DRAFT DRAFT DRAFT

GUIDANCE FOR DEMONSTRATING

ATTAINMENT OF AIR QUALITY GOALS FOR 

PM2.5 AND REGIONAL HAZE

draft 2.1

January 2, 2001
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Disclaimer

This is a draft document which reflects comments received as part of a technical review of an

earlier version. It presents a set of ideas for demonstrating attainment of air quality goals for

PM2.5 and regional haze. Our intent is to widely circulate this draft to receive comments and

further suggestions. At this stage of its development, the document should not be construed as

representing U.S. EPA policy. 
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Acknowledgments

We would like to thank members of an external review group as well as members of an

internal U.S. EPA review group. Individuals on these two groups spent several months

reviewing an earlier, preliminary draft of this guidance. We received many interesting and

thought-provoking comments from individuals on these two groups. Many of these comments

are reflected in this second draft of the guidance.
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Table of Contents

Disclaimer----------------------------------------------------------------------------------------------------2

Acknowledgments-------------------------------------------------------------------------------------------3

List Of Figures-----------------------------------------------------------------------------------------------9

List Of Tables-----------------------------------------------------------------------------------------------10

Foreword----------------------------------------------------------------------------------------------------11

Executive Summary----------------------------------------------------------------------------------------13

1.0 Introduction---------------------------------------------------------------------------------------------37

1.1 What Is The Purpose Of This Document?-------------------------------------------------37

1.2 Does This Guidance Apply To Me?--------------------------------------------------------37

1.3 How Does The Perceived Nature Of Particulate Matter And Tools For

Describing It Affect My Use/Interpretation of Model Results?--------------------38

1.4 What Topics Are Covered In This Guidance?--------------------------------------------45

Part I. How Do I Use Model Results To See If An Air Quality Goal Is Met?

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

The PM2.5 NAAQS And Regional Haze Goals-----------------------------------------------49

2.1 What Is The Recommended Modeled Attainment Test?--An Overview-------------49

2.2 What Is A Hot Spot Analysis And Why Is It Needed?----------------------------------50

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

An Overview-----------------------------------------------------------------------------51

2.4 What Does A Recommended Weight Of Evidence Determination 

Consist Of?--An Overview--------------------------------------------------------------51

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

Corroboratory Analyses Be Used In A Weight Of Evidence Determination?----52

3.0 What Are The Recommended Modeled Attainment Tests For The Two PM2.5 NAAQS?—57

3.1 What Is The Recommended Modeled Attainment Test

 For The Annual NAAQS?--------------------------------------------------------------59

3.2 What Is The Recommended Modeled Attainment Test

 For The 24-Hour NAAQS?-------------------------------------------------------------67

3.3 How Do I Select Appropriate Inputs For The Recommended Tests?------------------72

3.4 Why Do I Need To Consider A Hot Spot Analysis And How Do I Do It?------------82
Table of Contents, (continued)

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4.0 What Is The Recommended Modeled Test For Reasonable Progress 

Reducing Regional Haze?------------------------------------------------------------------------87

4.1 How Is Reasonable Progress Measured?--------------------------------------------------89

4.2 How Do I Apply A Modeled Test To Demonstrate Reasonable Progress?-----------96

4.3 How Do I Select Appropriate Inputs For the Reasonable Progress Test?------------106

5.0 If I Use A Weight Of Evidence Determination, What Does This Entail?---------------------111

5.1 What Broad Types Of Analyses Should I Consider In A Weight Of Evidence

Determination Related To The NAAQS For PM2.5?--------------------------------112

5.1.1 Air Quality Models----------------------------------------------------------------112

5.1.2 Analysis Of Air Quality And Emissions Trends------------------------------117

5.1.3 Use Of Observational Models---------------------------------------------------119

5.2 What If I Want To Consider Additional Corroborative Analyses?--------------------124

5.3 What Role Should Weight Of Evidence Play In Visibility-Related Analyses?------126 

6.0 What Should Be My Data Gathering Priorities?------------------------------------------------131

6.1 What Data And Other Needs Are Implied By The Modeled Demonstrations For

PM2.5 And Regional Haze?-------------------------------------------------------------131

6.1.1 Key Needs For Generating Credible Model Results Underlying

Attainment And Reasonable Progress Tests---------------------------------131

6.1.2 Key Needs For Performing Modeled Tests For Attainment

Or Reasonable Progress-------------------------------------------------------134

6.1.3 Key Needs For Performing Weight Of Evidence Determinations---------136

6.1.4 Overall Priorities------------------------------------------------------------------138

6.2 Why Is It Desirable To Plan For A Subsequent Review?-------------------------------139

7.0 What Documentation Do I Need To Support My Attainment Or 

Reasonable Progress Demonstration?---------------------------------------------------------141

Part II. How Should I Generate Modeling Results To Use In Attainment Or Reasonable

Progress Tests?

8.0 How Do I Apply Air Quality Models?-- An Overview------------------------------------------149

9.0 How Do I Develop And Use A “Conceptual Description”?-----------------------------------157

9.1 What Is A “Conceptual Description”?---------------------------------------------------157

9.2 What Sorts Of Analyses Might Be Useful For Developing And 

Refining A Conceptual Description?-------------------------------------------------161
Table of Contents (continued)

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10.0 What Does A Modeling/Analysis Protocol Do, And 

What Does Developing One Entail?-----------------------------------------------------------163

10.1 What Is The Protocol’s Function?-------------------------------------------------------163

10.2 What Subjects Should Be Addressed In The Protocol?-------------------------------164

11.0 What Should I Consider In Choosing Air Quality Model(s)?---------------------------------167

11.1 What Prerequisites Should An Air Quality Model Meet To Qualify 

For Use In An Attainment Or Reasonable Progress Demonstration?------------169

11.2 What Factors Affect My Choice of A Model For A Specific Application?--------171

11.3 What Are Some Examples Of Air Quality Models 

Which May Be Considered?-----------------------------------------------------------173

12.0 How Do I Decide Which Periods Or Meteorological Episodes To Model?-----------------177

12.1 What Are Generally Applicable Criteria For Choosing Days To Model?----------177

12.2 Additional Recommendations For The Annual PM2.5 NAAQS----------------------179 

12.3 Additional Recommendations For The 24-Hour PM2.5 NAAQS---------------------186 

12.4 Additional Recommendations For The Regional Haze Goals------------------------187

12.5 Some Other Considerations---------------------------------------------------------------189

 13.0 What Should I Consider When Selecting A Modeling Domain And 

Its Horizontal/Vertical Resolution?------------------------------------------------------------191

13.1 How Do I Decide What Domain Size And Resolution Are Appropriate 

To Address The Two PM2.5 NAAQS?------------------------------------------------191

13.1.1 How Do I Decide What Domain Size To Use?------------------------------191

13.1.2 What Horizontal Grid Cell Size Is Necessary?------------------------------193

13.1.3 How Many Layers Should I Consider?---------------------------------------195

13.1.4 Should I Consider Plume In Grid Modeling?--------------------------------197

13.1.5 What Else Should I Consider In Choosing Finely And 

Coarsely Resolved Portions Of Nested Regional Models?---------------198

13.2 How Do I Decide What Domain Size And Resolution Are Appropriate To 

Address The Reasonable Progress Goals For Reducing Regional Haze?--------199

14.0 How Do I Produce Meteorological and Air Quality Inputs Needed By 

An Air Quality Model?--------------------------------------------------------------------------201

14.1 What Approaches Exist To Generate Meteorological Input For

Grid Models?----------------------------------------------------------------------------201

14.2 How Do I Deal With Data Management And Computer-related 

Constraints When Applying Dynamic Meteorological Models?------------------203

14.3 How Do I Quality Assure Results Generated By A Meteorological Model?-------204

14.4 What Are Some Past Applications Of Dynamic Meteorological Models?----------207

14.5 How Do I Generate Meteorological Input For Lagrangian Models?----------------207

14.6 How Do I Address An Air Quality Model’s Need For Air Quality Inputs?--------208
Table of Contents (concluded)

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15.0 How Do I Produce Emission Inputs Needed For An Air Quality Model?-------------------211

15.1 What Emissions Information Is Needed?------------------------------------------------211

15.2 What Countywide Emission Estimates Are Needed To Support 

Air Quality Models?---------------------------------------------------------------------214

15.3 Can I Use the National Emissions Trends Inventory As A Starting Point?---------215

15.4 How Do I Convert Countywide Inventory Information Into Data 

Used In Air Quality Models?-----------------------------------------------------------216

15.5 What Should I Do To Quality Assure Emissions Estimates?-------------------------219

15.6 How Do I Estimate Emissions For Future Years?--------------------------------------221

15.6.1 How Far In The Future Should I Project Emissions?-----------------------221

15.6.2 What Emissions Should I Assume In The Future—

Actual Or Allowable?-------------------------------------------------222

15.6.3 Additional Guidance For Making Projections-------------------------------223

15.7 What Emissions-Related Issues Do I Need To Consider When Using

Lagrangian Models?---------------------------------------------------------------------224 

16.0 How Do I Assess Model Performance And Make Use Of Diagnostic Analyses?---------227

16.1 How Can I Evaluate Performance Of An Air Quality Model?-----------------------227

16.1.1 How Can I Use Graphics To Make A “Big Picture” Assessment 

Of Model Performance?-------------------------------------------------------228

16.1.2 What Metrics And Criteria Should I Consider For PM2.5

Components And Related Gaseous Pollutants?----------------------------229

16.1.3 What Metrics And Criteria Should I Consider For Predicted

PM2.5 Concentrations And Light extinction?-------------------------------237

16.1.4 How Can I Use Corroborative Analyses With Observational Models 

To Help Evaluate Air Quality Model Performance?-----------------------237

16.1.5 What Data Bases Reflecting Changes In Emissions Are Available To

Evaluate Model Performance?------------------------------------------------238

16.1.6 How Do I Use Ratios of Indicator Species To Evaluate Model

Performance?--------------------------------------------------------------------239

16.1.7 Are Retrospective Analyses Useful For Evaluating 

Model Performance?-----------------------------------------------------------240

16.1.8 All Of These Performance Tests Have Shortcomings, 

So What Do I Do?--------------------------------------------------------------241

16.2 How Can I Make Good Use of Diagnostic Tests?-------------------------------------246

16.2.1 Use Of Sensitivity Tests--------------------------------------------------------246

16.2.2 Use Of Process Analysis-------------------------------------------------------247

17.0 How Do I Evaluate Prospective Control Strategies?-------------------------------------------251

17.1 What Appears To Be The Most Effective Strategy?-----------------------------------251
Table of Contents (concluded)

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17.2 How Robust Is The Strategy I’ve Tentatively Selected?------------------------------252

17.3 How Certain Can I Be That A Strategy Will Work?-----------------------------------252

18.0 References --------------------------------------------------------------------------------------------255

Glossary------------------------------------------------------------------------------------------------------267
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List Of Figures

1.1----Conceptual Diagram Of Particulate Matter Properties-----------------------------------------41

3.1----Choosing Predictions To Estimate RRF’s-------------------------------------------------------80

12.1(a)–Coefficient Of Variation Of Rel. Red. Factor Vs. Samples Per Quarter

REMSAD Base Case Vs. Control (S3) Atlanta (64 Grid Cells)----------------------------182

12.1(b)–Coefficient Of Variation Of Rel. Red. Factor Vs. Samples Per Quarter

REMSAD Base Case Vs. Control (S3) Cincinnati (64 Grid Cells)------------------------183

12.1(c)--Coefficient Of Variation Of Rel. Red. Factor Vs. Samples Per Quarter

REMSAD Base Case Vs. Control (S3) Grand Canyon (64 Grid Cells)-------------------184
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List Of Tables

ES.1—Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze--------------------------------------------------------------22

3.1----Monitoring Data In Area “A” (with no sites qualifying for spatial averaging)-------------57

3.2----Monitoring Data In Area “B” (having two clusters of spatially averaged monitors

plus two additional monitoring sites which are not spatially averaged)--------------------58

3.3----Determining The 98th Percentile From Number Of Samples Collected In A Year---------59

3.4---–Recommended Default Assumptions To Derive Mass Associated With Major

Components Of PM2.5------------------------------------------------------------------------------77

3.5----Default Recommendations For Nearby Grid Cells Used To Calculate RRF’s--------------79

4.1----Default Assumptions Used To Derive Aerosol Species Concentrations For

Estimating Extinction Coefficients---------------------------------------------------------------91

4.2----Relative Humidity Adjustment Factors As A Function Of Relative Humidity--------------92

4.3—Correlations Between Hygroscopic Components Of PM And Relative Humidity-----------95

5.1----Recommended Analyses For A Weight Of Evidence Determination, Factors

 Affecting Their Credibility And Outcomes Consistent With Meeting The NAAQS-----113

7.1----Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze-------------------------------------------------------------142

11.1---Some Air Quality Models Used To Model PM2.5 Or Its Components----------------------174

14.1—Some Past Applications Of Dynamic Meteorological Models-------------------------------207

15.1—Some Emissions Models And Example Applications-----------------------------------------220

15.2—Default Suggestions For Using Projected “Actual” And “Allowable” Emissions--------223

16.1—Limited Observed Performance Statistics For Particulate Matter Models (Dec.2000)—235

16.2—Summary Of Methods To Evaluate Performance Of Air Quality Models-----------------241

16.3—Potentially Useful Diagnostic Tests At Various Stages Of Modeling----------------------249
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Foreword

Readers should note that the U.S. Environmental Protection Agency (EPA) is in the

process of completing its periodic review of the national ambient air quality standards (NAAQS)

for particulate matter. Therefore, these standards could be revised. Furthermore, the U.S. Court

of Appeals for the District of Columbia Circuit issued a decision on May 14, 1999 which

prohibited enforcement of the 1997 NAAQS for particulate matter and remanded them to EPA

for further justification. This decision has been appealed to the U.S. Supreme Court. Oral

arguments occurred on November 7, 2000. A decision by the Court is still pending. 

Nevertheless, concepts developed in the guidance are applicable to other standards for

pollutants, such as particulate matter, which are mixtures and which have an important secondary

component. Furthermore, issues related to use of models to help design effective control

strategies to reduce particulate matter or regional haze are complex and controversial. This draft

is being circulated to encourage a dialogue among interested parties. Our intent is to use the

information gained from this dialogue, along with the outcome of the pending EPA review of the

NAAQS for particulate matter and the results of ongoing analyses, to improve this draft.

This document is a guidance document rather than a regulation. Therefore, it does not

impose binding, enforceable requirements on any party, and may not apply to a particular

situation based upon the circumstances. U.S. EPA and State decision makers have the discretion

to adopt approaches on a case-by-case basis that differ from this guidance where appropriate. 

Any decisions by the U.S. EPA regarding adequacy of a particular State implementation plan

(SIP) to meet national ambient air quality standards (NAAQS) for particulate matter or

reasonable progress goals to reduce regional haze will be based on applicable statutes and

regulations. Therefore, interested parties are free to raise questions and objections about the

appropriateness of the application of this guidance to a particular situation. The U.S. EPA will,

and States should, consider whether or not the recommendations in the guidance are appropriate

in that situation. This guidance is a living document and may be revised periodically without

public notice. The U.S. EPA welcomes public comments on this guidance document at any time

and will consider those comments in any future revision of this document. 
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1

We define terms which appear in italics in greater detail in a glossary which appears at

the end of this guidance. 

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Executive Summary

An attainment demonstration1

 consists of (a) analyses which estimate whether selected

emissions reductions will result in ambient concentrations that meet specified air quality goals, 

and (b) an identified set of measures which will result in the required emissions reductions. In

this document we address only the first part of an attainment demonstration. That is, we describe

how to estimate if a control strategy to reduce emissions of particulate matter and its precursors

will lead to attainment of annual and 24-hour national ambient air quality standards (NAAQS)

for particles as small or smaller than 2.5 μm in diameter (PM2.5). 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). 

This guidance is divided into two major parts. In Part I, we explain how to use modeled

and monitored data to estimate if air quality goals for PM2.5 and regional haze will be met by a

proposed control strategy. We begin by describing a modeled attainment test for the annual

NAAQS for PM2.5 and then for the 24-hour NAAQS. Both tests are similar. The output of each

is an estimated future design value consistent with the respective forms of the NAAQS. If the

future design value does not exceed the concentration of PM2.5 specified in the NAAQS, the test

is passed. The modeled attainment test is limited to locations with monitored data. We also

describe a “hot spot analysis” which may be applied at other, unmonitored locations where there

is concern that concentrations of PM2.5 may be high. 

We next recommend a modeled test for reasonable progress meeting goals to reduce

regional haze. There are two such goals: (1) improve visibility on days when it is currently poor,

and (2) ensure visibility does not deteriorate on days when it is currently good. Like the modeled

tests for the PM2.5 NAAQS, the test for reasonable progress uses both monitored and modeled

data. Current visibility is estimated based on monitored components of PM2.5 and coarse

particulate matter. Models are used in a relative sense to estimate how these currently measured

concentrations will respond to a control strategy. Multiplying relative (modeled) response times

measured concentrations of particulate matter yields an estimate of future concentrations of

particulate matter. We then derive future visibility from the estimated future component

concentrations of PM2.5 and coarse particulate matter. We conclude by comparing future

estimated visibility with currently monitored visibility to see if the goals for reducing regional

haze are met.

States may use other analyses in addition to the modeled attainment test, hot spot analysis

and modeled test for reasonable progress to estimate whether future attainment of the NAAQS or

goals for reducing regional haze is likely. Attainment is likely if a preponderance of evidence

suggests so. We call this procedure a weight of evidence determination.
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Reliability of recommended tests for estimating future attainment or reasonable progress

depends on having reliable data bases. We identify and prioritize key data gathering activities

and analytical capabilities which will increase credibility of analyses used to estimate if the air

quality goals for PM2.5 and regional haze will be met. We conclude Part I of the guidance by

summarizing documentation and features which should be included in a sound attainment or

reasonable progress demonstration.

Part II of this guidance describes how to apply air quality models to generate results

needed by the modeled tests for attainment and reasonable progress. The recommended

procedure for applying a model(s) has 9 steps.

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

2. Develop a modeling/analysis protocol.

3. Select an appropriate model(s) to support the demonstration.

4. Select appropriate periods to model.

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

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.

9. Evaluate prospective control strategies.

Model applications require a substantial effort. States 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. Sections ES8.0 - ES15.0

summarize Part II of the guidance.

Part I: How Do I Use Modeled Results To See If An Air Quality Goal Is Met?

ES 1.0 What Are The Recommended Modeled Tests For Attainment Of The Two

NAAQS For PM2.5?

The two modeled attainment tests for the annual and 24-hour NAAQS use monitored data

to estimate current air quality. The attainment test for a given NAAQS is applied at each monitor

location within or near a designated nonattainment area for that NAAQS. Models are used in a

relative sense to estimate the response of measured air quality to future changes in emissions. 

Future air quality is estimated by multiplying current monitored values times modeled responses

to changes in emissions. Because PM2.5 is a mixture, States should use current observations and

modeled responses of major components of PM2.5 to estimate future concentrations of each

component. The predicted future concentration of PM2.5 is the sum of the predicted component

concentrations. We recommend that PM2.5 be divided into the following major components:

- mass associated with sulfates (SO4);

- mass associated with nitrates (NO3);
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- mass associated with organic carbon (OC);

- mass associated with elemental carbon (EC);

- mass associated with (primary) inorganic particulate matter other than primary sulfate or

nitrate particles (IP);

- unattributed mass (equal to the difference between measured PM2.5 and the sum of the

other 5 components of PM2.5 ) (U).

ES1.1 Modeled Attainment Test For The Annual NAAQS

Emissions and meteorological conditions vary during a year. Therefore, analyses related

to the annual NAAQS should estimate future concentrations for each of the 4 calendar quarters. 

Predicted annual concentrations should be estimated as the average of future concentrations

predicted for each of the 4 calendar quarters in a year. 

Key steps in the recommended modeled attainment test for the annual NAAQS are:

1. Derive current quarterly mean concentrations (averaged over 3 years) for each of the six major

components of PM2.5. This is done by multiplying the monitored quarterly mean concentration of

PM2.5 times the mean monitored composition of PM2.5 for that quarter in 3 consecutive years.

2. For each quarter, apply an air quality model to estimate current and future concentrations for

each of the six components of PM2.5. Take the ratio of future to current predictions for each

component. The result is a component-specific relative reduction factor (RRF).

3. For each quarter, multiply the current quarterly mean component concentration times the

component-specific RRF. This leads to an estimated future quarterly mean concentration for

each component.

4. Average the 4 quarterly mean future concentrations to get an estimated future annual mean

concentration for each component. Sum the annual mean concentrations of the six components

to obtain an estimated future annual concentration for PM2.5.

5. The test is passed if the estimated future annual mean concentration of PM2.5 is < 15.0 μg/m3

 at

each monitoring site.

The modeled attainment test for the annual NAAQS is described in Sections 3.1 and 3.3.

ES1.2 Modeled Attainment Test For The 24-Hour NAAQS

This test is applied at every monitoring site within or near a nonattainment area for the

24-hour NAAQS. It is very similar to the test recommended for the annual NAAQS. The main

differences are in estimating current concentrations for the six components of PM2.5 and in choice

of days used to develop values for the component-specific RRF’s. There are 4 key steps.
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1. Compute the 98th percentile ambient concentration for PM2.5 in each of three consecutive years. 

Average these three values to get a current design value. For each day with high measured PM2.5

(i.e., > 55 μg/m3), estimate the fraction of measured PM2.5 attributable to each of its six

components. Develop quarter-specific average fractional profiles using all days considered. 

Multiply the design value times the average fractional profile to get estimated values of current

concentrations of each component which correspond to the current design value for PM2.5.

2. Apply an air quality model to simulate current and estimated future quarterly emissions. This

leads to quarter-specific modeled estimates of current and future concentrations for each of the

six components of PM2.5. Compute quarterly component-specific RRF’s by taking the ratio of

modeled future to current concentrations.

3. Estimate future concentrations of components corresponding to a future 98th percentile

concentration of PM2.5. This is done by multiplying current concentrations of each component

obtained in step 1 times the component-specific RRF derived in step 2.

4. Sum the future concentrations of each component to get an estimated future 98th percentile

concentration of PM2.5. The test is passed if the future design value calculated at each monitor is

< 65 μg/m3.

The modeled attainment test for the 24-hour NAAQS is described in Sections 3.2 and 3.3.

ES2.0 What Is A Hot Spot Analysis And Why Do I Need It?

The modeled attainment tests are applied at monitoring sites. This is sufficient if the sites

are spatially representative and major gradients in measured particulate matter are unlikely. The

robust nature of the annual NAAQS (i.e., an annual mean averaged over three years), long

sampling time (24 hours) inherent in the data and importance of secondary particulate matter

(requiring several hours for formation) make numerous sharp gradients in ambient PM2.5

unlikely. However, there may be substantial sources of primary particulate matter in some

limited geographic locations. Further, atypically high concentrations of primary particulate

matter from one or more nearby sources are possible at a location on a given day. This may be of

particular concern for meeting the 24-hour NAAQS. Therefore, we suggest a procedure to assess

whether a hot spot analysis is necessary and describe what such an analysis entails.

States may determine whether a hot spot analysis is needed by reviewing spatial

distributions of primary PM2.5 emissions (i.e., directly emitted inorganic and organic particulate

matter) or by reviewing past model predictions within a grid superimposed over the

nonattainment area. If there are cells containing primary emissions which are well in excess of

any near a monitoring site and this excess is largely attributable to a limited number of sources, a

hot spot analysis may be warranted near these sources. Alternatively, a hot spot analysis may be

needed if past grid modeling results identify one or more grid cells with concentrations of

primary particulate matter and PM2.5 which are consistently well above concentrations predicted
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near any monitoring site.

A hot spot analysis needs to be designed on a case by case basis. Since the major focus is

on primary particulate matter, Lagrangian (e.g., Gaussian) or finely gridded models which do not

consider chemistry are suitable for hot spot analyses. We recommend focusing the analysis on

one or a limited number of sources and their proximity. The model should be applied to simulate

primary particulate concentrations everyday within the 3-year period used to calculate the current

design values for PM2.5. This information is reviewed to obtain the highest future mean 98th

percentile prediction (24-hour NAAQS) or the highest future average mean concentration (annual

NAAQS). Results are then superimposed on mean concentrations (averaged over 3 years) for the

other components of PM2.5 derived from the modeled attainment test for the annual NAAQS. If

the result is < 65 μg/m3 (24-hour NAAQS) and/or < 15.0 μg/m3 (annual NAAQS), no remedial

action is needed for the sources which are modeled in the hot spot analysis beyond that already

proposed in the SIP revision.

Hot spot analyses are discussed in Section 3.4.

ES3.0 What Is The Modeled Test For Reasonable Progress Reducing Regional

Haze?

We recommend a modeled test for reasonable progress which is conceptually similar

to the attainment tests for the PM2.5 NAAQS. The modeled test for reasonable progress needs to

reproduce as closely as possible the procedure to be used at a future date (e.g., 2018) to assess

whether observed trends in regional haze are consistent with reasonable progress. 

 The U.S. EPA’s 1999 rule for reducing regional haze identifies two goals. First,

visibility on the worst current days needs to be improved by an agreed upon number of deciviews. 

Second, visibility on the best current days may not deteriorate. A similar test is applied

separately to address each of these two goals. 

Light extinction is initially characterized in the test by an extinction coefficient (bext)

which reflects scattering and absorption of light. Values for bext are estimated using an

empirically derived equation which relates the extinction coefficient to relative humidity and the

following components of particulate matter:

 

- mass associated with sulfates (SO4);

- mass associated with nitrates (NO3);

- mass associated with organic carbon (OC);

- mass associated with elemental carbon (EC);

- mass associated with inorganic particulate matter other than primary sulfate or nitrate

particles(IP) (assumed to be all “crustal material”);

- coarse mass (CM) (i.e., PM10 - PM2.5).
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For each Class I area, the test uses monitored concentrations of these components,

together with a “relative humidity adjustment factor”, to estimate typical current values for bext on

days with worst and best visibility. The test uses location- and month-specific climatological

mean values for the humidity adjustment factor. These have been derived beforehand by the U.S.

EPA. Thus, we do not use monitored or modeled day-specific values for relative humidity to

derive day-specific humidity adjustment factors. Instead, our procedure is analogous to

normalizing a trend to account for meteorological differences. In assessing reasonable progress,

we are interested in seeing whether a control strategy has been effective in reducing haze. This is

difficult to do unless changes in visibility resulting from uncontrollable changes in relative

humidity are factored out.

The test uses an air quality model to estimate relative changes in each of the preceding

components of particulate matter (i.e., to derive component-specific relative reduction factors). 

Current, monitored components then get multiplied by the corresponding component-specific

relative reduction factors to estimate future concentrations for each component. Future values

for bext are estimated using future estimated concentrations of each component of particulate

matter and climatological values for the humidity adjustment factor. Finally, differences in

future vs. present values for bext are converted to differences in deciviews and compared to the

relevant goal for reasonable progress. The test is passed if both goals for reasonable progress are

met.

Summarizing, the recommended modeled test for reasonable progress has 6 steps.

1. For each Class I area, rank visibility 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 extinction coefficient

for the 20% of days with worst and 20% of days with best visibility. For each Class I area,

calculate the average mean extinction coefficients for worst and best days from the five yearspecific values.

3. Use an air quality model to simulate current emissions and future emissions. Use the resulting

information to develop relative reduction factors for each component of particulate matter

identified in an empirical relationship between bext and the components.

4. Multiply the relative reduction factors times the measured PM components to estimate future

values for each of these components on modeled days with poor (good) visibility.

5. Using the results in Step 4, recalculate the mean extinction coefficients for the previously

selected days with best and worst visibility in each of the five base years. Compute the future

average mean extinction coefficients for the worst and best days.

6. Convert the difference in average mean extinction coefficients to a difference in deciviews. 
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Compare the difference in deciviews to the goals for reasonable progress.

The recommended modeled test for reasonable progress is described in Section 4.0.

ES4.0 What Is A Weight Of Evidence Determination?

A State should always utilize available air quality, meteorological and emissions data to

complement a modeling analysis. As we discuss in Section 9.0, these data are used to develop a

conceptual description of an area’s nonattainment or visibility problem. This description is

useful for guiding a modeling analysis. If the modeled test is passed or nearly passed a State may

choose to use weight of evidence (WOE) to estimate if the air quality goal will be met. 

A weight of evidence determination is a diverse set of technical analyses performed to

corroborate findings of the modeled attainment test. If a weight of evidence determination is

used, a State should consider a recommended core set of analyses consisting of (1) a set of air

quality model results which includes the previously described tests plus additional analyses of

estimated concentrations, (2) an analysis of observed air quality and estimated emissions trends,

and (3) an analysis of outcomes produced by observational models.

We identify factors which enhance credibility of evidence produced by each of the core

analyses, as well as outcomes which would be consistent with the likelihood that a strategy

demonstrates attainment. 

A State may include other types of analyses, in addition to the core analyses, in a weight

of evidence determination. For another analysis to be considered three criteria should be

satisfied:

(1) a State should discuss why the proposed analysis is relevant for assessing attainment,

(2) a State should identify the procedure to be used and the data base available to support it, and

(3) a State should identify (in advance) outcomes which would be consistent with a hypothesis

that a proposed strategy will lead to attainment. 

Weight of evidence and its use is discussed in Section 5.0.

ES5.0 Why Do We Recommend These Modeled Tests And Offer An Option To

Perform Weight Of Evidence Analyses?

The modeled attainment and reasonable progress tests we recommend for the PM2.5

NAAQS divide PM2.5 into major components. The impact of a control strategy on PM2.5 or on

light extinction is the sum of the impacts attributable to individual components. To estimate

these impacts, models are used to predict the fractional decrease (increase) of each component. 
20 - DRAFT

These fractional changes are applied to currently monitored component concentrations to

estimate future concentrations for each component and, ultimately, future concentrations of PM2.5

and/or light extinction. Thus, model predictions are used in a relative rather than absolute sense. 

Results of other analyses (e.g., other model outputs, trend analysis, use of observational models)

may be used to complement the modeled attainment or reasonable progress tests. If the weight of

evidence produced by the tests and these other analyses suggest attainment is likely, we conclude

that the strategy, if implemented, will lead to attainment. We believe this approach is appropriate

for several reasons.

1. Particulate matter is a mixture. To accurately estimate effects of control measures on

ambient PM2.5 it is necessary to start out with the components in approximately the correct

proportion. This is made more likely by using monitored values to characterize current

composition of PM2.5. Models are best used to estimate how these correctly proportioned

components respond to simulated control measures. 

2. Secondary particulate matter constitutes an important fraction of PM2.5 and source of

light extinction. Modeling requirements for secondary and primary particulate matter differ in

their need to consider atmospheric chemistry and in the degree of spatial resolution needed for

the modeling. Treating components explicitly facilitates using a mix of models which are

responsive to primary vs. secondary PM and which is more cost effective than using a single

model to address all parts of a problem involving particulate matter.

3. It is easier to relate results of a modeled attainment test to the forms of the two NAAQS

and goals for regional haze if we use currently monitored data to establish current

conditions. The annual and 24-hour NAAQS both address a statistically determined

concentration which, in addition, is averaged over 3 consecutive years. These concentrations are

called “design values”. It may be difficult to relate model results to such NAAQS unless

numerous days are modeled from each year. Models which can address secondary particulate

matter are resource intensive and thus may be incompatible with considering many alternative

strategies simulating many days. However, monitoring data should be available for many days

each year. These data can readily be used to estimate current design values and corresponding

species concentrations. In effect, the monitoring data serve to “anchor” model results to the form

of the NAAQS as well as to current mean “worst” and “best” visibility.

4. Using models to predict future attainment has associated uncertainty. Our suggested

approach does not eliminate uncertainty, but reduces it in three important ways. First, monitored

data (e.g., current design values) are incorporated into the test. These data are likely measured

with greater accuracy than an absolute model prediction, and precision of the measurements is

better known. Second, the outcome of the test is based on a composite set of calculations from

several modeled days rather than a single day. This reduces the risk of choosing an inappropriate

strategy on the basis of a single outcome which is subject to uncertainty. Third, if the outcome of

the test is close to pass/fail, a weight of evidence determination may be used to see whether other

analyses provide corroborative evidence for conclusions drawn from the test.
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ES6.0 What Data Gathering And Other Priorities Should I Consider To Support 

This And Future SIP Revisions?

We note three major activities requiring data and/or enhanced analysis: 

generating modeling results; applying the results in attainment or reasonable progress tests and

applying weight of evidence analyses. We identify data needs and other priorities related to

these three activities. Data gathering priorities depend on the nature of the existing data base. 

However, in general, we suggest that highest priority be given to the following efforts.

1 (tie). Develop accurate emission factors and seasonal/diurnal activity levels for major

anthropogenic sources and source categories of PM2.5, SO2, NH3, VOC, NOx and CO.

1 (tie). Make speciated ambient measurements of PM2.5 at mass monitoring sites likely to exceed

the NAAQS and in Class I areas.

3. Develop locally applicable source speciation profiles for primary PM and VOC.

4. Make continuous, or at least several, upper air meteorological measurements/day and ensure

these are accompanied by surface meteorological measurements below the site of the resulting

vertical profiles.

5. Develop software to post process modeling and monitored data so that the modeled tests for

attainment and reasonable progress can be readily applied.

6. Arrange a limited duration study in which PM2.5 and its components are sampled continuously

or over periods not exceeding 6 hours at selected locations with routine 24-hour sampling. 

States should retain the ability to perform or sponsor modeling or related analyses subsequent to

approval of an initial demonstration of attainment or reasonable progress. Subsequent reviews

will likely be necessary.

Data needs and priorities are addressed in Section 6.0.

ES7.0 What Documentation Is Needed To Support The Modeled Demonstrations?

States should address the subject areas shown in Table ES.1. Documentation should be

accompanied by an executive summary which covers each of the areas shown in the table. 

Documentation requirements are covered in Section 7.0.
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Table ES.1. Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze

Subject Area Purpose of Documentation Issues Included

Conceptual Description of PM2.5

or Regional Haze Problem 

To provide a short narrative of

observational evidence which led

to the selected modeling

approach and strategies which

were investigated.

Measurements used

Analyses performed

Resulting rationale used to

support the modeling approach

and strategies investigated

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

 Days and domain considered

Emissions Preparations and

Results

Assurance of valid, consistent

emissions data base and that 

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 model-compatible

inputs 

Deviations from existing

guidance and underlying

rationale

Emissions model(s) used and

justification for choice of models

PM2.5, SO2, NH3, VOC, NOx, CO

emissions by State/county for

major source categories.
Table ES.1. Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze (continued)

Subject Area Purpose of Documentation Issues Included

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Air Quality/Meteorology

Preparations and Results

Assurance that representative air

quality and meteorological 

inputs are used in analyses

Extent of data base and

procedures used to derive &

quality assure inputs for analyses

used in the weight of evidence

determination 

Departures from guidance and

their underlying rationale

Meteorological model(s) used

Performance of meteorological

model if used to generate

meteorological inputs to the air

quality model

Performance Evaluation for Air

Quality Model (and Other

Analyses)

Show decision makers and the

public how well the model (or

other analyses) reproduced

observations or otherwise

performed on the days selected

for analysis

Summary of observational data

base available for comparison 

Identification of performance

tests used and their results for

major components of PM, mass

of PM2.5 and key gas phase

species

Ability to reproduce observed

temporal and spatial patterns for

major components of PM, mass

of PM2.5 and key gas phase

species

Overall assessment of what the

performance evaluation implies

Diagnostic Tests Ensure rationale used to adjust

model inputs or to discount

certain results is physically

justified and the remaining

results make sense.

Results from application prior to

adjustments 

Consistency with scientific

understanding and expectations 

Tests performed, changes made

and accompanying justification 

Short summary of final

predictions
Table ES.1. Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze (continued)

Subject Area Purpose of Documentation Issues Included

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Description of the Strategy

Demonstrating Attainment (or

Reasonable Progress)

Provide the EPA and the public

an overview of the plan selected

in the attainment or reasonable

progress demonstration.

Qualitative description of the

selected strategy 

Reductions in PM2.5, SO2, NH3,

CO, VOC, and/or NOx emissions

from each major source category

for each State/county from

current (identify) emission levels 

Clean Air Act mandated

reductions and other reductions 

Show relative reduction factors

estimated for each major

component of PM2.5 and (for

Regional haze applications) CM

Show predicted site-specific

future PM2.5 design values for the

selected control scenario and

identify any location for which a

hot spot analysis was needed

together with results of the hot

spot analysis 

Identification of authority for

implementing emission

reductions in the selected

strategy

Evidence that emissions will

remain at or below projected

levels throughout the 3-year

period used to determine future

attainment for PM2.5-related

applications, and the 5-year

period used to determine if

reasonable progress has

occurred for visibility-related

applications
Table ES.1. Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze (continued)

Subject Area Purpose of Documentation Issues Included

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Data Access Enable the EPA or other

interested parties to replicate

model performance and

simulation results for the selected

strategy, 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

Weight of Evidence

Determination (PM2.5 NAAQSrelated applications)

Assure the EPA and the public

that the strategy is likely to

produce attainment of the

NAAQS within the required

time.

Description of the modeled

attainment test and

observational data base used 

Identification of air quality

model(s) 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
Table ES.1. Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze (concluded)

Subject Area Purpose of Documentation Issues Included

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Weight of Evidence

Determinations (related to the

reasonable progress goals to

reduce regional haze)

Assure the EPA and the public

that the strategy is likely to meet

reasonable progress goals to

reduce regional haze

Description of the modeled test

for reasonable progress and

observational data base used 

Identification of air quality

model used 

Identification of other analyses

performed 

Outcome of each analysis,

including the modeled test for

reasonable progress 

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

visibility on the days with best

visibility will not deteriorate and

the goals for days with poor

visibility will be met

Review Procedures Used Provide assurance to the EPA

and the public that analyses

performed in the attainment or

reasonable progress 

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 thereto

Part II: How Should I Generate Modeling Results To Use In Attainment Or Reasonable

Progress Tests?

Generating model results for subsequent use in the attainment or reasonable progress tests

requires several steps.
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- develop a conceptual description of the problem at hand;

- develop and implement a modeling/analysis protocol;

- select appropriate air quality model(s) to use;

- select periods which need to be modeled;

- choose a modeling domain and select an appropriate spatial resolution;

- develop appropriate meteorological and air quality inputs to the model;

- develop emissions inputs to the air quality model;

- evaluate and diagnose model predictions;

- simulate and evaluate prospective control strategies.

ES8.0 What Is A “Conceptual Description”?

A conceptual description is a qualitative way of characterizing an area’s nonattainment or

light extinction problem. For example, is the problem one dominated by local emissions or are

regional factors also important? Do sites violating the NAAQS reflect a spatial or temporal

pattern in some way?, Do poor visibility days tend to be characterized by relatively high

concentrations of the same components of particulate matter?, etc.. A conceptual description is

based on use of readily available air quality, meteorological and emissions information. It may

be refined later as additional analyses are performed. States should develop a conceptual

description of the nonattainment or light extinction problem(s) they are seeking to solve as an

initial step in developing a solution. It serves as a means for guiding later decisions which are

needed in a modeling analysis. Suggestions for developing a conceptual description are found in

Section 9.0.

ES9.0 What Does A Modeling/Analysis Protocol Do And What Should It 

Contain?

A modeling/analysis protocol is a document which identifies methods and procedures to

be used in the analyses. The protocol also identifies ground rules to be followed in undertaking

analyses to estimate emission reductions needed to meet the NAAQS or a reasonable progress

goal. Ground rules include identifying the methods, data bases and procedures to be used to

obtain results, a description of how affected stakeholders in the modeling/analysis process will be

encouraged to participate, the process by which decisions will be made, and means used for

communicating issues and decisions. As the name implies, the protocol should address use of

other analyses as well as air quality modeling. The document is usually prepared by the

State/local agency(ies), or regional planning organization having lead responsibility for the

modeling/analysis, in consultation with stakeholders. The protocol should be kept up to date to

reflect major subsequent decisions made after the initial version is completed. Specific topics

which should be included in the protocol are identified in Section 10.0.

ES10.0 What Should I Consider In Choosing An Air Quality Model?

PM2.5 consists of primary and secondary particles. Modeling requirements for primary
28 - DRAFT

and secondary particulate matter differ. For example, spatial gradients in primary particulate

matter may require more spatial detail in the modeling, perhaps over a relatively limited domain. 

Relationships between secondary particulate matter and its gaseous precursors may be non-linear,

requiring use of a numerical grid model with an atmospheric chemistry algorithm applied over a

large domain. As a result, States may find it necessary to use one air quality model to address

primary components of PM2.5, and another model to address secondary components.

Modeling used to support attainment demonstrations for the PM2.5 NAAQS may consist

of the following combinations:

- a photochemical grid model to address secondary components plus a finer scale (nested)

grid model (without chemistry) to address primary components;

- a photochemical grid model for secondary components plus a Lagrangian model to

address primary components;

- a fine scale grid model (without chemistry) or a Lagrangian model to address primary

components, with no modeling of secondary components. 

If the last option is chosen, it should include a justification why an increase in secondary

components is not likely to lead to a violation of the NAAQS despite the prescribed reduction in

primary particulate matter.

A regional scale photochemical grid model should be used to support demonstrations of

reasonable progress reducing regional haze. Primary components may be modeled using the

same spatial resolution used for the regional analysis. Exceptions may be required on a case by

case basis.

For an air quality model to qualify as a candidate for use in an attainment demonstration

for PM2.5 or in a reasonable progresss demonstration, a State needs to show that it meets several

general criteria. 

1. The model has received a scientific peer review.

2. The model can be demonstrated applicable to the specific problem.

3. Data bases needed to perform the analysis are available and adequate.

4. Available past appropriate performance evaluations have shown the model is not

biased toward underestimates.

5. A protocol on methods and procedures to be followed has been established.

6. The developer of the model must be willing to make the source code available to users 
29 - DRAFT

for free or for a reasonable cost, and the model cannot otherwise be proprietary. 

 

So far as choosing individual models meeting the preceding requirements, States should

determine what attributes are needed for a qualifying model to address an area’s PM2.5 or regional

haze problem, and then choose among models possessing these attributes. Five factors should be

considered in selecting an air quality model for a specific application. Selection of an air quality

model should be concurred with by the appropriate U.S. EPA Regional Office and U.S. EPA

Model Clearinghouse. The five factors are listed approximately in order of importance. 

1. Nature of the air quality problem leading to nonattainment of the PM2.5 NAAQS or

light extinction should first be assessed, and the selected model(s) should have attributes

and capabilities consistent with the resulting conceptual description of the problem.

2. Availability, documentation and past performance should be satisfactory.

3. Relevant experience of available staff and contractors should be consistent with

choice of a model.

4. Time and resource constraints may be considered.

5. Consistency of the model with what was used in adjacent regional applications should

be considered.

Prior to using model results in a specific application, those implementing the modeling protocol

should show that the model performs adequately. We discuss choice of air quality models in

Section 11.0.

ES11.0 How Do I Choose Appropriate Periods To Model?

Choice of days to model depends on the air quality goal being addressed (i.e., annual

 NAAQS, 24-hour NAAQS or reasonable progress goals for days with current best and worst

visibility). However, there are some generally applicable criteria for selecting days to model.

1. Choose a variety of meteorological conditions to model.

2. Include some days with intensive data bases, to the extent feasible.

3. Model a sufficient number of days so that relative reduction factors (RRF) are likely to

be stable.

In addition, choice of days may reflect several practical considerations.

1. What days have already been modeled?
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2. Give preference to days occurring during the period corresponding to the current

design value used in the modeled attainment test or the base period used in the modeled

test for reasonable progress.

3. Include weekends among the selected days to improve the potential for evaluating

model performance.

4. If applying a regional model, choose periods meeting other selection criteria in as many

nonattainment or Class I areas as possible. 

For the modeled attainment test addressing the annual NAAQS, it is preferable to model

every day in a year which is representative of the 3-year period used to determine the current

annual design value. However, we believe that modeling 15 or more days per calendar quarter

(i.e., 60 or more days in a year) should yield relative reduction factors which are close to what

would be obtained if every day were modeled. States may instead choose to model a set of

prototypical meteorological conditions and then weight the results according to how frequently

each set of conditions occurs.

For modeled attainment tests addressing the 24-hour NAAQS, States should model

periods which include all observed exceedances of 65 μg/m3

 in the nonattainment area. If it is

not feasible to model 10 or more days with exceedances, States should model periods including

at least 10 days with observations > 55 μg/m3.

Hot spot analyses may sometimes be needed to supplement the modeled attainment test. 

Since these analyses are performed with non-resource intensive models, we recommend that the

model which is used be applied for every day during the 3-year period used to compute the

current design value. If this is not feasible, a similar 3-year period may be chosen.

Since it is likely that regional planning organizations/States will choose to model effects

of a strategy in many Class I areas simultaneously, it may be most efficient to model a single

representative year, drawn from within the base period. This will ensure that modeled estimates

for each Class I area are based on simulations of a large number of “worst” and “best” visibility

days. If this is not feasible, those implementing the modeling protocol should try to model at

least 10 days with current “worst” visibility and 10 days with current “best” visibility in as many

Class I areas as possible. States should avoid missing this minimal requirement by a large

margin in any Class I area, as it increases the possiblity that the estimated RRF may not be

representative of how air quality in such an area will respond to a control strategy.

Selecting periods to model is discussed in greater detail in Section 12.0.
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ES12.0 How Do I Select A Modeling Domain And Its Horizontal/Vertical

Resolution?

A modeling domain identifies the geographic bounds of the area which is modeled. In a

grid model, horizontal resolution is related to the horizontal dimensions of individual grid cells. 

Vertical resolution is determined by the number of grid cells (i.e., layers) considered in the

vertical direction. Appropriate domain size and resolution differ for applications related to the

NAAQS for PM2.5 vs. regional haze. Further, different recommendations are appropriate when

using models to assess effects of reducing emissions of primary vs. secondary particulate matter.

If secondary particulate matter is an important cause of a nonattainment problem and/or a

strategy includes altering SO4, NO3 or secondary OC, we recommend using a regional scale

modeling domain, at least 1000 km on a side. Smaller, urban scale modeling domains (< ~ 300

km on a side) may be used to consider strategies affecting concentrations of primary components

(IP, EC and primary OC). A State may consider a strategy which addresses both primary and

secondary components of PM2.5 by using a nested grid in which the regional scale domain

encompasses the urban scale one.

States should consider performing diagnostic tests to asses sensitivity of relative

reduction factors to horizontal grid cell size. However, as default recommendations, States may

use grid cells as large as 36 km on a side outside of nested urban areas. Horizontal cell size

within urban nonattainment areas and their immediate surroundings should be no larger than 12

km on a side. If a strategy depends on reducing primary emissions, maximum recommended

horizontal cell size in and near the nonattainment area should be reduced to 4-5 km on a side.

A State may choose to use a Lagrangian (e.g., Gaussian) model rather than numerical grid

models to consider portions of a strategy which address emissions of primary particulate matter. 

If this is done, the nested urban grid (used to consider secondary particulate matter in/near a

nonattainment area) may use horizontal cells as large as 12 km on a side. Lagrangian models

should not be applied to sources which are more than 50 km from a monitoring site where the

modeled attainment test is being applied. When Lagrangian models are applied in a hot spot

analysis, States may assume that the maximum impact from a source of primary particulate

matter occurs within 15 km of the source.

Because secondary particulate matter is likely to be an important cause of light extinction

and most Class I areas are located in rural/remote areas, a regional modeling domain at least

1000 km on a side should be used to support the modeled test for reasonable progress. 

Horizontal grid cell size as large as 36 km on a side may be used. States may use plume in grid

modeling to characterize effects of large point sources within ~3 grid cells of a Class I area, or a

monitoring site in a nonattainment area. Because regional planning organizations are likely to

consider effects of a strategy on numerous Class I areas simultaneously, they should consider

using one of two superdomains for their analyses: one for the western part of the contiguous U.S.

(with mostly low relative humidity) and one for the eastern part of the contiguous U.S. (with
32 - DRAFT

mostly high relative humidity).

Guidance for the number of grid cells in the vertical direction (i.e., layers) used in

modeling is the same for PM2.5 NAAQS-related and regional haze-related applications. We

recommend using a minimum of 9-11 layers. Demarcations between layers should correspond to

those used in meteorological models generating input to the air quality model. Priority should be

given to having fine vertical resolution near the earth’s surface where emissions occur. The

surface layer should be less than 50 meters thick. Defining maximum afternoon mixing height as

precisely as feasible should also be a goal.

Choice of domains, horizontal grid cell size, number of layers, use of plume in grid

models and geographic limits on use of Lagrangian (Gaussian) models are discussed in Section

13.0.

ES13.0 How Do I Produce Meteorological And Air Quality Inputs Needed By An

Air Quality Model?

We recommend that States use a dynamic meteorological model with four dimensional

data assimilation (FDDA) as the principal means for generating meteorological inputs needed by

numerical grid or Lagrangian models used for attainment or reasonable progress demonstrations. 

Any such meteorological model which has received a scientific peer review may be used. As

with the output from emissions models, it is critical that results of meteorological models be

quality assured. We identify several potentially useful means for doing so: 

1. comparison with upper air measurements “held back” from use in FDDA;

2. comparison of calculated trajectories with observed air quality patterns;

3. use of computer graphics to discern spatial discrepancies;

4. simulation of inert tracers to identify discontinuities or mass balance problems;

5. comparing results obtained with different meteorological models;

6. calculating and comparing divergence and/or dimensionless parameters and comparing

these with expected ranges;

7. comparing spatial air quality patterns obtained with a grid model vs. observed patterns, 

8. comparing outputs from a meteorological model for key variables vs. estimates for the

same variables used in the air quality model, and

9. using process analysis to flag contributions made to unexpected concentrations of
33 - DRAFT

PM2.5 components by meteorological factors. 

Applying meteorological models over extensive domains with fine scales (e.g., 4-12 km)

can be very resource intensive and present data base management problems. We suggest means

for reducing such problems. 

Air quality inputs are needed for initial conditions and for boundary values at the edges of

a numerical model’s domain. There is no satisfactory way to use available air quality

observations to specify initial conditions. Thus, States should diminish their importance by

beginning a simulation two or more days prior to the period of interest for urban applications and

three or more days earlier for regional applications. Nested regional models are the usual

preferred means for generating boundary conditions to a portion of the large regional domain

which is the focus of an attainment or reasonable progress demonstration. If an urban scale

model is used, the domain should be large enough so that emissions occurring in the center of the

domain just before sunrise remain within the domain until the end of the same calendar day. 

This should reduce importance of boundary conditions specified for such applications.

If a State chooses to use a Lagrangian air quality model to support an attainment

demonstration, meteorological input should be generated for a 3-year period, preferably the one

used to calculate the nonattainment area’s current design value. These data may be obtained

from National Weather Service or equivalent sites or by using a meteorological model (with 12

km or smaller horizontal grid cells). If a Lagrangian model is used, estimates are also needed for

“background” concentrations of primary particulate matter. Background estimates depend on the

geographic scale over which the Lagrangian model is applied. For urban scale applications (used

in the modeled attainment test), States should use observations or regional scale model estimates

just outside the area modeled with the Lagrangian model. For geographically more limited hot

spot analyses, background may be estimated by spatially averaging observations or modeled

estimates made within a defined distance of the source(s) being reviewed.

Derivation and use of meteorological and air quality inputs to an air quality model are

discussed in Section 14.0.

ES14.0 How Do I Produce Needed Emissions Inputs?

Whenever possible, States should derive current emissions estimates from locally

available data. This is called a “bottom-up” approach. Often however, it may be necessary to

rely on Statewide or national data, at least in some parts of the domain (i.e., it may be necessary

to use a “top-down” approach).

Producing needed emissions inputs using a top-down approach requires several steps. 

Many of these steps need to be followed with bottom-up approaches as well. First, compile

Statewide and then countywide estimates for inorganic and organic PM2.5, SO2, NH3, VOC, NOx

and CO emissions for point, area, mobile and biogenic emissions. Estimates are needed for each
34 - DRAFT

month of the year. Countywide estimates are also needed for PM10 emissions to estimate changes

in coarse particulate matter required for reasonable progress determinations. Second, quality

assure the outputs. Third, convert the resulting estimates into speciated, gridded hourly

emissions using emissions models. Fourth, once again, quality assure the results. Finally,

project gridded, speciated hourly emission estimates to a future year which corresponds to two

years prior to the deadline for meeting the NAAQS or for realizing the first increment of

reasonable progress toward reaching natural background visibility. We also recommend

projecting emissions to an intermediate year to facilitate a subsequent “mid-course review” to see

if a strategy is working or to diagnose why not.

The U.S. EPA has prepared a series of guidelines relating to these steps as a part of the

Emission Inventory Improvement Program (EIIP), as well as a guideline for developing

emissions inventories. States should be familiar with these guidelines. States should use the

most recent emission estimates commonly available when applying the modeled attainment or

reasonable progress tests. As of late 2000, the National Emissions Trends (NET) Inventory

compiled for 1999 is the preferred source of information for State and countywide estimates in

portions of the modeling domain for which States who are stakeholders have no better

information. If NET inventory estimates for 2002 become available in a timely manner, they will

be the preferred choice for use in modeled demonstrations for attainment or reasonable progress. 

Different means are used to obtain emissions information in the form needed by air

quality models. Ideally, location and daily/weekly emission patterns should be directly available

for point sources. Spatial distribution of surrogates for activity factors needs to be estimated for

major area source categories and for mobile sources in order to estimate gridded emissions. 

Diurnal and weekly activity patterns are also useful. Current, locally applicable PM2.5 and VOC

speciation profiles are desirable for point sources, mobile and major area source categories. 

Emission models are then used to characterize emissions from point sources, stationary area,

mobile and biogenic sources. We identify several commonly used emissions models.

Quality assurance of emissions estimates is necessary for results of a modeled test to be

credible. We recommend that quality assurance checks occur during several stages of the process

needed to derive required emission inputs to an air quality model. Comparing inventory

estimates made for different studies and in different States, computer graphics and comparisons

with available, speciated air quality data are useful means for quality assuring emission

estimates. 

In the modeled tests for the annual NAAQS and regional haze, projected emissions

should ordinarily reflect best estimates (i.e., projected “actual” emissions). For applications

related to the 24-hour NAAQS, States should ordinarily assume projected emissions reflecting

maximum feasible or permitted (i.e., “allowable”) emissions, whichever is less, for a small

number of sources. The number and types of sources for which this assumption is made needs to

be determined case by case. Projections for all other sources should reflect best estimates of

seasonally typical (i.e., “actual”) emissions.
35 - DRAFT

Hot spot modeling analyses may be needed in some nonattainment areas to provide

assurance that the 24-hour and annual NAAQS will be met in suspect locations where there are

no monitors. For analyses related to the 24-hour NAAQS, States should assume maximum

feasible or permitted emissions (whichever is less) for sources subject to this review, as well as

for a limited number of large, nearby sources. Number of sources for which projected

“allowable” or maximum feasible emissions is used needs to be determined case by case. Typical

emissions may be assumed for all other modeled sources contributing to ambient “background”

for these analyses. For analyses related to the annual NAAQS, typical (i.e., “actual”) emissions

should ordinarily be assumed for all sources, including those which are the subject(s) of the hot

spot analysis.

Generating emission inputs is addressed more fully in Section 15.0.

E15.0 How Do I Evaluate Model Performance And Make Use Of Diagnostic

Analyses?

In Section 16.0, we identify the following approaches as useful for evaluating

performance of an air quality model:

1. get a “big picture” assessment of discrepancies between observations and predictions

using computer graphics;

2. compute various metrics reflecting comparisons between predicted and observed

concentrations of PM components and gaseous precursor species;

3. compute metrics which compare predictions and observations of PM2.5 and extinction

coefficients derived from predicted vs. observed components of particulate matter;

4. compare source attribution estimates obtained with air quality models vs. those derived

from monitored data using receptor models;

5. compare observations vs. predictions of PM components and PM2.5 on weekends vs.

weekdays;

6. compare predicted vs. observed ratios of indicator species, and

7. perform retrospective analyses, in which observed trends are compared with past

modeled projections.

All of the preceding approaches have shortcomings. Thus, States should perform a

variety of tests and view all of the results in a “mini-weight of evidence” assessment of the

model’s performance. Greatest weight should be given to those tests which are most closely
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related to how the model is used in an attainment or reasonable progress demonstration (i.e., to

compute relative reduction factors for individual components of particulate matter). Therefore,

we suggest spending the most effort evaluating the model’s ability to reproduce observed

concentrations of components of PM and some related gaseous species (e.g., ozone, nitric acid). 

We identify a metric (comparison of predicted, spatially averaged component concentrations near

a monitor, averaged over several days with corresponding monitored observations, averaged over

the same days). This metric corresponds closely to how we compute the denominator of the

relative reduction factor. We also identify several additional, potentially useful metrics. 

We recommend that States gather the data and expertise to apply approaches which

evaluate the model’s ability to accurately predict changes in components of particulate matter. 

Thus, we identify weekend/week day and seasonal comparisons, comparisons between predicted

and observed ratios of indicator species and retrospective analyses as having the potential for

evaluating model performance in a most relevant way.

Diagnostic tests should be applied throughout a modeling analysis. We identify several 

key stages for use of these tests: (1) during model setup, (2) during model performance

evaluation, (3) to study predictions at specific times and locations in greater depth, (4) during the

process of choosing/comparing prospective control strategies, and (5) to help estimate

uncertainty in the resulting air quality projections after tentatively selecting a strategy.

 Two types of diagnostic tests are identified: sensitivity tests and process analysis. In

designing and evaluating results of diagnostic tests, States should consider how models are to be

used to support an attainment demonstration. That is, models should be used in a relative sense. 

Thus, diagnostic tests should consider how relative reduction factors (RRF) or other predicted

changes in the components of particulate matter are affected by various factors. Tests which are

useful for exploring, and perhaps improving, model performance are described in Section 16.0. 

Tests that are potentially useful for helping to choose an effective control strategy are identified

in Section 17.0. 
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1.0 Introduction

1.1 What Is The Purpose Of This Document?

This document has two purposes. The first is to recommend how to use

modeling/analysis to demonstrate that a control strategy will lead to air quality which meets

certain national goals. Goals include attaining national ambient air quality standards (NAAQS)

for particles with aerodynamic diameters < 2.5 micrometers (μm) (PM2.5) and making reasonable

progress reducing regional haze. Final determination of whether an air quality goal for PM2.5 or

regional haze is met will be based on a review of monitored data at the appropriate future date. 

Our intent in this guidance is to provide as reliable an indicator as possible of what these future

monitored attainment or reasonable progress tests will show. Because this is a guidance

document rather than a regulation, our recommendations are not requirements. A State may use

alternative procedures if it presents compelling arguments for doing so in a particular case to the

appropriate U.S. Environmental Protection Agency (EPA) regional office(s) and that office

agrees with the State’s assessment.

The likelihood of choosing effective strategies to meet goals for PM2.5 and regional haze

is greater if the strategies are derived using high quality emissions, meteorological and air quality

data. There are many desirable measurements or estimates which could be made to enhance

these data bases. However, time and resources to develop data for regulatory applications are

often limited. Therefore, the second purpose of this guidance is to identify how air quality,

emissions and meteorological data will be used in models or other analyses to demonstrate future

attainment of the NAAQS for PM2.5 or reasonable progress reducing regional haze. By

identifying how data will be used, our intent is to help States and others with an interest in

emission control strategies to prioritize their data needs.

1.2 Does This Guidance Apply To Me?

This guidance applies for all States which need to submit a State implementation plan

(SIP) revision to meet one or more NAAQS for PM2.5 (U.S. EPA, 1997). It also applies for 

States needing to submit a SIP revision to help meet goals to reduce regional haze in “Class I

areas” (i.e., generally, national parks or wilderness areas) subject to the regional haze rule (U.S.

EPA, 1999). 

Two NAAQS have been promulgated for PM2.5: an annual standard and a 24-hour

standard. The annual NAAQS is met at a monitoring site if the annual arithmetic mean PM2.5

concentration, averaged over 3 consecutive years, is < 15.0 μg/m3. The 24-hour NAAQS is met

at a monitoring site if the 98th percentile 24-hour concentration, averaged over 3 consecutive

years, is < 65 μg/m3. The “PM2.5" measurements referred to in the NAAQS must be measured

using a Federal Reference Method (FRM) or an equivalent measurement technique. FRM and

equivalent techniques are described in 40 CFR Part 50, Appendix L and in 40 CFR Part 53. 

Generally, a designated “nonattainment area” for PM2.5 is a geographic location containing one or
2

A deciview is a variable depicting maximum range of visibility. It is related

logarithmically to a physical measurement of attenuation, the extinction coefficient. Deciviews

increase as visibility impairment increases. They are analogous to the decibel system for sound. 

A deciview is calcuated by taking the logarithm of the ratio of a measured extinction coefficient

to a background value for the coefficient (see Section 4.0 for further details). The scale of

deciviews is set up so that changes are approximately linear to changes perceived by a human

eye. 

38 - DRAFT

more monitoring sites where observations do not meet one or both of the NAAQS for PM2.5.

The regional haze rule requires visibility to approach “natural background” levels by

2064. It requires reasonable progress to be made toward this goal over a series of consecutive

periods. The first of these periods covers 2004-2018. The guidance herein focuses exclusively

on this initial period. We expect that guidance for the following periods will be developed as

they become more imminent. The rule identifies two criteria which must be met in order for

reasonable progress reducing regional haze to be demonstrated for a Class I area during the initial

period.

1. Mean regional haze on the 20% of days with the worst visibility averaged over a consecutive

5-year period (i.e., 2000-2004) should be reduced by an agreed upon difference in deciviews2

between 2000/2004 and 2018, and

2. Mean regional haze on the 20% of days with the best visibility averaged over a consecutive 5-

year period (i.e., 2000-2004) should not increase.

1.3 How Does The Perceived Nature Of Particulate Matter And Tools For

Describing It Affect My Use/Interpretation Of Model Results?

Guidance for demonstrating attainment of NAAQS for PM2.5 or reasonable progress

reducing regional haze needs to be consistent with the perceived nature of PM2.5 and regional

haze. In this section, we identify probable attributes of PM2.5 in most locations of interest in

attainment or reasonable progress demonstrations. There may well be exceptions. As we discuss

in Section 9.0, 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.

This guidance is based on a conceptual description of PM2.5 , regional haze, the air quality

goals we are trying to meet and the tools that are available to estimate whether a prospective

strategy will meet the goals. This description is summarized by the following 9 premises.

1. Particulate matter is a mixture.

2. “Secondary” PM is a more important part of PM2.5 than it is of PM10.
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3. Regional haze is closely related to high concentrations of fine particulate matter.

4. The Federal Reference Method used to determine attainment of the NAAQS for PM2.5

is subject to potential interferences which are difficult to measure and model.

5. Many factors affecting formation and transport of secondary particles are the same as

those affecting formation and transport of ozone.

6. Spatial gradients for primary particulate matter may be more pronounced than those for

secondary particulate matter or ozone.

7. Seasonal differences exist in emissions of PM2.5 and its precursors as well as in

meteorological conditions affecting source/receptor relationships.

8. Causes for violations of PM2.5 NAAQS and poor visibility may be diverse.

9. Ability of models to predict future concentrations of PM2.5 and its components is

limited by a variety of factors.

These premises imply several things about how modeling should most appropriately be used to

demonstrate whether a prospective control strategy will meet goals for PM2.5 and/or regional

haze. We discuss these in the following paragraphs.

Premise 1. 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

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

composition of PM2.5 estimated from measurements of ambient species. Third, failure to model

components explicitly robs one of much diagnostic insight. For example, secondary components

of PM2.5 (e.g., sulfates and nitrates) are not independent of one another. Under some

circumstances reducing sulfates can increase nitrates. Thus, to realize the full potential of

reducing sulfates, it might be necessary to reduce precursors to nitrates in this example. To

consider these interrelationships, it is best to use a single air quality model to consider secondary
40 - DRAFT

components of PM.

Premise 2. “Secondary” PM is a more important part of PM2.5 than it is of PM10. Sizedifferentiated 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 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 being emitted as 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 may

take several days, as do some of the removal processes. Most require good mixing between

emissions from a source and its environment. Thus, many of the sources of measured secondary

particulate matter may not be in the immediate vicinity of a measured concentration of PM2.5. 

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 area or mobile sources of precursors may

not be necessary. Individual treatment of precursor emissions from relatively nearby large

sources of combustion may be needed on a case by case basis.
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42 - DRAFT

Premise 3. 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 presence of aerosols. For any given mass, fine particles (i.e., < 2.5

μm) are more efficient at scattering light than are particles > 2.5 μm 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 small (< 2.5 μm) coarse mode (i.e., primary) particles (e.g., composed of

crustal material). Light extinction is 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 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 is enhanced still 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 reducing regional haze will need to address secondary particulate matter. This, in turn,

means that large modeling domains will be necessary. Secondary particles arise from source

plumes being thoroughly mixed with their environment over a period of hours or days. Further,

Class I areas are generally likely to be far removed from most sources of precursors for secondary

particulate matter. Finally, 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, prerequisite

mixing for 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 4. Sampling anomalies associated with the Federal Reference Method for PM2.5 are

difficult to consider with an air quality model. 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. However, the

sampling protocol inherent with the FRM leaves some uncertainty about the water content of

material measured on the filter, as well as measured semi-volatile organic particulate matter and

nitrates. At present, it is not possible to adjust a model prediction to take account of positive or

negative sampling artifacts inherent in the FRM. Thus, one might expect some disagreement

between unadjusted model estimates and monitored concentrations of PM2.5. This has several

implications regarding how models might best be used to support attainment demonstrations for

the PM2.5 NAAQS. First, when evaluating model performance, it is important to assess how

accurately the model is able to reproduce observed components of particulate matter (i.e.,
43 - DRAFT

measured using different, often less uncertain, methodologies). Second, there may well be a

difference between the sum of measured components and the gravimetric mass of PM2.5

measured with the FRM.. This difference needs to be accounted for in some way in a modeled

test for attainment of the NAAQS. Since the FRM is included in the definition of the NAAQS,

the modeled attainment test we will recommend starts with measured concentrations of PM2.5 as

the “ground truth”. Models are used to assess how these measurements are likely to respond to

growth plus control measures applicable at some future date. Thus, we believe models are most

suitable for predicting relative changes in particulate matter and its components rather than using

them by themselves to make statements about future absolute concentrations of PM2.5. 

Premise 5. Many of the factors affecting formation and transport of secondary particles

are the same as those affecting formation and transport of ozone. Although there is not

necessarily a positive correlation between measured ozone and secondary particulate matter,

many of the same factors affecting concentrations of ozone also affect concentrations of

secondary particulate matter. For example, similarities exist in sources of precursors for ozone

and secondary particulate matter. Sources of NOx may lead to formation of nitrates as well as

ozone. Sources of VOC may also be sources or precursors for organic particles. Presence of

ozone itself may be an important factor affecting secondary particulate formation. For example,

as ozone builds up, hydroxyl (OH) radicals do also as a result of equilibrium reactions between

ozone, water and OH in the presence of sunlight. Hydroxyl (OH) radicals are instrumental in

oxidizing gas phase SO2 to sulfuric acid, which eventually gets absorbed by liquid aerosol and

converted to particulate sulfate in the presence of ammonia. Hydroxyl radical and NO are also

precursors for gas phase nitric acid, which gets absorbed by liquid aerosol and, in the presence of

ammonia, leads to particulate nitrate. Chemistry of secondary particulate matter and its

relationship to that for ozone are described in the U.S. EPA Criteria Document for Particulate

Matter (U.S. EPA, 2000).

Strategies to reduce ozone can also affect formation of secondary particulate matter. A

few of the more obvious examples are reducing VOC emissions could reduce ozone and OH. If

sulfate or nitrate production is limited by lack of availability of oxidizing agents, the ozone

reduction strategy could also reduce secondary particulate matter. Reducing NOx emissions

diminishes one of the precursors for nitric acid (i.e., NO2 which results from NO). Therefore, in

the presence of sufficient ammonia, reducing NOx emissions could reduce particulate nitrate

concentrations. There are also more subtle interfaces between strategies to reduce ozone and

secondary particulate matter. For example, reducing NOx in the presence of substantial

particulate sulfates and lack of sufficient ammonia could exacerbate the particulate sulfate

problem, or reducing SO2 in the presence of substantial NOx and ammonia could exacerbate the

particulate nitrate problem.

 

 The preceding discussion implies that models intended to address secondary particulate

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

factors. It also suggests that ability to predict ozone is a first step which States should consider in

evaluating performance of models for secondary particulate matter. Finally, it is clear from the
44 - DRAFT

discussion that States should include previously implemented or contemplated measures to

reduce ozone in their air quality model applications to predict secondary particulate matter.

Premise 6. 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, many 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.

The preceding implies that it is 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. It is unlikely that there

will be monitored PM2.5 data near each such location. 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 7. 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, agricultural

burning, prescribed forestry burns 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 each of four seasons and that a representative portion of

days may need to be modeled from each season. 

Premise 8. 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
45 - DRAFT

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 9. 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. In some cases,

most notably for secondary organic particulate matter, the extent to which current simplifications

are reasonable is uncertain. These limitations (and others) make the ability of a model to

accurately predict concentrations of PM2.5 and its components at a given time and location

doubtful.

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. Such corroboratory analyses should include periodic reviews of air quality data,

implementation schedules, meteorological measurements, etc. (e.g., one or more “mid-course

reviews”) using updated data bases and modeling tools (if available) so that a strategy can be

adjusted, if warranted.

Premises 1-9 represent a kind of “conceptual description” of likely PM2.5 and regional

haze problems viewed from a national perspective, as well as the ability of models to deal with

these problems. Guidance in subsequent chapters reflects needs and problems raised in this

conceptual description. 

1.4 What Topics Are Covered In This Guidance?

Part I (Sections 2.0-7.0) of this document describes how results of air quality models and

other analyses should be used to see if a proposed emissions control strategy will lead to

attainment of the NAAQS for PM2.5 and/or reasonable progress reducing regional haze. Section

2.0 provides an overview of a modeled attainment demonstration for the annual and 24-hour

NAAQS for PM2.5 . This demonstration consists of a modeled attainment test and, if a State so

chooses, a weight of evidence determination. Section 2.0 also summarizes the modeled test for

reasonable progress reducing regional haze. Section 3.0 describes attainment tests for each of the

two NAAQS for PM2.5. Section 4.0 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 5.0 explains how weight of evidence may be used to supplement the
46 - DRAFT

attainment test in an attainment demonstration or the modeled test for reasonable progress

reducing regional haze. Section 6.0 identifies key data gathering priorities and related activities

which could lead to better estimates of air quality for a SIP revision and in subsequent reviews. 

Section 7.0 identifies documentation which should accompany a SIP revision in order to

adequately describe the basis for assuming that the selected strategy demonstrates attainment or

reasonable progress. 

Part II (Sections 8.0 - 18.0) of this document identifies key data needed in modeling

which supports the analyses described in Part I. Part II also describes how the data should be

used in models and other analyses to generate results used in attainment or reasonable progress

demonstrations. Section 8.0 provides an overview of the steps needed to produce credible air

quality modeling results. Each of the major steps is outlined in the following sections. Section

9.0 discusses why a conceptual description is needed of each area’s PM2.5 or regional haze

problem, and explains how such a description should be developed. Section 10.0 addresses the

need to develop a modeling protocol. The protocol translates implications of a conceptual

description into a modeling/analysis plan to evaluate effects of proposed control strategies. 

Section 11.0 describes factors which a State should consider in selecting one or several models to

address goals for PM2.5 and regional haze. Section 12.0 provides guidance on choosing suitable

periods to model when addressing the annual and 24-hour NAAQS for PM2.5 and goals to reduce

regional haze. Section 13.0 covers choice of modeling domains and the degree of horizontal and

vertical spatial detail needed for the types of model applications we discuss. Section 14.0

indicates procedures which are appropriate for generating meteorological and air quality inputs to

models used to support demonstrations for PM2.5 and regional haze. Section 15.0 identifies

needed emissions data and describes how available data should be converted to a form needed by

air quality models. A discussion on estimating future emissions is also included. Section 16.0

identifies a series of tests which are potentially useful for assessing whether a model’s

performance is adequate for it to be used to support an attainment or reasonable progress

demonstration. Section 17.0 identifies several procedures which may be useful for identifying

control strategies which are effective in meeting air quality goals for PM2.5 or regional haze. 

Section 18.0 lists references cited in our guidance document. 

We have tried to make this guidance comprehensive. However, situations which we have

failed to anticipate will undoubtedly occur. Further, we expect that our knowledge about fine

particulate matter will evolve as data bases improve and the user community gains more

experience with the use of models for particulate matter. Thus, we recognize that States may

find it appropriate to deviate from the default recommendations in this guidance. Indeed, the

guidance itself is likely to be updated from time to time as our knowledge and experience

regarding fine particulate matter, regional haze and related analysis methods improves. States

should coordinate their approaches for demonstrating future attainment of air quality goals for

PM2.5 and regional haze with the appropriate U.S. EPA regional office(s) to reach consensus on

the most appropriate procedure(s) for the case at hand.
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Part I

How Do I Use Model Results To See If An Air Quality Goal Is Met?
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2.0 What Is A Modeled Demonstration?–An Overview For The PM2.5 NAAQS And

Regional Haze Goals

Modeled attainment demonstrations meeting goals for PM2.5 and regional haze consist of

(a) analyses which estimate whether selected emissions reductions will result in ambient

concentrations that meet the NAAQS or reasonable progress goals, and (b) an identified set of

measures which will result in the required emissions reductions. This guidance focuses on the

first component of an attainment demonstration--interpretation and conduct of analyses to

estimate the amount of emission reduction needed to reduce concentrations of PM2.5 or its

components to levels consistent with meeting the NAAQS or reasonable 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 should estimate the amount of emission reduction needed to demonstrate

attainment the NAAQS by using a modeled attainment test. In some cases, where there are

sources which are major emitters of primary particulate matter and no nearby air quality

monitoring, States should also perform a “hot spot analysis” for the source(s) in question. We

recommend a similar modeled test for demonstrating reasonable progress toward reducing

regional haze to natural background levels. In addition to the modeled tests and hot spot analysis

(for NAAQS-related applications), a State may consider a broader set of corroboratory analyses

to determine whether the “weight of evidence” indicates that a proposed emission reduction will

lead to attainment of the NAAQS or reasonable progress goals.

2.1 What Is The Recommended Modeled Attainment Test?–An Overview

The modeled attainment test for the annual or 24-hour NAAQS needs to be applied if

there is a violation of the corresponding NAAQS within or nearby a designated nonattainment

area. The test we recommend reflects PM2.5's nature as a mixture. This is done by dividing

monitored PM2.5 into major components. In the test, these are

- mass associated with sulfates

- mass associated with nitrates

- mass associated with organic carbon

- mass associated with elemental carbon

- mass associated with primary inorganic particulate matter other than primary sulfate and

nitrate particles

- mass in a catchall category reflecting the difference in measured mass of PM2.5 and the

sum of the mass from the other components of PM2.5 .

In the recommended modeled attainment test for the annual or 24-hour NAAQS, an air

quality model is used to simulate current and future air quality. Our recommended test uses

model estimates in a “relative” rather than “absolute” sense. That is, we take the ratio of the

model’s future to current predictions of the components of PM2.5 at FRM or equivalent PM2.5
50 - DRAFT

monitoring sites. We call each of these site-specific ratios, component-specific relative

reduction factors. 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 six future “component specific design

values” computed at the site. If the predicted future design value for PM2.5 is < 15.0 μg/m3

(applications related to the annual NAAQS) or < 65 μg/m3 (applications related to the 24-hour

NAAQS), the test is passed. If all such future site-specific PM2.5 design values are # the

concentration specified in the NAAQS, the test is passed.

2.2 What Is A Hot Spot Analysis And Why Is It Needed?

The modeled attainment test we recommend predicts whether or not all observed future

PM2.5 design values will be less than or equal to the concentration level specified in the NAAQS. 

By itself, the test makes no statement about future PM2.5 at locations where there is no nearby

monitor. Secondary particulate matter requires mixing and hours or days to materialize. Further,

the long sampling time (i.e., 24 hours) and averaging time inherent in the NAAQS is also likely

to reduce spatial gradients in PM2.5. Nevertheless, we recognize that concentrations of PM2.5

which are substantially higher than measured design values could exist near major sources of

primary PM2.5 emissions. Thus, we recommend States use a supplementary “hot spot” analysis to

identify other locations where passing the test might be a problem if monitoring data were

available. 

Like the modeled attainment test itself, the hot spot analysis is described more fully in

Section 3.0. Briefly however, it entails the following two steps.

- The first step is to identify locations where a hot spot analysis may be needed. This may

be done in one of two ways. The first approach is to review the spatial distribution of

estimated primary PM2.5 emissions for grid cells within a designated nonattainment area

for PM2.5. Note the location of grid cells in which primary emissions (i.e., from point,

area and mobile sources) are well above (e.g., > 20% higher) those “near” any monitoring

site for PM2.5 within the designated nonattainment area. Major sources within such cells

are candidates for a hot spot analysis. The second approach for flagging major sources

subject to a hot spot analysis makes use of available results from air quality modeling. In

this approach, a State should flag major sources in any grid cell for which predicted

concentrations of primary particulate matter are substantially (e.g., > 20% greater) and

consistently (e.g., > 50% of the days modeled) greater than predicted concentrations of

primary particulate matter near any site monitoring PM2.5.

- Apply a point/area/line source model to flagged sources for 3 consecutive years to

estimate a projected future highest 3-year average annual mean concentration and/or

highest 3-year average 98th percentile 24-hour concentration of primary particulate matter
51 - DRAFT

near each flagged source. Add each of these estimates to concentrations estimated from

interpolating future PM2.5 annual mean concentrations obtained with the modeled

attainment test for the annual NAAQS. If the resulting estimate exceeds the level

specified in the NAAQS, remedial measures are needed. 

2.3 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 primary inorganic particulate matter other than primary sulfate and

nitrate particles (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 coefficients. This ranking is used to identify the 20% of days

with worst and 20% 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 in 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 2 goals for reasonable progress are realized.

2.4 What Does A Recommended Weight Of Evidence Determination Consist Of?--

An Overview

As we note later in Section 9.0, States should perform complementary analyses of air

quality, emissions and meteorological data, and consider available modeling outputs other than

the results of the PM2.5 attainment test, related hot spot analyses or reasonable progress tests. 
52 - DRAFT

Such additional analyses are used to develop a conceptual description of an area’s problem. A

conceptual description is instrumental for guiding an air quality modeling application. 

Sometimes, States may choose to use results of corroboratory analyses in a weight of evidence

determination to conclude that attainment of the air quality goal is likely despite modeled results

which do not quite pass the attainment or reasonable progress test or for which a hot spot analysis

suggests remedial measures are needed to meet a NAAQS. If a conclusion differs from the

outcome of the modeled tests, the need for subsequent review (several years hence) with more

complete data bases is increased. 

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

diverse types of analyses, including results from the modeled attainment or reasonable progress

test and, if applicable, one or more hot spot determinations. States should next note whether or

not results from each of these analyses support a conclusion that the proposed strategy will meet

the air quality goal. States should then weigh each type of analysis according to its credibility as

well as its ability to address the question being posed (e.g., is the strategy adequate for meeting

the PM2.5 NAAQS by a defined deadline?). Next, 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 involving some subjectivity. If it is concluded that a

strategy is inadequate to demonstrate attainment or reasonable progress, a new strategy is

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

documenting how and why the conclusion is reached regarding 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 or

reasonable progress goals will be met. This follows from including ability to address the

question being posed as one of two criteria for weighing results from different analyses and from

a model’s ability to integrate information from scientific theory and observed data. For example,

in NAAQS-related applications, if the modeled attainment test is passed and the hot spot

analyses indicate no potential problem, this supports a hypothesis that the strategy is adequate to

meet the NAAQS. This information is included as one of several elements in a weight of

evidence determination to assess the strategy’s adequacy. The further model results are from

passing the modeled attainment test, the more compelling contrary results from other analyses

have to be for a control strategy to demonstrate attainment. If either the modeled attainment test

or a hot spot analysis produces results far in excess of the concentration specified in the NAAQS,

it is doubtful that other evidence will be sufficiently convincing to conclude that the NAAQS

will be attained. States should ordinarily consider a revised control strategy.

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

Corroboratory Analyses Be Used In A Weight Of Evidence Determination?

The procedures we recommend for using model results to address problems related to

PM2.5 and regional haze differ from those used in the past to address attainment demonstrations
53 - DRAFT

for PM10 and for total suspended particulate matter (TSP). Major differences include using

models in a relative sense in concert with monitored data, estimating changes in mass of

particulate matter as the sum of estimated changes in its major components, and recognizing use

of supplementary analyses in a weight of evidence determination in a more formal way. There

are several reasons why we believe these differences are appropriate.

1. The definitions of the air quality goals and the importance of secondary particulate

matter supports using such a test. Both NAAQS focus on a concentration which is averaged

over 3 consecutive years. The goals for regional haze focus on mean worst and best visibility

averaged over 5 consecutive years. Such goals present difficulties using models which address

secondary particulate matter. Non-linearities which may exist between secondary particulate

matter and its precursors mean that models addressing this important part of PM2.5 and light

extinction are likely to be resource intensive. This, in turn, means that it will probably not be

feasible to model 3 or 5 consecutive years to estimate the effects of a strategy on meeting the

goals. The problem of relating model results to the NAAQS is reduced by using monitored

design values as an inherent part of the modeled attainment test. The monitored design values

are calculated consistently with the form of the NAAQS, and serve to “anchor” a model response

to the form of the NAAQS. Problems relating model results to the goals for regional haze can be

addressed in a similar fashion. 

2. 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 possiblity is reduced if the

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

measurements.

3. Starting with an observed rather than modeled concentration as the base value subject to

improvement reduces problems in interpreting model results. This is especially true for air

quality goals, like the NAAQS, in which an absolute value is established as the goal. If one

relies on absolute model predictions, interpretive difficulties result if the model under (or over)

predicts an observed daily or annual concentration. For example, if a 24-hour PM2.5

concentration of 70 μg/m3 were observed and a model predicted 80 μg/m3 on that day, should the

target for the day, nevertheless, be 65 μg/m3? Although good model performance remains a

prerequisite for using a model in an attainment demonstration, problems posed by disagreements

which are within acceptable bounds are reduced by the new procedure. 

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

procedure we recommend recognizes this by allowing use of modeling plus other analyses to

determine whether weight of available evidence supports a conclusion that a proposed emission

reduction will suffice to meet the NAAQS or goals for regional haze.
54 - DRAFT

5. Focusing the modeled attainment test on monitoring sites could result in control targets

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

supplementing the recommended attainment test with a hot spot analysis performed for major

point sources of primary particulate matter not having a nearby monitoring site. This exercise

provides a means for remedial actions despite passing the modeled attainment test. Because

models for individual sources of primary particulate matter are significantly less resource

intensive than models needed for the modeled attainment test, it should be possible to apply these

models in a way which is consistent with the form of the NAAQS for PM2.5 .

Recommendations. States should estimate emission reductions needed to

demonstrate attainment using a modeled attainment test which focuses on sites

measuring PM2.5 with Federal Reference or equivalent methods .

 The modeled

attainment test specifically addresses 6 major components of measured PM2.5. States

should use model predictions in a relative sense to compute a relative reduction

factor for each component associated with a strategy. These factors should be

multiplied by “component specific design values” derived from speciated

measurements and PM2.5 measurements at different monitors. A site specific future

PM2.5 design value is the sum of effects of a strategy estimated for each of the 6

major components of measured PM2.5. If each estimated future site-specific design

value for PM2.5 is < the concentration specified in the NAAQS, the test is passed. 

States should review emissions and available modeled data to see if the modeled

attainment test needs to be supplemented by additional modeling performed near

major sources of primary particulate matter. If a hot spot analysis is performed, the

impact of an individual source, when superimposed on a background value

applicable to the designated nonattainment area should be < the concentration

specified in the NAAQS.

We recommend that States use a modeled test for reasonable progress reducing

regional haze which is similar to the recommended NAAQS attainment test. States

should use a model to estimate relative changes in 6 types of particulate matter

which have been previously related to visibility extinction. These relative reduction

factors should be used to estimate future concentrations for each type of particulate

matter. The resulting future concentrations are used to derive future values for the

mean 20% worst and mean 20% best extinction coefficients. Future extinction

coefficients should be compared to current ones to determine if the goals for

reasonable progress have been met.

Prior to applying the modeled tests, States should undertake complementary

analyses of air quality, meteorological and emissions data. These additional

analyses are needed to design and focus modeling which underlies the modeled

attainment or reasonable progress tests. Provided results of the modeled tests and

(if applicable) hot spot analyses are not failed by a wide margin, States may also use
55 - DRAFT

evidence produced by corroborative analyses together with results of the tests in a

weight of evidence determination. A weight of evidence analysis may be used either

to increase or decrease emission reductions identified by the modeled tests as

necessary to meet the NAAQS or goals for regional haze.
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3

 See 40 CFR Part 58, Appendix D, Section 2.8 for a description of criteria needed for

sites to qualify for spatial averaging to determine compliance with the annual NAAQS for PM2.5.

57 - DRAFT

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

Monitored data will ultimately provide the basis for determining whether the two

NAAQS for PM2.5 have been attained. The intent of this guidance is to identify a modeled

attainment test which is a good predictor of the monitored attainment test’s future outcome. 

Accordingly, our recommended modeled attainment tests for the annual and 24-hour NAAQS

attempt to closely mimic the corresponding monitored attainment tests. In this section, we begin

by illustrating the monitored tests for the annual and 24-hour NAAQS. We then recommend a

modeled attainment test for the annual NAAQS. This is followed by recommending a modeled

attainment test for the 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 hot spot analysis

which may be needed for unmonitored locations near one or more major sources of primary

particulate matter. 

By definition, the annual NAAQS 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 μg/m3

. 40CFR Part

58, Appendix N stipulates that the annual arithmetic mean at a monitoring site is to be calculated

by averaging the four quarterly arithmetic mean concentrations observed during a calendar year

at the site. Generally, the annual mean concentration averaged over three years must be < 15.0

μg/m3 at all monitoring sites. An exception occurs however at sites meeting criteria for spatial

averaging3. For such sites, the NAAQS is met if the 3-year average of the average annual

arithmetic means of all sites so lumped together is < 15.0 μg/m3

. Tables 3.1 and 3.2 illustrate

how to use monitored data to determine if the annual NAAQS for PM2.5 is met. A more

complete description of data handling conventions for the PM2.5 NAAQS can be found in U.S.

EPA (1999a).

Table 3.1. Monitoring Data In Area “A” (with no sites qualifying for spatial averaging)

(1) (2) 

Arithmetic

Mean, Year 1,

μg/m3

(3)

Arithmetic

Mean, Year 2,

μg/m3

(4)

Arithmetic

Mean, Year 3, 

μg/m3

(5)

Average

Arithmetic

Mean, μg/m3

Monitor 1 18.0 14.0 15.0 15.7

Monitor 2 16.0 14.0 14.0 14.7

Monitor 3 12.0 13.0 14.0 13.0
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Table 3.2. Monitoring Data In Area “B” (having two clusters of spatially averaged

monitors plus two additional monitoring sites which are not spatially averaged)

(1) (2)

Arith.Mean

Year 1, 

μg/m3

(3)

Arith.Mean

Year 2,

μg/m3

(4)

Arith.Mean

Year 3, 

μg/m3

(5)

Average

Arith.Mean,

μg/m3

(6)

Spatially

Averaged

Average

Arith.Mean,

μg/m3

Monitor 1 10.0 12.0 11.0 11.0 NA

Monitor 2 14.0 12.0 10.0 12.0 NA

Monitor 3

(in cluster 1)

17.0 14.0 17.0 16.0

Monitor 4

(in cluster 1)

13.0 12.0 12.0 12.3

Cluster 1

Spatial

Average

15.0 13.0 14.5 NA 14.2

(= (15.0+13.0+14.5)/3)

Monitor 5

(in cluster 2)

11.0 12.0 10.0 11.0

Monitor 6

(in cluster 2)

15.0 14.0 14.0 14.3

Monitor 7

(in cluster 2)

15.5 14.0 14.5 14.7

Cluster 2

Spatial

Average

13.8 13.3 12.8 NA 13.3

To determine whether the NAAQS is met, we compare each of the italicized numbers in column

(5) with 15.0 micrograms/m3

. Note that the NAAQS is not met in Area A, because the average

arithmetic mean at one of the sites, monitor 1, is greater than 15.0 μg/m3

.

 The NAAQS is met in Area “B”. This follows since the average arithmetic means at sites

1 and 2 are not greater than 15.0 μg/m3

, and the spatially averaged average means for sites in

cluster 1 and for sites in cluster 2 are also no greater than 15.0 μg/m3 (i.e., all the italicized

numbers in the table are < 15.0 μg/m3

). Note that the NAAQS is met in Area “B”, even though

the average annual arithmetic mean observed at site 3 is greater than 15.0 μg/m3. This follows,
4

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

monitored attainment test.

59 - DRAFT

because site 3 is one which qualifies to be treated as part of a spatial average (i.e., with site 4).

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 μg/m3.

4

 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. 

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 - 100 Highest Observed Concentration

101 - 150 2nd Highest Concentration

151 - 200 3rd Highest Concentration

201 - 250 4th Highest Concentration

251 - 300 5th Highest Concentration

301 - 350 6th Highest Concentration

351 - 366 7th Highest Concentration

 

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

samples/year), our recommended modeled attainment test would examine whether the 2nd highest

observation (averaged over 3 years) at the site becomes < 65 μg/m3 as a result of a simulated

control strategy.

3.1 What Is The Recommended Modeled Attainment Test For The Annual

 NAAQS?

In order to perform the recommended modeled attainment test, States should divide
5

Some of these components are not independent of one another. The model(s) whose

results are used in our recommended tests needs to take account of these interdependencies.

6

“Current emissions” are those corresponding to the period reflected by the monitored

design value used in the test. Regarding the recommended projection year, air quality used to

assess attainment in the future will be based on 3 years of observations. Emissions need to be

consistent with a projected attainment concentration throughout the 3-year period to increase the

likelihood that attainment will occur. Thus, if a nonattainment area were given an attainment

date of 2012, the projections in the modeled attainment test should be to 2010.

60 - DRAFT

observed mass concentrations of PM2.5 into 6 components5

:

- mass associated with sulfates (SO4);

- mass associated with nitrates (NO3);

- mass associated with organic carbon (OC);

- mass associated with elemental carbon (EC);

- mass associated with inorganic particulate emissions, excluding primary sulfate and

nitrate particles (IP);

- unattributed mass (i.e., the difference between measured PM2.5 and the sum of the other

5 components) (U).

To apply the test, States must first have run an air quality model at least twice--to

simulate current emissions and to simulate the net effects of a proposed control strategy and

growth projected to two years prior to the required attainment date.6

 We recommend a modeled

attainment test which has 6 steps.

Step 1. Compute observed quarterly mean PM2.5 and quarterly mean composition for each

monitor. 

For each monitoring site, compute quarterly arithmetic mean values for PM2.5, averaged

over 3 years, from monitored observations. For each day having corresponding speciated PM2.5

data, determine the fraction of PM2.5 comprised of mass associated with SO4, NO3, OC, EC, IP

and U. Using the resulting day-specific fractions, estimate observed quarterly average fractions

for each of the 6 components. For each monitoring site, multiply the averaged quarterly mean

observed PM2.5 concentration times the quarterly average fractions for each of the 6 components. 

This yields the “observed” average quarterly concentration for each of the 6 major components of

measured PM2.5. 

Note that we recommend a procedure which differs slightly from that in U.S. EPA,

(1999a). That is, for modeling purposes, States should average observations over a 3-year period

on a quarter by quarter basis rather than on an annual basis. Our recommendation recognizes that

not every day with measured PM2.5 is likely to have speciated data needed to derive day-specific

component fractions. However, quarterly mean compositional data (even if drawn from a more
61 - DRAFT

limited sample) should ordinarily be close to that which would be observed if speciated data were

available for every day with a PM2.5 measurement, provided the more limited sample is large

enough. Our recommendation is made to increase the sample size of speciated measurements

used to compute the fraction of observed PM2.5 which is comprised of each of the 6 components

in each quarter. Quarters should correspond to 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 comparisons with the NAAQS use calendar quarters, so the modeling

exercise should do so as well. 

Step 2. Derive relevant quarterly observed spatially averaged air quality values, where

appropriate.

For monitoring sites subject to spatial averaging, compute spatially averaged quarterly

mean PM2.5 values and spatially averaged component fractions for all sites which are associated

with one another. 

Step 3. Using model results, derive component-specific relative reduction factors (RRF) at

each monitor and apply these to observed air quality to obtain a projected quarterly PM2.5

estimate. 

For each monitoring site, obtain modeled estimates for 5 of the 6 major components of

observed PM2.5 (i.e., SO4, NO3, OC, EC and IP). For each 3-month quarter, calculate site (i) and

component (j) specific relative reduction factors (RRF)ij. The relative reduction 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 

current design value for annual PM2.5 (e.g., 2000-2002);

Cj, projected is the quarterly mean concentration predicted at or near the monitoring

site two years prior to the required attainment date (e.g., 2010 in an area

having a 2012 attainment date).

Assume that the relative reduction factor for the sixth previously identified component of

observed PM2.5 (i.e., “U”) is “1.00". Apply each component-specific relative reduction factor to

the observed average quarterly mean concentration of the corresponding component, derived in

step 1. Add the 6 components to obtain a projected average quarterly mean PM2.5 concentration.
62 - DRAFT

Step 4. Calculate site-specific projected average annual mean PM2.5 for all sites not subject

to spatial averaging.

For each monitoring site calculate the predicted projected average annual arithmetic mean

PM2.5 concentration by averaging the 4 projected site-specific average quarterly mean

concentrations obtained in step 3. Note those sites which are not subject to spatial averaging.

Step 5. Calculate spatially averaged projected average annual mean PM2.5 for sites subject

to spatial averaging.

For sites subject to spatial averaging, calculate the arithmetic mean of the projected

average annual mean PM2.5 concentrations for each site in a cluster of sites which have been

previously identified (by others) as subject to spatial averaging.

Step 6. Compare all projected average annual mean PM2.5 concentrations obtained in steps

4 and 5 with 15.0 μg/m3

.

For each site not subject to spatial averaging, compare the projected average annual

arithmetic mean PM2.5 concentration obtained in Step 4 with 15.0 μg/m3. Also compare each

spatially averaged projected average annual arithmetic mean PM2.5 concentration obtained in Step

5 with 15.0 μg/m3. If all values are < 15.0 μg/m3, the test is passed.

We illustrate application of the recommended test in example 3.1.

Example 3.1

Given: (1) Area “C” has 3 monitoring sites. Sites 2 and 3 meet the criteria for spatial averaging.

(2) Monitored air quality data show the following average quarterly mean PM2.5 

concentrations based on 3 years’observations at each site (values are in μg/m3 ).

Site Quarter 1 Quarter 2 Quarter 3 Quarter 4

1 17 16 16 14

2 15 17 18 13

3 17 19 15 15

(3) Monitored data also show the following regarding composition of each observed 

quarterly mean PM2.5 concentration. For clarity of presentation, we assume there is no

quarter to quarter difference in measured composition at sites 2 and 3.
7

Note: the RRF for the unattributed component (U) is always “1.00".

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Site Quarter EC OC SO4 NO3 IP U

1 1 10% 40% 30% 5% 5% 10%

1 2 10% 20% 50% 5% 5% 10%

1 3 10% 20% 50% 10% 5% 5%

1 4 5% 30% 40% 10% 10% 5%

2 All 10% 40% 40% 0% 5% 5%

3 All 5% 20% 45% 5% 5% 20%

(4) Modeled results show the following relative reduction factors (RRF)ij in predicted

mass of the 6 components of PM2.5:

Site Quarter RRFEC RRFOC RRFSO4 RRFNO3 RRFIP RRFU

7

1 1 .90 .80 .60 .80 1.00 1.00

1 2 1.00 1.00 .50 .60 .90 1.00

1 3 1.00 1.00 .50 .60 .90 1.00

1 4 .90 .80 .60 .80 .90 1.00

2 1 .90 .80 .60 .80 1.00 1.00

2 2 .90 .80 .60 .80 1.00 1.00

2 3 1.00 .90 .60 .60 .90 1.00

2 4 1.00 .90 .60 .60 .90 1.00

3 1 .90 .80 .70 .80 .90 1.00

3 2 .90 .80 .70 .80 .90 1.00

3 3 .90 .80 .70 .80 .90 1.00

3 4 .90 .80 .70 .80 .90 1.00

Find: Will Area “C” will meet the annual NAAQS for PM2.5 by the required attainment date?

 

Solution:

Step 1. Derive relevant quarterly observed site specific quarterly air quality values 
64 - DRAFT

 This information is provided in “given” items (2) and (3).

Step 2. Derive relevant quarterly observed spatially averaged air quality values for sites 2

and 3.

Using the information in “given” item (2), the spatially averaged average quarterly mean

PM2.5 concentrations for sites 2 and 3 are:

Quarter 1: 16.0 μg/m3 (i.e., (15 + 17)/2)

Quarter 2: 18.0 μg/m3

Quarter 3: 16.5 μg/m3

Quarter 4: 14.0 μg/m3

Using the information in “given” item (3), the spatially averaged composition data for sites 2 and

3 by quarter are:

Quarters 1: EC(7.5%), OC(30%), SO4 (42.5%), NO3 (2.5%), IP (5%), U (12.5%)

Quarters 2-4 are the same as Quarter 1 in this example.

Step 3. Apply quarterly component-specific relative reduction factors to quarterly

observations to obtain quarterly projected air quality estimates for each monitoring site.

Use information in “given” items (2), (3) and (4) for sites 1, 2 and 3.

Site 1:

Quarter 1: 

[PM2.5]1 projected = ([PM2.5]1 current) 3 [(RRF)j (Fraction of observed PM2.5 comprised of component j)]1 

= [17.0] [(.10)(.90) + (.40)(.80) + (.30)(.60) + (.05)(.80) + (.05)(1.00) + (.10)(1.00)]

= 13.3 μg/m3

Quarter 2: 

[PM2.5]1 projected = [16.0] [(.10)(1.00) + (.20)(1.00) + (.50)(.50) + (.05)(.60) + (.05)(.90) + (.10)(1.00)]

= 11.6 μg/m3

Quarter 3: 

[PM2.5]1 projected = [16.0] [(.10)(1.00) + (.20)(1.00) + (.50)(.50) + (.10)(.60) + (.05)(.90) + (.05)(1.00)]

= 11.3 μg/m3
65 - DRAFT

Quarter 4:

[PM2.5]1 projected = [14.0] [(.05)(.90) + (.30)(.80) + (.40)(.60) + (.10)(.80) + (.10)(1.00) + (.05)(1.00)]

= 10.6 μg/m3

Using the relevant information in “given” items (2), (3) and (4) and identical procedures

followed for site 1, average quarterly projected mean PM2.5 concentrations are computed for sites

2 and 3. The following estimates result.

Site 2: 

Quarter 1: [PM2.5]2 projected = 11.3 μg/m3

Quarter 2: [PM2.5]2 projected = 12.8 μg/m3

Quarter 3: [PM2.5]2 projected = 14.3 μg/m3

Quarter 4: [PM2.5]2 projected = 10.3 μg/m3

Site 3:

Quarter 1: [PM2.5]3 projected = 12.8 μg/m3

Quarter 2: [PM2.5]3 projceted = 14.3 μg/m3

Quarter 3: [PM2.5]3 projected = 11.3 μg/m3

Quarter 4: [PM2.5]3 projected = 11.3 μg/m3

Step 4. Calculate site-specific projected average annual mean PM2.5 for site 1.

Using the quarterly mean projections for site 1 obtained in step 3, we get

Annual [PM2.5]1 projection = (13.3 + 11.6 + 11.3 + 10.6)/4 = 11.7 μg/m3

Step 5. Calculate spatially averaged projected average annual mean PM2.5 for sites 2 and 3.

First, use the information generated in step 3, to compute spatially averaged quarterly

means for the two sites

Quarter 1 [PM2.5]projection = (11.3 + 12.8)/2 = 12.1 μg/m3

Quarter 2 [PM2.5]projection = (12.8 + 14.3)/2 = 13.6 μg/m3

Quarter 3 [PM2.5]projection = (14.3 + 11.3)/2 = 12.8 μg/m3

Quarter 4 [PM2.5]projection = (10.3 + 11.3)/2 = 10.8 μg/m3

Then, we average these 4 quarterly values to obtain the projected average annual mean PM2.5 for

the two sites.

Annual Spatially Averaged [PM2.5]projection = (12.1 + 13.6 + 12.8 + 10.8)/4 = 12.3 μg/m3
66 - DRAFT

Step 6. Compare the site specific annual projection at site 1 and the spatially averaged

annual projection for sites 2 and 3 to 15.0 μg/m3.

We note from Step 4, that the site-specific annual projection for site 1 is 11.7 μg/m3

. From Step

5, we see that the spatially averaged annual projection for sites 2 and 3 is 12.3 μg/m3. Neither of

these projections is greater than 15.0 μg/m3

. Therefore, the modeled attainment test is passed in

Area “C”.

Recommendations. Calculations underlying the modeled attainment test for the

annual NAAQS should be performed on a quarter by quarter basis. We

recommend measured PM2.5 be split into 6 components: mass associated with

sulfates, nitrates, organic carbon, elemental carbon, inorganic particulate matter

and unattributed mass. The calculations should be performed for each of 6

components of PM2.5 rather than on PM2.5 as a single entity. The test multiplies

quarterly component-specific relative reduction factors times concentrations of each

component, derived from observed data. Relative reduction factors are estimated

by noting the modeled response of each component to a proposed strategy. 

Resulting projected quarterly component concentrations are then aggregated to

estimate projected average annual mean PM2.5 concentrations. The test we

recommend has 6 steps:

1. Derive quarterly mean estimates for PM2.5 and for elemental carbon, organic

carbon, sulfates, nitrates, inorganic particulate matter and unattributed/unmodeled

particulate matter from current observed data at each PM2.5 monitoring site.

2. Using results of step 1, calculate observed spatially averaged air quality data for

sites which qualify for spatial averaging.

3. Using model results, derive component-specific quarterly relative reduction

factors for each monitoring site. Apply the relative reduction factors and results of

step 1 to estimate projected quarterly mean PM2.5 concentrations at each site.

4. Calculate site-specific average annual mean PM2.5 for all sites not subject to

spatial averaging using the information generated in step 3.

5. Calculate spatially averaged projected average annual mean PM2.5 for sites

subject to spatial averaging.

6. Compare all projected average annual mean PM2.5 values obtained in steps 4 and

5 with the level specified in the annual NAAQS (15.0 μg/m3

). 

If all values compared in step 6 are < 15.0 μg/m3

, the test is passed. 
67 - DRAFT

3.2 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 3 years). In the test, we are

interested in seeing what it takes to reduce current design values (i.e., 98th percentile

concentrations averaged over three consecutive years) at each site to < 65 μg/m3. 

Ideally, the modeled attainment test should reflect model results obtained for all days in

each season having observed PM2.5 concentrations below (but > 65 μg/m3) 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 reduction factors

reflect composite responses from many days. Therefore, we recommend modeling as many days

as feasible where observed PM2.5 is greater than 65 μg/m3. As with the annual NAAQS (and for

the same reasons), the preferred approach is also to develop relative reduction 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, from limited monitoring data available as of 2000, there is

not likely to be many days with observed concentrations greater than 65 μg/m3

. If this results in

a sample size of smaller than about 10 days per quarter, days with PM2.5 observations > ~55

μg/m3

 and speciated measurements should also be considered. If, after doing this, there is still a

small number of days in a single quarter, it may be combined with the most appropriate other

quarter. If the sample of high days is limited in 2 or more quarters, a State may develop relative

reduction factors which reflect a combined sample of days from the entire year.

We recommend a modeled attainment test for the 24-hour PM2.5 NAAQS with 5 steps.

Step 1. For each PM2.5 monitoring site, determine the 98th percentile concentration for each

of three years. Examine all days having observations > 55 μg/m3 to determine which have

speciated PM2.5 data.

Use the information in Table 3.3 plus knowledge of the number of PM2.5 mass

measurements to determine concentrations corresponding to the 98th percentile observation for

each year at each site. Determine all occasions having observations greater than 65 μg/m3 with

available speciated measurements. The goal of the modeled attainment test is to provide

assurance that measured PM2.5 with representative species profiles is reduced to < 65 μg/m3

. If

there is a limited number of days with observed concentrations > 65 μg/m3, we suggest States

consider days with observed PM2.5 > 55 μg/m3 to increase the available sample size for modeling. 
8

Ideally, quarter-specific composition profiles should be derived. If there is an

insufficient number of speciated observations on occasions with high observed PM2.5, it will be

necessary to use average annual profiles for days with high observed PM2.5. See Section 12.3 for

further discusion.

9

Excluding “ramp-up” days

68 - DRAFT

Identify all such days which also have speciated data. These are the candidate days for modeling.

Step 2. Compute the observed 3-year average design value at each monitoring site.

This step is performed to establish a site specific concentration to be reduced to 65 μg/m3

. It is

computed by taking the arithmetic mean of the 98th percentile concentration at a site for each of 3

consecutive years. Corresponding “observed” values for each of the 6 components of PM2.5 are

derived by computing an arithmetic mean composition profile8

 from speciated measurements on

the modeled days and multiplying this mean site-specific composition profile times the observed

site-specific PM2.5 design value.

Step 3. For each site, develop component-specific relative reduction factors to be applied to

the current site-specific “observed design values” derived for each component in step 2.

At each monitoring site, calculate the arithmetic mean concentration (using all modeled

days in a quarter9

) of each component corresponding with current emissions. Then calculate the

arithmetic mean concentration of each component corresponding to the projected future

emissions. For component j at site i:

(RRF)ij = ([C j projected]/ [C j Current ])i

where (RRF)ij is the relative reduction factor developed at site i for component j;

C j current is the predicted arithmetic mean 24-hr concentration of component j, computed

from modeled days, using emissions which correspond to the period in which the

design value is measured (e.g., 2000-2002);

C j projected is the predicted arithmetic mean 24-hr concentration of component j with

projected emissions corresponding to a time two years prior to the required

attainment date (e.g., 2010 for an area with a required attainment date of 2012). 
10Note that if the sample of days with observed concentrations > 55 μg/m3

 is sufficiently

large to use quarter-specific monitored data, this step is altered somewhat. In this case, one

should multiply the quarter-specific RRF values times an appropriate observed value. If the

design value occurs in a particular quarter, use the observed design value in that quarter. For

other quarters, use the highest observed concentration which is less than or equal to the design

value.

11We anticipate that there would be more candidate days for modeling in actual

applications. Indeed, it is likely that results obtained for a sample size as small as the one in this

example would be highly uncertain. We are using an example with few candidate days and few

monitoring sites for ease of presentation.

69 - DRAFT

Step 4. At each monitoring site, project future PM2.5 design values by multiplying each

component-specific relative reduction factor obtained in step 3 times the corresponding

component-specific “observed design value” derived in step 2. Add the results to obtain the

estimated future site-specific design value for PM2.5.

10

This is done using the following expression

[PM2.5]i projected = [[PM2.5]i current] 3[(RRF)j (Fraction of observed PM2.5 comprised of component j)]i

where [PM2.5]i projected is the projected PM2.5 design value at monitor site i,

[PM2.5]i current is the currently observed design value at site i, and 

(RRF)j

 is the relative reduction factor calculated for component j at site i.

Step 5. Compare each projected PM2.5 design value obtained in step 4 with 65 μg/m3

.

If all of the projected PM2.5 design values are < 65 μg/m3, the test is passed. 

The recommended test is illustrated in example 3.2.

Example 3.2

Given: (1) Area “D” has 2 monitoring sites. The average 98th percentile PM2.5 concentration

(i.e., the site specific design value) at site 1 is 80 μg/m3. The site specific design value at

site 2 is 70 μg/m3

.

(2) During the 3-year period used to compute the observed design values, there are 2 days

with observations > 55 μg/m3 at site 1 having speciated data. PM2.5 observations at site 1

on these days are 80 μg/m3 and 70 μg/m3. Site 2 has one candidate day with an observed

value > 55 μg/m3. PM2.5 concentration on this day is 66 μg/m3.

11
70 - DRAFT

(3) The following day specific species category fractions are observed:

Site Day EC OC SO4 NO3 IP U

1 1 5% 30% 50% 5% 5% 5%

1 2 5% 35% 45% 0% 5% 10%

2 3 10% 50% 20% 10% 10% 0%

(4) The following species-specific relative reduction factors have been calculated for the

2 sites shown in (3):

Site RRFEC RRFOC RRFSO4 RRFNO3 RRFIP RRFU

1 .80 1.00 .30 .50 1.00 1.00

2 .70 .70 .90 .90 1.00 1.00

Find: Is the attainment test for the 24-hour NAAQS passed in Area “D”?

Solution:

Step 1. Determine currently observed site-specific design values and candidate days for

modeling.

Candidate days for modeling (to compute the RRF values) are identified in “given” item

(2). Since there are so few of these days, we need to consider the year as a whole rather than

performing quarter-specific analyses.

Step 2. Compute the observed 3-year average design value at each monitoring site.

Information provided in “given” item (1) reflects application of this step. The design

values for sites 1 and 2 are 80 μg/m3

 and 70 μg/m3, respectively. Note that if one of the site

specific design values had been much less than 65 μg/m3

 (say, <55 μg/m3), it would not have

been necessary to apply the test at this site. It is generally a good idea to consider sites having

design values somewhat below 65 μg/m3 in the test, since relative “reduction” factors could be

greater than “1.00" as a result of growth with few control measures being applied.

Step 3. Choose days for modeling and develop site-specific relative reduction factors to be

applied to the design value obtained in step 2.

Note that we have chosen to include day 2 as well as day 1 in applying the test for site 1,
71 - DRAFT

despite measured PM2.5 having similar composition on the two days, and the measured PM2.5 on

day 2 being substantially less than on day 1. It is generally good practice to include all candidate

days. The resulting site- and day-specific relative reduction factors are shown in “given” item

(4).

Step 4. Project future PM2.5 concentrations applying relative reduction factors developed

in step 3 to design values developed in step 2.

From “given” item (4), we see that the relative reduction factors at site 1 for EC, OC,

SO4, NO3, IP and U are, respectively, 0.80, 1.00, 0.30, 0.50, 1.00 and 1.00.

Relative reduction factors at site 2 are EC (0.70), OC (0.70), SO4 (0.90), NO3 (0.90), IP

(1.00) and U (1.00). 

Therefore, for site 1,

[PM2.5]1 projected = [80] [(.80)(.05) + (1.00)(.325) + (.30)(.475) + (.50)(.025) + (1.00)(.05) + (1.00)(.075)]

= 52 μg/m3

and for site 2, 

[PM2.5]2 projected = [70] [(.70)(.10) + (.70)(.50) + (.90)(.20) + (.90)(.10) + (1.00)(.10) + (1.00)(0)]

= 55 μg/m3

Step 5. Compare the results in step 4 with 65 μg/m3.

Note that all projected site-specific PM2.5 design values are < 65 μg/m3

. Therefore, the

modeled attainment test is passed in Area “D”.

Recommendations. The modeled attainment test for the 24-hour NAAQS for PM2.5

should consider days with observations > 55 μg/m3 and with available speciated

data. Observed PM2.5 should be divided into 6 components on each day at each site. 

The six components are: mass associated with elemental carbon, organic carbon,

sulfates, nitrates, inorganic particulate matter and unattributed mass. Modeling is

used to develop site-specific relative reduction factors for five of the six components. 

The relative reduction factor for unattributed mass is always “1.00". Relative

reduction factors, along with compositional information, should be multiplied by

site specific observed design values to project a future PM2.5 design value at each

site. If the projected design value is < 65 μg/m3 for all sites, the modeled attainment

test is passed. The recommended test has 5 steps.

1. For each monitoring site identify the 98th percentile PM2.5 concentration for each
72 - DRAFT

year. Identify individual days having observed PM2.5 concentrations > 55 μg/m3

which also have speciated observations.

2. Compute the observed 3-year average PM2.5 design value and use speciated

observations to derive “observed” component-specific design values for each

monitoring site.

3. For each site, use model results to develop component-specific relative reduction

factors (RRF).

4. At each site, project future PM2.5 design values by multiplying each componentspecific RRF times the corresponding component-specific “observed” design value

derived in step 2. Add the results to obtain the estimated future site-specific design

value for PM2.5. See the previously identified footnote for this step if there is a large

number of days with speciated measurements which also have observations

exceeding 55 μg/m3

.

5. Compare each estimated future design value to 65 μg/m3

. If all projected

concentrations are <65 μg/m3, the test is passed.

3.3 How Do I Select Appropriate Inputs For The Recommended Tests?

The recommended attainment tests are very similar for the annual and 24-hour NAAQS

for PM2.5. Basically, a State should (a) estimate “current” measured site specific design values

for PM2.5, (b) derive corresponding “observed” site specific design values for each of 6 major

components of PM2.5, (c) take the ratio of modeled future to current conditions to derive sitespecific relative reduction factors for each of the 6 components, (d) for each site, multiply each

component-specific “observed” design value times the component’s relative reduction factor to

estimate a future concentration, (e) add the estimated future component concentrations to

estimate a corresponding future design value for PM2.5. All this sounds relatively

straightforward, but is it? It turns out there are several ways of deriving the information needed

to execute the preceding steps. In this section, we identify several preferences to promote

consistency among different applications. We do this by addressing several questions:

- how do I decide what “current period” and corresponding “current design value” to use

in the test?

- in the test for the 24-hour NAAQS, what do I do if each of the three years used to

estimate the 98th percentile concentration has a different sampling frequency?

- how do I estimate mass associated with each of the six components of PM2.5? 

- how do I decide what portion of the measured mass of PM2.5 should be categorized as
73 - DRAFT

“unattributed” (U)? 

- how do I derive “observed” component specific design values?

- in computing a site specific RRF value, how do I decide which predictions occur “near”

a monitor?

- which of several predictions “near” a monitor do I use to compute the RRF?

Choosing the current period and corresponding current design value. For the

recommended tests to be most sound, “current emissions” (used to calculate the denominator in

the relative reduction factor) should be consistent with emissions likely during the period

reflected by the “current (observed) design value”. Failure to meet this need could result in

projected future design values which are inappropriate. For example, if the “current inventory”

reflects an earlier period where emissions are substantially higher than those during the period

corresponding to the design value, a State could underestimate the future design value. This

follows, since the contrast between “current” and future emissions (and therefore predicted air

quality differences) could be greater than warranted. As a result, the RRF values would be too

low.

Ordinarily, if the year reflected by the most recent available inventory is included within

the 3-year period used to compute a design value, the need for the two to coincide is met. For

example, if the most recent available inventory is for 2002, design values reflecting 2000-2002,

2001-2003 and 2002-2004 are among the candidates for “current (observed) design value”. 

Although this should be confirmed for each attainment demonstration, major differences in

emissions are unlikely from year to year. Thus, differences in design values for three consecutive

overlapping 3-year periods most likely reflect meteorological differences. Calculating a design

value by averaging observations over three years will dampen fluctuations in the design values

attributable to meteorological differences. Nevertheless, looking only at three 3-year periods is

unlikely to adequately reflect potential variability in the design value due to meteorology. States

should ordinarily choose the highest site-specific design value of the three candidate periods for

use in the tests. This provides a safeguard against basing the test on design values observed

during an unusually mild 3-year period. Using this approach, it is conceivable that different 3-

year periods (all close to the period represented by the current inventory) could be used as the

basis for the current observed design value at different monitoring sites in a nonattainment area. 

This is acceptable. States may demonstrate that choice of a set of the most severe of three

candidate design values leads to one or more current design values which is inappropriately high

for use in the test. This latter argument could be considered as part of a weight of evidence

determination (see Section 5.0).

We need to mention one final issue before moving on to the next topic. What if the

period used to designate an area as “nonattainment” and the period reflected by the most recent

inventory do not coincide? For example, what if data from 2000-2002 were used as the basis for
12For ease of presentation in this document, we consistently omit valence notation for

ions. The valence associated with ammonium ion is +1, that for sulfate is -2, nitrate -1, etc. 

Occasionally, as with the radical nitrogen trioxide, this could lead to some ambiguity. Unless

otherwise noted, the reader should assume that our notation refers to sulfate, bisulfate, nitrate and

ammonium ions along with their appropriate valences. 

74 - DRAFT

a designation, but the most recent inventory reflected 1999? If a site-specific design value for the

period used for designation is higher than any of the three periods “straddling” the period

reflected by the current inventory, it should be used. Otherwise, States should use the highest of

the three site-specific design values straddling the period reflected by the inventory.

Estimating 98th percentile concentrations and observed design values when sample

sizes differ from year to year. States should use the guidance in Table 3.3 to compute the 98th

percentile PM2.5 concentration at each monitoring site for each of three consecutive years. The

observed site-specific design value is the arithmetic mean of these three 98th percentile values.

Estimating measured mass associated with components of PM2.5 and dealing with

differences between measured PM2.5 and the sum of identified components. We have been

very careful to note that it is important to estimate mass associated with each of the major

components of PM2.5, not just presence of the component itself. Estimating associated mass

requires us to make assumptions about which of several measured (or unmeasured) chemical

species are bound together. For example, SO4 aerosols could, in theory, be present in any one of

four forms: ammonium sulfate [(NH4)2SO4], ammonium bisulfate [(NH4)HSO4], sulfuric acid

[H2SO4] or letovicite [(NH4)3(HSO4)2]. Mass associated with a measured μg of sulfur depends on

the form of sulfate collected on a filter. That is, stoichiometry of the preceding four compounds

implies that the mass associated with 1 μg of measured sulfur could range from about 4.1 μg

(pure ammonium sulfate) to about 3.1 μg (pure sulfuric acid). 

For many locations in the western U.S., using a default assumption that measured sulfate

is in the form of [(NH4)2(SO4)] is probably valid (i.e., muliplying measured sulfur by “4.125" or

measured sulfate by “1.375" provides an appropriate estimate of the mass associated with

sulfate).

 However, in general, we recommend that States estimate the appropriate conversion

from measured SO4 (or sulfur) to mass associated with SO4 by noting the amount of measured

ammonium ion [NH4]

12 and nitrate [NO3] in the collected sample. The first step in estimating

mass associated with sulfate is to subtract the amount of measured ammonium associated with

nitrate. For the purpose of estimating mass associated with sulfates, a State may assume that all

measured nitrate is attributable to ammonium nitrate [NH4NO3]. Ammonium ion associated with

nitrate is subtracted from the measured concentration of ammonium ion. The remaining portion

of ammonium ion is considered in Equation 3.1a or 3.1b to estimate mass associated with

sulfates (Malm, et al., 2000).

If sulfate ion is measured,
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Mass associated with sulfate = 0.932 [NH4] + 1.02 [SO4] (3.1a)

If sulfur is measured,

Mass associated with sulfate = 0.932 [NH4] + 3.06 [S] (3.1b)

Example 3.3 illustrates how to estimate mass associated with measured sulfate ion.

Example 3.3

Given: (1) measured sulfate ion = 10 μg/m3

(2) measured nitrate ion = 2 μg/m3

(3) measured ammonium ion = 4 μg/m3

Find: measured mass associated with SO4

Solution:

(1) Subtract mass of ammonium ion associated with nitrate.

Assuming all nitrate is in the form of ammonium nitrate [NH4NO3], we see that 1 mole of

NH4 is equivalent to 1 mole of NO3.

We are given that 2 μg/m3

 of nitrate has been measured. Dividing this by the molecular

weight of nitrate (62 g/mole), we note that this measurement is equivalent to 2/62 = .032

μmoles of nitrate. Thus, there are .032 μmoles of ammonium associated with the

measured nitrate.

We determine the concentration of ammonium ion associated with nitrates by multiplying

the molecular weight of NH4 ion times .032 μmoles.

[NH4]associated with NO3 = (.032 μmoles) (18 μg/μmole) = 0.58 μg/m3

We obtain the mass of ammonium associated with sulfate by subtracting 0.58 μg/m3 from

the measured amount of ammonium (given as 4 μg/m3). Thus,

[NH4]associated with SO4 = 4 - 0.58 = 3.42 μg/m3

(2) Estimate mass associated with sulfate considering the amount of ammonium ion

associated with sulfate.
13In the unlikely event that the sum of the other 5 components exceeds the mass of PM2.5

measured with the FRM or equivalent method, assume U is zero, and adjust the component of

measured particulate matter with the greatest uncertainty (generally OC) downward so that the

sum of the 5 components and the measured mass of PM2.5 agree.

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Since sulfate ion, rather than sulfur, has been measured, use Equation 3.1a to estimate

mass associated with sulfate. We are given that the measured concentration of sulfate ion

is 10 μg/m3

.

Mass associated with sulfate = 0.932 [3.42] + 1.02 [10] = 13.4 μg/m3. (3.1a)

As previously noted, we believe States may generally assume that measured particulate

nitrate is largely in the form of ammonium nitrate. Thus, we recommend that mass associated

with nitrate be estimated by multiplying the measured nitrate ion concentration times “1.29". 

Other default recommendations for translating measurements into mass associated with each

component of particulate matter appear in Table 3.4. 

Default recommendations for translating EC, OC and the soil portion of IP are consistent

with recommendations found in Sisler (1996). There is uncertainty associated with the “1.4"

factor used to translate measured OC into mass associated with organic carbon. One recent study

suggests higher factors may be appropriate (Turpin and Lim, 2000). However, the value of “1.4"

is the one which has been most widely used as of late 2000. States may use an alternate factor in

a weight of evidence assessment, if justified. Composition of soil in a local area may also differ

from the recommended default assumptions in some parts of the country. Chow, et al., (1996)

and Solomon, et al., (1989) describe procedures for characterizing composition of soil dust used

for studies in central and southern California. States may use alternative procedures for

estimating measured mass associated with crustal material in a weight of evidence analysis if

they can present sufficient justification for doing so. We should note however, that the portion of

a nonattainment area’s measured PM2.5 attributable to soil dust may often be low. In such cases,

a major effort to refine these calculations may result in little change in the results of a modeled

attainment test.

Unattributed mass (U) is the difference between mass of PM2.5 measured with the Federal

Reference or equivalent method and the sum of the mass associated with the other five

components.13 If a certain portion of inert PM2.5 is unmodeled because it originates outside of the

United States, its mass may be included within component “U”. If this practice is followed, a

corresponding portion of the mass allocated to one or more other components should be reduced. 

For example, if monitored data are used to attribute a certain portion of measured PM2.5 to

Saharan dust, this may be subtracted from component “IP”, included as part of component “U”

and need not be modeled, either explicitly or as assumed background. On the other hand, if

Saharan dust were included as background in a model application, it should continue to be

included as part of component “IP”. 
77 - DRAFT

Table 3.4. Recommended Default Assumptions To Derive Mass Associated With Major

Components of PM2.5

(1)

Component

(2)

Formula To Derive Mass

Associated With

Component

(3)

Assumptions

SO4 See accompanying text All elemental sulfur is from

sulfate

NO3 1.29 (measured nitrate) Denuder efficiency is

~100% & all nitrate is from

ammonium nitrate

EC 1.00 (high + low

temperature EC)

All high temperature

carbon is elemental

OC 1.4 (organic carbon) Average organic molecule is

70% carbon (other

elements include hydrogen,

nitrogen and oxygen)

Inorganic Particulate

Matter (IP) 

= mass associated with

crustal material (soil) plus

combustion

Mass associated with soil = 

2.2(Al) + 2.49(Si) + 1.63(Ca)

+ 2.42(Fe) + 1.94(Ti)

Mass associated with

combustion = oxides of

remaining measured

metallic elements

(Soil K)=0.6(Fe), FeO &

Fe2O3 are equally

abundant, a factor of 1.16 is

used for MgO, Na2O, H2O

& CO3

Multiplication factors for

remaining elements

(associated with

combustion) depends on the

stoichiometry of the

compound when combined

with oxygen

Unattributed mass

(U)

U = PM2.5 - (SO4 + NO3 +

OC + EC + IP)

Six components account for

all measured PM2.5
78 - DRAFT

Deriving component-specific “observed” design values. For applications addressing

the annual NAAQS, States should compute the mean mass associated with each of the 6

components of PM2.5 at each monitoring site for each quarter over a continuous 3-year period. 

Each of these 6 values should then be divided by the mean PM2.5 measured on the corresponding

days at that site. The resulting 6 quotients reflect the composition of PM2.5 at the monitor. Each

of the 6 quotients should then be multiplied by the site-specific quarterly PM2.5 mean value,

obtained by averaging the observed quarterly mean concentration over 3 continuous years. The

product of this multiplication is the “observed” component specific mean value for each quarter.

For applications related to the 24-hour NAAQS, States should compute the mean mass

associated with each of the 6 components for all days chosen for modeling which have speciated

air quality data. These values should be divided by the corresponding mean PM2.5 measurement

on these days at the monitoring site in question. The resulting 6 quotients reflect the composition

of PM2.5 at the monitor. Each of these should be multiplied by the observed site-specific PM2.5

design value (i.e., the 98th percentile concentration averaged over 3 consecutive years) to derive

the “observed” component-specific design values for the 24-hour NAAQS. 

The preceding approaches are straightforward. However, what should a State do if the

speciated and PM2.5 mass measurements are not collocated? To minimize this problem, we

suggest States review preliminary FRM data and give priority to making speciated measurements

at sites showing a potential for violating one or both NAAQS. In the event an FRM monitoring

site is unaccompanied by collocated species measurements, we recommend that those

responsible for implementing a State’s modeling protocol review available speciated data,

monitor placement, meteorological conditions and local geographic features to choose among

available composition observations. For example, if available speciated data suggest little

gradient in components dominated by secondary particulate matter (e.g., SO4, NO3 and, perhaps,

OC), concentrations of these components could be assumed as constant from site to site and

further interpolation need only address components consisting largely of primary particulate

matter. Any remaining problem may be addressed by interpolating from existing speciated data

using objective analysis (e.g., such as weighing available observations according to the inverse

square of a site’s distance from a measurement).

One additional question arises. What should a State do if speciated and FRM

measurements are collected on different schedules (e.g. once every 3 days vs. once every 6 days)? 

The procedure we recommend in the attainment tests is to use composition estimates which

reflect measured speciation profiles that have been averaged over a number of days. Thus, States

should follow this procedure even if speciated data are only available for a subset of days used to

derive the monitored design values for PM2.5 used in the attainment tests.

Deciding what is “near” a monitoring site. 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. According to 40CFR Part 58,
79 - DRAFT

Appendix D, sites having “neighborhood” or “urban” scales of representativeness are generally

most suitable for estimating exposure or compliance with NAAQS. Because of the rather long

sampling (exposure) 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 were used

in recent draft guidance for ozone modeling (U.S.EPA, 1999b).

States may assume a distance of < 15 km to be “near” a monitoring site. However, for

ease in computation, States may assume that a monitor is at the center of the model grid 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 15 km of) a monitor is a function of the size of the

grid cells used in the modeling. Table 3.5 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

with12 km grid cells, “nearby” is defined by a 3 x 3 array of cells, with the monitor located in the

center cell. States may consider presence of topographic features, demonstrated mesoscale flow

patterns (e.g., land/sea, land/lake interfaces) or other factors to refine our default definitions for

the array of “nearby” grid cells, provided the justification for doing so is documented.

Table 3.5. Default Recommendations For Nearby Grid Cells Used To Calculate RRF’s 

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

< 5 7 x 7

>5 - 8 5 x 5

>8 - 15 3 x 3

>15 1 x 1

 

Choosing model predictions to calculate a relative reduction factor ((RRF)j ) near a

monitor. This issue is to decide which one(s) of the 24-hour predicted concentrations of PM2.5

components in cells near a monitor should we use to calculate the component-specific RRF’s? 

We recommend taking the spatially averaged value of the nearby predictions. Each componentspecific relative reduction 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 averaged daily predictions with current emissions. The procedure is illustrated in

Example 3.4. For ease of presentation, we illustrate the procedure for only one component (SO4)

for a limited number of days.
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81 - DRAFT

Example 3.4

Given: (1) Four primary days have been simulated using current and future emissions;

(2) The horizontal dimensions for each surface grid cell are 12 km x 12 km;

(3) Figure 3.1 shows predicted mass associated with SO4 in each of the 9 cells “near” a monitor

with (a) future emissions, and (b) current emissions.

Find: The site-specific relative reduction factor (RRF)j

 for mass associated with SO4.

Solution:

(1) For each day and for both current and future emissions, identify the 24-hour mass associated

with SO4. Since the grid cells are 12 km, a 3 x 3 array of cells is considered “nearby” (see Table

3.5). The numbers appearing beneath each 3 x 3 array in Figure 3.1 are the spatially averaged

mass associated with SO4 for each day.

(2) Compute the mean spatially averaged mass associated with SO4 for (a) future and (b) current

emissions.

Using the information in Figure 3.1,

(a) (mean mass associated with SO4)future = (7.5 + 7.0 + 7.0 + 7.2)/4 = 7.2 μg/m3

(b) (mean mass associated with SO4 )current = (8.3 + 8.8 + 8.0 + 8.0)/4 = 8.3 μg/m3

(3) The relative reduction factor SO4 near the site is

(RRF)j

 = (mean mass associated with SO4 )future/(mean mass associated with SO4 )current

 = 7.2/8.3 = 0.87

Recommendations. States should choose the period with the highest observed PM2.5

design value from 4 candidate 3-year periods: the three 3-year periods which

include the year represented by the most recent available inventory and the 3-year

period upon which the nonattainment designation is based (if this is not one of the

first three periods).

States should use stoichiometric relationships together with measured speciated data

to estimate mass associated with each of the 6 components of PM2.5 considered in the

modeled attainment test. Information presented in Table 3.4 may be used as default

assumptions.
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“Observed” component-specific design values should be estimated by computing the

mean mass associated with each component on all days with speciated

measurements (annual NAAQS) or on all modeled days with measurements (24-hr

NAAQS). These averages should be divided by the mean mass of PM2.5 on these

same days. This provides a mean estimate of the composition of PM2.5. The

“observed”component-specific design values are estimated by multiplying the

appropriate PM2.5 design value times the mean composition of PM2.5.

States should use predictions “near” a monitor to compute site-specific RRF values. 

“Near” is defined as a location within 15 km of a monitoring site, and may be

approximated by considering predictions in an array of grid cells centered on the

monitor. The number of cells considered is a function of cell size.

States should spatially average current and future predictions in cells near each

monitor. The site-specific RRF should be estimated by taking the ratio of spatially

averaged future predictions to spatially averaged current predictions.

3.4 Why Do I Need To Consider A Hot Spot Analysis And How Do I Do It? 

 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. Putting this another way, 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. 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. 

As a result, we expect the need for a hot spot analysis to be greatest when evaluating the

adequacy of a control strategy for meeting the 24-hour NAAQS. 

Given the potential for spatial gradients in the primary components of PM2.5 and the

variety of purposes for PM2.5 monitoring, it is not likely that monitors will be located so as to

detect potential large impacts from every major source of IP, EC and primary OC. Thus, we

recommend that States supplement the recommended modeled attainment test with a hot spot

analysis when warranted.

Determining whether a hot spot analysis is warranted. This may be done in one of
83 - DRAFT

two ways. The first approach is to review the spatial distribution of estimated primary PM2.5

emissions for grid cells within a designated nonattainment area for PM2.5. Note the location of

grid cells in which primary emissions (i.e., from point, area and mobile sources) are well above

(e.g., > 20% higher) those “near” any monitoring site for PM2.5 within the designated

nonattainment area. Major sources within such cells are candidates for a hot spot analysis. The

second approach for flagging major sources subject to a hot spot analysis makes use of available

results from air quality modeling. In this approach, a State should flag major sources in any grid

cell for which predicted concentrations of primary particulate matter are substantially (e.g., >

20% greater) and consistently (e.g., > 50% of the days modeled) greater than predicted

concentrations of primary particulate matter near any site monitoring PM2.5.

Size of the area considered in a hot spot analysis. The area over which a hot spot

analysis should be performed needs to be determined on a case by case basis. Size of the area

considered depends on the nature of a source’s plume. For example, analyses for sources whose

emissions are released aloft in heated plumes (e.g., combustion sources) will likely need to cover

a larger area than is necessary for sources emitting coarse mode particulate matter close to the

ground. However, since a hot spot analysis is primarily interested in estimating the maximum

impact from a flagged source, the area of coverage is seldom likely to be greater than about 15 -

20 km from the source. 

Resolving emissions-related issues. We recommend a focused quality assurance check

on emissions from sources flagged for a hot spot 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. If such

discrepancies are commonly reported, those implementing the modeling protocol (i.e., see

Section 10.0) 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 source subject to a

hot spot analysis. 

The next emissions-related issue is, what emissions level should a State assume (i.e., one

corresponding to maximum allowable emissions or one corresponding to a typical “actual”

level)? For the purpose of attainment demonstrations addressed in this guidance, emissions of

greatest interest are future estimates made for the required attainment date. Estimating future

emissions for sources subject to hot spot analyses is an issue which needs to be resolved on a

case by case basis, as the most reasonable course may depend on the particular source in

question. Generally however, for hot spot applications related to the 24-hour NAAQS, we

recommend using maximum potential emissions levels. If a source is subject to an operating

permit, maximum emissions allowed by that permit should ordinarily be used as its future

emissions in a hot spot analysis. For applications related to the annual NAAQS, it is not

reasonable to assume that maximum emissions will be sustained throughout the year. Therefore,

we recommend using future expected “actual” emissions levels for these applications. 
84 - DRAFT

Model application and interpretation. Once outstanding emissions-related issues are

resolved, States should apply a point, line or area source model (e.g., a Lagrangian (Gaussian)

model) for every day of the 3-year period corresponding to the current observed PM2.5 design

value used in the modeled attainment test. There is room for some judgment on the part of those

implementing the modeling protocol as to whether it is necessary to model every flagged source. 

For example, if mass and configuration of emissions and background concentrations are similar

for two flagged sources, it may only be necessary to model one of them. 

If the hot spot analysis is being performed to supplement the recommended test for the

24-hour NAAQS, note the 4th highest concentration in each of the three years at each receptor

site. Take the arithmetic mean of these three values at each site. The result is an array of

estimated mean 98th percentile impacts of the flagged source on IP, EC and primary portions of

the OC component of PM2.5. If an analysis is related to the annual NAAQS, compute the

arithmetic mean concentration for each quarter of each year at each receptor site. Use this

information to calculate the annual arithmetic mean concentration of primary components at each

receptor for each of the three years. Take the average of these three arithmetic mean values

calculated at each site. The result is an array of estimated average arithmetic mean impacts of the

flagged source on the primary components of PM2.5.

Superimposing estimated impacts on other ambient estimates. Once a State has

estimated the impact of a flagged source, it needs to superimpose this impact on an estimated

“ambient” value reflecting secondary components of PM2.5 plus other sources of primary

particulate matter. If the hot spot analysis is related to the annual NAAQS, this ambient value

should be obtained by spatially interpolating the average annual mean PM2.5 concentrations

projected at monitored locations in the modeled attainment test. Unless there is some

geographical feature or other documented reason for not doing so, we recommend using an

objective technique for doing the interpolation (e.g., such as weighing the projections according

to the inverse square of their distance from the site of the flagged source’s maximum projected

impact). If the sum of the maximum impact and interpolated ambient value exceeds 15.0 μg/m3,

some remedial action is needed.

If the hot spot analysis is related to the 24-hour NAAQS, choice of an appropriate

ambient value is more difficult. Ideally, States could perform a correlation analysis between the

maximum source impact predicted on each day with monitored observations and a background

value interpolated from the available monitored data. If there were no statistically significant

correlation or if the correlation coefficient were significant but low (e.g., < 0.50), it is appropriate

to assume the same background concentration used in the hot spot analysis related to the annual

NAAQS. If there were a perfect (positive) correlation between background values interpolated

from the monitored data and the estimated maximum impacts, then a background value which is

interpolated from the projected 98th percentile PM2.5 concentrations at monitoring sites would be

appropriate. Given the disparity between sources which may be responsible for the other

components of PM2.5 and those responsible for primary particulate emissions, we believe a

default approach which uses interpolated mean (rather than 98th percentile) projections for
85 - DRAFT

ambient values is appropriate. If there is another source flagged for a hot spot analysis which is

within 20 km, it may need to be modeled with a point/line/area source model as well. Resulting

estimated impacts from the “nearby”modeled sources should be estimated at each receptor site

used for the source which is subject to the hot spot analysis. If the sum of the maximum impact

calculated at any receptor site and the interpolated ambient value exceeds 65 μg/m3

, remedial

action is needed.

Projected emissions from nearby flagged sources. This issue is, should I assume

projected maximum allowable or projected typical “actual” emissions for nearby sources which

are explicitly modeled in a hot spot analysis addressing the 24-hour NAAQS? The question

needs to be addressed on a case by case basis. In cases where there is no reason to believe that

maximum emission levels among the sources are likely to coincide, we recommend using

projected actual emissions for flagged “nearby” sources. If some coincidence of maximum

activity is likely (e.g., as might possibly happen for nearby utilities on a hot summer day), States

may need to use maximum allowable emissions for projected estimates from nearby sources.

Recommendations. States should apply a supplementary hot spot analysis for

major sources of primary particulate matter. Size of the area considered for a hot

spot analysis depends on the nature of the modeled source, but is generally less than

20 km from the source. 

Prior to modeling, a quality assurance check should be performed on

emissions estimated from the source and corrections made as necessary. For hot

spot analyses related to the 24-hour NAAQS, States should assume future activity

levels which correspond to maximum potential or allowable activity, whichever is

less. For applications related to the annual NAAQS expected future activity levels

may be assumed.

States should apply a point/line/area source model for every day in the 3-year

period corresponding to that of the current PM2.5 design value used in the modeled

attainment test. For the 24-hour NAAQS, the mean 98th percentile primary

particulate matter (IP + EC + primary portions of OC) concentration should be

estimated at each receptor site. For the annual NAAQS, States should estimate the

average arithmetic mean primary PM concentration at each site. States should note

the maximum mean 98th percentile and the maximum average mean impacts.

States should superimpose the maximum impact on the annual mean or 98th

percentile daily concentration on ambient concentrations. “Ambient”

concentrations should be estimated by interpolating projected annual mean design

values obtained in the modeled attainment test at monitoring sites. If there are

nearby sources of primary particulate matter which have also been flagged for

hotspot analyses, point/line/area modeled concentrations estimated at the receptor

sites should also be added to impacts estimated from the source which is the subject
86 - DRAFT

of a particular hot spot analysis. If the sum of a source’s maximum impact, the

impact of nearby source(s) at that location and the interpolated ambient

concentration exceeds either of the two NAAQS for PM2.5, remedial action is

warranted.
87 - DRAFT

4.0 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 3.0, to demonstrate attainment of the NAAQS for PM2.5. They

share the following features:

- monitored data should be used to define current air quality;

- monitored concentrations of PM2.5 are divided into major components (e.g., SO4, NO3,

OC, EC, IP);

- it is necessary to estimate monitored and modeled mass associated with each major

component;

- models are used in a relative sense to develop relative reduction factors (RRF) between

future and current predicted concentrations of each component;

- component-specific relative reduction factors are multiplied by current monitored values 

 to estimate future component concentrations;

- estimates for future component concentrations are consolidated to provide an estimate

of future air quality which can be related to a goal for regional haze;

- future estimated air quality is compared to the goal to see if a simulated control strategy

results in the goal being met.

There are also several differences between the reasonable progress and attainment tests: 

- it is not necessary to include a component for unattributed mass (U) of PM2.5 in the

modeled test for reasonable progress;

- the modeled test for reasonable progress needs to consider coarse particulate matter, not

just particles as small or smaller than 2.5 μm;

- for the reasonable progress test, it is acceptable to assume that all measured sulfate is in

the form of ammonium sulfate ((NH4)2(SO4)). 

Regional haze is calculated by estimating light scattering and absorption by components

of PM2.5. It is not necessary for the sum of these components to equal gravimetric measurements

obtained with a Federal Reference or equivalent method for measuring PM2.5. Hence, it is not

necessary for us to introduce a component “U” to reconcile sum of the components with

gravimetric measurements of PM2.5. Coarse particles (aerodynamic diameters > 2.5 μm but < 10
88 - DRAFT

μm) scatter light. Although light scattering on a per mass basis is less efficient for coarse than

for fine particles, in some Class I areas measured coarse mass is comparable or even greater than

measured fine mass. Thus, we cannot ignore coarse mass in the modeled test for reasonable

progress. In some parts of the U.S. (most notably in the East), reviews of data collected in Class

I areas suggest that ammonium bisulfate (NH4HSO4) may represent mass associated with sulfate

better than does ammonium sulfate. However, in the modeled test for reasonable progress, we

are interested in the product of mass associated with sulfate and light scattering efficiency. This

product is very similar for ammonium sulfate and ammonium bisulfate (i.e., the lower mass of

ammonium bisulfate is compensated for by its greater light scattering efficiency) (Tang, 1996). 

This enables us to simplify the modeled test for reasonable progress by assuming that measured

sulfate is in the form of ammonium sulfate throughout the United States. 

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 may be asked to assume that the difference in

light extinction on worst visibility days during the base period and natural background is reduced

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)]). The rule provides States

with some flexibility to establish the goal for improving visibility on worst visbility days between

the base period and 2018 in the event this initial goal appears to be infeasible.

 

In essence, 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 is developing

guidance on how measurements should be used to determine these trends. This is expected to be

completed during the first half of 2001. In Section 4.1, we describe how it now appears that

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 4.2, we present and recommend a

modeled test which closely replicates the anticipated ambient test. Section 4.3 addresses how to

develop key inputs for the recommended modeled test for reasonable progress. 
14We recommend that States use Equation (4.1) as described. However, States may

derive a similar, but different equation which is specific for a given Class I area. We do not

recommend this, as it is likely to be expensive and time consuming to develop a sufficient data

base to develop such an equation. A State would need to collect non-routine data at the Class I

location over a sustained, multi-seasonal period. Data include transmissometer measurements,

size distribution measurements for fine as well as coarse particles, composition data, relative

humidity measurements and an assessment of the chemical form of sulfate particles at the

location in question. This implies need for an ambient nephelometer, a PM2.5 nephelometer, a

PM10 nephelometer, a variable humidity nephelometer, continuous absorption measurements

(multi-wavelength aethelometer), full ion measurements as a function of size (MOUDI sampler)

as well as the usual compliment of IMPROVE aerosol measurements for comparison purposes. 

Resulting data would then need to be analyzed and input into various aerosol/optical models as

described in Malm, et al., (1994).

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4.1 How Is Reasonable Progress 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 (4.1) may be applied in each Class I area to estimate bext (Sisler, 1996, Malm, et al.,

2000)14. The equation reflects empirical relationships derived between measured mass of

particulate matter components and transmissometer measurements of bext at monitoring sites in

39 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 (4.1)

where

the numerical coefficients on the right hand side of the equation represent the light

scattering or absorption efficiency, m2

/gm of the corrsponding component of particulate

matter,

f(rh), f’(rh) are relative humidity adjustment factors applied to the light scattering

efficiency (to be described in greater detail shortly), dimensionless,

SO4 is the mass associated with sulfates, μg/m3,
15Footnote has been deleted.

90 - DRAFT

NO3 is the mass associated with nitrates, μg/m3

,

OC is the mass associated with organic carbon, μg/m3,

EC is the mass associated with elemental carbon, μg/m3, 

IP is inorganic primary particulate matter (excluding primary sulfate and nitrate particles)

with aerodynamic diameter < 2.5 μm, μg/m3

,

CM is coarse particulate matter with aerodynamic diameter > 2.5 μm, but < 10 μm,

μg/m3

,

brayleigh is light-scattering attributable to Rayleigh scattering, Mm-1 (i.e., inverse “megameters”), assumed to be 10 Mm-1.

bext is the estimated extinction coefficient, Mm-1.

The regional haze rule stipulates that reasonable progress is to be measured in terms of changes

in “deciviews”. Deciviews are defined as the natural logarithm of the ratio of the extinction

coefficient to Rayleigh scattering (Pitchford and Malm, 1994)

Deciview = 10 ln(bext/10) (4.2)

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

(4.3). A 1 deciview change is equivalent to about a 10% change in bext.

dv = 10 ln ((bext)future / (bext)baselinet) (4.3)

Estimating Mass Associated With Components Of Particulate Matter. 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 (4.1). These are presented in Table 4.1. 

Information in Table 4.1 is very similar to that in Table 3.4 used for modeled attainment

demonstrations of NAAQS for PM2.5. Notice however, that fine inorganic particulate matter (IP)

is assumed to consist entirely of crustal material. As discussed previously, there is no term “U”

for unattributed mass. Finally, there is a term for coarse particulate matter included in Table 4.1

and in Equation (4.1)15. 
16As a result of field studies reported in Malm, et al., (2000), we assume the relative

humidity adjustment factor (f’(rh)) for organic carbon (OC) is equal to “1.00".

91 - DRAFT

Table 4.1. Default Assumptions Used To Derive Aerosol Species Concentrations For

Estimating Extinction Coefficients

(1)

Species

(2)

Formula

(3)

Assumptions

Sulfate 4.125 (measured sulfur) 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

Considering effects of relative humidity (rh). Presence of high relative humidity

increases the light scattering efficiency of fine particulate matter, particularly SO4 and NO3

species16. Thus, it is necessary to include a relative humidity adjustment factor, f(rh), for these

two components of PM2.5 on the right hand side of Equation (4.1). Table 4.2 is information

provided by Malm (2000a) which reflects data reported by Tang (1996). It denotes the

relationship between relative humidity and the relative humidity adjustment factor used to relate

bext to components of particulate matter.

The relative humidity adjustment factor in Equation (4.1) is applied to a 24-hour

concentration of particulate matter. Therefore, we need to compute a daily average value for

f(rh). This is done by noting hourly values of relative humidity and then using Table 4.2 to

estimate adjustment factors for each hour. For a given day, the f(rh) value used in Equation (4.2) 
17Malm (2000a)

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Table 4.2. Relative Humidity Adjustment Factors As A Function Of Relative Humidity17

(1)

Relative

Humidity (%)

(2)

Relative

Humidity

Adjustment

Factor (f(rh))

(3)

Relative

Humidity (%)

(4)

Relative

Humidity

Adjustment

Factor (f(rh))

(5)

Relative

Humidity (%)

(6)

Relative

Humidity

Adjustment

Factor (f(rh))

< 36% 1.00 57 1.58 78 2.70

37 1.02 58 1.62 79 2.78

38 1.04 59 1.66 80 2.86

39 1.06 60 1.70 81 2.94

40 1.08 61 1.74 82 3.03

41 1.10 62 1.79 83 3.12

42 1.13 63 1.83 84 3.22

43 1.15 64 1.88 85 3.33

44 1.18 65 1.93 86 3.45

45 1.20 66 1.98 87 3.58

46 1.23 67 2.03 88 3.74

47 1.26 68 2.08 89 3.93

48 1.28 69 2.14 90 4.16

49 1.31 70 2.19 91 4.45

50 1.34 71 2.25 92 4.84

51 1.37 72 2.31 93 5.37

52 1.41 73 2.37 94 6.16

53 1.44 74 2.43 95 7.40

54 1.47 75 2.50 96 9.59

55 1.51 76 2.56 97 14.11

56 1.54 77 2.63 98 26.37
93 - DRAFT

is the arithmetic mean of 24 hourly adjustment factors. This is illustrated in Example 4.1.

Example 4.1

Given: The following diurnal humidity information for a particular day.

Hour Relative Humidity Humidity Adjustment Factor (from Table 4.2)

 1 75 2.50

 2 80 2.86

 3 80 2.86

 4 90 4.16 

 5 95 7.40 

 6 98 26.37

 7 98 26.37 

 8 98 26.37 

 9 95 7.40

 10 90 4.16

 11 80 2.86 

 12 60 1.70 

 13 40 1.08 

 14 30 1.00

 15 30 1.00

 16 30 1.00

 17 30 1.00 

 18 35 1.00

 19 50 1.34 

 20 50 1.34 

 21 60 1.70 

 22 60 1.70 

 23 70 2.19 

 24 70 2.19 

 

 Total = 131.55

Find: The appropriate relative humidity adjustment factor for this day.

Solution: The appropriate adjustment for humidity is the mean relative humidity adjustment

factor for the day. Adding the hourly adjustment factors, we get “131.55". Divide this total by

“24" to get the mean daily relative humidity adjustment factor.

f(rh) = (131.55) / (24) = 5.48

If a State were interested in estimating the visibility extinction coefficient for the day in example
94 - DRAFT

4.1, it would need to multipy the extinction attributable to sulfates and nitrates under dry

conditions by “5.48". 

Normalizing trends in regional haze. It is clear from the preceding example and from

Equation (4.1) 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. On the day in Example 4.1 for instance,

light scattering efficiency of sulfates and nitrates increases from 3 m2/gm to 16.4 m2/gm. This

magnifies the effect of any change in sulfates or nitrates on the estimated extinction coefficient. 

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 lead to reasonable

progress reducing 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 (4.1), 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

(4.1) 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. Further, if the climatological estimates are made beforehand and published, this

approach simplifies the estimates for States. Recently, the U.S. EPA has sponsored a project to

examine measured hourly relative humidity data over a 5-year period within the United States to

derive month-specific climatological mean humidity correction factors for each Class I area (U.S.

EPA, 2000a). A potential downside of the “climatological” vs. “day-specific” approach could

occur if there is a strong correlation between concentrations of hygroscopic components (i.e., SO4

and NO3) and measured relative humidity. If such were the case, use of average month- and

location-specific climatological estimates could understate the importance of hygroscopic

components as contributors to light extinction.
95 - DRAFT

The U.S. National Park Service has examined data for each of 12 months from 20 Class I

areas where relative humidity measurements are made. In nearly all cases, no statistically

significant correlations were found between measured concentrations of SO4, NO3 and [SO4 +

NO3] vs. daily values of relative humidity in a large majority of months. Results are displayed in

Table 4.3. Further, deciview calculations have been made using day-specific vs. climatological

values for the relative humidity adjustment factor for each of 10 years in 15 Class I areas. In 14

of the 15 areas, little, if any, difference is observed in the year to year calculations for the mean

deciview values for the 20% worst and 20% best days nor in the trends. Some difference in the

mean deciview value for the worst 20% days was observed in one Class I area. However, the

overall trend in the mean worst and best deciview values for this site is similar using the two

methodologies for deriving f(rh).

Table 4.3. Correlations Between Hygroscopic Components of PM and Relative Humidity

# of Months with

stat.significant

positive correlation

between

hygroscopic species

& rel.humidity

# of Class I Areas in

this category for 

SO4 vs. rel.humidity

# of Class I Areas in

this category for 

NO3 vs. rel.humidity

# of Class I Areas in

this category for 

[SO4+NO3] vs.

rel.humidity

0 3 out of 20 6 out of 20 2 out of 20

1-2 9 out of 20 5 out of 20 6 out of 20

3-5 5 out of 20 8 out of 20 10 out of 20

> 6 (max.= 8) 3 out of 20 1 out of 20 2 out of 20

 

Results to date suggest there is a relatively weak correlation between hygroscopic

components of PM and relative humidity and that choice of a “climatological” vs. “day-specific”

method for computing f(rh) has little apparent effect on observed trends in visibility. As of this

writing (January 2001), we are examining information to see whether use of climatological vs.

day-specific values for f(rh) affects the identity of days chosen as the 20% best and 20% worst

visibility and, if so, whether this has any effect on conclusions about future visibility obtained

using the recommended modeled test for reasonable progress. At this time however, the

climatological approach for estimating f(rh) appears viable, and it is the approach we

recommend. Thus, States should use month- and Class I area-specific climatological relative

humidity adjustment factors in in Equation (4.1), to rank visibility based on measured

concentrations of major components of particulate matter. The resulting mean values of bext for

the worst 20% and best 20% of sampled days are those which need to be improved (worst 20%)

or not made worse (best 20%) in order for reasonable progress to be demonstrated. 

Recommendations. States should characterize visibility in a Class I area by
96 - DRAFT

estimating values for the light extinction coefficient (bext). The extinction coefficient

should be estimated using monitored mass of particulate matter associated with SO4,

NO3, OC, EC, IP and CM in Equation (4.1). States should use relative humidity

adjustment factors in Equation (4.1) which reflect climatological mean surface

relative humidity observations which are specific to the Class I area being analyzed

and to the month of the year in which an observation has occurred. 

4.2 How Do I Apply A Modeled Test To Demonstrate Reasonable Progress?

The purpose of a modeled test to demonstrate reasonable progress is to provide assurance

that a proposed control strategy will result in reasonable progress goals being met when

measured concentrations of particulate matter are used to estimate bext at some future date. The

test we recommend has 6 steps.

1. For each Class I area, rank visibility 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 extinction

coefficient for the 20% of days with worst and 20% of days with best visibility. For each

Class I area, calculate the average mean extinction coefficients 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 reduction factors for each component of

particulate matter identified on the right hand side of Equation (4.1).

4. Multiply the relative reduction factors times the measured PM data during the base

period to compute future daily values of the extinction coefficients. Rank these to

determine the 20% best and 20% worst visibility for each of the five base 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. Compute the

future average mean extinction coefficients for the worst and best days by averaging the

means from each of the 5 “future” years.

6. Convert the difference in average mean extinction coefficients between the future and

base periods to a difference in deciviews. Compare the difference in deciviews to the

goals for reasonable progress.

We describe each of these steps more fully below. States should follow the outlined

procedure for each Class I area subject to a reasonable progress review. We conclude this

subsection with an example illustrating the recommended modeled test for reasonable progress.
18How many and which days to simulate is discussed in Section 4.3 and in Section 12.0.

97 - DRAFT

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

period. The extinction coefficient, bext, should serve as the basis for ranking. This coefficient

should be calculated using Equation (4.1). Day-specific observations for mass associated with

SO4, NO3, OC, EC, IP and CM, as defined in Table 4.1, should be used to calculate bext for each

day. The appropriate month- and area-specific climatological mean value estimated in U.S. EPA

(2000a) should be used for the relative humidity adjustment factor (f(rh)) in Equation (4.1).

Step 2. Calculate the average baseline extinction coefficient 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 extinction coefficient) to best (lowest extinction coefficient) visibility. For each year,

note the 20% of days with worst and the 20% of days with best visibility. Calculate the

arithmetic mean extinction coefficient for the identified 20% worst- and best visibility days in

each year. Average the resulting 5 yearly mean extinction coefficients 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 extinction coefficients reflecting mean

visibility on the days with best visibility. The resulting average mean extinction coefficient may

not increase if reasonable progress is to be demonstrated.

Step 3. Estimate relative reduction 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 days18. Air quality corresponding with future

emissions (reflecting effects of growth and a control strategy under consideration) should be

simulated for the same days. Take the (temporal) arithmetic mean concentration for each PM2.5

component 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. Baseline and future

PM2.5 and PM10 are the predicted temporal quarterly arithmetic means of the predicted daily

values near the Class I monitoring site with current and future emissions. The relative reduction

factor for coarse mode (CM) particulate matter is given by the following expression. 

(RRF)CM = (PM10 - PM2.5)future / (PM10 - PM2.5)baseline

As we discuss in Section 4.3, we recommend that States derive separate sets of RRF values for

“worst” and “best” visibility days identified in step 2.
98 - DRAFT

Step 4. Multiply the relative reduction 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, its

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. Compute

mean values of bext for the sets of “worst” and “best” days in each year. Average the 5

mean extinction coefficients for “worst” and “best” visibility days.

Apply Equation (4.1) 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

(4.1) 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. For days included in a given year

of the base period, compute the arithmetic mean future extinction coefficient for the “worst” and

“best” visibility days. This leads to 5 future estimated mean extinction coefficients for the

“worst” and 5 future estimated mean extinction coefficients for the “best” visibility days. 

Compute the arithmetic mean of the 5 mean values for bext on the “worst” days, and the

arithmetic mean of the 5 mean values estimated for the “best” visibility days. The resulting

average mean values for bext on “worst” and “best” visibility days are used in Step 6.

Step 6. For “worst” visibility days and for “best” visibility days, convert the

difference in future and current values of average mean values of bext to a difference

expressed in deciviews. 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 in Equation (4.3) 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. The reasonable progress goal for “worst” visibility days is met if Equation (4.3) yields

a negative number and the absolute value of this negative number is greater than or equal to the

goal established for “worst” visibility days. The reasonable progress goal for “best” visibility

days is met if the deciview value resulting from Equation (4.3) is < 0. If goals for “worst” and

“best” visibility days are both met, the modeled test for reasonable progress is passed.

We use Example 4.2 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
99 - DRAFT

and then furnish information regarding mean extinction coefficients for the other four base years

to illustrate subsequent steps in the test.

Example 4.2

Given:

(1) Ten days have measured components of PM in a Class I area during the first year of a 5-year

base period. The table below shows the measurements (in μg/m3) for each of the 10 days. The

table 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.

Day Date f(rh) SO4 NO3 OC EC IP CM (bext) baselineRank

1 2/15 1.7 4.5 2.2 3.3 0.8 0.3 7.3

2 3/15 2.9 5.1 1.7 2.9 0.7 0.4 9.1

3 4/15 3.5 5.6 1.2 2.4 0.5 0.4 10.2

4 5/15 3.7 6.5 1.1 2.8 0.5 0.6 9.8

5 6/15 4.3 7.4 0.9 3.4 0.6 0.9 10.9

6 7/15 4.6 8.3 0.7 3.5 0.6 1.0 11.3

7 8/15 4.4 8.7 0.8 3.2 0.6 0.8 10.7

8 9/15 4.1 8.0 0.9 3.1 0.7 0.7 8.2

9 9/30 4.1 6.8 1.1 3.2 0.8 0.4 8.8

10 10/30 2.7 5.3 1.4 3.2 0.7 0.6 8.1

(2) Using procedures identical to those followed for year 1, the following table shows mean

current values for bext for “worst” and “best” visibility days in each of the other 4 years

comprising the base period.

Year Mean (bext)baselint, 

Worst Visibility Days, Mm-1

Mean (bext)baseline,

Best Visibility Days, Mm-1

2 190.3 62.1

3 170.1 87.4

4 180.6 75.3

5 165.0 82.6
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(3) The following table shows component-specific relative reduction factors estimated using an

air quality model to simulate future and current emissions. Component-specific relative

reduction factors are computed as described in Section 4.3. The table also shows predicted mean

future and current concentrations of PM2.5 and of PM10 predicted near the Class I area’s

monitoring site. The current and future mean PM2.5 and PM10 estimates are needed to calculate

the RRF for CM.. For simplicity in this example, we are assuming that RRF values are similar

for “worst” and “best” visibility days.

Modeled

Output

SO4 NO3 OC EC IP Est.

Baseline

Conc.

Est.

Future

Conc.

RRF 0.85 0.95 1.00 1.00 0.85 - -

Mean

PM10

- - - - -28 μg/m3 25 μg/m3

Mean

PM2.5

- - - - -11 μg/m3 8 μg/m3

(4) The following reasonable progress goals have been established: (a) mean visibility on the

20% of days with worst visiblity 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 extinction coefficients derived from

measured PM data.

First, estimate the extinction coefficient for each day with the needed PM measurements. 

This is done using the information in “given #1" with Equation (4.1). For day 1 in year 1, the

current extinction coefficient is:

bext = (3)(1.7)[4.5] + (3)(1.7)[2.2] + (4)(1)[3.3] + (10)[0.8] + (1)[0.3] + (0.6)[7.3] + 10

bext = 70.1 Mm-1

Current extinction coefficients 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 extinction

coefficient (i.e., worst visibility) is given a rank of “1". The results of these calculations are 
101 - DRAFT

Day Date f(rh) SO4 NO3 OC EC IP CM (bext) baselineRank

1 2/15 1.7 4.5 2.2 3.3 0.8 0.3 7.3 70.1 10

2 3/15 2.9 5.1 1.7 2.9 0.7 0.4 9.1 93.6 8

3 4/15 3.5 5.6 1.2 2.4 0.5 0.4 10.2 102.5 7

4 5/15 3.7 6.5 1.1 2.8 0.5 0.6 9.8 117.0 6

5 6/15 4.3 7.4 0.9 3.4 0.6 0.9 10.9 144.1 4

6 7/15 4.6 8.3 0.7 3.5 0.6 1.0 11.3 162.0 1

7 8/15 4.4 8.7 0.8 3.2 0.6 0.8 10.7 161.4 2

8 9/15 4.1 8.0 0.9 3.1 0.7 0.7 8.2 144.5 3

9 9/30 4.1 6.8 1.1 3.2 0.8 0.4 8.8 133.7 5

10 10/30 2.7 5.3 1.4 3.2 0.7 0.6 8.1 89.5 9

displayed in the last two columns (shown in italics) of the table above. 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.

Step 2. Calculate the average baseline extinction coefficient for the 20% of days with worst

visibility and the 20% days with best visibility.

For year 1, mean worst visibility = (162.0 + 161.4) / 2 = 161.7 Mm-1, and

mean best visibility = (70.1 + 89.5) / 2 = 79.8 Mm-1.

Mean worst and best visibility for years 2-5 is provided in “given #2". 

The table below summarizes mean worst and best visiblity for each of the 5 years in the base

period.

Year Mean (bext)current, 

Worst Visibility Days, Mm-1

Mean (bext)current,

Best Visibility Days, Mm-1

1 161.7 79.8

2 190 .3 62.1

3 170.1 87.4

4 180.6 75.3

5 165.0 82.6
102 - DRAFT

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

(bext)baseline = (161.7 + 190.3 + 170.1 + 180.6 + 165.0) / 5 = 173.5 Mm-1

The average mean best visibility is

(bext)baseline = (79.8 + 62.1 + 87.4 + 75.3 + 82.6) / 5 = 77.4 Mm-1

Step 3. Apply a model to develop component specific RRF’s for SO4, NO3, OC, EC, IP and

for coarse mode particulate matter (CM).

Using model results to develop values for the component-specific RRF’s is discussed

further in Section 4.3. Information in “given #3" provides RRF’s for SO4, NO3, OC, EC and IP. 

An RRF value for CM can be derived from the information in “given #3" as follows.

(RRF)CM = (25 - 8) / (28 - 11) = 1.00

Note that an RRF value for CM of “1.00" simply means that the strategy being simulated focused

on reducing fine particle concentations rather than coarse particulate matter.

Summarizing, the RRF values for the components of PM2.5 and CM are:

RRF SO4 NO3 OC EC IP CM

RRF 0.85 0.95 1.00 1.00 0.85 1.00

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.
103 - DRAFT

Worst Days

Day 6: [SO4]future = (RRF)SO4 [SO4]baseline = (0.85) [8.3] = 7.1 μg/m3

[NO3]future = (0.95) [0.7] = 0.7 μg/m3

[OC]future = (1.00) [3.5] = 3.5 μg/m3

[EC]future = (1.00) [0.6] = 0.6 μg/m3

(IP)future = (0.85) [1.0] = 0.9 μg/m3

[CM]future = (1.00) [11.3] = 11.3 μg/m3

Day 7: [SO4]future = (0.85) [8.7] = 7.4 μg/m3

[NO3]future = (0.95) [0.8] = 0.8 μg/m3

[OC]future = (1.00) [3.2] = 3.2 μg/m3

[EC]future = (1.00) [0.6] = 0.6 μg/m3

[IP]future = (0.85) [0.8] = 0.7 μg/m3

[CM]future = (1.00) [10.7] = 10.7 μg/m3

Best Days

Day 1: [SO4]future = (0.85) [4.5] = 3.8 μg/m3

[NO3]future = (0.95) [2.2] = 2.1 μg/m3

[OC]future = (1.00) [3.3] = 3.3 μg/m3

[EC]future = (1.00) [0.8] = 0.8 μg/m3

[IP]future = (0.85) [0.3] = 0.3 μg/m3

[CM]future = (1.00) [7.3] = 7.3 μg/m3

Day 10: [SO4]future = (0.85)[5.3] = 4.5 μg/m3

 [NO3]future = (0.95) [1.4] = 1.3 μg/m3

 [OC]future = (1.00) [3.2] = 3.2 μg/m3

 [EC]future = (1.00) [ 0.7] = 0.7 μg/m3

 [IP]future = (0.85) [0.6] = 0.5 μg/m3

 [CM]future = (1.00) [8.1] = 8.1 μg/m3

Similar calculations are performed for each of the “worst” and “best” days in each of the other 4

years in the base period. 

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 estimate the mean “worst”

and “best” visibility for each of the 5 meteorological 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 in Equation (4.1), we

can estimate corresponding values for bext. 
104 - DRAFT

For year 1

Worst Days

Day 6: bext = (3)(4.6)[7.1] + (3)(4.6)[0.7] + (4)[3.5] + (10)[0.6] + 0.9 + (0.6)[11.3] + 10 = 145.3 Mm-1 

Day 7: bext = (3)(4.4)[7.4] + (3)(4.4)[0.8] + (4)[3.2] + (10)[0.6] + 0.7 + (0.6)[10.7] + 10 = 144.2 Mm-1

Future mean visibility on worst days = (145.3 + 144.2) / 2 = 144.8 Mm-1

Best Days

Day 1: bext = (3)(1.7)[3.8] + (3)(1.7)[2.1] + (4)[3.3] + (10)[0.8] + 0.3 + (0.6)[7.3] + 10 = 66.0 Mm-1

Day 10: bext = (3)(2.7)[4.5] + (3)(2.7)[1.3] + (4)[3.2] + (10)[0.7] + 0.5 + (0.6)[8.1] + 10 = 72.1 Mm-1

Future mean visibility on best days = (66.0 + 72.1) / 2 = 69.1 Mm-1 

Similar calculations are performed for previously selected “worst” and “best” days in each of

years 2-5. To illlustrate the modeled test for reasonable progress, assume these other calculations

yield the following estimates for future mean bext on worst and best visibility days.

Year Future Mean bext On Worst

Visibility Days, Mm-1

Future Mean bext On Best

Visibility Days, Mm-1

1 144.8 69.1

2 137.7 62.0

3 169.4 79.3

4 153.0 67.8

5 154.0 69.4

 

Using results in the preceding table, we see that the estimated future average mean extinction

coefficient for the 20% days with worst visibility is

(bext)future = (144.8 + 137.7 + 169.4 + 153.0 + 154.0) / 5 = 151.8 Mm-1

The estimated future average mean extinction coefficient for the 20% days with best visibility is

(bext)future = (69.1 + 62.0 + 79.3 + 67.8 + 69.4) / 5 = 69.5 Mm-1.

Step 6. Convert the difference between future and baseline estimates of bext on “worst”

visibility days and on “best” visibility days to differences in deciviews. 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.
105 - DRAFT

Use Equation (4.3) and 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 = 10 ln(151.8 / 173.5) = - 1.3 deciviews

For the 20% days with best visibility,

dv = 10 ln(69.5 / 77.4) = - 1.1 deciviews 

Note that the reasonable progress goal for days with best visibility is met. This follows,

since Equation (4.3) yields a negative value, indicating that visibility is not predicted to

deteriorate on these days but, in fact, is estimated to improve. However, the reasonable progress

goal for the 20% days with the worst visibility is not predicted to be met. That is, the goal is to

decrease light extinction on these days by 2.0 deciviews. The simulated control strategy only

leads to a reduction in light extinction of 1.3 deciviews. If the modeled test for reasonable

progress is not passed, States may do well to examine the numerical coefficients in Equation

(4.1) on the days with “worst” (in this example) baseline visibility. Note that humidity

adjustment factors are a part of the numerical coefficient for SO4 and NO3. Generally, the most

effective strategies for improving visibility on days with poor visibility are those which realize

large relative differences in components of particulate matter having large numerical coefficients

on many of the base period’s “worst” visibility days.

Recommendations. States should apply a modeled test for reasonable progress

which has six steps.

1. Use extinction coefficients (bext’s) estimated from monitored components of

particulate matter to rank light extinction on each day with PM measurements in

each of five years comprising a base period. Days with the highest estimated

extinction coefficients (“worst” days) are ranked first.

2. For each year in the base period, calculate the baseline arithmetic mean value for

bext for the 20% of days in the sample with worst visibility and the 20% of days in

the sample with best visibility. Take the arithmetic average of the 5 yearly mean

extinction coefficients for days with “worst” and “best” visibility. 

3. Use an air quality model to simulate relative reduction factors (RRF) for each of

the six components of particulate matter used in Equation (4.1) to derive bext.

4. For each day included in the sample of 20% “worst” and 20% “best” visibility

days, multiply each monitored component concentration times the RRF value

specific for that component. The product is a day-specific estimate for the future

concentration of each component of particulate matter.
106 - DRAFT

5. Use Equation (4.1) to calculate future daily values for bext for each of the “worst”

and “best” days in each year. For each year, compute the future arithmetic mean

value for the “worst” and “best” days. Take the arithmetic average of the 5 yearly

mean future extinction coefficients for days with “worst” and “best” visibility.

6. Convert the difference between future and baseline average mean extinction

coefficents to deciviews. Do this twice. Once for the 20% of days with “worst”

visibility and once for the 20% days with “best” visibility. Compare the two

differences in deciviews with the corresponding two goals established for reasonable

progress. If the differences meet or exceed the goals, the modeled test for reasonable

progress is passed. 

4.3 How Do I Select Appropriate Inputs To The Reasonable Progress Test?

In Section 4.2, we described the recommended modeled test for reasonable progress. An

important part of the test requires using component-specific relative reduction factors (RRF’s),

obtained with models, to estimate future concentrations of these components and, subsequently,

future visibility. We did not say much about how to estimate the RRF’s. In this subsection, we

address this issue. 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 seven 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;

- 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 reduction factors;

- how many, what kind of days to use to develop RRF values, and

- use of relative reduction factors which are specific for days with poor vs. days with good

visibility 

Estimating baseline “worst” and “best” visibility in a Class I area without monitors. 

This issue will be addressed in guidance for estimating trends in regional haze from ambient data

which is being developed by the U.S. EPA . This guidance is expected to be available during the

first half of 2001. It is likely that the guidance for estimating regional trends in visibility will

suggest using observations from a nearby Class I area, or interpolating from observations made in

several nearby Class I areas. It is conceivable that the “observations” which get used will be

mean values for bext for days with “worst” and “best” visibility. If this is the case, the modeled

reasonable progress test will differ slightly from that applied in Class I areas with measured

components of particulate matter. If the only information available for a Class I area is a mean

value for bext for “worst” and “best” visibility days, the test will apply relative reduction factors
107 - DRAFT

(RRF) to these two single values rather than individually to days in a distribution of “worst” and

“best” days. Thus, the test becomes simplified. Derivation of RRF’s is discussed in greater

detail below.

What if a Class I area is so remote from other Class I areas that no available monitored

data appear applicable? In this case, we recommend that a State model every day during one of

the years in the base period. If this is not feasible, a State should follow guidance presented in

Section 12.0 for selecting days to model for an application related to the annual NAAQS for

PM2.5. Next, base period emissions should be modeled for the selected days. Equation (4.1)

should then be applied to estimate values for bext on each modeled day. Days should then be

ranked according to the resulting values for bext. Next, note the 20% of modeled days with the

worst visibility and the 20% of days with best visibility. Compute mean values of bext for days

with “worst” and “best” visibility. Use the modeled results to estimate typical concentrations of

SO4, NO3, OC, EC, IP and CM on the 20% best and worst (modeled) visibility days. Estimate

average RRF values for the 20% worst and best (modeled) visibility days for each of the 6

components of PM used to derive bext. Apply component-specific RRF’s to estimate future

values for each of the 6 PM components needed to estimate future mean worst and best mean bext. 

Apply Equation (4.1) to estimate future mean best and worst values for bext. Finally, apply

Equation (4.3) to see if the two reasonable progress goals have been met. 

Considering a base year with little or no monitored particulate matter. First, we

address a case where there are no monitored observations for one or two of the years in the 5-year

base period. If this happens, apply the reasonable progress test as described in Section 4.2. The

baseline average mean extinction coefficients should be estimated using the years within the base

period with data. The second case is one in which one year has less data than the other years in

the base period. If a year has less than half of the number of days with observations than the

other years in a base period, do not include it when computing the average mean current

extinction coefficient. Similarly, if a year does not have observations during a season in which

there are ordinarily many days with poor (good) visibility, do not include it when estimating

average mean values for baseline bext during days with “worst” (“best”) visibility.

Considering days on which measurements for one or more of the 6 components of

particulate matter needed to compute bext are missing. If a component is missing on a

particular day with measurements, that day should, nevertheless, be considered in ranking daily

bext values preparatory to identifying the 20% worst and 20% best visibility days. We

recommend that States compute the 90th percentile (i.e., close to the highest) and 10th percentile

(i.e., close to the lowest) concentration for the missing component from days where its

measurements are available. If a State is trying to determine “worst” visibility days, assume the

missing component’s concentration is equivalent to its 10th percentile value (i.e., is low). 

Conversely, If a State is addressing “best” visibility days, assume the missing component’s

concentration is equivalent to its 90th percentile value (i.e., is high). Following this procedure

will increase the sample size used to determine days with worst and best visibility. At the same

time, the procedure ensures that missing data are not likely to play a major role in flagging
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“worst” and “best” visibility days. That is, a day is identified as one of the worst or best visibility

days despite the missing component.

Using a constant sample of days to estimate baseline and future visibility. For a

typical Class I area, we anticipate there will be about 120 days per year having measurements

needed to estimate (bext)baseline with Equation (4.1). 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 4.2 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

conctrols 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. Meyer, et al. (1997) show that rankings are less stable

as one examines days which are not characterized by high concentrations of ozone. However, the

reason for this appears to be that the concentrations on such days are so similar to begin with. 

Thus, even though the identity of “best” days may be more subject to change, the resulting

differences in the predicted concentrations on the best days is small, because the variability in

these concentrations among different days is very small. 

Because the reasonable progress test relies on mean values for bext 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 expected shape of the

distribution of extinction coefficients, the arithmetic mean value for bext on the worst days is

more heavily influenced by extreme days rather than those on the borderline between “worst” and

more moderate light extinction. There could be differences in some “best” visibility days

corresponding with pre- and post-control emissions. However, because the differences in

concentrations of particulate matter on such days are likely to be relatively low, differences in the

computed mean value for bext on “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 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 post-control days changes) as part of a weight of evidence

determination.
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Selecting predictions to use in deriving RRF. Relative reduction 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 by Table 3.5, Figure 3.1 and Example 3.3 for a grid whose

cells are 12 km on a side. 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. 

If a Class I area does not contain a monitoring site, States should derive componentspecific RRF values using a procedure which differs only slightly from that used in Class I sites

with monitors. The difference occurs in how spatially averaged component concentrations are

calculated for each modeled day. In this case, daily spatially averaged component concentrations

should be estimated by taking the arithmetic mean value of predicted concentrations in all surface

grid cells which include any part of the Class I area. Once this is done, the recommended

procedure is identical to that for Class I areas with monitors. Note that days used to compute the

RRF correspond to those modeled days for which “monitored concentrations” have been derived

from measurements outside the Class I area.

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 12.0, this may make it adviseable to simulate a full year so that relative reduction 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 (derived using Equation (4.1)) 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 12.0 suggests that this number should be > ~10 days.
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Since meteorological conditions and/or emissions may be markedly different on “best”

visibility vs. “worst” visibility days, we believe it is prudent to develop 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

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. 

Recommendations. States may assume that days which are identified as having the

“worst” and “best” visibility with baseline emissions are representative of those with

“worst” and “best” visibility with future emissions.

States may use fewer than 5 years of observations to derive present estimates for bext

if this is dictated by available speciated estimates of particulate matter. In order to

ensure that as large a sample as feasible is used to identify “worst” and “best”

visibility days, States may assume a high value for a missing component of PM on

days being ranked to determine “best” visibility. For purposes of determining

“worst” visibility days, States may assume a low value for a missing component of

PM.

Component-specific relative reduction factors (RRF) should be estimated using

procedures similar to those used for the modeled attainment test for the NAAQS for

PM2.5. 

RRF estimates for each Class I area should be based on predictions of components

of particulate matter averaged over the 20% observed worst and 20% observed best

visibility days in a chosen year. If it is not feasible to model an entire year, RRF

values should be derived from modeled future and baseline concentrations averaged

over as close to 10 days or more with “worst” and “best” observations. 

Separate values for RRF should be derived for days with “worst” and “best”

visibility.
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5.0 If I Use A Weight Of Evidence Determination, What Does This Entail?

A weight of evidence determination examines results from a diverse set of analyses,

including outcomes of the modeled attainment or reasonable progress test and hot spot analysis. 

Each type of analysis has an outcome or set of outcomes consistent with a hypothesis that a

proposed control strategy is sufficient to meet the specified air quality goal within the required

time frame. If such an outcome occurs, then results of that analysis support the hypothesis that

the proposed strategy is adequate. Each analysis is weighed qualitatively, depending on its

ability to address adequacy of a strategy and on the credibility of the analysis. If most (i.e.,

overall weight of) evidence produced by the diverse analyses supports the hypothesis, then

attainment of the goal is demonstrated with the proposed strategy. The end product of a weight

of evidence determination is a document which describes analyses performed, data bases used,

key assumptions and outcomes of each analysis, and why a State believes that the evidence,

viewed as a whole, supports a conclusion that the area will attain the NAAQS or meet the goals

for reasonable progress. The further a modeled test (or hot spot analysis) is from clearly showing

attainment of the air quality goal(s), the more convincing other evidence needs to be to reach a

different conclusion in a weight of evidence determination. 

Each weight of evidence determination will be subject to area-specific conditions and

data availability. Area-specific factors may affect the types of analyses which are feasible , as

well as the significance of each for a nonattainment or Class I area. 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. In Section 5.1, we identify

several broad types of analyses which can be used to corroborate one another in a weight of

evidence determination addressing the NAAQS. 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 5.2, we note that additional, optional corroborative

analyses may be performed which address some of the default assumptions in the modeled

attainment test more specifically. We provide several examples of such analyses, and identify

conditions which should be met for them to be considered in a weight of evidence determination.

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, weight of

evidence analyses differ in some respects for visibility-related applications. Section 5.3 describes

how to use weight of evidence in reasonable progress determinations.

The types of weight of evidence analyses identified in this section do not represent a

comprehensive list. Analyses performed in addition to the recommended modeled attainment or

reasonable progress tests will likely depend, in part, on ways to remedy shortcomings in the tests

or underlying models perceived by those implementing the modeling/analysis protocol. Prior to

expending a substantial amount of resources, it is a good idea for States to consult with the

appropriate U.S. EPA regional office regarding the types of analyses they wish to include in a

weight of evidence determination.
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5.1 What Broad Types Of Analyses Should I Consider In A Weight Of Evidence

Determination Related To The NAAQS For PM2.5?

At a minimum, a weight of evidence determination should consider the following 3 types

of corroborative analyses: application of air quality models, trends in observed air quality trends

and estimated emissions and outcome of observational models. 

Table 5.1 addresses each of the 3 recommended types of analyses. In the table, we

identify factors which might cause those implementing the modeling/analysis protocol to give

greater credence to a particular set of results (column (2)). We also identify outcomes for each

analysis consistent with a hypothesis that emission reductions implied by a strategy are adequate

to demonstrate attainment (column (3)).

We discuss each of the recommended types of analyses in the following subsections.

5.1.1 Air Quality Models

Weight given to results obtained with air quality models depends on how good the model

performance is as well as the rigor with which the performance has been tested. Model

applications for which an extensive observational data base exists have greater credence,

especially if the data base includes monitored species concentrations which can be compared

with species-specific model estimates and can be used to derive concentrations of PM2.5

components which can be compared with model predictions. 

Model results are likely to be more credible if based on predictions made for several

rather than one or a small number of days. As explained by Hogrefe, et al. (2000) and in U.S.

EPA (1999b), RRF values computed for 8-hour daily maximum ozone concentrations are

apparently more stable (and, therefore, more reliably estimated) if they are based on a composite

response averaged over about 10 or more modeled days. Hogrefe, et al. (2000) also recommend

that model performance evaluation is most meaningful if based on a comparison of predictions

and observations averaged over many (e.g., > 10) days. The preceding references address ozone

rather than PM2.5 and its components. However, we believe the reasons given for basing model

performance evaluation and RRF values on a composite of several days should hold for PM2.5-

related model applications as well. 

One of the most uncertain inputs to a modeling analysis is the emission projections which

must be made to a future year(s) of interest. This uncertainty is reduced if the projection period

is short. Hence, weight of evidence provided by modeling is increased with short projection

periods. If rigorous quality assurance and review is provided for the model’s emissions and

meteorological inputs, this may increase confidence that the model is yielding correct answers for

the right reasons. Thus, rigor used in preparing model inputs also increases credibility given to

the results. 
Table 5.1. Recommended Analyses For A Weight Of Evidence Determination, Factors

Affecting Their Credibility And Outcomes Consistent With Meeting The NAAQS

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(1)

Type of Analysis

(2)

Factors Increasing Credibility of

the Analysis

(3)

Outcomes Consistent with

Hypothesis That a Candidate

Strategy will Lead to Attainment

Air Quality Models -good model performance

-extensive observational data

base available

-RRF are based on a composite

response on many days

-selected episode days have 

observations near the design

value (24-hr NAAQS)

-short projection periods

 

-carefully quality assured

inventory

-confidence in meteorological

inputs

-good ability to pose and address

questions about a strategy’s

adequacy

-other analyses tend to

corroborate conclusions

-the modeled attainment test is

passed, and 

-hot spot analyses do not produce

estimates > 65 μg/m3

 (24-hr

NAAQS) or > 15.0 μg/m3

 (annual

NAAQS), or 

-the attainment/hot spot tests

are nearly passed, the control

strategy requires additional

(unmodeled) reductions and

efforts are underway to

subsequently review/refine the

strategy

-commitment is made to deploy

monitors at locations not passing

the hot spot test

-substantial modeled 

improvement in air quality is

predicted using several measures

described in Section 5.1.1

-similar conclusions are reached

with other peer reviewed models
Table 5.1. Recommended Analyses For A Weight Of Evidence Determination, Factors

Affecting Their Credibility And Outcomes Consistent With Meeting The NAAQS

(continued)

(1)

Type of Analysis

(2)

Factors Increasing Credibility of

the Analysis

(3)

Outcomes Consistent with

Hypothesis That a Candidate

Strategy will Lead to Attainment

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Analysis of Air Quality and

Emissions Trends

-current or future (air quality

model) predicted design value is

within a few μg/m3

 above the

concentration specified in the

NAAQS

-extensive monitoring network

exists

- trends are available for all or

most of the 6 major components

of PM2.5, as well as for PM2.5

itself

-statistical model used to

normalize trend for

meteorological differences

explains much variance

-short projection periods used in

the analysis

-a pronounced, statistically

 significant downward trend is

apparent

-similar conclusions are reached

using several trend parameters

-continued, comparable relative

reductions in emissions are

provided for

-contemplated strategy focuses

on components which are similar

to those which were addressed by

past strategies.

-a pronounced downward

normalized trend exists in the

site-specific design value at all

sites with design values greater

than the level of the NAAQS. 

-Using projected emissions to

extrapolate the air quality trend

line to the required attainment

date indicates a 24-hr or annual

design value < the concentration

specified in the NAAQS.

-Other observed air quality trend

parameters also show a

substantial improvement.
Table 5.1. Recommended Core Analyses for a Weight of Evidence Determination, Factors

Affecting Their Credibility and Outcomes Consistent with Meeting the NAAQS

(concluded)

(1)

Type of Analysis

(2)

Factors Increasing Credibility of

the Analysis

(3)

Outcomes Consistent with

Hypothesis That a Candidate

Strategy will Lead to Attainment

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Use of Observational Models -an extensive monitoring

network exists

-precursor and indicator species

are measured using instruments

with appropriate sensitivity

-monitoring sites appear

spatially representative

-data have been quality assured,

and results are self-consistent

-results of a given observational

model are stable and plausible

physical explanations exist for

findings

-similar conclusions are reached

using a variety of observational

approaches 

-Findings indicate sources

controlled in the candidate

strategy are important causes of

observed PM2.5.

-Analysis of indicator species

suggests the direction of the

strategy (e.g., emphasis on NH3 

or NOx to reduce nitrates) is

appropriate.

Proper selection of episode days increases confidence in the results of a modeled

attainment test. For the annual NAAQS this means including a balance of days for each of the

four seasons, as well as a balance of days where PM2.5 concentrations are high and low. For the

24-hour NAAQS, considering days with concentrations near site-specific design values increases

confidence that relative reduction factors, developed for use in the modeled attainment test, are

appropriate. 

Confidence in the quantitative results of a modeled attainment test is greater if

corroborative, more qualitative, analyses support conclusions about appropriateness of a strategy. 

Finally, of the analyses available, modeling reflects the most comprehensive attempt to integrate

emissions and meteorological information with atmospheric chemistry. As such, modeling has

the greatest capability to address questions about adequacy of a strategy to meet air quality goals

in the future. Thus, States should include modeling results in a weight of evidence

determination, and these should ordinarily be very influential in deciding if attainment will occur.
116 - DRAFT

The outcome from modeling supports use of a proposed strategy for attainment if the

modeled attainment and hot spot tests, described in Section 3.0, are passed. If the hot spot test is

not passed, a commitment to deploy monitors at such locations should be an important

consideration in approving an attainment demonstration. In general, the closer modeled output is

to passing the attainment and hot spot tests, the easier it is for other analyses to produce evidence

which supports attainment. If a modeled attainment or hot spot test is not passed, selection of a

strategy which substantially reduces emissions of one or more of the major components of PM2.5

and an agreement to perform a subsequent review (based on improved data bases/tools) to refine

the strategy, if necessary, can be considered in a weight of evidence determination if other

modeling outputs and other analyses support a conclusion that the current selected strategy may

lead to attainment. Other model-produced indicators that a proposed strategy may be adequate

are (1) model predictions show major improvements in air quality using a variety of metrics, and

(2) other peer reviewed atmospheric simulation models suggest attainment occurs using modeled

attainment test results or other outputs. 

We recommend that at least 3 additional model outputs be examined in weight of

evidence determinations to provide assurance that passing or nearly passing the recommended

modeled attainment test indicates attainment. Like the test, and for similar reasons, each of these

additional outputs reflects relative changes in predicted air quality. States may use other model

outputs (not described herein) in a weight of evidence determination as well.

1. Compute the relative change in frequency of surface grid-days > 65 μg/m3

 (or

15.0 μg/m3 for annual applications) in the nonattainment area.

This output reflects the frequency with which predicted daily average concentrations

exceed the concentration specified in the NAAQS for PM2.5. Such a measure is not directly

related to the form of the NAAQS. Further, if current and future predictions are subject to a

systematic bias, this output could be misleading. However, if modeled episodes are chosen to

represent a variety of meteorological conditions under which concentrations specified in the

NAAQS are exceeded at one or more monitoring sites, and the model performs well using the

measures described in Section 16.0, a large reduction in the frequency of predicted daily

concentrations greater than 65 μg/m3 (or 15.0 μg/m3) is consistent with a conclusion that a

proposed strategy would meet the NAAQS. 

2. Compute the relative change in the number of grid cells in the nonattainment

area with predicted 24-hr (annual) PM2.5 > 65 μg/m3 (15.0 μg/m3)

This output estimates reduction in the pervasiveness of estimated PM2.5 concentrations in

excess of the concentrations specified in the NAAQS. It is subject to the same caveats as the

preceding output. One additional complication may occur if there are not many surface grid cells

in which current emissions lead to concentration estimates exceeding those specified in the

NAAQS. This measure should not be used if this number is small. 
117 - DRAFT

3. Compute the relative change in the total difference (μg/m3

 -day) of daily 

predictions > than the concentrations specified in one or both of the NAAQS for PM2.5.

Although not the same, this output is similar in concept to the change in the “dosage” to

concentrations greater than those specified in the NAAQS. For applications related to the 24-

hour NAAQS, we recommend using Equation (5.1). A similar equation can be used for

applications related to the annual NAAQS by replacing “65" in the numerator and denominator

with “15.0".

(5.1)

Where

RD = Relative Difference

Cij = Predicted 24-hour concentration for day i and grid cell j greater than 65 μg/m3

N = Total number of days

G = Total number of surface grid cells.

This metric is subject to the same caveats as the preceding two metrics. 

5.1.2 Analysis Of Air Quality And Emissions Trends

Using trend information in a weight of evidence determination requires two preliminary

steps: choosing a trend parameter(s) and normalizing fluctuations in the chosen parameter for

year to year differences in meteorology.

Choosing a trend parameter. The preferred approach is to look at trends in the

components which comprise most of PM2.5 (i.e., usually, SO4, OC, IP and/or NO3). If these

measurments are not available, then observed trends in measured PM2.5 is probably the next best

choice. As of 2000, the period of record for measurements of PM2.5 and/or its components is not

substantial in many locations. Thus, initially, States may wish to use visibility (e.g., measured

extinction coefficients) or PM10 as a surrogate trend parameter. If a surrogate trend parameter is

used, States should take precautions (identified shortly) in interpreting the results in a weight of

evidence determination. 

A second decision which needs to be made in choosing a trend parameter is, which

member in a temporal distribution of observed concentrations to track? That is, do I choose a

mean value, a 50th percentile value, a 98th percentile value, or what? We recommend that States
118 - DRAFT

choose trend parameters which are as close as possible to the entities used in the modeled

attainment test. Thus, for applications related to the annual NAAQS, the ideal is the observed

quarterly mean concentration averaged over 3 consecutive years. For the 24-hour NAAQS, the

ideal is the observed quarterly 98th percentile concentration averaged over 3 consecutive years. 

For a number of reasons however, the ideal may not be practical (e.g., insufficient number of

observations, large number of resulting trends leading to difficulties in intepretation, etc.). Thus,

for practical reasons, it is acceptable to look at fewer, more robust indicators (e.g., annual 50th

percentile concentrations, 90th (rather than 98th ) annual percentile concentrations). There are,

undoubtedly, additional trend parameters which might provide useful insights for some

applications. Additional trend parameters may be considered for use on a case by case basis. 

Normalizing observed trends for meteorological differences. The second step needed

prior to interpreting results of trend analysis is to normalize changes in the chosen trend

parameter(s) for year to year fluctuations in meteorology. There are several approaches which

have been used to do this. These include (a) using peaks averaged over multiple years

(California Air Resources Board, 1993), (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),

and (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). A brief summary of the strengths and

weaknesses of these and other techniques will be available in U.S. EPA’s PM2.5 Data Analysis

Workbook (U.S. EPA, 2001), expected to be completed early in 2001. 

Interpreting a normalized trend. A curve is fit through the normalized trend and

extrapolated to the year in which the air quality goal is to be met. Extrapolations are made by

considering past trends as well as past and projected emission reductions. If the trend is

statistically significant, the extrapolated value for the attainment year is at or below the air

quality goal, and projected relative emission reductions are comparable or greater than reductions

occurring during the period for which the trend is constructed, results of the trend analysis

suggest a strategy will be adequate. This procedure is illustrated in the following example.

Example. A proposed control strategy focuses on reducing sulfate concentrations. 

Estimate the relative reduction in emissions (e.g., SO2 or NH3) occurring during the

period corresponding to the observed normalized trend in the 98th (or 90th if data

limitations are a problem) percentile sulfate concentration in the nonattainment area. Use

the estimated emission trend in concert with the normalized air quality trend to determine

an “emission reduction sensitivity factor” (e.g., (μg/m3

 )/(percent emission reduction)). 

Multiply the sensitivity factor times the percent reduction in emissions projected between

the current period and the required attainment date. Note the resulting estimated

difference in sulfate concentrations. Since the strategy focuses on reducing sulfates (and

other components are not much affected), subtract the computed difference in the sulfate

concentration from the current design value for PM2.5 to get a projected design value. If
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the projected design value is # to the concentration specified in the NAAQS for PM2.5,

the trend analysis supports a hypothesis that a proposed control strategy will suffice to

reach attainment by the required date. Note that the procedure works even if a trend

parameter is used which differs from the form of the design value (e.g., 90th rather than

98th percentile for 24-hr NAAQS), provided the slopes in the trend parameters are similar.

Some precautions. The trend analysis we suggest assumes that a linear extrapolation of

observed past correspondence between monitored design values and estimated emission changes

will accurately describe future air quality. This assumption probably works best if the current

design value is already close to meeting the air quality goal. Weight given to trend analyses

depends on several other factors as well. The more air quality data available and the greater

variety of trend parameters which show major improvements, the more credible the results. 

Weight of evidence produced by trend results is higher 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. Trend analysis is most credible if the contemplated strategy is similar to a

past strategy (e.g., both strategies focus on reducing sulfates). 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 emissions. 

Finally, we previously noted that extra precaution is needed in interpreting results of trend

analysis when a surrogate for PM2.5, such as PM10, is used as a trend parameter. The

methodology described in the preceding example is only valid if the past emission reductions

corresponding with the observed air quality trend consisted largely of efforts to reduce the fine

fraction (i.e., < 2.5 μm) of PM10. We would not expect a past trend in PM10 to be indicative of

what might happen to future concentrations of PM2.5 if past emission reductions focused on the

coarse fraction of PM10. 

5.1.3 Use Of Observational Models

Observational models take advantage of monitored data to draw conclusions about the

relative importance of different types of PM2.5 emissions or precursors as factors contributing to

observed PM2.5, as well as inferences which might be drawn about the effectiveness of various

strategies to reduce PM2.5 concentrations. There are at least 3 approaches with potential for doing

this: source apportionment (i.e., “receptor”) models, hybrid approaches in which air quality

observations are combined with meteorological observations in some manner, 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. Our intent is merely to highlight major approaches and to illustrate how they might be

used in a weight of evidence determination. A review by Seigneur, et al. (1997), which is also

summarized in Seigneur, et al. (1999), contains a more complete description of the major
19Gray and Cass, (1986) and Schauer, et al., (1996) describe procedures to (a) distinguish

primary from secondary OC, or (b) identify organic species appropriate to use as fitting elements

in a CMB model.

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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. 

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). 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. This “optimum” combination is derived using statistical software which

calculates the square of the differences between observations and predictions of all of the fitting

elements. Each resulting difference is weighted according to the uncertainty attendant with

measuring or estimating the corresponding fitting element. It is the sum of these weighted

differences which gets minimized. The resulting solution represents each source category’s

estimated contribution to measured PM2.5 at a monitor on the day in question. States wishing to

apply CMB to address the annual NAAQS for PM2.5 should calculate source contributions for a

representative sample of days and use the resulting information to estimate the average relative

importance of each of the source categories. We address the issue of what is a “representative

sample of days” in Section 12.0. 

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. If such is the case, CMB can still be used to provide insights in

several ways. First the analysis can focus on apportioning only those components of PM2.5 which

largely consist of primary particulate matter (i.e., IP, EC and that portion of OC which is

identified as being emitted as primary organic particulate matter19). A second approach is to

continue to apportion total PM2.5 using “fictitious” source categories whose “source profiles”

consist entirely of “sulfate”, “nitrate” and “secondary OC”. The resulting solution, by itself, will

not lead to any insights about the sources of “sulfates”, “nitrates” or “secondary OC”, but insights

regarding contributors to the primary components of PM2.5 (IP, EC , primary OC) are possible. 

As we describe later, CMB estimates, when combined with meteorological analyses in hybrid

models, could provide insights into possible sources of secondary particulate matter as well.

Multi-variate statistical models comprise the second type of receptor model. Unlike with

CMB, States do not have to make a priori assumptions about contributing source categories. 

Another distinction from the CMB approach is that multi-variate models look at day to day
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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. Knowing which days are assoicated

with one another can lead one to examining other attributes of these days to provide clues about

significant sources. In particular, insights are possible when identified clusters of days are

considered in hybrid models incorporating meteorological variability. Knowing which species

have high temporal correlations with one another provides a clue about the possible identity of

significant source categories. One outcome of these analyses is an identification of statisticallyidentified “sources” accounting for variability in the monitored data. An essential part of the

analysis is to relate these “sources” to actual source categories in the area being studied. This

could be done by comparing each identified “source” with available source speciation profiles or

with other attributes of the area. Once actual, physically meaningful sources are identified, a

CMB-type approach can be used to provide an estimate of the contributions of identified source

categories to measured particulate matter on specific days.

Results obtained with multi-variate statististical methods, like cluster or principal

component analysis, can sometimes produce physically unrealistic solutions (e.g., negative

“factor loadings”for identified “marginal” sources). Recent multi-variate models include

constraints to avoid physically unrealistic solutions. The Positive Matrix Factorization (PMF)

technique (Paatero and Tapper, 1994) is one such technique. A user’s manual (including a

tutorial) for this approach is available for downloading at ftp://rock.helsinki.fi/pub/misc/pmf.

Papers by Huang, et al. (1999) and Polissar, et al. (1998) provide good examples of how PMF

can be used. UNMIX is a second recently available multi-variate statistical model which

incorporates constraints to avoid physically unrealistic solutions. Henry (1997) has prepared a

conceptual description of the principles underlying this model. A user’s manual is available

(Henry, 2000). UNMIX and PMF have recently been applied to a data base developed in

Phoenix. Information relating to this and other applications is described in Willis (2000). In

addition to identifying “sources”, UNMIX and PMF provide estimates of “source”contributions

to monitored concentrations of components of particulate matter. Unlike with CMB estimates,

these estimates represent “average” rather than day-specific contribution estimates. As such, this

output of PMF and UNMIX may be particularly useful for applications related to the annual

NAAQS.

Hybrid models. Hybrid models use results obtained with CMB or multi-variate receptor

models in concert with other information. These techniques are needed to provide insight about

contribution of potential sources to observed secondary particulate matter. Other information

used in hybrid models includes some or all of the following: location of sources of emissions,

composition of emissions associated with different identified sources, spatial correlation of PM2.5

or one or more of its components measured at different monitoring sites, correlation of high

PM2.5 or its composition with wind direction or other meteorological variables, and use of

trajectory analysis.
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Location and composition of potential sources of PM2.5 or its precursors is needed if one

is to make use of meteorological information to infer likely important contributors to secondary

particulate matter. Associations of high observed PM2.5 or secondary components of PM2.5 with a

subset of wind directions/speeds, suggests a locus for likely important contributors to secondary

particulate matter. If there are certain primary tracer species of particulate matter which seem to

track high observed secondary particulate matter, one could search locations along the wind track 

suggested by the wind data to locate areas where sources of the tracer and sources of precursors

of secondary particulate matter exist. Relating wind direction to high secondary particulate

matter observed at different monitoring sites allows one to locate potential important source

areas for secondary particulate matter with greater confidence through triangulation. Henry, et al.

(1994) and Henry (1997a, 1997b and 1997c) have published several papers which present an

approach utilizing data from several monitoring sites, and show how it can be used to infer

sources which likely contribute to observed sulfate concentrations.

Trajectory models can be used together with receptor models to identify potential regional

sources of secondary particulate matter. One trajectory model commonly used for similar

purposes with ozone-related applications is available through the National Oceanic and

Atmospheric Administration (NOAA, 1999). Trajectory models are used to trace likely previous

locations of pollutant laden air sampled at monitoring sites. Thus, they are referred to as, “backtrajectories”. Because we can only have a limited amount of confidence in the accuracy of any

single back-trajectory, and a number of potentially important factors are ignored in computing a

trajectory, these methods require trajectories to be constructed for many days. Similar

trajectories can then grouped to see whether each group is characterized by distinctive differences

in measurements of PM2.5 or its components. An alternative approach for using trajectory

models and receptor models together is to note days with high measured secondary components

of PM2.5 (i.e., SO4, NO3 or secondary OC) and contrast this with trajectories computed for other

days. If days with high measurements are characterized by trajectories which suggest longer

residence times over certain areas, this provides some evidence that sources of PM precursors in

such areas may contribute to high levels of measured secondary particulate matter. Much use has

been made of trajectory models to estimate possible need for making regional reductions in

ozone precursors. The methods used appear equally applicable for identifying possible sources

of secondary particulate matter. (Poirot, 1998, Schichtel and Husar, 1995, Schichtel, 1999).

Indicator species approaches. Indicator species approaches are based on the predicted

sensitivity of a secondary pollutant’s concentration to changes in different precursors for that

pollutant. Predicted sensitivity is determined from simulations performed with the chemical

kinetics mechanism used in air quality simulation models. Based on these simulations, 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. The approach is an

observational one, because the next step is to examine monitored data to calculate observed

ratios of the indicator species. Values of observed ratios are then compared to previously

identified ranges for which the secondary pollutant is sensitive to reductions in precursor A and

ranges for which reductions in precursor B appears most effective. Although results may
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sometimes fall into a “gray area” where more than one precursor may affect ozone or secondary

particulate matter, the indicator species approach provides a potential, quasi-independent means

for assessing whether a proposed strategy to reduce a secondary pollutant is likely to be efficient

in doing so.

As of late 2000, nearly all 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 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 gains

more experience with chemical mechanisms incorporating secondary particulate formation and

more speciated particulate and gas phase ambient measurements become available.

Potential utility of observational models in weight of evidence determinations.

Observational models are potentially useful for assessing whether a proposed strategy is oriented

toward source categories whose emissions appear to be associated with current observed PM2.5. 

Thus, they are useful for confirming whether a modeled strategy (a) is addressing likely

important source categories of primary particulate matter, and/or (b) is addressing likely sources

or areas in which precursors for secondary particulate matter originate. However, their ability to

estimate how much control is needed is limited, unless one can justify assuming an

approximately linear relationship between PM2.5 or precursor emissions and observed

concentrations of PM2.5 and/or its components. Thus, observational approaches are suitable to

corroborate results obtained for PM2.5 or its components with more quantitative techniques, like

air quality models. 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 representativeness of the responses

simulated with the air quality model for a limited sample of days. 

In summary, if conclusions drawn with one or more observational models suggest that the

types of sources to be controlled under a proposed strategy are those that appear associated with
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high PM2.5 and/or are those to which observed PM2.5 or its major components are sensitive, this

supports a hypothesis that the strategy is effective.

Interpreting results from observational models requires familiarity with the location being

studied, and requires considerable subjective judgment. 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. Results are more believable if they do not vary substantially in

response to different assumptions made in applying the statistical algorithms underlying many of

the models. Evidence produced by observational models is also more compelling if several

techniques are used which complement one another and produce results which are consistent. 

Results are also more believable if plausible underlying physical/chemical explanations for them

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. 

Recommendations. Weight of evidence determinations are best performed using a

variety of diverse analyses in a corroborative fashion. Prior to its application, each

selected analysis should have identified outcomes consistent with concluding a

proposed strategy is adequate. At a minimum, States should consider the following

corroboratory analyses in a weight of evidence determination: (1) output from air

quality model(s) (i.e., modeled attainment test results plus other indicators), (2) air

quality and emission trend analysis, and (3) interpretation of results obtained with

observational models. If it is not feasible to include one or more of the

recommended corroboratory analyses, the reasons should be documented. 

5.2 What If I Want To Consider Additional Corroborative Analyses?

The list of analyses in Section 5.1 is not an exhaustive one. A State may use other types

of analyses in a weight of evidence determination to supplement those identified in Section 5.1. 

To have another type of analysis considered, a State should identify why it believes the analysis

will produce information which has a bearing on attainment of the NAAQS. In addition, the

procedure to be used in applying the method and the extent of the data base available to support

it should be identified. Finally, prior to application of the method, a State should identify

outcomes which would be consistent with a hypothesis that a proposed emission reduction

strategy will lead to attainment.

Choice of additional corroborative analyses is, in part, a function of the available data

base and analytical tools, as well as questions posed by the outcomes of the analyses described in

Section 5.1. For purposes of illustration, we identify some additional analyses of the sort which

States might consider. None of these is required, and States may well choose to consider other

optional analyses or no optional analyses at all.
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Quantifying uncertainty associated with air quality model estimates. In this

guidance, we recommend that “uncertainty” be accounted for using a modeled attainment test

which uses models in a “relative” sense and by recognizing that use of corroboratory analyses

may be desirable in a weight of evidence determination. Thus, we account for uncertainty in a

qualitative way, without actually estimating it. 

States may find it useful to quantify estimates of uncertainty and then use these results

qualitatively in a weight of evidence determination. In Section 17.0, we identify three sensitivity

tests which may be useful for this purpose. The first of these is one which has been proposed by

Reynolds, et al., (1996). This test is to prepare “alternative base case” emission estimates,

reflecting reasonable alternative assumptions about current emissions which lead to comparable

or better model performance. Note differences in projected design values from these alternative

current emissions. A second test is to assume alternative (reasonable) growth assumptions. This

could reflect using differing growth rates or placement of new sources in different, equally

probable, locations. Note the differences in projected design values for the different growth

assumptions. Combinations of the first two tests are also possible. A third test is one in which a

control strategy under consideration is simulated with an alternative grid resolution or with

different (reasonable) meteorological assumptions. 

Other approaches for estimating uncertainty have been described in the literature (Gao, et

al., (1996) and Yang, et al., (1995)). Many of these approaches also assess sensitivity of model

predictions to uncertainties in input variables. For outcomes to be most relevant to the way we

recommend models be applied in attainment demonstrations, it is preferable that such procedures

focus on sensitivity of estimated relative reduction factors (RRF) and resulting projected design

values to the variations in inputs or model formulations. 

Once a range in projected design values is obtained using tests like those previously

described, a qualitative assessment can be made of how likely it is that a strategy will lead to

attainment of the NAAQS. For example, in applications related to the 24-hour NAAQS, if most

of the results lead to projected design values < 65 μg/m3, this supports a conclusion that if the

strategy is implemented, the NAAQS will be attained. Choice of tests and interpretation of the

outputs should be agreed upon beforehand in concert with the appropriate U.S. EPA Regional

Office.

Compare monitored design values for the current period used in the test with those

measured in other periods. In Section 3.3, we suggested choosing the highest monitor-specific

design values from a limited sample of recent 3-year periods. This is intended to provide some

protection against basing the modeled attainment test on a relatively mild period. However,

using such a limited number of periods to choose from opens the possibility that we have

inadvertantly chosen an extreme period upon which to base the test. The objective of this

analysis is to assess whether current design values used in the modeled attainment test are

atypically high or low due to natural or meteorological conditions. If the current design values

are lower (or much higher) than normal, the tests would yield overly optimistic (pessimistic)
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results. 

An analysis of current design values is complicated by trends in emissions as well as by a

limited period of observations. For example, one would expect design values measured several

years previously to be higher than current values if there has been an ongoing program to reduce 

PM2.5 or precursor emissions. This does not necessarily imply that the current design value is

atypically low. The problem of emissions trends can be addressed by examining statistical

relationships between meteorological conditions and daily PM2.5 concentrations to see whether

conditions corresponding to high PM2.5 occurred more or less frequently than usual during the

current period. Approaches which are analogous to that described by Cox and Chu, (1996) or by

Deuel and Douglas, (1998) might be tried to see how the current 3-year period ranks with other

3-year periods in terms of its frequency of days with meteorological conditions which appear to

correspond with high observed PM2.5.

Assess appropriateness of default compositional assumptions for major components

of PM2.5. This analysis examines available speciated PM observations to note proportions of 

elements or compounds. These proportions are compared with default composition assumptions

for major components of PM2.5 noted in Section 3.3 to check for consistency. If large

inconsistencies occur, alternative assumptions regarding measured mass associated with one or

more components may be proposed. This, in turn, could revise the estimated current

composition of PM2.5 used in the modeled attainment test. To illustrate, suppose measured

samples in a nonattainment area suggest that measured organic molecules typically consist of

60% carbon, rather than the 70% we assume as a default value (see Table 3.4). This would be

consistent with using a multiplication factor of “1.7" rather than “1.4" for estimating mass

associated with organic carbon (OC). A State could use this information to recalculate the mix

of the major components of currently measured PM2.5. The resulting changes could lead to

refined estimates regarding attainment using the modeled attainment test.

Recommendations. Optional analyses may be considered in addition to the 3

recommended analyses identified in Section 5.1. To use an optional analysis in a

weight of evidence determination, a State should (1) explain the rationale for the

analysis, (2) identify the data base underlying the analysis, (3) describe the

methodology to be used in applying the analysis, and (4) identify outcomes which

would be consistent with a hypothesis that a proposed control strategy will suffice to

attain the NAAQS.

5.3 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 described in Section 4.2 does not clearly show reasonable 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 uses for
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additional air quality modeling, observed ambient and emissions trends and observational

models. Trend analyses and observational models have already been discussed in detail in

Section 5.2. Therefore, in Section 5.3 we only identify some potential uses for these analyses.

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 and identify some analyses which a State might wish to

consider in addition to or instead of the modeled test for reasonable progress for “best” visibility

days.

Additional air quality modeling. Sensitivity tests could 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 current emissions and/or differing growth rate assumptions. 

One or more additional models could be used to develop relative reduction factors. These

RRF’s could then be used in the test described in Section 4.0 to see if consistent conclusions are

reached regarding trends in “worst” and “best” visibility days. used in the moded test for

reasonable progress. 

Further, visibility extinction can be estimating using a procedure which differs from the

approach of “reconstructing” light extinction from mass concentrations of components of

particulate matter described in Section 4.0. For example, models like the Community Multiscale

Air Quality (CMAQ) model can also utilize information about size distribution of fine particles

and associated light extinction efficiency factors to estimate light extinction in an independent

way (U.S. EPA, 1999c). Visibility trends which are calculated in different ways could be

compared to see if they corroborate each other. 

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 early ‘00's). This

information could be used to confirm that more control measures on previously reduced

emissions of a component or its precursors is likely to be useful. It may also be used to see

whether certain components are becoming increasingly important sources of light extinction.

Observational models. Observational 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
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nested grid model application or plume-in-grid modeling. Use of nested models and plume-ingrid algorithms are discussed in greater detail in Section 13.0. Observational 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.

Refinements to the recommended modeled test for reasonable progress. If a strategy

for meeting reasonable progress goals appears generally successful, but the modeled test for

reasonable progress is not passed 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. The list is not comprehensive and, in general, the effort needed to perform

the listed analyses would be substantial.

- Rerank 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).

- Use day specific estimates for relative humidity and relative humidity adjustment factors

(f(rh)) to compute light extinction attributable to hygroscopic compounds.

- Perform area specific analyses relating monitored visibility extinction to locally

applicable composition of OC, IP and SO4. Use results to develop an area-specific

version of Equation (4.1). 

- Background levels themselves are not constant from day to day. Available speciated

data and other information may be reviewed to see whether the outcome of the test is

being influenced by including several days with extraordinary events (e.g., a nearby major

forest fire lasting a number of days). 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. 

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 μg/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). Utililty 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
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16.0 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 (4.1). If it does, and diagnostic tests (also described in Section 16.0) are unable to

resolve a performance problem, a State may address the goal for “best” visibility days in the

particular Class I area(s) without using results from a model. We anticipate that observational

models may be helpful in such cases. They may be used to characterize meteorological

conditions under which “best” visibility occurs and may be used to identify zones where

increases in emissions may need to be limited in order to meet a goal of not degrading visibility

on “best” visibility days. 

Recommendations. States may use additional analyses in a weight of evidence

determination to corroborate or refine a strategy for which the modeled test for

reasonable progress has been applied. The decision of whether to perform a weight

of evidence analysis in a reasonable progress demonstration is best made for a

limited number of individual Class I areas on a case by case basis. If the ability of

air quality models to reproduce very low concentrations of particulate matter on

“best” visibility days is shown to be highly uncertain for a particular Class I area, a

State may consider a reasonable progress demonstration for that area in which an

atmospheric simulation model is not used to determine if the goal for “best”

visibility days will be met.
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6.0 What Should Be My Data Gathering Priorities?

A key purpose of this document is to identify and prioritize data collection activities

which may increase the credibility of modeled demonstrations of attainment or reasonable

progress. We recognize that a State’s data gathering priorities depend on what data are already

available. Thus, data gathering priorities need to be established on a case by case basis by those

implementing the modeling/analysis protocol for the attainment or reasonable progress

demonstration. In this section, we identify key data needs implied by the recommended modeled

tests for attainment or reasonable progress. We also identify needs for obtaining reliable model

results (prior to applying the tests) and for corroboratory analyses used in weight of evidence

determinations. Section 6.1 identifies data and analytical capabilities which are important for

these purposes. We recognize that it may not be feasible to collect all the data desirable during

the timeframes required for developing a revision to a State implementation plan (SIP). Thus, in

Section 6.2 we identify factors to consider in performing “mid-course reviews” of approved

SIP’s.

6.1 What Data And Other Needs Are Implied By The Modeled Demonstrations For

PM2.5 And Regional Haze?

There are three major activities included in demonstrations of attainment or reasonable

 progress for which a good data base is needed: 

- generating and evaluating model results which are subsequently used in the modeled

tests for attainment or reasonable progress (described in Part II of this guidance);

- applying modeled tests for attainment or reasonable progress (described in Sections 3.0

and 4.0);

- performing weight of evidence determinations (described in Section 5.0).

We list key needs for each of these three major activities. Highest priority needs are listed first. 

Of course, priorities for collecting data in any given geographical area are affected by the data

and capabilities which are already available. Further, those implementing the modeling protocol

may need to make case by case decisions regarding tradeoffs among the different needs to meet

budgetary constraints. Planning documents for a study in California’s San Joaquin Valley

provide an example of how to balance theoretical needs with existing data bases and budgetary

constraints when developing priorities for collecting data (California Air Resources Board,

1999).

6.1.1 Key Needs For Generating Credible Model Results Underlying Attainment

And Reasonable Progress Tests

1. Develop accurate emission factors, seasonal and diurnal activity levels for major sources
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and source categories of PM2.5, SO2, NH3, NOx, VOC and CO

These are the most important inputs to an air quality model, because they are the ones that

get altered in a control strategy. The effects of altering emissions are suspect if the emissions are

poorly characterized. Priorities for improving emissions estimates for different source categories

depend on the amount of uncertainty associated with current estimates, the potential for large

variability in a source category’s emission factors or activity levels among geographical areas and

the relative importance of related monitored components of PM2.5. 

2. Make speciated measurements of ambient PM2.5, and refine speciated source profiles so

that they are applicable locally

Speciated ambient measurements are needed to help evaluate performance of an air

quality model. Clearly, performance is likely to be enhanced for primary particulate matter (IP,

primary OC, EC) if speciated emission profiles for PM2.5 are applicable to the area and time of

the simulation. As we note in Section 15.0, speciated ambient measurements are potentially

useful as a means for quality assuring emission estimates to assess their applicability to the local

situation. Speciated ambient measurements and speciated emission profiles are also keys to

developing an accurate conceptual description of an area’s PM2.5 or visibilitiy problem. As we

discuss in Section 9.0, the conceptual description of a problem is instrumental in choosing a

modeling approach, making decisions about detail needed for various inputs and choosing

strategies to investigate with a model. 

Speciated measurements should be collocated with PM2.5 mass measurements. Highest

priority should be given to making measurements at sites violating or likely to violate the

NAAQS for PM2.5 and in Class I areas. We want to be sure that the model performs well here. 

In order to improve chances of developing an accurate conceptual picture of an area’s

nonattainment problem, it is also important to make some ambient speciated measurements in

rural areas to characterize regional levels and composition of particulate matter. Availability of

some rural mass and speciated data also makes it possible to better evaluate whether a model can

accurately predict buildup of particulate matter in urban areas. 

3. Make upper air measurements

These measurements should include wind velocity, temperature, dew point and pressure. 

Ideally, they should be made continuously. If this is not feasible, we recommend that

measurements be made 3-4 times per day—shortly after sunrise, midday, at twilight and during

the night. Similar surface measurements should be made so that a complete vertical profile can

be developed for these variables. As we note in Section 14.0, these measurements are used to

ensure that meteorological input generated for air quality models by dynamic meteorological

models is realistic. Meteorological models, in turn, develop the wind fields which define sourcereceptor orientations on any given day. As described in Section 12.0, meteorological factors may

also help in choosing appropriate, representative periods to model.
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If possible, short duration special studies should be performed, in which speciated air

quality measurements are made aloft. These measurements should include speciated PM2.5 (i.e.,

to derive SO4, NO3, OC, EC, IP), other oxidized forms of nitrogen, SO2 and ozone). These

measurements are useful for evaluating model performance, as well as for developing a

conceptual description of an area’s problem. 

4. Make short term measurements of PM2.5 and its species

“Short term”, in this context, refers to the sampling time. Measurements of PM2.5 and its

components should be made using sampling times no longer than 6 hours duration. 

Measurements of this nature are very important to meaningfully evaluate model performance. 

Comparing predictions and observations over 24-hour periods could result in failure to detect

poor performance in reproducing diurnal patterns of observed particulate matter. Poor

performance of this nature could result in source/receptor orientation being mischaracterized on a

particular day. Collection of 4 or more samples per day obviously greatly increases needed

resources and data management problems. It suffices to make measurements of this nature only

for limited duration field studies. Such short term studies should also include measurements of

air quality aloft. For purposes of evaluating model performance predicting relatively non-volatile

components of PM (e.g., SO4 , EC, IP) continuous surface measurements should be considered. 

As we describe in Section 16.0, efforts to evaluate model performance are best directed to these

periods with intensive data bases.

5. Develop accurate estimates for natural sources of VOC, primary OC and ammonia

 

Relative importance of natural emissions as a source of PM2.5 (especially of secondary or

primary OC) is not yet clear. We anticipate that the U.S. EPA will continue its efforts to

enhance/improve the BEIS emissions model (U.S. EPA, 1997a). However, States could focus on

items like distribution of land use and environmental factors (e.g., temperature) so that the

national model furnishes estimates which are as reliable as feasible.

6. Procure hardware, data storage capabilities and develop expertise to apply a regional

photochemical grid model with aerosol capabilities

As we discuss in Section 1.0, secondary particulate matter likely constitutes a major

portion of measured PM2.5, and is a major source of light extinction. In order to address

reasonable progress, it will be necessary to use such models. In most instances, regional scale

photochemical grid models will also be needed to address PM2.5 nonattainment problems. While

the tasks of running and interpreting results from such models can be performed externally, it is

important for State personnel to be sufficiently familiar with related issues to direct and critically

review these studies. If the work is to be done inhouse or with inhouse facilities, then necessary

hardware and data storage facilities are required.
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7. Make PM10 as well as PM2.5 mass measurements in Class I areas

This needs to be done to ensure that the model performs adequately in predicting

observed coarse particulate matter (CM). Changes in predicted concentrations of coarse

particulate matter affects predicted changes in light extinction (see Equation (4.1)).

8. Measure surface meteorological conditions routinely

Measurements should include, wind velocity, relative humidity, temperature and pressure. 

They should prove useful in helping to identify distinctive types of episodes to model. They may

also prove useful in developing a conceptual description of an area’s PM2.5 problem and provide

needed inputs for hot spot analyses (see Section 3.4).

9. Analyze speciated data to ascertain the importance of water as a component of measured

mass of particulate matter and to distinguish between primary and secondary parts of

organic carbon (OC)

Analyzing for presence of water may be needed to explain any apparent underprediction

of measured PM2.5 by an air quality model. Further, if water associated with measured sulfates

and nitrates can be estimated, a State could reduce the fraction of unattributed mass (U) in the

modeled attainment tests and instead associate this mass with sulfates and/or nitrates. 

Distinguishing between secondary and primary OC should help develop a better conceptual

description of an area’s PM2.5 or visibility problem. It should also facilitate a more complete

assessment of model performance. 

6.1.2 Key Needs For Performing Modeled Tests For Attainment Or Reasonable

Progress

1. Develop accurate emission factors and seasonal/diurnal activity levels for major

anthropogenic sources and source categories of PM2.5, SO2, NH3, VOC and NOx.

These estimates are needed to determine accurate relative reduction factors (RRF) and to

choose strategies likely to pass modeled tests for attainment or reasonable progress on an

informed basis.

2. Make ambient speciated measurements of PM2.5

These measurements are needed to establish current concentrations of the major

components of PM2.5. The attainment and reasonable progress tests estimate future

concentrations of major components by multiplying current component concentrations times

component specific relative reduction factors. Because we need to account for all measured mass

of PM2.5 with the major components (i.e., this is done through use of a component “U”, which

accounts for differences in the measured mass and the sum of mass associated with the remaining

components), it is best to collocate speciated measurements with FRM or equivalent monitors. 
135 - DRAFT

Priorities for making these measurements follow:

- make measurements at monitoring sites violating or likely to violate NAAQS for PM2.5;

- make measurements in rural areas so that as many nonattainment areas as possible have

at least one relatively nearby background site with speciated measurements.

3. Measure PM10 and PM2.5 in Class I areas

These measurements are needed to derive current estimates for coarse particulate matter

(CM). CM is used in Equation (4.1) to relate measured components of particulate matter to the

extinction coefficient used to characterize light extinction.

4. Develop post-processing software to facilitate application of model results and monitored

data in the modeled tests for attainment and reasonable progress

Software is needed to convert speciated measurements into mass associated with each

major component of PM. Information presented in Tables 3.4 and 4.1 should be used as the basis

for such software. In addition, it is clear from Examples 3.1, 3.2, 3.3 and 4.2 that the modeled

tests we recommend require substantial manipulation of model-generated and monitored data. 

Further, likely sample sizes will be much larger than those we have used for illustrative purposes

in the examples. Automated procedures will be needed to make the required data analysis

tractable. We plan to develop and provide States with such software.

5. Develop speciated emissions profiles for major source categories of particulate matter

These estimates are more important for generating model results and for applying weight

of evidence analyses. However, they are also useful in applying modeled tests for attainment or

reasonable progress. The information developed in the profiles can be used in concert with

monitored data to help focus control efforts on sources which appear to be contributing to

monitored exceedances of the NAAQS or to light extinction. This could be done using

observational models like those described in Section 4.1.3.

6. Develop accurate estimates for biogenic/geogenic sources of VOC, primary OC and

ammonia

 

These estimates are needed to improve accuracy of relative reduction factors associated

with strategies to reduce concentrations of PM2.5 and regional haze. The more important the

contribution that emissions of biogenic/geogenic sources make to a component of PM2.5, the less

responsive the RRF value for that component will be to control measures. We anticipate that the

U.S. EPA will continue its efforts to enhance/improve the BEIS emissions model (U.S. EPA,

1997a). However, States could focus on items like distribution of land use and environmental
136 - DRAFT

factors (e.g., temperature) so that the national model furnishes estimates which are as reliable as

feasible.

7. Make ambient PM2.5 and wind measurements at locations which are likely hot spots. 

Measurements of PM2.5 are intended to confirm that an identified hot spot is a potential

concern for meeting the NAAQS. Wind measurements are useful to confirm that the

monitor/source orientation is consistent with high PM2.5 measurements. If preliminary

measurements of PM2.5 mass suggests a problem is likely, States may wish to augment the mass

and wind measurements with speciated measurements. Availability of speciated measurements

will enable the hot spot to be considered more readily using the recommended modeled

attainment test in a mid-course review or subsequently.

8. Deploy routine instrumentation to estimate relative humidity at speciated monitoring

sites in Class I areas

As we note in Sections 4.1 and 4.2, the monitored and modeled tests for reasonable

progress utilize “climatological” estimates for humidity adjustment factors (f(rh)). However, not

every Class I area has relative humidity measurements which can be used to derive the

climatological values for f(rh). In these cases, relative humidity measurements made elsewhere

have been used after corrections for differences in altitude are made. As Examples 4.1 and 4.2

make clear, effectiveness of various control strategies to reduce regional haze can be significantly

affected by assumptions about relative humidity. The purpose of obtaining this information is to

develop more accurate climatological estimates of relative humidity which could be considered

subsequently in a mid-course review or in designing the next iteration of reasonable progress.

9. Analyze FRM data to determine water content associated with hygroscopic components

of particulate matter.

The purpose of this analysis is to reduce the amount of measured PM2.5 mass which is

assigned to component “U” (e.g., unattributed mass). Recall from Section 3.1 that mass assigned

to component “U” remains unaffected by a simulated control strategy. This analysis would

provide a rationale for assigning some currently unattributed mass of measured PM2.5 to

components which can be reduced by a control strategy (e.g., SO4 and NO3).

6.1.3 Key Needs For Performing Weight Of Evidence Determinations

Our discussion is limited to the three types of analyses discussed in Section 5.1 (i.e., other

air quality model outputs, trend analysis and use of observational models).

1. Monitor speciated concentrations of PM2.5

Highest priority should be given to making these measurments at PM2.5 monitoring sites
20See Section 14.0 for a more complete discussion of dynamic meteorological models.

137 - DRAFT

violating the NAAQS or likely to violate the NAAQS. A second priority is measurements at

locations which are often “upwind” from major source areas. Ideally, speciated and total mass

measurements should occur on the same days and at the same frequency. These measurements

are needed to use multivariate statistical receptor models. They are also one required input for a

chemical mass balance receptor model. Further, trend analysis can be more readily interpreted if

speciated ambient air quality data are available. Finally, these data may be potentially useful for

implementing indicator species approaches.

2. Develop speciated emission profiles for major source categories of PM2.5

These are prerequisites for using the chemical mass balance receptor model. They also

serve as a means for relating the outcome of multivariate statistical receptor models to source

categories likely affecting monitored concentrations of PM2.5.

3. Make an accurate assessment of the spatial configuration of major source categories of

PM2.5, SO2, NH3, NOx and VOC

This information is needed to be able to use hybrid receptor models. Hybrid models

combine results of statistically based receptor models (e.g., CMB, multivariate statistical

approaches) with meteorological information. They are potentially useful for identifying

potentially important sources of measured secondary as well as primary components of

particulate matter.

4. Make upper air meteorological measurements

Continuous or at least three or 4 such measurements should be made daily. Ideally, the 

measurements should be made at at least one location per urban area where speciated PM2.5

measurements are made. Measurements should be used to characterize winds aloft, as well as

temperature and moisture so that vertical profiles describing wind strength and direction,

turbulence, mixing heights and conditions suitable for cloud formation can be identified. 

Pressure measurements need to be included so that the other measurements can be related to

altitude above ground level. Wind information can be used to drive trajectory models or,

alternatively, to guide predictions made with dynamic meteorological models used to provide

wind data for trajectory models20. Trajectory models can be used as described in Section 5.1.3 in

concert with receptor models to help draw inferences about potential sources of observed

secondary particulate matter. Information regarding mixing and/or cloud cover may be useful in

explaining variability in observed concentrations of components of particulate matter which

accompanies a seemingly similar set of trajectories.

5. Make surface meteorological measurements at sites measuring speciated concentrations

of particulate matter
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Wind, temperature, moisture and pressure measurements should be included in the 

ground level measurements. Surface measurements such as these are potentially useful for

identifying distinctive clusters of days. Each cluster can then be examined to see if measured

speciated profiles have attributes in common or if any particular cluster corresponds to high

concentrations of PM2.5 .

 Surface meteorological data are also potentially useful in helping to

normalize observed trends in PM2.5 or its components for meteorological differences.

6. Develop software to facilitate post-processing of air quality model outputs

In Section 5.1.1, we identified several air quality modeling outputs (e.g., number of grid

cell-days greater than some threshold value such as 15.0 μg/m3

) which could prove useful in

making “weight of evidence” arguments. Calculating these outputs generally is not feasible

unless computer software is developed for doing so.

6.1.4 Overall Priorities

All of the previously mentioned data and capabilities are important. However, the list is

overwhelming. In this subsection, we attempt to identify the highest priority efforts. In

establishing these priorities, we have considered the following factors:

- presence as a priority for all three of the major activities (generating model results, 

applying the tests and performing weight of evidence analyses );

- fundamental nature of the need—do subsequent activities depend on it?

- is assessing attainment or reasonable progress practical without this need being met?

Using the preceding criteria, we judge the following activities to have the highest priority.

1 (tie). Develop accurate emission factors and seasonal/diurnal activity levels for major

anthropogenic sources and source categories of PM2.5, SO2, NH3, VOC, NOx and CO, and

1 (tie). Make speciated ambient measurements of PM2.5 at mass monitoring sites likely to exceed

the NAAQS and at a limited number of rural locations.

3. Develop locally applicable source speciation profiles for primary PM2.5 and VOC.

4. Make continuous or 4 or more upper air meteorological measurements/day and ensure these

are accompanied by surface meteorological measurements at the same site.

5. Develop software to post process modeling and monitored data so that the modeled tests for

attainment and reasonable progress can be applied.
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6. Arrange a limited duration study in which PM2.5 and its components are measured

continuously or sampled over periods not exceeding 6 hours at selected locations with routine

24-hour sampling. 

Recommendations. States should enhance available data bases and capabilities so

that the following activities can be performed credibly: generating model results for

use in the tests, applying the modeled tests for attainment and reasonable progress,

and performing weight of evidence analyses. A number of important priorities are

identified in this Section. Of these, we believe the following have the highest

priority.

1. (Tie) Improve emission factors and activity estimates for major source categories

of PM2.5, SO2, NH3, VOC, NOx and CO, and

1. (Tie) Make ambient speciated measurements of PM2.5 routinely at sites with mass

measurements which are likely to violate one or both NAAQS and in Class I areas.

3. Develop locally applicable source speciation profiles.

4. Make continuous or at least several (e.g., 4) upper air meteorological

measurements per day and corresponding surface measurements.

5. Develop software so that the recommended modeled tests for attainment and

reasonable progress can be readily applied.

6. Perform a limited duration study in which PM2.5 and its components are sampled

continuously or discretely for periods not exceeding 6 hours.

6.2 Why Is It Desirable To Plan For A Subsequent Review?

States should anticipate the need for a subsequent review of their SIP after its initial

approval by the U.S. EPA. As we note in Section 6.1, there are many data-related needs which,

if met, would enhance the credibility of a strategy to meet air quality goals for PM2.5 and regional

haze. Resources are limited and some of these needs will likely have to be met over a sustained

period of time. Second, our understanding of the science underlying formation, transport and

deposition of PM2.5 is evolving. Increased understanding of these processes will be reflected in

periodic updates of models and other analytical tools used to support the demonstrations of

attainment or reasonable progress. Third, formation and transport of secondary particulate matter

is closely related to processes which are important in the formation and transport of ozone. Thus,

it makes sense for programs designed to reduce PM2.5 and improve visibility to be cognizant of

programs to reduce ozone and vice versa. This can best be done after there has been an

opportunity to review changes in air quality resulting from implementation of plans to reduce

PM2.5, regional haze and ozone.
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We recommend that States consider the following in planning for subsequent reviews. 

First, continue to improve data bases. Second, retain the means to perform modeling/analysis. 

Third, make emission and air quality projections to some intermediate year which is about the

time a subsequent review is anticipated. These projections may subseqently prove useful in

assessing whether a “mid-course correction” to an emission reduction strategy is needed. Finally,

retain modeling input and output files used to simulate base emissions, emission projections for

the previously discussed intermediate year and projected emissions at the time of required

attainment or at the end of the first iteration of the plan to make reasonable progress reducing

regional haze. Only the files reflecting the strategy approved in the approved SIP revision need

be retained. 

Recommendations. States should maintain modeling and analysis capabilities after

approval of their initial SIP revisions addressing NAAQS for PM2.5 and regional

haze. Efforts should continue to enhance data bases which will underlie modeling

performed to support subsequent reviews and adjustments to the initial SIP

revisions. Emissions and air quality projections should be made for an intermediate

year to compare with monitored data during a “mid-course review” of an approved

plan. To facilitate such a review, States should archive modeled input and output

files associated with current emissions and with projections reflecting the

control/growth assumptions in the approved SIP revision. 
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7.0 What Documentation Do I Need To Support My Attainment Demonstration?

Recommendations regarding documentation for demonstrations showing that a SIP will

meet goals for PM2.5 or regional haze are similar to those for the 8-hour ozone NAAQS found in

U.S. EPA (1999b). We have added one requirement for demonstrations related to PM2.5 or

regional haze. That is, the documentation should contain a conceptual description of an area’s

PM2.5 or regional haze problem. This description includes a summary of the observational data

bases examined and the rationale that was used for focusing the modeling efforts in the way that

they were. We believe this requirement is useful, because of particulate matter’s nature as a

mixture. Attempting to reduce concentrations of a mixture of pollutants provides more potential

directions for modeling efforts and strategy development than would otherwise be the case. This

increases the possiblity of focusing on tangential issues and strategies. A conceptual description

provides some assurance that the modeling efforts and strategy are likely addressing the most

significant attributes of an area’s PM2.5 or regional haze problem. 

Recommendations. States should address the 11 subject areas shown in Table 7.1 in

the documentation accompanying an attainment or reasonable progress

demonstration. The documentation should contain a summary section which

addresses issues shown in the table. More detailed information should be included

in appendices, as necessary.
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Table 7.1. Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze

Subject Area Purpose of Documentation Issues Included

Conceptual Description of PM2.5

or Regional Haze Problem 

To provide a short narrative of

observational evidence which led

to the selected modeling

approach and strategies which

were investigated.

Measurements used

Analyses performed

Resulting rationale used to

support the modeling approach

and strategies investigated

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

 Days and domain considered

Emissions Preparations and

Results

Assurance of valid, consistent

emissions data base and that 

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 model-compatible

inputs 

Deviations from existing

guidance and underlying

rationale 

Emissions models used and

justification for choice of models

PM2.5, SO2, NH3, VOC, NOx, CO

emissions by State/county for

major source categories
Table 7.1. Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze (continued)

Subject Area Purpose of Documentation Issues Included

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Air Quality/Meteorology

Preparations and Results

Assurance that representative air

quality and meteorological 

inputs are used in analyses

Extent of data base and

procedures used to derive &

quality assure inputs for analyses

used in the weight of evidence

determination 

Departures from guidance and

their underlying rationale

Meteorological model(s) used

Performance of meteorological

model if used to generate

meteorological inputs to the air

quality model

Performance Evaluation for Air

Quality Model (and Other

Analyses)

Show decision makers and the

public how well the model (or

other analyses) reproduced

observations or otherwise

performed on the days selected

for analysis

Summary of observational data

base available for comparison

Identification of performance

tests used and their results for

components of PM, mass of

PM2.5 and key gas phase species

Ability to reproduce observed

temporal and spatial patterns for

components of PM, mass of

PM2.5 and key gas phase species

Overall assessment of what the

performance evaluation implies

Diagnostic Tests Ensure rationale used to adjust

model inputs or to discount

certain results is physically

justified and the remaining

results make sense.

Results from application prior to

adjustments 

Consistency with scientific

understanding and expectations 

Tests performed, changes made

and accompanying justification 

Short summary of final

predictions.
Table 7.1. Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze (continued)

Subject Area Purpose of Documentation Issues Included

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Description of the Strategy

Demonstrating Attainment (or

Reasonable Progress)

Provide the EPA and the public

an overview of the plan selected

in the attainment or reasonable

progress demonstration.

Qualitative description of the

selected strategy 

Reductions in PM2.5, SO2, NH3,

CO, VOC, and/or NOx emissions

from each major source category

for each State/county from

current (identify) emission levels 

Clean Air Act mandated

reductions and other reductions 

Show relative reduction factors

estimated for each major

component of PM2.5 and (for

Regional haze applications) CM

Show predicted site-specific

future PM2.5 design values for the

selected control scenario and

identify any location for which a

hot spot analysis was needed

together with results of the hot

spot analysis 

Identification of authority for

implementing emission

reductions in the selected

strategy

Evidence that emissions will

remain at or below projected

levels throughout the 3-year

period used to determine future

attainment for PM2.5-related

applications, and the 5-year

period used to determine if

reasonable progress has

occurred for visibility-related

applications
Table 7.1. Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze (continued)

Subject Area Purpose of Documentation Issues Included

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Data Access Enable the EPA or other

interested parties to replicate

model performance and

simulation results for the selected

strategy, 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

Weight of Evidence

Determination (PM2.5 NAAQSrelated applications)

Assure the EPA and the public

that the strategy is likely to

produce attainment of the

NAAQS within the required

time.

Description of the modeled

attainment test and

observational data base used 

Identification of air quality

model(s) 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
Table 7.1. Recommended Documentation For Demonstrating Attainment Of Air Quality

Goals For PM2.5 Or Regional Haze (concluded)

Subject Area Purpose of Documentation Issues Included

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Weight of Evidence

Determinations (related to

reasonable progress goals to

reduce regional haze)

Assure the EPA and the public

that the strategy is likely to meet

regional progress goals to reduce

regional haze

Description of the modeled test

for reasonable progress and

observational data base used 

Identification of air quality

model used

Identification of other analyses

performed 

Outcome of each analysis,

including the modeled test for

reasonable progress

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

visibility on the days with best

visibility will not deteriorate and

that goals for days with poor

visibility will be met

Review Procedures Used Provide assurance to the EPA

and the public that analyses

performed in the attainment or

reasonable progress 

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 thereto
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Part II

How Should I Generate Modeling Results To Use In Attainment Or

Reasonable Progress Tests?
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8.0 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 for PM2.5 or reasonable progress goals to reduce

regional haze. 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 tests for attainment and reasonable progress. The procedure we recommend consists of

9 steps:

1. formulate a conceptual description of an area’s nonattainment or regional haze

problem;

2. develop a modeling/analysis protocol;

3. select one or more appropriate air quality models to use;

4. select appropriate days to model;

5. choose a modeling domain with an appropriate number of layers and appropriately

sized grid cells;

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;

9. simulate and evaluate prospective control strategies.

In this Section (Section 8.0), 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 should take care to 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. Steps outlined in this Section are described in greater depth in

Sections 9.0 - 17.0.

1. Formulate a conceptual description of an area’s nonattainment or regional haze

problem. A State needs to have an understanding of the nature of an area’s problem before it

can proceed with a modeled attainment or reasonable progress demonstration. For example, it

would be difficult to identify appropriate stakeholders and develop a modeling protocol without

insights into whether resolution of the problem requires close coordination and cooperation with

other nearby States. 

In the case of a nonattainment problem, the State containing the designated nonattainment

area is expected to initially characterize the problem. For regional haze-related model

applications, the initiative for developing a conceptual description may be assumed by a State

containing a Class I area or, if its member States agree, by a regional planning organization 

formed to address regional haze. Initial characterization of the PM2.5 or regional haze problem 

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
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likely to affect what needs to be done locally? Which components of PM2.5 seem to constitute a

major portion of the measured mass of particulate matter? What seems to be the relative

importance of primary vs. secondary particulate matter? 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 emissions of PM or

its precursors? Are there any ambient measurements suggesting which precursors and sources

are important to further reduce secondary particulate matter? What priorities exist for gathering

additional air quality, meteorological and emissions data? What information might be needed

from potential stakeholders? 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 Section 5.0 (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 resource intensive analyses of available data. 

2. Develop a modeling/analysis protocol. A protocol describes how modeling will be

performed to support a particular attainment or reasonable progress demonstration. Its direction

and participating stakeholders are influenced by the previously developed conceptual description

of the problem to be resolved. The protocol outlines methods to be used to perform the

subsequent 7 steps needed to generate the modeling results and to subsequently apply the

modeled attainment/reasonable progress tests and hot spot analyses, as well as other

corroborating analyses in a weight of evidence determination. In addition, the protocol defines

procedures which will be followed in performing the analyses that support attainment or

reasonable progress demonstrations. These procedures include: a) identifying those responsible

for implementing the modeling, b) identifying those who will review each step as it occurs, c)

identifying procedures to be used to consider input/suggestions from those potentially affected by

the outcome (i.e., “stakeholders”), and d) outlining how decisions will be made concerning

technical analyses needed to complete each step in the modeling procedure. In short, the protocol

defines the “game plan” and the “rules of the game”.

3. Select one or more appropriate models for use. This step includes reviewing air quality

data to gain insight about the nature of a nonattainment area’s measured concentrations of

particulate matter and its components, reviewing rules established in the Guideline for Air

Quality Models (U.S. EPA, 2000b), and considering experience/expertise of those performing the

modeling. Identifying an 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. Major

components of particulate matter include primary particulate matter (i.e., inorganic particulate

matter, elemental carbon and some organic carbon) and secondary particulate matter (i.e.,

sulfates, nitrates and some organic carbon). Because modeling requirements may differ

significantly for primary and secondary particulate matter and for performing hot spot analyses, it

is conceivable that a State may choose to use more than one model to support its attainment

demonstration.
21See step 7 for definitions of these terms.

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4. Select appropriate days to model. Like the preceding step, this step requires review of

available air quality data. It also requires familiarity with the forms of the annual and 24-hour

national ambient air quality standards for particulate matter and the goals for reasonable progress

reducing regional haze (U.S. EPA, 1997, 1999). It is also important to thoroughly understand the

modeled tests described in Sections 3.0 and 4.0. In addition, it requires a review of

meteorological conditions accompanying monitored exceedances of the concentration specified

in the 24-hour NAAQS (65 μg/m3

) as well as those accompanying good and poor visibility. The

object of these reviews is to select periods which a) include days with observed concentrations

exceeding site-specific design values (24-hour NAAQS), b) select a representative mix of days

for each quarter in applications dealing with the annual NAAQS and c) select days which are

representative of those corresponding with good and poor visibility. 

5. Choose a modeling domain with an appropriate number of layers and appropriately

sized grid cells. Appropriate domain size is influenced by the air quality goal being addressed

(visibility or NAAQS for PM2.5), whether the model is being applied to address primary or

secondary particulate matter and choice of days modeled. 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 the number of required layers and size of

individual grid cells for that portion of the modeling grid. Other factors affecting choice of grid

cell size are the proximity of nearby sources and available spatial detail in the emissions data

used as input to an emissions model. Finally, feasibility of managing large data bases and

resources needed to estimate meteorological inputs and air quality in many grid cells are factors

which cannot be ignored in choosing size of a domain and its grid cells. Using a domain with

“nested” grids (i.e., fine grid cells near an area of principal interest, larger cells further away) is a

potential means for reconciling needs with available resources.

6. Generate meteorological and air quality inputs to the air quality simulation model. 

Unlike emissions, meteorological inputs remain constant during “performance-related”21,

“current” and “future” periods simulated with the air quality model. Nevertheless care needs to

be taken in specifying these, as they may affect relationships predicted between particulate matter

and emissions. Modeling applications which need to consider secondary particulate matter may

have to consider large geographical areas in many instances. Further, it is conceivable that

meteorological conditions aloft can have an important effect on predicted concentrations of

secondary particulate matter. Finally, meteorological monitoring is relatively sparse outside of

cities and, especially, aloft. Thus, we recommend that for applications addressing strategies to

reduce concentrations of secondary particulate matter, meteorological models ordinarily be used

to generate meteorological inputs. Application of meteorological models and choice of model

grid resolution in the preceding step are closely related. Meteorological conditions near the area

which is the focus of a modeled 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
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cost/feasibility of running air quality and meteorological models and resolution at which it might

be most desirable to treat dispersion of nearby emissions. The problem can be resolved in part by

considering primary and secondary components of particulate matter separately. Modeling for

primary components of particulate matter may be performed over a much more limited domain,

but with a greater degree of spatial resolution.

Air quality inputs consist of initial conditions and boundary conditions to the model

domain. Importance of initial conditions should be diminished by beginning a simulation at a

time prior to the period which is of interest. Nature of boundary conditions is an important factor

in deciding how large to make the size of the area modeled. The most satisfactory way to

generate future boundary conditions is through use of a regional air quality model. Therefore,

those implementing the protocol will once again be faced with a tradeoff between cost/feasibility

of data base management vs. a desire to limit the importance of an arbitrarily specified input to

the modeling exercise. 

7. Generate emissions inputs to the air quality simulation model. Emissions are the central

focus in a modeled attainment or reasonable progress demonstration. That is, they are the only

input to an air quality model which those implementing the protocol can control. Hence, they are

the major input which gets changed between the present and future. Emissions which are input

to a complex, multi-source air quality model are generated using an emissions 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 of

precursors for secondary particulate matter are affected by meteorological conditions. This

requires an interface between meteorological inputs and emissions. Emissions which are input to

an air quality model are also affected by the latter’s horizontal/vertical resolution and, of course,

the size of the area 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.

Emissions inputs may be needed for as many as 3 periods: (1) a “performance-related

period” corresponding to the time in which air quality observations used to evaluate an air quality

model’s performance are made, (2) a “current period”, corresponding to that represented by the

current monitored design value (or base period from which reasonable progress is measured), and

(3) a future period, corresponding to a time two years prior to the required attainment date or 2

years prior to when the next assessment of reasonable progress is required. If a model’s

performance evaluation is based on comparisons between predictions and observations on a

small number of days (e.g., with an intensive data base), performance-related emissions should

reflect day-specific conditions to the extent possible. For performance measures which compare

observed and predicted concentrations averaged over numerous days, emissions needs are similar

to those for the “current period” considered in the modeled tests for attainment or reasonable

progress. “Current period” emissions may ordinarily reflect quarterly average estimates. 

However, if seasonal differences in a location’s emissions do not closely correspond to calendar

quarters, States should use monthly average emission estimates to estimate “current emissions”. 
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8. Evaluate performance of the air quality model and perform diagnostic tests. To an

important extent, credibility of a modeled attainment or reasonable progress test’s results and

other modeled outputs is affected by how well the model replicates observed air quality. 

Evaluating model performance and conducting diagnostic tests depend on prior definition of the

modeling exercise and specification of model inputs. Hence, this is generally the last step prior

to using the model identify strategies which are likely to meet the air quality goal(s).

At first, it may seem that the obvious way to evaluate model performance is to simply

compare predicted and observed concentrations of PM2.5 or predicted/observed values of

extinction coefficients. It is not so simple, however. Because particulate matter is a mixture, it

is possible to get seemingly good agreement between observed and predicted values of PM2.5 but

poor performance predicting individual components. Badly mischaracterizing the mix of

secondary components of PM2.5 has the potential for deriving erroneous component-specific

relative reduction factors. Further, models needed to simulate formation, transport and

deposition of particulate matter often have many inputs. It is possible to get similar predicted

concentrations of PM2.5 or an individual component with different combinations of these inputs. 

There is no guarantee that PM2.5 or values of bext will respond the same way to controls with these

different combinations of inputs. Further, limited availability of observed samples for periods

shorter than 24 hours presents difficutlites in evaluating model performance. Thus, we place

greater emphasis on additional kinds of tests. These include ability to predict observed ozone,

use of precursor observations, ability to predict components of PM2.5, use of indicator species,

use of corroborative analyses with observational models and use of retrospective analyses. 

Performance tests which are most closely related to how we believe models should be

used in attainment/reasonable progress analyses should receive greatest emphasis. Thus, tests

which compare spatially paired observations/predictions which have been averaged over

numerous days are important, because they reflect how relative reduction factors should be

calculated. To the extent possible, States should also include performance tests which evaluate

accuracy of a model’s response to changes in emissions.

Diagnostic tests are separate simulations which are performed to determine the sensitivity

of a model’s predictions to various inputs to the model. As the name implies, these tests are

performed to diagnose why a model is performing poorly or to provide assurance that seemingly

good performance is not just a serendipitous outcome. These tests can be performed for a variety

of purposes, including prioritizing inputs needing greatest quality assurance and assessing

uncertainty associated with model predictions. In performing such tests, States should remember

how model results are used in the modeled attainment and reasonable progress tests

recommended in Sections 3.0 and 4.0. With the exception of hot spot analyses, model results are

used in a relative rather than absolute sense. In particular, the modeled tests require use of

relative reduction factors (RRF), generated by models. Thus, diagnostic tests should be used to

consider how RRF, as well as absolute predictions of primary particulate matter (in hot spot

analyses), are affected by changes to model inputs.
22We are not implying that controlling OC is less expensive than controlling SO4. We are

merely using this as an example to illustrate a potential use for sensitivity tests prior to choosing

a strategy for a State implementation plan.

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9. Simulate and evaluate prospective control strategies. This step is to identify

potential control strategies which appear consistent with a previously developed conceptual

description. Several of these may be tested to confirm that previous implications about what

might be an effective strategy to meet an air quality goal are correct. The intent of this step is to

narrow down and eventually select a strategy to propose in a SIP. 

The step entails modeling future control strategies, applying the recommended modeled

attainment or reasonable progress tests (or hot spot analyses) to see whether the air quality

goal(s) is met. This step includes a series of sensitivity tests performed with models. Nature of

these tests depends on the amount of confidence one has in the conceptual description of an

area’s nonattainment or regional haze problem. For example, if a State has reservations about

this description, initial sensitivity tests may be relatively coarse ones (e.g., do I control local

emissions only or is a regional approach needed?) Future predictions are, of course, subject to

uncertainty. Thus, this step also includes sensitivity tests to see the extent to which conclusions

about the ability of a strategy to meet a future air quality goal are subject to uncertainties in the

modeling analysis. Outcome of these sensitivity tests could serve as one factor which helps a

State decide which among several candidate strategies to choose. For example, if one apparently

successful (relatively inexpensive22) strategy depends on making reductions in a component of

particulate matter having large associated uncertainties (e.g., OC) and another, more expensive

strategy, depends on reducing a component with less associated uncertainty, (e.g., SO4) these

tests could help a State make a choice among strategies.

Recommendations. States should follow nine steps in applying models to generate

information required for use in modeled attainment demonstrations.

1. Formulate a conceptual description of an area’s nonattainment or regional haze

problem.

2. Develop a modeling/analysis protocol.

3. Choose one or more appropriate models.

4. Choose appropriate days to model.

5. Choose a modeling domain with an appropriate number of layers and 

appropriately sized grid cells.

6 .Generate appropriate meteorological and air quality inputs.

7. Generate quality assured emissions inputs.

8. Evaluate model performance and undertake diagnostic tests.

9. Simulate and evaluate prospective control strategies.

Execution of steps 3-9 should be performed in accordance with procedures

identified in the protocol. Rationale and outcome of the steps should be documented
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as described in Section 7.0. To increase the likelihood of an approvable

demonstration, States should carefully coordinate development and execution of

steps with the appropriate U.S. EPA regional office(s). 
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9.0 How Do I Develop And Use A “Conceptual Description”? 

The State which contains a nonattainment or Class I area should lead efforts to develop a

conceptual description of the related PM2.5 or visibility problem. If mutually agreed upon,

leadership may be delegated to the appropriate regional planning organization or another group. 

A State should start developing information to support a modeled attainment demonstration by

assembling and reviewing available air quality, emissions and meteorological data. Current

design values should be calculated at each PM2.5 monitoring site, as described in Section 3.0. 

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 to develop an initial conceptual description of the

nonattainment or regional 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 choice of initial diagnostic tests to identify potentially effective control

strategies. In general, a conceptual description is useful for helping a State to identify priorities

and allocate resources in performing a modeled demonstration. An example of a detailed

conceptual description can be found in Pun and Seigneur (1998) and in Pun and Seigneur (1999).

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, and that many of the analyses we describe would be more convincing with improved

data bases. Thus, a conceptual description is likely to evolve during the course of analyses

underlying attainment or reasonable progress demonstrations.

9.1 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. To illustrate, we pose two sets of questions below. The first illustrates what might

be asked in assessing the nature of a nonattainment problem.. The second focuses on preparing

for a reasonable progress demonstration. The examples are not necessarily comprehensive. 

There are other features of an area’s problem, such as data gathering priorities, which are also

important. For purposes of illustration later in the discussion, we have answered each of the

example questions posed below. Our responses appear in parentheses.

Example nonattainment application (annual 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
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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?

(Secondary components (i.e., SO4, NO3, OC) constitute about 80% of the measured mass

of PM2.5).

- 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 potential hot spots for primary particulate matter in 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 which may pose a hot spot problem).

- 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 ozonerelated 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, 4 measures believed to result in major reductions in VOC have been implemented

in the last 5 years. Reductions in SO2 emissions have resulted from the national program to

reduce acid deposition.)
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- 9. Are there discernible trends in design values or other air quality indicators which have

accompanied a change in emissions?

(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. Two emission scenarios

were modeled: current emissions and a substantial reduction in NOx emissions 

throughout a regional domain. Reduced NOx emissions led to substantial predicted reductions in

8-hour daily maximum ozone in most locations, but changes near the most built-up 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 PM2.5 concentrations in excess of 15.0 μg/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 always > 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 before simulating additional control

measures. The responses to questions 4 and 5 suggest that it is premature to determine whether

one or more hot spot analyses 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
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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.

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?

(Can’t tell yet).

- 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.)
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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 (4.1)) 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 two 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 10.0.

Recommendations. States should begin an analysis to support a modeled attainment

or reasonable progress demonstration by developing a conceptual description of an

area’s nonattainment or regional haze problem. This description is based on use of

readily available air quality, meteorological and emissions information. It may be

refined later as additional analyses are performed by those implementing the

modeling/analysis protocol.

9.2 What Sorts Of Analyses Might Be Useful For Developing And Refining A

Conceptual Description?

Questions like those posed in Section 9.1 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. In the following paragraphs, we

revisit key parts of the conceptual description identified for the nonattainment problem discussed

in Section 9.1. We note analyses which may help to develop a description of each part. The list

serves as an illustration. It is not necessarily exhaustive.

1. Is the nonattainment problem likely to be most affected by regional or local control measures?

- Note spatial distribution of observed design values.

- Note spatial correlation among day to day changes in observed PM2.5.

- Note spatial correlation in day to day changes in each of the components of PM2.5.

- Note typical amount of each component of PM2.5 relative to measured mass of PM2.5—what is

the relative importance of known secondary components (SO4, NO3) vs. known primary

components (IP, EC).

- Apply a receptor model like CMB or a multi-variate model–are any local source categories
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frequently identified as major contributors to observed PM2.5 ?

2. What control strategies appear potentially promising to simulate?

- Note typical relative importance of primary vs. secondary components—does this relative

importance differ on days with measured mass > 15.0 μg/m3?

- Apply one or more receptor models to identify source categories which appear to impact

monitoring sites frequently.

- Review inventory information to identify potentially important sources of PM2.5 and its

precursors.

- Map spatial configuration of sources.

- Develop pollution roses for PM2.5 and its components to see whether wind orientation on days

with high observations yields clues about origins of high observed PM2.5 or components.

- If secondary components appear important, combine receptor model results with trajectory

models or other meteorological analyses to provide clues about the origins of precursors

to secondary components.

- If secondary components appear important, apply indicator species methods to assess

precursor(s) which most likely formation of secondary particulate matter.

Resolving questions like the two preceding ones should provide further insights about the

scope of needed model simulations. For example, if the analyses suggest that the problem is a

regional one in which secondary components are important, this tells us that using a regional

scale model which considers atmospheric chemistry will likely be necessary. This, together with

emission maps, tells us things about needed domain and grid cell size. If, on the other hand, the

conceptual description suggests that the nonattainment problem can be addressed by controlling

emissions of primary particulate matter, scope and focus of the modeling can be quite different. 

Recommendations. States should analyze ambient air quality, meteorological and

emissions data in concert with an air quality modeling analysis. These analyses

perform at least 3 functions. First, they are needed to help develop a conceptual

description of a nonattainment area’s problem. Second, they help guide application

of a model in an air quality modeling analysis. Third, analysis of air quality,

meteorological and emissions data generates corroborative information which may

confirm conclusions drawn with an air quality model or cause some of the

underlying assumptions in the modeling to be reexamined. 
23For reasonable progress demonstrations, the “lead agency” for modeling/analysis may

be a regional planning organization if that is agreeable to its member States.

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10.0 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. Much of the information in U.S. EPA (1991)

regarding modeling protocols remains applicable. States 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 all types of analyses considered in a

weight of evidence determination, not just modeling. The nature of a potential PM2.5

nonattainment problem may be more diverse than one for ozone. This means that a State may

need to apply more than one model to design a strategy which will attain the NAAQS. If so, the

protocol will need to address how best to apply two or more models which are different in their

approach and coverage.

10.1 What Is The Protocol’s Function?

The most important function of a protocol is to serve as a means for planning and

communicating how a modeled attainment or reasonable progress demonstration will be

performed before it occurs. The protocol serves as a vehicle for review of substantive aspects of

the planned analyses as they occur, and establishes processes by which affected groups may

provide inputs to the analyses. The protocol is also the means by which States and other

stakeholders can assess applicability of default recommendations described herein and develop

alternatives. A good protocol should lead to widespread participation in developing the

demonstration. It should also reduce risk of spending time and resources on efforts which the

appropriate U.S. EPA Regional Office(s) believes are unproductive or inconsistent with Agency

policy.

The protocol also serves several important, more specific functions. First, it identifies

methods and procedures used to support the demonstration. Second, it identifies who will be

helping the State or local air quality agency (generally the lead agency23) to undertake or evaluate

analyses needed to support a defensible demonstration (i.e., the stakeholders). Third, it identifies

how communication will occur among stakeholders (e.g., States/tribes, environmental groups,

those in the private sector potentially affected by control strategies) to develop consensus on

various issues. Fourth, the protocol describes the review process applied to key steps in the

demonstration. Fifth, it describes how changes in methods and procedures or in the protocol

itself are agreed upon and communicated with stakeholders and the appropriate U.S. EPA

regional office(s). Major steps taken in implementing the protocol should be discussed with the

appropriate U.S. EPA regional office(s) as they are being decided. States should update the

protocol as major decisions are made concerning forthcoming analyses. 
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10.2 What Subjects Should Be Addressed In The Protocol?

There are two broad categories of subjects which should be addressed in a protocol. The

first of these is “substantive topics”. These include such things as the analyses which will be

performed, how they will be used, etc. The second category is “procedural topics”. This

category includes a discussion of how issues will be communicated and resolved as they arise. 

We list important substantive and procedural topics below.

(a) Substantive topics

1. Indicate choice of the air quality simulation model(s) to be used and how the model(s) meets

requirements in 40CFR51, Appendix W for using “alternative” models.

2. Provide assurance that proposed modeling procedures have been scientifically peer reviewed

and the protocol includes plans for technical review of how procedures are used in the specific

application and the resulting outputs.

3. Identify the emissions, air quality and meteorological data bases to be used to support air

quality modeling and other types analyses used in a weight of evidence determination.

4. Identify criteria/goals to be used to select periods to model and the process used to select the

specific candidate periods to be modeled. 

5. Note and discuss the underlying rationale for the domain size and spatial resolution to be used.

6. Identify methods used to quality assure meteorological and emissions inputs.

7. Note the rationale to be used to choose methods for generating meteorological inputs and for

selecting appropriate models to convert emissions into forms compatible with the chosen air

quality model(s). 

8. Identify model performance evaluation procedures and performance-related diagnostic tests

which are planned.

9. Identify sensitivity and other tests to be used to select a mix of control measures needed to

meet the air quality goal(s).

10. Identify the types of analyses to be included in a weight of evidence determination should

those implementing the protocol decide to use weight of evidence.

11. Identify outcomes for each selected weight of evidence analysis which would be consistent

with showing that a selected strategy will meet the air quality goal(s).
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(b) Procedural topics

1. Identify stakeholders participating in the process.

2. Discuss management/communication procedures to be used.

3. Identify methods for resolving conflicts.

4. Identify procedures for updating the protocol as new findings come to light during the

analyses.

5. Discuss procedures to be followed if a model’s performance is shown to be inadequate for

simulating a particular period or strategy.

6. Identify specific deliverables and the schedule for delivery to the appropriate U.S. EPA

regional office.

7. Identify procedures to be used to archive, document and report results consistently with our

guidance in Section 7.0.

Recommendations. States should prepare a modeling/analysis protocol as part of an

acceptable demonstration of attainment or reasonable progress. The protocol

should address substantive and procedural issues. Generally, procedures

recommended in the 1991 guidance and followed for the 1994 ozone SIP revisions

are also appropriate for PM2.5- and visibility-related model applications. These

procedures should be augmented to include a discussion of all analyses to be

included in the weight of evidence determination, not just modeling. The protocol

should also include provision for review of key parts of the analysis and data base

underlying the attainment or reasonable progress demonstration. The protocol

should be kept up to date to reflect major changes in initial plans.
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11.0 What Should I Consider In Choosing Air Quality Model(s)?

We begin this section by identifying the types of models which are most suitable for

addressing secondary and primary components of particulate matter. We then relate this to

choice of model types to address PM NAAQS- and regional haze-related applications. We next

identify a set of general requirements which an air quality model should meet to qualify for use in

an attainment or reasonable progress demonstration. We then identify several factors which will

help in choosing among qualifying air quality models for a specific application. We conclude by

identifying several air quality models which are available for use in attainment or reasonable

progress demonstrations. 

Models are, in reality, modeling systems which integrate an emissions model or

procedures for deriving emissions, a meteorological model or procedures for deriving

meteorological inputs and a chemical transport model. Meteorological and emissions models are

discussed in Sections 14.0 and 15.0, respectively. In this guidance, the term “air quality model”

means “air quality 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, 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 emissions/meteorological/air chemistry models differing

from the modeling system’s default combination). Thus, choice of an appropriate air quality

model(s) is among the earliest decisions to be made by those implementing the modeling/analysis

protocol.

To better understand our recommendations concerning choice of models, it is useful to

summarize key features of the conceptual description for PM2.5 presented in Section 1.3. PM2.5

consists of “fine mode” as well as some “coarse mode” particles. In PM2.5 attainment

demonstrations, the principal distinction between the two modes of particles is their origins. 

Fine mode particles are a mix of primary PM2.5 emissions (arising mainly from combustion) and

secondary particulate matter. Components of this mix of fine mode particles (i.e., mass

associated with sulfates, nitrates, secondary and primary organic carbon and elemental carbon)

can act independently of each other in some instances and interact in others. Coarse mode

particles (e.g., mass associated with “crustal” material and, in coastal areas, sea salt) occur as a

result of physical activity (e.g., grinding, crushing, resuspension). Coarse mode particles act

independently of fine mode particulate matter. While most mass associated with coarse mode

particles is associated with particles larger than 2.5 μm, some are < 2.5 μm. All of these smaller

coarse mode particles result from primary emissions of PM2.5. Because primary emissions of

PM2.5 undergo limited atmospheric transformations, resulting ambient concentrations of this

portion of PM2.5 are likely to be characterized by more pronounced spatial gradients than is the

case for secondary particulate matter.

The discussion in the preceding paragraph suggests that it may be cost-effective to model

primary and secondary components of PM2.5 differently (e.g., using different spatial resolution,

domain sizes and, possibly, different models). Thus, primary components of particulate matter
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(IP and EC) may be modeled differently than secondary components (SO4, NO3). The remaining

major component of PM2.5 (OC) consists of both primary and secondary particulate matter. 

Ideally, a State should breakup measured OC into primary and secondary origins and then model

the primary part of the OC consistently with other primary particulate matter and secondary OC

consistently with other secondary particulate matter. However, it is difficult, at present, to

distinguish that portion of ambient OC which is of primary vs. secondary origin. Since

secondary components of PM are not independent of one another, it is important to include

treatment of secondary OC in modeling which addresses secondary components of particulate

matter. Thus, if a distinction cannot be made between measured primary and secondary OC at a

monitoring site, we recommend including OC of primary and secondary origin in modeling

which addresses “secondary particulate matter”. In addition, it may be necessary in some hot

spot analyses to treat primary emissions of OC from a limited number of sources using modeling

approaches which are appropriate for addressing primary particulate matter. 

States should use a photochemical grid model to simulate effects of strategies to reduce

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 will be

needed to perform reasonable progress demonstrations. Based on its conceptual description of a

PM2.5 nonattainment problem, a State could conclude that the problem can be addressed by

reducing primary components of measured PM2.5. If this is the case, the State need not 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) model may be

used. If a Lagrangian model is used to estimate RRF’s for primary particulate matter, these

estimates should be made at the monitoring location. In some cases, it may be acceptable to use

source apportionment results obtained with a receptor model plus rollback to estimate future

concentrations of primary PM. This latter approach may be appropriate if (a) sources

contributing to monitored observations are ubiquitous (and subject to uniform control measures)

or are uniformly mixed, or (b) there is a single source or a single cluster of sources of observed

primary particulate matter which is confined to a small, well-defined area. Thus, a State may use

a regional photochemical grid model to address secondary components of particulate matter and

a less resource intensive inert model applied over a more limited domain with a finer

horizontal/vertical resolution 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
169 - DRAFT

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.

Recommendations. States should use a regional scale photochemical grid model to

estimate effects of a control strategy on secondary components of particulate matter. 

Changes in primary components may be estimated using a numerical grid model

(with no chemistry), a Lagrangian model or, in some cases, a receptor

model/rollback approach. 

Modeling used to support attainment demonstrations for the PM2.5 NAAQS may

consist of the following combinations:

- photochemical grid model (changes in secondary components) plus finer

scale grid model w/o chemistry (changes in primary components);

- photochemical grid model (secondary components) plus Lagrangian

model(s) or receptor/rollback model (primary components);

-fine scale grid model or Lagrangian model or receptor/rollback model

(primary components), no modeling of secondary components. 

If the last option is chosen it should include a justification why an increase in

secondary components is not likely to lead to a violation of the NAAQS despite the

prescribed reduction in primary particulate matter. 

A regional scale photochemical grid model should be used to support

demonstrations of reasonable progress reducing regional haze. Primary

components may be modeled using the same spatial resolution used for the regional

analysis. Exceptions may be required on a case by case basis.

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

An Attainment Or Reasonable Progress 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

proposed requirements in 40CFR Part 51, Appendix W (i.e., the “Model Guideline”) (U.S.EPA,

2000b). Note that we do not recommend a specific model for use in the attainment or reasonable

progress demonstrations. At present, there is no single model which has been extensively tested

and shown to be clearly superior or easier to use than several alternatives. Thus, the proposed

revision to 40CFR Part 51 Appendix W does not identify a “preferred model” for use in

attainment demonstrations of the NAAQS for PM2.5 or in reasonable progress demonstrations. 

Using language in 40CFR Part 51 Appendix W, models used for these purposes should meet

requirements for “alternative models”.

“Alternative models” may be used if they are non-proprietary. A “non-proprietary”

model is one whose source code is available for free or for a reasonable cost. Further, the user
170 - DRAFT

must be free to revise the code to perform diagnostic analyses. If such revisions are made, the

user needs to show that the introduced changes do not affect the model’s air quality estimates. 

For a model to be “non-proprietary”, users should also be able to revise the code in order to

improve the model’s ability to describe observations in a credible manner. If these latter

revisions are done, they need to be documented. Further, the rationale for any such changes

needs to be defended in a technical memorandum to the appropriate U.S. EPA regional office(s). 

If the U.S. EPA believes the changes are substantial, they should be subject to a scientific peer

review just as would any other “alternative model”. 

Several additional prerequisites should be met for an “alternative model” to be used to

support an attainment or reasonable progress demonstration. 

(1) It should have received a scientific peer review.

(2) It should be applicable to the specific application.

(3) It should be used with a data base which is adequate to support its application.

(4) It should have performed in past applications in such a way that estimates are not

likely to be biased low.

(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 the

basic approach followed in each of the major components of the modeling system (e.g., chemical

transport model, meteorological and emissions models) has been described in the peer reviewed

literature. Further, each component should have been tested, the results 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 to document that a “scientific peer review” has occurred. States should then reference

this documentation to gain acceptance of an air quality model for use in a modeled attainment or

reasonable progress demonstration. 

Should the U.S. EPA identify a “preferred model” at some future date, an “alternative

model” may still be used in a subsequent application if it is shown to be more appropriate for the

specific application. This could be demonstrated by side by side comparisons of predictions

obtained with the “preferred” and “alternative” models with observations. While such

comparisons may be desirable, they are not required. Criteria described in Section 11.2 may be

used to show that an “alternative model” is more appropriate than a “preferred model” for a

specific application.

Recommendations. For an air quality model to qualify as a candidate for use in an

attainment demonstration for PM2.5 or in a reasonable progresss demonstration, a

State needs to show that it meets several general criteria. 
171 - DRAFT

1. The model has received a scientific peer review.

2. The model can be demonstrated applicable to the problem being addressed.

3. Data bases needed to perform the analysis are available and adequate.

4. Available past appropriate performance evaluations have shown the model is not

biased toward underestimates.

5. A protocol on methods and procedures to be followed has been established.

6. The developer of the model must be willing to make the source code available to

users for free or for a reasonable cost, and the model cannot otherwise be

proprietary. 

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

States should consider several factors as criteria for choosing a qualifying air quality

model to support an attainment demonstration for the PM2.5 NAAQS or a reasonable progress

demonstration. These factors are: (1) ability to address the air quality problem described in the

conceptual description; (2) documentation and past track record of candidate models in similar

applications; (3) experience of staff and available contractors; (4) required time and resources vs.

available time and resources; (5) in the case of regional applications, consistency with regional

models applied in adjacent regions.. The first of these factors is used to identify attributes

needed for a model to be chosen. Factors (2)-(5) are used to help choose among candidate

models having these attributes. Finally, before results of a selected model can be used in an

attainment or reasonable progress demonstration, the model should be shown to perform

satisfactorily using the data base available for the specific application.

Ability to address the air quality problem described in the conceptual description. 

This is the most important criterion for selecting an appropriate model. Prior to selecting a

model, we recommend that those implementing the protocol review available air quality,

meteorological and emissions data, and take account of the geographic location of the

nonattainment or Class I area(s) relative to that of PM and precursor emissions. Section 9.0

identifies some types of analyses which may be useful for developing a conceptual description of

an area’s nonattainment problem. Once the conceptual description is developed, a State should

review the ability of candidate models to address the identified problem(s). 

Documentation and Past Track Record of Candidate Models. For a model to be used

in an attainment or reasonable progress demonstration, evidence should be presented that it has

been found acceptable for estimating concentrations of PM2.5 and most of its major components. 

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
172 - DRAFT

description of the model should be available. 

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.

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 11.1, a State

may use the factors described in this section (Section 11.2) to show that it is appropriate for use

in a specific application. Choice of an “alternative model” for use in a specific attainment or

reasonable progress demonstration needs to be reviewed and approved by the appropriate U.S.

EPA regional office and by the U.S. EPA Model Clearinghouse. 

Satisfactory Model Performance in the Specific Application. Prior to use of a selected

model’s results, it should be shown to perform adequately in the specific application. Means for

evaluating model performance are discussed in Section 16.0.

Recommendations. States should first determine what attributes are needed for a

qualifying model to address an area’s PM2.5 or regional haze problem, and then

choose among models possessing these attributes. Five factors should be considered

in selecting an air quality model for a specific application. Selection of an air

quality model should be concurred with by the appropriate U.S. EPA regional office

and U.S. EPA Model Clearinghouse. The five factors are listed approximately in

order of importance. 

1. Nature of the air quality problem leading to nonattainment of the PM2.5 NAAQS

or light extinction should first be assessed, and the selected model(s) should have

attributes and capabilities which enable it to address the conceptual description of

the problem.

2. Availability, documentation and past performance should be satisfactory.

3. Relevant experience of available staff and contractors should be consistent with

choice of a model.
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4. Time and resource constraints may be considered.

5. Consistency of the model with what was used in adjacent regional applications

should be considered.

Prior to using model results in a specific application, a State should show that the

model performs adequately in replicating observations available for that

application.

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

Seigneur, et al., (1997) provide a good review and a more complete discussion of air

quality and receptor models which have been used in applications related to particulate matter. 

Table 11.1 draws upon this information and lists several air quality models (available in 2000)

which have been used to simulate ambient concentrations of PM2.5 or its components. The list is

not intended to be comprehensive. States should review the U.S. EPA’s website which lists

models and provides short descriptions for updates (http://www.epa.gov/ttn/scram/). Exclusion

of a model from the list does not necessarily imply that it cannot be used to support a modeled

attainment or reasonable progress demonstration. By the same token, inclusion on the list does

not necessarily imply that a model may be used for a particular application. States should follow

the guidance presented earlier in Section 11 for selecting an air quality model for a specific

application. 
Table 11.1. Some Air Quality Models Used To Model PM2.5 Or Its Components

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Model Reference(s) Past Applications Comments

CMAQ U.S.EPA, 1998,

1999c

Eastern and

western halves of

continental U.S. by

U.S. EPA Office of

Research &

Development and

the Office of Air

Quality Planning &

Standards, 

Los Angeles Basin

(Seigneur, 2000)

secondary &

primary

components

simulated on

regional & urban

scales

SAQM-Aero Dabdub, et al., 1998 San Joaquin Valley,

California by

California Air

Resources Board, 

Los Angeles Basin

(Pai, et al., 2000)

secondary &

primary

components

simulated on

regional & urban

scales

URM Kumar, et al., 1996 Southern & midAtlantic regions as

part of the Southern

Appalachian

Mountain Initiative

(contact Dr.A.G.

Russell, GA Inst.of

Technology)

secondary &

primary

components

simulated on

regional & urban

scales

MAQSIP MCNC, 2000,

Odman and

Ingram, 1996

Eastern U.S.,

focused on North

Carolina,

Los Angeles Basin,

(Seigneur, 2000).

secondary &

primary

components

simulated on

regional & urban

scales
Table 11.1. Some Air Quality Models Used To Model PM2.5 Or Its Components (continued)

Model Reference(s) Past Applications Comments

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UAM-Aero Lurmann and

Kumar, 1996,

Lurmann, et al.,

1997

Kumar and

Lurmann, 1997

Los Angeles Basin,

San Joaquin Valley,

CA for SCAQMD

and for CARB

secondary &

primary

components

simulated on urban

scale

CALPUFF Strimaitis, et al.,

1998

Evaluated with data

sets for two power

plants

Lagrangian model

which considers

primary PM for a

limited number of

sources. Also

potentially useful

for hot spot

analyses.

REMSAD Systems

Applications,

International, 1998

Applied for

contiguous 48 States

by/for U.S. EPA,

Office of Air

Quality Planning

and Standards and

Office of Water

primary &

secondary PM using

condensed chemical

mechanism. Most

practical for annual

applications

AERMOD Cimorelli, et al.,

1998, 

Paine, et al., 1998

See Paine, et al.,

1998

primary particulate

matter only, nearby

(< 50 km) sources,

also useful for hot

spot analyses

ISC3 U.S.EPA, 1995,

Paine, et al., 1997

Numerous

applications

primary particulate

matter only, nearby

(< 50 km) sources, 

also useful for hot

spot analyses
Table 11.1. Some Air Quality Models Used To Model PM2.5 Or Its Components

(concluded)

Model Reference(s) Past Applications Comments

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CAL3QHCR Eckhoff and

Braverman, 1995

New York City and

other locations

line source model,

useful for

estimating

concentrations of

primary particulate

matter near

roadways

CMB (Chemical

Mass Balance

Model)

U.S. EPA, 1990 Numerous

applications (see

Seigneur, et al.,

1997)

receptor model,

most useful for

identifying source

categories of

primary particulate

matter impacting

monitoring sites

PMF (Positive

Matrix

Factorization)

Paatero and

Tapper, 1994

Narragansett, RI,

Alaska, Phoenix

receptor model,

most useful for

identifying source

categories of

primary particulate

matter impacting

monitoring sites

UNMIX Henry, 2000 Phoenix, AZ receptor model,

most useful for

identifying source

categories of

primary particulate

matter impacting

monitoring sites
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12.0 How Do I Decide Which Periods Or Meteorological Episodes To Model?

How many and which days to model depends primarily on the air quality goal the

modeling seeks to address. Therefore, our recommendations differ for the two NAAQS for

PM2.5 and for the goals reflecting reasonable progress reducing regional haze. Goal-specific

criteria are presented in Sections 12.2-12.4. Nevertheless, there are several criteria for choosing

days to model which are relevant for all of the air quality goals considered in this guidance. We

begin this section by identifying these generally applicable criteria. We then address each of the

two NAAQS and reasonable progress goals in turn, and identify additional, goal-specific criteria

for selecting days to model. We conclude by identifying some practical considerations in

choosing days to model.

12.1 What Are Generally Applicable Criteria For Choosing Days To Model?

There are three such criteria: choose a mix of days reflecting a variety of meteorological

conditions; choose days for which an extensive air quality/meteorological data base exists,

choose a sufficient number of days so that estimated relative reduction factors do not depend on

the outcome on one or a small number of days.

Simulating 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. In

the case of the 24-hour NAAQS for PM2.5 and the two goals for visibility, greatest priority should

be given to ensuring that this variety is drawn from days with high observed concentrations of

PM2.5 or days with poor (good) current visibility. We believe the most important indicator of

variety is differing wind fields. This affects source/source and source/receptor orientations and

may sometimes serve as a surrogate for other meteorological characteristics which affect mixing. 

Maximum daily temperature and moisture content may also have a major influence on the

effectiveness of a strategy in reducing concentrations of PM2.5 or improving visibility. 

Those implementing the modeling/analysis protocol should describe the rationale for

distinguishing among episodes which are modeled. If the analysis is to be focused on a limited

geographic area, the selection may reflect a number of area specific considerations. Qualitative

procedures such as reviewing surface and aloft weather maps, observed or modeled wind patterns

may suffice to distinguish episodes with distinctively different meteorological conditions. More

quantitative procedures, such as a CART analysis, to identify distinctive groupings of

meteorological/air quality parameters may sometimes be desirable. An example of a CART

analysis applied to select episodes in a study which addressed regional haze is described by

Deuel and Douglas (1998).

Choosing days with extensive data bases. If possible, days selected for modeling

should include some with extensive data bases, such as might be collected in a short duration

intensive field study. States should try to include days on which PM2.5 and speciated particulate

matter are collected for sampling periods not exceeding 6 hours (i.e., 4 samples/day). If it is not

possible to include any such days for a particular application, a State should provide information
24We consider these findings to be preliminary ones. We are performing additional tests

to provide more definitive analyses which address the issue of the minimum number of days

which have to be modeled in order for the model to provide an adequate indication of the effects

of a control strategy.

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showing that the model being used has performed adequately elsewhere when extensive data

bases have been available. 

An “extensive” data base should best include vertical profile measurements of wind

velocity, temperature, moisture and pressure taken 3-4 times/day or, better still, continuous

measurements. Preferably, upper air measurements for SO4, NO3, OC, EC, IP, SO2, NOy, NO,

NO2, and O3 or as many of these species as possible should be available. Surface measurements

for the previously identified meteorological and air quality data should also be available.

Although the tests we recommend place less emphasis on a model being able to predict

absolute concentrations of each component accurately, it is important that the model be able to

predict the mix of secondary components and ozone in approximately the right proportion. This

follows since there is an interdependency among these components which could affect accuracy

of estimated RRF’s if the model performs poorly. Thus, the need for the chosen model(s) to have

performed well on days with good data bases remains an important one.

Choose a sufficient number of days to derive robust estimates for the RRF values. 

Studies examining the stability of estimated RRF values for ozone suggest that the estimates

become more stable if based on an average of ten or more days (Milanchus, et al., 1998, Hogrefe,

et al. , 2000). As we will describe shortly, these findings also appear applicable to components

of PM2.5, but the number of days needed for RRF values to become “stable” may be somewhat

larger for PM than for ozone. Modeling a large number of days also increases the number of

days with observed PM2.5 and speciated data. This, in turn, increases the likelihood of having a

large data base to conduct a more meaningful model performance evaluation. Based on our

analyses to date, we suggest that States derive their estimated RRF values using modeled

concentrations averaged over several (e.g., ~ 15 or more, not including ramp-up) days.24

Recommendations. States should observe three criteria which are generally

applicable in choosing days to model, plus additional criteria which depend on the

air quality goal being addressed. The three general criteria are:

1. Choose a variety of meteorological conditions to model;

2. To the extent possible, include days with extensive data bases;

3. Model a sufficent number of days so that RRF values are based on several (e.g., >

~15) days.
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12.2 Additional Recommendations For The Annual PM2.5 NAAQS 

(NOTE TO REVIEWERS: we are continuing with our analyses of strategy simulations

obtained with the REMSAD model. Prior to finalizing the guidance, we hope to augment

these with results obtained using the CMAQ model. The analyses are intended to improve the

underlying basis for recommendations on choice of a minimum number of consecutive days to

model per quarter to support demonstrations related to the annual and 24-hour NAAQS and

the regional haze rule. Recommendations presented herein reflect findings as of December

2000.) 

Emissions of PM2.5 and its precursors are likely to vary seasonally, as are meteorological

conditions. Therefore, we recommend that for applications related to the annual NAAQS,

decisions regarding days to model should be made on a calendar quarter by calendar quarter

basis. Days chosen from each quarter should reflect a balance of days above and below the

observed quarterly arithmetic mean PM2.5 concentration, a mix of meteorological conditions and,

to the extent possible, include periods with intensive data bases. Approximately an equal number

of days should be drawn from each quarter. States may use any one of three approaches as the

basis for choosing days to model in applications related to the annual NAAQS. Each of these is

described below.

Approach 1. Model every day over a recent 3-year period. This is the method we

recommend for performing a hot spot analysis near sources of primary particulate matter. It is

also the preferred approach when a Lagrangian (e.g., Gaussian) or other relatively non-resource

intensive model is used to estimate relative reduction factors (RRF’s) for primary components of

particulate matter. One advantage of this approach is that the modeled results can be most readily

related to the form of the NAAQS. For example, for each quarter, the relative reduction factor is

the ratio of the average of the 3 consecutive mean quarterly future to current predictions for each

component of PM2.5. The modeling yields these estimates directly. It is not necessary to rely on

assumptions about the representativeness of RRF values calculated for a subset of chosen days. 

A second advantage is that it ensures that there is a large number of days for which both modeled

and monitored data exist. This provides a large data base which can be used to evaluate

performance of the model. 

A variation of this approach, which is acceptable for applications of photochemical grid

models, is to choose a single, recent “representative” year and model every day in that year. A

State should consider two factors in deciding whether a particular year is a “representative” one. 

First, the observed annual mean concentrations of PM2.5 should be close to the 3-year observed

design value at all, or most, monitoring sites. Second, the pattern of quarterly mean values

should be similar to the pattern of quarterly mean concentrations averaged over 3 years. For

example, if the 3-year mean quarterly PM2.5 concentration for winter is higher than that for the

other three seasons, this should also be the case for the selected “representative” year. Highest

priority should be given to ensuring that these two criteria are met at monitoring sites which

exceed the annual NAAQS for PM2.5.
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The disadvantage of modeling every day is the burden it places on central processing

(CPU) time and data storage needs. States will likely want to explore several emission control

strategies as well as perform diagnostic/sensitivity tests to estimate the extent to which

conclusions drawn from the modeling are affected by a variety of factors. Thus, the CPU and

data storage requirements could be large, especially using regional photochemical grid models. 

This disadvantage may diminish over time as computer hardware and related factors improve. In

the meantime however, it may be necessary to use simplified versions of photochemical grid

models if one chooses to model every day. This introduces a potential tradeoff between the

detail with which physicochemical phenomena are treated vs. possible errors estimating RRF

values introduced by simulating a subset of days which is not representative of a year. Thus, we

describe two additional approaches for States needing to apply regional photochemical grid

models to address the annual NAAQS.

Approach 2. Choose at least a minimum number of days to model from each

calendar quarter. The obvious question posed by this approach is, what is a “minimum number

of days”? We have reviewed past applications of the REMSAD model to address this question. 

The REMSAD model has been applied to the contiguous United States for an entire year using

meteorological inputs available for 1990. Thus far, we have investigated two emissions

scenarios: (1) emissions which are reflective of 1996 estimates, and (2) emissions which have

been projected to 2010. These latter emissions reflect Clean Air Act related-measures for VOC,

NOx and SO2 including reductions in NOx expected if the “NOx SIP call” is implemented, the

acid rain-related reductions and imposition of maximum available control technology (MACT--

mostly reflecting reductions in VOC). 

We used the modeled results to calculate quarterly mean concentrations for each

component with both emission scenarios. The four quarterly component-specific mean

concentrations corresponding to current emissions were averaged to get current annual mean

estimates for each component. An identical procedure was followed to get estimates of the

annual mean component concentrations corresponding to the 2010 emissions. The relative

reduction factor (RRF) for each component is the ratio of the annual mean predicted for 2010

divided by the predicted annual mean with 1996 emissions. We assumed these componentspecific RRF’s represent “ground truth”—RRF values you would find if every day were

modeled.

We next re-derived component-specific RRF’s as described above, but with one

difference. Only a limited number of modeled days were used to derive the RRF. For example,

we chose 4 days at random from each quarter to calculate the component-specific RRF values. 

Typically, this produces estimated component-specific RRF’s which differ somewhat from what

is obtained when every day is considered. We then replaced the 4 days/quarter chosen previously

and randomly redrew 4 days from each quarter. Once again, resulting RRF values were

calculated using the limited sample. We repeated this process 200 times. This gives a

distribution of component-specific RRF values which is attributable to not considering results

from every modeled day. We then calculated the mean (μ) and standard deviation (σ) of the RRF

values obtained with the 200 limited samples, as well as the coefficient of variation (σ/μ) for this
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distribution.

We can use the coefficient variation to get a sense of how many days need to be modeled

to get RRF values which agree closely with what would be obtained if all days were modeled. 

This insight is possible by considering what happens to the coefficient variation when all

(approximately 91) days are modeled in each quarter. If every day were modeled, the coefficient

of variation becomes zero. This follows, because the exact same days are used to calculate the

RRF in each of the 200 trials----there is no variability in the resulting RRF value (σ = 0). As the

number of sampled days included in each trial gets large, the variability in the estimate

approaches zero. Further, there would likely be little difference in the coefficient of variation as

the sample size increases.

Figures 12.1 (a) - (c) show the coefficient of variation in the RRF calculated for NO3 and

SO4 in each of 64 surface grid cells in and near Atlanta, Cincinnati and the Grand Canyon,

respectively. The coefficient of variation (the ordinate in the graphs) is expressed as a percentage

of the mean calculated RRF. The whisker diagrams in each of the figures indicate that, for any

given sample size, there is some cell to cell variability in the coefficient of variation. The thick

line in each plot represents typical values for the coefficient of variation. For example, in Figure

12.1 (a), we see that a typical coefficient of variation in the RRF for NO3 in Atlanta is about 7% 

of the estimated RRF value when the RRF is derived using 8 randomly chosen modeled days per

quarter. Notice in the figure how there is a relatively large increase in the typical coefficient

variation (to about 11%) when the RRF calculations are based on 4 modeled days/quarter. In

contrast, differences in the coefficient of variation become progressively smaller as the sample

size is increased. For example, the typical coefficient of variation obtained with a sample size of

16/quarter (~ 5%) is slowly reduced to about 3% with a sample size of 32 modeled days/quarter.

Note that our results suggest that the coefficient of variation of the RRF for SO4 is less

than that for NO3. For example, in Figure 12.1 (a) with a sample size of 8 modeled days/quarter,

the coefficient of variation of the RRF for SO4 is about 2%, whereas the value related to the RRF

for NO3 is about 7%. We believe this difference is due in part to much lower NO3 than SO4

concentrations which the model predicts in the vicinity of Atlanta. This introduces increased

“noise” in the model predictions for NO3. This hypothesis is supported by the generally higher

coefficients of variation of RRF’s for NO3 and SO4 which we calculate in the vicinity of the

Grand Canyon (Figure 12.1 (c)) compared to those estimated in Figures 12.1 (a) and (b) for

Atlanta and Cincinnati.
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Figure 12.1 (a).
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Figure 12.1 (b).
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Figure 12.1 (c).
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Choice of a limited number of days/quarter to model may sometimes be dictated by the

component of PM2.5 which has the highest coefficient of variation in its RRF value with a limited

sample size. Due to “noise” in model predictions, this is often likely to be a component for

which very low current concentrations are estimated. Why do we say a lower acceptable limit on

number of days modeled may only “sometimes” be dictated by the component with the highest

coefficient of variation? If the observations for this component are also very low relative to other

components, variability/uncertainty in the component’s RRF value may be unimportant (i.e., you

gain little by reducing the uncertainty in the RRF for this component by modeling many days). 

Recall that in the attainment and reasonable progress tests, we recommend that componentspecific RRF’s be multiplied by currently monitored component concentrations. If the

observations are also very low, uncertainty in the RRF (due to limited modeling) will likely have

little effect on the outcome of the test.

Looking at the information we have generated to date, we believe that modeling about 15-

20 days/quarter should provide an acceptable approximation of RRF values related to the annual

NAAQS. This is based on the relatively low values for the coefficient of variation in the RRF

seen in the figures for randomly chosen sample sizes of 16 days/quarter (i.e., typically only about

5% for the NO3 RRF, even at the Grand Canyon) and the relatively small reduction in the

uncertainty which occurs when the RRF estimates are based on more modeled days. Smaller

sample sizes may be used on a case by case basis, if justified (e.g., by choosing days in

something other than a random manner). 

Approach 3. Classify observed air quality data into groups defined by differences in

meteorological conditions. Model at least three days from each group. This approach is to

divide days within each calendar quarter into major meteorological categories. Modeling should

include at least three days from each identified category. Quarterly mean concentrations are then

computed for each major component of PM2.5 by weighting predictions obtained for each

category by the frequency with which that category is observed in the calendar quarter over a 3-

year period, preferably corresponding to the current measured design value for PM2.5. Quarterly

means obtained in this manner are then averaged to derive estimated annual means. Annual

means are calculated using current emissions and then with future estimated emissions reflecting

growth and controls. The component-specific RRF values are the ratios of annual mean

concentrations predicted for each component with future emissions to those predicted with

current emissions.

The biggest impediment to using approach 3 is developing a sound rationale for dividing

days into a finite number of meteorological classes and then being able to assign each day to the

appropriate class. If the model application is for a mesoscale or smaller problem, air quality

observations may be used to help identify distinctive classes. The CART procedure, illustrated

by Dueul and Douglas, (1998), is an example of such a methodology. Even though these analysts

were working with a regional (synoptic scale) air quality model, they were able to use this

approach because they were interested in applying model results to a limited number of receptor

locations (i.e., Shenandoah and Great Smokies National Parks). 
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Using air quality observations as a means for identifying meteorological classes becomes

impractical if one is applying a regional scale model to simultaneously derive solutions for

problems meeting the annual NAAQS or another air quality goal in numerous locations. Cohn,

et al., (2000) have developed a means for classifying days based solely on observed geographic

patterns of wind velocity observed aloft at 700 mb. Methods such as this one, which use

synoptic scale observations to identify meteorological classes, may be most practical for regional

scale model applications needing to consider many receptor sites.

Recommendations. The preferred approach for choosing days to model in

applications addressing the annual NAAQS for PM2.5 is to model every day for a 3-

year period which is as recent as feasible. For applications requiring use of a

photochemical grid model, use of a single, representative year is an acceptable

alternative.

If the preferred approach is not practical, States may use one of two alternatives for

choosing days to model.

1. Model all 4 calendar quarters, considering at least 15 days/quarter. Fewer

days/quarter may be acceptable if justified on a case by case basis.

2. Identify major classes of meteorological conditions occurring in each

calendar quarter. Model at least three days from each class in each quarter. 

Obtain quarterly mean concentration estimates by weighing each class

according to its relative frequency of occurrence. 

12.3 Additional Recommendations For The 24-Hour PM2.5 NAAQS 

The preferred approach for choosing days to model primary components of PM2.5 is to

model every day over a consecutive 3-year period, preferably corresponding to the period in

which the current design value is observed. This approach should generally be followed for hot

spot analyses. We also recommend it for use in grid models used to support the modeled

attainment test. If a nonattainment area violates both the annual and 24-hour NAAQS and our

recommended approach for selecting days for modeling to address the annual NAAQS is

followed (i.e., model every day for a year), a modeled data base suitable for use with the 24-hour

NAAQS may also exist. If it is not practical to model every day in a year to derive RRF values

for components of PM2.5 in the modeled attainment test, States may consider the suggestions

outlined below. 

When a photochemical grid model is used, an alternative approach for choosing days to

model for applications related to the 24-hour NAAQS is to model every day where there is an

observed daily concentration of PM2.5 which exceeds 65 μg/m3

. There could be a substantial

amount of uncertainty associated with predictions on any single day. Thus, the modeled

attainment test is most likely to be reliable when relative reduction factors reflect composite

responses from many days. Therefore, we recommend modeling as many days as feasible where
25We intend to perform model sensitivity tests to confirm or refine this estimate for PMrelated applications. Results will be reflected in the final version of the guidance.

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observed PM2.5 is greater than 65 μg/m3. States should make note of the quarter in which each

selected day occurs. As with the annual NAAQS, the preferred approach is to develop relative

reduction factors which are season (i.e., quarter) specific. Emissions, meteorological conditions

and thus composition of the mixture comprising PM2.5 could vary substantially from season to

season. 

We have noted that it is desirable to base our conclusions on a composite response of the

model(s) over many days. However, from limited monitoring data available as of 2000, there is

not likely to be many days with observed concentrations greater than 65 μg/m3. If this results in

a sample size of smaller than about 10 days per quarter, days with PM2.5 observations > ~55

μg/m3 and speciated measurements should also be considered. If, after doing this, there is still a

small number of days in a single quarter, it may be combined with the most appropriate other

quarter. If the sample of high days is limited in 2 or more quarters, a State may develop relative

reduction factors which reflect a combined sample of days from the entire year.

We expect relative reduction factors for components of PM2.5 to behave similarly to those

for ozone. That is, the larger the number of modeled days used to derive the RRF, the greater the

likelihood we will obtain a representative estimate for the RRF. Hogrefe, et al., (2000) have

noted that RRF values for ozone become relatively stable if derived using estimates obtained on

10 or more days25. Therefore, in applications related to the 24-hour NAAQS, States should base

the RRF on modeled results from 10 or more days with monitored observations > 55 μg/m3

. If

there are fewer than 10 such days available, use as many days as are available. 

Recommendations. In performing hot spot analyses, States should model every day

in a recent 3-year period. This is also the preferred procedure for deriving RRF

values for components of PM2.5 considered in the modeled attainment test. An

acceptable alternative is to model everyday in a representative year. If it is not

practical to use either of these approaches to derive RRF values, States may

consider the alternative identified below.

States should base RRF values obtained with photochemical grid models on at least

10 days/quarter with monitored concentrations of PM2.5 > 55 μg/m3

. If there is

fewer than 10 such days/quarter available, quarters may be pooled to obtain RRF

values based on approximately 10 or more days observing PM2.5 concentrations > 55

μg/m3

. Preference should be given to pooling quarters with as similar emissions

data and meteorological conditions as feasible. 

12.4 Additional Recommendations For The Regional Haze Goals

The goals for regional haze focus on the 20% of days with best and worst visibility. 
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Sampling in Class I areas is expected to occur 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 bext values

for “20% worst” and “20% best” days approximates mean values for bext averaged over 5 years

for the best and worst days). States can then base the RRF values on the 24 best and worst days

in each Class I area for that year. If it is not feasible to model an entire year, we offer some

alternatives in the following paragraphs.

Days with “worst” visibility. Earlier, we showed that RRF values based on a random

sample of 15 days/quarter was likely to provide a good estimate of the RRF which would be

obtained if one were to model every day in a year. This implies that modeling about 1/6 of a

large population of possible days produces a mean RRF value which is very close to what one

would get if one were to model every day. Modeling 1/6 of “worst” visibility days in a year

would mean modeling about 4 days with observed “worst” visibility. However, based on

information presented for ozone by Hogrefe, et al., (2000), our sense is that the reliability of RRF

estimates based on samples this small may not be reliable. Thus, as of late in 2000, we feel that a

minimum number of days to model is probably better described by the results obtained by

Hogrefe, et al. (2000) for ozone. Therefore, we recommend that each Class I area considered in a

regional modeling analysis have RRF values based on model results from at least 10 monitored

“worst” visibility days. States should consider seasonal patterns in observed “worst” visibility in

choosing days to model. 

Since regional scale models will likely be used for regional haze-related applications,

strategies for several Class I areas will likely be developed from the same model results. Thus,

having a criterion that the RRF for each Class I area be based on at least 10 modeled days drawn

from a population of “worst” days with monitored data will result in more than 10 “worst” days

being modeled for some Class I areas. Thus, meeting this criterion ensures that the RRF for most

Class I areas will be based on model results from more than 10 of their worst visibility days. 

Days with best visibility. The criterion for choosing days to model to meet the goal for

“best” visibility days is the same as that for “worst” days, except that the minimum number of 10

days should be drawn from the population of days with observed “best” rather than “worst”

visibility.

Recommendations. The preferred approach for regional haze-related modeling is to

simulate a full, representative year. RRF values should be based on model results

averaged over the 20% worst and 20% best days determined (using monitored PM

data) for each Class I area in the chosen year. If it is not feasible to model a full

year, States should derive RRF values for each Class I area based on modeling
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results from at least 10 days with observed “worst” (“best”) visibility.

12.5 Some Other Considerations

Choose periods which have already been modeled. That is, of course, provided that

past model performance evaluation for such an episode was successful in showing that the model

worked well in replicating observations. Given that criteria described in Sections 12.1, 12.2,

12.3 and 12.4 are met approximately as well by such episodes as they are by other candidate

episodes, a State could likely save a substantial amount of work in evaluating model

performance.

Choose periods which are drawn from the years upon which the current design

value is based (i.e. the “base period” for regional haze-related applications). Fewer

emission estimates and fewer air quality model simulations are needed if the period used to

evaluate model performance, and the “current period”, used in the recommended modeled

attainment test, are one in the same. 

It is desirable to include weekend days among those chosen. Weekend days reflect a

different mix of emissions than occurs on weekdays. Thus, for increased confidence that a

control strategy is effective it needs to tested on weekends as well as on weekdays. If emissions

and spatial patterns of high PM2.5 or its components do differ on weekends vs. weekdays,

including weekend days in the choice of episodes may provide a potential for evaluating accuracy

of a model’s response to changes in emissions. As we note in Section 16.0, such evaluations are

highly desirable.

If a State chooses to model several nonattainment areas simultaneously, choose

periods which meet the other criteria in as many of these nonattainment (or Class I) areas

as possible. A State or group of States may decide to apply a regional model or a nested regional

model to demonstrate attainment in several nonattainment areas or reasonable progress in several

Class I areas at once. Time and resources needed for this effort could be reduced by choosing

periods to model which meet the other criteria in several nonattainment or Class I areas which

are modeled.

Recommendations. The following are additional, practical criteria for choosing

which days to model for an attainment or reasonable progress test. States may

identify, document and present the rationale for practical criteria in addition to

these if they choose.

1. Give preference to previously modeled periods.

2. Give preference to periods occurring during the years corresponding to the

current design value (base period) used in the modeled attainment (reasonable

progress) test.
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3. Include weekends among the selected days.

4. If applying a regional model, choose periods to model which meet other,

previously identified selection criteria in as many nonattainment (or Class I) areas

as possible.
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13.0 What Should I Consider When Selecting A Modeling Domain And Its

Horizontal/Vertical Resolution?

A modeling domain identifies the geographic bounds of the area which is modeled. In a

grid model, horizontal resolution is related to the horizontal dimensions of individual grid cells. 

Vertical resolution is determined by the number of grid cells (i.e., layers) considered in the

vertical direction.

Appropriate domain size and resolution differ for applications related to the NAAQS for

PM2.5 vs. regional haze. Further, different recommendations are appropriate when using

Lagrangian models, such as Gaussian models, to assess effects of reducing emissions of primary

particulate matter. In Section 13.1, we address the issues of domain size/grid resolution as they

relate to model applications for the NAAQS for PM2.5. We begin by discussing appropriate

domain size and resolution for grid model applications. We then discuss Lagrangian model

applications which may be used for developing relative reduction factors for primary particulate

matter or for hot spot analyses. We conclude Section 13.1 by discussing what States should do

for analyses in which both grid and Lagrangian models are used. In Section 13.2, we turn our

attention to model applications addressing reasonable progress reducing regional haze.

13.1 How Do I Decide What Domain Size And Resolution Are Appropriate To

Address The Two PM2.5 NAAQS?

13.1.1 How Do I Decide What Domain Size To Use?

Grid model applications. Choice of domain size should be heavily influenced by a

State’s conceptual description of its nonattainment problem as well as by the remedies it wishes

to consider. For example, if a review of ambient measurements suggests that primary particulate

matter (IP, primary OC, EC) constitutes enough of measured PM2.5 at sites violating the NAAQS

so that control measures for these components may reduce them sufficiently to meet the NAAQS,

a State may choose to use an urban scale domain. Using an urban scale domain introduces an

issue about what to assume for boundary conditions (or “background” in Lagrangian

applications). We discuss this further in Section 14.0. Generally, an urban scale analysis is most

appropriate for nonattainment areas where monitored data suggest transport of particulate matter

or its precursors into the nonattainment area is well below the level of the NAAQS or is believed

unlikely to change. If a State’s attainment strategy is likely to depend on efforts to reduce

secondary components of PM2.5 (i.e., SO4, NO3 , secondary OC), the model application should

use a regional domain. Size of the regional domain will be influenced by the configuration of

sources and by wind patterns observed or predicted during periods in which the 24-hour or

annual NAAQS is frequently exceeded by monitored observations. 

What do we mean by “urban scale” and “regional” domains? An urban scale domain is

one having horizontal dimensions less than ~ 300 km on a side. Assuming the nonattainment

area is located near the center of the domain, the domain should be large enough to ensure that

emissions occurring shortly before sunrise in its center are still within the domain at the end of
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the same calendar day. If recirculation of the nonattainment area’s previous day’s emissions is

believed to contribute to an observed problem, the urban scale domain should be large enough to

characterize this. If recirculation encompasses distances larger than about 300 km, an urban

scale model is probably not sufficient to address an area’s problem. 

A regional domain is one having horizontal dimensions typically exceeding 1000 km on a

side. Data base management problems may make it infeasible to use the same horizontal grid

cell size in urban scale and regional models. Using nested regional models addresses this

problem. A nested regional model is one whose domain typically exceeds 1000 km on a side. 

However only a portion of that domain (e.g., < 300 km on a side) has grid cells with a size

similar to that recommended for urban scale models. States should ordinarily include all

monitoring sites considered in the modeled attainment test within the area covered by a grid with

size of individual cells comparable to that recommended for urban scale modeling.

Lagrangian model applications. The more pertinent way of expressing this issue is,

“how far downwind should I make concentration estimates using a Lagrangian model”? The

ability of the most commonly used form of Lagrangian model (i.e., a Gaussian model) to estimate

concentrations at distances greater than about 50 km has not been demonstrated. Therefore, use

of Gaussian models should be limited to urban scale applications. 

A limitation of 50 km introduces a potential inconsistency since, as we just noted, urban

scale domains may sometimes be larger than this. There are at least three ways of addressing this

discrepancy which may be considered by those implementing a modeling protocol. First,

perform the modeling anyway, recognizing that calculated impacts from remote sources will be

much less than those closer by. Further, effects of any error will likely be diminished by using

the model in a relative sense to adjust observed concentrations of primary components. A second

alternative is similar to the first, except use a Lagrangian puff model, like CALPUFF (Strimaitis,

et al., 1998) to consider effects of large, individual sources located more than 50 km away from

the monitor. A third alternative is to limit Gaussian modeling to within 50 km of each monitor

and assume that a part of the measured primary particulate matter is irreducible. The irreducible

portion may be estimated using available monitored speciated data at background sites. If those

implementing the modeling/analysis protocol conclude that none of these three alternatives is

acceptable, States may use a numerical grid model (with the chemistry “turned off”) to estimate

effects of a control strategy on RRF values for primary components of PM2.5.

Hot spot analyses. Gaussian or other Lagrangian approaches for estimating 24-hour

concentrations at hot spots are likely to be most reliable at distances less than about 15 km. In

addition, plumes from individual or small groups of sources are likely to be “smeared out” at

further distances. Hence, little is likely to be gained, beyond treatment in a grid model, using a

hot spot or plume-in-grid analysis of individual sources of primary particulate matter further than

~15 km from receptors of interest. Thus, hot spot analyses generally should be limited to

distances within about 15 km of large sources which are of potential concern. 

Recommendations. Size of a modeling domain depends on the importance of
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measured primary vs. secondary components of measured PM2.5 and whether a

State’s attainment strategy depends on secondary components being reduced. If

attainment depends on reducing secondary components, States should use a regional

scale modeling domain (> ~ 1000 km on a side).

Urban scale domains (< ~ 300 km on a side) are most suitable if a State’s control

strategy emphasizes reduction in primary components of PM2.5. Maximum domain

size may be restricted if Gaussian models are used to compute relative reduction

factors for primary components of particulate matter. We present three

alternatives for dealing with major sources of primary particulate matter located

within an urban scale domain, but more than 50 km from a particular monitoring

site. Those implementing the modeling/analysis protocol should consider which, if

any, of these alternatives is appropriate for their application. 

Hot spot analyses and use of plume in grid algorithms to estimate effects of

individual sources on 24-hour concentrations of primary particulate matter should

be limited to within about 15 km of the source.

13.1.2 What Horizontal Grid Cell Size Is Necessary?

(NOTE TO REVIEWERS: Several of the recommendations in this subsection are based on

past experience modeling ozone (e.g., 8-hour and 1-hour daily maxima) rather than

particulate matter. We are planning sensitivity tests using models which address PM2.5 and its

components to confirm or refine our current recommendations regarding horizontal grid cell

size for PM-related applications. We solicit information from reviewers which is related to the

effect of grid cell size on estimated relative reduction factors for components of particulate

matter.) 

This discussion, as well as the discussion in Section 13.1.3, applies solely to use of 

numerical grid models. If a Lagrangian model is used, estimates at the monitor locations should

be used to calculate RRF values for primary particulate matter. Centerline concentrations

estimated at breathing height (~ 2 m) should be used in hot spot analyses.

In grid model applications, there is some evidence which suggests that size of a horizontal

grid cell may be larger if we are concerned with longer averaging times. Analyses supporting

this hypothesis have been addressed toward the problem of predicting daily maximum ozone

concentrations. However, as we describe shortly, their findings should also be pertinent for

particulate matter. Sensitivity tests performed by Kumar, et al. (1994) in the South Coast Air

Basin compare hourly ozone predictions obtained with 5 km vs. 10 km vs. 20 km grid cells. 

Results indicate that use of finer grid cells tends to accentuate highest hourly ozone predictions

and increase localized effects of NOx titration during a 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 and
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Douglas (1996)). As in Los Angeles, use of smaller (i.e., 4 km) grid cells leads to higher domain

wide maximum hourly ozone concentrations. However, when reviewing concentrations at

specific sites, Haney and Douglas (1996) found that, for some hours, concentrations obtained

with the 12 km grid cells were higher than those obtained with the 4 km cells. Since signs of the

differences obtained with 4 km vs. 12 km grid cells vary for different hours, this may suggest that

8-hour daily maximum ozone predictions are less sensitive to the selected grid cell size than 1-

hour daily maxima. Recent sensitivity tests comparing relative reduction factors in predicted 8-

hour daily maxima near 272 sites in the eastern United States indicate generally small unbiased

differences (< .04, in 95% of the comparisons) using a grid with 12 km vs. 4 km grid cells

(LADCO (1999)). 

For particulate matter, the averaging time specified in the NAAQS and used for the

measurements is 24-hours. Since there appears to be little difference in RRF estimates for ozone

with 12 km vs. 4 km grid cells when one goes to 8-hour averaging times, we would expect this

finding to hold for secondary components of particulate matter which are computed for even

longer averaging times. But what about primary components of particulate matter? Sharpness of

spatial gradients in primary particulate matter should be much reduced over 24-hour averaging

times. This follows from variability in meteorological conditions as well as in emissions. We

would expect lack of atmospheric chemistry to have two counterveiling effects on sensitivity of

RRF values for primary particulate matter to changing grid cell size. Because there is no

chemistry, we expect spatial gradients in hourly 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. However, we would also expect these differences

between primary and secondary components to diminish as averaging times increase. Unlike

with secondary pollutants, exact positioning a plume relative to other sources has no effect on

concentrations within that plume. Hence we would expect gradients of primary components to

flatten out more rapidly as a function of averaging time. We do not know whether the second

effect is sufficiently large so that similar conclusions regarding maximum adviseable grid cell

size can be applied for primary and secondary components averaged over 24-hours. 

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

cells as small as 4-5 km 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 increases grid cell size to 12 km.

We expect that modeling analyses for nonattainment areas will model such areas 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. 

Because of the effects of 24-hour averaging times and the lack of close proximity of these

locations to a nonattainment area, grid cells as large as 36 km may be used.
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Recommendations. For urban scale applications or in nested portions of regional

grids focused on nonattainment areas, States may use grid cells as large as 12 km on

a side to estimate RRF’s for secondary particulate matter. RRF’s for primary

particulate matter should be estimated using cells which are 5 km or less on a side. 

Exceptions may be considered on a case by case basis.

States may use grid cells as large as 36 km on a side in portions of a regional grid

which do not include a nonattainment area.

13.1.3 How Many Layers Should I Consider?

(NOTE TO REVIEWERS: Several of the recommendations in this subsection are based on

past experience modeling ozone (e.g., 8-hour and 1-hour daily maxima) rather than

particulate matter. We are planning sensitivity tests using models which address PM2.5 and its

components to confirm or refine our current recommendations regarding number and

placement of layers for PM-related applications. We solicit information from reviewers which

is related to the effect of number and placement of layers on estimated relative reduction

factors for components of particulate matter.) 

As described in Section 14.0, the preferred means for generating meteorological data

fields for input to numerical grid models and Lagrangian models is to use a dynamic

meteorological model with four dimensional data assimilation (FDDA). Such models often

consider as many as 20-30 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 so many

layers may not be feasible nor cost effective. In this Section we identify factors affecting number

of layers chosen for use in an air quality model, as well as the placement of these layers. 

Diagnostic analyses to estimate an appropriate number of layers to use in an air quality

model have focused on the problem of predicting ozone accurately. However, with some

exceptions (which we will note), we believe the guidance for ozone related applications applies

for modeling related to the NAAQS for PM2.5 and regional haze. Accuracy of predicted ozone

concentrations depends, in part, on how accurately a model is able to characterize dilution of

precursors and ozone. This, in turn, depends on how precisely the model can estimate maximum

afternoon mixing heights (i.e., the planetary boundary layer). Precision of mixing height

estimates is affected by the thickness of the model’s layers aloft which are near the anticipated

mixing height (Dolwick, et al., (1999)). Because maximum 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 layers considered by the model. Dolwick, et al., (1999) have shown

that predictions are not sensitive to the number of layers considered above the planetary

boundary. Thus, States may assume as few as one layer above the highest conceivable maximum

afternoon mixing height with the rest of the layers occurring within the planetary boundary layer. 

Since wet chemistry occurring within clouds may have an important effect on secondary

particulate matter, States should be sure that the “highest conceivable maximum mixing height”
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is high enough to include strato-cumulous clouds, often present during summer or late spring. 

Earlier, we noted that it may often be necessary to consider a large modeling domain

when addressing PM2.5 nonattainment and (especially) regional haze-related problems. There

undoubtedly will be geographical differences in the maximum mixing height in a regional

modeling domain. This poses a problem in specifying an appropriate number and placement of

layers for use in a regional model application. Air quality models available as of late 2000

assume a fixed number and placement of layers throughout a modeling domain. This means that

a less than optimal specification may be necessary in parts of the domain. We suggest that States

give the greatest priority to choosing appropriate layers for areas which are the principal focus of

a modeled demonstration. For applications related to the NAAQS, this means the major

nonattainment area(s) being addressed. In cases where there are numerous areas which are the

focus of an analysis (e.g., as in reasonable progress demonstrations covering many Class I areas),

specify a set of layers which appears most appropriate for a majority of the areas. 

Placement of layers within the planetary boundary layer is also an important issue. For

practical reasons, it is best to have an air quality model’s layer placement coincide with layers

considered in the meteorological model used to generate meteorological inputs. So the

placement issue really is, which ones of the boundaries between the meteorological model’s

layers should I match with the boundaries between layers used in the air quality model? Modeled

concentrations used to compute component-specific RRF values are based on 24-hour estimates,

and nigttime periods may often be characterized by significant wind shear near the ground. 

Further, nearly all emissions are likely to occur within ~300 m of the ground. Thus, we suggest

that States give greatest attention to matching layers in the meteorological model within the first

300 m or so of the ground. The layer adjacent to the ground should generally be < 50 m deep. In

general, layers below the mixing height should not be too thick, or large, unrealistic step

increases in mixing may occur. States should try to avoid using layers within the planetary

boundary layer thicker than about 200-300 meters. 

 

Based on recent sensitivity studies performed for ozone (Dolwick, et al. (1999) and

LADCO (1999)), it appears as though as few as 7-9 layers (including one above the planetary

boundary layer) may suffice in a modeling study if care is taken in specifying placement of these

layers. Because the domain needs to extend high enough to include cloud tops and it is desirable

to have a shallow layers near the surface and avoid having thick layers further aloft, we raise this

minimum number slightly to 9-11 layers. Prior to modeling, we recommend that States review

available meteorological measurements aloft to get a sense of where the maximum afternoon

mixing height is likely to be on days which might be modeled. This information should be used

to avoid having layers below the planetary boundary layer which are so thick that they could lead

to large, discrete changes in mixing within very short periods. As we noted previously, air

quality models with which we are familiar all require that the number of layers considered in

coarse and fine portions of a nested regional grid be identical.

Recommendations. An air quality model may consider fewer layers than commonly

considered in a meteorological model. However, boundaries between layers used in
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an air quality model should coincide with selected boundaries in the meteorological

model. 

Care should be taken to configure an air quality model’s layers so that greatest

vertical detail is provided in the lowest 300 meters or so of the atmosphere. The

surface layer considered in the model should generally be no more than 50 meters

deep. States should also try to define the maximum afternoon mixing height

precisely. Except in a few instances where the the maximum mixing height is very

large, States should try to specify a model’s layer configuration so that no layer

beneath the mixing height is more than about 300 meters thick. The goal is to avoid

large, stepwise increases in mixing over very short time periods. 

The maximum height of the domain should be great enough to include tops of

strato-cumulous clouds. The minimum number of layers chosen depends on the

meteorological conditions and characteristics of the area to be simulated. To meet

the preceding criteria, it will most likely be necessary to use at least 9-11 layers

within the planetary boundary layer and 1-2 layers above it. 

13.1.4 Should I Consider Plume In Grid Modeling?

Imbedding a plume-in-grid model within a grid model application temporarily “isolates”

emissions of one or more large sources from those emanating from many nearby smaller sources. 

The reason for doing this to avoid having to assume that emissions from the large source(s)

disperse instantaneously within a relatively large grid cell. As of late 2000, we are unaware of

any air quality grid model which has a plume-in-grid capability that has been tested successfully

for use with particulate matter. However, plume-in-grid modeling offers a potentially costeffective alternative to using very fine grid cells for treating emissions from major sources in the

immediate vicinity . It is likely that one or more models with plume-in-grid capabilities will

have been developed and tested for particulate matter by the time States need to develop their SIP

revisions to meet goals for PM2.5 and regional haze. 

As noted in Section 13.1.1, we think it is unlikely that a plume-in-grid calculation will

change a relative reduction factor estimated for primary particulate matter, unless there is a major

point source of primary particulate emissions within about 15 km of a monitor. To reiterate, the

reason for this is the likelihood that the plume will meander during a day such that it gets

“smeared out” over a 24-hour period.

Plume-in-grid treatment for sources of SO2 and/or NOx may be justified over a longer

distance, however. This follows, because chemistry required for the formation of ozone and

secondary particulate matter is affected by the speed with which a major source’s NOx or SO2

plume mixes with the surrounding environment. For example, in their review/analysis of plume

data collected during an intensive field study near Nashville, TN in 1995, Gillani, et al. (1998)

show that it takes anywhere from about 30 - 40 km for a typical large plume and as much as 100

km from an extremely large source to become sufficiently diluted so that its chemistry is not
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affected by limited dilution. Thus, estimated production of secondary particulate matter both

within and outside of a plume could be affected by a model’s failure to properly consider limited

dilution of a major source’s plume. Even though these limits occur over timeframes which are

considerably less than the 24 hour period which serves as the basis for our PM2.5 NAAQS and

visibility calculations, we cannot rule out the possibility that failure to consider this delay in

mixing from nearby sources could affect relative reduction factors which are estimated for SO4,

NO3 or secondary OC.

Largest sources of NOx or SO2 are unlikely to occur in the middle of urban nonattainment

areas. That is, they are more likely to occur in the “regional portion” of a regional grid (i.e., with

36 km cells). More moderately sized sources could conceivably occur within an urban domain

(i.e., with 12 km grid cells). Therefore, a reasonable rule of thumb for plume in grid applications

is to limit them to sources which occur within ~3 grid cells of a monitoring site.

Recommendations. Plume in grid calculations for sources of primary particulate

matter are unlikely to be necessary. States may wish to perform a diagnostic test

including a plume in grid calculation if a major source of primary particulate

matter is located within 15 km of a monitor recording a violation of a NAAQS for

PM2.5 and the control strategy is focused on reducing primary particulate matter.

States should consider performing a diagnostic test to determine whether relative

reduction factors for SO4, NO3 or secondary OC are affected by applying a plume in

grid algorithm to major point sources of SO2 or NOx located within ~3 grid cells of a

monitor.

13.1.5 What Else Should I Consider In Choosing Finely And Coarsely Resolved

Portions Of Nested Regional Models?

Coarse Grid Domain. Size of a coarse grid domain should be consistent with the

chemical/physical lifetimes of pollutants to be modeled. It should also reflect the purpose for

which regional modeling is undertaken. For example, if a regional analysis is performed to

assess effects of a regional strategy simultaneously for a number of nonattainment areas, the

domain needs to be larger than if only a limited number of nearby areas were the focus of the

study. 

Lifetimes vary for ozone, secondary particulate matter and their precursors. Lifetime for

NOx (i.e., NO + NO2) may be less than a day. Regional analyses performed in the U.S. to date

suggest that lifetimes for sulfates and nitrates are two days or less (Dennis, 1994). Sources of

VOC are believed to be ubiquitous, due to natural emissions. Many of these natural emissions

are relatively reactive, so that multi-day transport of stable species of VOC or radical products

resulting from oxidation of more reactive species may not be a critical factor for selecting size of

a domain for modeling or secondary particulate matter. Transport of primary OC, such as results

from vegetative burning, may be important over several days. Lifetime for ozone is notoriously

difficult to estimate due to the recycling of this compound with free radicals, concentrations of
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oxidized species of nitrogen and emissions of fresh NOx and VOC precursors which occur in

transit. Given information about the lifetime of nitrates however, it is probably safe to assume a

lifetime for ozone which is on the order of 2-3 days. The foregoing information suggests that,

ideally, the size of a regional modeling domain should be large enough so that emissions

occurring two days prior to the beginning of daylight on a modeled day of interest are included

within the domain. Thus, we suggest States focus on their receptor areas of interest, perform

some screening analyses with trajectory models to ensure that major source areas within two

days’ travel time are included in the domain. 

Fine Grid Domain. Size of the fine grid domain should be influenced by several factors:

(1) proximity of receptor sites to major sources of PM2.5 and its precursors; (2) presence of

topographical features which appear to affect observed air quality, and (3) desire to limit resource

intensive efforts needed to use numerical models on a fine scale. The last factor is an important

concern for use of nested regional models. Size of a fine grid domain could be smaller than that

recommended for an urban scale analysis. This follows, since the coarse domain is available to

estimate impacts of sources located at intermediate distances from the receptor area, whereas this

information is not available for an isolated urban scale analysis. The issue of how far to extend a

fine scale grid is one which may need to be resolved on a case by case basis. We recommend

that States examine the issue using diagnostic sensitivity tests (see Section 16.0). For

consistency with the modeled attainment tests, we recommend that the fine grid should initially

extend 15 km beyond any receptor of interest.

Recommendations. Size of a coarse grid should be large enough to include

potentially important sources located two days’ travel time from receptor sites of

interest. Applications which need to consider numerous receptor sites located some

distance apart therefore need to use larger domains than do applications focusing

on receptors in close proximity to one another. Extent of a fine grid depends on the

number of receptor sites. States should perform diagnostic analyses to ascertain

how far a finely resolved grid needs to extend. As a starting assumption, we

recommend extending the finely resolved grid sufficiently so that it extends at least

15 km beyond all monitoring sites considered in the modeled attainment tests.

13.2 How Do I Decide What Domain Size And Resolution Are Appropriate To

Address The Reasonable Progress Goals For Reducing 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. Thus, a domain at least 1000 km on a side, in

which pollutants emitted at least two days prior to a day of interest remain within the grid is

needed. Regional modeling may be performed by or for regional planning organizations who

wish to consider effects of a strategy on many Class I areas simultaneously. Thus, for

applications in the contiguous United States, it may be most efficient to consider two
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superdomains—one covering the western (mostly low humidity) half of the nation, and a second

covering the eastern (mostly high humidity) half. 

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 perform diagnostic tests using plume-ingrid analyses, as described in Section 13.1.4. Guidance on vertical resolution presented in

Section 13.1.3 is also applicable for regional haze-related applications.

Recommendations. A regional domain is needed for model applications related to

regional haze. If an analysis is intended to address effects of a strategy on many

Class I areas simultaneously, using two superdomains covering the contiguous

United States may be appropriate.

Horizontal grid cell sizes up to 36 km on a side may be used. States may use a

plume in grid algorithm, if they choose, to assess effects of major point sources of

SO2 and/or NOx located within ~3 (36 km) grid cells of a monitor in a Class I area.

Modeling should consider at least 9-11 layers beneath a mixing height associated

with the highest strato-cumulous clouds and 1-2 layers above this. Layer

boundaries should coincide with boundaries used in meteorological models used to

generate input to the air quality model. Greatest detail is needed within ~300 m of

the ground, and the surface layer should be no more than ~50 m deep. No layer

should be more than about 300 m thick.
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14.0 How Do I Produce Meteorological And Air Quality Inputs Needed By An Air Quality

Model?

 

After episodes are selected for modeling, corresponding meteorological inputs need to be

generated for use in an air quality model. Although the resulting inputs remain constant, they can

affect outcomes of a number of the modeling outputs we have identified in Section 5.1 as

potentially useful for weight of evidence determinations. They may also potentially affect

relative reduction factors used in the attainment and reasonable progress tests. In contrast to

meteorological data, air quality inputs may change between times corresponding to “current” and

“future” emissions used in the modeled tests. This presents a potential problem which needs to

be addressed. 

In this Section, we describe two approaches for generating meteorological inputs to

numerical air quality grid models, and identify advantages/disadvantages associated with each. 

We note that using dynamic meteorological models with output “nudged” by observations is

usually the preferred approach for generating needed meteorological data. For some applications,

use of these models for horizontal grid cells smaller than 12 km may present practical problems. 

We identify ways to diminish these, if they occur. It is important to quality assure meteorological

inputs prior to their being used in an air quality model. We next discuss how these inputs can be

evaluated. We conclude our discussion of meteorological inputs by recommending procedures

for deriving them for use in Lagrangian (e.g., Gaussian) models. Meteorological needs of

Lagrangian models depend on how these models are to be used. We first discuss these needs

when the models are used to compute relative reduction factors for primary particulate matter. 

Then we address needs for meteorological inputs when Lagrangian models are used for hot spot

analyses. We next identify the role of air quality inputs as initial and boundary conditions for an

air quality simulation, and note ways to reduce limitations to the simulation resulting from

sparseness of these data. We conclude Section 14.0 by recommending procedures for estimating

background concentrations when Lagrangian models are used.

14.1 What Approaches Exist To Generate Meteorological Input To Grid Models?

Two approaches have been widely used to generate meteorological data needed in

gridded air quality models. The first of these (diagnostic models) relies primarily on observed

data and introduces some additional constraints on wind flow due to terrain features. Observed

surface temperatures and sounding data are used to develop other information needed to

characterize mixing. 

Most frequently used diagnostic wind models are described by Douglas, et al. (1990) and

by Scire, et al., (1998). The main advantage of diagnostic models is that they are relatively easy

and inexpensive to apply. Further, they make maximum use of wind observations. There are

several disadvantages, however. First, there are seldom enough observations to adequately

define a windfield, particularly aloft. Much of the input to the air quality model is derived

through interpolation or subjective methods. Further, mixing heights need to be derived through

external estimates. In addition, current (2000) generation air quality models feature a fixed
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vertical cell structure. Thus, it is necessary to somehow estimate vertical exchange rates between

these cells. Because of the sparseness of observations in many areas, we do not encourage use of

diagnostic models for generating inputs to regional scale air quality model applications. Finally,

meteorological estimates derived with a diagnostic model are not necessarily physically

consistent with one another. In the atmosphere, there is a physical dependency existing between

temperature, pressure and windfields. This interdependency is not extensively accounted for in

diagnostic models, and the extent to which it is considered depends on the expertise of those

applying the model. Nevertheless, if ambient concentrations of a pollutant (e.g., as for one or

more components of primary PM2.5) are believed to be primarily affected by winds and urban

scale source/receptor orientation, the disadvantages are not serious enough to preclude use of

diagnostic models. 

The second approach for generating needed meteorological data is to use dynamic

meteorological models with four dimensional data assimilation (FDDA). These models attempt

to characterize theoretical relationships between meteorological variables and

topographical/terrain characteristics. Use is made of relatively sparse observations aloft to help

steer (i.e., “nudge”) solutions so that they do not diverge from observed meteorological fields. 

Wind observations aloft are typically used for this purpose. See Seaman (1997) for a further

summary of the attributes of dynamic meteorological models. The MM5 (Grell, et al., 1994) and

Seaman and Stauffer, 1996) and the RAMS (Pielke, et al., 1992 and Lyons, et al., 1995) models

are among those which have been most widely used with numerical air quality models which

have the capability of considering precipitation events (i.e., potentially important for making PM

predictions). The major advantage of dynamic meteorological models is that they provide a way

of characterizing meteorological conditions consistent with theory, terrain and each other at times

and locations where observations do not exist. Further, their vertical structure is consistent with

that of current (2000) generation air quality models. Disadvantages have been large required

computer resources and considerable expertise needed to apply the approach. Recent advances in

computer technology have resulted in increased use of dynamic meteorological models for air

pollution applications. The MM5 model is used as the default approach with the CMAQ model

in MODELS3. States need to consider compatibility between candidate meteorological models

and the air quality model(s) chosen for use. We believe that use of dynamic meteorological

models with FDDA is generally the preferable approach for generating meteorological inputs to

air quality models for secondary particulate matter and ozone.

Although improvements in computers have made increased use of dynamic

meteorological models possible, we have found that data storage requirements and CPU time

increase dramatically as the horizontal grid cell size required of the meteorological model

becomes finer. For example, the CPU time needed to generate meteorological data resolved to

12 x 12 km grid cells is considerably greater than the expected factor of “9" increase in that

needed to process meteorology for a domain with 36 x 36 km grid cells. This suggests that States

may need to limit the spatial extent of areas and the number of episodes for which dynamic

meteorological models are used to process meteorological data for grids with horizontal

resolution <12 km. Generally, a finely resolved meteorological field needs to extend about 3 grid

cells beyond the bounds of the fine scale grid used to make air quality predictions. For example,
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if 4 km grid cells were used in the fine portion of a nested regional air quality model,

meteorological fields at this detail would need to extend 12 km beyond the bounds of the 4 km

grid used for air quality predictions.

Recommendations. States should ordinarily use a peer reviewed dynamic

meteorological model with four dimensional data assimilation as the means for

generating meteorological inputs to gridded air quality models. “Peer reviewed”

means the models basic formulation has been published in the peer reviewed

literature and subsequent changes have undergone third-party beta testing and

scientific review. Peer reviewed diagnostic models may be used on a case by case

basis. Grid cell size used in dynamic models should be chosen considering factors

discussed in Section 14.0.

14.2 How Do I Deal With Data Management And Computer-related Constraints

When Applying Dynamic Meteorological Models?

States should ordinarily use dynamic meteorological models resolved to the same level as

desired for making air quality predictions. Occasionally, this may not be feasible, or may lead to

poor performance of the dynamic model. In this Section, we identify possible means for

reducing one or both of these problems. The methods we discuss may increase the risk of

discontinuities at the bounds of a finely resolved grid. This may result when one matches up

surface (or, more properly, 10 m) observations used in diagnostic models with layer average

estimates provided by dynamic models. One means for diminishing potential mass conservation

problems might by to derive layer average estimates by applying an appropriate power law or

similarity theory to the meteorological observations. These should be checked and corrected to

the extent possible before proceeding. 

The first approach is to use available results from dynamic models on the next greatest

coarse scale (i.e., 36 km for a desired 12 km estimate, 12 km for a desired 4 km estimate) to

interpolate more finely resolved fields. An objective approach like bilinear interpolation could

be used (U.S. EPA, 1991). This approach would be particularly useful if the major reason for

desiring finely resolved meteorological estimates is related to a need to resolve emission

estimates more finely. For example, in the case of PM2.5, fine grid cells may be needed to most

accurately characterize gradients in emissions of primary particulate matter. 

A second approach for circumventing major resource requirements needed to apply

dynamic models for finely resolved grids considers topographic information (e.g., presence of

land/water interfaces) and measured meteorological data to refine coarser fields generated by a

dynamic model. This second approach may be preferred if the major reason for desiring finely

resolved meteorological inputs has to do with perceived importance of mesoscale features which

cannot be adequately considered through an objective interpolation procedure. In essence, the

second approach is to apply a diagnostic wind model to the wind field generated by the more

coarsely resolved dynamic model. 
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Finally, consequences of using coarse grid cells (e.g., 12 km when 4 km might be more

desirable) can be reduced by specifying a land use for each cell that corresponds to usage near the

major portion of emissions within a cell. This approach is most applicable at land/water

interfaces. By assuming the cell is entirely “land”, vertical dispersion of fresh emissions is likely

to be better characterized. This might also result in a better characterization of subsequent

transport of coastal emissions over adjacent large bodies of water. 

Recommendations. Prohibitive computer-related constraints associated with

applying a dynamic meteorological model to derive a finely resolved (4-12 km) set of

meteorological data can be addressed in one of two ways.

1. Interpolate more coarsely resolved data using objective analysis.

2. Apply a diagnostic wind model using “observations” generated by the dynamic

meteorological model for a coarser grid. Assume other variables remain the same

as for the coarser grid. 

Consequences of using coarser than desired grid cells may be reduced by assigning a

land use factor for each surface cell which corresponds to the location of most

emissions within the cell (e.g., at cells including an interface between land and a

large body of water).

14.3 How Do I Quality Assure Results Generated By A Meteorological Model?

There are several ways to evaluate performance of a meteorological model. Although it is

desirable to evaluate meteorological inputs before air quality predictions are made, some of the

means available for evaluating the meteorological model’s predictions must wait until the air

quality model is run. Important meteorological outputs warranting scrutiny include wind velocity

patterns, mixing heights (e.g., estimated by noting the vertical layer at which vertical diffusivity

(Kv) is suppressed), temperature, pressure, water vapor and cloud cover. Methods for evaluating

output from a meteorological model include comparison with selected upper air measurements,

derivation of trajectories, use of computer graphics, use of non-reactive tracers, comparing

results obtained with different models, use of dimensionless parameters, comparing spatial

patterns of observed and predicted components of PM2.5 and use of process analysis. Each of

these is briefly described in the following paragraphs.

Comparison with upper air observations. This can be done by excluding selected

upper air observations from use in four dimensional data assimilation (FDDA) so that they can be

used to assess model performance. Wind velocity, temperature, pressure and water vapor are

important variables to compare. If aloft measurements are available at more than one altitude,

they can provide a means for evaluating how well a model characterizes vertical exchange in the

lowest few layers. Generally, routine data bases (e.g., widely separated soundings taken twice

per day) are needed to support FDDA. Collection of continuous vertical meteorological profiles
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at a site is becoming more common with increasing use of radar profilers. Resulting data should

be helpful for checking performance of a dynamic model. Nevertheless, the data base is

insufficient to rely solely on these data to evaluate model performance. In Section 6.0, we noted

that it is desirable to increase measurements aloft. One reason for doing this is to provide better

means for evaluating performance of meteorological models.

 Derivation of trajectories. A State could select several locations in the grid and use

trajectory models such as HY-SPLIT (NOAA, 1999) to derive back- or forward-trajectories from

the hourly wind fields generated by a meteorological model. If surface trajectories were limited

to daylight hours, the computed trajectory could be compared with observed surface air quality

observations. Of course, this means of evaluation only works if air quality observations are

available on an hourly, or at most, 3-hourly basis. Thus, it is most appropriate to use observed

ozone (or some other hourly observations) to help evaluate performance of the chosen

meteorological model. If the timing of high ozone observed along the path of the trajectories is

consistent with expectations, given the configuration of sources, this is an indicator that the

meteorological model is performing adequately. A State could also derive daytime surface

trajectories using observed wind data. These trajectories could also be compared with air quality

patterns. Comparing the two sets of trajectories with observed air quality patterns makes it

possible to assess whether the meteorological model increases the skill with which ozone plumes

are oriented.

Use of computer graphics. Examining wind vectors for apparent discontinuities is

possible using graphics. It is also possible to construct difference diagrams between observed

and predicted temperatures and winds. Locations where agreement is poor may suggest areas

needing more finely resolved estimates. Geographical orientation between areas of poor

agreement and locations of major sources or observed poor air quality may be plotted to judge

potential significance of any disagreement. 

Simulation of inert tracers. This approach is to assume a uniform concentration field

(e.g., 10 ppb) of an inert tracer in an air quality model with grid cells identical to the horizontal

and vertical size of the cells used in the meteorological model (this may be feasible, since it is

unnecessary to consider atmospheric chemistry, deposition or fresh emissions). Identical,

constant boundary conditions should also be assumed. In theory, the concentration field should

remain uniform, and there should be no systematic drift in the mass of material remaining within

the grid. Predicted concentrations of the tracer can then be examined to see whether there are

major discontinuities in the concentration field or problems with mass balance. If there are, this

may suggest a problem with the meteorological model or with the ability of the air quality model

to consider divergence/convergence predicted with the meteorological model.

Compare results obtained with different models. This approach is to compare results

from two different models for a subset of days being considered. For example, MM5 and RAMS

results could be compared to note differences in predicted surface temperatures as well as wind

velocities at the surface and aloft. Reasons for major differences would then need to be

diagnosed.
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Compare estimated divergence or dimensionless parameters with expected ranges. 

Calculations can be made in selected portions of the grid to see whether they appear reasonable. 

 

Compare spatial patterns of air quality predicted with a grid model with observed

patterns on the days of interest. If the predictions are systematically skewed from the

observations, this could suggest a problem with the meteorological outputs generated by the

meteorological model.

Compare meteorological outputs from the meteorological model vs. those generated

from the air quality model. This comparison entails looking at a few key meteorological

variables (e.g., winds, temperature profiles). The purpose of this comparison is to ascertain

whether the source of a meteorological “problem” is the meteorological model itself or the data

processing used to convert the meteorological model’s outputs for use in the air quality model. 

Use process analysis. Process analysis applies to the output generated by an air quality

model. It is described by Jeffries (1997) and by Lo and Jeffries (1997). Its use with air quality

models is noted in Section 16.0. Process analysis determines the relative importance of different

chemical or physical factors as contributors to predicted air quality concentrations. If process

analysis suggests that a variable influenced by meteorological inputs, such as vertical exchange

(i.e., vertical diffusivity), plays a large, unanticipated role leading to a high or a low prediction of

one or more components of PM2.5, this might warrant a closer examination of what led to such a

prediction.

Recommendations. To the extent possible, States should quality assure results from

meteorological models prior to using them in the intended air quality model. States

should select a mix of approaches for evaluating meteorological inputs to an air

quality model on a case by case basis. Candidate approaches include:

1. comparison with upper air measurements “held back” from use in FDDA;

2. comparison of calculated trajectories with observed air quality patterns;

3. use of computer graphics to discern spatial discrepancies;

4. simulation of inert tracers to identify discontinuities or mass balance problems;

5. comparing results obtained with different meteorological models;

6. calculating and comparing divergence and/or dimensionless parameters and

comparing these with expected ranges;

7. comparing spatial air quality patterns obtained with a grid model vs. observed

patterns;
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8. comparing outputs from a meteorological model for key variables vs. estimates

for the same variables used in the air quality model, and

9. using process analysis to flag contributions made to unexpected concentrations

of PM2.5 components by meteorological factors. 

14.4 What Are Some Past Applications Of Dynamic Meteorological Models?

Table 14.1 lists some recent air quality modeling applications using the two most widely

available dynamic meteorological models. Choice of a meteorological model may be influenced

by compatibility with a chosen air quality model, as well as by past experience of those applying

the air quality model. The listing in Table 14.1 is not comprehensive. Inclusion on the list does

not necessarily imply an endorsement for a specific application. Exclusion does not necessarily

imply that an approach is inappropriate for a specific application. States should consider using

methods such as those in Section 14.3 to determine whether the output generated by a

meteorological model is adequate for use in a specific application.

Table 14.1. Some Past Applications Of Dynamic Meteorological Models

Meteorological Model References Describing

Model Performance

Sponsors (Applications)

MM5 Seaman, et al., (1995),

(1996b)

Tesche and McNally,

(1993a), (1993b)

CARB (San Joaquin Valley,

South Coast Air Basin, CA)

RAMS Tesche and McNally,

(1993c), (1993d), (1993e),

(1993f)

LADCO (eastern U.S., with

emphasis on Lake Michigan

States)

14.5 How Do I Generate Meteorological Input For Lagrangian Models?

States should review Section 9.3 in 40CFR Part 51, Appendix W, which discusses choice

of meteorological data to use in Lagrangian model applications and recommends preprocessor

programs to convert these data into formats required by the air quality models. Appendix W

suggests using one of two data bases: (a) at least one year of meteorological data collected at an

individual source to be modeled, or (b) 5 years of data collected at nearby National Weather

Service or other valid monitoring sites. Because of the nature of the NAAQS for PM2.5 , and the

recommended modeled attainment and hot spot tests, we suggest using at least at least 3 years of

meteorological data. Preferably, the meteorological data should cover the 3-year period upon

which the modeled attainment test’s “current” design value is based. If a Lagrangian model is

used to help estimate relative reduction factors in the modeled test for reasonable progress
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reducing regional haze, meteorological data for a 5-year period is needed. Preferably, these data

should cover the 5-year base period specified in the regional haze rule.

Recommendations. States should review information presented in Section 9.3 of

Appendix W to 40 CFR Part 51 prior to selecting meteorological data for use in

Lagrangian (e.g., Gaussian) models. For applications related to the NAAQS for

PM2.5 at least 3 years of data are needed. For applications related to assessing

reasonable progress reducing regional haze, 5 years of data are preferred.

14.6 How Do I Address An Air Quality Model’s Need For Air Quality Inputs?

Grid model applications. Air quality inputs are needed in gridded air quality models for

two purposes: to specify initial conditions, and to specify boundary conditions. There is no

satisfactory way to specify initial conditions in every grid cell. Thus, we recommend beginning a

simulation at least 2 days prior to a period of interest for urban scale applications, and 3 days

prior to periods of interest for regional applications to diminish importance of arbitrary

assumptions about initial conditions.

Boundary conditions can be specified in several ways. One way is to nest the area of

interest within a much larger domain. This approach is exemplified by using nested regional

models, as described previously. The need to diminish importance of boundary conditions is

why we recommended in Section 13.0 that States use a large regional domain, with the upwind

bound 2 or more days’ travel time from the area(s) which is the focus of an analysis. If it is not

practical to use a nested regional modeling approach, a second approach is to use a large single

domain in an urban scale analysis. The domain should be approximately symmetrical about the

major local sources affecting local monitoring sites of interest , and should be large enough so

that emissions occurring in the center of the domain just before sunrise remain within the domain

until the end of the same calendar day. If recirculation is thought to be part of the problem, the

domain size would need to be extended to be able to consider it. Use of a large, single domain

requires one to make use of monitored data and interpolation to estimate boundary conditions. 

This approach begs the question about what to assume for future boundary conditions. It works

best where boundary conditions are low and are expected to remain so.

Lagrangian applications. If a State uses a Lagrangian (e.g., Gaussian) air quality model

to estimate primary components of particulate matter, assumptions need to be made about

“background” air quality concentrations. Estimates made with the Lagrangian model should get

superimposed on this background. Definition of “background” depends on the scope of the

Lagrangian model application. If the model is applied to estimate RRF values for primary

components of PM2.5, a State will need to estimate effects of multiple sources of primary

particulate matter and, as discussed in Section 13.1.1, the model will need to be applied over an

urban area. In cases like this, “background” should be derived from regional modeled estimates

made outside the urban area or from monitored observations at remote/rural sites outside of the

urban area (i.e., we are using the model itself to estimate effects of nearby sources). To estimate

background, a State should use only the observations or regional modeled estimates for the same
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components being modeled with the Lagrangian model. If background is estimated using

monitored data, future background concentrations should remain unchanged.

 For hot spot analyses, Lagrangian model applications will often be limited to one or a

very small number of sources. Thus, “background” will need to consider effects of other sources

of primary particulate matter located within the urban area but which are not modeled. The best

procedure is to exclude the “hot spot” source(s) from the inventory in a multi-source model

application to estimate a “background” concentration at the site(s) of the source(s) subject to the

hot spot analysis. If this is not practical, States may use a spatially averaged set of gridded

surface estimates or spatially averaged monitored concentrations obtained within 50 km of the

source. If the distance from the source to the bounds of the urban nonattainment area is less than

50 km, averages within this smaller distance may be used instead. This procedure diminishes the

effect of “double counting” source(s) for which the hot spot analysis is performed. If monitored

data are used to estimate background, the default assumption for future background

concentrations is that it remains unchanged.

Recommendations. Simulations should begin at least two days prior to the period

of interest for gridded urban applications and three days for gridded regional

applications. Use of nested regional models is the preferred approach for

addressing boundary conditions for grid model applications. Where such an

approach is not feasible, States should consider a single domain large enough to

ensure that emissions occurring in the center of the domain just before sunrise

remain within the domain until the end of the same calendar day or that next-day

recirculation (if important) can be considered.

“Background” concentrations are needed for Lagrangian model applications. If a

Lagrangian model is used in the modeled attainment test, “background” should be

estimated using 24-hr air quality observations or regional model estimates obtained

just outside the domain considered with the multi-source Lagrangian (e.g.,

Gaussian) model. For hot spot analyses, States should exclude the “hot spot” source

from the inventory used in a multi-source model and use the multi-source model to

calculate “background” at the site of a hot spot. Alternatively, “background” for

hot spot analyses may be estimated by spatially averaging 24-hour gridded model

estimates or air quality observations at all locations within the urban area or within

50 km of the source, whichever is less. 
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15.0 How Do I Produce Emission Inputs Needed For An Air Quality Model?

(NOTE to reviewers: As we describe in this Section, there are a number of ongoing efforts to

improve emissions models and related inventories for PM and its precursors. The status of

emissions models to deal with PM2.5 is still very much in a state of flux. We anticipate that

much current uncertainty inherent in ongoing emission model development and

documentation will be resolved prior to the time the guidance herein is finalized.)

In this section, we first summarize emissions information needed to obtain the modeled

estimates used to assess model performance and to apply the modeled tests we recommend in

Sections 3.0 and 4.0 for attainment of the NAAQS and reasonable progress reducing regional

haze. We then discuss how to obtain the needed emissions estimates.

15.1 What Emissions Information Is Needed?

Inventories are needed for as many as three time periods: (1) a year which corresponds

closely to the air quality data base used to evaluate model performance (the “performance-related

inventory”); (2) a year having emissions corresponding to the air quality observations (i.e., the

design value) used in the modeled attainment test or in the modeled test for reasonable progress

(i.e., the 2000-2004 base period), called the “current inventory”, and (3) a year which is two years

prior to the deadline for meeting the NAAQS or initial reasonable progress goal (“future

inventory”). If the aerometric data base used to evaluate model performance is drawn from a

period corresponding to that represented by the “current inventory”, the emissions data base used

to derive the “performance-related inventory” and “current inventory” is one in the same. 

The specificity with which emissions estimates need to be made depends on whether a

State is evaluating model performance or applying one of the modeled tests for attainment or

reasonable progress. For model performance evaluations, States should estimate day-specific

inventories to the extent possible. We also recommend that this be done when applying the

modeled attainment test for the 24-hour NAAQS. In contrast, month-specific average inventory

estimates are adequate for use in the modeled annual attainment and reasonable progress tests. If

a State believes that there is not likely to be much variability in emissions from month to month

within a calendar quarter, quarterly average inventory estimates suffice. Air quality models

require hourly emissions estimates for each location (e.g., grid cell) considered. Hourly,

location-specific emissions estimates may be obtained using emissions models, as described in

Section 15.4. 

To provide emissions information needed for performance evaluation and the modeled

tests, States need to estimate emissions for the following in their inventories: SO2, NOx, NH3,

primary organic particulate matter, primary inorganic particulate matter (speciated), VOC

(speciated) and CO. For NAAQS-related applications, it is only necessary to estimate emissions

of primary organic and inorganic particulate matter having < 2.5 μm aerodynamic diameters. 

For visibility-related applications, it is also necessary to estimate emissions of primary particulate

matter with aerodynamic diameters < 10 μm but > 2.5 μm (i.e., “coarse” particulate matter). 
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Emissions need to be segregated into those emanating from numerous small stationary

(i.e., “area”) sources, roadway and non-road emissions from mobile sources, emissions from

large sources which need to be treated individually (“point” sources) and emissions from natural,

biogenic sources. Each of these types of emissions require certain associated pieces of

information to be considered adequately in models. 

Point sources. States should identify emissions according to the source’s SCC

process(es). Geographical location of the source, stack parameters affecting estimating plume

height and the operating schedule should also be estimated. To estimate a future inventory,

States should make note of which SIC category is appropriate for each point source, current

source activity levels and emission factors. Future emissions are the product of future estimates

for the emission factors and activity levels.

Area and non-road mobile sources. SCC process level emissions, typical operating

schedules, activity levels, an identified surrogate for apportioning emissions geographically and

current emissions factors are needed for each area source category and non-road mobile sources. 

Mobile sources. States need to estimate a level of vehicle activity (such as vehicle miles

traveled, “VMT”), develop a means for geographically apportioning this activity and be able to

estimate vehicle speeds.

Biogenic sources. As we describe in Section 15.4, States may use default estimates for

land use and biogenic emission factors to estimate emissions from biogenic sources.

The most reliable emissions estimates for a nonattainment area or for the vicinity of a

Class I area of particular interest are likely to be obtained if a State is able to provide areaspecific estimates of activity levels and/or emissions factors based on area-specific measurements

or data, rather than relying on national or even Statewide data bases/procedures. Deriving

emissions estimates based on locally specific information and data is often referred to as the

“bottom-up” approach for estimating emissions. We recommend this approach whenever it is

feasible. Examples include estimating VMT using locally specific transportation demand

models, using Metropolitan Planning Organization data to estimate activity levels for specific

area source categories and using continuous emissions monitoring (CEM) data to estimate point

source emissions.

Recommendations. States need to develop inventories for three time periods: a year

corresponding to the data base used to evaluate model performance, a year

corresponding to “current air quality” used in the modeled tests identified in

Sections 3.0 and 4.0 and a future year occurring two years before the identified

deadline for meeting an air quality goal. We recommend day-specific emissions

estimates for performance evaluations and for the 24-hr NAAQS. Monthly average

emissions estimates, and sometimes quarterly averages, suffice for modeled tests

related to the annual NAAQS and visibility.
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States should develop inventories for SO2, NOx, NH3, primary organic and

inorganic particulate matter < 2.5 μm and for “coarse” particulate matter

(visibility-related applications), VOC and CO. Speciated emissions estimates are

needed for primary particulate matter and for VOC. 

Emissions should be distinguished according to whether they are from individual

“point” sources, stationary area source categories, off-road mobile sources, onroad

vehicle sources and biogenic sources.

Emission estimates are a product of emission factors times activity levels. We

recommend that States use locally available data, and measurements to estimate

emission factors and activity levels whenever feasible. 

As noted in Sections 11.0, 12.0 and 13.0, we believe that many model applications will

need to cover large geographical areas over extended periods of time. For much of a modeling

domain it may not be feasible to use a “bottom-up” approach to estimate emissions. In Sections

15.2 - 15.6, we provide recommendations for using national data bases and guidance for

estimating emissions. Deriving local emissions from national (or Statewide) data bases and

guidance is often called the “top-down” approach. We identify information which needs to be

compiled on a State- and countywide basis. We then identify emissions models which can be

used to convert countywide estimates to the inputs needed by air quality models. We next

describe several approaches useful for quality assuring estimates obtained as the first two steps

proceed. We then present a short discussion of emission projection methods. We conclude

Section 15.0 by noting some emissions-related assumptions which are appropriate when using

Lagrangian models for hot spot analyses. 

While our discussions focus on air quality model needs, there is more extensive guidance

available on emissions inventory requirements and preparation (U.S. EPA, 1999d). The U.S.

EPA also maintains an internet website which serves as a good starting point for States and

others seeking information about estimating emissions related to PM2.5 or regional haze-related

problems (see http://www.epa.gov/ttn/chief/eiip/pm25inventory/pm25home.htm). Additional

guidance has been developed to prepare emission inventories. Much of this guidance has

resulted from a joint State/local agency and U.S. EPA effort called the Emission Inventory

Improvement Program (EIIP). A series of ten documents has been issued as a result of the EIIP. 

The most pertinent of these are listed below:

• Volume I: Introduction and Use of EIIP Guidance for Emissions Inventory

Development (U.S. EPA, 1997b)

• Volume II: Point Sources Preferred and Alternative Methods (U.S. EPA,

1997c)

• Volume III: Area Sources Preferred and Alternative Methods (U.S. EPA,

1997d)

• Volume IV: Mobile Sources preferred and Alternative Methods (U.S. EPA,

1997e)
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• Volume V: Biogenics Sources Preferred and Alternative Methods (U.S. EPA,

1997a)

• Volume VI: Quality Assurance Procedures (U.S. EPA, 1997f)

• Volume VII: Data Management Procedures (U.S. EPA, 1997g)

• Volume IX: Particulate Emissions (U.S. EPA, 1999e)

• Volume X: Emission Projections (U.S. EPA, 1999f) 

In addition, guidance exists which describes the National Emission Trends inventory

methodology (U.S. EPA, 1998a). The EIIP documents are available electronically through the

U.S. EPA Internet website at http://www.epa.gov/ttn/chief/eiip/techrep.htm. States should

consult these documents as they prepare their emission inventories.

15.2 What Countywide Emission Estimates Are Needed To Support Air Quality

Models?

Statewide and countywide emissions need to be divided into 4 broad categories:

stationary point source emissions, stationary area source emissions, mobile emissions for onroad and off-road sources and biogenic/geogenic emissions. Point sources should be classified

by SCC and have associated location information (e.g., latitude/longitude coordinates) as well as

diurnal and weekly operating schedules. Area source emissions should be classified by SCC and

reported by county. Surrogate factors, used to spatially allocate emissions from the source

category within an air quality model grid superimposed over the county, should be identified for

each area source category. Defaults for surrogates are available in current emissions models. 

Examples of surrogate factors might be such things as population or employment by census tract,

land use, etc. If information exists concerning weekly and diurnal emission patterns for different

area source categories, this information should also accompany the state- and countywide area

source emission estimates. On-road and off-road mobile source emissions should be estimated

using the most current version of the U.S. EPA MOBILE model (or, in California, the current

version of EMFAC) in concert with activity (i.e., vehicle miles traveled (VMT)) estimates. The

mobile source emission estimates should be accompanied by recommended surrogates for

spatially disaggregating the mobile emissions and by diurnal and weekly activity patterns so that

gridded, hourly estimates can be obtained for mobile emission estimates in subsequent steps. We

recommend States distinguish weekend vs. weekday activity levels for mobile and stationary area

sources. Estimates for biogenic emissions can be made using the most current BEIS emissions

model (Geron, et al., 1994, or updates approved by the U.S. EPA). A State should report

biogenic emissions on a county basis. Information regarding spatial pattern of land use is needed

within each county if a State wishes to distribute biogenic emissions within a county in a nonuniform manner.

For model applications addressing the PM2.5 NAAQS, emission estimates for each source

category should include countywide PM2.5, NH3, SO2, NOx, VOC and CO estimates for each

month of the year. Regional haze-related applications require the same information plus

countywide estimates for emissions of PM10. The PM2.5 and VOC estimates should be

accompanied by a recommended speciation profile for each source category. We recommend
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that States rely on local measurements to the maximum extent possible for the speciation profile

estimates. However, default information for PM and VOC species profiles is available in U.S.

EPA (1993), if needed. These data and updates can be obtained electronically through the U.S. 

EPA’s Internet website at www.epa.gov/ttn/chief/software.html#speciate. Finally, information

regarding PM size profiles may be needed for major source categories, since some previously

archived PM emissions may reflect estimates for PM10 rather than PM2.5.

Recommendations. States should be familiar with guidance in U.S. EPA (1999d)

and with U.S. EPA Emission Inventory Improvement Program guidance describing

appropriate procedures for estimating Statewide and countywide emissions needed

to support SIP revisions related to PM2.5 and regional haze. Air quality models

require emission estimates from point, area, mobile and biogenic sources. In order

to convert this information for use in air quality models, PM2.5 and VOC species

profiles, rationale for suballocating emissions within a county and for assuming

diurnal and weekday vs. weekend variability in emissions are needed for each point

source and for each major area source category, as well as for mobile sources. 

Default assumptions for spatial/temporal emission allocations are available in

emissions models, as are speciation profiles. However, assumptions which are more

appropriate for a specific area should be substituted for these, whenever possible. 

For applications related to the NAAQS for PM2.5, emission estimates for PM2.5, NH3,

SO2, NOx, VOC and CO are needed for each month of the year to support possible

use of regional model applications performed throughout the year. Needs are

similar for regional haze-related applications, except emission estimates for PM10

are also required. 

15.3 Can I Use the National Emissions Trends Inventory As A Starting Point?

If there are no previously available modeled inventories to serve as a starting point, we

recommend that States derive an inventory suitable for use with models starting from the

National Emissions Trends inventory (NET) (U.S. EPA , 1998a). As of late 2000, the most

recent NET inventory reflects statewide, annual emission estimates for 1996. The U.S. EPA

undertakes a major effort to update the NET estimates every three years. We plan to have a NET

inventory reflecting nominal 1999 emissions available in 2001. Since the 1999 estimates reflect

a greater effort to compile information regarding NH3 and primary PM2.5 emissions, we

recommend that it be used as the basis for initial estimates when needed. Such nominal NET

inventories are updated continually based on new information provided by the various States. 

Thus, it is important that those implementing the modeling protocol make consistent assumptions

about which version of the 1999 NET inventory is being used and that the version used is

included in the documentation described in Section 7.0. Assuming the lag time between the

nominal year of a NET inventory and the availability of that inventory remains approximately the

same, we anticipate that a NET inventory reflecting 2002 emissions should be available for use

early in 2004. 
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If the NET inventory is used, it should be for the same year as the current inventory used

by the State. This may require some minor adjustments reflecting growth and effects of known

national/regional control programs. Statewide emissions, by county, are in the NET inventory

and are available electronically through the U.S. EPA Internet website at

www.epa.gov/ttn/chief/net/index.html. If a State is performing a regional or nested regional

modeling analysis, the NET inventory can often serve to provide countywide estimates for

locations far removed from the area which is the focus of the modeled attainment or reasonable

progress demonstration. Closer in (within ~ 300 km all major source categories, ~1000 km for

large point sources), States should quality assure and improve emission estimates as necessary. 

Highest priority should be given to quality assuring and improving assumptions underlying the

NET inventory in areas where monitored data suggest violations of a NAAQS for PM2.5 are

likely. For more remote emissions, the NET inventory may be used as is, at a State’s discretion.

Recommendations. States may use the National Emissions Trends (NET) inventory

to provide initial emission estimates for PM2.5 and its precursors. States should

focus efforts to improve the NET estimates on all sources near (e.g., ~ 300 km) the

nonattainment or Class I area which is the focus of a modeling analysis. NET

estimates of emissions from large point sources of PM2.5 or its precursors should

undergo similar scrutiny if located within ~1000 km of the area which is the focus of

the analysis. We recommend that the NET inventory reflecting 1999 emissions be

used as a starting point for making emissions estimates. If its availability is timely,

2002 NET inventory estimates should replace those compiled for 1999 when

applying modeled tests for attainment of the PM2.5 NAAQS or reasonable progress

reducing regional haze. 

15.4 How Do I Convert Countywide Inventory Information Into Data Used In Air

Quality Models?

Air quality models predicting PM2.5 or its components require hourly emission estimates

which are either day-specific or applicable for types of days (e.g., week days, weekend days). 

Estimates are needed for primary organic and inorganic PM2.5, NH3, SO2, NOx, VOC and CO for

each cell of a grid superimposed over the area modeled. Typically, there are thousands of grid

cells in a model application. To utilize atmospheric chemistry in the air quality simulation model

and to perform computations for previously identified major components of PM2.5, PM2.5 and

VOC emissions also need to have their component chemical species identified. We recommend

that source specific, local information be used for this purpose whenever possible. The U.S.

EPA maintains the SPECIATE data base. SPECIATE can be used when more source-specific

information is lacking. It may be accessed electronically at

www.epa.gov/ttn/chief/software.html#speciate. In addition, it is a good idea to gather

information on size fractionated profiles for particulate matter so that past estimates of PM10

emissions can be more readily converted to estimates of PM2.5 emissions. Finally, emission

factors for some sources are dependent on meteorological conditions such as temperature. Thus,

meteorological conditions need to be known to estimate day specific emissions. Emissions

models should be used to account for the numerous and diverse factors which need to be
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considered to derive emissions inputs to air quality models. 

As of late 2000, documentation for three comprehensive emissions models is available or

is expected to be so shortly: EPS2.5 (Systems Applications International, 2000), EMS2000

(LADCO, 2000) and SMOKE (MCNC, 2000a). These comprehensive models treat stationary

point and area sources and incorporate information developed in models for onroad and offroad

mobile emissions and models for biogenic emissions. 

Comprehensive emissions models. Two comprehensive emissions models have been

used to convert estimated emissions from stationary, mobile and biogenic sources for use in air

quality simulation models for PM-related applications.. The first of these is EPS2.5. A

description and user’s guide for this model is expected to be available in 2001 (Systems

Applications International, 2000). EPS2.5 is an adaptation of an earlier model (EPS2.0) which

focuses on emissions of VOC, NOx and CO (Causley, et al., (1990), U.S. EPA, (1993a)). 

EPS2.5 is being used by the U.S. EPA in regional applications of the REMSAD air quality

model. 

EMS2000 is a second emissions model which will convert available emissions data into

emission estimates for PM2.5 and its precursors in the form needed by air quality models. 

EMS2000 is a result of a joint effort between LADCO and Alpine Geophysics, Inc. It is

expected to be documented and available by 2001 (LADCO, 2000). EMS2000 is an adaptation

of the EMS95 model developed by Alpine Geophysics, Inc., (1995). An adaptation of EMS95 is

currently being used in a regional air quality model to investigate causes of regional haze as part

of the Southern Appalachian Mountain Initiative (SAMI) (see

http://environmental.gatech.edu/SAMI/downloads.htm).

A third comprehensive emissions model, Version 2 of the Sparse Matrix Operator Kernel

Emissions (SMOKE), has had limited use to date (MCNC, 2000a). SMOKE is similar

theoretically to EMS95. However, it is computationally more efficient, reducing time and

memory required to formulate individual control strategies simulated in an air quality model. 

Anthropogenic emissions from mobile sources. As of late 2000, MOBILE5B is the

most recent available model to estimate mobile emission factors for ozone precursors from a

vehicle fleet representative of any specified year (U.S. EPA, 1996). Part 5 of MOBILE5B

specifically deals with emissions of primary particulate matter from or associated with mobile

sources. For all categories except road dust, States may use scaling factors provided in the

documentation to estimate PM2.5 emissions from the PM10 estimates provided by the model. Size

fractionated profiles for resuspended dust, or some other category which those implementing the

modeling/analysis protocol believe is similar to road dust, will need to be used in concert with

Part 5 emission factors to estimate PM2.5 emission factors associated with road dust. To estimate

mobile emissions, one must combine information generated with the MOBILE model (or, in

California, the EMFAC model) with vehicle activity estimates, such as vehicle miles traveled

(VMT). 
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The U.S. EPA’s Office of Transportation and Air Quality (OTAQ) (formerly the Office of

Mobile Sources) is developing the MOBILE6 model for highway vehicles as well as a

NONROAD model to improve estimates for off-highway vehicles. These two models are

expected to be available early in 2001. Although changes in MOBILE6 and NONROAD should

generally improve estimates for on- and off-road emissions, neither set of improvements focuses

specifically on emissions of primary components of PM2.5. Emissions of particulate matter are

expected to be addressed more specificallly in the next update to the MOBILE model. We do not

expect this to be available before 2002. Prior to the availability updated treatment of emission

factors for primary particulate matter from mobile sources, it will be necessary to use Part 5 in

concert with the most current available versions of the MOBILE and NONROAD models and

external information about size fractionated profiles for road dust. 

States may download, as well as track the status of the U.S. EPA’s mobile emissions

models by accessing the following internet address: http://www.epa.gov/otaq/models.htm. As

noted previously, emission factors estimated with MOBILE models need to be combined with

activity levels (e.g., vehicle miles traveled) to estimate emission levels which have been suitably

disaggregated spatially and temporally for use as inputs in air quality models. Methods for

estimating activity levels are included in U.S. EPA, (1997e). 

Biogenic Emissions. We recommend the Biogenic Emissions Inventory System (BEIS)

model as the default approach for estimating biogenic sources of precursors for PM2.5. Version 2

of BEIS (BEIS2) has been the most widely used procedure for estimating biogenic emissions

(Geron, et al. 1994 and U.S. EPA, 1997a). This model requires a mix of land uses to be specified

for each county, as well as hourly temperature information. If a State 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. The model makes

use of stored information regarding geographic distribution of plant species, as well as the

provided land use and temperature information, to generate gridded biogenic emissions. 

As of late 2000, an updated version of BEIS (BEIS3) is being tested by the U.S. EPA. 

BEIS3 includes estimates for mono-terpene compounds, needed for visibility- and PM2.5-related

model applications. Much of the information used as the basis for BEIS3 is currently available in

a NARSTO critical review paper by Guenther, et al., (2000) and in Geron, et al., (2000). Much

of the documentation for BEIS2 (Geron, et al., 1994) remains relevant. In addition, the Texas

Natural Resources Conservation Commission has developed a biogenics emissions model

(GLOBEIS) based on the Guenther, et al., (2000) work (TNRCC, 2000). Both BEIS3 and

GLOBEIS consider emissions of mono-terpenes. States should apply the most recent available

version of BEIS (i.e., BEIS3 or an equivalent model considering mono-terpene emissions) in

concert with land use data to support their modeled demonstrations of attainment or reasonable

progress. The BELD3 land cover data base, with 1 km resolution and vegetation speciation, has

recently become available and may be used to provide default land use estimates for use with the

BEIS models when county average figures are not believed to be adequate. BELD3 is described

on the following U.S. EPA website: http://www.epa.gov/asmdnerl/biogen.html.
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Table 15.1 summarizes available emissions models used in recent air quality model

applications, and identifies some example applications. Those which include estimates for

primary particulate matter are indicated. There is clearly much work ongoing to improve existing

emissions models and to make them more explicit for problems related to PM2.5 and regional

haze. The U.S. EPA has recently established a website which serves as a clearinghouse for new

developments in emissions modeling and related issues. It may be accessed at

http://www.epa.gov/ttn/chief/emch/. In addition, a website at

http://www.epa.gov/otaq/models.htm is a good source of current information on emissions

modeling related to mobile sources. We recommend that States routinely check these sites for

new developments. 

Recommendations. States should use emissions models to convert emission

inventory estimates into emissions inputs required by air quality models. Emission

models require additional inputs concerning chemical speciation, size fractionation,

spatial and temporal disaggregation. Generally, States should use the most current

available version of an emissions model. Because there is much current activity

underway to adapt/improve current emissions models to address ambient problems

related to fine particulate matter, States should check the websites identified in this

Section for latest developments. Choice of models also depends on compatibility

with the chosen air quality model and the application at hand, as well as past

experience of those implementing the modeling/analysis protocol. States should

quality assure outputs from emissions models prior to making air quality estimates.

15.5 What Should I Do To Quality Assure Emissions Estimates?

The most efficient means to quality assure (QA) emission estimates is to apply QA during

the initial emissions estimation process. The previously mentioned EIIP quality assurance

document, U.S. EPA (1997f), contains a number of QA procedures that should be used to

develop the basic countywide emission inventory. Once the basic emission inventory is ready for

modeling, there are three additional quality assurance techniques that may be appropriate. The

first is to compare emission estimates and estimating methodologies with those reported by other

States. States can use results from such comparisons to see whether their estimates are unusual

in any way and, if so, perhaps explain why. This focuses attention on portions of the inventory

which appear to differ from estimates made for other locations, so that a State can confirm

whether or not its initial estimates are appropriate. The NET inventory provided by the U.S.

EPA may be useful for this approach.

Displaying emissions estimates graphically is also a useful means for quality assuring

them. Emissions models identified in Section 15.4 can produce graphic displays useful for

quality assurance. For example, a tile plot of emissions made for a grid superimposed over the

area to be modeled is an effective means for identifying misplaced sources and for assuring

oneself that spatial patterns of emissions are consistent with where sources are believed to be. 

Other graphical displays include pie charts and time series plots. Pie charts are useful for

assessing whether distribution of emissions among source types or categories is consistent with 
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Table 15.1. Some Emissions Models And Example Applications

Emissions Model References Sponsors (Applications)

EMS95 (ozone precursors)

EMS2000 (precursors plus

primary PM)

Alpine Geophysics, (1995)

LADCO, (2000)

LADCO (eastern half of the

U.S. for ozone and soon for

PM),

U.S. EPA, OAQPS (eastern

half of the U.S., ozone),

NY DEC (eastern half of

the U.S., ozone).

SAMI (southeastern/midAtlantic States, ozone and

PM)

EPS2 (ozone precursors)

EPS2.5 (precursors plus

primary PM)

U.S. EPA (1993a)

Systems Applications

International, (2000)

U.S. EPA, Region IV (Gulf

States, ozone),

U.S. EPA, OAQPS

(contiguous 48 States,

ozone)

U.S. EPA, OAQPS

(contiguous 48 States, PM)

SMOKE (precursors plus

primary PM)

MCNC, (2000a) NC DEM (Charlotte, most

of NC and parts of

surrounding States, ozone)

MOBILE or EMFAC

emission factor models,

which must be used in

concert with activity

estimates to obtain

emissions estimates

(precursors plus primary

PM)

U.S. EPA, (1997e)

http://www.epa.gov/otaq/models.htm

MOBILE: Many sponsors

(throughout the U.S.

outside of California)

EMFAC: CARB

(California)

BEIS2 (ozone precursors)

BEIS3 (precursors plus

primary PM)

GLOBEIS (precursors plus

primary PM)

Geron, et al., (1994),

U.S. EPA, (1997e)

Guenther, et al., (2000)

Geron, et al., (2000)

TNRCC, (2000)

Guenther, et al., (2000)

U.S. EPA OAQPS

(contiguous 48 States,

ozone, PM)

TNRCC 

(Texas and portions of

nearby States)
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expectations. Time series displays enable a State to look at estimated diurnal patterns in

emissions to see whether these appear logical. They enable comparisons to be made for

weekends vs. weekdays to see whether estimated differences appear reasonable.

Comparison with monitored speciated data may become an increasingly important means

for quality assuring emissions estimates. Availability of speciated PM2.5 and VOC data in

ambient networks makes it possible to use monitored observations to apply source attribution

approaches (i.e., “receptor models”). A finding suggesting that air quality observations are the

product of a mix of emissions which differs greatly from that inferred from the inventory can

point the way toward parts of the inventory which may need further scrutiny. Receptor models

and their uses have been summarized by Seigneur, et al. (1997). Use of ambient data from the

PAMS network to quality assure VOC emissions estimates is described in U.S. EPA (1996a) and

in U.S. EPA (2001).

Recommendations. Quality assurance of emissions estimates is an essential part of

the modeling process, and should be performed continually. States should consider

the following approaches to quality assurance: primary QA emphasis during the

initial development of the basic emission inventory, comparison with available

emissions estimates and methods performed by others, computer graphics depicting

emissions model estimates, and comparison with speciated air quality data.

15.6 How Do I Estimate Emissions For Future Years?

15.6.1 How Far In The Future Should I Project Emissions?

Emissions projections are needed for sources within a modeling domain to determine if a

nonattainment area will meet air quality goals by a required date. For applications related to the

NAAQS for PM2.5, States should estimate future emissions for at least one future date--two years

before the date of required attainment. For example, if the required attainment date were 2012,

the emission projections should be made to 2010. This earlier date is chosen, because the form

of the NAAQS requires that the standard be attained over a 3-year period which ends at the

“deadline”. As discussed in Section 6.0, when a required attainment date is distant ( say, 5 or

more years) from the date required for a SIP submittal, we recommend that a State project

emissions to an intermediate period as well in order to support a subsequent mid-course review.

For reasonable progress-related applications, we recommend that States retain the

convention of projecting emissions to a time which is two years prior to the deadline. If this

convention is followed, the projection year would be in the middle of the 5-year period which

will later be used to see if air quality observations are consistent with the reasonable progress

goals. These projections are most likely to be representative of emissions occurring during this

5-year period. Thus, if the first set of goals for reasonable progress must be realized by 2018,

and data from 2013-2017 are to be reviewed to see if this goal is met, emission projections

should be made to 2015. The regional haze rule requires a mid-course review of progress to be

made at 5-year intervals (U.S. EPA, 1999). Although the rule does not require modeling to meet
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this obligation, past model results provide one means for assessing whether air quality observed

during 2009-2013 is consistent with expected reasonable progress by that time. Thus, States may

find it useful to project emissions to two years prior to the period used for the mid-course review

(i.e., 2011) so that air quality projections can be made which are consistent with the selected plan

to realize reasonable progress reducing regional haze.

15.6.2 What Emissions Should I Assume In Future Years—Actual Or Allowable?

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. It could happen that an allowable emissions limit exceeds the maximum

amount of emissions possible from a source. If this occurs, the maximum physical limit may be

used instead, provided the allowable limit is subsequently reduced (as part of the SIP revision) to

be consistent with the physical limits. We define future “actual emissions”as the best estimate of

future emissions for a source or source category. Often, future actual emissions may be obtained

by multiplying current typical month-specific emissions from a source or source category times

an annual growth rate in the activity level for the source compounded by the number of years

between the current and future period. However, this need not be the case. For example, if a

projection exceeds an allowable emission limit, it should be capped at that limit. In this case,

projected “actual” and “allowable” future emissions for a source are one in the same.

In Table 15.2, we provide some general guidance regarding when it is most appropriate to

use projected “actual” emissions and when it is most appropriate to use projected “allowable”

emissions. However, exceptions to this general guidance are likely to be necessary from time to

time. Typically there are a wide range of sources and source categories which will emit PM2.5 or

its precursors, and no individual source will make an important contribution to total PM2.5

concentrations. In such cases, emissions projections can be based upon actual emissions

estimates. This method is normally used in ozone SIP planning. However, there may also be

situations where a relatively small number of large stationary sources of PM2.5 are important

contributors to the total PM2.5 concentration. This would more likely involve the 24-hour

NAAQS than the annual average, and occur in an urban-scale, or topographically constrained

modeling domain. In such instances to ensure NAAQS protection, maximum allowable emission

rates should be used in preparing future year projections. This method has been used in SIP

planning for largely inert pollutants such as SO2, lead and PM10. We recommend that those

implementing the modeling protocol discuss the circumstances under which it is necessary to use

“allowable” rather than “actual” projected emissions with the appropriate U.S. EPA regional

office(s) and reach agreement on this issue prior to simulating prospective control strategies.

The entries in Table 15.2 contain several words which are subject to varying

interpretations. For example, a “major” source is likely to differ for different nonattainment

areas. That is, in some areas there may be a substantial number of large sources which do not

particularly stand out from one another. In another area there may be a small number of large

sources whose emissions are very much greater than those from other sources. In Table 15.2, our

meaning of “major” is more applicable to the large sources in the latter case. “Small number” is
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another subjective term in Table 15.2. Obviously, the larger the number of sources we are

talking about, the less likely it is that they are all operating at allowable limits simultaneously. 

We do not have a good basis for setting an upper limit in general guidance. Normally, the

number of stationary sources projected at allowable rates would be a small fraction of the total

number contained in the modeling domain.

Table 15.2. Default Suggestions For Using Projected “Actual” And “Allowable” Emissions

Type Of Modeled Test When To Use “Allowable”

Projected Emissions

When To Use “Actual”

Projected Emissions

Attainment Test For

Annual NAAQS For PM2.5

Rare instances of major

PM2.5 contribution from a

small number of extremely

large sources

Typically for all sources

Test For Reasonable

Progress Toward Meeting

Goals For Regional Haze

----- For all sources

Attainment Test For 24-

Hour NAAQS For PM2.5

For major sources whose

activity levels are likely to

be high during periods with

observed exceedances

For all remaining sources

Hot Spot Analysis (Related

To 24-Hour NAAQS)

For a source subject to the

hot spot review plus a small

number of nearby sources

which may be operating at

allowable limits

simultaneously with the

source under review

For background estimates

resulting from all other

sources

Hot Spot Analysis (Related

To Annual NAAQS)

Rare instances of major

PM2.5 contribution from a

small number of extremely

large sources

Typically for all sources

15.6.3. Additional Guidance For Making Projections

A State’s needs for inputs to an air quality model are a factor in making projections. 

Models require source locations (coordinates) as input. Thus, a projection approach that makes

its computations at this level is preferred. A less desirable alternative is to assume that all growth

and retirement occurs at existing facilities and that there is no variation in growth or control

within each source category. Information detailing the different types of projections that might
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be required of a State or local air pollution control agency can be found in the EPA publication

“Procedures For Preparing Emissions Projections” (U.S. EPA, 1991a). In addition to the

necessary types of projections, methods for projecting changes in future air pollution generating

activities, quantifying the effects of current and future controls, and combining effects of growth

and control are addressed in this document. Although last published in 1991, much of this

guidance for estimating future year emissions is still valid. There have been updates to some of

the information provided in the 1991 guidance (BEA projection phase-out and EGAS and

MOBILE model revisions, etc.) and therefore States should review additional documentation

concerning emissions projections in U.S. EPA (1999f).

States may find it useful to examine techniques that have been applied in other areas

where control strategy planning has been performed. In U.S. EPA (1991a), examples of emission

projection preparation are recorded in a form suitable for input to a grid-based photochemical

model. In the simplest sense, this approach relied on developing a growth factor and a control

factor for each major source category.

Recommendations. Emission estimates should be projected to two years earlier than

an established deadline for attaining the NAAQS for PM2.5 and for meeting the goals

for regional haze. States should consider making an additional projection to an

intermediate year so as to facilitate a subsequent mid-course review. 

States should generally use projected actual emissions in applications related to the

annual NAAQS and visibility goals. In applications related to the 24-hour NAAQS,

actual projected emissions should also generally be used. However, a small number

of the largest stationary sources of particulate matter or its precursors may need to

be projected at allowable emission rates. Projected allowable emissions should also

be used for a source subject to a hot spot analysis to assess whether the 24-hour

NAAQS is likely to be met near a source of primary particulate matter emissions. 

States may also need to assume allowable emissions for some nearby major sources

in a hot spot analysis related to the 24-hour NAAQS. If estimated allowable

emissions exceed what is physically possible, they may be reduced to physical

limitations or below, provided the allowable limit is reduced accordingly. 

States should review guidance on emission projections issued by the U.S. EPA in

1991 and in updates (U.S. EPA, 1999f). States should quality assure their emission

projections using several methods designed to validate the spatial and temporal

allocations, as well as any speciation that may be calculated. States should review

past emission projection efforts as part of any subsequent review performed for the

reasons identified in Section 6.0.

 

15.7 What Emissions-Related Issues Do I Need To Consider When Using

Lagrangian Models?

Information presented in Sections 15.1 - 15.6 is generally applicable for using Lagrangian
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as well as grid models. However, use of models in hotspot analyses differs from what is done in

the modeled attainment or reasonable progress tests. In hotspot analyses models are used in an

absolute sense. Thus, a State should undertake considerable effort to review emission factors and

activity levels for individual sources subject to a hotspot analysis. Prior to performing a hot spot

analysis, a State should assure itself that the current emission factor(s) and activity levels used for

a source’s emissions are as specific as possible for the source in question. Second, States should

compare model predictions for similar sources which are near monitoring sites with speciated

measurements made at the monitor to provide additional assurance that the emission factors are

appropriate. When there is some doubt, care should be taken to ensure that any applicable

allowable emission limit is physically possible for the source in question. If it is not, the limit

should be reduced so that a more realistic analysis can be performed.

Recommendations. Because a hot spot analysis requires using absolute model

predictions, States should make a substantial effort to ensure that emission factors

used for the source(s) subject to the review are source-specific and have been quality

assured. 
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16.0 How Do I Assess Model Performance And Make Use Of Diagnostic Analyses?

 States should evaluate performance of an air quality model in their specific application

prior to using model predictions to support an attainment or reasonable progress demonstration. 

Performance of an air quality model can be evaluated in two ways: (1) how well is the model

able to replicate observed concentrations of components of PM2.5 and total observed mass of

PM2.5, and (2) how accurately does the model characterize sensitivity of changes in component

concentrations to changes in emissions? The modeled attainment and reasonable progress tests

recommended in Sections 3.1 and 3.2 and in Section 4.2 use models to predict sensitivity of

components of particulate matter to controls and then apply resulting relative reduction factors to

observed (rather than modeled) concentrations. Thus, while both types of performance test are

important, the second type is the most important. Unfortunately, it is also more difficult to do. 

As noted in Section 12.1, 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 done. 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. 

Diagnostic analyses are potentially useful for several reasons. First, these analyses can be

used to better understand why the air quality model predicts what it does. This yields further

insight into whether or not the predictions are plausible. Second, diagnostic analyses provide

information which helps prioritize efforts to improve/refine model inputs. Third, diagnostic tests

can provide insight into which control strategies may be the most effective for meeting the goals

for PM2.5 and regional haze. Fourth, diagnostic analyses can be used to assess how “robust” a

control strategy is. That is, do I reach the same conclusion regarding adequacy of a strategy

when using a variety of assumptions regarding current conditions? 

In this section, we first identify methods which may be useful for evaluating model

performance. We then discuss each of these methods in greater detail. We next note that there is

no single method which offers a panacea for evaluating model performance. We recommend that

performance be assessed by considering a variety of methods, much as is done in a weight of

evidence determination. We then identify methods for performing diagnostic analyses. We

conclude by identifying several potentially useful diagnostic tests which States may consider at

various stages of the modeling analysis. Our discussion of diagnostic tests is limited to

procedures which may help explain reasons underlying a model’s poor (good) performance and

may perhaps point the way toward improving performance. In Section 17.0, we identify several

tests which may be useful for helping States to choose a control strategy to meet air quality goals. 

16.1 How Can I Evaluate Performance Of An Air Quality Model?

As noted above, model performance can be assessed in one of two broad ways: (1) how

accurately does the model predict observed concentrations?, and (2) how accurately does the

model predict responses of predicted air quality to changes in inputs? An example of the latter

type of assessment is, “how accurately does the model predict relative reduction factors (RRF)?” 
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Using terminology found in Seigneur, et al. (2000a), the first type of evaluation is an

“operational” evaluation and the second is a “diagnostic” evaluation.

Given existing data bases, nearly all analyses have been operational performance

evaluations. The underlying rationale is that if we are able to correctly characterize changes in

concentrations accompanying a variety of meteorological conditions, this gives us some

confidence that we can correctly characterize future concentrations under similar conditions. 

Computer graphics, PM component metrics, PM2.5 metrics and observational models are all

potentially useful for evaluating a model’s ability to predict observed air quality. 

 

Diagnostic model performance assessments can be made in several ways. One way is by

looking at predicted differences on weekends vs. week days, provided reliable emissions

estimates are available for both, and differences in weekend/week day emissions are substantial. 

A second way is to examine predicted and observed ratios of “indicator species”. If observed

ratios of indicator species are very high or very low, they provide a sense of whether further

production of secondary particulate matter at the monitored location is limited by availability of

NOx, SO2, NH3 or VOC. Agreement between paired observed and predicted high (low) ratios

suggests a model may correctly predict sensitivity of secondary particulate matter at the

monitored locations to emission control strategies. Thus, use of indicator species methods shows

potential for evaluating model performance in a way which is most closely related to how models

will be used in attainment demonstrations. We recommend that greater advantage be taken of

these methods in the initial demonstration and in subsequent reviews. A third way for assessing

a model’s performance in predicting sensitivity of PM2.5, its components and the visibility

extinction coefficient (bext) to changes in emissions is to compare projections after the fact with

observed trends. One reason States should retain data files and output generated in simulating

the control strategy selected for a SIP is to facilitate retrospective analyses. As explained in

Section 6.0, these 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 attainment and

reasonable progress demonstrations. 

States can assess model performance using graphics, metrics for components of PM2.5,

predicted total mass of PM2.5, corroborative analyses with observational models,

weekend/weekday comparisons, ratios of indicator species and retrospective analyses with

observed air quality and emission trends. These methods are described in the following

subsections. 

16.1.1 How Can I Use Graphics To Make A “Big Picture” Assessment Of Model

Performance?

 States should refer to guidance in U.S EPA (1991) regarding use of graphics to evaluate

model performance. Graphs plot predictions and observations. The 1991 guidance describes the

following graphical displays: time series plots, tile plots, scatter plots and quantile-quantile (QQ) plots. Each of these graphics can also be used to display differences between predictions and
229 - DRAFT

their paired observations. Graphics are useful means for understanding how predictions and

observations differ. For example, time series plots tell whether there is any particular time of day

or day(s) of the week when the model performs poorly. Utility of time series presentations is

greatly enhanced if some monitored data for PM2.5 and at least some components/species are

available continuously or for sampling periods of 6 hours or less. 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 and

Q-Q 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

observations and predictions which just happen to balance, producing low estimated aggegrated

bias. Since the NAAQS for PM2.5 and the regional haze goals will likely require modeling

different times of year, season-specific graphic displays may prove helpful for evaluating and

diagnosing model performance.

16.1.2 What Metrics And Criteria Should I Consider For PM2.5 Components And

Related Gaseous Pollutants?

Pollutants/Pollutant Categories of Interest. Because PM2.5 is a mixture, a meaningful

performance evaluation should include an assessment of how well the model is able to predict

individual components of particulate matter. In addition, performance metrics for certain

gaseous pollutants are useful for at least two reasons. First, their presence may affect response of

components of secondary particulate matter to strategies to reduce precursors. Second, the

model’s performance in predicting certain gaseous species may provide diagnostic clues which

help explain poor performance predicting secondary components of PM. Components of

particulate matter of interest include:

-SO4 and/or S

-NH4

-NO3

-mass associated with SO4

-mass associated with NO3

-EC

-OC

-IP (mass of inorganic primary particulate matter)

-mass of individual constituents of IP.

Gaseous pollutants of interest include:

-ozone

-HNO3

-NO2

-PAN

-NH3
230 - DRAFT

- NOy

- SO2

- CO

- H2O2.

A few words are needed about some of the less obvious entries listed above. For

purposes of model performance evaluation, comparing predicted and observed SO4 ion, sulfur

species and NO3 ion accomplishes the same purpose as comparing predicted and observed mass

associated with sulfate and nitrate. Comparing predicted and observed ammonium (NH4) ion

provides a means for checking whether assumptions made about the mass assumed to be

associated with measured sulfate ion or sulfur is correct. When considered in concert with the

predictions of sulfur (S) or sulfate ion, it may also help provide insight into whether the model is

accurately predicting if mass associated with sulfate and/or nitrate is limited by availability of

ammonia. Making continuous measurements of some key components of particulate matter, like

sulfates, would permit hourly comparisons to be made between observations and predictions.

This would be valuable in assessing whether the model is capturing observed diurnal patterns

correctly. Mass of individual constituents of IP is of use for determining whether the modeling is

predicting an appropriate mix of sources which contribute to IP.

Presence of hydrogen peroxide (H2O2), ozone and related OH radical concentrations can

affect both gas- and liquid phase oxidation of SO2 to particulate sulfate, as well as conversion of

NO2 to HNO3 (and subsequently to particulate nitrate). Further, ozone measurements are

relatively easy to make and are available on an hourly basis. Thus, a model’s performance in

predicting observed ozone has substantial diagnostic power, not available in 24-hour samples of

PM and its components. Comparison of observed and predicted concentrations of HNO3, PAN

and NO2 may provide an explanation for poor agreement between observed and predicted

concentrations of particulate nitrate. That is, it could provide insight into whether poor

agreement arises from the model’s mischaracterizing volatility (i.e., gaseous species too high,

nitrate too low or vice versa) or mischaracterizing deposition, emissions or meteorological

processes (i.e., disagreements of gaseous and particulate species are in the same direction). 

Comparisons between observed and predicted concentrations of ammonia (NH3) may provide

similar insights into whether apparent disagreements between predicted and observed nitrate

concentrations are attributable to mischaracterizing volatility of the nitrates on the filter or to

some more fundamental cause. 

Metrics. Recall that relative reduction factors for components of particulate matter are

estimated by taking the ratio of mean spatially averaged predicted concentrations near a monitor 

obtained with future emissions to that obtained with current emissions. Thus, a key question to

address is, how well does the model predict current spatially averaged concentrations near a

monitor which are averaged over the modeled days with corresponding monitored observations? 

For performance at a monitoring site, j, this metric is given by Equation (16.1).

Aij = [(1/n) Σall modeled days w. monitored data ABS [(C’p - Co) / Co]i, t ]j

 (16.1)
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where “ABS” denotes an absolute (unsigned) value of the difference between observations and

predictions,

C’p is the spatially averaged 24-hour prediction for component i of particulate matter on

day t;

Co is the monitored 24-hour observation of component i on day t;

n is the number of modeled days with monitored data at site j.

This metric should be applied at each monitoring site with the needed data. For applications

related to the NAAQS for PM2.5, greatest concern for good performance is at sites where

observations indicate the standard is not being met.

Other useful insights may result from metrics which are also frequently used in evaluating

a model’s ability to predict ozone concentrations. These include fractional bias between means

(i.e., averaged over several days) and standard deviations associated with predictions vs.

observations, normalized bias, normalized gross error and unpaired comparisons between

observed and predicted peak concentrations. Formulas for estimating the first four of these

metrics at an individual monitoring site, j, are shown in Equations (16.2) -(16.5).

Fractional bias

Fmj = [2 x (mp - mo)/(mp + mo)]j (16.2)

and

Fsj = [2 x (σp - σo)/(σp + σo)]j

 (16.3)

where 

Fmj = fractional bias of means at site j

Fsj = fractional bias of standard deviations at site j

mp, mo = the mean predicted and observed concentration averaged over several days

(hours)

σp, σo = the standard deviation in predicted and observed concentrations computed for

several days (hours).

Normalized bias

Bj = [1/n Gall modeled days with monitored data(Cp - Co)/Co]j (16.4)
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Normalized Gross Error

Gj

 = [1/n Gall modeled days with monitored data ABS [(Cp - Co)]/Co]j (16.5)

where

Bj

 = normalized bias estimated at site j

Gj = normalized gross error estimated at site j

Cp = predicted concentration at site j on a given day (or hour)

Co = observed concentration at site j on a given day (or hour)

n = the number of modeled days (hours) for which observations are available.

Note that units of time associated with Cp, Co, etc. can be days (i.e., usually for particulate matter

and its species) or hours (i.e., usually for species with continuous measurements, like ozone). 

Also note that the preceding metrics may not be that meaningful if the number of modeled days

with monitored data is limited at a site. There is no hard and fast rule about how many

comparisons are needed for the preceding metrics to be meaningful. However, States should be

cautious about interpretations which are made with sample sizes of less than ~ 15 or so.

If a State is able to model only a relatively few days with monitored observations, there

are “aggregated versions” of the preceding metrics which may be used to evaluate model

performance. By this, we mean predictions and observations occurring at different sites are

pooled into a single data base. Formulas which are very similar to those shown in Equations

(16.2) - (16.5) may then be applied. Aggregated statistics have been widely used to estimate

model performance in predicting observed ozone. In fact, performance criteria mentioned in past

U.S. EPA modeling guidance are specific for several of these aggregated metrics (U.S. EPA,

1991, U.S. EPA, 1996b, U.S. EPA, 1999b). Equations (16.6) - (16.9) reflect some widely used

aggregated metrics.

Aggregated Fractional Bias in Means

Fm = 1/N [Gall monitoring sites (Fmj) (16.6)

Aggregated Fractional Bias in Standard Deviations

Fs = 1/N [Gall monitoring sites (Fsj) (16.7)

where
26Performance goals for models used in hot spot analyses, where absolute predictions are

used, will be discussed shortly.

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N = the number of monitoring sites modeled, and the other terms are defined previously.

Aggregated Normalized Bias

B = 1/NTOT (Gall monitoring sites [Gall modeled days with monitored data(Cp - Co)/Co]j

) (16.8)

Aggregated Normalized Gross Error

G = 1/NTOT (Gall monitoring sites [ Gall modeled days with monitored data ABS [(Cp - Co)]/Co]j

) (16.9)

where

NTOT = the number of days (or hours) with paired predictions and observations at all

monitoring sites which are modeled. The other terms have been defined

previously.

Performance Goals. It is difficult to establish generally applicable numerical

performance goals for the previously identified metrics. This is, in part, a result of how we

recommend models be used in attainment and reasonable progress demonstrations. For a given

component of particulate matter, Equation (16.10) reflects our recommended methodology.

 (Cp)future = [(Modeled Conc.)future / (Modeled Conc.)current] [Current Obs.Conc.] (16.10)

Where (Cp)future is the estimated future concentration for the component in question.

If the current observed concentration of a component is small relative to observed concentrations

of other components, it is not particularly important how closely the model replicates its

observations—since the observed concentration is small, a poor prediction can have little effect

on the outcome of the modeled attainment test.

With some modification, the same conclusion reached in the preceding paragraph applies

to the modeled test for reasonable progress as well. Here however, the product of a component’s

observed concentration and that component’s light scattering/absorption efficiency needs to be

small relative to those for other components for model performance predicting the component’s

concentration to be unimportant. Recall that component-specific light scattering/absorption

efficiencies are represented by the numerical coefficients on the right hand side of Equation (4.1).

The other difficulty in establishing meaningful, generally applicable performance goals

results from the model’s use in a relative sense.26 For example, if a component is overpredicted
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with current emissions, a similar bias will probably occur with future emissions since the

projections assume constant meteorological conditions. Hence, the effects of the bias may be

reduced. Because the model is used in a relative sense, all things being equal, poor performance

may have its greatest effect if the model predicts low concentrations of a component with current

emissions. This could lead to low concentrations in the denominator of the component-specific

relative reduction factor and could potentially magnify the effects of any error accompanying

future estimates.

Based on the preceding discussion, States should be most concerned over model

performance if there are discrepancies between observations and predictions for components

constituting a major portion of the observed mass of PM2.5 or accounting for a major portion of

bext derived from observed components of particulate matter. In addition, large underpredictions

of these major components are likely to be of greatest concern.

An important additional goal which States should consider when evaluating model

performance is to be able to predict concentrations of components (e.g., SO4, NO3, OC) as well

as ozone in relative proportions which approximate those observed. This follows, since the

response of secondary components to a control strategy may depend on how much of the other

components are present. Thus, for example, if observations suggest sulfate concentrations are

about 3 times nitrate concentrations, good model performance should result in predictions which

approximate this 3:1 ratio.

Finally we come to the most difficult issue of all—how good should a State expect

performance of a model to be? Frankly, there is little basis for making recommendations at

present (January 2001). Seigneur (2000b) summarizes information based on very few sets of

comparisons. Some of the sets are based on data for as little as one day. Table 16.1 summarizes

the information presented by Seigneur (2000b). Note that the numbers presented in the table

suggest performances which are not as good as typically expected for ozone models. For

example, performance criteria for aggregated gross error and aggregated normalized bias for

ozone models are < 30% gross error and < ABS [15%] normalized bias. Poorer performance for

PM models should not be surprising. Since processes involved with ozone formation are also

instrumental in leading to secondary particulate matter, we believe that good performance

predicting ozone is a prerequisite for good performance predicting secondary particulate matter. 

However, there are additional physicochemical pheonomena affecting particulate matter. It

would be hard to achieve performance better than what is expected for ozone models. Thus,

States should regard previously identified performance goals for ozone models as upper bounds

for what one might expect for models of particulate matter and its components. We list these

goals following Table 16.1.
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Table 16.1. Limited Observed Performance Statistics For Particulate Matter Models

 (Dec. 2000)

Pollutant Aggregated Gross Error* Aggregated Normalized

Bias**

PM2.5 ~ 30 - 50% ~ +10% (only one set of

comparisons)

Sulfate ~ 30 - 50% ~ -20 to - 30%

Nitrate ~ 20 - 70% ~ -15 to + 50%

EC ~ 15 - 60% None Available

OC ~ 40 - 50% ~ +38% (only one set of

comparisons)

 

* Based on 3 sets of comparisons (different models & locations)

**Unless otherwise noted, based on 2 sets of comparisons (different models & locations). 

Note also that the statistics for aggregated gross error and aggregated normalized bias

were not obtained with the same models nor with the same data base. Thus, performances

shown for the two metrics in the table are not associated with each other.

Metric or Criterion Ozone-related Recommendations

Difference in 

Averaged Predictions < 0.15

Near Each Monitor (A)

(Eq. (16.1))

Unpaired 24-hr peaks + 20% difference

Aggregated Normalized Bias < 0.15 (absolute value)

& Normalized Bias

Aggregated Normalized Gross Error < 0.30

& Normalized Gross Error

Performance goals for predicting relative importance of components of PM differ,

according to whether a component comprises a major portion of measured PM. For major

components (i.e., those observed to comprise at least 30% of measured PM2.5), we propose that

the relative proportion predicted for each component averaged over modeled days with

monitored data agrees within about 20% of the averaged observed proportion. For example, if
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one observed that on average over a 20-day period measured mass associated with sulfates were

50% of measured mass associated with (sulfates + nitrates + organic carbon + elemental carbon),

this performance goal is that the model should predict that sulfate comprised 40-60% of the

modeled sum of (sulfates + nitrates + organic carbon + elemental carbon) on the same days as

those with the observations.

For minor observed components of PM, we suggest a goal that the observed and modeled

absolute proportion of each minor component agree within 5%. For example, if , on average,

mass associated with nitrates was observed to comprise 15% of the mass associated with

(sulfates + nitrates + organic carbon + elemental carbon), for the model to meet this goal, it

would need to predict that, on average, nitrates comprised 10-20% of the predicted mass of

(sulfates + nitrates + organic carbon + elemental carbon).

There may be some inconsistencies between the two preceding performance goals for

“minor” components which are almost “major” or vice versa. If this occurs, we suggest using the

least stringent of the two goals.

Although we have identified several performance goals, we believe that it is preferable

for those implementing the protocol to decide what constitutes acceptable performance on a case

by case basis using diagnostic analyses like those described in Section 16.2. 

Performance evaluation of hot spot models. Hot spot analyses superimpose predictions

of primary components of PM2.5 on assumed background levels for secondary components plus

primary components not attributed to the modeled source(s). Thus, performance evaluations

should focus on comparisons between predicted and observed concentrations of IP, EC and

primary portions of OC. One reason for performing a hot spot analysis, of course, is that there is

no monitor near a major source or major group of sources. Thus, in the absence of monitored

data, the performance evaluation should consist of two parts: (1) assurance that the model used

has a history of good performance, and (2) assurance that the emissions and meteorological data

used as inputs in the analysis at hand are reasonable.

States should review Chapter 10 in 40 CFR Part 51, Appendix W concerning evaluation

of Lagrangian (e.g., Gaussian) models. In particular, available results obtained for metrics

described in U.S. EPA (1984) and in U.S. EPA (1992) should be reviewed. Generally, models

identified as “preferred models” in Appendix W have a history of adequate performance. In

conducting this review, States should focus on results obtained in applications which are as

similar as possible to the one being considered. Efforts to quality assure emissions and

meteorological estimates used as input in the hot spot analysis should include the following. 

First, States should compare emission factors and activity levels assumed for the modeled

source(s) to those made elsewhere for similar source categories to ensure that the estimates are

reasonable. Engineering limits on activity levels should also be observed. Meteorological data

used to support the analysis should be reviewed to ensure that the data used do not reflect

physically inconsistent combinations of meteorological inputs.
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16.1.3 What Metrics And Criteria Should I Consider For Predicted PM2.5

Concentrations And Light Extinction? 

Since total measured mass of PM2.5 is the entity which is specified in the NAAQS, States

should review how well a model is able to reproduce these observations. We recommend using

the following metrics: difference in mean spatially averaged predictions near a monitor and

observations at the monitor (A), fractional bias, normalized bias, normalized gross error,

aggregated fractional bias, aggregated normalized bias, aggregated normalized gross error and

comparison of unpaired peak predictions and observations. These metrics and related

performance goals are described in Section 16.1.2. Whenever possible, States should make sure

that performance evaluations related to total PM2.5 are accompanied by information about how

well the model is able to replicate observed concentrations of components. Failure to do so

increases the possibility of (a) “getting the right answer for the wrong reasons”, or (b) observing

poor performance with few clues as to why.

Light extinction (i.e., bext) is a function of concentrations of components of particulate

matter plus relative humidity. However, as explained in Sections 4.1 and 4.2, our recommended

test for reasonable progress uses climatological mean estimates for relative humidity rather than

day-specific values. Therefore, the metrics and performance criteria for components of PM2.5, as

well as similar metrics for coarse particulate matter (PM10 - PM2.5) are the best way to evaluate

model performance predicting light extinction. 

Evaluation of a model’s ability to reproduce observed day-specific values for bext may

have some diagnostic value, however. For example, if the model reproduces observed

concentrations of particulate matter well, but doesn’t do well replicating day-specific values for

bext, this suggests that a poor job may be done predicting relative humidity. This could then lead

a State to focus on the predictions of humidity obtained with the meteorological model as a

possible source of error. Use of tile diagrams showing differences in predicted and observed

values for relative humidity and time series plots depicting diurnal differences may be useful to

assess sources of error. 

16.1.4 How Can I Use Corroborative Analyses With Observational Models To Help

Evaluate Air Quality Model Performance? 

Recently, techniques have been developed to embed procedures within the code of an air

quality model which enable users to assess contributions of specific source categories or of

specific geographic regions to predicted air quality at specified sites (ENVIRON, 1997, Yarwood

and Morris, 1997, Yarwood, et al., 1997a, Yang, et al., 1997, 1997a). These source attribution

procedures characterize what the air quality model says are the effects of targeted areas or

sources on predicted air quality. Provided speciated PM2.5 data are available at a site, source

attributions estimated with these approaches can be compared with those obtained using other

models which rely directly on observed air quality data. 

The chemical mass balance model (U.S. EPA, 1990, Seigneur, et al., 1997) is probably
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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. Available multi-variate statistical

models (see, for example, Paatero, et al., 1994 and Henry, 2000) may provide a more qualitative

means for assessing an air quality model’s performance. Multi-variate statistical models work by

examining temporal variability in monitored precursor species at a single site or spatial

variability on one or a few occasions at many sites. A qualitative comparison is possible if one

can contrast observations on days when winds suggest a source contribution is unlikely vs. days

when a contribution is likely, or at locations where a source category is important vs. those where

it isn’t. If the observational approach suggests a major change in a source category contribution,

and the air quality model also suggests that category is important or unimportant under similar

wind conditions, the observational model lends credence to the air quality model’s predictions.

16.1.5 What Data Bases Reflecting Changes In Emissions Are Available To Evaluate

Model Performance?

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. If these differences are

substantial, simulating weekend as well as week days could provide a means for evaluating how

accurately a model predicts the effects of changing emissions. Similar comparisons could be

made between seasons (e.g., does the model accurately reflect observed differences in component

concentrations in summer vs. winter?). We describe weekend/week day comparisons in the

following paragraphs. Where noted, parts of the discussion are also applicable for seasonal

comparisons. 

Weekend/week day information could be used in one of two ways. The first way is to

compare mean predicted and mean observed PM2.5 or, preferably, components of PM2.5 at each

monitoring site for weekends vs. week days. If there are a sufficient number of monitors

available, it is also desirable to make these comparisons for categories of monitors, grouped

according to whether they represent “downwind”, “center city” or “upwind” conditions. Metrics,

described in Section 16.1.2, as well as other tests, could be applied first for week days and then

for weekend days. If the performance is adequate for both weekends and weekdays, this suggests

that the model is accurately characterizing composite effects of different meteorological

conditions and different emissions. Seasonal comparisons could also be used for this purpose.

A second way for using weekend/week day information is to first screen the available

data to identify weekend days and week days for which meteorological conditions are “similar”. 

For example, for urban analyses, wind orientation, daily maximum surface temperature, presence

of precipitation and maximum mixing height might be considered for this purpose. 

Alternatively, a CART analysis might be performed to identify classes of “similar” days. If

similar sets of meteorological conditions are identified for weekends and week days, changes in

mean observed 24-hr PM2.5 or component concentrations can be compared with changes in mean

predictions made at each monitor site, as well as for groups of sites characterized as “upwind”,

“center city” and “downwind”. Metrics like those described in Section 16.1.2 may be used for
239 - DRAFT

this purpose. If predicted changes generally provide an unbiased estimate of observed changes,

this suggests that the model characterizes effects of changing emissions accurately. Since

meteorological conditions are likely to differ in different seasons, it probably will not be feasible

to use seasonal comparisons in this manner.

We need to mention several caveats regarding weekend/weekday comparisons. First,

changes in emissions between week days and weekends may be small compared to uncertainties

associated with the weekend and week day estimates. Second, dividing a (already small) sample

into weekends and week days may mean that conclusions are based on few comparisons. Third,

identifying “similar” meteorological conditions may be somewhat subjective. Finally, the

changes between weekend and week day emissions may be considerably less than the changes

needed to meet the NAAQS in some areas. Since the relationship between precursor and PM2.5

concentrations may be nonlinear in some cases, the weekend/weekday comparisons may not be

definitive evaluations. Although several of these caveats also apply to seasonal comparisons,

they may do so to a lesser extent. Despite these reservations, weekend/week day (and seasonal)

comparisons provide two of a relatively few means for evaluating a model’s ability to accurately

predict changes in PM2.5 or component concentrations. States should include these comparisons

in their efforts to evaluate model performance, whenever feasible. 

16.1.6 How Do I Use Ratios of Indicator Species To Evaluate Model Performance? 

A performance evaluation which includes comparisons between modeled and observed

ratios of indicator species carries with it a large potential advantage. 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. That is, when the model predicts ratios

within a certain range, predicted secondary components of PM2.5 are sensitive to changes in a

particular precursor. Within another range of ratios, predictions are sensitive to changes in

another precursor. 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.

As of late 2000, nearly all 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 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
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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 gains

more experience with chemical mechanisms incorporating secondary particulate formation and

more speciated particulate and gas phase ambient measurements become available.

Strength of the indicator species approach for assessing model performance depends on

an assumption that the model is accurately characterizing the relationships between indicator

species and secondary components of PM2.5. A second precaution 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 model

correctly predicts sensitivity of secondary particulate matter to changes in precursors. Third, this

method requires more measurements than are commonly made. In some cases, it may be difficult

to achieve the required precision with routine monitoring. Finally, much of the work done to

date with indicator species has focused on peak hourly concentrations of ozone. Applicability of

the approach to secondary particulate matter averaged over 24-hour sampling times has not yet

been extensively tested. Despite these precautions, the approach of comparing predicted and

observed ratios of indicator species provides a means of assessing a model’s ability to accurately

characterize sensitivity of predicted secondary components of PM2.5 to changes in precursors. 

States should use the method to help evaluate model performance, whenever feasible.

16.1.7 Are Retrospective Analyses Useful For Evaluating Model Performance? 

Retrospective analyses compare past model air quality projections with observed trends in

air quality and estimated trends in emissions. The approach is a direct assessment of what we are

most interested in---does the model accurately predict changes in air quality? However, it is not

as straightforward as it seems. Often, input estimates and assumptions used in past studies are

ambiguous and the emissions trends are qualitative. Also, the past studies generally assume

constant meteorology, which does not happen. One of the purposes of the reporting requirements

described in Section 7.0 is to make it possible for others to replicate modeled analyses at future

dates. 

In Section 6.0, we noted that a retrospective analysis is an important means for

diagnosing why an air quality goal has or has not been attained. Such an analysis provides

assurance that improved air quality results from changes in emissions rather than meteorology

and/or can identify reasons why satisfactory progress is not being observed. Retrospective

analyses will have an ancillary benefit of providing an additional means for evaluating model

performance. In order to ensure some planning for subsequent retrospective analyses and to

promote some uniformity in the methods used for these analyses, they are probably best

performed as part of a subsequent review rather than as supporting evidence in the initial SIP

revision.
241 - DRAFT

16.1.8 All Of These Performance Tests Have Shortcomings, So What Do I Do?

There is no single definitive test for evaluating model performance. All tests have

strengths and weaknesses. Credence given to model results is increased if a variety of tests is

applied and the outcomes either support a conclusion that the model is working well or, at least,

are ambiguous. Thus, one can think of a model performance evaluation as a “mini-weight of

evidence analysis” focused on the issue of how much credence to give model results in an

attainment or reasonable progress demonstration. Table 16.2 summarizes the tests and their

corresponding objectives or goals described in this guidance.

Table 16.2. Summary Of Methods To Evaluate Performance Of Air Quality Models

Method Test(s) Goals/Objectives

Do a “Big Picture” Assessment

Using Computer Graphics

-tile plots of observations &

predictions.

-tile plots of differences in

observations & predictions

-scatterplots & Q-Q plots

-time series plots

-complement metrics for

components of PM

-determine whether performance

is worse for high vs. low

observations

-look at spatial patterns of

performance--is performance

better downwind than upwind?

-focus diagnostic tests on certain

times/locations
Table 16.2. Summary Of Methods To Evaluate Performance Of Air Quality Models

(continued)

Method Test(s) Goals/Objectives

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Compare Predicted and

Observed Concentrations of

Components of PM

& Related Gaseous Species

SO4 or S

NH4

NO3

EC

OC

IP

Indivdual constituents of IP

Gaseous Species

O3

HNO3

NO2

NH3

PAN

NOy

H2O2

SO2

CO

-mean observed and predicted

component concentrations near

each monitor

-normalized bias near each

monitor

-fractional bias near each

monitor

-correlation coefficients, all data,

temporally paired means,

spatially paired means

-normalized bias, all monitors

-normalized gross error all

monitors

-compare predicted & observed

relative proportions of SO4, NO3

& secondary OC

-compare predicted & observed

ozone concentrations using

metrics identified in U.S. EPA

(1991, 1999e)

-partition pooled data base into

“upwind”, “center city” &

“downwind” sites. Repeat

analyses

-scatterplots & Q-Q plots of

metrics

- agreement within about 15%,

especially where observed

violations of the NAAQS occur

-within or near ~20% most

monitors

-within or near ~20% most

monitors

-moderate to large positive

correlations

-within or near ~ 15%

-within or near ~ 30%

-agreement within ~20%

(relative measure)–major

components,

-agreement within ~5% (absolute

measure)–minor components

-agreement within bounds

identified in U.S. EPA (1991,

1999e)

-get a better idea of what parts of

the distribution of predictions &

observations agree or disagree &

whether there is any obvious

pattern to the model’s

performance
Table 16.2. Summary Of Methods To Evaluate Performance Of Air Quality Models

(continued)

Method Test(s) Goals/Objectives

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Compare PM2.5 and bext

Predictions and Observations

-similar to metrics for

components of PM 

-this method should be

accompanied by evaluation of a

model’s ability to reproduce

observed concentrations of

components whenever possible

-provide means for assessing

whether model reproduces

observations of PM2.5

-provide means for assessing

whether model predicts relative

humidity sufficiently well

-no specific goal for performance

of model in predicting precursor

concentrations is given.

Compare with Observational

Models

-compare source attribution

estimates with observational

models.

-CMB

-multi-variate models

-source attribution & CMB

identify similar source categories

as being important contributors

to observed precursor or PM

component concentrations 

-day to day variability in air

quality model’s source

attribution is consistent with 

observations and/or with output

from mulit-variate models 

-these are qualitative

comparisons
Table 16.2. Summary Of Methods To Evaluate Performance Of Air Quality Models

(continued)

Method Test(s) Goals/Objectives

244 - DRAFT

Weekend/Week day or Seasonal

Comparisons

-compare previously identified

PM component metrics on

weekends vs. weekdays (or

between seasons)

-if data base permits, partition

data base into meteorological

classes. For each class compare

differences in weekday vs.

weekend (or seasonal)

predictions with differences in

weekday vs. weekend (or

seasonal) observations.

-pool data base to compute bias

and gross error on weekends and

week days or for the two seasons

being compared.

-if data base permits, partition

pooled data base into “upwind”,

“center city” and “downwind”

bins & perform the previously

identified pooled tests.

-objective is to test model’s

ability to accurately reproduce

effects of changing emissions

-same performance goals for

weekends and weekdays or for

each season as mentioned for PM

component metrics.

Compare Predicted and

Observed Ratios of Indicator

Species

compare predicted and observed

ratios at each site.

see Ansari and Pandis (1998) and

Blanchard, et al., (2000) for

specifics regarding potentially

useful ratios

predictions & observations

should fall into the same class

(e.g., SO2-limited cases, NH3-

limited cases, VOC-limited

cases, NOx-limited cases, cases

where it is too close to call)
Table 16.2. Summary Of Methods To Evaluate Performance Of Air Quality Models

(concluded)

Method Test(s) Goals/Objectives

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Perform Retrospective Analyses

to Compare Previously Made

Projections With Subsequently

Observed Trends

-project PM and its components

to a future (preferably sooner

than attainment or reasonable

progress date) year

-retain files

-update emission estimates at

future year & note observed

future concentrations of PM and

its components

-characterize future

meteorological episodes & model

in future

-recommended primarily for a

subsequent review.

-note agreement/disagreements

between projected PM

components & subsequent

observations

-perform diagnostic tests to

determine whether disagreement

is due to

--differences in projected

emissions vs. emissions estimated

at a future date

--differences in assumed

meteorlogical conditions

--a combination of different

meteorological and emissions

assumptions

--one or more limitations in the

model. 

Finally, we need to address the issue of adjusting model inputs to improve model

performance. One of the reasons we recommend a variety of tests for model performance is to

reduce the possibility of “getting the right answer for the wrong reason”. We recognize however,

that many of the inputs to models have associated (often unknown) uncertainties. It is acceptable

to adjust inputs within reasonable bounds to improve performance, providing it does not result in

poorer performance in any of the several measures of performance which we recommend in

Sections 16.1.1 - 16.1.7. If such an adjustment is made, it should be documented and

accompanied by an explanation as to why those implementing the protocol believe it is justified.

Recommendations. States should undertake a variety of performance tests. Results

from a diverse set of tests should be documented and weighed to qualitatively assess

model performance. Those implementing the modeling/analysis protocol should

review available data bases and decide the most appropriate mix of performance

tests relatively early in the modeling process. Provided suitable data bases are

available, greatest weight should be given to tests performed for components of

particulate matter and which assess model capabilities most closely related to how
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the model is used in the modeled attainment and reasonable progress tests. A

narrative describing overall assessment of model performance should be included

among the material submitted to support a recommended SIP revision requiring a

demonstration of attainment or reasonable progress. 

16.2 How Can I Make Good Use Of Diagnostic Tests? 

Diagnostic tests are used to explain model performance and to provide clues about how to

improve reliability of predictions. These tests are performed using one of two broad approaches. 

The first approach consists of tests in which sensitivity of air quality predictions to perturbations

in one or a combination of model inputs is examined. This is the more traditional of the two

approaches and has a longer track record. When it is applied, States should recall how model

outputs are used in the modeled attainment and reasonable progress tests recommended in

Sections 3.0 and 4.0. That is, models are used in a relative sense to provide relative reduction

factors. Relative reduction factors are obtained by taking the ratio of mean concentrations of PM

components obtained with future vs. current emissions. 

The second type of diagnostic test is one in which means for “tracking” the importance of

various phenomena contributing to predicted air quality at a location are embedded within the

code of an air quality model. This generally increases running time and should not be used

unless a vendor or someone very familiar with the computer code has installed this capability and

performed benchmark tests to ensure that the model, with and without the code revisions, yields

identical results. The major advantage of this latter type of diagnostic test is that it reduces the

number of model simulations needed to obtain insights about what is causing high or unexpected 

air quality predictions. Another advantage is that it can provide insights into what additional

sensitivity tests should be performed. Thus, once the initial effort to develop this capability has

been expended, a better understanding of why predictions are the way they are can be obtained

relatively efficiently.

16.2.1 Use Of Sensitivity Tests

Outcomes 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. 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. models used to simulate formation, transport and deposition of

secondary particulate matter are resource intensive. Third, the tests can be used to assess the

robustness of a control strategy. For example, States can consider effects of assumed boundary

conditions and meteorological assumptions on predicted effectiveness of a control strategy. If

the control strategy appears to work for a variety of assumptions, this increases confidence in the

strategy. Sensitivity tests also provide a means for prioritizing use of resources in applying the

model. For example, how sensitive are relative reduction factors to use of more layers or smaller

grid cells? Is using 12 km (rather than 36 km) grid cells more important than simulating many

days? Finally, sensitivity analyses could be useful for prioritizing control efforts or for noting
247 - DRAFT

sensitivity of predictions to uncertainties in the current or future emission inventory. The first

and second types of sensitivity tests are relating to diagnosing reasons for the outcome of model

performance tests. They are discussed in this Section. The third and fourth types of tests are

useful to help choose a strategy which will meet the air quality goal. They are discussed in

Section 17.0.

Sensitivity tests should be applied throughout the modeling process, not just when model

performance is being evaluated. Tests should be selected on a case by case basis by those

implementing the modeling/analysis protocol. We present several tests which may be useful for

setting up model runs and for evaluating performance. In Section 17.0, we identify additional

tests which may prove useful for selecting strategies and for estimating the degree of uncertainty

associated with model predictions and whether or not a particular strategy will be successful in

helping a State to meet an air quality goal. a sequence of activities likely to be followed in

applying an air quality model. The list of tests is intended for illustrative purposes. The

identified tests are not mandatory, nor is the list a comprehensive one.

Model Setup

-Change boundary conditions (is domain size adequate? do I need to consider using a

nested regional model?).

-Alter initial conditions (do I need to extend the ramp-up period I have selected?).

Performance Evaluation/Troubleshooting

-Alter grid cell size and/or number of layers considered (how are predicted 

component predictions affected?).

-Perturb specific inputs (e.g., mixing height, cloud cover, etc.) which might explain why 

certain processes are identified as important by process analysis (see Section

16.2.2) (are results affected by perturbations within reasonable bounds? what

additional measurements should I try to make for a better subsequent review?).

16.2.2 Use of Process Analysis

Occasionally a review of a graphical display, like a tile diagram, may indicate a limited

number of locations or incidents which bear further investigation. Diagnostic tests may be used

to perform focused analyses on these sites or incidents. These tests entail a more detailed look at

a time series of predictions and (if available) observations at or above a site, including chemical

species, winds and mixing. The examinations can be done qualitatively. However, more

quantification is possible using the second type of diagnostic test described at the beginning of

this subsection. A procedure called “process analysis” is an example of the second type of

diagnostic test. Process analysis has been used to assess relative importance of various model

assumptions as well as simulated physical and chemical phenomena contributing to a predicted

ozone concentration at a particular time and location (Jeffries, 1997, Jeffries, et al., 1996, Jang,

et al., 1995, Lo and Jeffries, 1997). The procedure works by breaking down a modeled

simulation into a sequence of physical and chemical processes leading to a predicted

concentration at a given time and location. Since models used to simulate ozone and secondary
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particulate matter are similar, process analysis should also be useful for addressing PM2.5- and

visibility-related applications. 

Process analysis requires a substantial amount of expertise to be interpreted to full

advantage. However, useful insights are also possible with less detailed analyses. The procedure

focuses on selected grid cells. Process analysis then takes advantage of the fact that a numerical

grid model addresses physical and chemical factors affecting ozone 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 discount that

prediction in the attainment or reasonable progress demonstration.

Table 16.2 shows examples of diagnostic tests which may be useful during and before

model performance evaluation. The table is intended to illustrate types of analyses. Choice of

tests needs to be made on a case by case basis by those implementing the modeling/analysis

protocol.
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Table 16.3. Potentially Useful Diagnostic Tests At Various Stages Of Modeling

Stage of Modeling Test(s) (Examples) Purpose(s)

Model Setup -change boundary conditions -is domain size sufficiently large?

-do I need to use a nested

regional model or will an urban

scale model suffice?

Performance Evaluation &

Troubleshooting

-alter specific (uncertain) inputs

(e.g., mixing heights, cloud

cover).

-alter grid cell size or number of

layers considered.

-is agreement with observations

improved?

-what is the effect on other

performance tests (e.g.,

comparisons with results

obtained with observational

models, weekend/weekday

differences, indicator species

ratios)?

-what priorities should I assign

to various kinds of improved

measurements?

Focused performance analysis -process analysis -do suspicious looking results

make physical sense?

-prioritize needs for additional

sensitivity tests

Recommendations. States should include diagnostic analyses throughout the

modeling process used to help understand model performance and to help develop

information which may help improve performance. Sensitivity of relative reduction

factors to input perturbations should be a prime focus of the tests. Provided

capabilities have been properly installed and tested, States may use versions of a

model’s code which contain capability for tracing importance different phenomena

as contributors to predicted ozone concentrations at selected locations.
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17.0 How Do I Evaluate Prospective Control Strategies?

One of the major purposes for a conceptual description, described in Section 9.0, is to

help a State to focus on a relatively small set of strategies which hold promise for meeting air

quality goals for PM2.5 or regional haze. In this Section, we identify additional analyses which

may help States to select among remaining strategies. These additional analyses address three

issues in more or less the following order:

(1) Given the choices I have made for model setup (e.g., grid cell size, domain, days

modeled, etc.), what appears to be an effective strategy?

(2) How “robust” is this strategy—does it appear to work for a variety of alternative,

reasonable assumptions regarding model inputs or setup?

(3) What associated uncertainty exists regarding likelihood of success for any strategy I

might choose? 

17.1 What Appears To Be The Most Effective Strategy?

To be “effective” a strategy needs to show attainment of the air quality goal. This may be

done using the recommended modeled attainment or reasonable progress tests, or through a

weight of evidence analysis which includes these tests. In the case of the reasonable progress

goal for days with good visibility, attainment of the goal may be shown through a weight of

evidence analysis which does not include modeling provided concentrations of particulate matter

on such days are already very low so that they are approach natural background.

The following analyses may be useful in helping to select among prospective strategies.

- If the conceptual description suggests it will be necessary to reduce secondary

components of PM, simulate across-the-board reductions in emissions of SO2, ammonia,

VOC, NOx and combinations of these with a relatively non-resource intensive

“engineering model”. This should help decide what direction I should be thinking of for

my control strategy to reduce secondary particulate matter.

- Compare effects of broad reductions in emissions from point vs. area vs. mobile sources

- Simulate across-the-board reductions in broad source categories (e.g., power generation,

motor vehicles, etc.)

- Simulate reductions in boundary conditions in concert with reduced emissions in the

area of primary interest to estimate the degree to which my strategy needs help from

regional controls in order to succeed. 

Although we have identified a prerequisite for a strategy to be effective (i.e., it meets the
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air quality goal). A number of factors which fall outside the scope of this guidance (e.g., costs,

political feasiblity, enforceability, etc.) are also important determinants of a strategy’s

effectiveness. The relative importance of these needs to be considered on a case by case basis.

17.2 How Robust Is The Strategy I’ve Tentatively Selected?

A State needs to assure itself that a tentatively selected strategy works under a variety of

reasonable assumptions regarding meteorological and emissions inputs. Ideally, a State should

also check to see that the success of a strategy does not hinge on having made a single, fortuitous

set of decisions regarding model setup (e.g., domain size, number of atmospheric layers

considered, etc.). If a State can show that a strategy works under a variety of conditions, the

modeling results have greater credence than otherwise. We list some sensitivity tests which may

be useful in helping to determine how “robust” a strategy may be. The list is not comprehensive,

nor do we require that all these tests be performed.

- Simulate the selected strategy starting with a different current inventory, reflecting

reasonable uncertainties in current emissions.

- Simulate the selected strategy, but with different (reasonable) growth projections.

- Consider different (reasonable) combinations of the first two tests.

- Perturb meteorological inputs, like mixing heights or cloud cover, which may be poorly

characterized but which earlier analyses have suggested may be important in affecting

current predictions. Take care that meteorological changes which are considered continue

to reflect a physically consistent combination of meteorological inputs.

- Simulate the selected strategy using different horizontal grid cell sizes and/or a different

number of atmospheric layers (i.e., grid cells in the vertical dimension).

 17.3 How Certain Can I Be That A Strategy Will Work?

Recall that we are recommending that models be used in concert with measured 

air quality to estimate whether attainment of future air quality goals is likely. That is, 

Future Air Quality = (model response) (current measured air quality).

Uncertainty in estimated values for future air quality arises from at least 3 sources: (1)

incomplete knowledge about all of the input data affecting a model’s response; (2) inherent

limitations in the model itself, and (3) variability in measured air quality data. 

Performing tests like the ones described in Section 17.2 provides a qualitative means for

addressing uncertainty attributable to incomplete knowledge about input data. That is, the

previously described sensitivity tests help identify strategies that are more likely to work under a
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variety of plausible input combinations. In addition, corroboratory information obtained in

weight of evidence assessments provides reassurance that a strategy will be successful. While

these analyses do not quantify uncertainty in future predicted air quality per se, they can be used

to reduce the uncertainty over whether a selected control strategy will be successful in meeting an

air quality goal.

At the present time (late 2000), we do not see a way to quantify uncertainty due to

inherent limitations in a model. However, there are several ways to reduce this uncertainty. 

First, use an air quality model(s) whose scientific basis is fully and satisfactorily explained in its

accompanying documentation. As we have noted, States may find it necessary to use a

simplified “engineering” version of a model. Uncertainty inherent in such results may be

reduced if it has been shown that the engineering and more complete versions of a model

produce similar results under the conditions which are of greatest interest for a particular

application.

A means to quantify uncertainty in future air quality estimates attributable to variability in

air quality observations may become available shortly. Rao and Hogrefe (2000) have developed

an approach which enables one to gain insight into the distribution of future air quality

predictions attributable to variability in currently observed air quality (i.e., such as an observed

design value) at a given location. The procedure is to fit a theoretical statistical distribution to

the tail of a set of daily observations at a monitoring site (e.g., over a 3-year period) and compute

a design value consistent with the form of the NAAQS. The next step is to perform a

bootstrapping operation several hundred times to obtain different sets of air quality data. For

each instance, a design value is determined from the resulting data. This leads to a distribution

of current design values which can be translated into a distribution of future air quality estimates

using the RRF approach recommended in this guidance. Work thus far has focused on the 1-hour

and 8-hour NAAQS for ozone. However, it may be possible to apply the methodology to PMrelated applications. 

Recommendations. States should use a previously developed conceptual description

to identify a relatively narrow set of strategy choices for attaining an air quality

goal. Choices may be narrowed further by performing several additional modeling

analyses (e.g., across-the board reductions, etc.) requiring fewer resources than a

full fledged analysis. Uncertainty regarding likelihood that a chosen strategy will be

successful can be reduced by choosing a model based on sound science with good

accompanying documentation, performing several sensitivity tests to ensure a

chosen strategy works under a variety of conditions and by considering year to year

variability in air quality observations at key monitoring sites considered in the

demonstration. 
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Glossary

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 one or more hot spot

analyses and a review of a diverse set of

model outputs and emissions, air quality and

meteorological data in a weight of evidence

determination to assess whether attainment

of the NAAQS is likely with the proposed

control strategy.

Class I areas Class I areas are geographical locations for

which the Clean Air Act requires particular

scrutiny to protect “air quality-related

values”. One of these values is visibility. 

Generally, Class I areas are locations of

particular scenic beauty, locations of

historical significance or locations which are

to be preserved as wilderness. They include

the nation’s national parks, national

monuments and wilderness areas. There are

156 Class I areas in the United States. 

These are identified on a map of the U.S.

contained in the final rule on regional haze

regulations (see Regional Haze Regulations;

Final Rule, Federal Register 64, No.126,

July 1, 1999, p.35716.).

Coarse mode particulate matter Coarse mode particulate matter is emitted

directly from sources as a result of physical

activities like grinding or crushing or

resuspension. Coarse mode particles range

in size from 1.0 - 20 micrometers or more,

with a relatively small fraction of the total

mass of coarse mode particulate matter

occurring in particles with aerodynamic

diameters < 2.5 micrometers. 
Glossary (continued)

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Coarse particulate matter The measured mass of particles with

aerodynamic diameters > 2.5 micrometers

and < 10 micrometers.

Components (of PM2.5) Major portions of measured mass of PM2.5,

grouped by common sets of chemical or

physical attributes. Components of PM2.5

considered in modeled demonstrations

should include mass associated with sulfates

(SO4), nitrates (NO3), organic carbon (OC),

elemental carbon (EC), inorganic primary

particulate matter (IP) and unattributed

portions of measured mass (U).

Conceptual description A qualitative way of characterizing the

nature of an area’s nonattainment or regional

haze problem. Attributes included in the

characterization include roles of primary and

secondary particulate matter, relative

importance of transport vs. local generation,

and mix of major source categories which

may be affecting measured concentrations of

particulate matter.

Deciview A measure of visibility derived by taking the

logarithm of the ratio of the light extinction

coefficient to the light extinction coefficient

which results solely due to air (without

particles). Deciview units are intended to

serve as a linear measure of human

perception of visibility degradation. 

Reasonable progress goals are expressed

using deciviews. 

Design values A design value is a concentration defined

consistently with the form of the NAAQS

for PM2.5. If the design value is < the

concentration defined in the NAAQS, the

NAAQS is met. Thus, for the annual

NAAQS, if the annual mean concentration,

averaged over 3 consecutive years (i.e., the
Glossary (continued)

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design value) is < 15.0 μg/m3

 at a monitor,

the NAAQS is met at that site.

Extinction coefficient This is a measure of light extinction

resulting from scattering and absorption of

light by particles and gases.

Fine mode particulate matter This is particulate matter emitted directly as

a result of combustion or formed in the

atmosphere as a result for reactions among

precursors. Fine mode particulate matter is

< 2.5 micrometers in aerodynamic diameter. 

Fine particulate matter All particles < 2.5 micrometers in

aerodynamic diameter. This includes all

fine mode particulate matter plus a small

fraction of emitted coarse mode particulate

matter.

Hot spot analysis A modeling analysis performed for one or a

limited number of sources of primary

particulate matter. These analyses may be

needed to supplement the modeled

attainment test for the PM2.5 NAAQS. Need

for them is most likely for problems related

to the 24-hour NAAQS. Analyses should

focus on large sources or concentrations of

sources situated in locations where there are

no monitored concentrations of PM2.5 and its

components.

Mid-course review An analysis of air quality and emissions data

performed sometime between the submittal

of a SIP revision and the date required for

attainment or the date required for the

current increment of reasonable progress

reducing regional haze to be realized. Its

purposes are to assess progress, compare

observed progress with expectations at the

time of the modeled attainment or

reasonable progress demonstration and to
Glossary (continued)

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identify possible means to improve progress,

if necessary.

Modeled attainment test The modeled attainment test uses monitored

design values for PM2.5 together with

monitored PM species to estimate current

concentrations for 6 major components of

PM2.5. Air quality models are then used to

estimate relative reduction factors for each

of the 6 components. A future concentration

for each component is the product of its

current concentration and calculated relative

reduction factor. Future concentrations of

each component are added to estimate the

future concentration of PM2.5. If the

resulting future concentration of PM2.5 is <

to the concentration specified in the

NAAQS, the test is passed. 

Modeled test for reasonable progress This test first entails ranking current

extinction coefficients derived from

monitored data for components of PM2.5 and

coarse particulate matter. An empirical

equation, based on a review of monitored

data by the National Park Service, is used to

derive the extinction coefficients which get

ranked. Results are used to identify 20% of

days with worst (best) current visibility,

along with the mean extinction coefficient

corresponding to the identified days with

current worst (best) visibility. Air quality

models are used to derive relative reduction

factors for components of PM2.5 and coarse

particulate matter. Future concentrations are

estimated by multiplying the current

observed component concentrations times

the corresponding relative reduction factors. 

The National Park Service equation is reapplied to estimate a future value for the

mean worst (best) extinction coefficient. 

The estimated future worst (best) mean
Glossary (continued)

271 - DRAFT

extinction coefficient (converted to

deciviews) is compared to the current value

(in deciviews) to see if the reasonable

progress goals will be met by the simulated

control strategy.

 

Modeling system This is a group of models used to predict air

qualilty concentrations. The group includes

an emissions model to convert countywide

emission information into gridded speciated

emissions which vary diurnally and reflect

environmental conditions. It also includes a

meteorological model to provide 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 pollutant

concentrations.

Relative reduction factor The relative reduction factor (RRF) is a key

part of the modeled tests for attainment and

reasonable progress. It is calculated for each

component of PM2.5 and, for visibilityrelated applications, for coarse particulate

matter as well. The RRF is a dimensionless

ratio of estimated future concentration of a

component divided by its current predicted

concentration.

PM2.5 This is the mass of all measured particulate

matter having an aerodynamic diameter <

2.5 micrometers. It consists of fine mode as

well as some coarse mode particulate matter.

Primary particulate matter This is all measured particulate matter which

is either emitted directly as particulate

matter or has been converted to particulate

matter, through condensation, within a few

dozen meters of its release. Generally, the

following components of PM2.5 consist of

primary particles: IP, EC and portions of
Glossary (concluded)

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OC. Coarse particulate matter also consists

of primary particulate matter.

Secondary particulate matter This is all measured particulate matter which

results from homogeneous or heterogeneous

chemical reactions occurring in the

atmosphere after release of gaseous

precursors. Generally, the following

components of PM2.5 consist of secondary

particles: SO4, NO3 and portions of OC.

Weight of evidence (WOE) determination This is a set of diverse analyses used to

judge whether an air quality goal is likely to

be met. The credibility of each analysis is

assessed and an oucome consistent with an

hypothesis that the goal will be met is

identified beforehand. If the set of oucomes,

on balance, is consistent with meeting the

goal, then the WOE can be used to show that

a proposed strategy will be adequate. A

weight of evidence determination is used

primarily for NAAQS-related applications. 

However, it may also be needed to assess

whether it is likely that the reasonable

progress goal for days with current best

visibility will be met. Weight of evidence

generally includes results from the modeled

attainment (or reasonable progress) test, hot

spot analyses, as well as other analyses of air

quality, emissions and modeled data. 
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