended to address secondary

particulate matter problems need also to be capable of simulating ozone formation and transport. 

U.S. EPA policy is to encourage “integration” of programs to reduce ozone, PM2.5 and regional

haze to ensure they do not work at cross purposes and to foster maximum total air quality benefit

for lower costs.

1.3.2 How is Measuring and Modeling Particulate Matter Different (and

Often More Complicated) than Ozone? 

Before modeling the expected PM2.5 benefits from emissions controls, it is important to

understand the unique and complicated aspects of measuring and modeling particulate matter. 

For many reasons, PM2.5 and regional haze modeling presents many more difficulties compared

to ozone modeling. In this section, we identify attributes of PM2.5 that are applicable to most

attainment or reasonable progress demonstrations. There are of course exceptions. As we

discuss in Section 11.0, States need to develop a conceptual description of the PM2.5 or regional

haze problem in each of their areas subject to a demonstration. If a substantially different picture

emerges from the general one presented in this section, modeling/analysis procedures which

differ from some of those we believe are generally applicable may be warranted.

Premise PM1. Particulate matter is a mixture. Unlike a compound (e.g., ozone) or an

element (e.g., lead), a mixture has components which (a) can behave independently of one

another (e.g., primary vs. secondary components) or (b) are related to one another in a complex

way (e.g., different secondary components). Thus, if one only considers PM2.5 as a single entity,

rather than as the sum of its major components, there is a greater risk of choosing an ineffective

control strategy. This follows, because models may not perform equally well in predicting major

components of PM2.5. Nevertheless, balancing errors could (erroneously) indicate good model

performance predicting PM2.5. If a control strategy focused on reducing a component of PM2.5

which was overestimated by the model, the subsequently observed impact on PM2.5 could be less

than expected. 

Characteristics of PM2.5 as a mixture and the possibility that models perform unevenly in

predicting the major components of the mixture have two important implications for our

guidance. First, the modeling should divide PM 2.5 into a half a dozen or so major components

and note the effects of a strategy on each. The effect on PM2.5 should be estimated as a sum of

the effects on individual components. Second, to reduce the effects of uneven performance and

possible major bias in predicting absolute concentrations of one or more components, models are

best used in a “relative” sense in concert with measured PM2.5 and estimated composition of the

measured PM2.5 derived from speciated measurements. That is, responses predicted by models
Draft 3.2- September 2006 6

should be applied to observed component concentrations derived from PM2.5 measurements and

composition of PM2.5 estimated from measurements of ambient species. 

Premise PM2. In most parts of the country, “Secondary” PM is a more important part of

PM2.5 than it is of PM10. Size-differentiated ambient particulate data suggest that mass of

particulate matter follows a bimodal distribution, with one peak (fine mode) reflecting particles

with aerodynamic diameters ~ 0.1-1.0 micrometers arising from nucleation and accumulation

phenomena, and a second (coarse mode) occurring with aerodynamic diameters in the range of

1.0-20 micrometers. As shown in Figure 1.1, derived from Wilson and Suh, 1997, mass of fine

particulate matter (i.e., PM2.5) attributable to coarse mode particulate matter < 2.5 micrometers is

relatively small. Mass attributable to fine mode particulate matter with aerodynamic diameters >

2.5 micrometers (i.e., coarse particulate matter) is also relatively small.

Origins of coarse and fine mode particulate matter are usually quite different. The former

results mostly from physical types of activities (e.g., crushing, grinding, resuspension due to

motions, etc.). Nearly all these activities result in particulate matter, with little subsequent

chemical change. We call such emissions of particulate matter “primary emissions”, because

they are measured in more or less the same form in which they are emitted. In contrast, origins

of fine mode particulate matter are more diverse. For example, some fine mode particulate

matter is directly emitted to the atmosphere as a result of combustion. Such emissions occur

either directly as particles or as a result of condensation which occurs very shortly after the

emissions occur. These are primary emissions, because what is measured in the ambient air is

essentially unchanged (chemically) from what is released. However, many fine mode particles

are the result of physicochemical reactions which occur in the atmosphere among gaseous

precursors or through absorption or adsorption onto previously existing aerosols. Such particles

constitute “secondary” particulate matter, because they undergo transformations in the

atmosphere causing the chemical and/or physical nature of what is measured to be different from

what is emitted. 

Because of the size distribution of ambient particulate matter, most measured PM2.5 is

likely to be fine mode particulate matter. As a result, it is dominated to a much larger extent

than PM10 by “secondary” particulate matter and primary particulate emissions arising from

combustion. Some of the physicochemical processes leading to secondary particulate matter

formation may take hours or days, as do some of the removal processes. Thus, many of the

sources of measured secondary particulate matter may not be local emitted sources. This implies

that modeling to support attainment demonstrations for PM2.5 (and, as we will discuss later,

regional haze-related applications) will need to cover a very large domain, and will need to

include chemical/physical mechanisms important in formation/removal of secondary particulate

matter. Because several of the processes are slow and first require thorough mixing with the

environment, spatially detailed treatment near sources of precursors may not be necessary. 

Individual treatment of precursor emissions from relatively nearby large sources of primary

PM2.5 may be needed on a case by case basis.
Draft 3.2- September 2006 7

Particle Size (aerodynamic diameter), micrometers

0.1 0.2 0.5 1.0 2.0 2.5 5.0 10.0 20.0

Incremental Mass / Incremental Log (diameter) 

Fine Mode Particles

Coarse Mode Particles

PM 2.5 "Coarse"

 PM

PM 10

Figure 1.1. Conceptual Diagram Of Particulate Matter Properties

(primary + secondary 

 particulate matter)

(primary particulate matter only)

Premise PM3. Regional haze is closely related to presence of high concentrations of fine

particulate matter. Light extinction results from scattering and absorption of light. Some

scattering occurs by gas molecules in pristine air (i.e., Rayleigh scattering). Nearly all remaining

light extinction is caused by the presence of aerosols. For any given mass, fine particles (i.e., <

2.5 :m) are more efficient at scattering light than are particles > 2.5 :m aerodynamic diameter. 

Further, certain components of PM2.5 are more efficient at scattering or absorbing light than

others. Many of the most efficient are secondary particulate species. For example, sulfates

(secondary), nitrates (secondary) and organic (secondary and primary) components scatter light

more efficiently than do primary particles composed of crustal material. Light extinction is also

exacerbated by high relative humidity. Water vapor combines with hygroscopic particulate

matter (e.g., sulfates and nitrates) to greatly increase the light scattering efficiency of these

species. Previously, we noted that secondary particulate matter is likely to comprise an
Draft 3.2- September 2006 8

important fraction of measured PM2.5. Secondary particulate matter will be even more important

as a cause of regional haze. This follows from the greater efficiency with which these already

important components of PM2.5 scatter light. This importance can be enhanced further by high

relative humidity, which is especially relevant in the Eastern U.S.

The discussion in the preceding paragraph suggests that modeling to assess reasonable

progress for regional haze will need to address secondary particulate matter. This, in turn, means

that large modeling domains will be necessary. Class I areas are generally likely to be far

removed from most sources of precursors for secondary particulate matter. Additionally, the

measure of visibility (deciviews) with which we are most concerned, addresses maximum range

of visibility. This measure reflects an effect which is an integrated one over a relatively large

distance. Thus, the need for a large domain, the prevalence of secondary particles, the relative

remoteness of Class I areas from most sources of precursors and the visibility measure of

greatest interest suggest that modeling related to reasonable progress may be done without a fine

degree of spatial resolution.

Premise PM4. Sampling anomalies associated with the Federal Reference Method for PM2.5

need to be considered as part of the model attainment demonstration. A NAAQS needs to

be related to observed health effects. In order to establish this link most clearly, the U.S. EPA

has adopted a Federal Reference Method (FRM) for measuring PM2.5 similar to procedures used

in epidemiological studies in which adverse health effects are associated with exposure to

PM2.5.(reference) Since the FRM is included in the definition of the NAAQS, the modeled

attainment test we recommend uses the measured concentrations of FRM PM2.5 as the “ground

truth”. The FRM sampling protocol is known to be a precise measurement, but it may not

accurately measure PM2.5 in the air. There are many known positive and negative sampling

artifacts associated with the measurement and analysis protocol. Among them are positive

artifacts associated with organics and water retained on the Teflon filter and negative artifacts

associated with volatile nitrates and organics. 

The attainment test recommends using speciated ambient data to estimate PM2.5 components. 

These measurements are from the Speciation and Trends Network (STN) for urban sites and the

IMPROVE network for Class I areas. The Speciation measurements use different protocols than

FRM measurements. Therefore, the species measured by STN and IMPROVE are often not

directly comparable to the FRM PM2.5. The attainment test recommends a set of default

procedures to adjust the speciation measurements to make them more comparable to FRM

measurements.

Premise PM5. Spatial gradients for primary particulate matter may be more pronounced

than those for secondary particulate matter or those for ozone. As previously noted,

secondary particulate matter and ozone result from an interaction of meteorology and chemistry

taking several hours to days. Because of the time scales and mixing required, sharp spatial

gradients in concentrations of these pollutants are unlikely. In contrast, primary particulate

matter is emitted in the form it appears at monitoring sites. It is likely that concentrations of

primary particulate matter are greatest near major source areas of primary particulate matter.
Draft 3.2- September 2006 9

The preceding implies that it may be necessary to estimate concentrations of primary

particulate matter using models with finer spatial resolution than is necessary for secondary

particles. Further, there may be several sources or concentrations of sources of primary

particulate matter within an area designated as “nonattainment” for PM2.5. The guidance will

need to address how to evaluate model performance and how to estimate whether attainment of

the NAAQS is likely in such locations.

Premise PM6. Seasonal differences are likely in emissions of PM2.5 and its precursors, as

well as in meteorological conditions affecting source/receptor relationships. Emissions from

several potentially important sources of PM2.5 , such as residential wood burning, wildfires,

agricultural burning, prescribed burning and biogenic sources have distinctive seasonal patterns. 

Further, meteorological factors which may affect PM2.5 or regional haze, such as relative

humidity, sunlight intensity, mixing heights, precipitation and temperature, have marked

seasonal differences in many parts of the United States. The annual NAAQS for PM2.5 and the

goals for regional haze address a composite of conditions measured over many days. To

understand how such composites respond to changes in emissions, it will be necessary to model

a variety of days with varying emissions and meteorological conditions. This implies that States

will need to develop base emissions estimates for a full year (or multiple years) or a

representative portion of days may need to be modeled from each season. 

Modeling for the 24-hour PM2.5 standard is further complicated by the fact that violations of the

24-hour standard may occur during different times of the year and may be caused by a variety of

different PM2.5 species and precursors. 

Premise PM7. Causes of PM2.5 concentrations which violate NAAQS may be diverse. 

Modeling approaches needed to address primary vs. secondary particulate matter differ in their

requirements. Earlier, we noted differing requirements for size and resolution of the modeling

domain. Another difference is the need to consider atmospheric chemistry in the modeling. It is

essential to have some understanding of the nature of an area’s PM2.5 or visibility problem before

modeling begins. Otherwise, a State runs the risk of selecting inappropriate analysis tools as

well as selecting a strategy which will prove to be ineffective at reducing its problem. Thus, a

State needs to perform data analysis before using air quality models. This analysis should be

used to develop a conceptual description of the problem at hand. The conceptual description

may then be used to select a broad strategy (e.g., do I focus on reducing primary or secondary

particulate matter or both?) as well as to help implement a modeling protocol to best address the

nature of the problem and the qualitative strategy which has been tentatively selected to address

it. The guidance needs to provide States with flexibility in choosing model(s) to address specific

problems.

Premise PM8. Ability of models to predict future concentrations of the components of

PM2.5 is limited by a variety of factors. Our ability to characterize emissions on a day to day

basis or on a source-specific basis is limited. Fully characterizing meteorological conditions on

any given day is also problematic. Further, for regulatory models to be tractable, they must

characterize chemical and physical processes by simplifying them in some reasonable manner. 
Draft 3.2- September 2006 10

In some cases, most notably for secondary organic particulate matter, the extent to which current

simplifications are reasonable is uncertain. These limitations (and others) add to the uncertainty

of the model’s ability to accurately predict concentrations of PM2.5 and its components at a given

time and location.

The preceding paragraph has several implications for using models to demonstrate future

attainment of a NAAQS for PM2.5 or reasonable progress goals for regional haze. It suggests that

we should focus on composite responses of the model averaged over several days to help

circumvent the problem of not knowing all of the details on an individual day. This composite

response then needs to be related to the form of the air quality goal in some manner. Limitations

in available models and their underlying data bases also suggest that the guidance should

recognize a need for performing other, corroboratory analyses to confirm conclusions reached

with a model. 

1.4 What Topics Are Covered In This Guidance?

This guidance addresses two broad topics: Part I, “How do I use results of models and

other analyses to help demonstrate attainment?”, and Part II, “How should I apply air quality

models to produce results needed to help demonstrate attainment?”. Part I is divided into 8

sections (i.e., Sections 2-9). Part II consists of 9 sections (Sections 10-18).

Part I ( “How do I use results of models and other analyses to help demonstrate

attainment?”) begins in Section 2 with an overview of the procedure for using modeling results

to help demonstrate attainment of the 8-hour ozone NAAQS and the annual and 24-hour

NAAQS for PM2.5 . Section 2 also summarizes the modeled test for regional haze reasonable

progress.

Section 3 describes the recommended modeled attainment tests. 

Section 4 describes the attainment test for ozone in more detail.

Section 5 describes the attainment tests for each of the two NAAQS for PM2.5.in more

detail. 

Section 6 outlines a recommended test to assess whether a proposed control strategy will

lead to air quality changes which meet reasonable progress goals to reduce regional haze. 

Section 7 describes how supporting analyses should be performed to complement the

attainment test and the reasonable progress test, as well as how it should be used in a weight of

evidence determination. 

Section 8 identifies several data gathering activities and analyses which States/Tribes

could undertake to enhance the credibility of the modeling and corroborative analyses to support

subsequent reviews on progress toward attainment and/or reasonable progress. 
Draft 3.2- September 2006 11

Section 9 identifies the documentation necessary to adequately describe the analyses

used to demonstrate attainment of the ozone NAAQS.

Part II (“How should I apply air quality models to produce results needed to help

demonstrate attainment?”) begins in Section 10 with an overview of the topics to be covered.

Section 11 identifies a series of meteorological, emissions and air quality data analyses

which should be undertaken to develop a qualitative description of an area’s nonattainment

problem prior to a model application. As we describe, this qualitative description should be used

to guide the subsequent model application. 

Section 12 describes the purpose, function, and contents of a modeling protocol.

Section 13 addresses what criteria should be considered in choosing a model to support

the ozone and PM attainment demonstrations and reasonable progress demonstrations. Several

guidelines are identified for accepting the use of a model for this purpose. 

Section 14 provides guidance for selecting suitable time periods (or episodes) to model

for an attainment demonstration or reasonable progress demonstration. Topics include a

discussion of the form of the NAAQS and its resulting implications for episode selection.

Section 15 identifies factors which should be considered in choosing; a model domain,

the horizontal and vertical resolution, and the initial/boundary conditions for an air quality

modeling application.

Section 16 addresses how to develop and evaluate meteorological inputs for use in a

modeling exercise supporting an attainment demonstration or reasonable progress

demonstration.. 

Section 17 discusses how to develop appropriate emissions estimates for use in the

selected air quality model. 

Section 18 outlines the structure of model performance evaluations and discusses the use

of diagnostic analyses. 

The guidance concludes with references and a glossary of important terms which may be

new to some readers.
Draft 3.2- September 2006 12

Part I. How Do I Use Results Of Models And Other

Analyses To Help Demonstrate Attainment?
7

As detailed in Section 7, additional corroborative analyses are still needed to supplement

the modeled attainment test, even when predicted ozone and/ or PM2.5 concentrations are less

than the NAAQS.

Draft 3.2- September 2006 13

2.0 What Is A Modeled Attainment Demonstration?--An Overview

A modeled attainment demonstration consists of (a) analyses which estimate whether

selected emissions reductions will result in ambient concentrations that meet the NAAQS or

progress goals and (b) an identified set of control measures which will result in the required

emissions reductions. As noted in Section 1, this guidance focuses on the first component of an

attainment demonstration, that is, completion and interpretation of analyses to estimate the

amount of emission reduction needed to attain the NAAQS or progress goals. Emission

reduction strategies should be simulated by reducing emissions from specific source categories

rather than through broad “across-the-board” reductions from all sources.

States/Tribes should estimate the amount of emission reduction needed to demonstrate

attainment of the NAAQS using the modeled attainment test. We recommend a similar modeled

test for demonstrating reasonable progress toward reducing regional haze. In addition, a

State/Tribe should consider a broader set of model results, as well as perform a set of other

corroboratory analyses to further support whether a proposed emission reduction will lead to

attainment of the NAAQS or reasonable progress.

2.1 What Is The Recommended Modeled Attainment Test for Ozone and PM2.5?-

An Overview 

A modeled attainment test is an exercise in which an air quality model is used to simulate

current and future air quality. If future estimates of ozone and/or PM2.5 concentrations are less

than the NAAQS, then this element of the attainment test is satisfied7

. Our recommended test is

one in which model estimates are used in a “relative” rather than “absolute” sense. That is, we

take the ratio of the model’s future to current (baseline) predictions at monitors. We call these

ratios, relative reduction factors. Future ozone and/or PM2.5 concentrations are estimated at

existing monitoring sites by multiplying a modeled relative response factor at locations “near”

each monitor by the observation-based, monitor-specific, “baseline” design value. The resulting

predicted “future concentrations” are compared to NAAQS. 

The test for ozone is based on the calculation of a single mean ozone RRF for each

monitor. The PM2.5 attainment test is more complicated and reflects the fact that PM2.5 is a

mixture. In the test, ambient PM2.5 is divided into major components. These are

- mass associated with sulfates

- mass associated with nitrates

-mass associated with ammonium

- mass associated with organic carbon
Draft 3.2- September 2006 14

- mass associated with elemental carbon

-mass associated with particle bound water

- mass associated with “other”primary inorganic particulate matter

-and passively collected mass

A separate RRF is calculated for each of the PM2.5 components (except passive mass). 

We call each of these site-specific ratios, component-specific RRFs. Future PM2.5 design values

are estimated at existing monitoring sites by multiplying modeled relative reduction factors 

“near” each monitor times the observed “component specific design value”. This latter quantity

is estimated using measured site-specific design values for PM2.5 in concert with available

measured composition data. Future site-specific PM2.5 design values at a site are estimated by

adding the future year values of the seven PM2.5 components. If all future site-specific PM2.5

design values are # the concentration specified in the NAAQS, the test is passed.

2.2 What Is The Recommended Modeled Test For Reasonable Progress?—An

Overview

The recommended modeled test for reasonable progress reducing regional haze is similar

conceptually to the recommended tests for the two NAAQS for PM2.5. Models are used to

develop relative reduction factors for each of 6 components of particulate matter between a base

period (2000-2004) and a future 5-year period which will be reviewed in 2018. Components

used for regional haze-related applications differ slightly from those used for NAAQS-related

applications. They are:

- mass associated with sulfates;

- mass associated with nitrates;

- mass associated with organic carbon;

- mass associated with elemental carbon;

- mass associated with inorganic particulate matter (i.e., crustal material);

- mass associated with coarse particulate matter (i.e., PM10 - PM2.5).

 

Current speciated measurements in a Class I area are used in an empirically derived

equation to estimate light extinction for each day with measurements. Days are ranked

according to their resulting light extinction (measured in deciviews). This ranking is used to

identify the 20% of days with worst and 20% of days with best visibility during each year in the

base period. The 20% worst and best days are examined to estimate appropriate observed

concentrations for the components of PM on “best” and “worst” days. 

Observed component concentrations are multiplied by the corresponding relative

reduction factors to estimate future concentrations for each component on “best” and “worst”

days. Future component concentrations are then inserted into the equation relating light

extinction to concentrations of particulate matter. The resulting estimates for future light

extinction on “best” and “worst” days are compared with observations made during the base

period to see whether the goals for reasonable progress are realized.
Draft 3.2- September 2006 15

2.3 What Does A Recommended Supplemental Analysis/Weight Of Evidence

Determination Consist Of? --An Overview

As we describe in more detail in Section 7, States/Tribes should always perform

complementary analyses of air quality, emissions and meteorological data, and consider

modeling outputs other than the results of the attainment test. Such analyses are instrumental in

guiding the conduct of an air quality modeling application. Sometimes, the results of

corroboratory analyses may be used in a weight of evidence determination to show that

attainment is likely despite modeled results which may be inconclusive. The further the

attainment test is from being passed, the more compelling contrary evidence produced by

corroboratory analyses must be to draw a conclusion differing from that implied by the modeled

attainment test results. If a conclusion differs from the outcome of the modeled test, then the

need for subsequent review (several years hence) with more complete data bases is increased. If

the test is failed by a wide margin (e.g., future design values outside the recommended range at

an individual site or multiple sites/locations), it is far less likely that the more qualitative

arguments made in a weight of evidence determination can be sufficiently convincing to

conclude that the NAAQS will be attained. Table 2.1 contains guidelines for assessing when

corroboratory analyses and/or weight of evidence determinations may be appropriate. 

Table 2.1 Guidelines For Weight of Evidence Determinations

Results of Modeled Attainment Test Supplemental Analyses 

Ozone Annual PM2.5 24-Hour PM2.5

Future Design

Value < 82 ppb,

all monitor sites

Future Design

Value <14.5

ug/m3, all

monitor sites 

Future Design

Value <62 ug/m3,

all monitor sites 

 Basic supplemental analyses

should be completed to

confirm the outcome of the

modeled attainment test 

Future Design

Value 82 - 87

ppb, at one or

more sites/grid

cells

Future Design

Value 14.5-15.5

ug/m3, at one or

more sites/grid

cells

Future Design

Value 62-67

ug/m3, at one or

more sites/grid

cells

 A weight of evidence

demonstration should be

conducted to determine if

aggregate supplemental

analyses support the

modeled attainment test
Results of Modeled Attainment Test Supplemental Analyses 

Ozone Annual PM2.5 24-Hour PM2.5

8

 Regional modeling completed by EPA indicates that, on average, considerable amounts

of precursor control (e.g., 20-25 percent or more) will be needed to lower projected ozone design

values by 3 ppb or PM2.5 annual design values by 0.5 ug/m3 or 24-hour PM2.5 design values by 3

ug/m3.

Draft 3.2- September 2006 16

Future Design

Value > 88 ppb,

at one or more

sites/grid cells

Future Design

Value > 15.5

ug/m3, 

at one or more

sites/grid cells

Future Design

Value > 68

ug/m3, 

at one or more

sites/grid cells

 More qualitative results are

less likely to support a

conclusion differing from the

outcome of the modeled

attainment test8.

In a weight of evidence (WOE) determination, States/Tribes should review results from

several diverse types of air quality analyses, including results from the modeled attainment test. 

As a first step, States/Tribes should note whether or not the results from each of these analyses

support a conclusion that the proposed strategy will meet the air quality goal. Secondly,

States/Tribes should weigh each type of analysis according to its credibility, as well as its ability

to address the question being posed (i.e., is the strategy adequate for meeting the NAAQS by a

defined deadline?). The conclusions derived in the two preceding steps are combined to make an

overall assessment of whether meeting the air quality goal is likely. This last step is a qualitative

one. If it is concluded that a strategy is inadequate to demonstrate attainment, a new strategy is

selected for review, and the process is repeated. States/Tribes should provide a written rationale

documenting how and why the conclusion is reached regarding the adequacy of the final selected

strategy. Results obtained with air quality models are an essential part of a weight of evidence

determination and should ordinarily be very influential in deciding whether the NAAQS will be

met. 

2.4 Why Should A Model Be Used In A “Relative” Sense And Why May

Corroboratory Analyses Be Used In A Weight Of Evidence Determination?

The procedure we recommend for estimating needed emission reductions differs from that in

past guidance (U.S. EPA, 1996c) for ozone and PM in two major respects. First, we recommend

a modeled attainment test in which model predictions are used in a relative rather than absolute

sense. Second, the role of the weight of evidence determination, when used, has been expanded. 

That is, these results can now be used as a rationale for concluding that a control strategy will

meet the NAAQS, even though the modeled attainment test alone may not be conclusive. There

are several reasons why we believe these changes are appropriate.
Draft 3.2- September 2006 17

1. Starting with an observed concentration as the base value reduces problems in

interpreting model results. If a model under (or over) predicts an observed daily maximum

concentration, the appropriate target prediction is not as clear as might be desired. For example,

if an 8-hour daily maximum ozone concentration of 120 ppb were observed and a model

predicted 100 ppb on that day, should the target for the day still be 84 ppb? In the relative

attainment test, observed data is used to define the target concentration. This has the effect of

anchoring the future concentrations to a “real” ambient value. Although good model

performance remains a prerequisite for use of a model in an attainment demonstration, problems

posed by less than ideal model performance on individual days are reduced by the new

procedure. 

1. The form of the 8-hour ozone and PM2.5 NAAQS necessitates such an attainment test. 

The NAAQS for ozone and PM2.5 requires ambient data to be averaged over three consecutive

years. This presents difficulties using the resource-intensive Eulerian models we believe are

necessary to capture spatially differing, complex non-linearities between ambient ozone and

precursor emissions. That is, it is difficult to tell whether or not a modeled exceedance obtained

on one or more days selected from a limited sample of days is consistent with meeting the

NAAQS. To do so would require modeling several years and, perhaps, many strategies. This

problem is reduced by using the monitored design value, as an inherent part of the modeled

attainment test. 

3. PM2.5 consists of a diverse mix of primary and secondary components. This raises a

concern about a model’s potential inability to correctly predict values for each component which

are proportional to the observed mix of components. Failure to predict major measured

components of PM2.5 in the correct proportion increases the possibility of choosing ineffective

control strategies on the basis of incorrect model predictions. This possibility is reduced if the

model responses are instead applied to components of PM2.5 which are derived from

measurements.

4. Model results and projections will continue to have associated uncertainty. The procedure

we recommend recognizes this by including modeling plus other analyses to determine whether

all available evidence supports a conclusion that a proposed emission reduction plan will suffice

to meet the NAAQS. For applications in which the modeled attainment test is not passed, a

weight of evidence analysis may be used to support a determination that attainment will be

achieved, despite the results of the modeled attainment test. The weight of evidence

determination includes several modeling results which are more difficult to relate to the form of

the 8-hour ozone and 24-hour PM2.5 NAAQS. These results address relative changes in the

frequency and intensity of high modeled ozone or PM2.5 concentrations on the sample of days

selected for modeling. If corroboratory analyses produce strong evidence that a control strategy

is unlikely to meet the NAAQS, then the strategy may be inadequate, even if the modeled

attainment test is passed.
Draft 3.2- September 2006 18

5. Focusing the modeled attainment test only at monitoring sites could result in control

targets which are too low if the monitoring network is limited or poorly designed. We

recommend a test which includes a review of the strategy’s impact at locations without monitors. 

This exercise provides a supplemental test to determine whether there is a need for further action

despite passing the modeled attainment test at all monitoring sites. While this test may indicate

potential unmonitored violations, ultimately, the best way to account for a limited or poorly

designed monitoring network is to use the model results, or other available analyses, to help

determine locations where additional monitors should be sited. 
9

The units for ozone is ppb and the units for PM2.5 is ug/m3.

Draft 3.2- September 2006 19

3.0 What Is The Recommended Modeled Attainment Test?

In Section 2, we provided an overview of the recommended modeled attainment test. In this

section we provide more details on the calculation of baseline design values, the definition of

“nearby grid cells”, and an “unmonitored area analysis” which provides estimates of future year

values in unmonitored areas. Sections 4, 5, and 6 provide more specific details and examples for

attainment tests for ozone and PM2.5, and the reasonable progress test for regional haze

respectively. 

We begin by establishing the basic equation on which all of the tests are built upon. 

Equation (3.1) describes the recommended modeled attainment test, applied near monitoring site

I.

(DVF)I = (RRF)I (DVB)I (3.1)

where

(DVB)I

 = the baseline concentration monitored at site I, units in ppb or ug/m39;

(RRF)I = the relative response factor, calculated near site I, unitless 

The relative response factor is the ratio of the future concentration predicted near a

monitor (averaged over multiple days) to the baseline concentration predicted near the

monitor (averaged over the same days), and

(DVF)I = the estimated future design value for the time attainment is required, ppb or ug/m3.

Equation (3.1) looks simple enough. However, several issues must be resolved before

applying it. 

(1) How is a “site-specific” baseline design value ((DVB)I) calculated? 

(2) In calculating the (RRF)I

 , what do we mean by “near” site I? 

(3) Several surface grid cells may be “near” the monitor, which one(s) of these should be used to

calculate the (RRF)I

 ?

(4) How do you calculate future design values in unmonitored areas?

(5) Which base year emissions inventory should be projected to the future for the purpose of

calculating RRFs?
10See 40CFR Part 50, Appendix N for a discussion of data completeness in applying the

monitored attainment test.

Draft 3.2- September 2006 20

(6) Which future year should emissions be projected to in order to assess attainment using the

modeled attainment test? 

3.1 Calculating site-specific baseline concentrations. 

The modeled attainment test is linked to the form of the ozone and PM2.5 NAAQS through

the use of monitored design values. Official design values are calculated in different ways for 8-

hour ozone, annual average PM2.5, and 24-hour average PM2.5. The following is a brief

description of the calculations:

Ozone

The 8-hour ozone design value is calculated as the 3 year average of the fourth highest

monitored daily 8-hour maximum value at each monitoring site. The standard is met if the

design value is <0.8 ppm (in practice, this translates to < 85 ppb). The detailed description of the

methodology can be found in 40 CFR Part 50.10, Appendix I.

Annual Average PM2.5

The annual average PM2.5 design value is met if, over a consecutive three year period, the

average arithmetic mean concentration of PM2.5 is less than or equal to 15.0 :g/m3

. The annual

arithmetic mean at a monitoring site is calculated by averaging the four quarterly arithmetic

mean concentrations observed during a calendar year at the site. The annual mean concentration

averaged over three years must be < 15.0 :g/m3 at all monitoring sites. The details can be found

in 40CFR Part 58, Appendix N

In some cases, two nearby sites can be spatially averaged to determine the annual average design

value for both sites. For details on spatial averaging requirements see 40 CFR Part 58, Appendix

D, Section 2.8. 

24-Hour Average PM2.5

The 24-hr NAAQS for PM2.5 is met if the 98th percentile 24-hour average concentration of

particulate matter with aerodynamic diameter < 2.5 micrometers, averaged over three

consecutive years, is < 65 :g/m3

.

10 The test applies at all monitoring sites--spatial averaging is

not allowed for the 24-hour NAAQS. The “98th percentile” concentration (and, thus, the design

value concentration) depends on the number of days on which PM2.5 is monitored during a year. 

Table 3.3 is derived from information presented in 40CFR Part 50, Appendix N. The table

shows how to determine the 98th percentile concentration for a given year. 
Draft 3.2- September 2006 21

Table 3.3. Determining the 98th Percentile from Number of Samples Collected in a Year

(1)

Number of Samples Collected in a Year

(2)

Value Corresponding to 98th Percentile for

the Year

1 - 50 Highest Observed Concentration

50 - 100 2nd Highest Concentration

101 - 150 3rd Highest Concentration

151-200 4th Highest Concentration

201 - 250 5th Highest Concentration

251 - 300 6th Highest Concentration

301 - 350 7th Highest Concentration

351 - 366 8th Highest Concentration

 

Thus, if valid samples were collected at a monitoring site once every 3 days (i.e., ~ 122 valid

samples/year), the design value would be based on the 3rd highest observation from each year

(averaged over 3 years). Attainment is reached if the design value at the site is < 65 :g/m3

.

How is a “site-specific” baseline design value ((DVB)I) calculated? 

The baseline measured concentrations at each monitoring site is the anchor point for future

year projected concentrations. The baseline design values are projected to the future using

RRFs. In practice, the choice of the baseline design value can be critical to the determination of

the estimated future year design values. Therefore, careful consideration should be given to the

calculation of baseline values. The baseline design values should have the following attributes:

1) Should be consistent with the form of the applicable NAAQS.

2) Should be easy to calculate.

3) Should represent the baseline inventory year.

4) Should take into account the year-to-year variability of meteorology.

5) Should take into account the year-to-year variability of emissions.

Several possible methodologies to calculate baseline design values are:

1) The designation design value period (i.e. 2001-2003).

2) The design value period that straddles the baseline inventory year (e.g., the 2001-2003 design

value period for a 2002 baseline inventory year).

3) The highest (of the three) design value periods which include the baseline inventory year (e.g.
Draft 3.2- September 2006 22

the 2000-2002, 2001-2003, and 2002-2004 design value periods for a 2002 baseline inventory

year).

4) The average (of the three) design value periods which straddle the baseline inventory year.

For the modeled attainment tests we recommend using the average of the three design value

periods which include the baseline inventory year (choice number 4 from above). Based on the

attributes listed above, the average of the three design value periods best represents the baseline

concentrations, while taking into account the variability of the meteorology and emissions (over

a five year period).

The three design values that are averaged in the calculation cover a five year period, but the

average design value is not a straight five year average. It is, in effect, a weighted average of the

annual averages. For example, given a baseline inventory year of 2002, the years used to

calculate the average design value range from 2000-2004. In the average of the 2000-2002,

2001-2003, and 2002-2004 periods, 2002 is “weighted” three times, 2001 and 2003 are weighted

twice, and 2000 and 2004 are weighted once. This has the desired effect of weighting the

projected ozone or PM2.5 values towards the middle year of the five year period, which is the

emissions year (2002 in this example). The average design value methodology is weighted

towards the inventory year (which is the middle year) and also takes into account the emissions

and meteorological variability that occurs over the full five year period (although the emissions

and meteorology from the other years are weighted less than the middle year of the 5 year

period). Because of this, the average weighted design value is thought to be more representative

of the baseline emissions and meteorology period than other methodologies such as choosing the

highest single design value period. 

Additionally, the average design value will be more stable (less year to year variability) than

any single design value period. An analysis of recent ambient design value data at 471 ozone

monitors, over the period 1993-2004, shows that the median standard deviation of design values

was 3.3 ppb whereas the standard deviation of the 5 year weighted average design values was 2.4

ppb (Timin, 2005a). Also, moving from the period ending in 2003 to the period ending in 2004,

the median change in the ozone design values was 4.0 ppb. The median change in the 5 year

weighted average ozone design values was only 0.8 ppb (there was not a long enough data

record to do the same analyses for PM2.5) . These analyses show that the average design values

are clearly more stable and will therefore provide a “best estimate” baseline year design value

(DVBI

) for use in future year model projections.

The recommended averaging technique assumes that at least five complete years of ambient

data is available at each monitor. In some cases there will less than five years of available data

(especially at relatively new monitoring sites). In this case we recommend that data from the

monitor is used if there is at least three consecutive years of data. If there are three years of data

then the baseline design value will be based on a single design value. If there are four years of

data then the baseline design value will be based on an average of two design value periods. If a

site has less than three years of data, then the site should not ordinarily be used in the attainment

test. 
11The “baseline design value” is an average of several design values and thus is

technically not a design value. The guidance continues to refer to the average design values as

“design values” even though they are based on averages of observed design values.

12 The average design value should carry one significant figure to the right of the decimal

point for ozone and 24-hour PM2.5 and 2 significant figures for annual PM2.5. There are several

calculations in the modeled attainment test which carry the tenths digit or hundreths digit. We

have found that rounding and/or truncating concentrations and RRFs can lead to an overestimate

or underestimate of the impact of emissions controls. In some cases, a few tenths of a ppb

change (or a few tenths of a percent reduction) can be meaningful. 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

hundreths 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.

Draft 3.2- September 2006 23

Calculating site-specific “baseline” design values11 (DVB) to use in the attainment test

Example 3.1

Given:

(1) The baseline inventory year is 2002 (i.e., 2002 emissions are being modeled).

(2) For purposes of illustration, suppose the area contains only three ozone monitors.

Find: The appropriate site-specific baseline design values to use in the modeled attainment test.

Solution: Since the inventory reflects 2002, we need to examine monitored design values for

overlapping 3-year periods that include 2002. The three design values are then averaged for

each site. These are the values for site-specific baseline design values (DVB) in the modeled

attainment test. The procedure is shown in Table 3.1.

Table 3.1 Example Illustrating Calculation Of Baseline Design Values

Monitor 2000-2002

Design Value,

ppb

2001-2003

Design

Value, ppb

2002-2004

Design

Value, ppb

Baseline Design Value

(DVB) Used In The

Modeled Attainment

Test, ppb

1 88 87 90 88.312

2 86 84 91 87.0
Monitor 2000-2002

Design Value,

ppb

2001-2003

Design

Value, ppb

2002-2004

Design

Value, ppb

Baseline Design Value

(DVB) Used In The

Modeled Attainment

Test, ppb

Draft 3.2- September 2006 24

3 88 86 85 86.3

3.2 Identifying surface grid cells near a monitoring site. 

There are three reasons why we believe it is appropriate, in the modeled attainment test, to

consider cells “near” a monitor rather than just the cell containing the monitor. First, one

consequence of a control strategy may be “migration” of a predicted peak. If a State were to

confine its attention only to the cell containing a monitor, it might underestimate the RRF (i.e.,

overestimate the effects of a control strategy). Second, we believe that uncertainty in the

formulation of the model and the model inputs is consistent with recognizing some leeway in the

precision of the predicted location of daily maximum ozone concentrations. Finally, standard

practice in defining a gridded modeling domain is to start in the southwest corner of the domain,

and determine grid cell location from there. Considering several cells “near” a monitor rather

than the single cell containing the monitor diminishes the likelihood of inappropriate results

which may occur from the geometry of the superimposed grid system.

Earlier guidance (U.S. EPA,1996a) has identified 15 km as being “near” a site. This is also

consistent with the broad range of intended representativeness for urban scale ozone monitors

identified in 40CFR Part 58, Appendix D and . As noted in section 1.3, secondary particulate

matter is likely to constitute an important fraction of PM2.5. Further, a major purpose of urban

monitoring performed to determine attainment of the NAAQS for PM2.5 is to estimate likely

exposure to PM2.5 concentrations over 24-hour periods. For PM2.5, sites having “neighborhood”

or “urban” scales of representativeness are generally most suitable for estimating exposure or

compliance with the NAAQS. Because of the rather long sampling time (i.e., 24 hours) inherent

in the PM2.5 NAAQS, locations within the lower range of “urban” scale (4-50 km) would seem

consistent with a definition of “near” a site. Therefore, we recommend following the same

ground rules used for defining “near” as for ozone modeling (U.S.EPA, 1999b).

For ease in computation, States/Tribes may assume that a monitor is at the center of the cell

in which it is located and that this cell is at the center of an array of “nearby” cells. The number

of cells considered “nearby” (i.e., within about a 15 km radius of) a monitor is a function of the

size of the grid cells used in the modeling. Table 3.2 provides a set of default recommendations

for defining “nearby” cells for grid systems having cells of various sizes. Thus, if one were

using a grid with 4 km grid cells, “nearby” is defined by a 7 x 7 array of cells, with the monitor

located in the center cell. 

The use of an array of grid cells near a monitor may have a large impact on the RRFs in

“oxidant limited” areas (areas where NOx decreases may lead to ozone or secondary PM

increases) and in areas with large gradients in primary PM2.5. The array methodology could lead
13The appropriate size of the array for horizontal grid cells < 4km should be discussed

with the appropriate U.S EPA Regional Office.

Draft 3.2- September 2006 25

to unrealistically small or large RRFs, depending on the specific case. Care should be taken in

identifying an appropriate array size for these areas. States/Tribes may consider the presence of

topographic features, demonstrated mesoscale flow patterns (e.g., land/sea, land/lake interfaces),

the density of the monitoring network, the density of emissions (especially primary PM), and/or

other factors to deviate from our default definitions for the array of “nearby” grid cells, provided

the justification for doing so is documented. 

Table 3.2. Default Recommendations For Nearby Grid Cells Used To Calculate RRF’s 

Size of Individual Cell, km Size of the Array of Nearby Cells, unitless

4 - 513 7 x 7

>5 - 8 5 x 5

>8 - 15 3 x 3

>15 1 x 1

3.3 Choosing model predictions to calculate a relative response factor (RRF)I

near a monitor. 

8-Hour Ozone NAAQS

Given that a model application produces a time series of estimated 1-hour ozone

concentrations (which can be used to calculate running 8-hour averages), what values should be

chosen from within the time series? We recommend choosing predicted 8-hour daily maximum

concentrations from each modeled day (excluding “ramp-up” days) for consideration in the

modeled attainment test. The 8-hour daily maxima should be used, because they are closest to

the form of concentration specified in the NAAQS.

The second decision that needs to be made is, “which one(s) of the 8-hour daily maxima

predicted in cells near a monitor should we use to calculate the RRF?” We recommend choosing

the nearby grid cell with the highest predicted 8-hour daily maximum concentration with

baseline emissions for each day considered in the test, and the grid cell with the highest

predicted 8-hour daily maximum concentration with the future emissions for each day in the test. 

Note that, on any given day, the grid cell chosen with the future emissions need not be the same

as the one chosen with baseline emissions. 

We believe selecting the maximum (i.e., peak) 8-hour daily maxima on each day for

subsequently calculating the relative response factor (RRF) is preferable for several reasons. 
Draft 3.2- September 2006 26

First, it is likely to reflect any phenomenon which causes peak concentrations within a plume to

migrate as a result of implementing controls. Second, it is likely to take better advantage of data

produced by a finely resolved modeling analysis.

The relative response factor (RRF) used in the modeled attainment test is computed by taking

the ratio of the mean of the 8-hour daily maximum predictions in the future to the mean of the 8-

hour daily maximum predictions with baseline emissions, over all relevant days. The procedure

is illustrated in Example 3.2.

Example 3.2

Given: (1) Four primary days have been simulated using baseline and future emissions.

(2) The horizontal dimensions for each surface grid cell are 12 km x 12 km.

(3) In each of the 9 grid cells “near” a monitor site I, the maximum daily predicted future

concentrations are 87.2, 82.4, 77.5, and 81.1 ppb.

(4) In each of the 9 grid cells “near” a monitor site I, the maximum daily predicted baseline 8-

hour daily maximum ozone concentrations are 98.3, 100.2, 91.6, and 90.7 ppb.

Find: The site-specific relative response factor for monitoring site I, (RRF)I

Solution:

(1) For each day and for both baseline and future emissions, identify the 8-hour daily maximum

concentration predicted near the monitor. Since the grid cells are 12 km, a 3 x 3 array of cells is

considered “nearby” (see Table 3.2). 

(2) Compute the mean 8-hour daily maximum concentration for (a) future and (b) baseline

emissions.

Using the information from above,

(a) (Mean 8-hr daily max.)future = (87.2 + 82.4 + 77.5 + 81.1)/4 = 82.1 ppb

 and

(b) (Mean 8-hr daily max.)baseline = (98.3 + 100.2 + 91.6 + 90.7)/4 = 95.2 ppb

(3) The relative response factor for site I is

(RRF)I = (mean 8-hr daily max.)future/(mean 8-hr daily max.)baseline

 = 82.1/95.2 = 0.862
Draft 3.2- September 2006 27

Figure 3.1. Choosing Ozone Predictions To Estimate RRF's

(a) Predictions With Baseline Emissions

(b) Predictions With Future Emissions

Day 1 Day 2 Day 3 Day 4

Day 1 Day 2 Day 3 Day 4

97.2 95.5 96.2

97.1 95.2 89.1

97.2 98.3 97.6

100.2 98.5 98.1

99.1 97.3

95.4 97.9

100.0

99.5

87.8 90.1 89.9 

90.9 91.6 88.7 

88.5 89.4 90.2 

85.9 

87.9 

86.9 

90.5 90.7 

87.3 88.4 

87.9 88.9

86.1 86.8

84.3

85.8 87.2

86.2

85.4

84.5

86.9

82.2

82.4

80.8

79.9

81.4 77.8

81.2

80.5

80.1

72.1 76.1 75.5

74.3

77.4 75.6 

74.6 77.5

76.9

75.4 78.8

80.8 79.5 80.9

80.4 76.9

79.8

81.1 

98.3 100.2 91.6 90.7

87.2 82.4 77.5 81.1

Mean Baseline Ozone Concentration = (98.3 + 100.2 + 91.6 + 90.7) / 4 = 95.2 ppb 

Mean Future Ozone Concentration = (87.2 + 82.4 + 77.5 + 81.1) / 4 = 82.1 ppb

Day 1 Day 2 Day 3 Day 4

97.2 96.2

97.1 95.2 89.1

97.2 98.3 97.6

100.2 98.5 98.1

99.1 97.3

95.4 97.9

100.0

99.5

87.8 90.1 89.9 

90.9 91.6 88.7 

88.5 89.4 90.2 

85.9 

87.9 

86.9 

90.5 90.7 

87.3 88.4 

87.9 88.9

98.3 100.2 91.6 90.7

PM2.5 NAAQS

For PM2.5 applications, we recommend choosing predicted 24-hour average concentrations

from each modeled day (excluding “ramp-up” days) for consideration in the modeled attainment

test. The 24-hour average values should be used, because they are the basis for design value

calculations. 

The second decision that needs to be made is, “which of the 24-hour average values

predicted in cells near a monitor should we use to calculate the RRF?” We recommend taking

the spatially averaged value of the nearby predictions. Each component-specific relative

response factor (RRF)j

 used in the modeled attainment test is computed by taking the ratio of the

mean of the spatially averaged daily predictions in the future to the mean of the spatially
Draft 3.2- September 2006 28

averaged daily predictions with current emissions. The procedure is the same as the ozone

example presented above, except the spatial mean is used instead of the highest concentration in

near the monitor. 

3.4 Estimating design values at unmonitored locations: what is an unmonitored area

analysis and why is it needed?

An additional review is necessary, particularly in nonattainment areas where the ozone or

PM2.5 monitoring network just meets or minimally exceeds the size of the network required to

report data to Air Quality System (AQS). This review is intended to ensure that a control

strategy leads to reductions in ozone or PM2.5 at other locations which could have baseline (and

future) design values exceeding the NAAQS were a monitor deployed there. The test is called

an “unmonitored area analysis”. The purpose of the analysis is to use a combination of model

output and ambient data to identify areas that might exceed the NAAQS if monitors were located

there. 

The unmonitored area analysis should identify areas where future year design values are

predicted to be greater than the NAAQS. The unmonitored area analysis for a particular

nonattainment area is intended to address potential problems within or near that nonattainment

area. The analysis should include, at a minimum, all nonattainment counties and counties

surrounding the nonattainment area (located within the State). In large States, 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 downwind areas should be evaluated on a case by case basis. In order to examine

unmonitored areas in all portions of the domain, it is recommended to use interpolated spatial

fields of ambient data combined with gridded modeled outputs.

3.4.1 Why does the unmonitored area analysis need to use both ambient

data and model output?

Ambient data can be interpolated to provide a set of spatial fields. The spatial fields will

provide an indication of concentrations in monitored and unmonitored areas. But a simple

interpolation of the 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, such as adding a nugget effect or a trend

surface). The interpolation technique does not account for emissions or chemistry information

that may be needed to identify potential unmonitored violations.

The gridded model output (absolute) concentrations can also be used to examine

unmonitored area concentrations. The model provides an hourly concentration for every grid
Draft 3.2- September 2006 29

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. 

The model output is only as accurate as the emissions and meteorological input. But unlike the

interpolated ambient data, the model output explicitly accounts for emissions, chemistry, and

meteorology over the entire domain.

Both the interpolated ambient data and the model outputs have major weaknesses. But they

also both have strengths. We can take advantage of the strengths of each dataset by combining

the two types of data. The interpolated spatial fields of ambient data provide a strong basis for

estimating accurate pollutant concentrations at monitors and near monitors. Given that

information, 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. The best way to

use the model to adjust the spatial fields is to use modeled gradients. It is preferable to assume

that the model is predicting areas of generally high or low ozone or PM, as compared to

assuming that the absolute predictions from the model are correct. For example, in areas where

the model predicts relatively high ozone or PM concentrations, the spatial fields can be adjusted

upward. In areas where the model predicts relatively low ozone or PM concentrations, the

spatial fields can be adjusted downward. In this way, it may 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 ozone near urban areas), may be adjusted

downward. It may also be possible to predict high PM2.5 concentrations in unmonitored areas

which contain large sources of primary PM emissions. The combination of interpolated spatial

fields and modeled output will be referred to as “gradient adjusted spatial fields”

3.4.2 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 output gradients (base year values).

3) Apply gridded model RRFs to the gradient adjusted spatial fields.

4) Determine if any unmonitored areas are predicted to exceed the NAAQS in the future.

EPA has developed a software package called “Modeled Attainment Test Software” (MATS)

which will spatially interpolate data, adjust the spatial fields based on model output gradients

and multiply the fields by model calculated RRFs. States will be able to use the EPA-provided

software or are free to develop alternative techniques that may be appropriate for their areas or

situations.
14Other metrics (such as the 2nd or 3rd highest predicted ozone value) may be more

appropriate if less than a full summer is modeled. 

Draft 3.2- September 2006 30

Step 1

The first step in the analysis is to interpolate ambient data. Ideally, design values should be

interpolated. The same 5 year weighted average design values that are used in the monitor based

model attainment test 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. In some cases, it may be preferable to interpolate

individual years of data or individual design values, and then average those up to get the 5 year

weighted average. 

We are not recommending a single interpolation technique. EPA has provided example

analyses in the past using the Kriging interpolation technique (U.S.EPA, 2004b). Alternatively,

EPA’s BenMAP software, which was used to create interpolated fields for the CAIR, uses the

Voronoi Neighbor Averaging (VNA) technique (Abt, 2003). 

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 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. To be consistent with the attainment test, we

recommend interpolating the PM2.5 concentration in each quarter which is equal to or less than

the 98th percentile value for the year. 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. The BenMAP software contains an example of this technique called eVNA. It uses

seasonal average model output data to adjust interpolated spatial fields (MATS also uses the

same technique). The eVNA technique has been used in health benefits assessments (U.S. EPA,

2005a). 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 cell14. 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
Draft 3.2- September 2006 31

averages in each quarter. This could be for all days with 24-hour predicted (or measured) PM2.5

> 65 ug/m3 (or some relatively high value) or simply the top 10% or 25% of all (PM2.5) days in

each quarter. 

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 times the RRF in each grid cell. The future year gradient adjusted

spatial fields are then analyzed to determine if any grid cells are predicted to remain above the

NAAQS. 

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 quarter.

3.4.3 Using the Results of the Unmonitored Area Analysis

It should be stressed that due to the lack of measured data, the examination of ozone and

PM2.5 concentrations as part of the unmonitored area analysis is more uncertain than the monitor

based attainment test. As a result, the unmonitored area analysis should be treated as a separate

test from the monitor based attainment test. While it is expected that additional emissions

controls are needed to eliminate predicted violations of the monitor based test, the same

requirements may not be appropriate in unmonitored areas. 

The cause of predicted violations of the unmonitored area analysis may not be obvious. 

While careful analysis of the emissions in the area may reveal likely sources of the ozone

violation, elimination of the violation may not be simple or straightforward. Alternatively,

predicted violations of the unmonitored area analysis for PM2.5 may, in some cases be simple to

diagnose. They may occur in areas with high emissions of primary PM2.5. Elimination of the

predicted violation may be possible by placing controls on one or more sources. Additional

dispersion modeling may be helpful in diagnosing the source(s) of high primary PM2.5 in a

particular grid cell. The results of a more refined dispersion modeling analysis may help identify

emissions controls or locations where additional monitoring may be appropriate. 

It is recommended that predicted violations of the unmonitored area analysis are carefully

scrutinized to determine whether they are likely to exist in the ambient air or whether they may

be caused by an error or uncertainty in the modeling system. Due to the uncertainty of the

analysis, at a minimum, it is appropriate to commit to additional deployment of ambient
15It would also be appropriate to commit to additional emissions controls in lieu of

additional monitoring in unmonitored areas. 

16 The year may be the same, but the emissions may still differ. The base case inventory

may include day specific information (e.g. wildfires, CEM data) that is not appropriate for using

in future year projections. Therefore the baseline inventory may need to replace the day specific

emissions with average or “typical” emissions (for certain types of sources).

Draft 3.2- September 2006 32

monitors in areas where the unmonitored area analysis predicts future violations15. This

monitoring would allow a better assessment in the future of whether the NAAQS is being met at

currently unmonitored locations. 

Violations of the unmonitored area analysis should be handled on a case by case basis. As

such, additional analyses and/or tracking requirements may be needed depending on the nature

of the problem and the uncertainty associated with the potential violation(s). 

3.5 Which base year emissions inventory should be projected to the future for the

purpose of calculating RRFs? 

The modeled attainment test adjusts observed concentrations during a baseline period (e.g.,

2000-2004) to a future period (e.g., 2009) using model-derived “relative response factors”. It is

important that emissions used in the attainment test correspond with the period reflected by the

chosen baseline design value period (e.g., 2000-2004). Deviations from this constraint will

diminish the credibility of the relative response factors. Therefore, it is important to choose an

appropriate baseline emissions year. There are potentially two different base year emissions

inventories. One is the base case inventory which represents the emissions for the meteorology

that is being modeled. These are the emissions that are used for model performance evaluations. 

For example, if a State is modeling a base year of 1998 (or episodes from 1998), “base case”

emissions and meteorology would be for 1998. As described in Section 18, it is essential to use

base case emissions together with meteorology occurring in the modeled episode(s) in order to

evaluate model performance. 

Once the model has been shown to perform adequately, it is no longer necessary to model the

base case emissions. It now becomes important to model emissions corresponding to the period

with a recent observed design value. The second potential base year inventory corresponds to

the middle year of the baseline average design value (e.g 2002 for a 2000-2004 average design

value). This is called the baseline inventory. The baseline emissions inventory is the inventory

that is ultimately projected to a future year. 

In section 17 we recommend using 2002 as the baseline inventory year for the current round

of ozone, PM2.5, and Regional Haze SIPs. If States/Tribes model the full year of 2002 (or

episodes from 2002) then the base case and baseline inventory years will be the same16. But if

States/Tribes model other years or episodes or full seasons from other years, then the base case
Draft 3.2- September 2006 33

inventories should be projected (or “backcasted”) to 2002 to provide a common starting point for

future year projections.

Alternatively, the baseline emissions year could be earlier or later than 2002, but it should be

a relatively recent year (preferably within the 5 year design value window). In order to gain

confidence in the model results, the emissions projection period should be as short as possible. 

For example, projecting emissions from 2002 to 2009 (with a 2000-2004 baseline average design

value) should be less uncertain than projecting emissions from 1995 to 2009 (with a 1993-1997

baseline average design value). Use of an older baseline average design value period is

discouraged. 

It is desirable to model meteorological time periods occurring during the period reflected by

the baseline design value (e.g., 2000-2004). However, modeling time periods need not be

selected from the period corresponding to the baseline design value, provided they are

representative of meteorological conditions which commonly occur when exceedances of the

ozone or PM2.5 standard occur (or represent typical annual concentrations of PM2.5) . The idea is

to use selected representative time periods to capture the sensitivity of predicted ozone or PM2.5

to changes in emissions during commonly occurring conditions. There are at least three reasons

why using time periods outside the period with the baseline design value may be acceptable: (1)

availability of air quality and meteorological data from an intensive field study, (2) the desire to

use meteorological data which may be “more representative” of typical conditions compared to

the baseline design value period and (3) availability of a past modeling analysis in which the

model performed well. 

Under the regional haze rule, the period for establishing baseline visibility is defined as

2000-2004. Therefore, emissions and meteorological time periods should be chosen from the

base time period. Modeling the same year (or time period) for PM2.5 NAAQS applications and

reasonable progress demonstrations is a logical way to conserve resources.

3.6 Choosing a year to project future emissions. 

States/Tribes should project future emissions to the attainment year or 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). Specifically, it states that

emission reductions needed for attainment must be implemented by the beginning of the ozone

season immediately preceding the area’s attainment date. Attainment dates are expressed as “no

later than” three, five, six, or nine years after designation and nonattainment areas are required to

attain as expeditiously as practicable. For example, moderate nonattainment areas that were

designated on June 15, 2004, have an attainment date of no later than June 15, 2010, or as

expeditiously as practicable. States/Tribes 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. Requirements for the RACM analysis can be found in (U.S. EPA, 1999c).
Draft 3.2- September 2006 34

The PM2.5 implementation rule contains similar provisions. One key difference is that all

PM2.5 nonattainment areas have a single classification and a single set of attainment deadlines. 

The PM2.5 nonattainment areas are required to attain the standard within 5 years of designation or

as expeditiously as practicable. They can also request up to a 5 year extension of the attainment

deadline. The procedures for justifying an extension of the attainment date are contained in the

final implementation rule (reference). In this guidance document, we will assume that areas will

model a future year based on attaining within the first 5 year period. Areas that request and are

granted attainment date extensions should consult with their Regional Office regarding future

year modeling analyses and requirements. 

 For areas with an attainment date of no later than June 15th 2010 (for moderate ozone

nonattainment areas) or April 5th, 2010 (for PM2.5 nonattainment areas), the emission reductions

need to be implemented no later than the beginning of 2009 (for PM2.5) or the 2009 ozone season

(for 8-hour ozone). A determination of attainment will likely be based on air quality monitoring

data collected in 2007, 2008, and 2009. Therefore, the year to project future emissions should be

no later than the last year of the three year monitoring period; in this case 2009. Since areas are

required to attain as expeditiously as practicable and perform a RACM analysis, results of the

analysis may indicate attainment can be achieved earlier, (e.g., 2008). In this case, the timing of

implementation of control measures should be used to determine the appropriate projection year. 

For example, if emission reductions (sufficient to show attainment) are implemented no later

than the beginning of the 2008 ozone season, then the future projection year should be no later

than 2008. In all cases, the selection of the future year(s) to model should be discussed with the

appropriate EPA Regional Office as part of the modeling protocol development process. 

For reasonable progress demonstrations, the first review of progress will occur in 2018. This

will likely be based on monitored data from the 2013-2017 period. Therefore, a logical future

period to model is the middle of the five year review period (2015). But a uniform rate of

progress level can be calculated for any year in the future between the base period and 2064. 

Therefore, it is appropriate to model any future year between 2015 and 2018. The calculation of

the uniform rate of progress should be consistent with the emissions projection period. For

example, if 2002 emissions are modeled (representing the 2000-2004 base period) and then

projected to 2015, the uniform rate of progress calculation should be based on the same number

years in the future, which in this case is 13 (2002-2015). Similarly, if a future year of 2018 is

modeled, then the rate of progress should be based on 16 years of progress (2002-2018). 
17See http://www.epa.gov/cair/pdfs/finaltech02.pdf for documentation of the base case

and future year modeling.

Draft 3.2- September 2006 35

4.0 Ozone Attainment Test

Section 3 contained the details of the modeled attainment test that are common to both ozone

and PM2..5. This section describes several additional details related to the 8-hour ozone

attainment test. The section concludes with an example calculation. 

4.1 Limiting modeled 8-hour daily maxima chosen to calculate RRF. 

On any given modeled day, meteorological conditions may not be similar to those leading to

high concentrations (i.e., values near the site-specific design value) 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). Using equation (3.1) could

then lead to an erroneously high projection of the future design value.

In order to examine this issue, we analyzed modeled baseline and future emissions for 30

episode days during 1995 using a grid with 12 km x 12 km cells and 9 vertical layers17. We

examined modeled RRF’s computed near each of 299 monitoring sites in the eastern half of the

United States (Timin, 2005b). The study examined the day to day variability of (daily) RRFs at

each site. One purpose of the study was to assess the extent to which a relative response factor

(RRF) is dependent on the magnitude of modeled current 8-hour daily maxima. 

Figure 4.1 shows an example of the raw data from the analysis for all of the monitoring sites

in the Baltimore region. The plot shows the daily RRFs vs. the base case daily maximum

modeled concentrations for all days (above 60 ppb in this case). In this example, it can be seen

that the model tends to respond more to emissions reductions (lower RRFs) at higher predicted

ozone concentrations. There appears to be a general bias in the model results such that the

model predicts less benefit from emissions reductions at lower concentrations. Since we are

generally interested in the model response on high ozone days, these results tend to suggest that

the RRF calculation should be limited to days when the model predicts high ozone

concentrations. 

The analysis examined daily RRFs, but in practice, the RRFs are not calculated on a daily

basis. A mean RRF is calculated based on the mean base case concentration (across model days)

divided by the mean future case concentration (across the same model days). As such, we also

calculated mean RRFs using various minimum concentration thresholds. The minimum
18It was clear from the plots that a minimum threshold value of less than 70 ppb would

not be appropriate. An upper threshold of 85 ppb was examined because it is equal to the

NAAQS. We are generally concerned about the model response on days that exceed the

NAAQS. But at the same time, it is necessary to have a sufficient number of days in the mean

RRF calculation to develop a robust estimate (this is addressed in more detail in section 14.1.1). 

19The same set of sites was used for each of the threshold concentrations (70-85 ppb).

Draft 3.2- September 2006 36

0.8

0.85

0.9

0.95

1

1.05

60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135 140

2001 Base Model Value (ppb)

Relative Reduction Factor (RRF)

240030014

240030019

240051007

240053001

240130001

240251001

240259001

245100053

Power (240030014)

Power (240051007)

Power (240030019)

Figure 4.1 - Daily relative response factors as a function of daily maximum base modeled

concentrations for monitors in the Baltimore nonattainment area.

concentration thresholds examined ranged from 70-85 ppb in 5 ppb increments18. The 

monitoring sites were screened to eliminate sites that had a limited number of “high” modeled

ozone days. All sites that had less than 10 days with an 8-hour average maximum modeled

concentration of 85 ppb were dropped from the analysis. This left 206 sites in the analysis19. 

Table 4.1 shows that the mean RRF (averaged across all sites) is sensitive to the minimum

ozone threshold. As the threshold is raised from 70 ppb to 85 ppb, the RRFs become larger. On

average, the model predicts a 0.2% greater ozone reduction for every 5 ppb increase in the

minimum threshold. Additionally, the variability of the daily RRFs (as measured by the standard
20The analysis suggests a threshold of greater than 85 ppb may be appropriate, but 85 ppb

was chosen as the upper end of the threshold because it is equal to the NAAQS. For areas with

very high baseline design values (>110 ppb) it may be necessary to use a higher threshold. 

Draft 3.2- September 2006 37

deviation of the daily RRFs) is reduced as the threshold is increased. This is an important

finding because lower variability in day to day RRFs indicates lower uncertainty in the mean

RRFs, and hence the attainment test results. 

Minimum Threshold Mean RRF Mean Standard

Deviation

70 ppb 0.879 0.030

75 ppb 0.876 0.029

80 ppb 0.874 0.028

85 ppb 0.872 0.026

Table 4.1- Mean RRFs and standard deviations as a function of various minimum

thresholds.

As a result of the apparent bias in model response at concentrations below the NAAQS,

combined with the increased variability of the RRFs at lower concentrations, we recommend

using a minimum concentration threshold of 85 ppb20. This will result in less bias and provide

for more robust RRFs and future design values. 

Example 4.1

Example 4.1 illustrates how to apply the minimum concentration threshold.

Given: The same simulations as performed in Example 3.2 yield low predictions near site I with

baseline emissions on day 3, such that the 8-hour daily maximum ozone concentration predicted

for that day is 65.0 ppb (rather than the 91.6 ppb shown in Example 3.2).

Find: The relative response factor near site I ((RRF)I

).

Solution: (1) Calculate the mean 8-hour daily maximum ozone concentration obtained near site

I for baseline and future emissions. Exclude results for day 3 from the calculations. From

Example 3.2, 

(a) (mean 8-hr daily max)future = (87.2 + 82.4 + 81.1)/3 = 83.6 ppb

(b) (mean 8-hr daily max)baseline = (98.3 + 100.2 + 90.7)/3 = 96.4 ppb.
21States are responsible for submitting SIPs for all areas of their State. As such, the

monitored and unmonitored area attainment tests should be applied both within and outside of

the nonattainment area. In the modeling protocol, States should identify the appropriate areas to

apply the respective tests. States should work with their EPA Regional Office to help determine

the appropriate areas. 

2240CFR Part 50.10, Appendix I, paragraph 2.3

Draft 3.2- September 2006 38

(2) Compute the relative response factor by taking the ratio of future/baseline.

(RRF)I

 = 83.6/96.4 = 0.867

4.2 How Do I Apply The Recommended 8-Hour Ozone Modeled Attainment Test?

States/Tribes should apply the modeled attainment test at all monitors within the

nonattainment area plus other nearby counties within the State21. Inputs described in Section 3.1

are applied in Equation (3.1) to estimate a future design value at all monitor sites and grid cells

for which the modeled attainment test is applicable. When determining compliance with the 8-

hour ozone NAAQS, the standard is met if, over three consecutive years, the average 4th highest

8-hour daily maximum ozone concentration observed at each monitor is < 0.08 ppm (i.e., # 84

ppb using rounding conventions)22. Thus, if all resulting predicted future design values (DVF)

are # 84 ppb, the test is passed. The modeled attainment test is applied using 3 steps.

Step 1. Compute baseline design values. Compute site-specific baseline design values

(DVBs) from observed data by using the average of the design value periods which include

the baseline inventory year. 

This is illustrated in Table 3.1 for specific sites. The values in the right hand column of

Table 3.1 are site-specific baseline design values. 

Step 2. Estimate relative response factors. Use air quality modeling results to estimate a

relative response factor for each grid cell near a monitoring site. 

This step begins by computing the mean 8-hour daily maximum ozone concentrations for

future and baseline emissions. The relative response factor for site I is given by Equation 4.1. 

(RRF)I = (mean 8-hr daily max)future/ (mean 8-hr daily max)baseline (4.1)

Using Equation (4.1), the relative response factor is calculated as shown in example 3.2. 

Note that the RRF is calculated to three significant figures to the right of the decimal place. The

last significant figure is obtained by rounding, with values of “5" or more rounded upward. For

the illustration shown in Table 4.2, we have assumed that the same four days described

previously in Example 4.1 have been simulated. Note that on day 3, model baseline 8-hour daily
23This effectively defines attainment in the modeled test as <= 84.9 ppb and

nonattainment as >= 85.0 ppb.

Draft 3.2- September 2006 39

maximum ozone concentration was < 85 ppb. As discussed in Section 4.1, predictions for this

day are not included in calculating the mean values shown in the last row of the table. We have

also assumed that the monitored baseline design value (DVB) at site I is 102.0 ppb. 

Step 3. Calculate future design values for all monitoring sites in the nonattainment area. 

Multiply the observed baseline design values obtained in Step 1 times the relative response

factors obtained in Step 2. 

In Table 4.2, we see (column (2)) that the baseline observed design value at monitor site I is

102.0 ppb. Using Equation (3.1), the predicted future design value for monitor site I is, 

(DVF)I = (102.0 ppb) (0.867) = 88.4 ppb = 88 ppb

Note that the final future design value is truncated23 and in this example, the modeled attainment

test is not passed at monitor site I.

 

Day

 

Calculated

baseline design

value, (DVB)I, 

(ppb)

 

Baseline 8-hr daily

max. concentration

at monitor (ppb)

 

Future predicted

8-hr daily max.

concentration at

monitor (ppb)

 

Relative

response

factor(RRF), 

 

Future design

value, (DVF)I,

(ppb)

1 98.3 87.2 - -

2 100.2 82.4 - -

3 65.0 Not Considered - -

4 90.7 81.1 - -

Mean 102.0 96.4 83.6 0.867 

(i.e.,

83.3/96.0)

88.4=

88 ppb

Table 4.2 Example Calculation of a Site-Specific Future Design Value (DVF)I
24Concentrations 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

Draft 3.2- September 2006 40

5.0 What Are The Modeled Attainment Tests For The Two PM2.5

NAAQS?

In this section, we begin by illustrating the monitored tests for the annual and 24-hour

NAAQS. We next identify how to derive needed inputs from available modeled data for the two

modeled attainment tests and highlight some implications that result from the recommended

tests. We conclude the section by describing a primary PM2.5 analysis which may be used to

better capture the contributions of primary PM2.5 to nonattainment and the benefits of controlling

primary PM2.5 in areas with relatively large contributions from primary sources. 

5.1 What Is The Recommended Modeled Attainment Test For The Annual

 NAAQS?

As described in section 2, the monitored attainment test for PM2.5 utilizes both PM2.5 and

individual PM2.5 component species. We therefore call the attainment test for PM2.5, the

Speciated Modeled Attainment Test (SMAT). A separate RRF is calculated for each PM2.5

species. In order to perform the recommended modeled attainment test, States should divide

observed mass concentrations of PM2.5 into 7 components (plus passive mass):

- mass associated with sulfates

- mass associated with nitrates

-mass associated with ammonium

- mass associated with organic carbon

- mass associated with elemental carbon

-mass associated with particle bound water

- mass associated with “other”primary inorganic particulate matter 

-and passively collected mass

To apply the test, States must first have run an air quality model at least twice--to simulate

current emissions and to simulate future year emissions. We recommend a modeled attainment

test which has 4 basic steps. Additional details are provided later in this section.

Step 1. Compute observed quarterly mean PM2.5 and quarterly mean composition for each

monitor. 

Derive current quarterly mean concentrations24 for each of the major components of PM2.5. This
basis before calculating annual averages. 

Draft 3.2- September 2006 41

is done by multiplying the monitored quarterly mean concentration of Federal Reference Method

(FRM) (EPA, 1997) derived PM2.5 by the monitored fractional composition of PM2.5 species for

each quarter. (e.g., 20% sulfate x 15.0 ug/m3 PM2.5 = 3.0 ug/m3 sulfate).

Step 2. Using model results, derive component-specific relative response factors (RRF) at

each monitor for each quarter.

For each quarter, apply an air quality model to estimate current and future concentrations for

each of the components of PM2.5. Take the ratio of future to current predictions for each

component. The result is a component-specific relative response factor (RRF).

 The relative response factor for component j at a site i is given by the following expression:

(RRF)ij = ([Cj, projected]/[Cj, current])i

where Cj, current is the quarterly mean concentration predicted at or near the monitoring site

with emissions characteristic of the period used to calculate the baseline design value for

annual PM2.5.

Cj, projected is the future year quarterly mean concentration predicted at or near the monitoring

site. 

 (e.g., given model predicted base year sulfate of 10.0 ug/m3 and future year concentration of 8.0

ug/m3, then RRF for sulfate is 0.800).

Step 3. Apply the component specific RRFs to observed air quality to obtain projected

quarterly species estimates.

For each quarter, multiply the current 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 ug/m3

 sulfate x 0.800 = future sulfate of 2.4 ug/m3).

Step 4. Sum the quarterly mean components to get quarterly mean PM2.5 values and then 

average the quarterly mean PM2.5 concentrations to get 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.
25References to STN monitors in this document always refer to the overall speciation

network which includes both trends sites and SLAMS sites.

26There are ~1100 FRM measurement sites and ~200-250 urban speciation sites (trends

and SLAMS). 

Draft 3.2- September 2006 42

Step 5. Compare all projected average annual mean PM2.5 concentrations obtained in step

4 with 15.0 :g/m3.

Compare the projected average annual arithmetic mean PM2.5 concentration obtained in Step 4

with 15.0 :g/m3. If all values are < 15.0 :g/m3, the test is passed.

5.1.1 What Ambient PM2.5 Data is Used in the Attainment Test?

PM2.5 data collected at FRM sites is used for nonattainment designations. Therefore we

recommend using FRM data as the base value 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 FRM

filter measurements and PM2.5 speciation measurements do not always measure the same mass. 

There are numerous known issues with positive and negative sampling artifacts. And second,

the majority of FRM sites do not have co-located STN25 speciation samplers26. Both of these

issues are addressed below.

5.1.2 FRM Monitors that Don’t Have Speciation Data

There are approximately 1200 FRM sites and only ~200 urban speciation monitoring sites. 

This creates a problem when trying to apply the attainment test at the majority of FRM sites. 

Species concentration data and/or species fractions are needed in order to apply SMAT. 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 gridded

model outputs to gradient adjust the species concentrations.
27The draft PM2.5 modeling guidance recommended a mass reconstruction scheme that

was identical to the IMPROVE technique. We are still recommending use of the IMPROVE

assumptions for regional haze modeling (see section 6).

Draft 3.2- September 2006 43

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. For the CAIR analysis, a relatively simple interpolation technique

was used to estimate species concentrations at all FRM sites in the country. The analysis used a

Voronoi Neighbor Averaging (VNA) technique. Other interpolations have been done using

Kriging and other more complicated methodologies. States are encouraged to explore

techniques that are most appropriate for their area and situation. EPA’s MATS software

contains routines to interpolate data and model output using the VNA 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 great promise but

has not been used extensively for PM2.5 analyses. The technique is further limited by the

representativeness of the model outputs and emissions inventories.

5.1.3 New Species Calculations and Definitions

Recent 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

Speciation Trends Network (STN) By design, the FRM mass measurement does not retain all

ammonium 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 concentrations (and percent contributions to PM2.5 mass)

which may be different than the ambient levels of some PM2.5 chemical constituents. For the

purposes of predicting changes in PM2.5 components, constructed PM2.5 mass should match the

composition of mass retained by the FRM. As such, we are recommending changes to the

calculation and definition of PM2.5 species compared to the draft PM2.5 modeling guidance27. 

We recommend a SMAT technique which uses an FRM mass construction methodology

which results in reduced nitrates (relative to the amount measured by routine speciation

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,
Draft 3.2- September 2006 44

2006). This characterization of PM2.5 mass also reflects crustal material and other minor

constituents. 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.

5.1.4 Recommended Treatment of Species Data

As noted above, special treatment of species component mass is recommended in the SMAT

methodology. 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 the following equation:

RCFMFRM = [Ammoniated Sulfate Mass] + [Retained Nitrate Mass] + [Retained Carbonaceous

Mass] + [Crustal ] + [Other Components] [5.1]

In the above characterization, RCFM 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. Also, sulfates, nitrates and

carbonaceous mass includes particle bound water associated with these hygroscopic aerosols. In

this characterization, crustal mass is intended to be a more general term that includes fine soil,

and oxides that result from other PM emissions. The following section describes the

recommended treatment for each of the components. 

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 accurately 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 higher 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. More NO3 is retained at

low temps and high humidity which varies by sampling location and time of year.

Because nitrate retention varies by site and season, an 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 (ug/m3)= 745.7/TR* 1/24* E (Ki

1⁄2 ) [5.2]

 i=1

where delta NO3 is the amount of volatilized nitrate mass, TR is the reference temperature for the

sampled air volume in degrees Kelvin and Ki is the dissociation constant for ammonium nitrate
Draft 3.2- September 2006 45

evaluated at the ambient temperature for hour i. The nitrate loss can be predicted for each day,

based on hourly temperature and relative humidity data. The SMAT analysis for CAIR used

National Weather Service temperature and relative humidity data for 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). 

Ammonium Associated with Sulfates and Retained Nitrates

 To determine the mass associated with nitrates, we first assume retained nitrate is probably

all ammonium nitrate. Thus the ammonium associated with nitrates can be derived directly from

the measured or predicted NO3FRM as 

NH4NO3 = 0.29 * NO3FRM [5.3]

The difference between total FRM NH4 (amount associated with nitrates and sulfates),

termed NH4FRM, and the measured NH4, termed NH4STN,, is needed to determine the ammoniated

form of sulfates as described by equation 5.1. Recent 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. During sampling conditions associated with nitrate

volatilization, ammonium nitrate dissociates but the HNO3 downstream of the denuder is

recaptured on the basic nylon media and the result is reported as particle nitrate. On the other

hand, the NH4+ volatilizes to gaseous NH3 and apparently passes thru the filter. At several FRM

study sites, the STN NH4 which is adjusted for evaporated NH4NO3 tends to more closely

corresponds to the measured NH4 from the FRM Teflon filter. However, for other sites, the

measured STN NH4 appear to agree with FRM NH4. 

In the CAIR analysis, 50% of the ammonium associated with volatilized nitrate was assumed

to also volatilize. But more recent information suggests that using measured ammonium

(assuming none is volatilized) may be a better assumption. We recommend using measured

ammonium, but further analysis of this issue is warranted. 

There are several ways to estimate ammonium mass for use in SMAT. The most direct way

is to use measured ammonium concentrations from the STN network (IMPROVE does not

measure ammonium ion). 

A second, more indirect method is 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 was used for the CAIR

analysis. The ambient data is such that all of the ammonium data is from urban sites (STN), but

the sulfate and nitrate data is from both urban (STN) and rural (IMPROVE) sites. This leads to

an overestimation of ammonium concentration in rural areas when ammonium is directly

interpolated. In the CAIR analysis, calculated DON, SO4 and NO3FRM were interpolated to get
Draft 3.2- September 2006 46

quarterly average concentrations at each FRM site. The interpolated species concentrations were

then used to calculate NH4FRM using the following equation: 

NH4FRM = DON * SO4 + 0.29*NO3FRM [5.4]

The indirect calculation of ammonium mass from interpolated fields tends to smooth out the

gradients in mass. This was deemed to be beneficial, due to the uncertainty in the measurements. 

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 CAIR analysis used the Aerosol Inorganic Model (AIM) (Clegg, 1998)

to calculate PBW. PBW was derived from quarterly average FRM concentrations of sulfate,

ammonium, and nitrate as describe above. Estimated hydronium ion, H+, needed to achieve

ionic balance was 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 filter equilibration conditions of 35% RH and 22 deg C

(295 deg K) temperature were used. 

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). 

For computational convenience, a polynomial regression equation was fit to the

calculated water mass from AIM and the three input values that fed into AIM (sulfate, nitrate

and ammonium). A polynomial equation can then be used in all SMAT analyses to estimate

water. 

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.

Other Non-Carbon PM2.5 Components (blank mass)

The other quantifiable components of PM2.5 mass include passively collected mass,

represented by a field blank concentration of 0.3-0.5ug/m3 (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 ug/m3. This value can be modified based on local FRM blank mass measurements. The
28The 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 particularly be a problem in

Draft 3.2- September 2006 47

blank mass is assumed to remain constant through time (RRF=1.0).

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. 

Due to the many uncertainties in estimating carbonaceous mass from carbon

measurements (Turpin, 2001; Chow, 2004); such as 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 recommend an alternative approach to estimate

the organic carbon contribution to PM2.5 mass.

The revised SMAT approach estimates organic carbon by mass balance, comparing

precisely measured FRM PM2.5 mass (EPA, 2003) with the sum of its non-organic carbon

components. The latter are sulfates, ammonium, nitrates, estimated particle bound water,

elemental carbon, estimated crustal material plus 0.5 ug/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] + [crustal material] + [0.5] } 

[5.5]

In this expression, all of the above components represent the mass retained on FRM Teflon

filters. 

This approach completely accounts for FRM mass28 and OCMmb is often greater than the
areas that do not have nearby speciation data and have relatively large concentrations of primary

PM2.5. The OC by mass balance technique may inadvertantly apportion mass to organic carbon

which may actually be EC or “other” primary PM2.5 mass. All available ambient data and

modeling data should be used to evaluate the species apportionment results.

29The measured OCM is only used to calculate the “floor” for OCMmb

Draft 3.2- September 2006 48

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 the CAIR analysis, the

floor was set so that OCMmb could not be more than 30% lower than measured OCM (OC*1.4). 

We used interpolated measured values of OCM to calculate the floor. The lower limit was equal

to interpolated OC * 1.4 * 0.7. If the OCMmb concentration was less than the lower limit, it was

set equal to the lower limit. 

There may also be situations where an OCM “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% or more 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 many regions of the country. 

Summary of PM2.5 Composition Calculations

Equation 5.6 shows the final composition of PM species as they relate to the measured

FRM values for each quarter. Quarterly average FRM mass is equal to the sum of the seven

species plus blank mass.

PM2.5FRM = { [OCMmb] + [EC] + [SO4] + [NO3FRM] + [NH4FRM] + [water] + [crustal material]

+ [0.5] } [5.6]

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)), crustal mass , and measured OCM.29

3) Quarterly average ammonium is calculated from the adjusted nitrate, sulfate, and DON

values (if measured ammonium is not used directly).
30The species concentrations can be derived from co-located speciation data, nearby

speciation data or from interpolated spatial fields. The FRM mass is not interpolated.

Draft 3.2- September 2006 49

4) Calculated ammonium, sulfate, and nitrate values 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, crustal, and blank mass values.

6) The sum of the 7 species plus blank mass is equal to the FRM mass.

We illustrate application of the recommended test in example 5.1.

Example 5.1

Given: (1) Area “C” has 2 monitoring sites. 

(2) Monitored air quality data show the following average quarterly mean PM2.5 concentrations

based on a 5 year weighted average of observations from 2000-2004 at each site (values are in

:g/m3 ).

Table 5.1

Site Quarter 1 Quarter 2 Quarter 3 Quarter 4

1 17.21 ug/m3 16.34 20.30 14.76

2 15.39 17.98 18.23 13.76

(3) The area only has 1 urban speciation site which is co-located with FRM site 2. The

speciation data is only available for 2003 and 2004. Therefore, the species data for the two years

has been interpolated to derive estimated species concentrations at each site. The species

concentrations (derived from 2003 and 2004 data) are matched up with FRM data for 2003 and

2004 to derive representative species fractions. 

Average monitored air quality for the 3rd quarter of 2003 and 2004 at site 1 is as follows30: 

 Table 5.2

FRM

Mass

(ug/m3)

Blank

Mass

(ug/m3)

Non-blank

Mass

(ug/m3)

 Sulfate

(ug/m3)

Nitrate

(ug/m3)

Organic

Carbon

Mass

(ug/m3)

Elemental

Carbon

(ug/m3)

Water

(ug/m3)

Ammonium

(ug/m3)

Crustal

(ug/m3)

22.57 0 .5 22.07 8.51 1.11 5.21 0.91 2.31 3.31 0.71
31If there are less than 5 years of speciation data, then an assumption of

representativeness needs to be made. It is generally assumed that the inter-annual variability of

the species fractions is small compared to the variability of species concentrations. In the near

future, 5 full years of concurrent FRM and speciation data available. 

Draft 3.2- September 2006 50

The blank mass is subtracted before species fractions are calculated because the blank mass is

held constant at 0.5 ug/m3 throughout the analysis. In the example, the measured FRM mass for

quarter 3 is 22.57 ug/m3. The non-blank FRM mass is 22.07 ug/m3. The mass of the seven

species add up to the non-blank mass. 

(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 seven species. Blank mass remains fixed

at 0.5 ug/m3. 

Table 5.3

FRM

Mass

(ug/m3)

Blank

Mass

(ug/m3)

Non-blank

Mass

(ug/m3)

 %

Sulfate

%

Nitrate

%

Organic

aerosol

%

Elemental

Carbon

%

Water

%

Ammonium

%

Crustal

22.5 0 .5 22.0 38.6 5.0 23.6 4.1 10.5 15.0 3.2

The percentages in table 5.3 above are the relative composition for the 3rd quarter of 2003 and

2004. It is assumed that these species fractions are representative of the 2000-2004 time

period31. 

(5) The weighted quarterly average FRM design values are used as the baseline FRM value for

each monitoring site (2000-2004). The species fractions from the 2003/2004 speciation data

were used to estimate the species concentrations for the baseline year FRM PM2.5 data. The

percentage compositions for 2003/2204 are applied to the quarterly weighted average design

values as shown in table 5.4. In the example below, the weighted average design value for the

3rd quarter for the site from table 5.1 is 20.30 ug/m3. This leads to the following concentrations

of PM2.5 species:

Table 5.4 Calculation of the “current” species concentrations

Weighted

Avg.

FRM

Mass

(ug/m3)

Blank

Mass

(ug/m3)

Non-blank

Mass

(ug/m3)

 Sulfate

(ug/m3)

Nitrate

(ug/m3)

Organic

aerosol

(ug/m3)

Elemental

Carbon

(ug/m3)

Water

(ug/m3)

Ammonium

(ug/m3)

Crustal

(ug/m3)

20.30 0 .5 19.8 7.64 0.99 4.67 0.81 2.08 2.97 0.63

 This procedure is repeated for each PM2.5 site and quarter to complete the calculation of
32In the CAIR analysis, the DON was assumed to stay constant through time due to the

uncertainty in the ammonium measurements. The water calculation is sensitive to the ammonium (and

therefore the DON value) concentrations. Keeping 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. 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.

Draft 3.2- September 2006 51

current ( baseline) 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)ij in predicted mass of 5

components of PM2.5 for the 3rd quarter:

Table 5.5

RRF

Sulfate

RRF

Nitrate

RRF

Organic

aerosol

RRF

Elemental

Carbon

RRF

Crustal

RRF

Ammonium*

0.876 0.943 0.971 0.932 1.042 0.890

*In the example, the ammonium RRF is not used. 

(7) The quarterly mean RRFs from table 5.5 are multiplied by the weighted quarterly average

species concentrations from table 5.4 to derive future year concentrations.

From the example above, the future year 3rd quarter concentrations are:

SulfateFuture = 7.64 * 0.876 = 6.69 ug/m3

NitrateFuture= 0.99 * 0.943 = 0.93 ug/m3

Organic carbon massFuture= 4.67 * 0.971 = 4.53 ug/m3

Elemental CarbonFuture= 0.81 * 0.932 = 0.75 ug/m3

CrustalFuture= 0.63 * 1.042 = 0.66 ug/m3

(8) The future year concentrations derived in step 5 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

(current year) DON values. Assuming that the DON is unchanged from the current year32, the

ammonium is calculated using the following formula:
33In the final step, the future year concentration should be rounded to the tenths digit. A

(rounded) value of 15.0 ug/m3 meets the NAAQS. A value of 15.1 ug/m3 or greater violates the

NAAQS. 

Draft 3.2- September 2006 52

NH4future = DON * SO4future + 0.29*NO3future, 

In the example above, assuming the base year DON is 0.336, 

AmmoniumFuture= 0.336 * 6.69 + 0.29 * 0.93 = 2.52 ug/m3

The NH4future, SO4future, and NO3future concentrations can then be input into an equilibrium

model (AIM or another alternative model) or through a polynomial equation to predict future

year particle bound water concentration. 

(9) The future species concentrations at each FRM site are then summed over the seven species

plus blank mass to estimate the future quarterly average PM2.5 concentration. 

In the example above, the total PM2.5Future= 

6.69 + 0.93 + 4.53 + 0.75 + 0.66 + 2.52 + 1.73 + 0.5 = 18.31 ug/m3

(assuming that the future year water concentration is equal to 1.73 ug/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 15.0 ug/m333. 

5.2 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 seeing what it takes to reduce current design values at each site to < 65 :g/m3

. 

Ideally, the modeled attainment test should reflect model results obtained for days in

each season having observed PM2.5 concentrations slightly below as well as above the design

value. This may seem strange at first. The underlying reasons are that PM2.5 consists of a

mixture of pollutants. Composition of the mixture could vary substantially from season to

season. Second, 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
34The top 25% of days may seem like too many days per quarter to use for a 98th

percentile based standard, but at many sites this may not be very many monitored days. For sites

with a 1 in 6 day sampling schedule meeting minimal completeness criteria (assuming 11

samples per quarter), the top 25% of days is only 3 days per quarter.

35In some cases it may not be necessary to model and evaluate the 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. 

Draft 3.2- September 2006 53

reflect composite responses from many days. Therefore, we recommend modeling as many days

as feasible where observed PM2.5 is greater than 65 :g/m3. Alternatively, the test can focus on

the high end of the distribution of days in each quarter, (e.g. the top 25% of PM2.5 days)

assuming that many of the high days violate the NAAQS. As with the annual NAAQS (and for

the same reasons), the preferred approach is to develop relative response factors which are

season (i.e., quarter) specific. 

We have noted that it is desirable to base our conclusions on a composite response of the

model(s) over many days. However, there is not likely to be many days with observed

concentrations greater than 65 :g/m3. If this results in a sample size of smaller than about 5 days

per quarter then the analysis should focus on the high end of the distribution of PM2.5 days in

each quarter (e.g. the top 25% of PM2.5 days34).

The 24-hour 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. We recommend

applying the attainment test on an annual basis and then averaging the annual average results up

to 3 year averages and then averaging again to get a 5 year weighted average. 

PM2.5 is a complex mixture of components that have significant seasonal variations. If the

observed 98th percentile value came from the 1st quarter, it is possible that an observed

concentration from a different quarter, which may have a slightly lower concentration, could

become the 98th percentile value in the future. It is important to demonstrate that the standard

will be met for all seasons. Therefore we recommend applying the test by quarter for each

year35. 

We recommend a modeled attainment test for the 24-hour PM2.5 NAAQS with 6 steps. 

Step 1. Compute observed 98th percentile values for each year and next highest

concentrations for each quarter.

The first step in projecting the daily design value is to identify the maximum daily average
Draft 3.2- September 2006 54

PM2.5 concentration in each quarter that is less than or equal to the annual 98th percentile value

over the entire year. This results in data for each year (for five years) for each site which

contains one quarter with the 98th percentile value and three quarters with the maximum values

from each quarter which are less than or equal to the 98th percentile value.

Step 2. Identify the species composition for each quarter for each monitor. 

These quarterly PM2.5 concentrations are then separated into their component species by 

multiplying the quarterly maximum daily concentration at each site by the estimated fractional

composition of PM2.5 species, by quarter, based on observed species fractions for the days > 65

ug/m3 or from the days in the high end of the distribution of PM2.5 concentrations (using the

same methodology as used in the annual average calculations) (e.g., 30% nitrate x 70.0 ug/m3

PM2.5 = 21.0 ug/m3 nitrate).

Step 3. Using model results, derive component-specific relative response factors (RRF) at

each monitor for each quarter.

For each quarter, apply an air quality model to estimate current and future concentrations for

each of the components of PM2.5. Take the ratio of future to current predictions for each

component on the modeled days with PM2.5 > 65 ug/3 or the high end of the distribution of

modeled days. The result is a component-specific relative response factor (RRF). (e.g., given

model predicted base year nitrate of 20.0 ug/m3 and future year concentration of 15.0 ug/m3, then

RRF for nitrate is 0.750).

 The relative response factor for component j at a site i is given by the following expression:

(RRF)ij = ([Cj, projected]/[Cj, current])i

where Cj, current is the mean concentration (for the high modeled days for each quarter)

predicted at or near the monitoring site with emissions characteristic of the period used to

calculate the baseline design value for 24-hour PM2.5.

Cj, projected is the future year mean concentration (for the high modeled days for each

quarter) predicted at or near the monitoring site with emissions characteristic of the

future year period. 

Step 4. Apply the component specific RRFs to observed air quality to obtain projected

quarterly species estimates.

For each quarter, multiply the quarterly component concentration (step 2) times the componentspecific RRF obtained in step 3. This leads to an estimated future quarterly concentration for

each component. (e.g., 21.00 ug/m3

 nitrate x 0.750 = future nitrate of 15.75 ug/m3).
Draft 3.2- September 2006 55

Step 5. Sum the quarterly components to get quarterly “potential 98th percentile” PM2.5

values.

Sum the quarterly components to get quarterly “potential 98th percentile” PM2.5 values. Repeat

this procedure for each quarter and for each of the 5 years of ambient data. The highest (of the

4) daily value for each year at each monitor is considered to be the estimated 98th percentile

value for that year.

Step 6. Calculate future year 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 design values and then the 3 design values are averaged to create the 5 year weighted

average for each monitor. Compare the projected 24-hour design values to 65 ug/m3.

The recommended test is illustrated in example 5.2.

Example 5.2

Given: (1) Area “D” has 1 monitoring site. The 98th percentile values for 5 years of observed

data are as follows (year 3 is being modeled for the attainment test):

Year 1- 80 ug/m3

Year 2- 68 ug/m3

Year 3- 71 ug/m3

Year 4- 58 ug/m3

Year 5- 74 ug/m3

Step 1- Compute observed 98th percentile values for each year and next highest

concentrations for each quarter.

The maximum quarterly values from the 5 years which are equal to or less than the 98th

percentile concentration are as follows:

Year 1 Year 2 Year 3 Year 4 Year 5

Quarter 1 80.0 ug/m3 65.0 57.0 58.0 74.0

Quarter 2 67.6 45.5 46.4 45.3 52.2

Quarter 3 41.7 47.6 32.5 39.4 45.3

Quarter 4 52.8 68.4 71.3 49.5 64.2
36The percentages are derived from several steps in the processing. The nitrate, sulfate,

elemental carbon, and crustal come from measured or interpolated data. The ammonium comes

from interpolated sulfate, nitrate, and DON (or directly interpolated ammonium). Water is

calculated from ammonium, nitrate, and sulfate using a model or equation. The OMC is

calculated by mass balance.

Draft 3.2- September 2006 56

Step 2- Identify the species composition for each quarter for each monitor. 

The mean species composition36 for the observed high PM2.5 days in quarter 1 is:

Nitrate- 30%

Sulfate- 10%

Organic carbon- 23%

Elemental carbon- 5%

Crustal- 5%

Ammonium- 15%

Water- 12%

This leads to the following baseline concentrations for quarter 1 (ug/m3):

PM2.5 Nitrate Sulfate OCM EC Crustal Ammonium Water

Year 1 79.5 23.85 7.95 18.29 3.98 3.98 11.93 9.54

Year 2 64.5 19.35 6.45 14.84 3.23 3.23 9.68 7.74

Year 3 56.5 16.95 5.65 13.00 2.83 2.83 8.48 6.78

Year 4 57.5 17.25 5.75 13.23 2.88 2.88 8.63 6.90

Year 5 73.5 22.05 7.35 16.91 3.68 3.68 11.03 8.82

Step 3- Using model results, derive component-specific relative response factors (RRF) at

each monitor for each quarter.

The following are RRFs for the 1st quarter derived from the modeled baseline and future year

concentrations for the high PM2.5 days.

RRF

Sulfate

RRF

Nitrate

RRF

Organic

aerosol

RRF

Elemental

Carbon

RRF

Crustal

RRF

Ammonium*

RRF

Water*

0.942 0.843 0.917 0.932 1.042 0.873 0.882

* The RRFs for ammonium and water may not be calculated directly. These represent the effective RRFs after

calculating future year ammonium and water based on the future year sulfate and nitrate concentrations.
Draft 3.2- September 2006 57

Step 4- Apply the component specific RRFs to observed air quality to obtain projected

quarterly species estimates.

The quarter 1 concentrations for each year are multiplied by the component RRFs. Ammonium

concentrations are calculated from the future year sulfate, nitrate, and (baseline) DON value. 

Water concentrations are calculated from the future year nitrate, sulfate, and ammonium

concentrations, using an empirical equation.

Nitrate Sulfate OCM EC Crustal Ammonium Water

Year 1 22.47 6.70 16.77 3.70 4.14 10.41 8.41

Year 2 18.23 5.44 13.60 3.01 3.36 8.45 6.83

Year 3 15.97 4.76 11.92 2.63 2.94 7.40 5.98

Year 4 16.25 4.85 12.13 2.68 3.00 7.53 6.09

Year 5 20.77 6.20 15.50 3.43 3.83 9.62 7.78

Step 5. Sum the quarterly components to get quarterly “98th percentile” PM2.5 values.

The year 1 PM2.5 concentration is equal to the sum of the 7 components plus 0.5 ug/m3 blank

mass:

Year 1= 22.47 + 6.70 + 16.77 + 3.70 + 4.14 + 10.41 + 8.41 + 0.5 = 72.6

Filling in the quarter 1 values for the other 4 years, plus the other 3 quarters for each year; the

full matrix of quarterly values is as follows:

1999 2000 2001 2002 2003

Quarter 1 72.6 ug/m3 58.9 51.6 52.5 67.1

Quarter 2 57.5 38.7 39.4 38.5 44.4

Quarter 3 38.8 44.3 30.2 36.6 42.1

Quarter 4 46.6 60.5 63.0 43.6 57.1

Step 6. Calculate future year 5 year weighted average 24-hour design values and compare

to the NAAQS.

For this example, let’s assume that the quarter1 future year values were the highest quarterly

values for each year. Therefore, the 3 year average design values are:
Draft 3.2- September 2006 58

DV1 = 72.6 + 60.5 + 63.0 = 65.4

DV2 = 60.5 + 63.0 + 52.5 = 58.7 

DV3 = 63.0 + 52.5 + 67.1 = 60.9

And finally, the 5 year weighted average is equal to the average of the 3 future year design

values:

5 year weighted average future DV= 65.4 + 58.7 + 60.9 = 61.7 = 62 ug/m3

5.3 Special Considerations for Primary PM2.5

 Primary particulate matter does not undergo physical/chemical transformation between 

its being emitted and its arrival at a receptor location. Thus, a relatively lengthy travel time from

source to receptor (to enable thorough mixing and chemistry to proceed) is not needed for high

concentrations of primary particulate matter to occur. Therefore, unlike secondary particulate

matter, we would often 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 a

few (as opposed to many which are spread out) concentrated sources of primary particulate

matter, we would expect there to be some substantial spatial gradients in the primary portion of

the organic carbon component and in the inorganic particulate matter (IP) and elemental carbon

(EC) components of ambient PM2.5 (these are often called “hotspots”). Substantial gradients are

most likely to be a potential problem in addressing whether a proposed control strategy is

sufficient to attain the 24-hour NAAQS for PM2.5. This follows, because orientation of a

source’s plume varies. For many hours, one might expect to find no impact from such a source

at a given location. Occasions with no likely impact tend to balance those large impacts

occurring over some periods.

PM2.5 monitor siting guidance (EPA, 1999) and PM2.5 NAAQS preamble (July 18, 1997;

62 FR at 38780) indicates that high PM2.5 concentrations that occur in “unique” population

oriented areas should not be compared to the annual NAAQS and should only be compared to

the 24-hour NAAQS (especially when caused by a single source). Furthermore, high PM2.5

concentrations measured in non-population oriented areas should not be compared to either

NAAQS. 

High primary PM 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. To

address the issue of PM2.5 concentrations in unmonitored areas, we have recommended an

“unmonitored area analysis” (see section 4.3). The unmonitored area analysis is intended to be

the primary means for identifying high PM2.5 concentrations outside of traditionally monitored

locations. The spatial resolution of the modeling that is the underlying basis of the unmonitored

area analysis will determine how well it addresses primary PM hotspot issues. The finer the

resolution of the grid model, the more likely that primary PM hotspots will be recognized. 
Draft 3.2- September 2006 59

Based on the monitoring guidance, we believe that an unmonitored area analysis conducted at

12km or finer resolution is sufficient to address unmonitored PM2.5. Conducting the

unmonitored analysis at 4km or finer resolution will provide an even more detailed analysis of

the spatial gradients of primary PM2.5.

5.3.1 Analysis of Primary PM2.5 Impacts at Monitors

Most PM2.5 monitors are located at “urban scale” sites. These sites tend to represent large

spatial scales and do not have large gradients compared to other monitors in the area. Some sites

are “neighborhood” scale and may be influenced by more local sources. For reasons stated

above, local influences creating spatial gradients are likely to consist mostly of primary PM2.5

(OC, EC, and other inorganic particulate). These sources may be local point sources, or they

may be nearby roads or other mobile or area sources. 

It may be necessary to evaluate the impact of local primary PM sources for contributions

to both the 24-hour and annual NAAQS. As stated earlier, population oriented FRM sites that

are dominated by point sources should not be compared to the annual NAAQS. But there are

numerous cases where the impact from local sources is not dominant, but 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 1) help explain the

causes of nonattainment at the monitor and 2) may lead to the more efficient ways to attain the

standard by controlling emissions from local sources. 

There are several modeling tools that can be used to evaluate contributions of local PM2.5

sources. A grid model can be run at very high resolution (down to 1 or 2 km) or a Gaussian

dispersion can be used. Grid based models usually simulate chemical transformation and

complex meteorological conditions, while dispersion models are generally more simplistic;

being limited to a local-scale and using Gaussian approximations with little or no chemistry. 

Therefore, while dispersion models may not be an appropriate tool for determining secondary

PM2.5 concentrations, they work well for use in determining local primary PM2.5 impacts in a

small area. 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

monitors, we recommend that the model results be used in a relative manner. This is consistent

with the already described attainment test. If a grid model is used at very high resolution, the

attainment test as described in Section 5.1 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 photochemical grid based modeled attainment test and the results from localscale dispersion modeling. If such an approach is taken, the suggested variations on the

attainment test are discussed in the following section.
37The 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.

Draft 3.2- September 2006 60

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

Dispersion Model

To apply a dispersion model in an attainment test, it becomes important to determine the

local component of primary PM2.5 at the monitor and the sources that are contributing to that

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. In most cases, the local component will include contributions

from more than one source. 

Sources identified as contributing to the monitor will be modeled with a dispersion

model. It is common practice to run dispersion models for limited numbers of point sources in

relatively small areas (out to a distance of ~50 km). Dispersion models can also be run with all

sources of primary PM in a limited area (including mobile, non-road, and area sources). 

When applying the model to evaluate changes in primary PM2.5, one should determine the

PM2.5 species that make up the local primary particulate matter37. We recommend that the

individual components of primary PM2.5 are tracked separately in the dispersion model. This will

allow more thorough evaluation of the dispersion modeling results (comparing model output to

speciated ambient data) and may aid in the development of potential primary PM2.5 control

strategies

 The majority of the primary PM will consist of the primary portion of the organic carbon

component, elemental carbon (EC), and a general category of inorganic particulate matter (IP). 

In some cases, directly emitted sulfate (and in rare cases nitrate) may also be a significant

component of the local primary PM2.5. 

The following sections discuss a suggested methodology for examining decreases or

increases in PM concentrations due to local primary sources for the Annual and 24-hour PM2.5

NAAQS:

Methodology for the Annual PM2.5 NAAQS

The suggested steps outlined below assume that the attainment test has been performed using the

photochemical modeling and that the annual DV has been computed as discussed in Section 5.1.

(1) Identify local primary PM2.5 sources that are thought to be are contributing to the monitor

and causing non-attainment of the annual NAAQS. 
38Receptors should only be located in areas that are appropriate for placing FRM

monitors for comparison to the annual NAAQS (see EPA, 1999).

39The selection of these sources should be representative of the 5-year period used to

calculate the 5 year weighted average design values. If large emissions changes have occurred

Draft 3.2- September 2006 61

(2) By quarter, identify at the monitor primary versus secondary components of 

PM2.5.

(3) Identify the percentage of total primary PM2.5 at the monitor due to the local sources 

identified in (1). Convert the percentage to an estimated concentration in ug/m3

(4) Model with a dispersion model the current and future concentrations of these 

local sources using actual emissions. 

-Place receptors at the monitor location(s) and in nearby areas38. 

-The receptor spacing should be fine enough to get an accurate picture of the

concentration at the monitor. 

-Model the base year and future annual average 

-For both base and future year use the same 1 year of met data – the 

same year as used for the photochemical modeling 

(5) From the modeling in (4), calculate quarterly RRFs for total 

primary PM2.5 modeled for the selected sources (using concentration values 

at receptors at monitors).

(6) Multiply the primary PM2.5 identified in (3) by the RRFs from (5).

(7) Subtract the result in (6) from the primary PM2.5 identified in (3) 

(8) For each quarter, subtract the amount in (7) from the future year quarterly DV provided by

the photochemical modeling.

(9) Average the four quarters to get an annual average DV.

Methodology for the 24-Hour NAAQS 

The suggested steps outlined below assume that the attainment test has been performed using the

photochemical modeling and that the quarterly DV’s have been computed as discussed in

Section 5.2. As with the annual design value, monitor data for 5 years are used as the basis for

the projection. 

(1) Determine the local primary PM2.5 sources that are thought to be contributing to the monitor

for each of the 4 quarters39. 
over the 5 year period then it may be necessary to determine the local contribution of identified

sources for each year. The identification of the contribution to the monitor should be most

influenced by the 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.

40Because the test is relative, in most cases, actual emissions should be used. Since the

absolute predicted concentrations are not used directly, allowable emissions may overestimate

the changes in concentrations due to the identified sources. This should be evaluated on a caseby-case basis. Allowable emissions may be appropriate if there are sources that often emit above

their typical levels on high PM2.5 days. 

41Receptors should only be located in areas that are appropriate for placing FRM

monitors for comparison to the 24-hour NAAQS (see EPA, 1999).

42High days may be days > 65 ug/m3 or the high end of the distribution of modeled days

(e.g top 25% days)

Draft 3.2- September 2006 62

(2) For each of the four representative quarters, determine the primary versus secondary

components of PM2.5 at the monitor on the 98th percentile day and all high days.

(3) Identify percentage of total primary PM2.5 at monitor due to the local sources 

identified in (1). Convert the percentage to an estimated concentration in ug/m3.

(4) Model with a dispersion model the current and future concentrations of these 

local sources using actual emissions40. 

-Place receptors at the monitor location(s) and in nearby areas41. 

-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 met data used for the

photochemical modeling.

-Calculate the 24-hour average for each day. Average the high days42 within each quarter

to determine a modeled high concentration for each quarter in the base and future year. 

(5) From the modeling in (4), calculate quarterly RRFs for total primary PM2.5 modeled for the

selected sources.

(6) Multiply the primary PM2.5 identified in (3) by the RRFs from (5).

(7) Subtract the result in (6) from the primary PM2.5 identified in (3) 

(8) For each of the 5 years used to calculate the 5 year weighted average design value, subtract

the result from (7) from each quarter for each year. The “new” annual 98th percentile value is the

highest quarterly concentration in each year. 
Draft 3.2- September 2006 63

(9) Average the annual 98th percentile concentrations to get 3 design values and then average the

3 DV to get the final future year 5 year weighted average DV.

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 counted once in the

photochemical modeling and again in the dispersion modeling. There are several ways to

account for this issue.

1) Run the photochemical model without the primary PM2.5 emissions from the flagged sources

in both the base case and future case(s). 

2) 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.

See (references). 

3) Assume that double counting is small enough to be ignored. 

The grid resolution of the photochemical model and the number of flagged sources in the

dispersion model will determine the magnitude of the potential double counting. The higher the

resolution of the grid model, the more double counting will be a problem (because the local

sources will be more finely resolved). And the more sources that are flagged, the more double

counting will be a problem. Therefore, the nature of the problem should be evaluated on a case

by case basis. The simplest way to evaluate the magnitude of the problem is to run the

photochemical model with and without the flagged sources from the dispersion model. This will

indicate the maximum impact the sources can have on the photochemical modeling results. A

very small impact means that double counting is not a problem. A large impact indicates that

double counting needs to be explicitly accounted for.

Annual NAAQS Example:

For quarter 4, a monitor has a 5 year weighted mean concentration of 17.0 ug/m3. 

(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 comparison to other monitors in the area, it is believed that the local PM2.5

component at the monitor for quarter 4 is 2 ug/m3.

(3) Using receptor modeling, it is determined that the steel mills are contributing a local 

PM2.5 component in quarter 4 of approximately 1 ug/m3.

(4) Modeling with a dispersion model shows the following contribution at the monitor from the 

identified local sources (steel mills):
Draft 3.2- September 2006 64

Base: 2.0 ug/m3

Future: 1.5 ug/m3 

(5) Quarter 4 RRF_local = 1.5/2.0 = 0.750.

(6) 1 ug/m3 * 0.750 = 0.75 ug/m3.

(7) 0.75 ug/m3 - 1 ug/m3 = -0.25 ug/m3 (total Q4 reduction from identified local sources is 0.25

ug/m3)

(8) Quarter 4 New DV = 17.0 – 0.25 ug/m3 = 16.75 ug/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

24 Hour NAAQS Example:

The baseline design values are calculated from the 5 year base period centered around the

modeling year. The dispersion model is run with emissions and meteorology for year 3 (the

center year of the 5 year period). The maximum quarterly values from the 5 years which are

equal to or less than the 98th percentile concentration are as follows (from example 5.2):

Year 1 Year 2 Year 3 Year 4 Year 5

Quarter 1 80.0 ug/m3 65.0 57.0 58.0 74.0

Quarter 2 67.6 45.5 46.4 45.3 52.2

Quarter 3 41.7 47.6 32.5 39.4 45.3

Quarter 4 52.8 68.4 71.3 49.5 64.2

The future year PM2.5 concentrations from the photochemical modeling described in Section 5.2,

Step 5 are as follows: 

Year 1 Year 2 Year 3 Year 4 Year 5

Quarter 1 72.6 ug/m3 58.9 51.6 52.5 67.1

Quarter 2 57.5 38.7 39.4 38.5 44.4

Quarter 3 38.8 44.3 30.2 36.6 42.1

Quarter 4 46.6 60.5 63.0 43.6 57.1
Draft 3.2- September 2006 65

(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 comparison to other monitors in the area, it is believed that the primary local PM2.5

component at the monitor is the following for each of the quarters in 2001:

Quarter 1: 15 ug/m3

Quarter 2: 13 ug/m3

Quarter 3: 8 ug/m3

Quarter 4: 20 ug/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 of the following for each of the quarters in 2001:

Quarter 1: 8 ug/m3

Quarter 2: 4 ug/m3

Quarter 3: 4 ug/m3

Quarter 4: 5 ug/m3

It is believed that this is representative of the other four years within the 5-year period.

(4) Modeling with a dispersion model shows the following contribution at the monitor from the 

identified local sources (steel mills) for total primary PM2.5 each of the quarters in 2001:

Quarter 1: Base: 11.7 ug/m3 Future: 8.3 ug/m3 

Quarter 2: Base: 6.3 ug/m3 Future: 4.4 ug/m3 

Quarter 3: Base: 5.3 ug/m3 Future: 3.1 ug/m3 

Quarter 4: Base: 8.3 ug/m3 Future: 6.2 ug/m3 

For each quarter, we computed the 24-hr average for each day and computed the mean of the

high days for each quarter to get the base and future year concentrations.

(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 ug/m3 * 0.709 = 5.7 ug/m3

Quarter 2: 4 ug/m3 * 0.698 = 2.8 ug/m3

Quarter 3: 4 ug/m3 * 0.585 = 2.3 ug/m3

Quarter 4: 5 ug/m3 * 0.747 = 3.7 ug/m3

(7) Subtracting the result in (6) from the primary PM2.5 identified in (3) 

Quarter 1: 5.7 ug/m3 - 8 ug/m3 = -2.3 ug/m3

Quarter 2: 2.8 ug/m3 - 4 ug/m3 = -1.2 ug/m3

Quarter 3: 2.3 ug/m3 - 4 ug/m3 = -1.7 ug/m3

Quarter 4: 3.7 ug/m3 - 5 ug/m3 = -1.3 ug/m3
Draft 3.2- September 2006 66

(8)Subtracting the result from (7) from each quarter for each of the five years:

Year 1: 

Q1: 72.6 ug/m3 - 2.3 ug/m3 = 70.3 ug/m3

Q2: 57.5 - 1.2 = 56.3

Q3: 38.8 - 1.7 = 37.1

Q4: 46.6 - 1.3 = 45.3

Year 2: 

Q1: 58.9 - 2.3 = 56.6

Q2: 38.7 - 1.2 = 37.5

Q3: 44.3 - 1.7 = 42.6

Q4: 60.5 - 1.3 = 59.2

Year 3: 

Q1: 51.6 - 2.3 = 49.3

Q2: 39.4 - 1.2 = 38.2

Q3: 30.2 - 1.7 = 28.5

Q4: 63.0 - 1.3 = 61.7

Year 4: 

Q1: 52.5 - 2.3 = 50.2

Q2: 38.5 - 1.2 = 37.3

Q3: 36.6 - 1.7 = 34.9

Q4: 43.6 - 1.3 = 42.3

Year 5: 

Q1: 67.1 - 2.3 = 64.8

Q2: 44.4 - 1.2 = 43.2

Q3: 42.1 - 1.7 = 40.4

Q4: 57.1 - 1.3 = 55.8

The “new” 98th percentile values are:

Year 1: 70.3

Year 2: 59.2

Year 3: 61.7

Year 4: 50.2

Year 5: 64.8

(9) Averaging the 3 design values:

DV1 = (70.3 + 59.2 + 61.7)/3 = 63.7 

DV2 = (59.2 + 61.7 + 50.2)/3 = 57.0

DV3 = (61.7 + 50.2 + 64.8)/3 = 59.9
Draft 3.2- September 2006 67

Averaging the 3 DV to get the final future year 5 year weighted average DV:

(63.7 + 57.0 + 59.9)/3 = 60.2 = 60 ug/m3

In the above examples, emissions reductions from steel mills were modeled with a

dispersion model, which provided additional reductions to the future year 5 year weighted

average annual and 24-hour design values. Changes in design values derived from

photochemical modeling should be evaluated to determine the magnitude (on concentrations) of

emissions changes from the flagged steel mills. The change in concentration in the

photochemical model from emissions controls on the steel mills may need to be subtracted from

the final design value to ensure that there is no double counting of emission reductions. 

Quality Assurance

As with any modeling exercise, it is important to quality assure the model inputs and

outputs. In particular, we recommend a focused quality assurance check on emissions from

sources flagged for a dispersion modeling analysis. Prior to applying a model, States should

review available information to ensure that there are no commonly reported major discrepancies

between modeled estimates applying available emission factors and estimated activity levels for

similar sources and nearby monitored data for particulate matter. The 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 12) should

consult with the appropriate U.S. 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 with the

same percent reduction. If a speciation monitor exists, the speciated data from the monitor may

also help quality assure the dispersion model results. However, the relative application of the

dispersion model results 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.
Draft 3.2- September 2006 68

6.0 What Is The Recommended Modeled Test For Reasonable Progress Reducing Regional

Haze?

Our recommended modeled test to demonstrate reasonable progress has many similarities

to the tests, described in Section 5.0, to demonstrate attainment of the NAAQS for PM2.5. The

recommended reasonable progress test and the attainment tests both use monitored data to define

current air quality. They divide PM2.5 into major species, and component-specific relative

response factors are multiplied by current monitored values to estimate future concentrations. 

Regional haze is calculated by estimating light scattering and absorption by components

of PM2.5. Goals for reducing regional haze are defined in U.S. EPA , 1999. There are two goals

for reducing regional haze.

- Mean regional haze on the 20% of days with worst current visibility should be reduced by an

agreed upon relative amount between a base period (2000-2004) and 2018, and

- mean regional haze on the 20% of days with best visibility during the base period should not

increase.

The “agreed upon” relative amount of improvement on worst visibility days needs to

consider the long term goal stated in the rule, which is to reduce regional haze to “natural

background” levels by 2064. Thus, initially, States are asked to evaluate reductions in light

extinction (on the on worst visibility days) by an amount which is proportional to the number of

years between the base period and 2018 vs. the number of years between the base period and

2064. For example, if a Class I area’s mean worst visibility during the 2000-2004 base period

were 35 deciviews and natural background were estimated to be 8 deciviews, the goal for 2018

would be to reduce light extinction on the worst days by 6.3 deciviews (i.e., [35 -8][(2018-

2004)/(2064-2004)]). Examples of this calculation is contained in (EPA, 2003) and is referred to

as uniform rate of progress or the “glide path”. 

The regional haze rule provides States with some flexibility to establish the goal for

improving visibility on worst visibility days between the base period and 2018 in the event this

initial goal appears to be infeasible. The procedures for determining the reasonable progress

goals are contained in (reference). The actual determination of reasonable progress is a multistep process which looks at the uniform rate of progress, the predicted rate of progress, and a

multi-factor test. In the end, the definition of “reasonable progress” will be determined by states

and FLMs for Class I area by considering the factors and the modeling. This document (the

modeling guidance) will assume that goal is the glide path (uniform rate of progress), but that

may not always be the case. We will refer to meeting the glide path as “uniform rate of

progress” which, in the end, may be more or less visibility improvement than “reasonable

progress”.

 

The goals for regional haze between now and 2018 set requirements for trends in air

quality rather than for meeting some absolute air quality level. The U.S. EPA has developed
Draft 3.2- September 2006 69

guidance on how to track visibility for the regional haze rule. In Section 6.1, we describe how

the ambient trends in regional haze will be determined. The modeled test for reasonable

progress needs to provide a good predictor for what the trend in observed air quality will be

determined to be in 2018. This is most likely to happen if we reproduce the test which will be

applied to the ambient observations as closely as practical. In Section 6.2, we present and

recommend a modeled test which closely replicates the ambient data test. Section 6.3 addresses

how to develop key inputs for the recommended modeled test for reasonable progress. 

6.1 How are Regional Haze Trends Measured?

Regional haze is measured by an extinction coefficient (bext) which represents light

attenuation resulting from scattering and absorption of light from ambient particulate matter plus

scattering of light due to gas molecules in the air (i.e., Rayleigh scattering). Although bext can be

estimated differently, the regional haze rule requires that it be estimated using measured ambient

particulate matter. This follows since, for a given set of meteorological conditions, visibility can

be improved by reducing concentrations of ambient particulate matter. Thus, deriving bext in this

manner provides a direct link between regional haze and related pollutant concentrations. 

Equation (6.1) may be applied in each Class I area to estimate bext (Sisler, 1996, Malm, et al.,

2000). The equation reflects empirical relationships derived between measured mass of

particulate matter components and transmissometer measurements of bext at monitoring sites in

Class I areas within the IMPROVE network. 

 bext = 3((f(rh))[SO4] + 3((f(rh))[NO3] + 4(f’(rh))[OC] + 10[EC] + 1[IP] +

0.6[CM] + brayleigh (6.1)

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, ug/m3

,

NO3 is the mass associated with nitrates, ug/m3

,

OC is the mass associated with organic carbon, ug/m3

,

EC is the mass associated with elemental carbon, ug/m3

, 

IP is inorganic primary particulate matter (excluding primary sulfate and nitrate particles) with

aerodynamic diameter < 2.5 um, :g/m3

,

CM is coarse particulate matter with aerodynamic diameter > 2.5 um, but < 10 um, ug/m3

,
Draft 3.2- September 2006 70

brayleigh is light-scattering attributable to Rayleigh scattering, Mm-1 (i.e., inverse “mega-meters”),

assumed to be 10 Mm-1.

bext is the estimated extinction coefficient, Mm-1.

Revised IMPROVE Algorithm

The IMPROVE program has recently revised the IMPROVE algorithm (IMPROVE,

2006); (Hand, 2006) . The new algorithm is intended to reduce biases in light extinction

estimates compared to the old algorithm. The new algorithm is somewhat more complicated

than the old algorithm 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]

+ 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)]

 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 current

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 for 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 ug/m3 x [Total Sulfate], for [Total Sulfate] < 20 ug/m3

[Large Sulfate] = [Total Sulfate], for [Total Sulfate] $ 20 ug/m3

[Small Sulfate] = [Total Sulfate] - [Large Sulfate]
43See U.S. EPA 2003a, Appendix A, Table A-2.

Draft 3.2- September 2006 71

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).

Testing of the new algorithm reveals that, in most regions of the country, the new

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. Therefore, the revised equation it is not expected to make a

large difference in the uniform rate of progress calculations. However, the new equation could

lead to differences in the relative change in light extinction predicted by the models. Either of

the IMPROVE equations are acceptable for use in the estimation of model predicted changes in

regional haze. The old algorithm is easier to apply. But States can use either or both of the

algorithms. 

Deciview Haze Index

The regional haze rule 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) (6.2)

Where the units of bext and light scattering due to Rayleigh scattering (i.e., the “10" in the

denominator of the logarithmic expression) are both expressed in Mm-1.

A change in deciviews, which is how we are to assess reasonable progress, is given by Equation

(6.3). A 1 deciview change is equivalent to ~10% change in bext.

)dv = dvfuture - dvbaseline (6.3)

A negative number indicates a reduction in deciviews, which is an improvement in visibility.

Estimating Mass Associated With Components Of Particulate Matter. 

Regional haze calculations for the modeled uniform rate of progress test, utilize

IMPROVE measurements at Class I areas. All Class I areas have speciated PM2.5 measurements

or are close to a representative monitoring site with measurements43. Therefore, it is generally
44There may be a few 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).

Draft 3.2- September 2006 72

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 areas44. 

FRM data is not used in the test. 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 SANDWICH procedure introduced in Section 3. 

Sisler (1996) has developed a set of default assumptions for mass associated with each of

the components of particulate matter on the right hand side of Equation (6.1). These are

presented in Table 6.1. The components in Table 6.1 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 1.4 times measured OC mass; there is no

water component; and there is a term for coarse particulate matter. 

Table 6.1. Default Assumptions Used To Derive Aerosol Species Concentrations For

Estimating Extinction Coefficients

(1)

Species

(2)

Formula

(3)

Assumptions

Sulfate 4.125 (measured sulfur) or 1.375

(measured SO4)

All elemental sulfur is from

sulfate, & all sulfate is from

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 1.4 (organic carbon) Average organic molecule is 70%

carbon

IP

consists entirely of 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

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
Draft 3.2- September 2006 73

Normalizing trends in regional haze- f(rh) factors. It is clear from equation (6.1) that

relative humidity can have a substantial effect on estimated extinction coefficients, as well as on

the relative importance of changes in different components of particulate matter can have 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 us to assess 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 obvious potential ways 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. Subject to the uncertainties posed by the empirically derived coefficients

in Equation (6.1), this approach is the most likely to identify the 20% best and worst visibility

days during the base period at a Class I site. 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 a State or regional planning organization 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 to normalize trends in the extinction coefficient 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 Equation

(6.1) 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 is to use a single climatological value. In the context of

modeling, there is a choice. We can use climatological data or we can use the modeled (or

measured) relative humidity data from the air quality model. Since the 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.

 Throughout this section we make every attempt to be consistent with the “Tracking

Guidance”. It will be easier to interpret the modeling results and compare them to future

measured values, if the data are calculated in the same manner. We therefore 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. 
45For 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. 

Draft 3.2- September 2006 74

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%45. 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. These values

have also been incorporated into precalculated daily extinction and deciview data that can be

found on the Visibility Information Exchange Web System (VIEWS) website (

http://vista.cira.colostate.edu/views/ ). The use of precalculated visibility data will make it more

simple for States and RPOs to calculate visibility changes and will also provide for more

consistency.

6.2 How Do I Apply A Modeled Test To Demonstrate Uniform Rate of Progress?

The purpose of a modeled test to demonstrate uniform rate of progress is to provide

assurance that a proposed control strategy will result in progress goals being met when measured

concentrations of particulate matter are used to estimate visibility impairment at some future

date. The test we recommend has 6 steps.

1. For each Class I area, rank visibility (in deciviews) on each day with 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 five 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

particulate matter identified on the right hand side of Equation (4.1).

4. Multiply the relative response factors times the measured PM data during the base

period to compute future daily values of deciviews. Rank these to determine the 20%

best and 20% worst visibility for each of the five years.

5. Using the results in Step 4, recalculate the future mean extinction coefficients for the

20% best and 20% worst visibility days in each of the five base years. 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..

6. Compare the difference in deciviews to the progress goals. 

We describe each of these steps more fully below. States should follow the outlined
46Pre-calculated and ranked extinction calculations for all Class I areas for the base

period 2000-2004 are available on the VIEWS website at http://vista.cira.colostate.edu/views/.

This data can be used to satisfy steps 1 and 2.

47How many and which days to simulate is discussed in Section 6.3 and in Section 14.1.

Draft 3.2- September 2006 75

procedure for each Class I area subject to a reasonable progress review. The methodology

follows the same basic procedures outlined in the “Tracking guidance.” Further details can be

found in that document. We conclude this subsection with an example illustrating the

recommended modeled test for reasonable progress.

Step 1. Using monitored data, rank current 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

period46. The deciview (dv) should serve as the basis for ranking. Day-specific observations for

mass associated with SO4, NO3, OC, EC, IP and CM, as defined in Table 6.1, should be used to

calculate bext for each day. The appropriate month- and area-specific climatological relative

humidity adjustment factor (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 reasonable progress goal for regional haze. 

Average the 5 yearly mean deciview values reflecting mean visibility on the days with best

visibility. The resulting average mean deciviews may not increase if reasonable progress is to be

demonstrated.

Step 3. Estimate relative response factors (RRF) for each component of PM2.5 and

for CM.

This should be done by using emissions during the base period in air quality model

simulations performed for a large number of days47. Air quality corresponding with future

emissions (reflecting effects of growth and controls) should be simulated for the same days. 

Take the (temporal) arithmetic mean concentration for each PM2.5 component (and coarse mass)

computed near the Class I monitoring site with future emissions and divide this by the

corresponding arithmetic mean concentration for each component obtained with current

emissions. The resulting quotients 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
Draft 3.2- September 2006 76

are calculated using the identified 20% best and 20% worst monitored days at each Class I area. 

This will likely be a different set of days in each area. . 

Step 4. Multiply the relative response factors derived in Step 3 times measured

daily values for each component of PM2.5 and CM to get future daily estimates of PM2.5

components and CM on “worst visibility” and “best visibility”days.

These multiplications produce future estimates for SO4, NO3, OC, EC, IP and CM for

each of the previously selected “worst” and “best” visibility days.

Step 5. Use the information developed in Step 4 to compute future values for bext for

each of the previously identified “worst” and “best” days with monitored data. Sum the

future bext values for each day and then convert to deciviews. Compute mean values of

deciviews for the sets of “worst” and “best” days in each year. Average the 5 mean

deciview values for “worst” and “best” visibility days.

Apply Equation (6.1) using the future predicted PM component concentrations for each

of

the previously identified 20% “worst” and “best” days. Use the area- and month-specific

climatological mean default value for the humidity adjustment factor (f(rh)). Applying Equation

(6.1) in this manner produces an estimate of the future value of bext for each of the previously

identified “worst” and “best” visibility days for a Class I area. The daily total bext for each day

should then be converted to deciviews. This gives a future year daily deciview value for each of

the best and worst days. 

Then 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 deciview values estimated for the “best” visibility days. The resulting average

mean values for deciviews on “worst” and “best” visibility days are used in Step 6.

Step 6. Compare the future year mean deciview values calculated in step 5 to the

baseline mean deciview values calculated in step 2. Compare these differences with the two

previously established goals for reasonable progress to see if reasonable progress is

demonstrated.

Apply the information produced in Step 5 to estimate a difference in

deciviews for previously identified days with future “worst” visibility and future “best”

visibility. If the resulting change in deciviews is a negative number, this represents an

improvement in visibility. Compare the change in visibility to the uniform rate of progress

and/or the reasonable progress goals.

Example 6.2
Draft 3.2- September 2006 77

We use example 6.2 to illustrate the modeled test for reasonable progress. For ease of

presentation, we assume there are only 10 speciated samples for PM in the first of 5 years

comprising the base period. Since sampling will occur every third day, we anticipate a usual

sample size of about 120 days per year. We go through 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.

Given:

Ten days have measured components of PM in a Class I area during the first year of a 5-year

base period. Table 6.2 below shows the measurements (in :g/m3) for each of the 10 days. Table

6.2 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.

Table 6.2

Day Date f(rh) SO4 NO3 OC EC IP CM

1 2/15 1.7 4.53 2.23 3.37 0.89 0.32 7.33

2 3/15 2.9 5.12 1.78 2.92 0.78 0.44 9.17

3 4/15 3.5 5.67 1.25 2.44 0.54 0.43 10.25

4 5/15 3.7 6.59 1.12 2.86 0.50 0.66 9.80

5 6/15 4.3 7.47 0.93 3.49 0.61 0.98 10.99

6 7/15 4.6 8.33 0.79 3.50 0.69 1.08 11.38

7 8/15 4.4 9.22 0.89 3.24 0.67 0.89 10.78

8 9/15 4.1 8.05 0.97 3.12 0.73 0.71 8.25

9 9/30 4.1 6.84 1.15 3.21 0.85 0.42 8.82

10 10/30 2.7 5.32 1.41 3.26 0.76 0.64 8.11

The following uniform rate of progress has been calculated: (a) mean visibility on the 20% of

days with worst visibility should be improved by 2.0 deciviews; (b) mean visibility on the 20%

of days with best visibility should not deteriorate. 

Find: Is the control strategy simulated in this model analysis sufficient to meet the two goals for

reasonable progress reducing regional haze in this Class I area?

Solution:

Step 1. Rank days according to their baseline daily deciview values derived from measured

PM data.

First, estimate the extinction coefficient for each day with the needed PM measurements. 
Draft 3.2- September 2006 78

This is done using the information in table 6.2 with Equation (6.1). 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

Current 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 last three columns (shown in italics) of table 6.3 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 6.3

Day Date f(rh) SO4 NO3 OC EC IP CM (bext)

baseline

Deciv

iews

Rank

1 2/15 1.7 4.53 2.23 3.37 0.89 0.32 7.33 71.6 19.7 10

2 3/15 2.9 5.12 1.78 2.92 0.78 0.44 9.17 95.5 22.6 8

3 4/15 3.5 5.67 1.25 2.44 0.54 0.43 10.25 104.4 23.5 7

4 5/15 3.7 6.59 1.12 2.86 0.50 0.66 9.80 118.6 24.7 6

5 6/15 4.3 7.47 0.93 3.49 0.61 0.98 10.99 146.0 26.8 4

6 7/15 4.6 8.33 0.79 3.50 0.69 1.08 11.38 164.7 28.0 1

7 8/15 4.4 9.22 0.89 3.24 0.67 0.89 10.78 170.5 28.4 2

8 9/15 4.1 8.05 0.97 3.12 0.73 0.71 8.25 146.4 26.8 3

9 9/30 4.1 6.84 1.15 3.21 0.85 0.42 8.82 135.3 26.1 5

10 10/30 2.7 5.32 1.41 3.26 0.76 0.64 8.11 90.7 22.0 9

Step 2. Calculate the average baseline 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 6.4. Table 6.4 below
Draft 3.2- September 2006 79

summarizes mean worst and best visibility for each of the 5 years in the base period.

Table 6.4

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 baseline worst and best visibility”, needed for reasonable progress

calculations, is obtained by taking the arithmetic average of the mean worst and best visibility

for the 5 years. Thus, the average mean 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, IP and

for coarse mode particulate matter (CM).

Tables 6.5 and 6.6 show the procedure for calculating component-specific relative

response factors using an air quality model. Component-specific relative response factors are

computed as described in Section 4.3. The example shows the calculation of RRFs for the worst

days in the year modeled. The same calculation is repeated for the best days.

Table 6.5 Base year modeled species concentrations

Modeled

Output

SO4 NO3 OC EC IP

(crustal)

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
Draft 3.2- September 2006 80

Table 6.6 Future year modeled species concentrations

Modeled

Output

SO4 NO3 OC EC IP

(crustal)

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

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

SO4 NO3 OC EC IP

(crustal)

CM

RRF (mean

future/mean

base)

0.780 0.752 0.956 0.768 1.022 1.043

Step 4. Estimate future daily concentrations for each component of PM2.5 and for CM for

each of the previously identified “worst” and “best” visibility days by multiplying

component-specific RRF’s times the corresponding observed component concentration on

each day.

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 need to 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.

Worst Days

Day 6: [SO4]future = (RRF)SO4 [SO4]baseline = (0.780) [8.33] = 6.50 :g/m3

[NO3]future = (0.752) [0.79] = 0.59 :g/m3

[OC]future = (0.956) [3.50] = 3.35 :g/m3

[EC]future = (0.768) [0.69] = 0.53 :g/m3

(IP)future = (1.022) [1.08] = 1.10 :g/m3

[CM]future = (1.043) [11.38] = 11.87 :g/m3

Day 7: [SO4]future = (0.780) [9.22] = 7.19 :g/m3
48The same mean RRFs (for each component) are applied to the concentrations on all of

the worst days (and best days) for each of the 5 years of data. The mean RRFs are derived from

the modeled days.

Draft 3.2- September 2006 81

[NO3]future = (0.752) [0.89] = 0.67 :g/m3

[OC]future = (0.956) [3.24] = 3.10 :g/m3

[EC]future = (0.768) [0.67] = 0.51 :g/m3

(IP)future = (1.022) [0.89] = 0.91 :g/m3

[CM]future = (1.043) [10.78] = 11.24 :g/m3

Best Days

Day 1: [SO4]future = (0.780) [4.53] = 3.53 :g/m3

[NO3]future = (0.752) [2.23] = 1.68 :g/m3

[OC]future = (0.956) [3.37] = 3.22 :g/m3

[EC]future = (0.768) [0.89] = 0.68 :g/m3

(IP)future = (1.022) [0.32] = 0.33 :g/m3

[CM]future = (1.043) [7.33] = 7.64 :g/m3

Day 10: [SO4]future = (0.780) [5.32] = 4.15 :g/m3

[NO3]future = (0.752) [1.41] = 1.06 :g/m3

[OC]future = (0.956) [3.26] = 3.12 :g/m3

[EC]future = (0.768) [0.76] = 0.58 :g/m3

(IP)future = (1.022) [0.64] = 0.65 :g/m3

[CM]future = (1.043) [8.11] = 8.46 :g/m3

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 period48. 

Step 5. Use the estimated future PM component concentrations to estimate future values

for bext on each of the “worst” and “best” visibility days. Then convert the daily total bext

values to deciviews. Then estimate the mean “worst” and “best” visibility for each of the 5 

years in the base period. Finally, estimate future values for average mean visibility on

“worst” visibility days and average mean visibility on “best” visibility days.

Using future concentrations of PM components obtained in Step 4, we can estimate

corresponding values for 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
Draft 3.2- September 2006 82

Convert to deciviews 

134.8 Mm-1 = 10* ln(134.8/10) = 26.0 dv

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

Convert to deciviews 

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: bext = (3)(1.7)[3.53] + (3)(1.7)[1.68] + (4)[3.22] + (10)[0.68] + 0.33 + (0.6)[7.64] + 10 =

61.2 Mm-1

Convert to deciviews 

61.2 Mm-1 = 10* ln(61.2/10) = 18.1 dv

Day 10: bext = (3)(2.7)[4.15] + (3)(2.7)[1.06] + (4)[3.12] + (10)[0.58] + 0.65 + (0.6)[8.46] + 10 =

76.2 Mm-1

Convert to deciviews 

76.2 Mm-1 = 10* ln(76.2/10) = 20.3 dv

Future mean visibility on best days = (18.1 + 20.3) / 2 = 19.2 dv 

Similar calculations are performed for previously selected “worst” and “best” days in each of

years 2-5. To illustrate the modeled test for reasonable progress, assume these other calculations

yield the following estimates for future mean dv on worst and best visibility days.

Table 6.7

Year Future Mean dv On Worst

Visibility Days

Future Mean dv On Best

Visibility Days

1 26.2 19.2

2 27.3 19.3

3 27.6 17.6

4 25.9 18.3

5 26.1 18.6

 

Using results in table 6.7, we see that the estimated future average mean visibility for the 20%

days with worst visibility is
Draft 3.2- September 2006 83

dvfuture = (26.2 + 27.3 + 27.6 + 25.9 + 26.1) / 5 = 26.6

The estimated future average mean extinction coefficient for the 20% days with best visibility is

dvfuture = (19.2 + 19.3 + 17.6 + 18.3 + 18.6) / 5 = 18.6

Step 6. Compare the two resulting differences to the two goals for reasonable progress. If

both goals are met, the modeled test for reasonable progress is passed.

Use the results generated in Step 5 to first estimate the difference in deciviews for days

with worst visibility and then the difference in deciviews for days with best visibility.

For the 20% days with worst visibility,

dv difference = 26.6 - 28.8 = - 2.2 deciviews

For the 20% days with best visibility,

dv difference = 18.6 - 19.7 = - 1.1 deciviews

The uniform rate of progress goal for the 20% days with the worst visibility is predicted

to be met. That is, the goal is to improve visibility on these days by 2.0 deciviews. The future

year strategy leads to an improvement in visibility of 2.2 deciviews. The goal for days with best

visibility is also met. This follows, since visibility is not predicted to deteriorate on these days

but, in fact, is estimated to improve (by 1.1 deciviews). The final step in the process is to

determine if the predicted improvement in visibility meets reasonable progress. This step is

beyond the modeling guidance. The modeling can only be used to determine the predicted

improvement in visibility (and whether the improvement is on the “glide slope”. It cannot

determine if reasonable progress is met.

6.3 How Do I Select Appropriate Inputs To The Reasonable Progress Test?

In Section 6.2, we described the recommended modeled test for reasonable progress. An

important part of the test requires using component-specific relative response factors (RRF’s),

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 RRFs. 

Second, there are several assumptions inherent in the recommended modeled test for reasonable

progress. We identify these in this subsection and comment on their underlying rationale. More

specifically, we address eight issues: 

- how to estimate base period air quality in a Class I area without monitored data;

- how to handle a base year without data or with a small sample size;

- how to consider a day with missing data for one or several components of PM;
49Bering 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.

Draft 3.2- September 2006 84

- 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;

- which predictions to use to derive relative response factors;

- how many and what kind of days to use to develop RRF values

- use of relative response factors which are specific for days with poor vs. days with good

visibility 

-alternative RRF calculations

Estimating baseline “worst” and “best” visibility in a Class I area without monitors.

There are 156 Class I areas and there are 110 IMPROVE sites in or near Class I areas. 

Therefore, approximately 46 Class I areas do not have 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) values for each Class I area as well as the

representative site for each Class 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 sites49. 

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 sites will have at least 3

complete years of data. The guidance also contains procedures for filling in missing data as part

of the calculation of the 20% best and worst days. We recommend that these same procedures

are followed for calculations in the modeled test. 

There are several data completeness issues that may cause problems with the modeling

test. First, a site may have less than 3 years of complete data. This will be a problem for the

purpose of modeling and for tracking progress. 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 occurs when data is missing

during the meteorological time period that was modeled. It is likely that most States will only be

modeling a single year (or less). Therefore it is possible that some or all of the ambient data at

one or more Class I areas is missing during that year. Without ambient data, it is impossible to

identify the 20% best and worst days (used to calculate modeled RRFs). 
50This is only recommended if the model performance for all species is good (at sites with

data). While it is expected that the air quality models will do an adequate job of predicting the

PM2.5 components which are responsible for the largest visibility degradation, the models may

not perform as well in estimating total visibility impairment. Accurately estimating the 20% best

and worst days depends on accurately estimating all of the PM components. In particular,

overprediction of certain components (e.g nitrates in the winter) could lead to identification of

some of the worst days in the wrong season and for the wrong reasons. 

Draft 3.2- September 2006 85

Again, if this occurs, States should work with their Regional Office and FLM 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. Another alternative is to estimate the 20% best

and worst days from the model outputs50. 

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 (6.1). 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 6.2 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.

It is unlikely that there would be any wholesale change in the identity of “worst” or

“best” days with future vs. current 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 reasonable progress test 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 reasonable progress test. 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 preand 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 reasonable

progress test for “best” days is likely to be protective of the environment. If our recommended
Draft 3.2- September 2006 86

procedure leads to suspected problems in the outcome of a test, a State may perform a more

rigorous version of the reasonable progress test (in which the identity of pre-control and postcontrol 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. “Near” is defined

in Section 3.3. For each day, daily average surface predictions of each component of PM made

near a monitor should be estimated. 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-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 baseline emissions. The

recommended procedure is illustrated in section 3.3 for a grid whose cells are 12 km on a side. 

Similar to the PM2.5 NAAQS attainment test, nearby grid cells should be averaged. Note that for

cells larger than 15 km on a side, no spatial averaging is necessary—States should just use the

prediction in the cell containing the monitor. 

Selecting days to derive RRF values. RRF values should be estimated by taking the

ratio of future predictions averaged over several days to current predictions averaged over the

same several days. It may often happen that a regional planning organization or a group of

States decides to model effects of a strategy for numerous Class I areas simultaneously. As we

note in Section 14, 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 baseline emissions. Thus, the RRF should reflect values

averaged over ~ 24 “best” and “worst” days in that year. 

If it is not feasible to model an entire year, or if only a small number of Class I areas is to

be considered, a State should examine when worst visibility is observed to occur. Choose a

sample of days from each quarter in which an incident of “worst” visibility occurs and calculate

a RRF estimate for each component of PM. The appropriate RRF value would be applied to

monitored “worst visibility” days. There is not, as yet, a good basis for suggesting a minimum

number of days to choose for this purpose. However, information presented in Section 14.1.4

suggests that this number should be > ~10 days.

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. If this is not feasible or only a limited number of Class I areas are to be

considered in an analysis, States may review when “best” visibility days are observed to occur in

the base period. Model > ~ 10 days with observed “best” visibility and average predicted
Draft 3.2- September 2006 87

baseline and future concentrations for each PM component. The RRF values are the ratios of the

future to baseline 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. 

Alternative RRF calculations. The default reasonable progress test is relatively simple

in that a single mean RRF is calculated for each PM component (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 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 felt

that the default recommendation is too simplistic to accurately capture the change in future

visibility at any particular Class I area. Alternative methods should be submitted as part of a

weight of evidence submital.
Draft 3.2- September 2006 88

7.0 How Can Additional Analyses Can Be Used to Support the

Attainment Demonstration?

By definition, models are simplistic approximations of complex phenomena. The

modeling analyses used to demonstrate that various emission reduction measures will bring an

individual area into attainment for the NAAQS contains many elements that are uncertain (e.g.,

emission projections, meteorological inputs, model response). These uncertain aspects of the

analyses can sometimes prevent definitive assessments of future attainment status. The

confidence in the accuracy 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 Eulerian air quality models

represent the best tools for integrating emissions and meteorological information with

atmospheric chemistry and no single additional analysis can replace that, EPA believes that all

attainment demonstrations will be strengthened by additional analyses that can help confirm that

the planned emissions reductions will result in attainment.

Corroboratory 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 (e.g., less than 82 ppb for ozone, 14.5 ug/m3 for annual PM2.5, and 62

ug/m3 for 24-hour PM2.5) 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. As noted earlier (see section 2.2), there may be some

cases in which it is possible to demonstrate attainment via a "weight of evidence" demonstration

despite failing the model attainment test, or conversely, demonstrate that reaching attainment is

not likely despite passing the model attainment test. This section of the guidance will discuss

some specific additional analyses that can be used to corroborate the model projections, or refute

them in the case of a weight of evidence determination. Additional examples of possible weight

of evidence determinations are provided in existing EPA guidance (USEPA, 1996c). Again, it

should be noted that no single corroborating analysis can serve as an adequate substitute for the

air quality model, however, in aggregate, many such analyses can help inform the process.

 In Section 7.1, we identify several broad types of analyses which can be used to

corroborate one another as part of a supplemental analysis. States should utilize each of these

types of analysis to ensure that conclusions regarding adequacy of a proposed strategy are based

on a variety of independent analyses. In Section 7.2, we more specifically address the submital

of a weight of evidence demonstration and recommend a framework for compiling and

submitting a weight of evidence demonstration. 

The goals for regional haze between the base period and 2018 relate to trends in light

extinction rather than to some absolute value, as is the case for the NAAQS. Thus, supplemental

analyses differ in some respects for visibility-related applications. Section 7.3 describes how to

use supplemental analyses in reasonable progress determinations.
51 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.

Draft 3.2- September 2006 89

7.1 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

2) Trends in ambient air quality and emissions

3) Observational models and diagnostic analyses

These broad groups of supplemental analyses are discussed in more detail below:

Modeling: The relative attainment tests described in sections 3, 4, and 5 are the primary

modeling tools used in an attainment demonstration. The application of a photochemical grid

model developed on a regional or local scale, is the best tool available to judge the impacts of

changes in future year emissions. In addition to this “primary” modeling analysis, there are

various other models, applications, and tools that can be used to supplement the results of the

modeled attainment test. These include, but are not limited to: 

! Use of available regional or national scale modeling applications that are suitable51 for

the local area, 

! Use of other appropriate local modeling attainment demonstrations that include the

nonattainment area of interest, 

! Use of photochemical source apportionment 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.). 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 in

inputs or model formulations. 

! Use of dispersion models to address primary PM2.5 contributions to monitors. In areas

with large spatial gradients of primary PM2.5, dispersion models are best suited to
52 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.

53For each of these metrics, the appropriate comparison to the level of the NAAQS is 85

ppb for 8-hour ozone; 65 ug/m3 for 24-hour PM2.5; and 15 ug/m3 for annual PM2.5.

Draft 3.2- September 2006 90

characterizing the change in primary PM2.5 in the future.

! Use of the same modeled attainment demonstration but with future design values that are

calculated in an alternative manner than that recommended in Sections 3, 4, and 5 of this

guidance. Any alternate approaches for the calculation of the future design value 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.

As discussed in Section 2, 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, for some

model applications there may be strong evidence from the performance evaluation that the

model is able to reproduce detailed observed data bases with relatively little error or bias. 

Particularly for cases such as these, some types of “absolute” modeling results may be used to

assess general progress towards attainment from the baseline inventory to the projected future

inventory52. There are several metrics that can be considered as part of this type of additional

analysis:

• percent change in total amount of ozone or PM2.5 >= NAAQS53 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., >= 85 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 show 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. 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 modeling metrics may help provide confidence that

attainment will be reached. 
54This is particularly true in areas with little or no ambient speciation data.

Draft 3.2- September 2006 91

In cases where attainment is demonstrated at all monitors, the above metrics are useful in

showing the amount of expected reduction in other portions of the area where the unmonitored

area analysis was applied. The results of the unmonitored area analysis can also be examined to

indicate the magnitude and spatial extent of predicted remaining nonattainment. If application of

the modeled test in all grid cells indicates that most or all of the unmonitored areas will be in

attainment, then that information can support a modeled attainment demonstration and add

positive evidence to a weight of evidence determination. 

Uncertainty estimates associated with the spatial interpolation technique can also be

considered when reviewing and interpreting the results of an unmonitored area analysis. When

making a decision on whether attainment is likely to occur, areas with very high uncertainty

estimates for interpolated design values should be given less weight than areas with low

uncertainty estimates54. If model predicted future design values are close to or above the level of

the NAAQS, placing a monitor in the area, may be the only way to address this issue in the

future (absent of requiring additional emissions controls).

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 extrapolate future trends in ozone or PM2.5 based

on measured historical trends of air quality and emissions. There are several elements to this

analysis that are difficult to quantify. First, in most cases, the ambient data trends must be

normalized 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 formed

in the local area (e.g., NOx-limited, ammonia limited, transport-influenced, etc.). Assuming all

of these prerequisites can be met, then it may be possible to develop a curve that relates past

emissions changes to historical and current air quality. Once the relationship between

past/present emissions and air quality is established, this “reduction factor” can be applied to the

expected emissions reductions from a particular control strategy. 

 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 and Zurbenko, 1994, Flaum, et al., 1996, Milanchus, et al.,

1997, 1998, Hogrefe, et al., 2000), (c) using a probability distribution of meteorological severity

based on climatological data (Cox and Chu, 1993, 1996), (d) 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 (Stoeckenius, 1990, 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,

the established relationship between ozone and meteorological variables are stronger (higher r-
Draft 3.2- September 2006 92

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 difficult to meteorologically adjust PM2.5

concentrations with much certainty. 

 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. 

A simpler (and more uncertain) way to qualitatively assess progress toward attainment is

to examine recently observed air quality and emissions trends. A downward trend in observed

air quality and a downward trend in emissions (past and projected) is 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. EPA recently prepared a report

that analyzed statistically significant trends in ozone (U.S. EPA, 2004c and U.S. EPA, 2005b)

and ozone precursor emissions as well as a report which examined trends in PM2.5 concentrations

and precursors (U.S. EPA, 2005c). 

Weight given to trend analyses depends on several factors. The more air quality data

available and the greater variety of trend parameters which show major improvements, the more

credible the 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 it is not necessary to extrapolate

very far into the future. Additionally, 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

controlling OC, there is no guarantee that ambient OC will respond similarly to changes in past

emissions. 

Observational Models In some cases ambient data can be used to corroborate the effects of a

control strategy (e.g., Blanchard et al, 1999; Croes et al, 2003; Koerber and Kenski, 2005). 

Observational models take advantage of monitored data to draw conclusions about the relative

importance of different types of emissions and precursors as factors contributing to observed

PM2.5 and ozone, as well as inferences which might be drawn about the effectiveness of various

strategies to reduce concentrations. Observational models can be used to examine days which

have not been modeled with an air quality model, as well as days which have been modeled. 

The resulting information may be useful for drawing conclusions about the general

representativeness of the responses simulated with the air quality model for a limited sample of
Draft 3.2- September 2006 93

days. However, their ability to estimate how much control is needed is limited. Thus,

observational approaches are suitable to corroborate results from more quantitative techniques,

like air quality models. There are at least two types of observational models: source

apportionment (i.e., “receptor”) models and indicator species approaches. 

Receptor models There is a large body of literature describing the theory and use of receptor

models to identify and/or apportion sources which may be contributing to monitored air quality. 

Reviews by Seigneur, et al. (1997), which is also summarized in Seigneur, et al. (1999), and

(EPA,2003) contains a more complete description of the major approaches, summarizes findings

obtained in a number of applications, and provides an extensive list of references. Receptor

models are most useful for identifying contributions of various source categories to observed

primary components of particulate matter, although, to a lesser degree, they can also be used to

infer contributions from ozone and secondary PM precursors. 

There are two major types of receptor models. The first type is the chemical mass

balance model (CMB). A description and user’s guide is available for the CMB model (U.S.

EPA, 1990, 2004). This model assumes that the user already has a good idea of what source

categories potentially contribute to observations at a monitoring site. Speciated emissions

profiles for all source categories are then compared with speciated air quality measurements at a

monitor on a given day. A combination of source category contributions which minimizes

observed differences between calculated and observed concentrations for a set of PM species

(i.e., “fitting elements”) is derived. One key assumption of the CMB approach is that there is no

substantial chemical transformation of particulate matter between the point of emissions and the

site of the ambient measurements. Thus, the technique is limited when a large portion of

measured PM2.5 is secondary particulate matter.

The second type of receptor models are multi-variate statistical models. Unlike with

CMB, a priori assumptions about contributing source categories are not needed. Another

distinction from the CMB approach is that multi-variate models look at day to day variations in

speciated observations rather than focusing on individual days. The object of this is to identify

sets of chemical species which track one another well. Statistical methods like cluster analysis

or principal component analysis are used to identify groups of days or groups of chemical

species which are associated with one another. Common multi-variate statistical models include

positive matrix factorization (PMF) (Paatero and Tapper, 1994) , and UNMIX (Maykut, 2003;

Poirot et. al, 2001) These model s may be useful when confirming whether a strategy is reducing

the right sorts of sources. 

Indicator Species Indicator species approaches are based on the predicted sensitivity of a

secondary pollutant’s concentration to changes in different precursors for that pollutant. It is

possible to identify ratios of certain species which are good indicators of whether a secondary

pollutant is sensitive to reductions in precursor A or precursor B. Measurement of “indicator

species” is a potentially useful means for assessing which precursor category (VOC or NOx)

limits further production of ozone or secondary PM2.5 at a monitor’s location at various times of

day and under various sets of meteorological conditions. Several indicator ratios have been

developed to examine the sensitivity of ozone to changes in NOx and VOC, and the sensitivity of
Draft 3.2- September 2006 94

particulate nitrate to changes in NOX, VOC, and ammonia.

Sillman (1998, 2002) and Blanchard, (1997, 1999, 2000, 2001) identify several sets of

indicator species which can be compared to suggest whether ozone is limited by availability of

VOC or NOx. Comparisons are done by looking at ratios of these species. The following appear

to be the most feasible for use by a regulatory agency: O3/NOy, O3/(NOy - NOx) and O3/HNO3. 

Generally, high values for the ratios suggest ozone is limited by availability of NOx emissions. 

Low values suggest availability of organic radicals (e.g., attributable to VOC emissions) may be

the limiting factor. For these ratios to be most useful, instruments should be capable of

measuring NOy, NOx, NO2 and/or HNO3 with high precision (i.e., greater than that often

possible with frequently used “routine” NOx measurements). Thus, realizing the potential of the

“indicator species method” as a tool for model performance evaluation and for diagnosing why

observed ozone concentrations do or do not meet previous expectations may depend on

deploying additional monitors and/or measurements. States/Tribes should consult the Sillman

(1998, 2002) and Blanchard, (1997, 1999, 2000, 2001) references for further details on

measurement requirements and interpretation of observed indicator ratios.

Ansari and Pandis, (1998) have developed an indicator ratio of species and applied it to

several combinations of secondary particulate matter present under different environmental

conditions. They use this ratio to predict how mass of particulate matter will respond to

reductions in sulfate, nitrate and ammonia. Blanchard, et al. (2000) have also examined how

indicator species might be used to assess whether particulate nitrate concentrations are limited by

NOx, VOC, or by ammonia emissions using mechanisms which incorporate reactions dealing

with secondary particulate matter. These authors identify two ratios of indicator species which

appear potentially useful for identifying limiting precursors for secondary nitrate particulate

matter: (1) the ratio of particulate ammonium plus gas-phase ammonia over the sum of nitric

acid plus particulate nitrate plus particulate sulfate, and (2) the ratio of particulate to total nitrate. 

As with some of the ozone indicator ratios, many of the gas phase species needed for PM

indicator ratios are not routinely measured. In particular, additional measurements of nitric acid

and gas phase ammonia are needed to apply many of the indicator species ratios. 

The strength of the evidence produced by observational models is increased if an

extensive monitoring network exists and at least some of the monitors in the network are capable

of measuring pollutants to the degree of sensitivity required by the methods. Evidence produced

by observational models is more compelling if several techniques are used which complement

one another and produce results for which plausible physical/chemical explanations can be

developed. Indications of a strong quality assurance analysis of collected data and

measurements that are made by a well trained staff also lend credence to the results.

7.2 If I Use A Weight Of Evidence Determination, What Does This Entail?

As discussed in Section 2, it may be possible through the use of supplemental analyses to

draw a conclusion differing from that implied by the modeled attainment test results. Past
55Few studies have been done to examine similar uncertainties for PM2.5. Based on recent

modeling analyses, a similar range of +- 2-4% of the NAAQS seems appropriate for PM2.5. That

translates to roughly 0.3-0.6 ug/m3 for the annual PM2.5 standard. Consequently, the

recommended weight of evidence range for PM2.5 is nominally +- 0.5 ug/m3.

Draft 3.2- September 2006 95

modeling analyses have shown that future design value uncertainties of 2-4 ppb for ozone55, can

result from use of alternate, yet equally appropriate, emissions inputs, chemical mechanisms, and

meteorological inputs (Jones, 2005; Sistla, 2004). Because of this uncertainty, EPA believes that

weight of evidence determinations can be used in some cases to demonstrate attainment

conclusions that differ from the conclusions of the model attainment test.

As part of their recommendations to transform the SIP process into one that is more

performance-oriented, the Clean Air Act Advisory Committee (CAAAC) recommended

increased use of weight of evidence within State/Local attainment demonstrations (AQMWG,

2005). One of the workgroup’s recommendations to EPA was that “EPA, in conjunction with

affected stakeholders, should modify its guidance to promote weight-of-evidence (WOE)

demonstrations for both planning and implementation efforts. In particular, these demonstrations

should reduce reliance on modeling data as the centerpiece for SIP/TIP planning, and should

increase use of monitoring data and analyses of monitoring data, especially for tracking progress. 

Enhanced tracking and ambient monitoring data is a better use of available resources than

intensive local modeling.”

A weight of evidence (WOE) determination examines results from a diverse set of

additional 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 submital 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 weight of

evidence determination will be subject to area-specific conditions and data availability. Areaspecific factors may also affect the types of analyses which 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 needs to be done on a case by case basis by those implementing

the modeling/analysis protocol. States/Tribes are encouraged to consult with their EPA Regional

office in advance of initiating a WOE analysis to determine which additional analyses may be

most appropriate for their particular area. 

In a WOE determination, each type of analysis has an identified outcome that is

consistent with the hypothesis that a proposed control strategy is sufficient to meet the NAAQS

within the required time frame. 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 weight of evidence produced by the various 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
Draft 3.2- September 2006 96

analyses performed, data bases used, key assumptions and outcomes of each analysis, and why a

State/Tribe believes that the evidence, viewed as a whole, supports a conclusion that the area

will, or will not, attain the NAAQS despite a model-predicted DVF concluding otherwise. 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.

2) Multiple supplemental analyses in each of the three various categories discussed above

(modeling, air quality/emissions trends, observational models).

3) A weighting for each separate analysis based on its ability to quantitatively assess the

ability of the proposed control measures to yield attainment.

4) A description of each of the individual supplemental analyses and their results. 

Analyses that utilize well-established analytical procedures and are grounded with sufficient data

should be weighted accordingly higher.

5) A written description as to why the aggregate analyses leads to a conclusive

determination regarding the future attainment status of the area that differs from the modeled

attainment test.

7.3 What Role Should Weight Of Evidence Play In Visibility-Related Analyses?

We believe a weight of evidence analysis is an appropriate option for States to use if the

modeled test for visibility does not clearly meet the uniform rate of progress. Unlike the

NAAQS, reasonable progress goals address trends in air quality rather than some absolute level

of air quality. Thus, the focus of weight of evidence analyses differs from those performed in

NAAQS attainment demonstrations. In this subsection, we note some potential supplemental

analyses that may be used to examine reasonable progress. We then identify several refinements

to our recommended modeled test for reasonable progress if a State or regional planning

organization believes these are warranted for a weight of evidence determination. We conclude

by noting some potential concerns about the ability of models to address days with very good

visibility. 

Additional air quality modeling. Sensitivity tests can be performed to see if

conclusions about trends in “worst” and “best” visibility are robust. One example of such an

analysis is applying a model with and without a more finely resolved nested grid near one or

more Class I areas. A purpose of this 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
Draft 3.2- September 2006 97

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 supports conclusions reached in

the modeled test for reasonable progress. 

Review of trends. This includes some qualitative comparisons between past trends in

reconstructed visibility and estimated changes in emissions (e.g., early ‘90's to mid ‘00's). The

IMPROVE dataset has a longer record than the corresponding urban FRM and speciation sites. 

Some IMPROVE sites have data going back to 1989. 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.

Observational models. Receptor models are potentially useful for flagging potential

importance of local sources, if any, in influencing measurements made in a Class I area. This

could lead to a refined treatment of a local source, either through a more finely resolved nested

grid model application or plume-in-grid modeling. Trajectory models may also 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 States 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 tagging,

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. 

Refinements to the recommended modeled test for reasonable progress. If a strategy

for meeting reasonable progress goals appears generally successful, but the uniform rate of

progress is not met in a limited number of Class I areas, States may consider refining the

recommended test for reasonable progress 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 test may not be applicable. We list some potential refinements which could

be considered. The list is intended to illustrate types of additional analyses which could be

performed. 

- Use an alternative light extinction equation such as the revised IMPROVE 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 of more days with extraordinary

events (e.g., a nearby major forest fire 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 U.S.

EPA regional office.
Draft 3.2- September 2006 98

-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 recompute mean future “best” and “worst” visibility (i.e., do not

assume that the identity of baseline “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 particulate matter used in visibility calculations may

be within a :g/m3

 or two of 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 particulate matter are very low (and light extinction is

also low). Utility of weight of evidence determinations is heightened in such cases. If a State

has reason to believe that an atmospheric simulation model’s ability to estimate concentrations

of components of particulate matter is limited on such days, performance tests described in

Section 18 should be applied to the extent feasible for the specific Class I area in question. Next,

a State should see whether a model’s inability to accurately predict one or more individual

components of particulate matter has a substantial effect on the extinction coefficient calculated

with Equation (6.1). If it does, and diagnostic tests (also described in Section 18) 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. 
56PM nonattainment areas that are granted an attainment date extension later than 2013

will be required to perform a Mid-Course Review.

Draft 3.2- September 2006 99

8.0 What Additional Analyses and Data Collection Can Be Completed to

Assess Progress towards Attainment 

The purpose of an attainment demonstration is to provide a best estimate as to whether

the control measures included in a State Implementation Plan will result in attainment of the

NAAQS by a specific date in the future. In most cases, it will be desirable to periodically track

the air quality improvements resulting from the SIP to ensure that the plan is going to result in

attainment by the appropriate dates. One possible tracking approach is a mid-course review

(MCR)56. Also, the regional haze rule requires periodic tracking of progress towards visibility

goals (every 5 years). In this section, we identify measurements and activities which will

provide better support for mid course reviews, future modeling exercises and other supplemental

analyses designed to determine the progress toward attainment of the NAAQS or reasonable

progress. Improved data bases will increase the reliability of reviews and enable identification

of reasons for attainment or non-attainment of the NAAQS. 

Deploying additional air quality monitors. One type of additional monitoring which

should be considered has already been mentioned in Section 3. Additional ozone and/or PM2.5

monitors should be deployed in unmonitored locations where future design values are predicted

to exceed the NAAQS via the unmonitored area test. This would allow a better future

assessment of whether the NAAQS is being met at unmonitored locations. Also, additional PM

speciation monitors should be deployed in areas that are expected to continue to exceed the

NAAQS in the future (particularly if there is not a speciation monitor at the highest FRM sites in

a nonattainment area). Additional ambient data can provide an increased understanding of the

nature of the problem, which can help lead to the most cost efficient solution.

Collecting Measurements for Indicator Species Measurement of “indicator species” is

a potentially useful means for assessing which precursor category limits further production of

ozone or secondary PM2.5 at a monitor’s location at various times of day and under various sets

of meteorological conditions. Many of the gas phase species needed to calculate indicator ratios

are not routinely measured. Some of the species needed to calculate ozone indicator ratios are

NOy and HNO3. Some of the species needed to calculate the gas ratio and excess ammonia (for

nitrate analyses) include NH3 and HNO3. Thus, realizing the potential of the “indicator species

method” as a tool for model performance evaluation and for diagnosing why observed

concentrations do or do not meet previous expectations may depend on deploying additional

monitors and/or measurements. 

Making measurements aloft. Almost all measured ambient air quality and

meteorological data are collected within 20 meters of the earth’s surface. However, the

modeling domain extends many kilometers above the surface. Further, during certain times of

day (e.g., at night) surface measurements are not always representative of air quality or

meteorological conditions aloft. Concentrations aloft can have marked effects when they are
Draft 3.2- September 2006 100

mixed with ground-level emissions during daytime. Thus, the weight given to modeling results

can be increased if good agreement is shown with air quality measurements aloft. The most

important of these measurements are ozone, NOy, NO, NO2, as well as several relatively stable

species like CO and selected VOC species. Measurements of SO2 may also be helpful for

identifying presence of plumes from large combustion sources.

Measurements of altitude, temperature, water vapor, winds and pressure are also useful. 

Continuous wind measurements, made aloft in several locations, are especially important. They

provide additional data to “nudge” meteorological model fields, but more importantly also allow

for construction of more detailed conceptual models of local ozone formation (Stehr, 2004). 

This provides greater assurance that the air quality model correctly reflects the configuration of

sources contributing to ozone formation.

Collecting locally applicable speciated emissions data. While the U.S. EPA maintains

a library of default VOC and PM emissions species profiles (U.S. EPA, 1993); (U.S. EPA,

2006), some of these may be dated or may not properly reflect local sources. Use of speciated

emissions data is a critical input to air quality models. For example, the accurate representation

of the VOC speciation of current and future gasoline emissions may have an important impact on

future ozone concentrations. Efforts to improve speciation profiles for local sources (especially

for large sources of primary PM2.5) should enhance credibility of the modeling as well as several

of the procedures recommended for use in supplemental analyses and the weight of evidence

determinations.
Draft 3.2- September 2006 101

9.0 What Documentation Do I Need To Support My Attainment 

Demonstration?

States/Tribes should follow the guidance on reporting requirements for attainment

demonstrations provided in U.S. EPA (1994b). The first seven subjects in Table 9.1 are similar

to those in the 1994 guidance. The 1994 guidance envisions an air quality model as the sole

means for demonstrating attainment. However, the current guidance (i.e., this document)

identifies supplemental analyses as well as a possible weight of evidence determination as a

means for corroborating/refuting the modeled attainment test in an attainment demonstration. In

addition, feedback received since the earlier guidance has emphasized the need for technical

review of procedures used to identify a sufficient control strategy. Thus, we have added two

additional subject areas which should be included in the documentation accompanying an

attainment demonstration. These are a description of the supplemental analyses and/or weight of

evidence determination, and identification of reviews to which analyses used in the attainment

demonstration have been subject. In the end, the documentation submitted by the States/Tribes

as part of their attainment demonstration should contain a summary section which addresses the

issues shown in Table 9.1.

Table 9.1 Recommended Documentation for Demonstrating Attainment of the

8-hour NAAQS for Ozone

Subject Area Purpose of

Documentation

Issues Included

Conceptual Description Characterization

(qualitative and

quantitative) of the

area’s nonattainment

problem; used to guide

the development of the

modeling analysis.

Emissions and air quality

assessment;

Measurements used; 

Analyses performed; 

Processes, conditions, and

influences for ozone, PM,

and/or regional haze

formation.
Subject Area Purpose of

Documentation

Issues Included

Draft 3.2- September 2006 102

Modeling/Analysis

Protocol

Communicate scope of

the analysis and

document stakeholder

involvement.

Names of stakeholders

participating in preparing and

implementing the protocol;

Types of analyses performed;

Steps followed in each type of

analyses;

Rational for choice of the

modeling system and model

configurations.

Emissions Preparations

and Results

Assurance of valid,

consistent emissions data

base. Appropriate

procedures are used to

derive emission

estimates needed for air

quality modeling.

Data base used and quality

assurance methods applied; 

Data processing used to

convert data base to modelcompatible inputs; 

Deviations from existing

guidance and underlying

rationale; 

VOC, NOx, SO2, NH3, PM2.5,

PM10, and CO (as

appropriate) total emissions

by State/County and for major

source categories.

Air Quality/Meteorology

Preparations and Results

Assurance that

representative air

quality and

meteorological inputs

are used in analyses

Description of data base and

procedures used to derive and

quality assure inputs for

modeling; 

Departures from guidance and

their underlying rationale.

Performance of meteorological

model used to generate

meteorological inputs to the

air quality model.
Table 6.1 Recommended Documentation for Demonstrating Attainment of the 8-hour

NAAQS for Ozone (continued)

Subject Area Purpose of

Documentation

Issues Included

Draft 3.2- September 2006 103

Performance Evaluation

for Air Quality Model (and

Other Analyses)

Show decision makers

and the public how well

the model (or other

analyses) reproduced

observations on the days

selected for analysis for

each nonattainment area

and appropriate subregions.

Summary of observational

data base available for

comparison; 

Identification of performance

tests used and their results

(including diagnostic

analyses); 

Ability to reproduce observed

temporal and spatial patterns; 

Overall assessment of what the

performance evaluation

implies.
Table 6.1 Recommended Documentation for Demonstrating Attainment of the 8-hour

NAAQS for Ozone (continued)

Subject Area Purpose of

Documentation

Issues Included

Draft 3.2- September 2006 104

Description of the Strategy

Demonstrating Attainment

Provide the EPA and the

public an overview of the

plan selected in the

attainment demonstration.

Qualitative description of the

attainment strategy; 

Reductions in VOC, NOx, SO2,

NH3, and/or CO emissions from

each major source category for

each State/county/Tribal land

from current (identify) emission

levels; 

Clean Air Act mandated

reductions and other reductions; 

Show predicted relative response

factors for ozone and/or each

component of PM2.5 and regional

haze(as applicable)

Show predicted future design

values for the selected control

scenario and identify any

location(s) which fails the

unmonitored area analysis

described in Section 3; 

Identification of authority for

implementing emission

reductions in the attainment

strategy.

Evidence that emissions remain

at or below projected levels

throughout the 3-year period

used to determine future

attainment for PM2.5 and ozone

and/or 5-year period for

reasonable progress. 
Table 6.1 Recommended Documentation for Demonstrating Attainment of the 8-hour

NAAQS for Ozone (continued)

Subject Area Purpose of

Documentation

Issues Included

Draft 3.2- September 2006 105

Data Access Enable the EPA or other

interested parties to

replicate model

performance and

attainment simulation

results, as well as results

obtained with other

analyses.

Assurance that data files are

archived and that provision

has been made to maintain

them; 

Technical procedures for

accessing input and output

files; 

Identify computer on which

files were generated and can

be read, as well as software

necessary to process model

outputs; 

Identification of contact

person, means for

downloading files and

administrative procedures

which need to be satisfied to

access the files.
Table 6.1 Recommended Documentation for Demonstrating Attainment of the 8-hour

NAAQS for Ozone (concluded)

Subject Area Purpose of

Documentation

Issues Included

Draft 3.2- September 2006 106

Supplemental

Analyses/Weight of

Evidence Determination

Assure the EPA and the 

public that the strategy

meets applicable

attainment tests and is

likely to produce

attainment of the

NAAQS and/or

reasonable progress by

the required time.

Description of the modeled 

test and observational data

base used; 

Identification of air quality

model used; 

Identification of other analyses

performed; 

Outcome of each analysis,

including the modeled

attainment test; 

Assessment of the credibility

associated with each type of

analysis in this application; 

Narrative describing process

used to conclude the overall

weight of available evidence

supports a hypothesis that the

selected strategy is adequate to

attain the NAAQS.

Review Procedures Used Provide assurance to the

EPA and the public that

analyses performed in

the attainment

demonstration reflect

sound practice

Scope of technical review

performed by those

implementing the protocol; 

Assurance that methods used

for analysis were peer

reviewed by outside experts; 

Conclusions reached in the

reviews and the response.
Draft 3.2- September 2006 107

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Draft 3.2- September 2006 108

Part II. How Should I Apply Air Quality Models To

Produce Results Needed To Help Demonstrate

Attainment?
Draft 3.2- September 2006 109

10.0 How Do I Apply Air Quality Models?-- An Overview

In Part I of this guidance, we described how to estimate whether a proposed control strategy

will lead to attainment of the NAAQS or meet reasonable progress goals within a required time

frame. We noted that air quality models play a major role in making this determination. We

assumed that modeling had been completed, and discussed how to use the information produced. 

We now focus on how to apply models to generate the information used in the modeled

attainment demonstration. The procedure we recommend consists of nine steps:

1. Formulate a conceptual description of an area’s nonattainment problem;

2. Develop a modeling/analysis protocol;

3. Select an appropriate air quality model to use;

4. Select appropriate meteorological episodes to model;

5. Choose a modeling domain with appropriate horizontal and vertical resolution and

establish the initial and boundary conditions to be used;

6. Generate meteorological and air quality inputs to the air quality model;

7. Generate emissions inputs to the air quality model;

8. Evaluate performance of the air quality model and perform diagnostic tests, as necessary.

9. Perform future year modeling (including additional control strategies, if necessary) and

apply the attainment test

In this section, we briefly describe each of these steps to better illustrate how they are interrelated. Because many of these steps require considerable effort to execute, States/Tribes should

keep the appropriate U.S. EPA Regional Office(s) informed as they proceed. This will increase

the likelihood of having an approvable attainment demonstration when the work is completed. 

The steps outlined in this section are described in greater depth in Sections 11 - 18.

1. Formulate a conceptual description of an area’s nonattainment problem. A State/Tribe

needs to have an understanding of the nature of an area’s nonattainment problem before it can

proceed with a modeled attainment demonstration. For example, it would be difficult to identify

appropriate stakeholders and develop a modeling protocol without knowing whether resolution

of the problem may require close coordination and cooperation with other nearby States. 

The State/Tribe containing the designated nonattainment area or the RPO containing the

Class I area is expected to initially characterize the problem. This characterization provides a

starting point for addressing steps needed to generate required information by those

implementing the protocol. Several examples of issues addressed in the initial description of a

problem follow. Is it a regional or local problem? Are factors outside of the nonattainment area

likely to affect what needs to be done locally? Are monitoring sites observing violations located

in areas where meteorology is complex or where there are large emission gradients? How has

observed air quality responded to past efforts to reduce precursor emissions? Are there ambient

measurements suggesting which precursors and sources are important to further reduce ozone,

PM2.5, and/or regional haze? What information might be needed from potential stakeholders? 
Draft 3.2- September 2006 110

As many of the preceding questions imply, an initial conceptual description may be based

largely on a review of ambient air quality data. Sometimes, methods described in Sections 7 and

8 (e.g., trend analysis, observational models) may be used. Other times, these types of analyses

may be deferred until after a team is in place to develop and implement steps following a

modeling/analysis protocol. The initial conceptual picture may be based on less resourceintensive analyses of available data. 

2. Develop a modeling/analysis protocol. A protocol describes how modeling will be

performed to support a particular attainment demonstration or reasonable progress

demonstration. The content of the protocol and identification of participating stakeholders are

influenced by the previously developed conceptual description of the problem. The protocol

outlines methods and procedures which will be used to perform the subsequent steps needed to

generate the modeling results and then apply the modeled attainment test and unmonitored area

analysis as well as other corroborating analyses. This is accomplished by: a) identifying those

responsible for implementing the modeling, b) outlining the specific steps needed to complete

the attainment demonstration, c) identifying those who will review each step as it occurs, and d)

identifying procedures to be used to consider input/suggestions from those potentially affected

by the outcome (i.e., “stakeholders”). In short, the protocol defines the “game plan” and the

“rules of the game”.

3. Select an appropriate model for use. This step includes reviewing non-proprietary, gridbased photochemical models to select the model that is most appropriate for the application in

terms of (a) state-of-the science algorithms to represent the chemical and physical processes

associated with ozone and/or PM formation, transport, and removal, (b) peer review, (c) model

performance in prior applications, and (d) ease of use. Identifying the air quality model to be

used is an early step in the process, since it may affect how emissions and meteorological

information are input to the model. It could also affect size of the area modeled and choice of

the horizontal/vertical resolution considered. For PM2.5 nonattainment applications where large

spatial gradients of primary PM2.5 exist, a dispersion model may be needed in addition to a

photochemical model.

4. Select appropriate meteorological time periods to model. Like the preceding step, this

step requires review of available air quality and meteorological data. It also requires a thorough

understanding of the form of the ozone and PM2.5 NAAQS, the goals for reasonable progress, 

and the modeled attainment tests described in Sections 3-6. Finally, it requires a review of

meteorological conditions which represent annual PM2.5 concentrations, both good and bad

visibility days, and time periods which have been observed to accompany monitored

exceedances of the 8-hour ozone or 24-hour PM2.5 NAAQS. The object of these reviews is to

select periods which a) include days with observed concentrations exceeding site-specific design

values (8-hour ozone or 24-hour PM2.5 NAAQS), b) select a representative mix of days for each

quarter in applications dealing with the annual NAAQS and/or c) select days which are

representative of those corresponding with good and poor visibility.
Draft 3.2- September 2006 111

Due to increased computer speeds, it is now prudent to recommend modeling relatively long

time periods. For 8-hour ozone and 24-hour PM2.5, at a minimum, modeling episodes which

cover full synoptic cycles is desirable. Depending on the area and the time of year, a synoptic

cycle may be anywhere from 5-15 days. Modeling even longer time periods of up to a full

season may simplify the episode selection process and provide a rich database with which to

apply the modeled attainment test. For annual average PM and regional haze applications, we

recommend modeling (at least) a full year. 

5. Choose a modeling domain with appropriate horizontal and vertical resolution and

establish the initial and boundary conditions. Nested grid models will typically be used to

support the modeled attainment test. In order to provide reasonable boundary conditions for the

local nonattainment area, in many cases it is important to model a large regional domain with

relatively coarse resolution, and a smaller sub-regional domain with relatively fine horizontal

resolution. Meteorological and air quality data corresponding to the time periods that will be

modeled need to be reviewed prior to choosing size of the area modeled. Appropriate domain

size is influenced by the air quality goal being addressed (visibility or NAAQS for ozone or

PM2.5), whether the model is being applied to address ozone or primary or secondary particulate

matter and choice of days modeled.

The presence of topographical features or mesoscale meteorological features (e.g., land/sea

breeze) near or in the nonattainment area of principal interest are factors to consider in choosing

size of individual grid cells and the number of required vertical layers for that portion of the

modeling grid. Another factor affecting the choice of grid cell size is the available spatial detail

in the emissions data used as input to an emissions model. Finally, factors which cannot be

ignored in choosing size of a domain and its grid cells include the feasibility of managing large

data bases and the resources needed to estimate meteorological inputs and air quality in many

grid cells.

6. Generate meteorological inputs to the air quality simulation model. Prognostic

meteorological models will ordinarily be used to generate the meteorological inputs used in the

attainment demonstration modeling. The application of meteorological models and the choice of

model grid resolution in the preceding step are closely related. Meteorological conditions near

the area which is the focus of an attainment demonstration may dictate the required spatial

resolution. On the other hand, cost and data management difficulties increase greatly for finely

resolved grids. Thus, those implementing the protocol will likely be faced with a tradeoff

between cost/feasibility of running air quality and meteorological models and resolution at which

it might be most desirable to treat dispersion of nearby emissions.

7. Generate emissions inputs to the air quality simulation model. Emissions are the central

focus in a modeled attainment demonstration because they are the only input which is altered

between the present and future case scenarios and represent the model input to which control

strategies are applied. Emissions inputs to an air quality model are generated using an emissions
Draft 3.2- September 2006 112

model. Applying such a model is as complicated as the air quality model itself, and demands at

least as much attention. In current emissions models, emissions from some of the major source

categories are affected by meteorological conditions. This requires an interface between

meteorological inputs and emissions. The development of emissions data must also take into

account the horizontal/vertical model resolution of the model configuration and the size of the

area to be modeled. In short, treatment of emissions is a central and complex one which, itself,

involves several steps. These include deriving emission inventories, quality assuring results,

applying results in an emission model(s), and (again) quality assuring results. Emission inputs

may be needed for number of scenarios including; (1) a base case corresponding to that of the

selected meteorological time periods, (2) a baseline corresponding to that represented by the

baseline monitored design value, (3) a future base case when attainment of the NAAQS or

reasonable progress needs to be demonstrated, and (4) control scenarios in which additional

emissions controls are applied to emissions in the future base case.

8. Evaluate performance of the air quality simulation model and perform diagnostic tests. 

The credibility of a modeled attainment test and other model outputs is affected by how well the

model replicates observed air quality in a historical case. Evaluating model performance and

conducting diagnostic tests depend on the prior definition of the modeling exercise and

specification of model inputs. Hence, this is generally the last step prior to using the model to

support an attainment or reasonable progress demonstration. In the past, the performance

evaluation for ozone has relied almost exclusively on numerical tests comparing predicted and

observed ozone, or visual inspection of predictions and observations. These are still important

tools. However, photochemical grid models have many inputs, and it is possible to get similar

predicted ozone and PM2.5 concentrations with different combinations of inputs. There is no

guarantee that ozone and/or secondary PM2.5 will respond the same way to controls with these

different combinations of inputs. Thus, we place greater emphasis on additional kinds of tests

than was the case in past guidance. These include use of precursor observations, indicator

species, and corroborative analyses with observational models. Diagnostic tests are separate

simulations which are performed to determine the sensitivity of a model’s predictions to various

inputs to the model. This can be done for a variety of purposes, including selection of effective

control strategies, prioritizing inputs needing greatest quality assurance and assessing uncertainty

associated with model predictions. In performing such tests, States/Tribes should remember how

model results are used in the modeled attainment tests recommended in Sections 3-6. Model

results are used in a relative rather than absolute sense. Thus, diagnostic tests should be used to

consider how relative, as well as absolute predictions, are affected by changes to model inputs.

9. Perform future year modeling (including additional control strategies, if necessary) and

apply the attainment test. The base case model runs for performance evaluations should

contain emissions inventories on a highly resolved basis which can best simulate the ozone

and/or PM2.5 concentrations that were measured. For some sources, it may not be appropriate to

project day specific emissions to the future because they may not be representative of typical

base case conditions. This is commonly the case for wildfire and continuous emissions monitor

(CEM) based utility emissions. If needed, a separate baseline model run should be completed for
Draft 3.2- September 2006 113

the purpose of calculating relative response factors.

The next step is to run the future year base case model run. The inventory should contain all

known emissions controls expected to be in place in the future year, as well as projected growth

of emissions to the future. The attainment test should be performed using the future base case

and the base year baseline. If attainment cannot be shown, then model runs which contain

additional control measures are needed. Multiple future year control strategy runs may need to

be completed until the attainment test is passed. 
Draft 3.2- September 2006 114

11.0 How Do I Get Started?- A “Conceptual Description”

A State/Tribe should start developing information to support a modeled attainment

demonstration by assembling and reviewing available air quality, emissions and meteorological

data. Baseline design values should be calculated at each monitoring site, as described in

Section 3. For PM applications, speciated data should be reviewed to get a sense of what

component(s) might be contributing most significantly to nonattainment or light extinction. If

past modeling has been performed, the emission scenarios examined and air quality predictions

may also be useful. Readily available information should be used by a State/Tribe to develop an

initial conceptual description of the nonattainment or reasonable haze problem in the area which

is the focus of a modeled demonstration. A conceptual description is instrumental for identifying

potential stakeholders and for developing a modeling/analysis protocol. It may also influence a

State’s choice of air quality model, modeling domain, grid cell size, priorities for quality

assuring and refining emissions estimates, and the choice of initial diagnostic tests to identify

potentially effective control strategies. In general, a conceptual description is useful for helping

a State/Tribe identify priorities and allocate resources in performing a modeled demonstration.

In this Section, we identify key parts of a conceptual description. We then present examples

of analyses which could be used to describe each of these parts. We note that initial analyses

may be complemented later by additional efforts performed by those implementing the protocol.

11.1 What Is A “Conceptual Description”?

A “conceptual description” is a qualitative way of characterizing the nature of an area’s

nonattainment or regional haze problem. It is best described by identifying key components of a

description. Examples are listed below. There are 3 different examples. One each for ozone,

annual PM2.5, and regional haze. The examples are not necessarily comprehensive. There could

be other features of an area’s problem which are important in particular cases. For purposes of

illustration later in the discussion, we have answered each of the questions posed below. Our

responses appear in parentheses.

11.1.1 8-Hour Ozone NAAQS

1. Is the nonattainment problem primarily a local one, or are regional factors important? 

(Surface measurements suggest transport of ozone close to 84 ppb is likely. There 

are some other nonattainment areas not too far distant.)

2. Are ozone and/or precursor concentrations aloft also high?

(There are no such measurements.)
Draft 3.2- September 2006 115

3. Do violations of the NAAQS occur at several monitoring sites throughout the

nonattainment area, or are they confined to one or a small number of sites in proximity to

one another?

(Violations occur at a limited number of sites, located throughout the area.)

4. Do observed 8-hour daily maximum ozone concentrations exceed 84 ppb frequently or

just on a few occasions?

(This varies among the monitors from 4 times up to 12 times per year.)

5. When 8-hour daily maxima in excess of 84 ppb occur, is there an accompanying

characteristic spatial pattern, or is there a variety of spatial patterns? 

(A variety of patterns is seen.)

6. Do monitored violations occur at locations subject to mesoscale wind patterns (e.g., at a

coastline) which may differ from the general wind flow? 

(No.)

7. Have there been any recent major changes in emissions of VOC or NOx in or near the

nonattainment area? If so, what changes have occurred?

(Yes, several local measures [include a list] believed to result in major reductions in VOC

[quantify in tons per summer day] have been implemented in the last five years. 

Additionally, the area has seen large regional NOx reductions from the NOx SIP call.)

8. Are there discernible trends in design values or other air quality indicators which have

accompanied a change in emissions?

(Yes, design values have decreased by about 10% at four sites over the past [x] years.

Smaller or no reductions are seen at three other sites.)

9. Is there any apparent spatial pattern to the trends in design values?

(No.)

10. Have ambient precursor concentrations or measured VOC species profiles changed?

(There are no measurements.)

11. What past modeling has been performed and what do the results suggest?
Draft 3.2- September 2006 116

(A regional modeling analysis has been performed. Two emission scenarios were modeled: 

current emissions and a substantial reduction in NOx emissions throughout the regional

domain. Reduced NOx emissions led to substantial predicted reductions in 8-hour daily

maximum ozone in most locations, but changes near the most populated area in the

nonattainment area in question were small or nonexistent.)

12. Are there any distinctive meteorological measurements at the surface or aloft which

appear to coincide with occasions with 8-hour daily maxima greater than 84 ppb?

(Other than routine soundings taken twice per day, there are no measurements aloft. There

is no obvious correspondence with meteorological measurements other than daily maximum

temperatures are always > 85 F on these days.)

Using responses to the preceding questions in this example, it is possible to construct an

initial conceptual description of the nonattainment area’s ozone problem. First, responses to

questions 1 and 11 suggest there is a significant regional component to the area’s nonattainment

problem. Second, responses to questions 3, 4, 7, 8, and 11 indicate there is an important local

component to the area’s nonattainment problem. The responses to questions 4, 5 and 12 indicate

that high ozone concentrations may be observed under several sets of meteorological conditions. 

The responses to questions 7, 8, and 11 suggest that ozone in and near the nonattainment area

may be responsive to both VOC and NOx controls and that the extent of this response may vary

spatially. The response to question 6 suggests that it may be appropriate to develop a strategy

using a model with 12 km grid cells. 

The preceding conceptual description implies that the State/Tribe containing the

nonattainment area in this example will need to involve stakeholders from other, nearby

States/Tribes to develop and implement a modeling/analysis protocol. It also suggests that a

nested regional modeling analysis will be needed to address the problem. Further, it may be

necessary to model at least several distinctive types of episodes and additional analyses will be

needed to select episodes. Finally, sensitivity (i.e., diagnostic) tests, or other modeling probing

tools, will be needed to assess the effects of reducing VOC and NOx emissions separately and at

the same time.

11.1.2 Annual PM2.5 NAAQS

1. Is the nonattainment problem primarily a local one, or are regional factors important? 

(Surface measurements suggest that only design values in or immediately downwind of

the city violate the NAAQS. However, other nearby design values come close to the

concentration specified in the NAAQS)

2. What is the relative importance of measured primary and secondary components of PM2.5

measured at sites violating the NAAQS?
Draft 3.2- September 2006 117

(Secondary components (i.e., SO4, NO3, OC) constitute about 80% of the measured mass

of PM2.5 . There are higher concentrations of primary PM2.5 in the core urban area compared

to the suburbs and more rural areas.)

3. What are the most prevalent components of measured PM2.5?

(The most important components in ranked order are mass associated with SO4, OC and

inorganic primary particulate matter (IP)).

4. Does the measured mix of PM components appear to roughly agree with mix of emission

categories surrounding the monitoring sites?

(No. Relative importance of measured crustal material (IP) appears less than what

might be inferred from the inventory).

5. Do there appear to be any areas with large gradients of primary PM2.5 in monitored or

unmonitored areas?

(Cannot really tell for sources of crustal material until we resolve the preceding

inventory/monitoring discrepancy. There are no other obvious major sources of primary

particulate matter).

6. Is there any indication of what precursor might be limiting formation of secondary

particulate matter?

(No indicator species analyses have been performed. Past analyses performed for

ozone-related SIP revisions suggest that ozone in this area may be limited by availability of

VOC).

7. Do monitored violations occur at locations subject to mesoscale wind patterns (e.g., at a

coastline) which may differ from the general wind flow? 

(No.)

8. Have there been any recent major changes in emissions of PM or its precursors in or near

the nonattainment area? What?

(Yes, measures believed to result in major reductions in VOC and NOx have been

implemented in the last 5 years. Reductions in power plant NOx have resulted from the

NOx SIP call and SO2 emissions reductions have resulted from the national program to

reduce acid deposition.)

9. Are there discernible trends in design values or other air quality indicators which have
Draft 3.2- September 2006 118

accompanied a change in emissions?

(The trend appears to be downward, but the most recent air quality data has been higher. 

Overall, the period of record is insufficiently long to tell).

10. Is there any apparent spatial pattern to the trends in design values?

(No.)

11. What past modeling has been performed and what do the results suggest?

(A regional modeling analysis has been performed for ozone and PM2.5. Two emission

scenarios were modeled: current emissions and a substantial reduction in NOx and SO2

emissions throughout a regional domain. Reduced NOx emissions led to substantial

predicted reductions in 8-hour daily maximum ozone in most locations. Modeled SO2

reductions from the CAIR rule had a strong impact on sulfate concentrations)

12. Are there any distinctive meteorological measurements at the surface or aloft which

appear to coincide with occasions with PM2.5 concentrations in excess of 15.0 :g/m3

?

(Other than routine soundings taken twice per day, there are no measurements aloft. 

There is no obvious correspondence with meteorological measurements other than daily

maximum temperatures are often > 85F on days with the highest PM2.5 observations.)

13. Do periods with high measured particulate matter or components of particulate matter

appear to track each other or any other measured pollutant?

(There appears to be some correspondence between measured high concentrations of

SO4 and ozone).

Using responses to the preceding questions in this example, it is possible to construct an

initial conceptual description of the nonattainment area’s ozone problem. First, responses to

questions 1, 2 and 3 suggest there is a significant regional component to the area’s nonattainment

problem. Second, responses to questions 1 and 3 indicate there is a local component to the

problem.. The responses to questions 11,12 and 13 suggest that there may be a link between

reducing ozone and reducing particulate matter. Thus, it may be appropriate to assess effects of

previously committed to strategies to reduce ozone and national PM control measures before

simulating additional control measures. The responses to questions 4 and 5 suggest that it is

premature to determine whether a “local area analysis” will be needed. The response to question

7 suggests that it may not be necessary to model with very small grid cells, at least for the

secondary components of PM2.5. 

The preceding conceptual description implies that the State containing the nonattainment
Draft 3.2- September 2006 119

area in this example will need to involve stakeholders from other, nearby States to develop and

implement a modeling/analysis protocol. It also suggests that a nested regional modeling

analysis will be needed to address the problem.

11.1.3 Example reasonable progress application 

1. What components of particulate matter appear to have high concentrations on days with

poor visibility?

(Mass associated with SO4 and coarse particulate matter (CM) seem to have the highest

concentrations on most such days).

2. What are typical values for the humidity adjustment factor during the times of year when

most of the days with poor visibility occur?

(Typical values appear to be about “4.0").

3. Does visibility appear to track well among nearby Class I areas?

(Yes, but not always).

4. Does poor visibility seem to occur under any specific meteorological conditions?

(This information is not readily available).

 5. Does poor visibility seem to coincide with high observed concentrations of any particular

other pollutant?

(There seems to be some correspondence with high regional ozone concentrations)

6. What components of particulate matter appear to have relatively high concentrations on

days with good visibility?

(Coarse particulate matter and OC)

7. What are typical values for the humidity adjustment factor during times of year when

most of the days with good visibility occur?

(About “2.3")

8. Does good visibility appear to occur under any specific meteorological conditions?

(Don’t know.)
Draft 3.2- September 2006 120

Answers to the preceding questions suggest that strategies to reduce sulfate concentrations

and, perhaps, regional ozone concentrations might be effective in reducing light extinction on

days when visibility is currently poor. The responses suggest that a strategy which focuses on

this alone should first be tried for the days with good visibility as well. Even though sulfate

concentrations appear low on such days, the fact that sulfates scatter light efficiently (see

Equation (6.1)) and relative humidity is still high enough to enhance this effect is worth

considering. Responses suggest that further meteorological analyses would be worthwhile prior

to selecting strategies to simulate with a resource intensive regional model.

It should be clear from the preceding examples that the initial conceptual description of an

area’s nonattainment problem draws on readily available information and need not be detailed. 

It is intended to help launch development and implementation of a modeling/analysis protocol in

a productive direction. It will likely be supplemented by subsequent, more extensive modeling

and ambient analyses performed by or for those implementing the modeling/analysis protocol

discussed in Section 12.0.

Questions like those posed in Section 11.1 can be addressed using a variety of analyses

ranging in complexity from an inspection of air quality data to sophisticated mathematical

analyses. We anticipate the simpler analyses will often be used to develop the initial conceptual

description. These will be followed by more complex approaches or by approaches requiring

more extensive data bases as the need later becomes apparent. These analyses are intended to

channel resources available to support modeled attainment demonstrations onto the most

productive paths possible. They will also provide other pieces of information which can be used

to reinforce conclusions reached with an air quality model, or cause a reassessment of

assumptions made previously in applying the model. As noted in Section 7, corroboratory

analyses should be used to help assess whether a simulated control strategy is sufficient to meet

the NAAQS.
Draft 3.2- September 2006 121

12.0 What Does A Modeling/Analysis Protocol Do, And What Does

Developing One Entail?

Developing and implementing a modeling/analysis protocol is a very important part of an

acceptable 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,

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 reasonable progress goals, and producing

documentation to be submitted for EPA Regional Office review and approval. Much of the

information in U.S. EPA (1991a) regarding modeling protocols remains applicable. 

States/Tribes should review the 1991 guidance on protocols. In this document, we have revised

the name of the protocol to “Modeling/Analysis Protocol” to emphasize that the protocol needs

to address modeling as well as other supplemental analyses.

12.1 What Is The Protocol’s Function?

As noted above, the most important function of a protocol is to serve as a means for planning

and communicating up front how a modeled attainment demonstration will be performed. The

protocol is the means by which States/Tribes, U.S. EPA, and other stakeholders can assess the

applicability of default recommendations and develop alternatives. A good 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 policy.

The protocol also serves several important, more specific functions. First, it should identify

who will help the State/Tribe or local air quality agency (generally the lead agency) undertake

and evaluate the analyses needed to support a defensible demonstration (i.e., the stakeholders). 

Second, it should identify how communication will occur among States/Tribes and stakeholders

to develop consensus on various issues. Third, the protocol should describe the review process

applied to key steps in the demonstration. Finally, it should also describe how changes in

methods and procedures or in the protocol itself will be agreed upon and communicated with

stakeholders and the appropriate U.S. EPA Regional Office(s). Major steps to implement the

protocol should be discussed with the appropriate U.S. EPA Regional Office(s) as they are being

decided. States/Tribes may choose to update the protocol as major decisions are made

concerning forthcoming analyses. 
57States may want to document detailed meteorological modeling decisions in a separate

document. At least a brief description of the meteorological model and model setup should be

contained in the air quality modeling protocol.

Draft 3.2- September 2006 122

12.2 What Subjects Should Be Addressed In The Protocol?

At a minimum, States/Tribes should address the following topics in their modeling/analysis

protocol:

1. Overview of Modeling/Analysis Project

a. Management structure

b. Technical committees or other communication procedures to be used

c. Participating organizations

d. Schedule for completion of attainment demonstration or reasonable progress analyses

e. Description of the conceptual model for the nonattainment area (or Class I area(s))

2. Model and Modeling Inputs

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

b. Modeling domain

c. Horizontal and vertical resolution

d. Specification of initial and boundary conditions

e. Episode selection

f. Description of meteorological model setup57

g. Development of emissions inputs 

h. Geographic area identified for application of the attainment test(s)

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

3. Model Performance Evaluation

a. Describe ambient data base

b. List evaluation procedures

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

4. Supplemental Analyses

a. List additional analyses to be completed to corroborate the model attainment test

b. Outline plans for conducting a weight of evidence determination, should it be necessary

5. Procedural Requirements

a. Identify how modeling and other analyses will be archived and documented

b. Identify specific deliverables to EPA Regional Office

 
Draft 3.2- September 2006 123

13.0 What Should I Consider In Choosing An Air Quality Model?

Photochemical grid models are, in reality, modeling systems in which an emissions model, a

meteorological model and an air chemistry/deposition model are applied. In this guidance, we

use the term “air quality model” to mean a gridded photochemical modeling system. Some

modeling systems are modular, at least in theory. This means that it is possible to substitute

alternative emissions or meteorological models within the modeling system. Often however, the

choice of an emissions or meteorological model or their features is heavily influenced by the

chosen air quality model (i.e., an effort is needed to develop software to interface combinations

of components differing from the modeling system’s default combination). Thus, choosing an

appropriate air quality model is among the earliest decisions to be made by those implementing

the protocol. In this section, we identify a set of general requirements which an air quality

model should meet in order to qualify for use in an attainment demonstration or regional haze

application. We then identify several factors which will help in choosing among qualifying air

quality models for a specific application. We conclude this section by identifying several air

quality models which are available for use in attainment or reasonable progress demonstrations. 

Meteorological and emissions models are discussed in Sections 15 and 16, respectively.

13.1 Types of Air Quality Models

States should use a photochemical grid model to simulate the effects of strategies to reduce

ozone and the secondary components of particulate matter (i.e., mass associated with SO4, NO3

and secondary OC). Because of the regional nature of “regional haze” and relatively high

efficiency with which secondary particulate matter scatters light, we believe that photochemical

grid models are needed to perform reasonable progress demonstrations. Based on its conceptual

description of a PM2.5 nonattainment problem, a State could conclude that the PM2.5 problem can

be addressed solely by reducing primary components of measured PM2.5. If this is the case, the

State may not need to use a photochemical grid model in their attainment demonstration if it can

present convincing qualitative arguments that an increase in the secondary components of PM

will not cause reductions in the primary components to be insufficient to meet the NAAQS. 

Greater flexibility is possible in choosing a modeling approach to address primary

components of PM2.5 (i.e., IP, EC and primary portions of OC) and coarse particulate matter (i.e.,

needed for regional haze-related applications) than is true for secondary components. That is, it

is not necessary to use a model which considers atmospheric chemistry in addressing changes in

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

model may be used. In general, modeling primary PM components with a grid model is

acceptable, but further refined dispersion modeling may be necessary in areas with large spatial

gradients of primary PM. If a dispersion model is used to estimate RRF’s for primary particulate

matter, these estimates should be made at the monitoring location (see Section 5.3). Thus, a

State may use a regional photochemical grid model to address both primary and secondary

components of particulate matter or they may use a photochemical model to address secondary
58Air quality models are generally identified by a version number. The version of the

model that is used in SIP applications should be identified. Code revisions to standard versions

of models should be noted and documented. 

Draft 3.2- September 2006 124

PM and an inert model applied over a more limited domain to address primary components. 

In regional haze-related applications it may suffice when modeling primary particulate

matter to use grid cells which are the same size as those used to model secondary components of

particulate matter. If there is no reason to believe that there are major individual sources of

primary PM within about 50 km which affect the monitor site in a Class I area, primary

components can be considered using the same coarse grid used for the photochemical grid

model. This is generally a good default assumption.

13.2 What Prerequisites Should An Air Quality Model Meet To Qualify For Use

In An Attainment Demonstration?

A model should meet several general criteria for it to be a candidate for consideration in an

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

requirements in 40 CFR 51.112 and 40 CFR part 51, Appendix W (U.S. EPA, 2003). Note that,

unlike in previous guidance (U.S. EPA, 1991a), we are not recommending a specific model for

use in attainment or reasonable progress demonstrations. At present, there is no single model

which has been extensively tested and shown to be clearly superior than its alternatives. Thus,

40CFR 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 demonstrations for

regional haze. Based on the language in 40CFR Part 51 Appendix W, models used for these

purposes should meet requirements for “alternative models”.

States/Tribes 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 must be able to revise the

code58 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 or reasonable progress demonstration. 

(1) It should have received and been revised in response to a scientific peer review.

(2) It should be appropriate for the specific application on a theoretical basis.

(3) It should be used with a data base which is adequate to support its application.

(4) It should be shown to have performed well in past ozone or PM modeling applications. 

(If the application is the first for a particular model, then the State should note why it
Draft 3.2- September 2006 125

believes the new model is expected to perform sufficiently.) 

(5) It should be applied consistently with a protocol on methods and procedures.

An air quality model may be considered to have undergone “scientific peer review” if each of

the major components of the modeling system (i.e., air chemistry/deposition, meteorological and

emissions models) has been described and tested, and the results have been documented and

reviewed by one or more disinterested third parties. We believe that it should be the

responsibility of the model developer or group which is applying an air quality model on behalf

of a State/Tribe to document that a “scientific peer review” has occurred. States/Tribes should

then reference this documentation to gain acceptance of an air quality model for use in a

modeled attainment demonstration. 

13.3 What Factors Affect My Choice of A Model For A Specific Application?

States/Tribes should consider several factors as criteria for choosing a qualifying air quality

model to support an attainment or reasonable progress demonstration. These factors are: (1)

documentation and past track record of candidate models in similar applications; (2) advanced

science and technical features (e.g., probing tools) available in the model and/or modeling

system; (3) experience of staff and available contractors; (4) required time and resources versus

available time and resources; and (5) in the case of regional applications, consistency with

regional models applied in adjacent regions. Finally, before the results of a selected model can

be used in an attainment demonstration, the model should be shown to perform satisfactorily

using the data base available for the specific application.

Documentation and Past Track Record of Candidate Models. For a model to be used in

an attainment demonstration, evidence should be presented that it has been found acceptable for

estimating hourly and eight-hourly ozone concentrations and/or hourly and 24-hour average

PM2.5 and PM component concentrations. Preference should be given to models exhibiting

satisfactory past performance under a variety of conditions. Finally, a user’s guide (including a

benchmark example and outputs) and technical description of the model should be available. 

Advanced Technical Features. 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.

Experience of Staff and Available Contractors. This is a legitimate criterion for choosing

among several otherwise acceptable alternatives. The past experience might be with the air

quality model itself, or with a meteorological or emissions model which can be more readily

linked with one candidate air quality model than another.
Draft 3.2- September 2006 126

Required vs. Available Time and Resources. This is a legitimate criterion provided the

first two criteria are met. 

Consistency of a Proposed Model with Models Used in Adjacent Regions. This criterion

is applicable for regional model applications. If candidate models meet the other criteria, this

criterion should be considered in choosing a model for use in a regional or nested regional

modeling application.

Demonstration that an “Alternative Model” is Appropriate for the Specific Application.

If an air quality model meets the prerequisites identified in Section 13.2, a State/Tribe may use

the factors described in this section (Section 13.3) to show that it is appropriate for use in a

specific application. Choosing an “alternative model” needs to be reviewed and approved by the

appropriate U.S. EPA Regional Office.

Satisfactory Model Performance in the Specific Application. 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 approach for evaluating model performance are

discussed in Section 18.

13.4 What Are Some Examples Of Air Quality Models Which May Be Considered?

 Air quality models continue to evolve and have their own strengths and weaknesses

(Russell, 2000). Table 13.1 lists several current generation air quality models which have been

used to simulate ambient ozone, PM, and regional haze concentrations. Table 13.2 lists several

air quality models which have been used for various ozone and PM applications over the past

decade, but are not widely used at this time. Table 13.3 lists several current dispersion models

that have used to model primary PM. Table 13.4 lists several receptor models that have been

used to identify sources of PM. The list is not intended to be comprehensive. Exclusion of a

model from the list does not necessarily imply that it cannot be used to support a modeled

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

the list does not necessarily imply that a model may be used for a particular application. 

States/Tribes should follow the guidance in Sections 13.1 and 13.2 in selecting an air quality

model for a specific application. 
Draft 3.2- September 2006 127

Table 13.1 Current Air Quality Models Used To Model Ozone and PM

Air Quality Model References

CAMx Environ (2004)

CMAQ U.S. EPA (1998a)

UAM-V Systems Applications

International (1996)

Table 13.2 Other Air Quality Models Used to Model Ozone and PM

Air Quality Model References

CALGRID Scire, et al. (1989)

MAQSIP MCNC (1999)

Odman, et al. (1996)

SAQM Chang, et al., (1997)

CARB (1996)

URM Kumar, et al., (1996)

Table 13.3 Current Dispersion Models Used to Model Primary PM

Air Quality Model References

AERMOD U.S. EPA (2004a) 

U.S EPA (2004b)

CALPUFF Scire (2000)

ISC3 U.S. EPA (1995a) 

U.S EPA (1995b)

Table 13.4 Current Receptor Models

Air Quality Model References

PMF Eberly (2005)

Hopke (2001)

UNMIX Lewis (2003)

CMB U.S. EPA (2004c)
Draft 3.2- September 2006 128

14.0 How are the Meteorological Time Periods (Episodes) Selected?

Historically, attainment demonstrations have been based on a limited number of episodes

consisting of several days each. In the past, the number of days modeled has been limited by the

speed of computers and the ability to store the model output files. With the advancement in

computer technology over the past decade, computer speed and storage issues are no longer an

impediment to modeling long time periods. In fact, many groups have recently modeled entire

summers and/or full years for ozone, PM2.5, and regional haze (Baker, 2004) (U.S. EPA, 2005). 

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, 2000). 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. we recommend modeling “longer” episodes that encompass full

synoptic cycles. 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.

More limited research has been conducted related to PM2.5 modeling. Most model

applications for the annual PM2.5 NAAQS have simply modeled a full year (CAIR, RPOs, etc). 

This is a logical goal since every day of the year is included in the calculation of the annual

NAAQS. Several tests have also been made using less than a full year of meteorological data. 

One study (Environ, 2004) found that modeling 14 days from each quarter provided results with

a reasonable amount of uncertainty compared to modeling the full year. The following sections

contain further recommendations for choosing appropriate time periods to model for attainment

demonstrations and reasonable progress demonstrations.

 

At a minimum, four criteria should be used to select time periods which are appropriate to

model:

 

1) Simulate a variety of meteorological conditions:

a) 8-Hour Ozone- Choose time periods which reflect a variety of meteorological

conditions which frequently correspond with observed 8-hour daily maxima > 84 ppb

at multiple monitoring sites. 

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

conditions which frequently correspond with observed 24-hour averages > 65 ug/m3 at

violating monitoring sites.

c) Annual PM2.5- Choose time periods from each quarter which reflect the variety of

meteorological conditions which represent average concentrations for that quarter and

year

d) 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.

2) Model time periods in which observed concentrations are close to the appropriate baseline
Draft 3.2- September 2006 129

design value or visibility impairment. 

3) Model periods for which extensive air quality/meteorological data bases exist. 

4) Model a sufficient number of days so that the modeled attainment test applied at each

monitor violating the NAAQS is based on multiple days (see section 14.1.1). 

These four criteria may sometimes conflict with one another. For example, there may only

be a limited number of days with intensive data bases, and these may not cover all of the

meteorological conditions which correspond with monitored concentrations close to site-specific

design values during the base period. Thus, tradeoffs among the four primary criteria may be

necessary in specific applications. 

Those implementing the modeling/analysis protocol may use secondary episode selection

criteria on a case by case basis. For example, prior experience modeling an episode or year, may

result in its being chosen over an alternative. Another consideration should be to choose time

periods occurring during the 5-year period which serves as the basis for the baseline design value

(DVB). If observed ozone or 24-hour PM2.5 exceedances occur on weekends, weekend days

should be included within some of the selected time periods. If it has been determined that there

is a need to model several nonattainment areas simultaneously (e.g., with a nested regional scale

model application), a fourth secondary criterion is to choose time periods containing days of

common interest to different nonattainment areas. 

In this section, we first discuss each of the four identified primary criteria for choosing

meteorological episodes to model. We then discuss the secondary criteria, which may be

important in specific applications.

14.1 What Are The Most Important Criteria For Choosing Episodes?

In the following section, we identify how the four selection criteria apply to ozone. We then

discuss issues related to time period selection for PM2.5 and regional haze. We also recommend

minimum time periods to model for 8-hour ozone, 24-hour PM2.5, annual PM2.5, and regional

haze.

14.1.1 8-Hour Ozone NAAQS

Simulate a variety of meteorological conditions This criterion is important, because we want

to be assured that a control strategy will be effective under a variety of conditions leading to

elevated ozone concentrations. The time periods chosen should reflect the variety of conditions

that lead to 8-hour ozone concentrations > 85 ppb. 

Those implementing the modeling/analysis protocol should describe the rationale for

distinguishing among episodes which are modeled. The selection may reflect a number of areaspecific considerations. Qualitative procedures such as reviewing surface and aloft weather

maps, and observed or modeled wind patterns may suffice for distinguishing episodes with
Draft 3.2- September 2006 130

distinctively different meteorological conditions. More quantitative procedures, such as a

Classification and Regression Tree (CART) analysis or a principal component analysis (PCA), to

identify distinctive groupings of meteorological/air quality parameters corresponding with high

daily maxima or averages, may sometimes be desirable. An example of a CART analysis

applied to select episodes is described by Deuel (1998). LADCO used CART to rank historical

years for Midwestern cities by their conduciveness to ozone formation (Kenski, 2004). A PCA

may also be used to characterize predominant meteorological conditions and relate those

conditions to ozone concentrations (Battelle, 2004). This information can be used to quantify the

relative "ozone forming potential" of different days, regimes, and years. 

 The interpretation of results of a wind rose analysis or a statistical analysis such as PCA or

CART should focus on episodic time periods, rather than individual days. The winds may be

blowing from different directions on consecutive days, but that does not necessarily mean that

those days represent different meteorological regimes. Preference should be given to modeling

episodic cycles.

Additionally, statistical analyses such as PCA normally limit the number of identified

meteorological regimes to a relatively small number of generalized patterns. The analysis may

indicate that only one or two of these patterns are responsible for most or all of the exceedance

days in an area. But no two days and no two episodes are exactly the same. Further analysis

should be performed on potential episode periods to differentiate subtle, but often important,

differences between episodes. For this reason, it may be beneficial to model more than one

episode from the most frequently occurring meteorological regimes which lead to ozone

exceedances. Modeling a continuous time period which encompasses several episodes or a full

ozone season will make it easier to adequately account for all of the potential meteorological

conditions which correspond to high measured concentrations.

Choose episodes having days with monitored 8-hour daily maxima close to observed

average 4th high daily maximum ozone concentrations. 

We want to use episodes whose severity is comparable to that implied by the form of the

NAAQS (i.e., an episode whose severity is exceeded, on average, about 3 times/year at the time

of the selected episode). Note that we said, “at the time of the selected episode” (i.e., the “base

case period”) rather than “current or baseline period” in the preceding sentence. The objective is

to choose episodes with days which are approximately as severe as the average 4th high 8-hour

daily maximum concentration specified in the NAAQS. 

Air quality measurements recorded during the baseline/current period can also be used to

characterize episode severity. This is done by selecting a 5-year period which “straddles” a

modeled episode. For example, if an episode from 2002 were modeled, we recommend looking

at measured 8-hour daily maxima at each site in the nonattainment area during 2000-2004. 

Using this information it should be possible to assess the relative severity of the days chosen for

modeling at each site. Limiting this characterization to the five years straddling an episode

avoids problems posed by long term trends in emissions in assessing episode severity. However,

it leaves unanswered the question of whether the 5-year period selected to assess severity of a

modeled day is typical or atypical. If there is an underlying long term trend in ambient ozone
59The analysis in section 4.1 showed that low ozone predictions can introduce a bias in

the relative results from the model. Therefore, ambient (and modeled) concentrations that are

more than 10 ppb above the design value are preferable to episodes with ambient concentrations

that are more than 10 ppb below the design value. 

Draft 3.2- September 2006 131

attributable to meteorological cycles or other causes, it may not be appropriate to compare

different periods with one another using air quality observations. Thus, if one uses a 10-year old

episode with an exceptional data base, there is greater uncertainty in ranking its severity relative

to the current period of interest than if the episode were drawn from the current period.

Note that if the episode is drawn from a recent time period (especially the three years upon

which the nonattainment designation is based), days which are chosen are likely to have

monitored observations very close to the baseline design value. In the absence of such

information, we suggest “+ 10 ppb” as a default recommendation for purposes of prioritizing

choice of episodes59. If the base and baseline/current periods do not coincide, “close to” is

within + 10 ppb of the design value during the base period straddling the episode. If it is not

feasible to meet this default criterion for all monitoring sites, meeting it at sites with

baseline/current design values > 85 ppb should receive greatest priority.

Choose days with intensive data bases. 

Preference should be given to days with measurements aloft, available measurements of

indicator species (see Section 18) and/or precursor measurements. These preferences result from

a desire to incorporate a rigorous model performance evaluation as a part of the attainment

demonstration. This reduces the likelihood of “getting the right answer for the wrong reason”. 

Thus, the likelihood of mischaracterizing ozone/precursor sensitivity is reduced.

Choose a sufficient number of days to enable the monitored attainment test to be based on

multiple days at each monitoring site violating the NAAQS. 

Figure 4.1 indicates that the relative response factor computed at any given site appears to be

affected by the minimum threshold value. Based on an analysis of modeled data, the

recommended minimum (baseline) threshold value is 85 ppb. The minimum threshold value

analysis (detailed in section 4.1) was also used to examine how the number of days contained in

the mean RRF calculation influences the mean RRF. It was found that, on average, a minimum

number of 10 modeled days (in the mean RRF calculation) produces mean RRFs that are

relatively robust.

The analysis cited earlier in the guidance (Timin, 2005b) was used to help determine the

minimum number of days to use in a mean RRF calculation. The dataset consisted of 206

monitoring sites which had at least 10 days with predicted 8-hour daily baseline maximum ozone

concentrations > 85 ppb. In the analysis we assumed that a mean RRF calculated from a “large”

set of days is more stable than an RRF calculated from a small set of days. Using information on

the variability of the model response on individual days, we are able to measure the variability of
60The 25, 50, and 100 day datasets were created by calculating the standard deviation of

the daily RRFs for the monitoring sites with at least 10 days > 85 ppb. The distribution of the

RRF was calculated from the standard deviation. The original dataset had an actual maximum

number of 30 days.

61The standard deviation is in “RRF units”. For example, an RRF of 0.90 is equal to

a10% ozone reduction. A standard deviation of 3.4% is a measure of the variability such that

±3.4% is equal to a range in mean RRF of 0.866-0.934.

Draft 3.2- September 2006 132

the mean RRF on any subset of days. The analysis used datasets of 25, 50, and 100 days60. The

standard deviation of the daily RRFs was used to create the datasets and measure the variability

of the RRFs. 

Figure 14.1 shows an example of the variability of the mean RRF as a function of the number

of days in the mean RRF calculation. The example plot is for a monitoring site in Harford

County, MD. The mean RRF for a 50 day sample size is 0.90 (10% ozone reduction). The

standard deviation of the daily RRFs was 0.034 (3.4%)61. The plot shows the range of the mean

RRFs calculated using a sample size ranging from 3 to 25 days (a subset of the 50 days). Each

subset (3 days, 4 days, 5 days, etc.) was sampled 1000 times. As can be seen in the plot, the

range of mean RRFs varies widely for a small sample size (3 days) and is relatively stable for a

large sample size (25 days). As the number of days increases, the variability of the mean RRF

decreases. A similar conclusion was reached in a different study (Hogrefe, 2000) which found

that the RRF is more variable when based on a small number of days. 

The ability to accurately capture a mean RRF with a small number of days is dependent on

the variability of the daily RRFs (as measure by the standard deviation). Sites with a small

standard deviation of the daily RRFs will be able to replicate the large dataset mean RRF with

relatively few days. 
Draft 3.2- September 2006 133

Figure 14.1- Mean RRF as a function of the subset of days (3-25 days) for a Harford County,

MD ozone monitoring site. The full dataset was 50 days. 

Using the available information, we were able to calculate, for each monitoring site, the number

of days needed to provide a mean RRF calculation that is within ±1% and ±2% of the “large

dataset” mean, with a 95% confidence interval. The number of days needed to produce a robust

mean RRF is dependent on the variability of the daily RRFs (as measured by the standard

deviation). Therefore, more days are needed to produce a stable RRF if the standard deviation of

the daily RRFs is high.

Table 14.1 summarizes the results for the 25th, 50th (median) and 75th percentile of the

standard deviation for the 206 monitoring sites. The table presents results for a range of standard

deviations, a range of large datasets (25, 50, and 100 days), and both ±1% and ±2% accuracy. 

The table shows that for the median standard deviation of the monitoring sites (2.4%), a

minimum number of 10-16 days is needed to replicate the mean RRF to within ±1% (95% of the

time) and a minimum number of 5-6 days is needed to replicate the mean RRF to within ±2%

(95% of the time). The table also shows that a smaller standard deviation requires fewer days

and a larger standard deviation requires more days. 
62As noted in section 4.1, for areas with a very high design value (>110 ppb), a higher

threshold might be appropriate. 

Draft 3.2- September 2006 134

75th Percentile 3.1% 12/17/23 6/8/9

Median 2.4% 10/13/16 5/5/6

25th Percentile 1.9% 9/10/12 3/4/4

± 1% (25/50/100 days) ± 2% (25/50/100 days)

Standard 

Value (206 sites) Deviation

75th Percentile 3.1% 12/17/23 6/8/9

Median 2.4% 10/13/16 5/5/6

25th Percentile 1.9% 9/10/12 3/4/4

± 1% (25/50/100 days) ± 2% (25/50/100 days)

Standard 

Value (206 sites) Deviation

Table 14.1- Number of days needed to replicate the 25/50/100 day dataset mean RRF to

within ±1% and ±2%, with a 95% confidence interval.

Based on these results, we recommend a minimum number of 10 days to be included in the

mean RRF calculation for each monitoring site. This will ensure a relatively robust mean RRF

value that is within ±1% (on average) of the large dataset mean. If relatively few ozone days are

being modeled or certain monitors have relatively few exceedances (above 85 ppb), then we

recommend using an absolute minimum number of 5 days in the calculation. 

 The minimum number of days recommendations can be combined with the minimum

threshold recommendation to create a hierarchy of number of days/threshold combinations that

can address any situation. The recommended minimum concentration threshold identified in

section 4.1 is 85 ppb. But similar to the minimum number of days, there may be situations

where there are relatively few “high” modeled ozone days at certain monitors. Therefore, when

possible, we recommend using the 85 ppb threshold62, but it is acceptable to use a threshold as

low as 70 ppb. 

Therefore, the following criteria should be applied to determine the number of days and the

minimum threshold at each ozone monitor:

! If there are 10 or more days with daily maximum 8-hour average baseline modeled ozone >

85 ppb then use an 85 ppb threshold.

! If there are less than 10 days with daily maximum 8-hour average baseline modeled ozone >

85 ppb then reduce the threshold down to as low as 70 ppb until there are 10 days in the

mean RRF calculation.

! If there are less than 10 days with daily maximum 8-hour average modeled ozone > 70 ppb

then use all days > 70 ppb.
63Any situation where there are less than 5 days available for RRF calculations at

monitoring sites with relatively high concentrations, (above the NAAQS and/or close to the areawide design value) should be discussed with the appropriate U.S. EPA Regional office(s). 

Draft 3.2- September 2006 135

! Don’t calculate an RRF for sites with less than 5 days > 70 ppb63.

The following table illustrates several examples of the recommended hierarchy of choosing the

number of days vs. the minimum threshold.

Number of Days

> 70 ppb

Number of Days

> 85 ppb

Number of Days in

Mean RRF

Threshold < 85 ppb?

50 15 15 No

20 12 12 No

12 7 10 Yes

11 3 10 Yes

9 6 9 Yes

6 1 6 Yes

3 0 N/A N/A

Table 14.2- Examples of the recommended hierarchy in choosing the number of days in the

mean RRF calculation vs. the minimum threshold. The “number of days” refers to the

number of days (at each monitor) when the daily modeled 8-hour ozone maximum is > 70

or 85 ppb. 

In summary, States should try to model enough episode days so that the mean RRF

calculation at each monitor contains a minimum of 10 days with a modeled concentration > 85

ppb. If there are less than 10 days > 85 ppb, then the threshold should be lowered until 10 days

are included in the calculation. The threshold should not go below 70 ppb and the number of

days should always be at least 5. In trying to meet these recommendations, the greatest priority

should be given to identifying episode days with appropriate ozone concentrations at the

monitoring sites with the highest design values. Sites with design values below the NAAQS

should be given a low priority.

14.1.2 Recommendations for the 24-Hour PM2.5 NAAQS

The episode selection criteria for the 24-hour PM2.5 NAAQS are similar to the ozone criteria. 

The complicating factor for the 24-hour NAAQS is that exceedances of the standard can occur

under a variety of meteorological conditions at different times of the year and can be due to a

variety of different PM components. Therefore the universe of episodes could be large and much

more varied. The form of the standard is a 3 year average of the 98th percentile concentration
64A careful analysis is needed for the days that exceed the 24-hour NAAQS. It may be

necessary to divide days by species as well as by quarter. For example, exceedence days with

high nitrates may respond differently than exceedence days with high organics (in the same

quarter). Averaging the response from many dis-similar days may give an unrealistic mean

response.

Draft 3.2- September 2006 136

from each year. Therefore, modeling 3 full years of meteorological data would best capture the

full range of conditions that lead to 24-hour design values. But the standard is only influenced

by the very high end of the distribution of PM2.5 days (top 2% from each year). This argues for

modeling a smaller set of days on an episodic basis. We recommend one of two possible

approaches:

1) Model every day for a full year (or multiple years). This is recommended for both dispersion

modeling of primary PM2.5 components and photochemical modeling of secondary and primary

components. Many areas that violate the 24-hour PM2.5 NAAQS will also violate the annual

PM2.5 NAAQS. Therefore, full year modeling may already exist or is being planned for the

annual NAAQS attainment test. States that have nonattainment areas that violate both PM2.5

standards should take advantage of planning opportunities to address both standards at the same

time. Modeling at least a full year will also help ensure that a sufficient number of days are

included in the RRF calculations. 

2) Model episodes when high PM2.5 concentrations occur. Similar to ozone, episodes should be

selected where PM2.5 concentrations are greater than the NAAQS (> 65 ug/m3) and are close to

the baseline design value. Similar to ozone, data analyses can be completed to help select a

variety of meteorological episodes which lead to high PM2.5 concentrations. In some cases, there

may be very limited conditions which lead to high 24-hour average PM2.5 concentrations, and in

other cases there may be a wide variety of cases. The specific situation in each nonattainment

area will determine the number of episodes and the time periods which need to be modeled. For

example, if exceedance level PM2.5 concentrations in an area only occur in the winter, then a

limited number of winter days can be modeled. In other areas, exceedance days may occur in all

seasons.

Calculation of RRFs- Regardless of the time periods modeled, component specific RRFs

should be calculated for all four quarters or for all quarters in which high 24-hour PM2.5

concentrations occur (following the recommendations in section 5.2). We do not have specific

analyses that have analyzed an appropriate minimum threshold concentration for 24-hour

average PM2.5. If there are very few days that exceed the 65 ug/m3 standard, then a secondary

recommendation is to include all days with concentrations > 50 ug/m3, or calculate RRFs based

on the highest modeled PM2.5 days from each quarter. A reasonable estimate of the high end of

the distribution might be between the top 10% of days to the top 25% of days. We recommend

that RRF calculations contain a similar number of days as for ozone. This means that each

quarterly RRF calculation should contain no less than 5 days64.
Draft 3.2- September 2006 137

14.1.3 Annual PM2.5

Similar to episode selection for the daily standards, the most important consideration for the

annual PM2.5 NAAQS is to model a variety of meteorological conditions throughout the year. 

The easiest way to accomplish this is to model a full year. Since all days count toward the

annual average, each season should be represented in the modeling. If less than a full year is

modeled then a minimum number of days need to be modeled from each quarter. 

One study (Environ, 2004) examined how well modeling 7-28 days in each quarter

represented modeled quarterly and annual averages. The study calculated the uncertainty

associated with absolute model predictions and RRFs that were based on 7, 14, or 28 days from

each quarter (the periods were contiguous). They found that the relative uncertainty of the

annual average concentrations was in the range of 40-70% for SO4, 30-50% for anthropogenic

organics, and 60-90% for nitrates (the low percentage is for 28 days and the high percentage is

for 7 days). The uncertainty was lowered by about half when considering the uncertainty in

RRFs for the 7-28 day periods vs. the annual average. This shows the advantage of using the

models in a relative sense. But it also shows that modeling less than a full year introduces

additional uncertainty into the model results (compared to modeling a full year). The conclusion

from the study was that at least 14 days are needed to represent each quarter. This assumes that

the model results are being used in a relative sense. Out of all of the PM components, nitrate is

the most difficult of the species to adequately represent with a reduced number of modeling

days. 

Based on current information, for annual average PM2.5, we recommend modeling a full year

(or more). Reduced modeling of 15 or more days per quarter may be acceptable in some cases,

but areas where nitrate is an important component of PM2.5 should consider modeling a full year

(or at least the time periods when nitrates are high). Modeling less than a full year (e.g. one

month per quarter) can be useful for initial strategy development and sensitivity analyses. In this

way, information can be learned from partial year runs and can be supplemented with full year

model runs later in the process. This may save resources and be a way to get more model runs

done (relatively) quickly.

14.1.4 Regional Haze

The goals for regional haze focus on the 20% of days with best and worst visibility. 

Sampling in Class I areas occurs once every three days. Thus, each year there will be about 24

“worst” days and 24 “best” days to choose from. Since the base period against which reasonable

progress is to be gauged is 5 years long, there could be as many as 120 “best” and “worst” days

to choose among for modeling.

It is likely that numerous Class I areas will be considered simultaneously in a modeled

reasonable progress demonstration. Thus, the preferred approach for regional haze-related

model applications is to simulate an entire, representative year (i.e., one whose mean derived

deciview values for “20% worst” and “20% best” days approximates mean values for deciviews

averaged over 5 years for the best and worst days). States can then base the RRF values on the
65 http://www.arb.ca.gov/aqd/weekendeffect/weekendeffect.htm

Draft 3.2- September 2006 138

best and worst days in each Class I area for the modeled year (the ~24 best and worst days from

the modeling year). If it is not feasible to model an entire year, then RRFs are likely to be

relatively stable if at least 10 worst days (and 10 best days) are modeled (similar to the ozone

recommendations). If the worst days occur throughout the year, then more time periods may

need to be modeled. Worst days that occur in the same season are more likely to have similar

PM components and are likely to respond in a similar way. Therefore, a more limited set of

model days may be able to represent the worst days from a single season. This is also true of the

best days. 

14.2 What Additional, Secondary Criteria May Be Useful For Selecting Time

Periods to Model?

In Section 14.0, we noted that there may often be conflicts among the four primary criteria

recommended as the basis for choosing episodes to model. Several additional, secondary

selection criteria may be helpful for resolving these conflicts.

Choose time periods which have already been modeled. That is, of course, provided that

past model performance evaluation for such a time period was successful in showing that the

model worked well in replicating observations. Given that the four primary criteria are met

approximately as well by such time periods they are by other candidate time periods, a

State/Tribe could likely save a substantial amount of work in evaluating model performance. 

However, it should be noted that large changes in emissions and precursor levels or ratios may

make the use of older time periods undesirable. 

Choose time periods which are drawn from the period upon which the baseline design

value is based. As we note in Section 3, fewer emission estimates and fewer air quality model

simulations may be needed if the base case period used to evaluate model performance, and the

baseline period used in the recommended modeled attainment test are the same. Following this

criterion could also make the second primary criterion more straightforward. 

Choose episodes having observed concentrations “close to” the NAAQS on as many

days and at as many sites as possible. This criterion mainly applies to the standards with short

term averaging times (8-hour ozone and 24-hour PM2.5). It is related to the modeled attainment

test and to the fourth primary criterion for episode selection. The more days and sites for which

it is reasonable to apply the test, the greater the confidence possible in the modeled attainment

test.

It is desirable to include weekend days among those chosen, especially if concentrations

greater than the NAAQS are observed on weekends. Weekend days often reflect a different

mix of emissions than occurs on weekdays65. This could also lead to different spatial patterns of

8-hour ozone and/or 24-hour PM2.5 concentrations. Thus, for increased confidence that a control
Draft 3.2- September 2006 139

strategy is effective it needs to be tested on weekends as well as on weekdays. If emissions and

spatial patterns of high ozone or PM2.5 do differ on weekends versus weekdays, including

weekend days in the choice of episodes will provide a mechanism for evaluating the accuracy of

a model’s response to changes in emissions.

If it has been determined that there is a need to model several nonattainment areas

simultaneously, choose time periods which meet the primary and secondary criteria in as

many of these nonattainment areas as possible. As discussed in Section 15, a State/Tribe or

group of States/Tribes may decide to apply a model on a regional or a nested regional scale to

demonstrate attainment or reasonable progress in several nonattainment areas or Class I areas at

the same time. Time and resources needed for this effort could be reduced by choosing time

periods which meet the primary and secondary criteria in several nonattainment areas (or Class I

areas) which are modeled.
66Due to relatively long lifetimes of PM2.5 aerosols (compared to ozone), PM modeling

domains may need to generally be larger than ozone domains. 

Draft 3.2- September 2006 140

15.0 What Should Be Considered When Selecting The Size And

Horizontal/Vertical Resolution Of The Modeling Domain?

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

appropriate domain size depends on the nature of the strategies believed necessary to meet the

air quality goal. This, in turn, depends on the degree to which air quality observations suggest

that a significant part of an observed exceedance is attributable to regional concentrations which

approach or exceed levels specified in the NAAQS. The choice of domain size is also affected

by data base management considerations. Generally, these are less demanding for smaller

domains. 

Horizontal resolution is the geographic size of individual grid cells within the modeling

domain. Vertical resolution is the number of grid cells (i.e., layers) considered in the vertical

direction. The choice of suitable horizontal and vertical resolution depends on spatial variability

in emissions, spatial precision of available emissions data, temporal and spatial variation in

mixing heights, the likelihood that mesoscale or smaller scale meteorological phenomena will

have a pronounced effect on precursor/pollutant relationships, data base management constraints,

and any computer/cost constraints.

We begin this section by discussing factors States/Tribes should consider in choosing domain

size. Next, we address the selection of horizontal grid cell size and the number of vertical layers. 

We conclude by discussing factors affecting the decision on the size and resolution of coarse

scale and fine scale grids within a nested model.

15.1 How is the Size of the Modeling Domain Chosen?

Historically (until ~1995), ozone attainment demonstrations used urban scale modeling

domains which were typically several hundred kilometers (or less) on a side. With the advent of

nested grid models, most model applications began to use either relatively fine regional grids, or

urban-scale inner grids nested within relatively coarse regional-scale outer grids. Recent PM

modeling has generally used nationwide coarse grid domains with regional scale nested domains.

We expect that most urban scale ozone and PM2.5 attainment demonstrations will utilize a

national or regional nested grid modeling approach. 

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

problem and the scale of the emissions which impact the nonattainment area. Isolated

nonattainment areas that are not impacted by regional transport of ozone and/or PM and its

precursors may be able to use a relatively small domain66. Some areas of the western U.S. may

fall into this category. Most nonattainment areas in the eastern U.S. have been shown to be
Draft 3.2- September 2006 141

impacted by transported ozone and/or PM and ozone and/or PM precursors from hundreds of

miles or more upwind of the receptor area (U.S EPA, 1998b) (U.S. EPA, 2004). 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. The influence of boundary conditions

should be minimized to the extent possible. 

The inner domain of a nested model application should include the nonattainment area and

surrounding counties and/or States. The size of the inner domain depends on several factors. 

Among them are: 

1) The size of the nonattainment area.

2) Proximity to other large source areas and/or nonattainment areas.

-Relatively isolated areas may be able to use a smaller fine grid domain. 

-Nearby source areas should be included in the fine grid. 

3) Proximity of topographical features which appear to affect observed air quality.

4) Whether the model application is intended to cover multiple nonattainment areas.

5) Typical wind speeds and re-circulation patterns.

-Very light wind speeds and re-circulation patterns may obviate the need for a large fine grid

domain. 

6) Whether the photochemical model utilizes one-way or two-way nested grids. 

-The fine grid domain of a model with one-way nested grids may need to be larger

(compared to a model with two-way nested grids) due to the fact that air that leaves the fine

grid never returns. The grid needs to be large enough to capture re-circulation due to shifting

wind directions. A two-way nested grid model allows for continuous feedback from the fine

grid to the coarse grid and vice versa. 

7) Computer and time resource issues.

 

15.2 How are the Initial and Boundary Conditions Specified?

Air quality models require specification of initial conditions for model species in each grid

cell in the model domain (in all layers) and boundary conditions for all grid cells along each of

the boundaries (in all layers). Generation of initial and boundary conditions for individual model

species include gas-phase mechanism species, aerosols, non-reactive species and tracer species. 

There is no satisfactory way to specify initial conditions in every grid cell. Thus, we recommend

using a “ramp-up” period by beginning a simulation prior to a period of interest to diminish the
67Sensitivity simulations can be completed to determine the necessary length of the rampup period. A longer ramp-up period may be needed for very large domains where the

characterization of long range transport is important. 

68One atmosphere model applications for ozone and PM may commonly use global

models to specify boundary conditions. This is especially important for PM species due to their

long lifetimes. A number of recent studies show that long-range, intercontinental transport of

pollutants is important for simulating seasonal/annual ozone and particulate matter (Jacob, et al.,

1999; Jaffe, et al., 2003; Fiore, et al., 2003).

Draft 3.2- September 2006 142

importance of initial conditions67. We recommend a ramp-up period of at least 2-3 days for

ozone modeling. Due to longer lifetimes of fine particulate matter, we recommend a ramp-up

period of at least 5-10 days for PM2.5. In this way, the choice of initial conditions are not likely

to influence the modeling results. For nested model applications, initial conditions can be

specified using model predictions from the outer grid if the nested grids are started after the

beginning of the simulation for the outer grid. 

Boundary conditions can be specified in several ways. One option is to nest the area of

interest within a much larger domain using nested regional models, as described previously. As

noted above in Section 15.1, use of a large regional domain acts to diminish the importance of

boundary conditions. Alternatively, initial and boundary conditions can be derived from another

regional nested modeling application or from a global model68. Another option is to use default

initial and boundary concentration profiles representing relatively clean conditions which have

been formulated from available measurements or results obtained from prior modeling studies,

(e.g. prescribing 35 ppb for annual ozone). Another approach to consider is using the model’s

simulated pollutant values (generated for emissions) averaged over one or more of the upper

layers to specify a value for the lateral and top boundary conditions. This approach allows for

better representation of spatial and temporal variation of boundary conditions (i.e.,

diurnal/seasonal/vertical profiles). This method also avoids arbitrarily guessing at the futureyear boundary conditions. 

If there is no larger regional model application available, then it is recommended that

background boundary conditions be used to specify initial and boundary concentrations for the

attainment demonstration modeling. However, concentration fields derived from a larger

domain regional or global chemistry model (i.e. nesting approach) is considered more credible

than the assumption of static concentrations, since the pollutant concentration fields reflect

simulated atmospheric chemical and physical processes driven by assimilated meteorological

observations. Therefore, we recommend using boundary conditions derived from a regional or

global scale model, whenever possible. 

Diagnostic testing which indicates a large impact on the model results from initial or

boundary conditions may indicate that the domain is not large enough or the ramp-up period is
Draft 3.2- September 2006 143

too short. In either case, it should generally be assumed that initial and boundary conditions do

not change in the future. The use of lowered initial or boundary conditions in the future year

should be documented and justified. 

15.3 What Horizontal Grid Cell Size Is Necessary?

As we discuss in Section 16, most applications will use a prognostic meteorological model to

provide meteorological inputs needed to make air quality estimates. Typically, these models are

set up to produce meteorological fields for nested grids with a 3:1 ratio. In past modeling

applications, the most commonly used grid cell sizes have been 108, 36, 12 and 4 km cells. In

this section we provide recommendations for choosing the grid size to use as an upper limit for

regional and urban scale models or for fine portions of nested regional grids. 

This section only applies to the use of numerical grid models. If a Lagrangian dispersion

model is used, estimates at (or near) the monitor locations should be used to calculate RRF

values for primary PM. 

In past guidance, we have recommended using horizontal grid cell sizes of 2-5 km in urban

scale ozone modeling analyses (U.S. EPA, 1991a). Sensitivity tests performed by Kumar (1994)

in the South Coast Air Basin compared hourly base case predictions obtained with 5 km versus

10 km versus 20 km grid cells. Results indicate that use of finer grid cells tends to accentuate

higher hourly ozone predictions and increase localized effects of NOx titration during any given

hour. However, statistical comparisons with observed hourly ozone data in this heavily

monitored area appear comparable with the 5 and 20 km grid cells in this study. Comparisons

between hourly ozone predictions obtained with 4 km vs. 12 km grid cells have also been made

in an Atlanta study (Haney, 1996). As in Los Angeles, the use of smaller (i.e., 4 km) grid cells

again leads to higher domain wide maximum hourly ozone concentrations. However, when

reviewing concentrations at specific sites, Haney (1996) found that for some hours

concentrations obtained with the 12 km grid cells were higher than those obtained with the 4 km

cells. Other studies have shown that model performance does not necessarily improve with finer

resolution modeling. In several studies, the model performance at 12 km resolution was equal to

or better than performance at 4 km resolution (Irwin, 2005).

Another important aspect in choosing the horizontal grid cell size is the relative response of

the model at various grid cell resolutions. Recent sensitivity tests comparing relative response

factors in predicted 8-hour daily maxima ozone near 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 grid cells (LADCO, 1999; Arunachalam, 2004). The largest differences in the relative

response of models at varying resolution is likely to occur in oxidant limited areas. The

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). 

Horizontal resolution will also have an impact on primary particulate matter. Because there is no
Draft 3.2- September 2006 144

chemistry, we expect spatial gradients in concentrations to be much higher for primary PM

components compared to secondary components. The model’s ability to capture these gradients

will depend in large part upon the horizontal resolution of the model. Therefore, emissions

density is an important factor in considering grid resolution. Additionally, terrain features

(mountains, water) should be considered when choosing grid cell size. 

Intuitively, one would expect to get more accurate results in urban applications with smaller

grid cells (e.g., 4 km) provided the spatial details in the emissions and meteorological inputs

support making such predictions. Thus, using 4 km grid cells for urban or fine portions of nested

regional grids and 12 km cells in coarse portions of regional grids are desirable goals. However,

extensive use of urban grids with 4 vs. 12 km grid cells and regional grids with 12 vs. 36 km grid

cells greatly increases computer costs, run times and data base management needs. Further,

elsewhere in this guidance we identify needs to model large domains, many days, and several

emission control scenarios. We also identify a number of diagnostic tests which would be

desirable and suggest using more vertical layers than has commonly been done in the past. Also,

there may be ways of dealing with potential problems posed by using larger than desired grid

cells. For example, use of plume-in-grid algorithms for large point sources of NOx should be

considered as an alternative with coarser than desired grid cells. And dispersion modeling of

primary PM can be used to offset the need of fine spatial resolution in a grid model. 

The relative importance of using a domain with grid cells as small as 4 km should be

weighed on a case by case basis by those implementing the modeling/analysis protocol. Thus, in

this guidance, we identify upper limits for horizontal grid cell size which may be larger than

desired for some applications. This is intended to provide flexibility for considering competing

factors (e.g., number of modeled days versus grid cell size) in performing a modeling analysis

within the limits of time and resources.

15.3.1 Horizontal Resolution Recommendations for Ozone

For coarse portions of regional grids, we recommend a grid cell size of 12 km if feasible, but

not larger than 36 km. For urban and fine scale portions of nested regional grids, it may be

desirable to use grid cells about 4 km, but not larger than 12 km. States/Tribes should examine

past model applications and data analyses for their area when choosing the fine grid resolution. 

Past model applications and data analyses may help determine whether a grid cell size as small

as 4 km (or smaller) is necessary for a particular area. Model performance and the relative

response to emissions controls should be considered in the decision. States/Tribes should

consider diagnostic tests to assess the difference in model performance and response from

varying model grid resolution, particularly in oxidant-limited areas. 

All ozone monitor locations within a nonattainment area should ordinarily be placed within

the fine scale portion of a nested regional grid if nested models are used. States/Tribes choosing

an urban grid or fine portion of a nested grid with cells 12 km or larger should consider applying

plume-in-grid algorithms to major point sources of NOx. The use of plume-in-grid should be
Draft 3.2- September 2006 145

discussed with the appropriate EPA Regional Office.

15.3.2 Horizontal Resolution Recommendations for the PM2.5 NAAQS

The formation of secondary particulate matter shares the same basic photochemical processes

as ozone. In fact, they are closely interconnected in many respects. Since there appears to be

little difference in RRF estimates for 8-hour ozone with 12 km vs. 4 km grid cells, we would

expect this finding to hold for secondary components of particulate matter. But what about

primary components of particulate matter? Because there is no chemistry, we expect spatial

gradients in concentrations to be much higher for primary particulate matter than for secondary

particulate matter. This follows, because it takes some time (less than 24 hours) for the

chemistry to occur. Although it is clear that spatial resolution of primary PM will impact the

predicted concentrations, it is not clear how it will impact the relative change in concentrations

due to controls (RRFs). Areas without strong gradients in primary PM will likely have little

benefit from fine scale resolution. Areas that have large gradients in primary PM may need to

use finer resolution grid models or may need to supplement the grid modeling with dispersion

modeling. This a particularly true if violating monitors are strongly impacted by one or more

large sources of primary PM.

The bottom line of the preceding discussion is that we feel comfortable recommending that

States may use grid cell sizes as large as 12 km for urban scale applications addressing

secondary components of particulate matter. We are less sure about an acceptable upper limit

for cell size in applications addressing primary components. We believe it is prudent to assume

that, in some cases, cells as small as 4 km (or possibly smaller) are needed. Those implementing

the modeling/analysis protocol may wish to perform a diagnostic test using a grid model without

chemistry to see whether estimated RRF’s for primary components are affected if one decreases

the grid cell size from 12 km to 4km. Alternatively, large sources of primary PM can be

modeled with a dispersion model or a combination of grid and dispersion models (see Section

5.3).

We expect that modeling analyses for nonattainment areas will model using grid cell sizes of

12 km or less. If a regional scale model is applied, most of the domain will likely cover

rural/remote areas or locations which are not out of compliance with the NAAQS. For the

regional outer nest of the domain, grid cells as large as 36 km may be used

15.3.3 Horizontal Resolution Recommendations for Regional Haze

The reasonable progress test focuses on Class I areas. Most of these are in remote or rural

locations. Regional haze is not likely to be dominated by local sources. Further, light extinction

is more likely to be dominated by secondary particulate matter, due to the latter’s efficiency of

scattering light. All these attributes indicate that it will be necessary to model a regional scale

domain for regional haze related applications. Because of the remoteness of Class I areas, grid

cell sizes up to 36 km on a side should suffice for regional haze-related modeling. States may
Draft 3.2- September 2006 146

wish to perform diagnostic tests using plume-in- grid analyses, as well as finer horizontal

resolution to determine if results may differ using more finely resolved emissions and

meteorology. Guidance on vertical resolution presented in Section 15..4 is also applicable for

regional haze-related applications.

15.4 How Should the Vertical Layers Be Selected? 

As described in Section 16, the preferred approach for generating meteorological data fields

for input to air quality simulation models is to use a prognostic meteorological model with four

dimensional data assimilation (FDDA). Such models normally use more than 30 vertical layers. 

To minimize a number of assumptions needed to interface meteorological and air quality models,

it is better to use identical vertical resolution in the air quality and meteorological models. 

However, application of air quality models with as many as 30 vertical layers may not be

feasible or cost effective. In this section we identify factors to consider in choosing the number

of vertical layers chosen for the air quality model applications.

In the past, short ozone episodes of only several days usually encompassed periods of mostly

clear skies with very little precipitation. As such, ozone models often did not explicitly model

clouds or precipitation. However, we are recommending modeling longer episodes (or even a

full ozone season) for ozone and a full year for PM2.5 and regional haze applications. Therefore

the photochemical model needs to account for cloud processes and a full range of precipitation

types. In order to adequately parameterize these processes, the top of the modeling domain

should typically be set at the100 millibar level (~16,000 meters). In turn, this means that many

more vertical layers will be needed to capture the meteorological processes both below and

above the boundary layer, up to the top of the model. 

The accuracy of predicted base case ozone and PM concentrations will be affected by how

well the model is able to characterize dilution of ozone, PM, and precursors. This, in turn,

depends in part on how precisely the model can estimate mixing heights (i.e., the PBL). The

precision of mixing height estimates is affected by the thickness of the model’s vertical layers

aloft which are near the anticipated mixing height (Dolwick, 1999). Ozone concentrations are

most heavily influenced by the rate of rise in the morning mixing height and the maximum

afternoon mixing height. Since PM is measured as a 24-hour average concentration, PM is also

influenced by the strength of nighttime mixing and the presence of a low level inversion. 

Because mixing heights may vary on different days and it is necessary to simulate numerous

days and locations, model predictions can be influenced by the number of vertical layers

considered by the model. 

Placement of vertical layers within the planetary boundary layer is also an important issue. 

For practical reasons, it is best to have an air quality model’s vertical layers align with the

interface between layers in the meteorological model. In view of the importance of carefully

specifying the temporal variation in mixing height, we recommend high precision below and
Draft 3.2- September 2006 147

near the anticipated maximum afternoon mixing height. In addition, specifying the vertical

extent of mixing overnight during stable conditions is also an important consideration in

determining the vertical layer structure. In this regard, we recommend that the lowest layer in

the air quality model be no more than 50 meters thick. In general, layers below the daytime

mixing height should not be too thick, or large unrealistic step increases in mixing may occur. 

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

are less important to the daily mixing processes of pollutants. Therefore, vertical resolution

above the boundary layer is typically much coarser.

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

demonstration. The vertical resolution will vary depending on the application. Recent

applications of one atmosphere models (with model tops at 100mb) have used anywhere from 12

to 21 vertical layers with 8-15 layers approximately within the boundary layer (below 2500m)

and 4-6 layers above the PBL (Baker, 2004b), (Hu et. al, 2004).

There are also ozone model applications which may not need to consider the full set of

meteorological data through the tropopause. These applications typically use vertical domains

which extend up to 4 or 5 km. These applications are most appropriate for short ozone episodes

that occur under high pressure conditions (little cloud cover or precipitation). In these cases,

fewer vertical layers are needed to represent the atmosphere up to the top of the domain (4-5

km). However, where appropriate, EPA encourages the use of full-scale one-atmosphere models

which account for all atmospheric processes up to ~100 mb. 
Draft 3.2- September 2006 148

16.0 How are the Meteorological Inputs Prepared for Air Quality

Modeling?

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

models require certain meteorological inputs that help simulate the formation, transport, and

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

but consistently involve parameters such as wind, vertical mixing, temperature, moisture, and

solar radiation. While model inputs can be derived strictly from ambient measurements, a more

credible technical approach is to use 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 yielding temporally and spatially complete data sets that are grounded by actual

observations. 

This section provides recommendations for generating, or otherwise acquiring, the

meteorological data sets sufficient for air quality modeling purposes. Additional suggestions are

provided to assist in the configuration of standard meteorological modeling analyses. The last

section outlines procedures for evaluating whether the meteorological input is of sufficient

quality for input into the air quality model. In general, it is recommended that States/Tribes

spend appropriate effort in accurately characterizing the meteorological fields in view of several

sensitivity runs which show that relatively small perturbations in meteorological inputs can have

large impacts on resultant air quality modeling results (Dolwick, 2002).

16.1 What Issues are Involved in the Generation and Acquisition of

Meteorological Modeling Data?

The recommended approach for generating the meteorological data needed to conduct the

attainment demonstration is to apply 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 past

events, 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 meteorological

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

meteorological models are listed below: 

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

known as MM5 (Grell et al., 1994), 

• The Regional Atmospheric Modeling System (RAMS) (Pielke et al., 1992), and 

• The Weather Research and Forecasting Model (WRF) (Skamarock, et al., 2005). 

Recent advances in relatively low-cost computational power have resulted in widespread use
Draft 3.2- September 2006 149

of MM5 and similar models for air pollution applications over the past decade (Olerud et al.,

2000; Doty et al., 2001; Johnson, 2003, Baker, 2004). EPA expects that the large majority of

future attainment demonstration analyses will be based on meteorological data from these types

of dynamic meteorological models. However, there are other acceptable means of generating

meteorological data for an air quality modeling simulation. Over the next several years, EPA

further expects that more meteorological input data sets will be developed from archived

National Weather Service (NWS) model simulations. It is possible that data from archived WRF

model simulations could be used to feed air quality simulations. Some of these prognostic

meteorological models are already being used to drive real-time air quality forecasts (McQueen

et al., 2004). Additionally, in some cases the dynamic meteorological model may not adequately

capture key meteorological elements of an airshed's conceptual model (e.g., source-receptor

transport vectors to key monitoring locations). In 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. 

A description of the methods used to generate the meteorological fields should be included in

the modeling protocol. In cases in which standard meteorological modeling (e.g., MM5, RAMS,

or WRF in a retrospective analysis mode) is not used, it is recommended that a detailed

description of the technique that is used to generate the three-dimensional meteorological fields

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

modeling analysis.

16.2 How Should the Meteorological Modeling Analysis be Configured?

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

models can be particularly challenging because of the amount of flexibility available to the user. 

The following are recommendations on how to establish the configuration of a meteorological

model for air quality analyses. The focal point in establishing the proper meteorological

modeling configuration should be to get the best possible meteorological model performance.

Selecting a Model Domain: As noted in Section 15, it is expected that most attainment

demonstrations will cover large areas and use nested grids. 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 3 to 6 cells beyond the domains of each air quality modeling grid to avoid

boundary effects. For example, if 4 km grid cells are to be used in the fine portion of a nested

regional air quality model, then the meteorological fields at this detail would need to extend

12-24 km beyond the bounds of the 4 km grid used for air quality predictions. In terms of grid

resolution, EPA recommends that the dynamic meteorological models use the same grid

resolution as desired for the air quality model applications. In some cases, however, this may

not always be feasible. One possible reason for modeling with meteorology using a different
Draft 3.2- September 2006 150

grid resolution is in the case of unacceptable model performance from the meteorological model

at the desired grid resolution. In other instances, the need for finer resolution may be

emissions-driven more than meteorologically-driven and the costs do not warrant the generation

of additional resolution in the meteorological data. In these specific situations it is

recommended that the air quality model application use available results from meteorological

models on the next coarser scale (i.e., 36 km for a desired 12 km estimate, 12 km for a desired 4

km estimate). The coarse grid meteorological fields can be mapped to the more finely resolved

air quality modeling domain.

Selecting Physics Options: Most meteorological models have a suite of "physics options"

that allow users to select how a given feature will be simulated. For example, there may be

several options for specifying the planetary boundary layer scheme or the cumulus

parameterization. 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 best-suited

for the domain and period being simulated. Examples of sensitivity analyses can be found in

McNally (2002), Olerud, (2003), and Huang, et al., (2005). 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. As an example, a

wintertime PM simulation in the Midwest with high measured nitrate concentrations may need a

meteorological model configuration that employs a land-surface model that properly handles

snow cover fields and their effects on boundary layer humidities and temperatures.

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. 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. nocturnal low-level wind jets). In general, analysis nudging strengths should be no

greater than 1.0 x 10-4 for winds and temperatures and 1.0 x 10-5 for humidity. 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 below the boundary layer for thermodynamic variables like temperature and
Draft 3.2- September 2006 151

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

without FDDA, it is suggested that the simulation durations be shorter than 24 hours.

Conversion of Meteorological Outputs to Air Quality Model Inputs: Even before

determining how the meteorological model is configured, careful thought should be given to the

compatibility between candidate meteorological models and the air quality model(s) chosen for

use. A variety of post-processors exist to convert the outputs from the meteorological models

into the input formats of the air quality models. Some examples include: the Meteorology

Chemistry Interface Processor (MCIP) (Otte, 2004), MM5CAMx (Environ, 2005), and

RAMSCAMx (Environ, 2005). These meteorological preprocessors provide a 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, 3) extraction of data for appropriate

window domains, 4) reconstruction of the meteorological data on different grid and layer

structures, and 5) calculation of additional variables.

16.3 How Should the Performance of the Meteorological Modeling Be Evaluated?

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 are accepted as is, even though this component of the modeling is arguably more

complex and contains a higher quantity of potential errors that could affect the results of the

analysis (Tesche, 2002). EPA recommends that States/Tribes 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).

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
69 It is difficult to say which meteorological parameters will most affect any particular modeling exercise

and it will vary by parameter and location. However, in general, it is expected that following key variables should

be most closely evaluated: temperature, water vapor mixing ratio, wind speed, wind direction, clouds/radiation,

precipitation, and the depth and evolution of vertical mixing over the modeling periods.

Draft 3.2- September 2006 152

in space and time. Typical statistical comparisons of the key meteorological parameters69 will

include: comparisons of the means, mean bias, mean normalized bias, mean absolute error, mean

absolute normalized error, root mean square error (systematic and unsystematic), and an index of 

agreement. For modeling exercises over large domains and entire ozone seasons or years, it is

recommended that the operational evaluation be broken down into individual segments such as

geographic subregions or months/seasons to allow for a more comprehensive assessment of the

meteorological strengths and weaknesses. Other useful ways of examining model performance

include: aloft, surface, individual episodes (e.g., high ozone / PM2.5 days), diurnal cycle, as a

function of synoptic regimes, or combinations of the above. It is recommended that the ambient

data used in these statistical comparisons be quality checked by doing standard range check and

buddy analyses. To the extent that modelers can set aside a portion of the ambient data strictly

for evaluation purposes (i.e., data not used in the FDDA), that is also encouraged. Figure 16.1

shows a sample operational evaluation summary. This graphic was developed using the

Atmospheric Model Evaluation Tool (AMET) (Gilliam, 2005), though other evaluation tools are

available.
Draft 3.2- September 2006 153

Figure 16.1 Sample operational evaluation summary for a 2002 MM5 case. This

particular analysis looked at April through June temperature performance at a single

site. The upper left quadrant shows a scatter plot of observed versus predicted

temperatures, the lower left traces show how model performance varies as a function of

the observation range, the upper right table lists some of the statistical outputs, and the

lower right compares the spectrum of predictions against the observational spectrum. 
Draft 3.2- September 2006 154

2002 MM5 performance for temperature at three sites, by season

0.00

1.00

2.00

3.00

-2.50 -1.50 -0.50 0.50 1.50 2.50

Bias

Error

DET 1st Q

DET 2nd Q

DET 3rd Q

DET 4th Q

BHM 1st Q

BHM 2nd Q

BHM 3rd Q

BHM 4th Q

SEA 1st Q

SEA 2nd Q

SEA 3rd Q

SEA 4th Q

Figure 16.2: Sample "soccer plot" summary of temperatures from a 2002 MM5 case. This

particular analysis looked at seasonal temperature performance at three sites. The benchmarks

in this case were set at +/- 1.0 K for bias and 2 K for error. This analysis called attention to a

relatively large cold bias in the winter. 

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 in a

"pass/fail" mode, but only 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 an issue that should be given further examination. Figure 16.2 shows a sample analysis

in this vein.
70 Possible examples include: lake/sea breezes, low-level jets, or amount of convection

on a given day.

Draft 3.2- September 2006 155

In most cases the performance evaluation will be completed on the raw meteorological fields. 

However it is also important to compare the results before and after the meteorological

post-processing to ensure that the meteorological fields going into the air quality model have not

been adversely affected.

Phenomenological Evaluation: As discussed in Chapter 11, 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 of a particular modeling exercise, it is

recommended that the specific meteorological parameters70 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 thought to strongly affect air pollution

formation and transport within the scope of a specific analysis. It is expected that this

event-oriented evaluation will need to 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 emissions from one area to a downwind area with an intent to establish

source-receptor relationships. For these types of modeling analyses, accurate transport wind

trajectories are needed to properly establish these source-receptor linkages. In this type of model

application, 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.
36 2002 is the recommended inventory year for the baseline modeling (the starting point

for future year projections). Other years may be modeled for the base case modeling (for

performance evaluation) if episodes are chosen from years other than 2002. If all of the episodes

are from 2002, then the base case and baseline inventories will be for the same year.

Draft 3.2- September 2006 156

17.0 How Are the Emission Inputs Developed?

Air quality modeling for 8-hour ozone, PM2.5, and regional haze requires emission inputs for

base case, baseline, and future modeling years. As explained in the EPA Emission Inventory

Guidance (U.S. EPA, 2005c), 2002 is designated as a new base year for 8-hour ozone and PM2.5

SIPs and regional haze plans; therefore, wherever possible, 2002 should be used for baseline

modeling36. The use of an alternative baseline year should be discussed with the appropriate

EPA regional office. The future-year depends on the nonattainment classification of the

individual State or Tribe, as described in Section 3.6. Note that emissions data should be

consistent with the data used in the modeled attainment test, described in Sections 3-6.

Preparation of emissions data for air quality models for the base and future years requires

several steps. First, States and Tribes need to compile base-year inventories for their modeling

region (e.g., the States and Tribes in the modeling grid). For ozone model applications, emission

inventories should include a complete accounting of anthropogenic and biogenic VOC

(speciated), NOx, and CO. For PM and regional haze model applications, emission inventories

should also include emissions of SO2, NH3, PM2.5 (speciated), and PM coarse (PMC). Second,

modelers must collect “ancillary data” associated with the inventories, which prescribes the

spatial, temporal, and chemical speciation information about the emission inventory. Third,

modelers use the ancillary data for “emissions modeling”. Emissions models spatially,

temporally, chemically, and vertically allocate emission inventories to the resolution needed by

AQMs. Fourth, modelers must collect data on growth rates and existing control programs for

use in projecting the base year emission inventories to the future year, and then use an emissions

model to prepare that future year inventory data for input to the air quality model. Fifth,

emissions inventories that reflect the emissions reductions needed for attainment will have to be

prepared for air quality modeling.

Sections 17.1 and 17.2 summarize the issues for preparing emission inventories. Section

17.3 describes the needs for ancillary data. Section 17.4 summarizes the emissions modeling

steps. Section 17.5 and Section 17.6 summarizes the issues associated with modeling of future

year emissions data. Finally, Section 17.7 provides guidance on emissions to be used for local

area primary PM2.5 modeling.

17.1 What Emission Inventory Data are Needed to Support Air Quality Models? 

Air quality models require hourly emissions for each grid cell and model layer in the

modeling domain. Depending on the application, models may be run for episodic periods or for
Draft 3.2- September 2006 157

up to a full year or multiple years. The emissions need to be allocated to the chemical species

required for the chemical mechanism and version of the model being used. The speciated

emissions of VOC and PM2.5 depend on the air quality model and chemical mechanism being

used. For primary PM2.5 emissions, the model divides the PM into organic carbon, elemental

carbon, primary sulfate, primary nitrate, and “all other” PM2.5 emissions. Calculating the

speciated emissions is part of the emission modeling step, described in Section 17.4 based on

data described in Section 17.3.

Emission inventory data from five categories are needed to support air quality modeling:

stationary point-source emissions, stationary area-source emissions (also called non-point),

mobile emissions for on-road sources, mobile emissions for nonroad sources, and

biogenic/geogenic emissions. The emission inventory development and emissions modeling

steps can be different for each of these categories. 

Point Sources- Point source inventories for modeling should be compiled at a minimum by

country, State/Tribe, county, facility, stack, and source category code (SCC) but often are further

subdivided by “point” and “segment”(see references below to point-source inventory

development). The point source data must include information on the location of sources (e.g.,

latitude/longitude coordinates); stack parameters (stack diameter and height, exit gas temperature

and velocity); and operating schedules (e.g., monthly, day-of-week, and diurnal). 

Stationary Area Sources- Stationary-area source emissions data should be compiled by

country, State/Tribe, county, and SCC. 

On-Road Mobile- On-road mobile source emissions should be estimated using the most current

version of the U.S. EPA MOBILE model ( http://www.epa.gov/omswww/m6.htm), and for

California, the most current version of EMFAC (http://www.arb.ca.gov/msei/onroad/latest_version.htm) in concert with vehicle miles traveled (VMT) data representative of the

time periods modeled. The MOBILE model allows modelers to override default settings to

obtain a local-specific on-road inventory, and modelers should consider using these options to

improve their inventories. On-road emissions and VMT should be compiled at least at the

country, State/Tribe, county, and SCC level, though modelers may optionally compile and use

data for individual road segments (called “links”). The link approaches requires starting and

ending coordinates for each road link. As noted in Section 17.3, link-based emissions can be

based on Travel Demand Models (TDMs).

Nonroad Mobile- For nonroad mobile sources, the emissions should be compiled as country,

state/tribe, county and SCC totals using the most current version of EPA’s NONROAD model

(http://www.epa.gov/oms/nonrdmdl.htm) or alternative model.

Biogenic Emissions - Biogenic emissions from plants and soil contribute VOC (including

Terpenes that are imported for PM and regional haze modeling), NOx, and CO emissions, which

are best calculated as part of the emissions modeling step described in Section 17.4. These
37Day specific emissions data, such as CEM data or wildfire data may be useful in the

base case modeling to improve model performance. But in some cases, it may not be appropriate

to project day-specific data to the future. For example, if a large power plant was shutdown for

maintenance during the base case period, it would not be logical to project zero emissions for

that source in the future (if it is assumed that the plant will be operating in the future year). 

Therefore, certain day-specific inventory information should be removed and replaced with

average data in the baseline inventory, before projecting the baseline to the future. This issue is

not a concern for day-specific mobile source or biogenic emissions data which are dependent on

day specific (or even hourly) meteorological data for the time periods modeled. 

Draft 3.2- September 2006 158

emissions require land use data, which is described as an ancillary dataset in Section 17.3. 

Geogenic emissions (such as volcanic emissions) are often not relevant for many areas, but if

geogenic sources in the modeling domain are expected to contribute in a significant way to air

quality problems, they should be included.

Other Considerations - For all sectors, emissions data must be compiled at a minimum as an

annual total for the base modeling year. Emissions can also be compiled as monthly total

emissions or (for ozone) an average-summer-day inventory. In any case, the temporal allocation

step during emissions modeling (see Sections 17.3 and 17.4) must adjust the inventory resolution

for the modeling time period. Additionally, we encourage the use of more temporally specific

data where it is available and can be expected to improve model performance. For example,

hour-specific Continuous Emissions Monitoring (CEM) data may be used as a source for hourspecific NOx and SO2 emissions and exit gas flow rates37, and the hourly heat-input CEM data

can be used to allocate emissions of other pollutants.

Inventories should be built using the most current, accurate, and practical methods available. 

Several references are available for guidance on building emission inventories. The first is the

“Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter NAAQS

and Regional Haze Regulations” (U.S. EPA, 2005c). Additionally, modelers may want to

consider EPA’s approaches used for developing the 2002 NEI. Available NEI documentation

may be used to help guide the development of the modeling inventory

(http://www.epa.gov/ttn/chief/net/2002inventory.html). Lastly, 10 volumes (many with several

chapters comprised of separate documents) have been issued by the Emission Inventory

Improvement Program (EIIP) for use in inventory development. Nine of these volumes are

relevant to creating emission inventories for ozone, PM, and regional haze modeling:

• Volume I: Introduction

• Volume II: Point Sources

• Volume III: Area Sources and Area Source Method Abstracts

• Volume IV: Mobile Sources

• Volume V: Biogenics Sources

• Volume VI: Quality Assurance/Quality Control

• Volume VII: Data Management Procedures
Draft 3.2- September 2006 159

• Volume IX: Particulate Emissions

• Volume X: Emission Projections

The EIIP documents are available electronically through the EPA website at

http://www.epa.gov/ttn/chief/eiip/techreport/. The quality assurance procedures contain

essential steps for inventory preparation, which help assure that the emission inventory is

appropriate for SIP air quality modeling.

17.2 Can The National Emission Inventory Be Used As a Starting Point? 

We recommend that States and Tribes start with available inventories suitable for air quality

modeling of the selected time period(s). If no such inventory is available, States/Tribes may

derive an inventory suitable for use with models starting from the National Emission inventory

(NEI), available from http://www.epa.gov/ttn/chief/net/. The 2002 NEI can be used as a starting

for inventory development. However, the detail and accuracy of the emissions in the NEI may

not be sufficient for use in attainment demonstration modeling. Thus, States/Tribes should

review the contents of the NEI for accuracy and completeness for regional and local scale

modeling and amend the data where it is insufficient to meet the needs of the local air quality

model application. While some benefits can be realized for states using the same inventory

(such as the NEI) as a starting point for their work, the more important goal for SIP inventory

development is to ensure that the data are representative and accurate enough for making good

control strategy decisions.

17.3 What Other Data are Needed to Support Emissions Modeling?

 The emission inventories must be converted (through emissions modeling) from their

original resolution (e.g., database records) to input files for air quality models. These input files

generally require emissions to be specified by model grid cell, hour, and model chemical species. 

This section describes the ancillary data that modelers should collect that allow emissions

models to convert the emission inventory data.

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 profiles)

must be created to convert annual emissions to specific months (monthly profiles), average-day

emissions to a specific day of the week (weekly profiles), and daily emissions to hours of the day

(hourly profiles). Additionally, a cross-reference file is needed to assign the temporal profiles to

the inventory records by SCC, facility, 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. EPA provides a starting point for the temporal

profiles and cross-reference files, available at: http://www.epa.gov/ttn/chief/emch/temporal/.
38The 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 provides an

appropriate platform for comparisons between the base year and future years.

Draft 3.2- September 2006 160

For point sources, hourly Continuous Emissions Monitoring data are recommended for use in

model-evaluation runs. For future-year runs, we recommend creating an “average-year”or

“typical year” temporal allocation approach that creates representative emissions for the

“baseline inventory” but that also includes similar daily temporal variability as could be

expected for any given year. Care should be taken to not reduce or increase day-to-day

variability in the averaging approach, with the exception of eliminating year-specific outages or

other year-specific anomalies38 within the years used for the model-attainment test.

The emissions models also need information about the chemical species of the VOC and

PM2.5 emissions for stationary point, stationary area, and all mobile 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”. EPA provides a starting point for

the VOC and PM2.5 speciation data, which are available at:

http://www.epa.gov/ttn/chief/emch/speciation/. For large or critical VOC and PM2.5 sources in

the modeling domain, States/Tribes 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 improved speciation profiles for the critical facilities or source categories. 

These speciation profiles should be assigned to the inventory by a speciation cross-reference file,

which also needs to be created or updated from the available defaults. The cross-reference files

typically assign speciation profiles based on SCC code, though facility-specific assignments for

point source code is also possible if plant-specific data are available.

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. Such sectors include stationary area, nonroad mobile, and non-link on-road mobile

sources. The spatial allocation process assigns fractions of county-total emissions to the model’s

grid cells intersecting the county based on a “surrogate” data type (e.g., population or 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.cep.unc.edu/empd/projects/mims/spatial/srgtool/ ), which is based on the 

Multimedia Integrated Modeling System (MIMS) Spatial Allocator

(http://www.cep.unc.edu/empd/projects/mims/spatial/ ). Additionally, 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. Additionally, EPA provides spatial surrogates and cross-
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.

Draft 3.2- September 2006 161

references for a limited set of modeling grids and surrogate types at:

http://www.epa.gov/ttn/chief/emch/spatial/. Finally, for future-year modeling, emissions

developers can choose to change their spatial surrogate data based on predicted changes in land

use patterns, population growth, and demographics, however, the impact and utility of such

approaches is not well characterized39.

For biogenic emissions modeling, the Biogenic Emission Inventory System, version 3

(BEIS3) (http://www.epa.gov/asmdnerl/biogen.html ) model comes with all needed ancillary

data, except the gridded land-use data and meteorology data for a specific air quality modeling

domain and grid. Land use and meteorology data that are compatible with BEIS3 are needed for

the specific grid and grid-cell resolution that is being used. For BEIS3, land use data can be

created with the MIMS Spatial Allocator (http://www.cep.unc.edu/empd/projects/mims/spatial/),

using raw Biogenic Emissions Landcover Data (BELD), version 3

(http://www.epa.gov/ttn/chief/emch/biogenic/). For future-year modeling, 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. The impact and

model characterization of biogenic terpene and sequiterpene on secondary aerosol format for PM

modeling is currently evolving. Emissions modelers should consider the latest available

information on this issue where biogenic sources are key sources in their modeling region.

On-road emissions for fine-scale model grids (e.g., 4-km grid cells or smaller) may be based

on a link-based mentioned in Section 17.2. 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 linkbased 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).

Emissions models have other input files that must be created. For example, criteria may be

needed for selecting elevated from non-elevated point sources. Each model has a large number

of files and settings which work together in fairly complex ways; therefore, care must be taken to

determine the files needed for the emissions model, and to prepare all needed input files in a way

that will support using the emissions model for the specific air quality modeling episode.

17.4 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 17.3, additional ancillary data is needed to complete the process. The

emissions data from each of the five emissions sectors are temporally allocated, chemically
Draft 3.2- September 2006 162

speciated, and spatially allocated. 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 additional issues that are

key for emissions modeling.

Emissions models Several emissions models are available for use in SIPs. While no single

model has been specifically created for all situations of SIP development, each model is

generally capable of performing the temporal, chemical, and spatial allocation steps as well as

various other steps. Users of these models are responsible for ensuring that the emissions

processing steps are transforming the emission inventories as intended and are not changing the

emissions in any unexpected way. The models each have 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 establish which model is best for the

specific application.

Currently there are three main emissions models being used to process emissions for input

into photochemical grid models and a fourth model that is under construction at the time that this

document was written. They are: Sparse Matrix Operator Kernel Emissions (SMOKE);

Emissions Modeling System (EMS-2001); and Emissions Preprocessor System - Version 2.5

(EPS 2.5). The Consolidated Community Emissions Processing Tool (CONCEPT) is under

development by the Regional Planning Organizations.

The Sparse Matrix Operator Kernel Emissions (SMOKE), software and User’s Guide are

available through the University of North Carolina, Carolina Environmental Program

(http://www.cep.unc.edu/empd/products/smoke). SMOKE supports processing of criteria,

mercury, and toxics inventories for stationary point, stationary area, mobile, and biogenic

emissions. It can create input files for the CMAQ, CAMX, UAM-V, and REMSAD air quality

models. SMOKE was the basis for development of the BEIS3 system, so BEIS3 in its original

form can be used easily with SMOKE. Applications of SMOKE have been presented at several

of the International Emissions Inventory Workshops (Houyoux et al., 2000; Strum et al., 2003). 

SMOKE is available for UNIX and Linux operating systems and is not recommended for use on

a PC. It does not require third party software. It does not include utilities for creating speciation

profiles, biogenic land use, or spatial surrogates, though the latter two datasets can be built using

the Multimedia Integrated Modeling System (MIMS) Spatial Allocator Tool

(http://www.cep.unc.edu/empd/projects/mims/spatial/). Support for the SMOKE system is

available through the Community Modeling and Analysis System (CMAS) help desk

(http://www.cmascenter.org/html/help.html).

The Emissions Modeling System, (EMS 2001, http://www.ladco.org/tech/emis/ems_2001/) is

a later version of EMS-95, which was used in the modeling underlying the U.S. EPA NOx SIP

call rule to reduce regional NOx emissions (U.S. EPA 1997h), as well as in other applications of

nested regional air quality models. It can create inputs for the CAMX and UAM-V models. It

includes the BIOME3 model, which provides access to similar calculations of biogenic
Draft 3.2- September 2006 163

emissions as are available in the BEIS3 system. EMS-2001 can be run on either Linux or

Windows NT, and users must purchase a license for the SAS® software to use it. It includes

utilities for creating speciation profiles, biogenic land use, and spatial surrogates. An updated

version has new spatial processors, which limit the need for GIS software. 

The Emissions Preprocessor System - Version 2.5 (EPS-2.5), software and User's Guide are

available through Systems Applications International/ICF Consulting (www.uamv.com). EPS2.5 is a comprehensive emissions processing system that supports processing of stationary point,

stationary area, and mobile emissions for the development of base and future-year modeling

inventories for input to the CAMX, UAM-V, and REMSAD models. EPS-2.5 consists of a set of

stand-alone FORTRAN programs that do not require third-party software. The system is capable

of preparing local, regional, and continental-scale emission inventories for ozone, particulate

matter, mercury, and air toxics modeling applications. EPS 2.5 is available for UNIX and Linux

operating systems. It includes utilities for creating source-specific temporal and chemical

speciation profiles based on locally provided detailed information for episode specific emission

inventories. It also includes utilities for preparing spatial surrogates. In addition, EPS-2.5 has

the capability of creating modeling inventories required for the application of source

apportionment techniques such as UAM-V's Ozone and Precursor Tagging Methodology

(OPTM).

The Consolidated Community Emissions Processing Tool (CONCEPT) is an open source

model written primarily in PostgreSQL. Users are encouraged to make additions and

enhancements to the model. The database structure of the model makes the system easy to

understand, and the modeling codes themselves 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 easily 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 work by the emissions modeling community has begun

development of CEM preprocessing software, graphical QA tools, and an interface to the traffic

demand models for on-road motor vehicle emissions estimation.

Biogenic emissions Estimates for biogenic emissions can be made using the BEIS emissions

model (Geron, et al., 1994). 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., (2000) contains the technical development and review of BEIS3. 

Vukovich, et al., (2002) summarizes new 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, a State or Tribe should review the biogenic emissions on a county

basis and update as needed the spatial patterns of land use data. BEIS3.13 was the latest version
40Point 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.

Draft 3.2- September 2006 164

of BEIS3 in use by EPA at the time this document was written.

Other models for biogenic emissions include the BIOME model that is a part of EMS-2001

and the Global Biosphere Emissions and Interactions System (GloBEIS). The latter estimates

emissions from natural (biogenic) sources and is designed for use in combination with

photochemical modeling systems, such as CAMx (http://www.globeis.com/).

All of the biogenic models require a mix of land uses to be specified for each county or grid

cell, as well as hourly temperature and in some cases other meteorological data. If a State or

Tribe believes the average land use mix characterized for a county is inappropriate for certain

gridded locations within a county, this may be overridden for the grid cells in question on a case

by case basis.

17.5 Are there Other Emissions Modeling Issues?

In addition to the general emissions modeling steps and the biogenic emissions modeling,

there are several other issues of which the air quality modeler should be aware. These are:

• Elevated point sources

• Advanced approaches for mobile source modeling

• Quality assurance

In the remainder of this section, we briefly address each of these issues.

Elevated Point Sources Point sources need to be assigned to an appropriate model layer40 (the

vertical modeling dimension). Depending on the air quality model that is being used, different

emissions modeling steps can be taken. Models such as UAM-V and CAMX, expect input

emissions files separately for layer-1 emissions and elevated point-source emissions. 

Additionally, elevated point sources may be flagged for treatment with a plume-in-grid (PinG)

approach. For these models, emissions modelers must supply a criteria for specifying which point

sources will be treated as elevated and as PinG sources. In this case, the air quality model

calculates the plume rise of the point source emissions when the model is run. 

Models such as CMAQ expect (1) a 3–D emissions input file, for which the elevated plume

rise has already been calculated and (2) a separate optional PinG emissions file. Emissions

modelers may optionally specify which sources to treat as elevated and PinG. Since the

emissions model must calculate plume rise in advance, it must use meteorological data such as

temperature, pressure and wind speeds. For the sake of consistency, the meteorological data that
Draft 3.2- September 2006 165

is used in calculating plume rise is usually the same as what is used by the air quality model.

Mobile Source Modeling Mobile source emissions modeling takes two primary approaches. 

The first approach is to compute emissions from VMT and emission factors from the MOBILE

model prior to use by an emissions model. The second approach is to allow an emissions model,

such as SMOKE, to drive the MOBILE model using gridded meteorology data. Many more

assumptions must be made about using average temperatures in the first approach, since

emissions are calculated on an annual total, monthly, or average-day basis and therefore do not

include the day-to-day temperature variability that the second approach includes. It is widely

assumed that the second approach is more robust for local-scale modeling, though we do not

recommend one approach over the other. States/Tribes are encouraged to choose an approach

that gives sufficient model performance for their attainment demonstration modeling.

Quality Assurance The third additional emissions modeling topic we have summarized here is

emissions modeling quality assurance (QA). A brief synopsis of appropriate quality assurance

(QA) approaches for emissions modeling is available in Section 2.19 of the SMOKE manual

(http://cf.unc.edu/cep/empd/products/smoke/version2.1/html/ch02s19.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 through inventory QA procedures, as referenced in Section 17.3.) 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 checking modeling-specific parameters

such as stack parameters.

Additionally, at the time this document was written, the EPA is developing the Emissions

Modeling Framework (EMF), which includes extensive data management and quality assurance

features intended to assist emissions modelers with the enhanced quality of their data. The EMF

also includes a graphical analysis tool called “EmisView”. Both of these tools will eventually be

available for use in ozone, PM2.5, and regional haze SIPs, though they are not at this time.

17.6 How Are Emissions Estimated for Future Years?

Emissions estimates for future years are called “emissions projections”. These projections

include both emissions growth (due to increased or decreased activities) and emissions controls

(due to regulations that reduce emissions in specific ways in the 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 State/Tribe 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
Draft 3.2- September 2006 166

time of their base year and future years. For details on which future year(s) should be modeled

for attainment demonstrations, refer to Section 3.6.

A report entitled “Procedures For Preparing Emissions Projections” describes emissions

projections issues and approaches (U.S. EPA, 1991b; also available at

www.epa.gov/ttn/chief/eiip/techreport/volume10/x01.pdf). It is currently the most

comprehensive resource available about emissions projections. In this section, we will address an

overall approach for tackling emissions projection issues that incorporates use of this document

and points out where other approaches or new data are available to amend the information

included in that report.

Developers of projection-year emissions are advised to take the steps in the bulleted list below.

Each of these steps corresponds to a subsequent subsection.

# Identify sectors of the inventory that require projections and sectors for which projections are

not advisable, and prioritize these sectors based on their expected impact on the modeling

region (Section 17.6.1).

# Collect the available data and models that can be used to project emissions for each of the

sectors (Section 17.6.2).

# For key sectors, determine what information will impact the projection results the most, and

ensure that the data values reflect conditions or expectations of the modeling region (Section

17.6.3)

# Create inputs needed for emissions models, create future year inventories, quality assure

them, and create air quality model inputs from them (Section 17.6.4)

The remainder of this section provides additional details about these steps.

17.6.1 Identify and prioritize key sectors for projections

Emissions modelers should evaluate their inventories for those sectors that are most critical to

their modeling results. The purpose of identifying these key sectors is to direct more resources

and efforts to estimating future-year emissions for these sectors in the subsequent steps.

Sectors can be subsets of the larger groups of stationary area, on-road mobile, nonroad

mobile, and point sources. For ozone modeling, in regions that are NOX-limited, sectors with

higher NOX emissions will be more important for ensuring correct projections, and for VOClimited regions, sectors with higher VOC emissions will be more important. Mobile sources are

typically large contributors to both VOC and NOx and are therefore usually a priority in

estimation of future-year emissions, particularly for urban areas. For PM and regional haze

modeling, the relative importance of primary PM2.5 emissions and precursors will depend on the
Draft 3.2- September 2006 167

area of the country. Primary PM2.5 emissions may be important in urban areas. In the East, SO2

emissions may be of primary importance. And NOx, VOC, and NH3 will vary in importance

depending on the particular situation. 

In some cases, there are sectors that are very difficult or highly uncertain to project into the

future. Wildfire emissions is a common example of such a sector, since models are not readily

available that estimate the magnitude and location of future-year emissions. In these cases, it may

be advisable to create an “average year” inventory and temporal distribution of the emissions. 

17.6.2 Collect available data and models for each sector needing projections

The EIIP projections document (referenced previously) provides a starting point for this step. 

The EIIP document provides the majority of information about sources of data on growth and

controls. This section supplements that document.

For each sector needing projections (and in particular for the priority sectors), emissions

modelers should collect and review growth and control information. As noted in the EIIP

document, the sources of such information depend upon the sector of interest. New data and

approaches beyond those in the EIIP document are available and provided here; likewise, new

data and approaches will become available subsequent to the publication of this document. The

information provided here is a snapshot of information that will grow and adapt over time, and

emissions modelers should seek out such new information as part of this step.

In addition to the sources of control information about non-EGU point and stationary area

sources listed in the EIIP projection document, EPA has provides control assumptions with its

modeling data used for promulgating air quality rules. For example, emissions growth and

control assumptions were provided with the Clean Air Interstate Rule (See

http://www.epa.gov/air/interstateairquality/pdfs/finaltech01.pdf for additional information on

obtaining actual data files) More current information is usually available than what is available

from EPA, which is best obtained by working with state and local regulatory officials for the SIP

modeling region. The remainder of this section focuses on sources of growth information.

Point sources.

Section 2 of the EIIP projections document gives details about projection of point-source

emissions. There are two major subsets of point sources: electric generating utilities (EGUs) and

non-EGUs. The Clean Air Markets Division (CAMD) of the U.S. EPA uses the Integrated

Planning Model (IPM) to model 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/epa-ipm/ ). Additionally, IPM-based emissions are posted by

CAMD on EPA’s website (http://www.epa.gov/airmarkets/epa-ipm/iaqr.html). Other trading

models may exist and could be used for estimation of future-year emissions.
Draft 3.2- September 2006 168

To prevent double-counting of emissions sources, emissions modelers who use IPM should be

careful to match the sources of emissions from IPM with the base year emissions sources. This

helps to ensure that the EGU part of their point source inventory is separated from the non-EGU

part based on the facilities included in IPM. The facilities included in IPM are defined using the

National Electric Energy Database System (NEEDS). The NEEDS dataset should be compared to

the point inventory using the Office of Regulatory Information Systems (ORIS) Plant ID field

from both. In some cases (e.g., co-generation facilities), only some of the units at these facilities

are included in IPM; therefore, the separation of EGUs from non-EGUs should be done by unit

and not by facility.

Since base-year emissions of EGU point sources can be based on hour-specific emissions

from the CEM program, emissions modelers must choose a temporal allocation approach for

estimated future-year emissions. Emissions modelers should choose an approach that is

representative of future expected behavior and not limited to any single year’s closures and

maintenance schedule. Ideally, types of EGUs that are run during high demand conditions would

have temporal allocation approaches that reflected those peaks, and units that are run

continuously would be temporally allocated in a more uniform way. Analysis of several years of

CEM data could be helpful to determine these unit-by-unit trends and develop profiles

accordingly. 

Additional considerations for projecting point source emissions without the IPM model are

provided in the EIIP projection document. However, several of the references in that document

are now out of date, as follows.

# The latest SCC codes are available at http://www.epa.gov/ttn/chief/codes/ and not the website

provided in the document.

# The AP-42 emission factor website is now http://www.epa.gov/ttn/chief/ap42/index.html.

# The website with further information about point source inventories is

http://www.epa.gov/ttn/chief/eiip/techreport/

# The references in Table 2.1-1 web link is no longer applicable. Instead, the following

information can be used to help provide similar information:

- Energy consumption by industry report, 2002:

http://www.eia.doe.gov/oiaf/analysispaper/industry/consumption.html

- Annual Energy outlook report, 2005: 

http://www.eia.doe.gov/oiaf//aeo/pdf/0383(2005).pdf

The references in Table 2.1-2 are also out of date and should be updated as follows:

- US EPA Emissions trends reports. These reports can be used to provide the historical

trends of emissions sectors based on the National Emission Inventory:

http://www.epa.gov/air/airtrends/

- Integrated Planning Model:

http://www.epa.gov/airmarkt/epa-ipm/

- Multiple Projections System - is no longer in use
Draft 3.2- September 2006 169

- California Emission Forecasting System:

http://www.arb.ca.gov/html/databases.htm

The tools and data sources described in the “Additional resources” section below should also be

evaluated for use for projection of point source emissions.

Stationary area sources.

Section 3 of the EIIP projections document provides details about projecting area-source

(a.k.a nonpoint emissions). However, several of the references or statements in that document are

now out of date, as follows.

# The web sites for SCCs, AP-42 emission factors, and more information about area-source

inventories are out of date. These should be updated to the same sites as were listed in the

point sources section above.

# The ASEM model mentioned in Section 3.2 of the EIIP projection document was not

completed and is not available.

# The references in Table 3.1-2 are out of date and should be updated as follows:

- US EPA Emissions trends reports. These reports can be used to provide the historical

trends of emissions sectors based on the National Emission Inventory:

http://www.epa.gov/air/airtrends/

- Multiple Projections System - is no longer in use

- California Emission Forecasting System:

http://www.arb.ca.gov/html/databases.htm

The tools and data sources described in the “Additional resources” section below should also be

evaluated for use for projection of point source emissions.

On-road mobile sources.

Section 3 of the EIIP projections document provides details about projecting on-road mobilesources. It is still a very relevant snapshot of on-road mobile projection approaches; however,

several of the references or statements in that document are now out of date, as follows.

Additionally, several new capabilities are available, which also are included in the list below.

# The newer versions of the MOBILE model support 28 vehicle types instead of the 8 types

listed in this document, however, the NEI and other inventories often group these 28 types to

the same 8 described in the document.

# The web sites for SCCs, AP-42 emission factors, and more information about area-source

inventories are out of date. These should be updated to the same sites as were listed in the

point sources section above.

# The EPA Office of Mobile Sources is now called the Office of Transportation and Air Quality

(OTAQ). Its website is http://www.epa.gov/otaq/index.htm.
Draft 3.2- September 2006 170

# The new website for the MOBILE model is http://www.epa.gov/otaq/mobile.htm.

# The PART5 model is no longer used, because it has been superceded by the MOBILE model,

starting with version 6 (MOBILE6).

# The increased popularity of sport utility vehicles has continued, but may be affecting more

than only the percentage of light duty gasoline vehicles. Additionally, since MOBILE6

includes 28 vehicle types, the increases in larger and heavier vehicles can be more carefully

accounted for using MOBILE6 than the EIIP projection document mentions.

# Updates to the SMOKE model and other emissions models permit the temperature

information used in MOBILE6 to come from the prognostic meteorological models (e.g.,

MM5 and MCIP) that are used to prepare inputs to air quality models.

# OTAQ has created the National Mobile Inventory Model, which drives both the MOBILE6

model and the NONROAD model to create base-year and future-year inventories. Additional

information on this tool is available at: http://www.epa.gov/otaq/nmim.htm.

# The references in Table 4.1-1 are out of date and should be updated as follows:

- MOBILE6 - is an operational model and can be used

See http://www.epa.gov/otaq/mobile.htm

- The PART5 model is no longer needed because MOBILE6 replaces it

- The California mobile sources emissions page is now:

http://www.arb.ca.gov/msei/msei.htm

- The EIIP mobile source document is now available at:

http://www.epa.gov/ttn/chief/eiip/techreport/volume04/index.html

- Emission Inventory Guidance documentation is now available at:

http://www.epa.gov/ttn/chief/eidocs/eiguid/index.html

Additionally, the Economic Growth Analysis System (EGAS) contains VMT growth information

and can be used to grow VMT by SCC. This system is further described in the “Additional

resources” section below.

Nonroad mobile sources.

Section 4 of the EIIP projections document provides details about projecting nonroad mobile

sources. Several of the references or statements in that document are now out of date, as follows. 

Additionally, several new capabilities are available, which also are included in the list below.

# As noted above for on-road sources, the Office of Mobile Sources has changed to the Office

of Transportation and Air Quality and has a different web site.

# The EPA’s NONROAD model has been completed and has been used in a variety of ways by

EPA, states, and local agencies. The website for the NONROAD model is:

http://www.epa.gov/otaq/nonrdmdl.htm

# California’s OFFROAD model website is now:

http://www.arb.ca.gov/msei/off-road/updates.htm

# OTAQ has created the National Mobile Inventory Model, which drives both the MOBILE6

model and the NONROAD model to create base-year and future-year inventories. Additional

information on this tool is available at: http://www.epa.gov/otaq/nmim.htm.
Draft 3.2- September 2006 171

Additional resources.

In addition to sector-specific resources, there are other sources of information that can be used

for more than a single sector as information to help determine the most applicable growth rates

for the modeling region. These include the Economic Growth Analysis System (EGAS), Bureau

of Economic Analysis data, Bureau of Labor Statistics “Employment Outlook”, Census Bureau

data, Trade Organizations and individual facilities, and the Chemical Economics Handbook. The

following paragraphs provide a brief description and additional references for each of the sources

of data just listed. Section 17.6.3 provides information about how to choose the best information

for a given sector.

Multiple sources of information can be used to better inform emissions modelers. For

example, if a state and/or industry has shown steady decreases in gross product over the past five

years, that information could temper (or be used in place of) a projected 30% growth predicted

from EGAS over the next 10 years. Such a discrepancy would simply highlight the fact that all

models, including REMI or those used by DOE can produce results that are not consistent with

other expectations. In addition, because of changes in technologies and fuel efficiency, economic

growth is not necessarily a sufficient indicator of emissions growth. While this guidance does not

provide a prescriptive approach for combining such information, the authors recognize that

predicting the future of emissions is not an exact science and therefore multiple sources of

information should be used to develop an informed approach.

EGAS. The latest EGAS model at this time is version 5 (http://www.epa.gov/ttn/ecas/egas5.htm)

The default version of EGAS relies primarily on three sources of data: (1) state-specific economic

data from the Regional Economic Model, Inc. (REMI) Policy Insight ® model (version 5.5) that

includes population growth estimates, (2) Region-specific fuel-use projections from the U.S.

Department of Energy (DOE) Annual Energy Outlook 2004, and (3) VMT projections included in

the MOBILE6.0 model. EGAS outputs growth factors that do not include control information for

a user-define base year and multiple future years through 2035. EGAS uses default cross-walks

from these data sources to SIC, SCC and MACT codes. The DOE data are used to compute

growth factors assigned by default to stationary source fuel combustion sectors, the VMT

projections are used for on-road mobile, and the REMI data are used for the remaining sectors.

Additionally, EGAS5 supports emissions modelers adding new data sources, changing

assignments of data sources to SICs, SCCs, and MACT codes, and creating custom configurations

of data that best represent the growth expected in their modeling region. The additional sources

of information listed below can be input into EGAS to develop such custom scenarios. It should

be noted that these sources have very little, if any, projection information. Instead, they provide

historical data that could be extrapolated to produce projections or to derive projections using

other methods. 

EGAS results should be assigned to the inventory to give the best mapping of the raw data to

the inventory. Generally, SIC is the preferred cross-walk for point sources and SCC is the

preferred cross-walk for non-point sources. However, the DOE-based fuel consumption forecasts
Draft 3.2- September 2006 172

(assigned by SCC) can also be useful for some sectors that use fuel combustion.

Bureau of Economic Analysis. The Bureau of Economic Analysis (BEA) provides historical

data about macroeconomic factors at the state level such as gross state product, personal income

by industry, employment by industry, wages and earnings by industry, and population by state. 

The website to obtain state and/or state-industry data is

http://www.bea.doc.gov/bea/regional/gsp/. Such information may be used to verify emissions

growth forecasts in EGAS by comparing available historical BEA data (1997-2004) with the

REMI economic activity data driving emissions in EGAS for the same set of years. A good use

of the gross state product data is to verify growth in sectors/codes that use REMI output and

value-added data. Additionally, employment and population statistics can be compared with

sectors/codes using REMI employment and population data. The BEA also provides industryspecific national information at http://www.bea.doc.gov/bea/dn2/home/annual_industry.htm.

However, since these data are at the national level, the use of the data should be limited to

comparisons with national simulations in EGAS.

Bureau of Labor Statistics. The Bureau of Labor Statistics (BLS) provides historical statespecific and national employment data. The BLS database is fairly large and provides more detail

than needed for verifying the growth factors generated in EGAS using REMI employment data. 

However, BLS employment data is likely to be as accurate if not more accurate than employee

data provided by BEA and US Census. The data are available at

http://www.bls.gov/sae/home.htm.

Census Bureau. The US Census Bureau provides data on total employees, total payroll, number

of establishments, and value of shipments by NAICS code (2 digit - 6 digit) for 1997 and 2002. 

This database is ideal for examining changes in the number of establishments from 1997 - 2002. 

However, it does not provide data from 1998-2001 so its trend information is limited to two

“snapshots”. Therefore, this data is useful primarily to evaluate the state/SCC and state/SIC

combinations from EGAS that show zero emissions growth, which indicates an assumption by

EGAS or its input data that those processes or industries do not exist in the state or region of

interest. The census data are available at

http://www.census.gov/http://www.census.gov/econ/census02/guide/geosumm.htm.

Trade Organizations and individual facilities. Trade organizations can also be a helpful

resource because they often have projections for future-year growth of their industry and may be

aware of pending facility closures or new facility construction. This resources is most relevant

for large industrial sources that usually are included in point source inventories. In addition,

emissions modelers should consider contacting large sources of emissions in their modeling

region for their expectations of emissions growth or reduction. In many cases, large industries

may be willing to provide such information when presented with what will be used if no

additional information can be provided. For these types of data sources, emissions modelers

should be aware of possible conflicts of interest, ie. industry groups or facilities may feel an
Draft 3.2- September 2006 173

incentive to under-represent future-year emissions in hopes of avoiding additional requirements

for reducing emissions; however, many industrial representations have been valuable stakeholders

who are interested in providing accurate information.

Chemical Economics Handbook. The Chemical Economics Handbook, produced by Access

International, Inc., is a series of reports on prices, production and consumption of hundreds of

chemical industry products and commodities. Past and current information on chemical products

and commodities is available, and projections of future prices, production and consumption are

often available. Reports on specific industries are also available. Reports at an industry level can

often be used to verify the efficacy of future industry modeling results. Each report is updated

every 3 years. Projections, some up to 5 years from the current day, are often prepared using

proprietary methods. Reports are available by subscription, and can be obtained as hard copy,

CD, or through the Internet at http://www.sriconsulting.com/CEH/Public/index.html.

17.6.3 Evaluate and refine data for projections in modeling region

For key sectors, emissions modelers should determine what information will impact the

projection results the most, and ensure that the data values reflect conditions or expectations of

the modeling region. The key information is identified based on a combination of base-year

emissions data and growth and control rates. An iterative process is helpful to complete this step,

as follows:

# Estimate future-year emissions by combining the base-year emissions with the projection

information.

# Review future-year emissions data to identify large sectors of key pollutants (e.g. NOx, SO2,

and VOC). Emissions summaries by state/SCC or state/SIC can be helpful for this step to

consolidate the data to review.

# For the largest future-year emissions sectors, review the growth and control assumptions,

compare among all available data sources, and/or collect additional information to refine the

assumptions. A representative growth rate should be identified from the available data

sources and all information known about the sources and sectors. Stakeholder review of the

data can be helpful during this step; for example, an industrial facility with large projected

emissions may be able to review the data and provide additional information for a more

informed future-year emissions estimate.

# Additionally, emission modelers should also compare the future-year emissions to base-year

emissions to identify overall growth rates for industrial sectors and reconsider excessively

high growth rates, especially when associated with significant emissions.

# Using the new information, repeat step 1 and continue these steps until a satisfactory futureyear projection across the entire inventory has been completed.
41In the emissions baseline modeling, the actual emissions may sometimes be replaced

with “typical” emissions (e.g. EGU and fire emissions).

Draft 3.2- September 2006 174

Emissions models (e.g., SMOKE, MOBILE6, NONROAD, etc.) 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.

Prior to starting this step, emissions modelers should determine which emissions model will be

used to perform the calculations and make sure that the type of information needed by the model

is being collected.

17.6.4 Create future-year inventories and air quality model inputs

Using the final projection data determined in the previous step, create the final inputs to

emissions models being used. Then, use the emissions model to create the future-year inventory. 

Other inputs to emissions models, such as spatial surrogates, speciation profiles, or temporal

allocation factors may also need to be adjusted to reflect conditions expected in the future.

Once a future-year inventory and other data have been created, it must undergo the same steps

as for the base-year modeling, such as temporal allocation, speciation, spatial allocation, elevated

source selection, special on-road mobile processing, and quality assurance. 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 baseyear modeling can lead to artificial differences in air quality modeling results that can affect

conclusions. Therefore, it is critical to avoid such differences whenever possible.

17.7 Emissions Preparation for a Local Area Analysis

In section 5.3 we discuss modeling of local primary PM2.5 sources with a dispersion model. 

The emissions for these sources should closely match those used in photochemical models. For

example, every effort should be made to temporalize and speciate the primary emissions using

available profiles. Information on input and formatting of the emissions for dispersion models

can be found in the respective users’ guides. Pre-processing of the primary emissions with an

emissions model may be needed to derive the detailed information needed to temporalize and

speciate the emissions.

In section 5.3 we recommend using “actual emissions” in the local area analysis, as compared

to using “allowable emissions”. By “allowable emissions”, we mean the maximum amount of

emissions from a source which is consistent with emission limitations contained in an applicable

operating or construction permit for that source. We define “actual emissions”as the best estimate

of the actual emissions that occurred during the baseline period being modeled41. 
Draft 3.2- September 2006 175

Modeling with allowable emissions is sometimes warranted. For example, for permit

modeling, we generally compare the absolute predicted modeled concentrations against the

NAAQS or the PSD increments. In these cases we want the modeling to be conservative. That

is, we want to make sure that a maximum permitted level of emissions cannot cause of violation

of the NAAQS or PSD increment. Therefore it is appropriate to model with allowable emissions. 

But in this case (local area analysis) we are 1) trying to determine the actual (or typical)

contribution from sources to a monitor and 2) we are using the model results in a relative sense. 

Therefore, it is only appropriate (and in fact to conservative) to use actual emissions. Using

actual emissions should lead to a reasonable estimate of benefits from reducing emissions at the

flagged sources. Using allowable emissions might lead to an overestimate of benefits from

emissions controls at flagged sources.
Draft 3.2- September 2006 176

18.0 What are the Procedures for Evaluating Model Performance and

What is the Role of Diagnostic Analyses? 

The results of a model performance evaluation should be considered prior to using modeling

to support an attainment or reasonable progress demonstration. The performance of an air quality

model can be evaluated in two ways: (1) how well is the model able to replicate observed

concentrations of PM2.5 components, ozone and/or precursors (surface and aloft), and (2) how

accurate is the model in characterizing the sensitivity of ozone and/or PM2.5 to changes in

emissions? The first type of evaluation can be broadly classified as an "operational evaluation"

while the second type of evaluation can be classified as a "diagnostic evaluation". The modeled

attainment tests recommended in Sections 3-6 use models to predict the response of ozone and

PM2.5 to changes in emissions and then applies the resulting relative reduction factors 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 evaluation even more

important. 

In addition to the model performance evaluation, diagnostic analyses are potentially useful to

better understand whether or not the predictions are plausible. Diagnostic analyses may also be

able to provide: (1) information which helps prioritize efforts to improve and refine model inputs,

(2) insight into which control strategies may be the most effective for meeting the NAAQS, and

(3) an indication of the "robustness" of a control strategy. That is, diagnostic tests may help

determine whether the same conclusions would be reached regarding the adequacy of a strategy if

alternative, plausible, assumptions were made in applying the model for the attainment test.

In this section, we first discuss the general concept of operational and diagnostic evaluations. 

We then identify and discuss issues related to the ambient measurements of ozone and PM2.5. We

then discuss methods which may be useful for evaluating model performance. It is recommended

that performance be assessed by considering a variety of methods. The section continues by

identifying potentially useful diagnostic tests which States/Tribes should consider at various

stages of the modeling analysis to increase the confidence in the model predictions of future

ozone and/or PM2.5 levels. The section concludes with a discussion on interpreting the results of

the operational and diagnostic evaluations.

18.1 How Do I Assess Model Performance And Make Use of Operational and Diagnostic

Evaluations?

As noted above, model performance can be assessed in one of two broad ways: how

accurately does the model predict observed concentrations for specific cases, and how accurately

does the model predict responses of predicted air quality to changes in inputs (e.g. relative

reduction factors)? Given existing data bases, nearly all analyses have addressed the first type of

performance evaluation. The underlying rationale is that if we are able to correctly characterize

changes in concentrations accompanying a variety of meteorological conditions, this gives us
Draft 3.2- September 2006 177

some confidence that we can correctly characterize future concentrations under similar

conditions. Typically, this type of operational evaluation is comprised principally of statistical

assessments of model versus observed pairs. Operational evaluations are generally accompanied

by graphical and other qualitative descriptions of the model's ability to replicate historical air

quality patterns. The robustness of an operational evaluation is directly proportional to the

amount and quality of the ambient data available for comparison. 

The second type of model performance assessment, a diagnostic evaluation, can be made in

several ways. 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 and 1998; Ansari and

Pandis, 1998; Blanchard et al., 2000). 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). 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 has the potential to evaluate models in a way which is most

closely related to how they will be used in attainment demonstrations. A second way 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 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. More types of diagnostic analyses are provided in

Section 18.5. We recommend that diagnostic analyses be performed during the initial phase of

the model application and during any mid-course review.

18.2 What Pollutants Are of Interest for Model Performance?

There are a number of gas phase species that can be used to evaluate both ozone and

secondary PM2.5. Their presence may affect the response of ozone or 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 in predicting ozone or secondary

PM2.5. Gaseous pollutants of interest include:

- Ozone (O3)

- Nitric acid (HNO3)

- Nitric oxide (NO)

- Nitrogen dioxide (NO2)

- Peroxyacetyl nitrate (PAN)

- Volatile Organic Compounds (VOCs)

 - Ammonia (NH3)
42 For predicted/observed comparisons, organic carbon is preferred over organic carbon

mass to allow for transparency between model predictions and observed measurements. Organic

carbon mass includes hydrogen, oxygen, and other components. 

43 Crustal (soil) can contribute up to 10% of PM2.5 mass in certain locations. Unspeciated

inorganic PM (sometimes referred to as “other PM”) is often used interchangeably with “crustal”

or “soil” PM. The term “soil” is usually refers to the specific definition of soil from the

IMPROVE program. “Other inorganic PM” includes heavy metals and other inorganic species

in addition to those commonly found in soil. 

Draft 3.2- September 2006 178

- NOy (sum of NOx and other oxidized compounds)

- Sulfur dioxide (SO2)

- 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). Model performance can

best be assessed using extensive data bases, such as those obtained in major field studies. 

However, we recognize that such data may not be available for every model application which

needs to be performed. At a minimum, a State should supplement its performance tests with

available data with a review of results from performance tests for the model(s) which were

completed elsewhere where extensive data bases were used. 

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) and/or Sulfur (S)

- Nitrate ion (NO3)

- Ammonium ion (NH4)

- Elemental Carbon (EC)

- Organic Carbon (OC) and/or Organic Carbon Mass42

- Crustal (weighted average of the most abundant trace elements in ambient air)

- Mass of inorganic primary particulate matter (IP)43

- Mass of individual constituents of IP

18.3 What Are the PM2.5 Measurement versus Model Concentration Issues? 

Ambient measurements are needed to perform a model evaluation. Historically, ozone model

evaluations have used routinely measured ambient data (e.g. ozone) collected on a continuous
44 Most of the issues identified in Table 18.1 are discussed in Frank, 2006.

Draft 3.2- September 2006 179

basis. The measurements tend to be of high frequency and quality. Conversely, PM2.5 and PM2.5

component measurements are (mostly) filtered based measurements which have relatively long

sampling times (usually 24 hours) and aren't collected every day (usually once every three or six

days). Speciated data play an especially important role, as the deterministic models predict exact

chemical components which can be compared to some of the corresponding measured analytes. 

In addition, there are known positive and negative sampling artifacts as well as analysis artifacts

associated with FRM and speciation monitors. For example, 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 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. This section attempts to address the range of measurements and

measurement issues associated with PM2.5 and its components.

Table 18.1 summarizes many of the PM2.5 species measurements and networks and lists

sampling time and measurement issues. This data will commonly be used as part of an

operational evaluation. 

Table 18.1 PM2.5 Species Measurements by Monitoring Network and Related Sampling Time

Issues.44

Network Measurement Notes Sampling Time

FRM PM2.5 - Negative artifacts- nitrate, organic

carbon

- Positive artifacts- organic carbon,

water

24-hour average 1/3

days or 1/6 days

(some daily sites)

STN

PM2.5 -STN total measured mass may not be

reliable (cutpoint issues)

24-hour average 1/3

days or 1/6 days

SO4

NO3 - Differences among sampler

measurements across network. 

Different sampler types use different

measurement techniques. 

NH4 - Negative artifacts may occur on the

Nylon filters due to volatilization of

ammonium from the filter because the

alkaline filter may not retain NH3

released in the decomposition of

ammonium nitrate. 
Network Measurement Notes Sampling Time

45 Network average blank corrections for many of the STN samplers are large (up to 1.53

ug/m3).

Draft 3.2- September 2006 180

Organic Carbon - Blank correction is needed to account

for positive artifact.45

- OC/EC split is operationally defined

using TOT method (NIOSH).

Elemental

Carbon

- OC/EC split is operationally defined

using Thermal Optical Transmittance

(TOT) method (NIOSH)

CASTNet

SO4

weekly average

NO3 Negative artifact 

NH4

SO2

HNO3 Split between NO3 and HNO3 may not

be reliable in warm weather due to

conversion from NO3 to HNO3

IMPROVE

PM2.5

24-hour average 1/3

days

SO4 and S - Sulfur measurements are usually used

to estimate sulfate

NO3

NH4 - Only measured at 3 Eastern sites.

Organic Carbon - OC/EC split is operationally defined

using Thermal Optical Reflectance

(TOR) analysis.

- Blank corrections are applied to all

data before they are reported.

Elemental

Carbon

- OC/EC split is operationally defined

using Thermal Optical Reflectance

(TOR) analysis.

In general, the speciation 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. The STN and IMPROVE networks use different

analysis techniques when estimating EC and OC. This will generally have a larger impact on EC

concentrations. It may not be appropriate to compare EC concentrations between networks. The

Clean Air Scientific Advisory Committee (CASAC) has recommended to transition the
Draft 3.2- September 2006 181

IMPROVE carbon protocol (TOR) to the STN in order to provide data consistency among the

two networks (U.S. EPA, 2005d). Over the next two to three years (2007-2009), EPA plans to

convert all the STN sites (Trends and supplemental sites, approximately 200 sites) to IMPROVE

carbon sampling and analysis protocols. 

Due to both positive and negative artifacts, the FRM data may not be directly comparable

with model outputs without some adjustments. 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 in Section

5 we recommended default methodologies to adjust speciation (STN and IMPROVE) data to

better approximate the FRM data. A similar adjustment could be made to the model output data

to allow the direct comparison to FRM measurements. But due to the relatively limited

information to be gained by evaluating total measured PM2.5 mass (as compared to PM2.5

components), it may not be worth the effort.

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

systematic review and/or updates to the current monitoring network/program. Please note, there

are other available monitoring networks which are not mentioned here and more details on the

networks and measurements should be obtained from other sources.

AQS The Air Quality System (AQS) is 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. 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 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, Total Carbonaceous Mass (TCM), and

Crustal Material), air toxic data, photochemical assessment data, and meteorological data. The

data are measured and reported on an hourly basis. The AQS system continues to expand as

regulations consider to include more ambient air pollutants (U.S. EPA, 2006a). 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 Air Explorer tool

(http://www.epa.gov/mxplorer/aboutthedata.htm) which is a collection of user-friendly
Draft 3.2- September 2006 182

visualization tools for air quality analysts. The tools generate maps, graphs, and data tables

dynamically.

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, 2001). Data are collected at Class I

areas across the United States mostly at National Parks, National Wilderness Areas, and other

protected pristine areas. Currently, there are approximately 160 IMPROVE rural/remote sites

that have complete annual PM2.5 mass and/or PM2.5 species data. Of the total sites, 110 sites are

for regional haze and visibility (and are located at or near Class I areas) and 50 are protocol sites

(determined by the needs of state and local air pollution control agencies to meet their respective

State Implementation Plan (SIP) requirements). The website to obtain IMPROVE documentation

and/or data is http://vista.cira.colostate.edu/improve/.

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

deposition monitoring network where data are collected and reported as weekly average data

(U.S. EPA, 2002b). CASTNet provides atmospheric data on the dry deposition component of

total acid deposition, ground-level ozone and other forms of atmospheric pollution. The data are

collected in filter packs that sample the ambient air continuously during the week. CASTNet now

comprises over 70 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/.

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

consistent speciated PM2.5 data for the assessment of trends at representative sites in urban areas

in the U.S. The STN was established by regulation and is a companion network to the

mass-based Federal Reference Method (FRM) network implemented in support of the PM2.5

NAAQS. As part of a routine monitoring program, the STN quantifies mass concentrations and

PM2.5 constituents, including numerous trace elements, ions (sulfate, nitrate, sodium, potassium,

ammonium), elemental carbon, and organic carbon. There are currently 54 Trends sites

nationally in operation. Trends 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 long-term trends and provide an accountability mechanism to access effectiveness

of control programs. In addition, there are approximately 181 supplemental speciation sites

which are part of the STN network and are SLAMS sites. The STN data at trends sites are

collected 1 in every 3 days, whereas supplemental sites collect data either 1 in every 3 days or 1

in every 6 days. Comprehensive information on the STN and related speciation monitoring can

be found at http://www.epa.gov/ttn/amtic/specgen.html and

http://www.epa.gov/aqspubl1/select.html.

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
Draft 3.2- September 2006 183

utilities, and other industries to determine geographical patterns and trends in precipitation

chemistry in the U.S. NADP collects and reports wet deposition measurements as weekly

average data (NADP, 2002). The network is now known as NADP/NTN (National Trends

Network) 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/.

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 et al., 2005; Hansen et al., 2003;

Zheng et al., 2002). SEARCH data are collected and reported on an hourly/daily basis. There are

currently six SEARCH measurement sites: Birmingham, Alabama (urban), Centreville, Alabama

(rural), Gulfport, Mississippi (urban), Jefferson Street, Atlanta, Georgia (urban), Oak Grove,

Mississippi (rural), Yorkville, Georgia (rural), 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.atmospheric-research.com/studies/SEARCH/index.html. 

Supersites The Supersite (SS) monitoring program began in 2001 as an EPA OAQPS and

ORD co-funded project which measures speciated PM2.5 at eight air shed sites. Specifically, these

eight monitoring supersites are located in Baltimore, Maryland; Pittsburgh, Pennsylvania; New

York City, New York; Atlanta, Georgia; Houston, Texas; St. Louis, Missouri; Fresno, California

and Los Angeles, California. These supersites were developed as special study platforms for

research and measurement methods to address the scientific uncertainties associated with PM2.5. 

The goals of the SS program include (1) fine particulate characterization to understand processes

and source-receptor relationships and to support PM2.5 and ozone SIPs, (2) testing of advanced

sampling methods to enable a smooth transition from established routine techniques, and (3)

development of monitoring data and samples shown in (1) to support health effects and exposure

studies. PM Supersites information can be obtained at

http://www.epa.gov/ttn/amtic/supersites.html.

18.4 How Should the Operational Evaluation of Performance Be Completed?

As noted above, an operational evaluation is used to assess how accurately the model predicts

observed concentrations. Therefore, an operational evaluation can provide a benchmark for

model performance and identify model limitations and uncertainties that require diagnostic

evaluation for further model development/improvement. Computer graphics, ozone and PM

metrics/statistics, and observational models are all potentially useful for evaluating a model’s

ability to predict observed air quality. This section describes the recommended statistical
Draft 3.2- September 2006 184

MNB Model Obs)

Obs N

N

= ⎛ −

⎝

⎜ ⎞

⎠ ∑ ⎟ ⋅ 1

1

100% ( MNB Model Obs)

Obs N

N

= ⎛ −

⎝

⎜ ⎞

⎠ ∑ ⎟ ⋅ 1

1

100% (

MNGE Model Obs

Obs N

N

= ⎛ −

⎝

⎜ ⎞

⎠ ∑ ⎟ ⋅ 1

1

100%

measures, graphical displays, and other analytical techniques which should be considered as part

of an operational evaluation of ozone and PM2.5 model performance. Note that model predictions

from the ramp-up days should be excluded from the analysis of model performance. 

18.4.1 Ozone Operational Evaluation

Statistics: It is recommended that, at a minimum, the following three statistical measures be

calculated for hourly ozone and 8-hourly maxima ozone over the episode days in an attainment

demonstration.

• Mean Normalized Bias (MNB): This performance statistic averages the model/observation

residual, paired in time, normalized by observation, over all monitor times/locations. A value

of zero would indicate that the model over predictions and model under predictions exactly

cancel each other out. The calculation of this measure is shown in Equation 18.1.

 

 (18.1)

• Mean Normalized Gross Error (MNGE): This performance statistic averages the absolute

value of the model/observation residual, paired in time, normalized by observation, over all

monitor times/locations. A value of zero would indicate that the model exactly matches the

observed values at all points in space/time. The calculation of this measure is shown in

Equation 18.2.

 (18.2)

• Average Peak Prediction Bias and Error: These are measures of model performance that

assesses only the ability of the model to predict daily peak 1-hour and 8-hour ozone. They are

calculated essentially the same as the mean normalized bias and error (Equation 18.1 and

18.2), except that they only consider daily maxima data (predicted versus observed) at each

monitoring location. In the attainment test, models are used to calculate relative reduction

factors near monitoring sites by taking the ratio of the average 8-hour daily maximum

concentrations calculated for the future and current cases. Thus, the model's ability to predict

observed mean 8-hour daily maxima is an important indicator of model performance.

Appendix A provides other statistics such as mean bias, mean error, mean fractional bias,
46 Past ozone modeling applications have used a minimum cutoff of either 40 ppb or 60

ppb. Due to the interest in predicted ozone concentrations at or above the 8-hour standard (85

ppb), the higher cut off (60 ppb) is recommended. 

47 The use of a 0 ppb threshold can add valuable information about the ability of the

model to simulate a wide range of conditions. Because of the tendency of the MNB and MNGE

metrics to inflate the importance of biases at the lowest observed values (which are in the

denominator), it is recommended that the alternate metrics of normalized mean bias (NMB) and

normalized mean gross error (NMGE) be used as substitutes for evaluations with no minimum

threshold.

48 In certain instances, States/Tribes may also want to conduct performance evaluations

using the “near the monitor” grid cell arrays. 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. Furthermore, a “near the monitor” performance evaluation is

consistent with the RRF methodology. 

Draft 3.2- September 2006 185

mean fractional error, root mean square error, correlation coefficients, etc. which should also be

calculated to the extent that they provide meaningful information. Wherever possible, these types

of performance measures should also be calculated for ozone precursors and related gas-phase

oxidants (NOx, NOy, CO, HNO3, H2O2, VOCs and VOC species, etc.) and ozone (and

precursors) aloft. 

Thresholds: EPA recommends that the three metrics above be calculated two ways: 1) for

pairs in which the 1-hour or 8-hour observed concentrations are greater than 60 ppb46, and 2) for

all pairs (no threshold)47. This will help to focus the evaluation on the models ability to predict

NAAQS-relevant ozone and minimize the effects of the normalization. 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 locations48. States/Tribes should

recognize that, even in the case of perfect model performance, model-observed residuals are

unlikely to result in exact matches due to differences between the model predictions which are

volume averages and the observations which are point values.

Averaging: The statistics should initially be calculated for individual days (averaged over all

sites) and individual sites (averaged over all days). As appropriate, States/Tribes should then

aggregate the raw statistical results into meaningful groups of subregions or subperiods. 

Averaging of suitable time periods for graphical analysis is incorporated below based upon the

different types of graphical displays. 

Plots/Graphics: Along with the statistical measures, EPA recommends that the following

four sets of graphical displays be prepared and included as part of the performance analysis.
Draft 3.2- September 2006 186

• Time series plots of model and predicted hourly 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. Figure 18.1 shows a sample time series plot. This graphic was developed

using the air quality analysis module of the Atmospheric Model Evaluation Tool (AMET)

(Appel et al., 2005), though other evaluation tools exist and can be used.

Figure 18.1. Sample time series analysis plot for a 12-km model vs. observed 2001 case. The

upper plot shows a particular analysis for 1-hour maxima ozone, July through September

performance averaged over five sites in Cleveland, Ohio. The bottom plot shows the bias and

error (percentage) calculated based on the predicted 1-hour maxima ozone from the upper plot.

• Scatter plots of predicted and observed ozone at each site within the nonattainment area

(and/or an appropriate subregion). These plots should be completed using: a) all hours within

the modeling period for hourly ozone, and b) all 8-hour daily maxima within the modeling

period. It may also be useful to develop separate plots for individual time periods or key

subregions. These plots are useful for indicating if there is a particular part of the distribution
49 Quantile-quantile (Q-Q) plots may also provide additional information with regards to

the distribution of the observations vs. predictions. But due to the fact that Q-Q plots are not

paired in time, they may not always provide useful information. Care should be taken in

interpreting the results. 

Draft 3.2- September 2006 187

of observations that is poorly represented by the model49. See Figure 18.2 below for a scatter

plot example, which was generated using the AMET.

Figure 18.2. Scatter plot analysis for a 12-km model vs. observed 2001 case. The scatter plot

shows a performance analysis for daily 8-hour maxima ozone pairs for the month of July. 

• Daily tile plots of predicted ozone across the modeling domain with the actual observations as

an overlay. Plots should be completed for both daily 1-hour maxima and daily 8-hour

maxima. These plots can reveal locations where the model performs poorly. Superimposing

observed hourly or daily maximum concentrations on the predicted isopleths reveals useful

information on the spatial alignment of predicted and observed plumes.
50Predicting very low concentrations is not critical for modeling the PM2.5 NAAQS. 

However, predicting low concentrations of PM species is more important for regional haze

modeling, where in the cleanest Class I areas, PM concentrations may be less than a few ug/m3

(especially on the 20% best days). 

Draft 3.2- September 2006 188

• Animations of predicted hourly ozone concentrations for all episode days or for certain

periods of interest. Animations are useful for examining the timing and location of ozone

formation. Animations may also reveal transport patterns (especially when looking at ozone

aloft). 

18.4.2 PM/RH Operational Evaluation

An operational evaluation for PM2.5 and regional haze is similar to that for ozone. Some

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

a 24-hour averaging time. The individual components of PM2.5 should be evaluated individually. 

In fact, it is more important to evaluate the components of PM2.5 than to evaluate total PM2.5 itself. 

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

predicted for “the right reasons” (the proper mix of components). If performance of the major

components is good, then performance for total PM2.5 should also be good.

This section contains some additional recommended statistics that have typically not been

calculated for ozone performance, but have been found to be particularly useful for PM analyses

(such as fractional bias and error). We also show examples of some new types of display plots

such as “soccer plots” and “bugle plots”. Soccer plots provide a convenient way to display a

summary of model performance (including bias and error at the same time). Bugle plots have

variable bias and error goals, based on ambient concentrations. This allows for a higher

percentage error and bias at very low concentrations. This recognizes the fact that models often

have difficulty in accurately predicting near background concentrations50.

Statistics: Statistics for PM and regional haze performance are similar to those calculated for

ozone. We recommend calculating statistics for components of PM2.5. PM2.5, and PM precursors. 

Useful metrics include mean fractional bias and mean fractional error, normalized mean bias, and

normalized mean error. Formulas for estimating these metrics at an individual monitoring site, j,

are shown in Equations (18.4) -(18.7). Other statistics such as mean bias, mean error, root mean

square error, correlation coefficients, etc. should also be calculated to the extent that they provide

meaningful information (see Appendix A for definitions). 

• Mean Fractional Bias (percent): Normalized bias can become very large when a minimum

threshold is not used. Fractional bias is used as a substitute. The fractional bias for cases

with factors of 2 under- and over-prediction 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
Draft 3.2- September 2006 189

estimates. The single largest disadvantage is that the predicted concentration is found in both

the numerator and denominator. 

Equation 18.4:

•

Mean Fractional Error (percent): Normalized error can become very large when a

minimum threshold is not used. Therefore fractional error is used as a substitute. It is similar

to the fractional bias except the absolute value of the difference is used so that the error is

always positive. 

Equation 18.5:

• Normalized Mean Bias (percent): This performance statistic is used as a normalization to

facilitate a range of concentration magnitudes. This statistic averages the difference (model -

observed) over the sum of observed values. Normalized mean bias is a useful model

performance indicator because it avoids over inflating the observed range of values.

Equation 18.6:

• Normalized Mean Error (percent): This performance statistic is used as a normalization of

the mean error to facilitate a range of concentration magnitudes. This statistic averages the

difference (model - observed) over the sum of observed values. Normalized mean error is a
Draft 3.2- September 2006 190

useful model performance indicator because it avoid over inflating the observed range of

values.

Equation 18.7:

Averaging Times: Note that units of time associated with model and observed

concentrations can be days (i.e., usually for particulate matter and its species), hours (i.e., usually

for species with continuous measurements, like ozone) or sometimes weeks (CASTNet filter pack

measurements). Also note that the preceding metrics may not be meaningful if the number of

modeled days with monitored data is limited at a site. 

Since the NAAQS for PM2.5 and the regional haze goals will likely require modeling different

times of year, season-specific statistics and graphic displays are helpful for evaluating and

diagnosing model performance. Statistics and graphics can be averaged for various time scales. 

For example, statistical metrics and scatterplots can show daily averaged ambient-modeled pairs,

monthly averaged pairs, quarterly (or seasonal averaged) pairs, or annual average pairs. Each of

these averaging times can provide useful information. We recommend a range of different

averaging times for annual or seasonal modeling. At a minimum, States should examine daily

averaged pairs and seasonal (or quarterly) averaged pairs. It should be noted that statistics and

plots tend to look “better” as the averaging time goes 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 STN measurements (24-hour average). 

Plots/Graphics: As mentioned above in the ozone operational evaluation, graphics are a

useful means for understanding how predictions and observations differ. For PM evaluations we

recommend creating time series plots, tile plots, and scatter plots. Time series plots tell whether

there is any particular time of day, day(s) of the week, or months (seasons) of the year when the

model performs poorly. Tile plots reveal geographic locations where the model performs poorly. 

Information from tile plots and time series may provide clues about where to focus quality

assurance efforts for model inputs. Scatter plots show whether there is any part of the distribution

of observations for which the model performs poorly. These plots are also useful for helping to

interpret calculations of bias between observations and predictions. For example, they could

show large differences between component PM2.5 observations and predictions which just happen

to balance, producing low estimated aggegrated bias. As mentioned above, since the NAAQS for
Draft 3.2- September 2006 191

PM2.5 and the regional haze goals will likely require modeling different times of year, seasonspecific graphic displays are helpful for evaluating and diagnosing model performance.

These above mentioned graphical plots have been provided as examples in the ozone

operational evaluation section. Other graphical analysis displays can also be developed to better

inform a model performance evaluation. Additional types of graphical plots are shown below. 

These plots were generated using the AMET tool.

Figure 18.3 shows an example of spatial plots of normalized mean bias and mean bias by

monitoring site. These plots allow an analysis of regional differences in model performance (e.g.

inland vs. coastal areas). 

(a) (b)

Figure 18.3. Example of (a) Spatial plot of NMB; (b) model-observed difference plot (mean

bias). 

 

Figure 18.4 shows an example of a “soccer plot” and “bugle plots”. The soccer plot is so

named because the dotted lines resemble a soccer goal. The plot is a convenient way to visualize

model performance, as measures of both bias and error are shown on a single plot. As bias and

error approach zero, the points are plotted closer to or within the “goal”, represented here by the

dashed boxes. The “bugle plot”, 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
Draft 3.2- September 2006 192

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. We recommend allowing for

larger bias and error when the ambient concentration falls below 2 ug/m3.

 (a) 

 

 (b) (c)

Figure 18.4. (a) The “soccer goal” plot measures both bias and error. The “goal” is represented

by the dashed boxes. (b) A “bugle plot” of bias and (c) a “bugle plot” of error. 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.
Draft 3.2- September 2006 193

Figure 18.5 shows an example of two types of box plots can be developed for model

performance evaluation. One displays monthly (left) and one that displays hourly (right)

information. The box plots show several quantities: the shading represents the 25% to 75%

quartiles, while the lines represent the median values, for both model and observation

concentrations. 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 used with hourly data, and shows how the model predictions compare against

observations throughout an entire day. 

(a) (b)

Figure 18.5. The box plots show several quantities: the shading represents the 25% to 75%

quartiles, while the lines represent the median values, for both model and observation

concentrations. (a) displays monthly data (left) and (b) displays hourly data (right). 

18.5 How Do I Assess Model Performance and What Types of Analyses Can be Done to

Evaluate the Accuracy of the Model Response: Diagnostic Evaluations?

This section lists possible analyses that could be performed to investigate the ability of the

model to accurately forecast changes in predicted concentrations of ozone and PM resulting from

changes in ozone and PM2.5 precursor emissions. Diagnostic evaluations/tests are used to explain

model performance and to provide clues about how to improve the reliability of predictions. 

The diagnostic analyses discussed in this section include observational models, probing tools,

receptor models, retrospective analyses, and sensitivity analyses. This represents a large variety
Draft 3.2- September 2006 194

of tools that can aid in diagnostic analyses. Some of these techniques are based on analysis of

ambient data, some require running the photochemical many times, and some of the techniques

involve coding of the photochemical model to allow additional outputs. States/Tribes are

encouraged to complete as many of these types of diagnostic analyses as possible throughout the

modeling process in order to help understand model performance and to help develop information

which may help improve performance, as well as increase confidence in the modeled attainment

projections.

18.5.1 Observational models 

A performance evaluation which includes comparisons between modeled and observed ratios

of indicator species carries with it a large potential advantage. 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) (Sillman, 1995,

1997, 1998, and 2002; Lu and Chang, 1998; Pun and Seigneur, 1999). 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, 2001). If a model accurately predicts observed ratios of

indicator species, then one can conclude with additional confidence that the predicted change in

ozone or PM may be accurate. One precaution with respect to the use of indicator species is that

there may be a range of observed ratios for which the preferred direction of 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 ratios of indicator

species such that observed and predicted ratios fall within the same range of ratios, this provides

some confidence that the predicted change in particulate matter may be accurate. A second

precaution is that application of this method often requires more measurements than are

commonly made. In some cases, it may be difficult to achieve the required precision with current

routine monitoring. Finally, much of the work done to date with indicator species has focused on

peak hourly concentrations of ozone. Despite these precautions, comparing predicted and

observed ratios 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. 

Many studies and applications of indicator species approaches have addressed ozone-related

problems. Sillman (1995), Sillman (1998) and Lu and Chang (1998) provide good descriptions of

the method, identify key ratios and illustrate application of the approach. Even though the

preceding ratios are oriented toward ozone, they can provide insight into why modeled

concentrations of secondary particulate matter are sensitive to changes in VOC, or NOx emissions

(Pun and Seigneur, 1999). Ansari and Pandis (1998) have developed an indicator ratio of species

and applied it to several combinations of secondary particulate matter present under different

environmental conditions. They use this ratio to predict how mass of particulate matter will

respond to reductions in sulfate, nitrate and ammonia. Blanchard et al., (2000) have also
Draft 3.2- September 2006 195

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 two ratios of indicator species

which appear potentially useful for identifying limiting precursors for secondary nitrate

particulate matter: (1) the ratio of particulate ammonium plus gas-phase ammonia over the sum of

nitric acid plus particulate nitrate plus particulate sulfate, and (2) the ratio of particulate to total

nitrate. It is likely that additional indicator species approaches will be identified as the user

community continues to gain more experience with chemical mechanisms incorporating

secondary particulate formation and more speciated particulate and gas phase ambient

measurements become available.

Other observational methodologies exist and can be used in a similar manner. The Smog

Production (SP) algorithm is another means by which ambient data can be used to assess areas

that are NOx or VOC-limited (Blanchard et al., 1999). Additionally, it has been postulated that

differences in weekend-weekday ozone and PM2.5 patterns may also provide real-world

information on which precursors are most responsible for ozone and PM2.5 formation in any given

area (Altshuler et al., 1995; Pun, et al., 2001 and 2003; Heuss et al., 2003; Fujita, et al., 2003;

Blanchard and Tanenbaum et al., 2003 and 2004; Yarwood et al., 2003; Qin et al., 2004). For

example, activity levels and patterns, leading to PM2.5 and precursor emissions from mobile, area

and some point sources, may differ on weekends vs. week days. In areas where there are large

differences between average weekend and weekday ambient ozone and PM2.5 concentrations over

the span of several seasons, it would be useful to compare statistical model performance for

weekends versus weekdays (e.g., does the model accurately reflect observed differences in

component concentrations in summer vs. winter?). This would allow one to assess whether the

model is capturing the effect of the emissions differences which are presumably driving the

real-world concentration differences. 

18.5.2 Probing Tools

Recently, techniques have been developed to embed procedures within the code of an air

quality model which enable users to assess the contributions of specific source categories or of

specific geographic regions to predicted model concentrations at specified sites (e.g. for ozone

predictions, Zhang et al., 2003). Various techniques have been implemented into various air

quality models, but three of the most commonly used probing tools are photochemical source

apportionment (Dunker et al., 2002a; ENVIRON, 2004, 2005; Tonnesen and Wang, 2004; SAI,

2005; Yarwood et al., 2005; Meyers, 2006), the direct decoupled method (DDM) (Dunker, 1980

and 1981; Yang, et al., 1997a and 1997b; Dunker et al. 2002b; ENVIRON, 2004; Cohan, 2002

and 2004), process analysis (Jeffries, 1994 and 1997; Jeffries, 1996; Jang, 1995; Lo, 1997; Byun

and Ching, 1999; Morris et al., 2001 and 2002; ENVIRON, 2005; Henderson et al., 2006). In the

context of model performance evaluation, these attribution procedures are useful in that they

allow one to "track" the importance of various emissions categories or phenomena contributing to

predicted ozone and particulate matter at a given location. This can provide valuable insight into

whether the model is adequately representing the conceptual description of ozone and PM

patterns in the nonattainment area. In the cases where model performance is subpar, these

analyses can be useful for indicating where model input or model algorithm improvements are
Draft 3.2- September 2006 196

most needed. 

Caution should be applied to the tools and techniques mentioned here as they are limited by

the accuracy of the underlying air quality photochemical model, when the tools utilize the same

algorithms/modules and assumptions. An overview of these aforementioned probing tools are

provided below.

Photochemical source apportionment: A photochemical source apportionment tool tags

and tracks the release, transport, chemical transformation, and deposition of precursor species

from primary emission sources, source categories, source regions, initial conditions and/or

boundary conditions from a photochemical grid model. Therefore, the contribution of tagged

sources to simulated concentrations (including secondary pollutants) and deposition can be

measured in a single model run. This source apportionment tool can be used to estimate how

emissions from individual source areas and regions affect modeled ozone and PM concentrations

over space and time. For example, this is achieved by using multiple tracer species to track the

fate of ozone precursor emissions (VOC and NOx) and the ozone formation caused by these

emissions within an air quality model simulation. There are challenges to consider during

implementation of this methodology, i.e., tracking the spatial and temporal relationships between

separate groups of emission sources and ozone and particulate matter formation (ENVIRON,

2004). Among them are insuring compatibility with the underlying air quality model formulation

so that derived source-receptor relationships will be consistent with model response to emission

changes. 

The information gleaned from source apportionment can be useful in considering potential

control strategies. However, a key consideration in weighing alternative strategies is which types

of emissions are cost effective and practical to control, a consideration not addressed by source

apportionment modeling.

Direct decoupled method (DDM): The DDM calculates the local, first-order sensitivity

coefficients of gas-phase pollutant concentrations to perturbations of model inputs, e.g. initial

conditions, boundary conditions, emissions. Theoretically, the DDM is applicable to the

calculation of higher order sensitivity coefficients as well, but these are much more numerous

than the first order coefficients and generally will be less useful (ENVIRON, 2004). DDM can

also be used to explore model sensitivities to perturbations in meteorological parameters and

chemical rate constants, although resource intensive and at present somewhat less useful. The

decoupled method is advantageous both in terms of computational efficiency and stability of the

numerical solution. DDM has been used as a sensitivity analysis tool in several studies. A single

model run using DDM can potentially provide the information equivalent to tens, if not hundreds

of brute force emissions reductions model runs.

Process analysis: Process analysis techniques allows one to quantitatively track (mass

balancing) the contributions of individual physical and chemical atmospheric processes to the

overall change in pollutant concentrations at a particular time and location. Thus, process

analysis will assess the relative importance of each physical and chemical process for a particular

model simulation as well as explain how model predictions are obtained, which is valuable when

modeling nonlinear systems such as atmospheric photochemistry. Since models used to simulate
Draft 3.2- September 2006 197

ozone and secondary particulate matter are similar, process analysis should also be useful for

addressing both ozone and PM2.5 (and visibility related) applications. 

Three of the most common process analysis tools implemented in grid models are the

integrated process rate (IPR) analysis, the integrated reaction rate (IRR) analysis, and the

chemical process analysis (CPA). The IPR analysis is used to determine the relative contributions

of individual physical (e.g., advection, diffusion, emissions) and chemical (e.g., chemical

reaction, aqueous chemistry, aerosol production) processes, potentially revealing unexpected low

or high process contributions. The IRR analysis is used to identify and explain the details of the

chemical transformations (chemical pathways and key chemical characteristics) in a particular

model simulation. Typically, IRR analyses have been used to determine and understand model

prediction differences between chemical mechanisms (e.g., CB-IV, SAPRC). Likewise, the CPA

is an improvement on the IRR analysis technique where part of the processing of IRR information

is internalized within the air quality grid model to yield key chemical parameters directly (e.g.,

budget terms for ozone and NOx). 

Process analysis requires a substantial amount of expertise to be interpreted to full advantage. 

However, useful insights are also possible with less detailed analyses. An analysis can focus on

selected grid cells in a small area. Process analysis then takes advantage of the fact that a

numerical grid model addresses physical and chemical factors affecting secondary pollutants in a

sequential manner. For example, a typical sequence followed in a model for each time step (e.g.,

1 hour) might be (1) advection of PM2.5 components and precursors present at the beginning of

the time step, (2) PM2.5 and precursor emissions added during the time step, (3) vertical diffusion

of the advected material and fresh emissions, (4) estimated cloud cover and its effects on

photolysis rates, (5) atmospheric chemistry involving advected and diffused material with fresh

emissions, and (6) deposition of certain compounds. Process analysis examines incremental

effects on changes in component and/or PM2.5 predictions from hour to hour attributable to each

of the processes described above. In this way, one gets a sense of how important each process is

as a contributor to predicted air quality at a specified time and location. 

If a focused diagnostic analysis, such as one obtained with process analysis, suggests a

particular model prediction may be an artifact of a model assumption rather than a result of real

chemical/physical atmospheric processes, States may wish to go back to the meteorological or

emissions model to verify that the inputs and assumptions that have been used are correct. If a

prediction is the result of an apparent artifact which cannot be resolved, States may wish to

discount that prediction in the attainment or reasonable progress demonstration.

18.5.3 Receptor Models (Multi-variate Source Apportionment)

Receptor models (also referred to as multi-variate source apportionment models) are

mathematical or statistical procedures for identifying and quantifying the sources of air pollutants

at a receptor location (Henry, 1991; Hopke, 1991, 1999, 2001; Lewis et al., 2003; Coulter et al.,

2000; Willis, 2000; Watson, et al., 2002; Maykut et al., 2003; Mukerjee et al., 2004). Unlike

photochemical and dispersion air quality models, receptor models do not use pollutant emissions,

meteorological data and chemical transformation mechanisms to estimate the contribution of
Draft 3.2- September 2006 198

sources to receptor concentrations. Instead, receptor models use the chemical and physical

characteristics of gases and particles measured at source and receptor to both identify the

presence of and to quantify source contributions to receptor concentrations. Receptor models can

identify possible air quality management strategy solutions especially when used in conjunction

with examination of the local emission inventory (Brook, et al., 2004). These models are

therefore a natural complement to other air quality models and can be used as part of State

Implementation Plans (SIPs) for identifying sources contributing to air quality problems. 

Receptor models provide scientific support for current ambient air quality standards and for

implementation of those standards by identifying and quantifying contributions of various source

types. As mentioned in sections 18.2 and 18.3, the richness of the ambient air quality data sets

has been increasing, due to more species being measured, species being stratified by particle size,

shorter durations of sampling, and measurements not only at the surface but also aloft. To take

advantage of these richer data sets the receptor models have become more complex. To ensure

that receptor modeling tools, both simple and complex, are available for use in the development

and implementation of air quality standards, the United States Environmental Protection Agency's

Office of Research and Development (ORD) has and continues to develop a suite of receptor

models (multivariate statistical techniques) that are freely distributed to the air quality

management community. The EPA has developed the Chemical Mass Balance (CMB) and

UNMIX models as well as the Positive Matrix Factorization (PMF) method for use in air quality

management. CMB fully apportions receptor concentrations to chemically distinct source-types

depending upon the source profile database, while UNMIX and PMF internally generate source

profiles from the ambient data. Details of CMB, UNMIX and PMF are described below.

The Chemical Mass Balance (CMB) Model (most recent version, EPA-CMBv8.2) is one of

several receptor models that has been applied to air quality problems over the last two decades

(U.S. EPA, 1990, Seigneur et al., 1997, Coulter et al., 2000). Based on an effective-variance least

squares method (EVLS), EPA has supported CMB as a regulatory planning tool through its

approval of numerous State Implementation Plans (SIPs) which have a source apportionment

component. The chemical mass balance model is probably the most directly applicable

observational approach for this purpose, since it can focus on the same day(s) considered with the

air quality model. Cautions raised previously about representativeness of the monitored data

continue to apply. CMB requires speciated profiles of potentially contributing sources and the

corresponding ambient data from analyzed samples collected at a single receptor site. CMB is

ideal for localized nonattainment problems and has proven to be a useful tool in applications

where steady-state Gaussian plume models are inappropriate, as well as for confirming or

adjusting emissions inventories. 

UNMIX is named for its function, which is to "unmix" the concentrations of chemical species

measured in the ambient air to identify the contributing sources. The particular mathematical

approach used by UNMIX is based on a form of Factor Analysis, but its novelty is that

physically-meaningful constraints are imposed which are intended to remove the undesirable

ambiguity of the multiple solutions that are characteristic of ordinary Factor Analysis. For a

given selection of species, UNMIX estimates the number of sources, the source compositions, and

source contributions to each sample. Chemical profiles of the sources are not required, but
Draft 3.2- September 2006 199

instead are generated from the ambient data.

The PMF technique is a form of factor analysis where the underlying co-variability of many

variables (e.g., sample to sample variation in PM species) is described by a smaller set of factors

(e.g., PM sources) to which the original variables are related. The structure of PMF permits

maximum use of available data and better treatment of missing and below-detection-limit values. 

Also available is a document which discusses the PMF methodology: A Guide to Positive Matrix

Factorization (PDF). 

18.5.4 Retrospective Analyses 

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

model-predicted trend over the same time period. The approach is a direct assessment of what we

are most interested in---does the model accurately predict changes in air quality as a result of

changes in emissions? As part of this analysis the model is run for current episodes or time

periods and 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.

Because differences in meteorology between years can confound the apparent change in

pollutants, EPA recommends that, in most cases, retrospective analyses use constant meteorology

and backcasted emissions. The model should respond in a predictable way if the emissions

changes are large enough. Hence, backcasting 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) in the last ten years, then that may be a good test of the response of the model. In

Section 8, we noted that a retrospective analysis can be a useful tool for diagnosing why an area

has not attained the NAAQS. Therefore, 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. 

18.5.5 Alternative Base Cases (Sensitivity tests) 

In some cases it may be useful to evaluate how the response of the model to emissions

reductions varies as a function of alternative model inputs or model algorithms. Outcomes of

these types of sensitivity tests are useful for several purposes. First, the tests can be used to see

whether model performance is especially sensitive to a particular input or combination of inputs

(Arnold, et al., 2003; Gilliland et al., 2006; Morris et al., 2006). Second, sensitivity tests may

help prioritize additional data gathering efforts so that a better subsequent review/diagnosis can

be performed at the time of a mid-course review or required attainment date. Third, the tests can

be used to assess the robustness of a control strategy. As an example, States/Tribes could
Draft 3.2- September 2006 200

consider the effects of assumed boundary conditions on predicted effectiveness of a control

strategy. If the model response does not differ greatly over a variety of alternative plausible

configurations, this increases confidence in the model results. 

The parameters for sensitivity tests can include, but are not limited to: different chemical

mechanisms, finer or coarser grid resolution, meteorological inputs from alternative, credible

meteorological model(s), different initial/boundary conditions, and multiple sets of reasonable

emission projections. Sensitivity tests can and should be applied throughout the modeling

process, not just when model performance is being evaluated. In cases where the operational

model performance is considered to be poor, these tests may help indicate where base case

input/algorithm changes are warranted.

A new technique has been developed to more efficiently examine a large range of emissions

control scenarios. Response Surface Modeling (RSM), has been developed by utilizing advanced

statistical techniques to characterize the relationship between model outputs and input parameters

in a highly economical manner (U.S. EPA, 2006b). The RSM is a metamodel of a model (i.e., air

quality model); it is a reduced-form prediction model using statistical correlation structures to

approximate model functions through the design of complex multi-dimension experiments.

The RSM is based on a new approach known as air quality metamodeling that aggregates

numerous pre-specified air quality modeling simulations into a multi-dimensional air quality

“response surface”. Simply, this metamodeling technique is a “model of the model” and can be

shown to reproduce the results from an individual modeling simulation with little bias or error

(U.S. EPA, 2006b and 2006c, Hubbell, 2005). The RSM is based on statistical relationships

between model inputs and outputs to provide real-time estimate of these air quality changes. The

RSM provides a wide breadth of model outputs, which can be used to develop emissions control

scenarios. The RSM approach can inform the selection and evaluation of various control

scenarios. This approach allows for the rapid assessment of air quality impacts and changes of

different combinations of emissions reductions. While the RSM may not provide a complete

picture of all changes necessary to reach various alternative standards nationwide, it is highly

useful in the context of providing illustrative control scenarios for selected areas, and

understanding the contribution of different source categories, source regions and pollutant

emissions to air quality across the U.S. The RSM can be used in a variety of ways: (1) strategy

design and assessment (e.g. comparison of urban vs. regional controls; comparison across sectors;

comparison across pollutants); (2) optimization (develop optimal combinations of controls to

attain standards at minimum cost); (3) model sensitivity (systematically evaluate the relative

sensitivity of modeled ozone and PM levels to changes in emissions inputs.

18.6 How Should the Results of the Model Evaluation be Assessed (Interpreted)? 

In EPA guidance for the 1-hour ozone attainment demonstrations (U.S. EPA, 1991a), several

statistical goals were identified for operational model performance. These goals were identified

by assessing past modeling applications of ozone models and determining common ranges of

bias, error, and accuracy (Tesche et al., 1990). The 1-hour guidance noted that because of
51 In practice, however, most 1-hour ozone modeling applications using the 1991 

guidance tended to focus almost entirely on meeting the three statistical "goals" for bias, error,

and accuracy at the expense of more diagnostic evaluation.

Draft 3.2- September 2006 201

differences in the quality of the applications considered, it was inappropriate to establish "rigid

criterion for model acceptance or rejection" (i.e., no pass/fail test). It was recommended that

these ranges should be used in conjunction with the additional qualitative procedures to assess

overall model performance.51

With the additional experience of another decade of photochemical modeling, it is clear that

there is no single definitive test for evaluating model performance. All of the tests identified in

Sections 18.2 and 18.3 have strengths and weaknesses. Further, even within 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 (like that described in Section 7) be used to determine whether a particular modeling

application is valid for assessing the future attainment status of an area. EPA recommends that

States/Tribes undertake a variety of performance tests and weigh them qualitatively to assess

model performance. Provided suitable data bases 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. Generally, additional confidence should be attributed to model base

case applications in which a variety of the tests described above are applied and the results

indicate that the model is performing well. From an operational standpoint, EPA recommends

that States/Tribes compare their evaluation results against similar modeling exercises to ensure

that the model performance approximates the quality of other applications. To aid in this

comparison, we have summarized performance metrics (including information on models and

notes on inputs) of recent ozone, PM2.5, and regional haze applications in Appendix B.

In summary, we continue to believe that for ozone, PM2.5, and regional haze; 1) there should

be no bright line performance criteria; 2) the evaluation of statistical performance measures

should be compared to current and past modeling applications; and 3) statistical measures should

be used in conjunction with diagnostic tests and other qualitative analyses to assess overall model

performance.
Draft 3.2- September 2006 202

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Draft 3.2- September 2006 215

Glossary

Modeled attainment demonstration - A modeled attainment demonstration consists of two

parts: an analysis estimating emission levels consistent with attainment of the NAAQS, and a list

of measures that will lead to the desired emission levels once growth is accounted for. The first

(analysis) part consists of a modeled attainment test. It may also include an additional screening

analysis and a review of a diverse set of model outputs and emissions, air quality and

meteorological data for consideration in a weight of evidence determination to assess whether

attainment of the NAAQS is likely with the proposed control strategy. 

Modeled attainment test - This test takes the ratio of mean predicted future and current 8-hour

daily maximum ozone concentrations averaged over several days and multiplies this ratio times

the site-specific monitored design value at each monitoring location. If the product is < 84 ppb

near all monitoring sites, the test is passed. 

Modeling system - This is a group of models used to predict ambient ozone concentrations. 

The group includes an emissions model which converts countywide emission information into

gridded speciated emissions which vary diurnally and reflect environmental conditions. It also

includes a meteorological model which provides gridded meteorological outputs and an air

chemistry/deposition model which takes information provided by the emissions and 

meteorological models and uses it to develop gridded predictions of hourly pollutant

concentrations. 

Relative response factor (RRF) - The ratio of predicted 8-hour daily maximum ozone

averaged over multiple days near a monitoring site with future emissions to corresponding

predictions obtained with current emissions.

 

Unmonitored Area Analysis - An analysis used to ensure that a proposed control strategy will

be effective in reducing ozone at locations without air quality monitors so that attainment is

shown throughout a nonattainment area. The purpose of the analysis is to use a combination of

model output and ambient data to identify areas that might exceed the NAAQS if monitors were

located there. 

Weight of evidence determination (WOE) - This is a set of diverse analyses used to judge

whether attainment of the NAAQS is likely. The credibility of each analysis is assessed and an

outcome consistent with an hypothesis that the NAAQS will be met is identified beforehand. If

the set of outcomes, on balance, is consistent with attainment, then the WOE can be used to

show attainment. A weight of evidence determination includes results from the modeled

attainment test, the unmonitored area analysis, other model outputs and several recommended

analyses of air quality, emissions and meteorological data. 
Draft 3.2- September 2006 216

APPENDIX A

Below are the definitions of model performance statistics suggested as part of this ozone

modeling guidance. 

Mean Observation: The time-average mean observed value (in ppb)

Mean Prediction: The time-average mean predicted value (in ppb) paired in time and space

with the observations.

Ratio of the Means: Ratio of the predicted over the observed values. A ratio of greater than 1

indicates on overprediction and a ratio of less than 1 indicates an underprediction.

Mean Bias (ppb): This performance statistic averages the difference (model - observed) over

all pairs in which the observed values were greater than zero. A mean bias of zero indicates that

the model over predictions and model under predictions exactly cancel each other out. Note that

the model bias is defined such that positive values indicate that the model prediction exceeds the

observation, whereas negative values indicate an underestimate of observations by the model. 

This model performance estimate is used to make statements about the absolute or unnormalized

bias in the model simulation.

Normalized Mean Bias (percent): This statistic averages the difference (model - observed)
Draft 3.2- September 2006 217

over the sum of observed values. Normalized mean bias is a useful model performance indicator

because it avoids over inflating the observed range of values.

Mean Fractional Bias (percent): Normalized bias can become very large when a minimum

threshold is not used. Fractional bias is used as a substitute. The fractional bias for cases with

factors of 2 under- and over-prediction 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. The single largest

disadvantage is that the predicted concentration is found in both the numerator and denominator. 

Mean Error (ppb): This performance statistic averages the absolute value of the difference

(model - observed) over all pairs in which the observed values are greater than zero. It is similar

to mean bias except that the absolute value of the difference is used so that the error is always

positive. 

Normalized Mean Error (percent): This performance statistic is used to normalize the mean

error relative to the observations. This statistic averages the difference (model - observed) over

the sum of observed values. Normalized mean error is a useful model performance indicator

because it avoids over inflating the observed range of values.
Draft 3.2- September 2006 218

Mean Fractional Error (percent): Normalized error can become very large when a minimum

threshold is not used. Therefore fractional error is used as a substitute. 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. 
Draft 3.2- September 2006 219

Appendix B: Summary of recent model performance evaluations conducted

by other modeling groups

1. Boylan, J., VISTAS, “PM Model Performance Goal and Criteria”, National RPO Modeling

Meeting, Denver, CO, 2004a. 

• Based on benchmarking with a combination of data from a number of PM modeling

studies (SAMI, VISTAS, WRAP, EPA, MANE-NU, EPRI, and Midwest RPO). Proposed

performance goals (close to best achievable) and performance criteria (acceptable) for

these metrics and showed where the modeling studies fall for the various components of

PM. 

• Proposed to use mean fractional bias (MFB) and mean fractional error (MFE) as the

standard performance metrics. 

 Goal: MFE <= 50%, MFB <= +-30%

 Criteria: MFE <= 75%, MFB <= +-60%

 Less abundant species should have

 less stringent goal & criteria

• Proposed to use asymptotically approaching goals & criteria when data are greater than 2.5

um, approaching +200% MFE and +-200% MFB for extreme small model & observed data

(formula of logarithmic MFB & MFE are proposed)

• Based on combined modeling studies described above, for more abundant conditions, MFE

and MFB are typical in the range of 

 Sulfate: MFE = 30% ~ 77%, MFB = -45% ~ +51% (> 2 ug/m3)

 Nitrate: MFE = 55% ~ 125%, MFB = +3% ~ +82% (> 1 ug/m3) 

 Organic: MFE = 35% ~ 95%, MFB = -70% ~ +35% (> 1.5 ug/m3)

 EC: MFE = 50% ~ 95%, MFB = -45% ~ +50% (> 0.5 ug/m3)

 PM 2.5: MFE = 50% ~ 85%, MFB = -55% ~ +60% (> 5 ug/m3)

• Suggested to conduct performance evaluation on episode-by-episode basis or month-bymonth for annual modeling

• Different performance goals & criteria may be needed for gaseous precursors and wet

depositions.

• Benchmarking should be done for the entire modeling system (meteorology, emissions

inventory, and model).

2. Morris, R., et al., “Application of Multiple Models to Simulation Fine Particulate in the

Southeastern US”, National RPO Modeling Meeting, Denver, CO, 2004a.

• Based on model multiple model applications over VISTAS modeling:

1July 1999 & July 2001: CMAQ and CMAQ 36-km & 12-km

2January 2002 & July 2001: CMAQ and CMAQ MADRID 36-km only

3Used same horizontal & vertical grids, CMAQ-to-CAMx emissions, ICs/BCs, but
Draft 3.2- September 2006 220

different MM5 interface (MCIP vs. MM5CAMx).

• Both models performed reasonably well, with CMAQ performing better for SO4 and

CAMx performing better for OM. Both models did not perform well for NO3, soil, and

coarse PM, but CAMx seemed to have much higher positive bias in winter nitrate than

CMAQ

• Used fractional bias and fractional error (instead of normalized ones) to illustrate model

performance because these statistics do not exhibit such extreme fluctuation. 

• Demonstrated the usefulness of “soccer goal” plots for MFB & MFE. Suggested

15%/35% (MFB/MFE) for O3, and illustrated 50%/75% for PM2.5 species and bounded at

100%/200% 

• Based on modeling studies described above (July 1999 & 2001), MFE and MFB are

typical in the range of 

 Sulfate: MFE = 25% ~ 70%, MFB = -25% ~ +51%

 Organic: MFE = 30% ~ 75%, MFB = -50% ~ +45%

 EC: MFE = 35% ~ 65%, MFB = -30% ~ +40%

• CMAQ performance improved for OM when they adjusted the Kz min value to 1.0 m2

/sec

(from 0.1m2

/sec).

• Data (July 2001) of SO4 and total carbon mass (TCM). Both models seemed to captured

the diurnal and daily trends well for both SO4 & TCM, but the magnitude can be

significantly off for some of the days. 

3. Boylan, J. and Baker, K., “Photochemical Model Performance and Consistency”, National

RPO Modeling Meeting, Denver, CO, 2004.

• Illustrated model performance comparisons of 36-km daily modeling results by MRPO

(CAMx4), VISTAS (CMAQ-CB4), and MANE-VU (CMAQ-SARPC99) for two episodes

(July 1999 and January 2002) at three IMPROVE sites; and by MRPO (CAMx4) and

VISTAS (CMAQ-CB4 for one episode (July 1999) for the Pittsburgh supersite. 

• Indicated that in some cases there was good agreement among the models and selected

IMPROVE sites, but in others cases there were noticeable variations (even between the

two RPOs running CMAQ). 

• Sulfate were more consistent among the three models; nitrate and OM have higher

discrepancies; All three models overpredicted nitrate and underpredicted OM (based on

model vs. observed scatter plots comparisons) 

• Suggested that where models diverge, they may show a different response to control

strategies. Reasons for the model variations may include differences between CB4 and

SAPRC99, and potential differences in emissions inventories, differences in Kz_min

values, differences in met. & land use/soil methodologies, etc. 

• Also described a comparison of hourly modeling results by MRPO and VISTAS for a July

1999 episode at the Pittsburgh super site. In some cases these hourly results were better

than daily results and generally captured the diurnal patterns for PM2.5 species and gaseous

species (O3, NOx, HNO3, SO2, etc.), although the magnitude was considerably off in

some cases.
Draft 3.2- September 2006 221

4. Seigneur, C., “Review of CMAQ and REMSAD Performance for Regional PM Modeling”,

AER Report for UARG, Doc# CP163-03-01b, March 2003.

• Describe a review of summary performance of CMAQ, CMAQ-MADRID 1, CMAQMADRID 2, and REMSAD. Based on the comparison studies by others given below:

a. BRAVO 1999 study: CMAQ-MADRID 1 & REMSAD

b. WRAP 1996 studies: CMAQ & REMSAD

c. WRAP Aug. 1999 & Jan. 2000 studies: CMAQ & REMSAD

d. EPA/ORD July 1999 studies: CMAQ & REMSAD

e. Southeast PM - July 1999 study (Nashville/Atlanta):CMAQ,

 CMAQ-MADRID1,2 & REMSAD

• AER Suggested normalized errors of 50% as the benchmark for sulfate and PM2.5 (note:

this suggestion was not agreed by other modeling groups, see references #2, #5, #7: the

MNE is most biased metrics and has high fluctuations). The review showed only SE PM

study meets this criteria for sulfate and PM2.5, and all the rest of species failed. No model

showed consistently better performance than the others. Suggested that the model inputs

has more effect on performance than model formulations.

• Indicated that “the current performance of air quality models for PM is poor”...”There is a

dire need for improving model inputs and model formulations in order to obtain acceptable

model performance”... “3-D air quality models are the best tools available to address the

PM source-receptors relationships because they take into account the non-linearities

that affect the formation of secondary PM”.

5. Boylan, J., VISTAS, “Calculating Statistics: Concentration Related Performance Goals”,

PM Model Performance Workshop, RTP, NC, 2004b.

• Illustrated a set of standard bias and errors calculations commonly used for model

performance statistics and proposed model performance goal. 

• Indicated Mean Normalized Bias and Errors (MNBE) are most biased and least useful

among “MNBE”, “NMBE” and “MFBE”. The Mean Fractional Bias and Errors (MFBE)

is least biased and most useful among the three metrics. 

• Recommended MFB & MBE and proposed performance goal: MFE<=50% and MFB <=+-

30% for more abundant species (eg., sulfate & PM2.5) and less stringent for less abundant

species (eg., nitrate, OC, EC, soil, etc.). Performance goal is not criteria and should be

prohibit the modeling from being used if it fails to meet the goal. 

• Proposed to use “2.5ug/m3" as the “grayline” for 50% MFE & +-30% MFB and

asymmetrically approaching 200% MFE & +-200% MFB to extremely small

concentrations. 

• Recommended to use monthly avg. for annual modeling.
Draft 3.2- September 2006 222

6. Morris, R., et al., “Model and Chemistry Inter-comparison: CMAQ with CB4, CB4-2002,

SAPRC99”, National RPO Modeling Meeting, Denver, CO, 2004b. 

• Based on US (36-km) and VISTAS (12-km) January 2002 modeling, conducted chemistry

mechanisms inter-comparisons for CMAQ with CB4, CB4-2002,and SAPRC99. 

• The performance of CB4 and CB4-2002 was similar for PM, and superior to SAPRC99

overall (for the Jan02 case). 

• The model performance for CMAQ/CB4, US 36-km domain is in the range of:

 Sulfate: MFE = 42% ~ 73%, MFB = -21% ~ +14%

 Nitrate: MFE = 62% ~ 105%, MFB = -21% ~ +46%

 Organic: MFE = 50% ~ 77%, MFB = +3% ~ +59%

 EC: MFE = 59% ~ 88%, MFB = +2% ~ +70%

 Soil: MFE = 165% ~ 180%, MFB =+164% ~ +180%

 PM 2.5: MFE = 48% ~ 88%, MFB = +25% ~ +81%

• Given that the computational cost of SAPRC99 is twice that of CB4, suggested to use 36

and 12 km grids with CB4 chemistry for PM modeling for the time being. 

• Noted that both CB4 and SAPRC underpredicted winter O3 significantly.

7. Tonnesen, G., et al., “Regional Haze Modeling: Recent Modeling Results for VISTAS and

WRAP”, CMAS Annual workshop, RTP, NC, 2003.

• Illustrated the WRAP 1996 CMAQ 36-km modeling and performance evaluation in the

Western U.S. and VISTAS CMAQ 12-km modeling for 3 episodes: January 2002, July

1999, July 2001. Recommended to use the performance metrics of Mean Fractional Bias

(MFB)and Mean Fractional errors (MFE) over mean normalized bias & errors (used in

earlier WRAP model evaluation).

 Sulfate: MFB = -47% ~ +48% (1996 WRAP domain)

 Nitrate: MFB = -95% ~ +30% (1996 WRAP domain)

 Organic: MFB = -20% ~ +70% (1996 WRAP domain)

 OC: MFB = -45% ~ +3% (1996 WRAP domain)

• VISTAS modeling key findings (1) sulfate performance reasonably well (2) nitrate

overpredictions in the winter, underpredictions in summer, may need better NH3 emissions

(3) Kv min =1 improved performance, mixing height is important (4) minor differences in

19 vs. 34 layers

8. Zhang, Y., et al., “Performance Evaluation of CMAQ and PM-CAMx for July 1999 SOS

Episode”, AER Report for CRC, Doc# CP131-03-01, April 2003. 

• Conducted CMAQ (2002 version) and PM-CAMx performance evaluation based on July

1999 SOS episode (6/29-7/11) modeling study (32-km nested w/ 8-km in the SE U.S.,

including Atlanta & Nashville)

• Ozone performance: use MNB & MNE w/ 60 ppb threshold for O3 (CMAQ performed
Draft 3.2- September 2006 223

better):

 CMAQ: MNB < 1%, MNE = 18% 

 CAMx: MNB = 27%, MNE = 33%

• PM performance: CMAQ & PM-CAMx are generally consistent in the rural areas (vs.

IMPROVE); differs significantly over urban/suburban; in general, CMAQ performs much

better than PM-CAMx

 PM 2.5: CMAQ: MNB = 38%, MNE = -7% 

 CAMx: MNB = 55%, MNE = 35%

 Sulfate: CMAQ: MNB = 9%, MNE = 45% 

 CAMx: MNB = 44%, MNE = 63%

 Nitrate: CMAQ: MNB = -50%, MNE = 98% 

 CAMx: MNB =137%, MNE = 158%

• The performance of CMAQ for PM and O3 is consistent with the performance expected

for air quality models (however, nitrate issue existed); the performance of CAMx does not

generally meet current expectations for AQM.

9. Morris. R., et al.,”Evaluation of CAMx: Issues Related to Section Models ”, PM Model

Performance Workshop,RTP, NC, 2004c. 

• Illustrated a WRAP comparison of CMAQ (v4.3), REMSAD (v7), CAMx (bimodal PM),

and CAMx (4-section PM) based on January and July 1996 and annual 1996 over the 36-

km WRAP domain. 

• Indicated that all models exhibited variations in performance, but no clear winner across

all species and periods. Sulfate predictions were reasonable, but nitrate was significantly

overpredicted. For all three models,

 Sulfate : MFE = 40% ~ 60%, MFB = -40% ~ +14% (July 96)

 Nitrate : MFE = 105% ~ 200%, MFB = +45% ~> +100% (Jan. 96)

 Organic: MFE = 50% ~ 75%, MFB = -65% ~ +5% (July 96)

 EC : MFE = 47% ~ 105%, MFB = -48% ~ +25% (Jan. 96)

• The 1996 model performance is less than stellar, indicating potential issues in 1996 MM5

and emissions. 

• Showed the effects of sectional PM distribution on PM Modeling in the Western US.

Based on a study in the South Coast Air Basin using CAMx4+, which allows side-by-side

comparisons of aqueous-phase chemistry modules (bulk vs. variable size resolution) and of

PM size distribution treatments (bimodal vs. sectional). 

10. Tonnesen, G., et al..,”Model Performance Metrics, Ambient Data Sets and Evaluation

Tools”, PM Model Performance Workshop, RTP, NC, 2004a.

• Illustrated model performance metrics, available PM2.5 and O3 data, and evaluation

software tool. Suggested that air quality modeling should include model evaluation as part

of the system.

• Comparing performance metrics may not be enough since performance metrics often show
Draft 3.2- September 2006 224

mixed response and it is possible for a better model to have poorer metrics (e.g., bias in

met & emissions inputs). Diagnostic evaluation is needed to judge finer grid performance

since coarser grid may have compensating errors. But should we assume that finer grid

modeling always gives better simulations/physics?

• An example of VISTAS modeling (July 1999) showed differences of hourly sulfate and its

wet deposition between CMAQ results using 12-km and 36-km grids, possible due to

regional transport (wind speed & direction), precipitations/clouds, and numerical diffusion.

• Recommended bias factor as the best metric for evaluating haze.

11. Wang, Z., et al.,” Comparison and Diagnostic Evaluation of Air Quality Models for

Particulate Matter: CAMx, CMAQ, CMAQ-MADRID”, National RPO Modeling Meeting,

2004.

• Conducted model evaluation based on 1999 SOS episode (6/29-7/10) EPRI modeling

study, 

• CAMx has higher positive bias than CMAQ and CMAQ-Madrid in predicting sulfate;

CMAQ underestimated nitrate and CAMx and CMAQ-MADRID overestimated nitrate. 

• All three models underestimated OM. However, there was no clear winner in model

performance. 

• The three models responded differently to a 50% increase in ammonia emissions,

indicating a need to further look at the models’ responsiveness to changes in emissions.

12. Ku, C., CENRAP, “CMAQ and CAMx Simulations for January and July 2002”, National

RPO Modeling Meeting, Denver, CO, 2004.

• Compared CMAQ and CAMx 36-km simulations Based on January and July 2002 (basB)

over a continental US domain.

• Indicated that the results were mixed for the models over CENRAP generally. Both

models performed acceptably for PM2.5 and sulfate in the summer (when sulfate is

abundant) but they were overpredicted in the winter compared against IMPROVE network. 

• CAMx significantly overpredicted nitrate in the winter (higher prediction than CMAQ),

but has better performance in OM (lower prediction than CMAQ), based on performance

measures of normalized bias and errors.

• The study also showed mixed results for the models in three climatically different regions

in CENRAP that contain Class I areas, with varying performance depending on region and

season. This finding points up the difficulty in improving model performance over the

whole CENRAP domain.

13. Eder B. and Yu S., “An Evaluation of the 2003 Release of Models-3/CMAQ”, CMAS

Annual workshop, RTP, NC, 2003.

• Illustrated CMAQ 2003 evaluation for two episodes (winter 2002 and Summer 1999) for
Draft 3.2- September 2006 225

O3 and PM 2.5 species against AIRS, CASTNet, IMPROVE, and STN. Suggested the use

of the performance metrics of Normalized Mean Bias (NMB)and Normalized Mean errors

(NMB) (and correlation coefficient, R).

• Ozone is fairly unbiased and accurate (NMB<10%) and NME=~20%); sulfate performance

is quite well, even for STN. NO3 was the worst in the winter (NME ~67% for STN and

~96% for IMPROVE)

14. Frank, N.,”Use of National PM2.5 and Speciation Network Measurements for Model

Evaluation ”, PM Model Performance Workshop, RTP, NC, 2004.

• Compared correlated speciated monitoring sites (e.g., IMPROVE, CASTNet, STN) for

ambient PM species measurements. 

• The sulfate agrees very well, but nitrate has both positive & negative bias site-by-site. 

• OM is more uncertain than other species, but is still somewhat robust for model evaluation

(ie., uncertainty is relatively small compared to current range of uncertainty in modeling). 

OM is about 50%-80% of urban excess of PM2.5.

15. Hu, Y., et.al., “Evaluation of CMAQ with FAQS Episode of August 11th-20th”, 2000,

CMAS Annual workshop, RTP, NC, 2003.

• Conducted CMAQ (36/12/4 km nesting) performance evaluation for O3 based on Fall Line

Air Quality Study (FAQS) 8/11-20. 

• Indicated that CMAQ had a good O3 performance, but has a nighttime problem, which

could be due to min Kz used. Analysis suggested that an optimal of min Kz may lie

between 0.1~1 m2/s. 

• The Isoprene emissions may be overestimated in the rural area and CO emissions may be

underestimated.

16. Tonnesen, G., et al., “Prelim Preliminary Results CMAQ and CMAQ-AIM with

SAPRC99”, National RPO Modeling Meeting, 2004b. 

• Compared CMAQ and CMAQ-AIM with SAPRC99 based on 2001 VISTAS modeling

study. CMAQ-AIM with SAPRC99 has larger negative bias and lower predictions of

Sulfate PM than standard released CMAQ.

• Conducted model & chemistry inter-comparisons: CMAQ with CB4, CB4-2002,

SAPRC99. Some chemistry differences were observed in the models based on the 36-km

U.S. and the 12-km VISTAS modeling; the performance of CB4 and CB4-2002 was

similar, and superior to SAPRC99 overall.

• Given that the computational cost of SAPRC99 is twice that of CB4, suggested to use 36

and 12 km grids with CB4 chemistry for current VISTAS modeling study.
Draft 3.2- September 2006 226

17. Yu, S., et al., “Simulation of Primary and Secondary Organic Aerosols over the US by

CMAQ: Evaluation and Analysis”, CMAS Annual workshop, RTP, NC, 2003. 

• Evaluated CMAQ w/ SAPRC99 performance of Primary Organic Aerosols (POA), EC,

and secondary Organic Aerosols (SOA) based on IMPROVE, SEARCH, and SOS data. 

• Suggested that model captured general trends & patterns of most EC & POA within a

factor of two (based on IMPROVE & SEARCH). 

• Slight underprediction in the Eastern U.S., likely due to underprediction in biogenic SOA.

18. Chien, C. Y., et al., “CMAQ Model Performance Evaluation with the Updated CB4-2002",

CMAS Annual workshop, RTP, NC, 2003. 

• Evaluated CMAQ w/ CB4 vs. CB4-2002 (and CB4-2002 with removed N2O5 gaseous

reaction) against IMPROVE, CASTNet and AQS based on January & July 1996 cases. 

Described the key updates of CB4-2002 (HNO3, N2O5 & PAN rxns). 

• CB4-2002 has lower O3, lower N2O5, lower PAN, higher HNO3, higher nitrate, slightly

higher sulfate, and slightly lower SOA prediction than CB4. 

• The performance of CMAQ CB4 and CB4-2002 was similar for PM species. However,

CB4-2002 has higher positive bias for winter nitrate (since it predicted higher nitrate).

19. Dennis, R.,”Time-Resolved and In-Depth Evaluation of PM and PM Precursors Using

CMAQ ”, PM Model Performance Workshop, RTP, NC, 2004. 

• Evaluated the CMAQ (2003 version) performance of inorganic PM, EC, and gaseous

precursors against Atlanta 1999 supersite summer data and 2002 Pittsburgh supersite

winter data.

• Suggested that collapse of evening PBL is too fast and rise of morning PBL too slow

(based on higher modeled EC, NOy & CO against Atlanta data at sunrise & sunset)

• Performance for sulfate & ammonium was fairly good; 

• Overprediction for nitrate, but updated chemistry in CMAQ (included in 2004 version) has

improved the nitrate performance (updated reaction probability for HNO3 heterogeneous

rxn and remove gaseous N2O5->HNO3 rxn)

• Use ratio to define nitrate PM formation being HNO3- or NH3-limited: (NHx2*SO4)/(HNO3+NO3)

• Nitrate PM predictions are very sensitive to NHx, and thus the NH3 emissions need serious

attention. 

20. Kumar, N.,”PM2.5 Model Performance: Lessons Learned and Recommendations ”, PM

Model Performance Workshop, RTP, NC, 2004.

• Illustrated the use of model performance statistics based on two CMAQ-MADRID
Draft 3.2- September 2006 227

applications, SOS 1999 episode in the SE U.S. and 12-km BRAVO summer 1999 episode

(nested within REMSAD BRAVO 36-km modeling results)

• Indicated model evaluation issues regarding local vs. regional, daily/weekly vs.

month/seasonal

 Sulfate : MNB= +20% / +51%, MNE = 51% / 89% (SEARCH/IMPROVE)

 Nitrate : MNB= +72% / -25%, MNE = 72% / 46% (SEARCH/IMPROVE)

 EC : MNB= +14% / -8%, MNE = 52% / 54% (SEARCH/IMPROVE)

 PM 2.5 : MNB= -19% / -8%, MNE = 32% / 49% (SEARCH/IMPROVE)

• Described the available performance metrics and illustrated the use of logarithmic and

fractional bias and errors as potential benchmarks

21. Baker K., Midwest RPO, “Fine Grid Model Performance”, National RPO Modeling

Meeting, Denver, CO, 2004. 

• Based on the Midwest RPO 36-km and 12-km Modeling study using CAMx4

• Indicated that the model performance for PM2.5 species generally was similar for 36 and 12

km grids; there were some differences in running control scenarios for sulfate/nitrate, but

we could not determine which one is better

22. Morris, R., et al., “VISTAS Grid Resolution Sensitivity”, National RPO Modeling

Meeting, Denver, CO, 2004d.

• Based on VISTAS Southeast U.S. modeling study for Phase I episode (Jan. 2002, July

1999, and July 2001), compared results for CMAQ w/ CB4 and SAPRC99 using the 36 km

national RPO domain and the 12 km grid VISTAS domain 

• Effects of grid resolution on model performance was mixed (performance was not

necessarily improving using 12-km)

• CMAQ w/ SAPRC99 (mixed for sulfate, worse for nitrate) was not performing better than

w/ CB4

• Compared July 1999 episode for ozone using 36-km & 12-km CMAQ and CAMx. 

CMAQ O3 performance degraded to underprediction with finer grid; CAMx is similar at

36-km and 12-km.

• Examined the performance of the MM5 model configurations using various cloud schemes

(including Kain-Fritsch and Reisner schemes) for the 12 km (WRAP) and 36 km domains. 

The results showed that cool and moist bias found in the West, overprediction of

convective precipitation using KF.

23. Zhang, Y., et al., “Development and Application of MADRID: A New Aerosol Module in

CMAQ”, CMAS Annual workshop, RTP, NC, 2003.

• Evaluated CMAQ-MADRID against SCAQS 1987 episode. The performance of CMAQMADRID seemed to perform well in O3, PM2.5, and sulfates, and comparable in other PM

2.5 species to other models (UAM-IV/CALGRID/UAM-AERO, GATOR, SMOG, CIT,

SAQM-AERO) 
Draft 3.2- September 2006 228

• Described the development and sciences of MADRID aerosol module included in CMAQ. 

Key features: sectional (size bins), hybrid equilibrium, more sophisticated SOA treatment,

CMU aqueous chemistry

24. Morris, R., et al., “WRAP Multi-Model Evaluation Using the 1996 36 km Section 309

Database”, National RPO Modeling Meeting, Denver, CO, 2004e.

• Conducted WRAP 1996 multi-model evaluation. Compared CMAQ (v4.3), REMSAD

(v7), CAMx (bimodal PM), and CAMx (4-section PM) using the 1996 36-km section 309

database. 

• Indicated that sulfate was the best performing species on an annual basis, with a winter

overprediction compensating for a summer underprediction; NO3 was predicted poorly by

all models; OC, EC, and CM were underpredicted by all models. concluded that modeling

is more challenging in the West than in the Midwest and Southeast.

25. Seigneur, C., “Current Status of Air Quality Models for Particulate Matter”, NARSTO

2000—Tropospheric Aerosols: Science and Decisions in an International Community, October

23-26, 2000, Queretaro, Mexico, 2000b.

• Based on a limited set of comparisons with limited temporal and spatial model studies.

• Statistics calculated for aggregated gross error (AGE) and aggregated normalized bias

(ANB). Note that the statistics for AGE and AGB were not obtained with the same models

nor the same data base. Thus, performance for the two metrics in the table are not

associated with each other.

 PM2.5 : AGE ~ +30% - +50%, AGB ~ +10% (based on one study)

 Sulfate : AGE ~ +30% - +50%, AGB ~ -20% - -30%

 Nitrate : AGE ~ +20% - +70%, AGB ~ -15 - +50%

 EC : AGE ~ +15% - +60%, AGB = none available 

 OC : AGE ~ -40% - +50%, AGB ~ 38% (based on one study)
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United States Office of Air Quality Planning and Standards Publication NEnvironmental Protection Emissions, Monitoring and Analysis DivisionAgency Research Triangle Park, NC