Policy Assessment for the Review of the 

Particulate Matter National Ambient Air 

Quality Standards 
 

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EPA 452/R-11-003 

April 2011 

Policy Assessment 

for the Review of the Particulate Matter 

National Ambient Air Quality Standards 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, North Carolina 
DISCLAIMER 

 This document has been reviewed by the Office of Air Quality Planning and Standards 

(OAQPS), U.S. Environmental Protection Agency (EPA), and approved for publication. This 

OAQPS Policy Assessment contains conclusions of the staff of the OAQPS and does not 

necessarily reflect the views of the Agency. Mention of trade names or commercial products is 

not intended to constitute endorsement or recommendations for use. 
ACKNOWLEDGMENTS 

This Policy Assessment is the product of the Office of Air Quality Planning and 

Standards (OAQPS). It has been developed as part of the Environmental Protection Agency’s 

(EPA) ongoing review of the national ambient air quality standards (NAAQS) for particulate 

matter (PM). The PM NAAQS review team has been led by Ms. Beth Hassett-Sipple. Dr. Karen 

Martin has managed the project. For the chapter on health effects associated with fine particle 

exposures and the primary PM2.5 standards, the principal authors include Ms. Beth HassettSipple, Dr. Pradeep Rajan, and Dr. Zach Pekar. For the chapter on health effects associated with 

thoracic coarse particle exposures and the primary PM10 standard, the principal author is Dr. 

Scott Jenkins. For the chapter on visibility-related effects and the secondary PM2.5 standards, the 

principal authors include Dr. Marc Pitchford (National Oceanic and Atmospheric 

Administration, NOAA) and Mr. Phil Lorang. For the chapter on other welfare effects and 

secondary PM standards, the principal author is Dr. Meredith Lassiter. The principal 

contributors of ambient monitoring information, as presented in the introductory chapter, 

Appendix B, and other analyses, are Mr. Tim Hanley and Ms. Joann Rice. The authors would 

also like to acknowledge Mr. Mark Schmidt, Dr. Adam Reff, Dr. Michael Rizzo, and Mr. Mark 

Evangelista for conducting analyses on specific topics. Staff from other EPA offices, including 

the Office of Research and Development and the Office of General Counsel, also provided 

valuable comments and contributions. 

 Earlier drafts of this document were reviewed by the Clean Air Scientific 

Advisory Committee (CASAC) and made available for public comment. This final document 

has been informed by the expert advice and comments received from CASAC, as well as by 

public comments. 
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 i 

TABLE OF CONTENTS 

List of Tables................................................................................................iv 

List of Figures................................................................................................v 

List of Acronyms/Abbreviations.........................................................................vi 

EXECUTIVE SUMMARY ...........................................................................ES-1 

1. INTRODUCTION.......................................................................................................... 1-1 

1.1 PURPOSE .................................................................................................................. 1-1 

1.2 BACKGROUND ....................................................................................................... 1-2

1.2.1 Legislative Requirements ..................................................................................... 1-2

1.2.2 Previous PM NAAQS Reviews ........................................................................... 1-4

1.2.3 Litigation Related to the 2006 PM Standards ...................................................... 1-9

1.2.4 Current PM NAAQS Review............................................................................. 1-10

1.3 CURRENT AMBIENT MONITORING NETWORKS FOR PM .......................... 1-12

1.3.1 PM2.5 Mass ......................................................................................................... 1-13

1.3.2 PM2.5 Speciation ................................................................................................. 1-14

1.3.3 PM10 Mass .......................................................................................................... 1-14

1.3.4 PM10-2.5 Mass ...................................................................................................... 1-15

1.4 GENERAL APPROACH AND ORGANIZATION OF THIS DOCUMENT ........ 1-15

1.5 REFERENCES ........................................................................................................ 1-17 

2. REVIEW OF THE PRIMARY STANDARDS FOR FINE PARTICLES ............... 2-1 

2.1 APPROACH .............................................................................................................. 2-1

2.1.1 Approaches Used in Previous Reviews ............................................................... 2-2 

2.1.1.1 Review Completed in 1997 .......................................................................... 2-2 

2.1.1.2 Review Completed in 2006 .......................................................................... 2-4 

2.1.2 Remand of Primary Annual PM2.5 Standard ........................................................ 2-6 

2.1.3 General Approach Used in Current Review ........................................................ 2-8 

2.2 ADEQUACY OF CURRENT STANDARDS ........................................................ 2-17 

2.2.1 Evidence-based Considerations ......................................................................... 2-17 

2.2.2 Risk-based Considerations ................................................................................. 2-36 

2.2.3 CASAC Advice .................................................................................................. 2-47 

2.2.4 Staff Conclusions on Adequacy of Current Standards ...................................... 2-48 

2.3 CONSIDERATION OF ALTERNATIVE STANDARDS ..................................... 2-49 

2.3.1 Indicator ............................................................................................................. 2-49 

2.3.2 Averaging Times ................................................................................................ 2-56 
 ii 

2.3.3 Forms ................................................................................................................. 2-58 

2.3.3.1 Form of the Annual Standard ..................................................................... 2-59 

2.3.3.2 Form of the 24-Hour Standard .................................................................... 2-60 

2.3.4 Alternative Levels .............................................................................................. 2-62 

2.3.4.1 Evidence-based Considerations .................................................................. 2-63 

2.3.4.2 Risk-based Considerations .......................................................................... 2-92 

2.3.4.3 CASAC Advice ........................................................................................ 2-100 

2.3.4.4 Staff Conclusions on Alternative Standard Levels...........................2-101 

2.4 SUMMARY OF STAFF CONCLUSIONS ON PRIMARY FINE PARTICLE 

STANDARDS........................................................................................................ 2-104 

2.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

COLLECTION ...................................................................................................... 2-106 

2.6 REFERENCES ...................................................................................................... 2-112 

3. REVIEW OF THE PRIMARY STANDARD FOR THORACIC COARSE 

PARTICLES ................................................................................................................... 3-1 

3.1 APPROACH .............................................................................................................. 3-1

3.1.1 Approaches Used in Previous Reviews ............................................................... 3-2 

3.1.1.1 Reviews Completed in 1987 and 1997 ......................................................... 3-2 

3.1.1.2 Review Completed in 2006 .......................................................................... 3-3 

3.1.2 Litigation of 2006 Final Rule for Thoracic Coarse Particles ............................... 3-5 

3.1.3 General Approach Used in Current Review ........................................................ 3-6 

3.2 ADEQUACY OF THE CURRENT PM10 STANDARD ........................................... 3-9 

3.2.1 Evidence-Based Considerations ........................................................................... 3-9 

3.2.2 CASAC Conclusions and Recommendations .................................................... 3-23 

3.2.3 Staff Conclusions on Adequacy of Current PM10 Standard ............................... 3-24 

3.3 CONSIDERATION OF POTENTIAL ALTERNATIVE STANDARDS .............. 3-27 

3.3.1 Indicator ............................................................................................................. 3-27 

3.3.2 Averaging Time ................................................................................................. 3-30 

3.3.3 Form ................................................................................................................... 3-31 

3.3.4 Level .................................................................................................................. 3-35 

3.4 SUMMARY OF STAFF CONCLUSIONS ON PRIMARY THORACIC COARSE 

PARTICLE STANDARD ........................................................................................ 3-50 

3.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

COLLECTION ........................................................................................................ 3-51 

3.6 REFERENCES ........................................................................................................ 3-54 

 
 iii 

4. REVIEW OF THE SECONDARY STANDARDS FOR VISIBILITY-RELATED 

EFFECTS........................................................................................................................ 4-1 

4.1 APPROACH .............................................................................................................. 4-1

4.1.1 Approaches Used in Previous Reviews ............................................................... 4-2 

4.1.1.1 Review Completed in 1997 ......................................................................... 4-2 

4.1.1.2 Review Completed in 2006 ......................................................................... 4-4 

4.1.2 Remand of Secondary PM2.5 Standards ............................................................... 4-8 

4.1.3 General Approach Used in Current Review ........................................................ 4-9 

4.2 ADEQUACY OF CURRENT STANDARDS ........................................................ 4-12 

4.2.1 Evidence-based and Impact-Based Considerations ........................................... 4-12 

4.2.2 CASAC Advice .................................................................................................. 4-39 

4.2.3 Staff Conclusions on Adequacy of Current Standards ...................................... 4-39

4.3 CONSIDERATION OF ALTERNATIVE STANDARDS ..................................... 4-40 

4.3.1 Indicator ............................................................................................................. 4-41 

4.3.1.1 Evidence-based and Impact-based Considerations ..................................... 4-41 

4.3.1.2 CASAC Advice on Indicator ...................................................................... 4-51 

4.3.1.3 Staff Conclusions on Indicator ................................................................... 4-51 

4.3.2 Averaging Time and Related Considerations .................................................... 4-52 

4.3.2.1 Sub-daily Averaging Times ........................................................................ 4-52 

4.3.2.2 24-Hour Averaging Time ........................................................................... 4-55 

4.3.2.3 CASAC Advice on Averaging Time .......................................................... 4-56 

4.3.2.4 Staff Conclusions on Averaging Time ....................................................... 4-57 

4.3.3 Form ................................................................................................................... 4-58 

4.3.4 Level .................................................................................................................. 4-60 

4.4 SUMMARY OF STAFF CONCLUSIONS ON SECONDARY STANDARDS FOR 

VISIBILITY-RELATED EFFECTS ....................................................................... 4-63 

4.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

COLLECTION ........................................................................................................ 4-66 

4.6 REFERENCES ........................................................................................................ 4-68 

 5. REVIEW OF THE SECONDARY STANDARDS FOR OTHER WELFARE 

EFFECTS........................................................................................................................ 5-1

5.1 APPROACH .............................................................................................................. 5-1

5.1.1 Approaches Used in Previous Reviews ............................................................... 5-2 

5.1.1.1 Review Completed in 1997 .......................................................................... 5-2

5.1.1.2 Review Completed in 2006 .......................................................................... 5-2 

5.1.2 Scope of Current NAAQS Reviews ..................................................................... 5-3 

5.1.2.1 Scope of the Current Secondary PM NAAQS Review ................................ 5-3 

5.1.2.2 Scope of the Current NOx/SOx Secondary NAAQS Review ...................... 5-5 

5.1.3 General Approach Used in Current Review ........................................................ 5-5 

 
 iv 

5.2 CLIMATE .................................................................................................................. 5-6

5.2.1 Scope .................................................................................................................... 5-6 

5.2.2 Adequacy of the Current Standards ..................................................................... 5-7 

5.2.3 Staff Conclusions ............................................................................................... 5-10 

5.2.4 Key Uncertainties and Areas for Future Research and Data Collection ............ 5-12 

5.3 ECOLOGICAL EFFECTS ...................................................................................... 5-13 

5.3.1 Scope .................................................................................................................. 5-13 

5.3.2 Adequacy of the Current Standards ................................................................... 5-16 

5.3.3 Staff Conclusions ............................................................................................... 5-23 

5.3.4 Key Uncertainties and Areas for Future Research and Data Collection ............ 5-25 

5.4 MATERIALS ........................................................................................................... 5-25 

5.4.1 Scope .................................................................................................................. 5-25 

5.4.2 Adequacy of the Current Standards ................................................................... 5-26 

5.4.3 Staff Conclusions ............................................................................................... 5-28 

5.4.4 Key Uncertainties and Areas for Future Research and Data Collection ............ 5-29 

5.5 REFERENCES ....................................................................................................... 5-30 

APPENDICES 

A. Clean Air Scientific Advisory Committee Letter on Second Draft Policy Assessment, 

September 10, 2010.................................................................................A-1 

B. Ambient PM Monitoring Networks...............................................................B-1 

C. Predicted Percent of Counties with Monitors Not Likely to Meet Current and Alternative 

Primary Annual and 24-hour PM2.5 Standards..................................................C-1 

D. Predicted Percent of Counties with Monitors Not Likely to Meet Current and Alternative 

Primary 24-hour PM10 Standards.................................................................D-1 

E. Information Regarding a 1-hour PM2.5 Mass Indicator..........................................E-1 

F. Two Simplified Approaches to Calculate Hourly PM2.5 Light Extinction Values from 

Hourly PM2.5 Mass and Relative Humidity Data Plus 24-hour Mean PM2.5 Composition 

Data....................................................................................................F-1 

G. Calculated 24-hour Average PM2.5 Light Extinction and Adjusted Candidate Protection 

Levels.................................................................................................G-1 

H. Predicted Percent of Counties with Monitors Not Likely to Meet Current and Alternative 

Secondary PM2.5 Standards........................................................................H-1 
 v 

LIST OF TABLES 

Table 1-1. Summary of National Ambient Air Quality Standards Promulgated for Particulate 

Matter 1971-2006 ...................................................................................................... 1-5 

Table 2-1. Summary of Causality Determinations for PM2.5 .................................................... 2-19 

Table 2-2. Estimated Incidence and Percent of Total Annual Incidence Associated with 

 Long-term PM2.5 Exposure Based on Simulation of the Current Suite of Standards 

(for IHD mortality based on 2007 PM2.5 Concentrations) ...................................... 2-44 

Table 2-3. Estimated Incidence and Percent of Total Annual Incidence Associated with ShortTerm PM2.5 Exposure Based on Simulation of the Current Suite of Standards (CV 

mortality and hospital admissions based on 2007 PM2.5 concentrations) ............... 2-45 

Table 3-1. Summary of Causality Determinations for PM10-2.5 ................................................ 3-10 

Table 3-2. Predicted Counts of Counties, and Population (x 1,000) within those Counties, Not 

Likely to Meet the Current PM10 Standard and Alterantive PM10 Standards with 98th

Percentile Forms (based on air quality in 2007-2009)..................................3-44 

Table 3-3. PM10 Concentrations in Locations that Met Current PM10 Standard and Where 

Positive and Statistically Significant Associations with PM10-2.5 Have Been 

Reported.......................................................................................3-45 

Table 4-1. Logit Model-estimated Visual Air Quality Values (in deciviews) Corresponding to 

Various Percent Acceptability Values for the Four Cities. ..................................... 4-25 

Table 4-2. Percentage of 2005-2007 Daily Maximum Daylight Hourly Values of PM10 Light 

Extinction Exceeding CPLs (excluding hours with relative humidity > 90%). ...... 4-31 

Table 4-3. Percentage of 2007-2009 Daily Maximum Daylight Hourly Values of PM2.5 Light 

Extinction Exceeding CPLs (excluding hours with relative humidity > 90%). ...... 4-32 

Table 4-4. Percentage of Daily Maximum Daylight Hourly Values of PM10 Light Extinction 

Exceeding CPLs when Just Meeting the Current PM2.5 NAAQS (excluding hours 

with relative humidity > 90%). ................................................................................ 4-34 

Table 4-5. Percentage of Daily Maximum Daylight 4-Hour Average Values of PM10 Light 

Extinction Exceeding CPLs when Just Meeting the Current PM2.5 NAAQS 

(excluding hours with relative humidity > 90%). .................................................... 4-36 

Table 4-6. Percentage of Daily Maximum Daylight 1-Hour Average Values of PM2.5 Light 

Extinction Exceeding CPLs when Just Meeting the Current PM2.5 NAAQS 

(excluding hours with relative humidity > 90%). .................................................... 4-37 

Table 4-7. Percentage of Daily Maximum Daylight 4-Hour Average Values of PM2.5 Light 

Extinction Exceeding CPLs when Just Meeting the Current PM2.5 NAAQS 

(excluding hours with relative humidity > 90%). .................................................... 4-38 

Table 5-1. Scope of the Current Secondary PM NAAQS Review and Current NOx/SOx 

secondary NAAQS review. ....................................................................................... 5-4 
 vi 

LIST OF FIGURES 

Figure 2-1. Overview of Approach for Review of Primary PM2.5 Standards ............................ 2-16

Figure 2-2. Distribution of Site-level Variation in 98th and 99th Percentile Concentrations, as 

Measured by Coefficient of Variation (SD/Mean) Computed by Site Across Years, 

 2000-2008 ................................................................................................................ 2-62

Figure 2-3. Confidence Intervals Around PM2.5 Concentration-Response Relationships – 

Information from Multi-city Epidemiological Studies ............................................ 2-67

Figure 2-4. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for 

 Multi-city, Long-term PM2.5 Exposure Studies of the General Population and 

 Older Adults ............................................................................................................ 2-73

Figure 2-5. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for 

 Multi-City, Long-term PM2.5 Exposure Studies of Children .................................. 2-74

Figure 2-6. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for MultiCity, Short-term PM2.5 Exposure Studies of the General Population and Older Adults

 ................................................................................................................................. 2-75

Figure 2-7. Distribution of Population-Level Data and Corresponding PM2.5 Concentrations for 

 Selected Multi-City Epidemiological Studies ......................................................... 2-76

Figure 2-8. Translating Epidemiological Evidence from Multi-City Exposure Studies into 

 Annual PM2.5 Standards .......................................................................................... 2-80

Figure 2-9. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for 

Single-City, Short-term PM2.5 Exposure Studies .................................................... 2-87

Figure 2-10. County-level 24-hour DVs versus Annual DVs, 2007-2009 ................................ 2-90

Figure 2-11. Percent Reduction in Long-term Exposure-related Mortality Risk ...................... 2-95

Figure 2-12. Percent Reductions in Short-term Exposure-related Mortality and Morbidity 

 Risk ........................................................................................................................ 2-96 

Figure 3-1. Overview of Approach for Review of Primary PM10 Standard.........................3-8 

Figure 3-2. PM10 Air Quality and PM10-2.5 Effect Estimates in Locations of U.S. Single-City 

PM10-2.5 Mortality Studies..................................................................3-19 

Figure 3-3. PM10 Air Quality and PM10-2.5 Effect Estimates in Locations of U.S. Single-City 

PM10-2.5 Morbidity Studies..................................................................3-20 

Figure 3-4. Site-Level Ratio of 24-Hour PM10-2.5 to 24-Hour PM10 Concentrations...............3-28 

Figure 3-5. 98th Percentile PM10 Concentrations in Locations of U.S. Single-City PM10-2.5 

Mortality Studies.............................................................................3-37 

Figure 3-6. 98th Percentile PM10 Concentrations in Locations of U.S. Single-City PM10-2.5 

Morbidity Studies............................................................................3-38 

Figure 3-7. Composite 3-year PM10 98th percentile 24-Hour Average Concentration Versus the 

PM10 Expected Exceedance Concentration-equivalent Design Value 

 (1988-2008)...................................................................................3-41 

Figure 3-8. Regional 3-year PM10 98th percentile 24-Hour Average Concentration Versus the 

PM10 Expected Exceedance Concentration-equivalent Design Value 

 (1988-2008)...................................................................................3-42 

 
 vii 

Figure 4-1. Overview of Approach for Review of Secondary PM2.5 Standards ........................ 4-11 

Figure 4-2. Summary of Results of Urban Visibility Studies in Four Cities, showing the 

Identified Range of the 50% Acceptance Criteria. .................................................. 4-24 

Figure 4-3. Distribution of Estimated Daily Maximum Daylight 1-hour PM10 Light Extinction 

Across the 2005-2007 Period, by Study Area (excluding hours with relative humidity 

greater than 90%) .................................................................................................... 4-31 

Figure 4-4. Distribution of Estimated Daily Maximum Daylight 1-hour PM2.5 Light Extinction 

Across the 2007-2009 Period, by Study Area (excluding hours with relative humidity 

greater than 90%). ................................................................................................... 4-32 

Figure 4-5. Distribution of Daily Maximum Daylight 1-hour PM10 Light Extinction when Rolled 

Back to Just Meet Current PM2.5 NAAQS, by Study Area (excluding hours with 

relative humidity greater than 90%). ...................................................................... 4-34 

Figure 4-6. Distribution of Daily Maximum Daylight 4-hour Average PM10 Light Extinction 

when Rolled Back to Just Meet Current PM2.5 NAAQS, by Study Area (excluding 

hours with relative humidity greater than 90%). .................................................... 4-36 

Figure 4-7. Distribution of Daily Maximum Daylight 1-hour Average PM2.5 Light Extinction 

when Rolled Back to Just Meet Current PM2.5 NAAQS, by Study Area (excluding 

hours with relative humidity greater than 90%). .................................................... 4-37 

Figure 4-8. Distribution of Daily Maximum Daylight 4-hour Average PM2.5 Light Extinction 

when Rolled Back to Just Meet Current PM2.5 NAAQS, by Study Area (excluding 

hours with relative humidity greater than 90%). .................................................... 4-38 

Figure 4-9. Relationship between Daylight 1-hour PM2.5 Mass Concentration vs. Same-Hour 

PM10 Calculated Light Extinction forTwo Cities. ................................................... 4-43 
 viii 

LIST OF ACRONYMS/ABBREVIATIONS 

AAMMS Ambient Air Monitoring and Methods Subcommittee 

ACS American Cancer Society 

AOD Aerosol optical depth 

ANPR Advance notice of proposed rulemaking 

ANS Autonomic nervous system 

AQCD Air Quality Criteria Document 

AQS EPA’s Air Quality System 

AR4 Fourth Assessment Report of the Intergovernmental Panel on Climate Change 

BC Black carbon 

BMA Bayesian model averaging 

C Carbon 

Ca Calcium 

CAA Clean Air Act 

CAPs Concentrated ambient particles

CASAC Clean Air Scientific Advisory Committee 

CBSA Consolidated Business Statistical Area 

CBVD Cerebrovascular disease 

CCN Cloud Condensation Nuclei 

CCSP US Climate Change Science Program 

Cd Cadmium 

CF Cystic fibrosis 

CFR Code of Federal Regulations 

CHD Coronary heart disease 

CHF Congestive heart failure 

CHS Children’s Health Study 

CH4 Methane 

CO Carbon monoxide 

CO2 Carbon dioxide 

COPD Chronic obstructive pulmonary disease 

CPL Candidate protection level 

Cr Chromium 

C-R Concentration-response 
 ix 

CSN Chemical Speciation Network 

Cu Copper 

CV Cardiovascular 

CVD Cardiovascular disease 

DE Diesel Exhaust 

DEP Diesel Exhaust Particles 

DHEW US Department of Health, Education, and Welfare 

DLEE Dry light extinction efficiency 

dv deciview 

EC Elemental carbon 

ED Emergency department 

EPA Environmental Protection Agency 

FEM Federal Equivalent Method 

FEV1 Forced expiratory volume in one second, volume of air exhaled in first 

second of exhalation 

FRM Federal Reference Method 

GHG Greenhouse gas 

HA Hospital admissions 

HEI Health Effects Institute 

HF Hygroscopic fraction 

Hg Mercury 

IHD Ischemic heart disease 

IMPROVE Interagency Monitoring of Protected Visual Environment 

IPCC Intergovernmental Panel on Climate Change 

IRP Integrated Review Plan 

ISA Integrated Science Assessment 

IT Intratracheal 

IUGR Intrauterine growth restriction, intrauterine growth retardation 

Km Kilometer 

LC Local conditions 

LML Lowest measured level 

μg microgram 

μm micrometer, micron 

μg/m3 micrograms per cubic meter 

MCAPS Medicare Air Pollution Study 
 x 

MEA Millennium Ecosystem Assessment 

MI Myocardial infarction 

MLEE Moist light extinction efficiency 

Mm Megameter 

Mm-1 Inverse megameters, 1/(million meters) 

MSA Metropolitan Statistical Area 

N Nitrogen 

NAAQS National Ambient Air Quality Standards 

NCEA National Center for Environmental Assessment 

NCore National Core Monitoring Network 

Ni Nickel 

NMMAPS National Morbidity, Mortality, and Air Pollution Study 

NO Nitric oxide 

NO2 Nitrogen dioxide 

NO3

- Nitrate 

NOx Nitrogen oxides (NO+NO2) 

NOAA National Oceanic and Atmospheric Administration 

NRC National Research Council 

O3 Ozone 

OAQPS Office of Air Quality Planning and Standards 

OAR Office of Air and Radiation 

OC Organic carbon 

OCM Organic carbonaceous material 

OMB Office of Management and Budget 

ORD Office of Research and Development 

PA Policy Assessment 

PAH Polyaromatic hydrocarbon 

Pb Lead 

PBDES Polybrominated diphenyl ethers 

PM Particulate matter 

PM0.1 In general terms, particulate matter with a mobility diameter less than or 

equal to 0.1 μm; a measurement of ultrafine particles; since no reference 

method has been established, no 50% cut-point has been specified 
 xi 

PM2.5 In general terms, particulate matter with an aerodynamic diameter less 

than or equal to a nominal 2.5 μm; a measurement of fine particles 

In regulatory terms, particles with an upper 50% cut-point of 2.5 μm 

aerodynamic diameter (the 50% cut point diameter is the diameter at 

which the sampler collects 50% of the particles and rejects 50% of the 

particles) and a penetration curve as measured by a reference method 

based on Appendix L of 40 CFR Part 50 and designated in accordance 

with 40 CFR Part 53, by an equivalent method designated in accordance 

with 40 CFR Part 53, or by an approved regional method designated in 

accordance with Appendix C of 40 CFR Part 58 

PM10 In general terms, particulate matter with an aerodynamic diameter less 

than or equal to a nominal 10 μm; a measurement of thoracic particles 

(i.e., that subset of inhalable particles thought small enough to penetrate 

beyond the larynx into the thoracic region of the respiratory tract) 

In regulatory terms, particles with an upper 50% cut-point of 10± 0.5 μm 

aerodynamic diameter (the 50% cut point diameter is the diameter at 

which the sampler collects 50% of the particles and rejects 50% of the 

particles) and a penetration curve as measured by a reference method 

based on Appendix J of 40 CFR Part 50 and designated in accordance with 

40 CFR Part 53 or by an equivalent method designated in accordance with 

40 CFR Part 53 

PM10-2.5 In general terms, particulate matter with an aerodynamic diameter less 

than or equal to a nominal 10 μm and greater than a nominal 2.5 μm; a 

measurement of thoracic coarse particulate matter or the coarse fraction of 

PM10

In regulatory terms, particles with an upper 50% cut-point of 10 μm 

aerodynamic diameter and a lower 50% cut-point of 2.5 μm aerodynamic 

diameter (the 50% cut point diameter is the diameter at which the sampler 

collects 50% of the particles and rejects 50% of the particles) as measured 

by a reference method based on Appendix O of 40 CFR Part 50 and 

designated in accordance with 40 CFR Part 53 or by an equivalent method 

designated in accordance with 40 CFR Part 53 

POM Particulate organic matter 

POP Persistant organic pollutants 

PRB Policy-relevant background 

QAPP Quality Assurance Project Plan 

RA Risk Assessment 
 xii 

REA Risk and Exposure Assessment 

RF Radiative forcing 

RH Relative humidity 

SANDWICH Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbonaceous mass 

approach 

SCAB South Coast Air Basin (CA) 

SD Standard deviation 

SES Socioeconomic status 

S Sulfur 

SIP State implementation plan 

SLAMS State and local air monitoring stations 

SO2 Sulfur dioxide 

SOx Sulfur oxides 

STN Speciation Trends Network 

STP Standard temperature and pressure 

TB Tracheobronchial 

TSP Total suspended particulate 

UFPs Ultrafine particles 

UFVA Urban-Focused Visibility Assessment 

V Vanadium 

VAQ Visual Air Quality 

VOC Volatile organic compounds 

W/m2 Watts per square meter 

WACAP Western Airborne Contaminants Assessment Project 

WHI Women’s Health Initiative 

Zn Zinc 
 ES-1

EXECUTIVE SUMMARY 

This Policy Assessment (PA) has been prepared by staff in the Environmental 

Protection Agency’s (EPA) Office of Air Quality Planning and Standards (OAQPS) in 

conjunction with the Agency’s ongoing review of the national ambient air quality 

standards (NAAQS) for particulate matter (PM), which include primary (health-based) 

and secondary (welfare-based) standards. It presents staff conclusions regarding the 

adequacy of the current suite of PM standards as well as potential alternative standards 

for consideration in this review. 

Staff conclusions are based on the scientific and technical information, as well as 

uncertainties and limitations related to this information, assessed in other EPA 

documents, including the Integrated Science Assessment for Particulate Matter (Final 

Report) (ISA, US EPA, 2009a), the Quantitative Health Risk Assessment for Particulate 

Matter (Final Report) (RA, US EPA, 2010a) and the Particulate Matter Urban-Focused 

Visibility Assessment (Final Report) (UFVA, US EPA, 2010b). This PA is intended to 

“bridge the gap” between the relevant scientific evidence and technical information and 

the judgments required of the EPA Administrator in determining whether, and if so how, 

to revise the PM NAAQS. The current and potential alternative PM standards are 

considered in terms of the basic elements of the NAAQS: indicator, averaging time, 

form, and level. 

Primary Standards for Fine Particles (Chapter 2): 

In assessing the adequacy of the current suite of annual and 24-hour PM2.5

standards meant to protect public health against long- and short-term exposures to fine 

particles, staff concludes that the currently available information clearly calls into 

question the adequacy of the current standards and that consideration should be given to 

revising the suite of standards to provide increased public health protection. In 

considering alternative PM2.5 standards, staff concludes that protection from both longand short-term PM2.5 exposures can most effectively and efficiently be provided by 

relying primarily on the annual standard, with the 24-hour standard providing 

supplemental protection for days with high peak concentrations. 

Taking into account both evidence-based and risk-based considerations, staff 

concludes that consideration should be given to revising the current annual PM2.5

standard level of 15 μg/m3

 to a level within the range of 13 to 11 μg/m3. Staff further 

concludes that the evidence most strongly supports consideration of an alternative annual 

standard level in the range of 12 to 11 μg/m3

. In conjunction with consideration of an 

annual standard in the range of 12 to 11 μg/m3, staff concludes it is appropriate to 

consider retaining the current 24-hour PM2.5 standard level at 35 μg/m3. In conjunction 

with consideration of an annual standard level of 13 μg/m3

, staff concludes there is 

limited support to consider revising the 24-hour PM2.5 standard level to somewhat below 

35 μg/m3

, such as down to 30 μg/m3. 

In reaching these conclusions, staff recognizes that uncertainties and limitations 

remain in the currently available evidence and quantitative risk estimates. We note that 

no discernible thresholds have been identified for any health effects associated with long- 
 ES-2

or short-term PM2.5 exposures. Therefore, a primary focus of our consideration of 

alternative standard levels has been related to identifying the broader range of ambient 

PM2.5 concentrations which has the most influence on generating health effect estimates 

in the epidemiological studies. We also recognize that there remain uncertainties in our 

understanding of the relationship between differences in the ambient PM2.5 mixtures 

and/or exposure-related factors and the heterogeneity in health effects observed in the 

epidemiological evidence. Staff concludes that there is insufficient information at this 

time to consider supplementing the mass-based PM2.5 indicator by considering a separate 

indicator for ultrafine particles or for a specific PM2.5 component or group of components 

associated with any source categories of fine particles, or for eliminating any individual 

component or group of components from the mix of fine particles included in the PM2.5

mass-based indicator. 

Primary Standard for Thoracic Coarse Particles (Chapter 3): 

In assessing the adequacy of the current primary 24-hour PM10 standard, which is 

meant to protect public health against short-term exposures to thoracic coarse particles 

(i.e., PM10-2.5), staff concludes that it would be appropriate to consider either retaining or 

revising the current standard, depending on the relative weight placed on the evidence 

supporting associations with PM10-2.5 and the uncertainties and limitations in this 

evidence. Important uncertainties and limitations include those associated with the air 

quality estimates used in PM10-2.5 epidemiologic studies; the extent to which PM10-2.5 air 

quality concentrations reflect exposures to PM10-2.5; the extent to which PM10-2.5 itself is 

responsible for health effects reported in epidemiologic studies; and the extent to which 

the chemical and/or biological composition of PM10-2.5 affects particle toxicity. 

To the extent consideration is given to revising the current standard, which has a 

one-expected-exceedance form and a level of 150 μg/m3

, staff concludes that 

consideration should be given to revising both the form and level. In this case, 

consideration should be given to a 98th percentile form and a level within the range of 85 

μg/m3

 down to about 65 μg/m3

, in conjunction with retaining the PM10 indicator and the 

24-hour averaging time. Staff also concludes that standard levels in the upper part of this 

range are supported by the strongest evidence. 

Secondary Standards for PM-related Visibility Impairment (Chapter 4): 

In assessing the adequacy of the current suite of secondary annual and 24-hour 

PM2.5 standards (which are identical to the primary PM2.5 standards) meant to protect 

against PM-related visibility impairment, staff concludes that the currently available 

information clearly calls into question the adequacy of the current standards and that 

consideration should be given to revising the suite of standards to provide increased 

public welfare protection. Staff also concludes that the current PM2.5 mass indicator is 

not appropriate for a national standard intended to protect against PM-related visibility 

impairment since such a standard is inherently confounded by regional differences in 

relative humidity and species composition of PM2.5, which are critical factors in the 

relationship between ambient particles and associated visibility impairment. 

 Taking into account both evidence-based and impact assessment-based 

considerations, staff concludes that consideration should be given to establishing a new 
 ES-3

standard defined in terms of a calculated PM2.5 light extinction indicator. Such an 

indicator would use speciated PM2.5 mass and relative humidity data to calculate PM2.5

light extinction, similar to how light extinction is now calculated in the Regional Haze 

Program. In conjunction with a calculated PM2.5 light extinction indicator, staff 

concludes that consideration should be given to a 24-hour averaging time and a level of 

28 deciviews (dv) or somewhat below, down to 25 dv. Staff concludes that it would also 

be appropriate to consider a multi-hour, sub-daily averaging period (e.g., 4 hours) to the 

extent that data quality issues that have recently been raised about data from continuous 

PM2.5 monitors classified as Federal Equivalent Methods (FEMs) can be appropriately 

addressed. In conjunction with consideration of a standard with a 4-hour averaging time, 

staff concludes that consideration should be given to a level of 30 dv or somewhat below, 

down to 25 dv. In all cases, staff concludes that consideration should be given to a 90th

percentile form, averaged over three years. 

Secondary Standards for Non-visibility Welfare Effects (Chapter 5):

In assessing the adequacy of the current suite of secondary PM standards (which 

are identical to the primary PM2.5 and PM10 standards) meant to protect against PMrelated effects other than visibility impairment, staff has considered PM-related effects on 

climate, ecological effects, and effects on materials. Staff concludes that the currently 

available information supports retaining control of both fine and coarse particles to 

address PM-related effects on ecosystems and materials damage and soiling, but that 

there is insufficient information to assess the adequacy of protection afforded by the 

current standards. Staff also concludes that there is insufficient information at this time 

to base a NAAQS on climate impacts associated with current ambient concentrations of 

PM or its constituents. 
This page left intentionally blank. 
 1-1 

1 INTRODUCTION 

1.1 PURPOSE 

The U.S. Environmental Protection Agency (EPA) is presently conducting a review of 

the national ambient air quality standards (NAAQS) for particulate matter (PM). The plan and 

schedule for this review were presented in the Integrated Review Plan for the National Ambient 

Air Quality Standards for Particulate Matter (IRP; US EPA, 2008a). The IRP identified key 

policy-relevant issues to be addressed in this review as a series of questions that frame our 

consideration of whether the current NAAQS for PM should be retained or revised. 

This Policy Assessment (PA), prepared by staff in the EPA’s Office of Air Quality 

Planning and Standards (OAQPS), is intended to help “bridge the gap” between the relevant 

scientific information and assessments and the judgments required of the EPA Administrator in 

determining whether, and if so how, it is appropriate to revise the NAAQS for PM.1

 This PA 

presents factors relevant to EPA’s review of the primary (health-based) and secondary (welfarebased) PM NAAQS. It focuses on both evidence- and risk-based information in evaluating the 

adequacy of the current PM NAAQS and in identifying potential alternative standards for 

consideration. In so doing, we are seeking to provide as broad an array of options as is 

supportable by the available information, recognizing that the selection of a specific approach to 

reaching final decisions on the primary and secondary PM standards will reflect the judgments of 

the Administrator. 

In this PA, we consider the scientific and technical information available in this review as 

assessed in the Integrated Science Assessment for Particulate Matter (Final Report) (ISA, US 

EPA, 2009a), the Quantitative Health Risk Assessment for Particulate Matter (Final Report) 

(RA, US EPA, 2010a) and the Particulate Matter Urban-Focused Visibility Assessment (Final 

Report) (UFVA, US EPA, 2010b). In so doing, we focus on information that is most pertinent to 

evaluating the basic elements of NAAQS: indicator2

, averaging time, form,3 and level. These 

elements, which together serve to define each standard, must be considered collectively in 

evaluating the health and welfare protection afforded by the PM standards. 

Although this PA should be of use to all parties interested in this PM NAAQS review, it 

is written with an expectation that the reader has familiarity with the technical discussions 

 1

 Preparation of a PA by OAQPS staff reflects Administrator Jackson’s decision to modify the NAAQS review 

process that was presented in the IRP. See http://www.epa.gov/ttn/naaqs/review.html for more information on the 

current NAAQS review process. 

2

 The “indicator” of a standard defines the chemical species or mixture that is to be measured in determining 

whether an area attains the standard. 

3

 The “form” of a standard defines the air quality statistic that is to be compared to the level of the standard in 

determining whether an area attains the standard. 
 1-2 

contained in the ISA (US EPA, 2009a) and in the quantitative health risk and visibility 

assessment documents (US EPA, 2010a,b). 

1.2 BACKGROUND 

1.2.1 Legislative Requirements 

Two sections of the Clean Air Act (CAA) govern the establishment and revision of the 

NAAQS. Section 108 (42 U.S.C. section 7408) directs the Administrator to identify and list 

certain air pollutants and then to issue air quality criteria for those pollutants. The Administrator 

is to list those air pollutants that in her “judgment, cause or contribute to air pollution which may 

reasonably be anticipated to endanger public health or welfare;” “the presence of which in the 

ambient air results from numerous or diverse mobile or stationary sources;” and “for which . . . 

[the Administrator] plans to issue air quality criteria...” Air quality criteria are intended to 

“accurately reflect the latest scientific knowledge useful in indicating the kind and extent of all 

identifiable effects on public health or welfare which may be expected from the presence of [a] 

pollutant in the ambient air . . .” 42 U.S.C. § 7408(b). Section 109 (42 U.S.C. 7409) directs the 

Administrator to propose and promulgate “primary” and “secondary” NAAQS for pollutants for 

which air quality criteria are issued. Section 109(b)(1) defines a primary standard as one “the 

attainment and maintenance of which in the judgment of the Administrator, based on such 

criteria and allowing an adequate margin of safety, are requisite to protect the public health.” 4 A 

secondary standard, as defined in section 109(b)(2), must “specify a level of air quality the 

attainment and maintenance of which, in the judgment of the Administrator, based on such 

criteria, is requisite to protect the public welfare from any known or anticipated adverse effects 

associated with the presence of [the] pollutant in the ambient air.”5 

The requirement that primary standards provide an adequate margin of safety was 

intended to address uncertainties associated with inconclusive scientific and technical 

information available at the time of standard setting. It was also intended to provide a reasonable 

degree of protection against hazards that research has not yet identified. See Lead Industries 

Association v. EPA, 647 F.2d 1130, 1154 (D.C. Cir 1980), cert. denied, 449 U.S. 1042 (1980); 

American Petroleum Institute v. Costle, 665 F.2d 1176, 1186 (D.C. Cir. 1981), cert. denied, 455 

 4

 The legislative history of section 109 indicates that a primary standard is to be set at “the maximum permissible 

ambient air level . . . which will protect the health of any [sensitive] group of the population,” and that for this 

purpose “reference should be made to a representative sample of persons comprising the sensitive group rather than 

to a single person in such a group” S. Rep. No. 91-1196, 91st Cong., 2d Sess. 10 (1970). 

5

 Welfare effects as defined in section 302(h) (42 U.S.C. § 7602(h)) include, but are not limited to, “effects on soils, 

water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and climate, damage to and 

deterioration of property, and hazards to transportation, as well as effects on economic values and on personal 

comfort and well-being.” 
 1-3 

U.S. 1034 (1982); American Farm Bureau Federation v. EPA, 559 F. 3d 512, 533 (D.C. Cir. 

2009); Association of Battery Recyclers v. EPA, 604 F. 3d 613, 617-18 (D.C. Cir. 2010). Both 

kinds of uncertainties are components of the risk associated with pollution at levels below those 

at which human health effects can be said to occur with reasonable scientific certainty. Thus, in 

selecting primary standards that provide an adequate margin of safety, the Administrator is 

seeking not only to prevent pollution levels that have been demonstrated to be harmful but also 

to prevent lower pollutant levels that may pose an unacceptable risk of harm, even if the risk is 

not precisely identified as to nature or degree. The CAA does not require the Administrator to 

establish a primary NAAQS at a zero-risk level or at background concentration levels, see Lead 

Industries v. EPA, 647 F.2d at 1156 n.51, but rather at a level that reduces risk sufficiently so as 

to protect public health with an adequate margin of safety. 

In addressing the requirement for an adequate margin of safety, the EPA considers such 

factors as the nature and severity of the health effects involved, the size of sensitive population(s) 

at risk, and the kind and degree of the uncertainties that must be addressed. The selection of any 

particular approach to providing an adequate margin of safety is a policy choice left specifically 

to the Administrator’s judgment. See Lead Industries Association v. EPA, 647 F.2d at 1161-62; 

Whitman v. American Trucking Associations, 531 U.S. 457, 495 (2001). 

In setting primary and secondary standards that are “requisite” to protect public health 

and welfare, respectively, as provided in section 109(b), EPA’s task is to establish standards that 

are neither more nor less stringent than necessary for these purposes. In so doing, EPA may not 

consider the costs of implementing the standards. See generally, Whitman v. American Trucking 

Associations, 531 U.S. 457, 465-472, 475-76 (2001). Likewise, “[a]ttainability and 

technological feasibility are not relevant considerations in the promulgation of national ambient 

air quality standards.” American Petroleum Institute v. Costle, 665 F. 2d at 1185. 

Section 109(d)(1) requires that “not later than December 31, 1980, and at 5-year intervals 

thereafter, the Administrator shall complete a thorough review of the criteria published under 

section 108 and the national ambient air quality standards . . . and shall make such revisions in 

such criteria and standards and promulgate such new standards as may be appropriate . . . .” 

Section 109(d)(2) requires that an independent scientific review committee “shall complete a 

review of the criteria . . . and the national primary and secondary ambient air quality standards ... 

and shall recommend to the Administrator any new . . . standards and revisions of existing 

criteria and standards as may be appropriate . . . .” Since the early 1980's, this independent 

review function has been performed by the Clean Air Scientific Advisory Committee (CASAC).6

 

 6 Lists of CASAC members and of members of the CASAC PM Review Panel are available at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/WebCASAC/CommitteesandMembership?OpenDocument . 
 1-4 

1.2.2 Previous PM NAAQS Reviews 

The EPA initially established NAAQS for PM under section 109 of the CAA in 1971. 

Since then, the Agency has made a number of changes to these standards to reflect continually 

expanding scientific information, particularly with respect to the selection of indicator7 and level. 

Table 1-1 provides a summary of the PM NAAQS that have been promulgated to date. These 

decisions are briefly discussed below. 

In 1971, EPA established NAAQS for PM based on the original air quality criteria 

document (DHEW, 1969; 36 FR 8186, April 30, 1971). The reference method specified for 

determining attainment of the original standards was the high-volume sampler, which collects 

PM up to a nominal size of 25 to 45 micrometers (μm) (referred to as total suspended particles or 

TSP). The primary standards (measured by the indicator TSP) were 260 μg/m3

, 24-hour average, 

not to be exceeded more than once per year, and 75 μg/m3

, annual geometric mean. The 

secondary standard was 150 μg/m3

, 24-hour average, not to be exceeded more than once per 

year. 

In October 1979, EPA announced the first periodic review of the criteria and NAAQS for 

PM, and significant revisions to the original standards were promulgated in 1987 (52 FR 24634, 

July 1, 1987). In that decision, EPA changed the indicator for PM from TSP to PM10, the latter 

including particles with an aerodynamic diameter less than or equal to a nominal 10 μm, which 

delineates thoracic particles (i.e., that subset of inhalable particles thought small enough to 

penetrate beyond the larynx into the thoracic region of the respiratory tract). The EPA also 

revised the primary standards by: (1) replacing the 24-hour TSP standard with a 24-hour PM10

standard of 150 μg/m3 with no more than one expected exceedance per year; and (2) replacing 

the annual TSP standard with a PM10 standard of 50 μg/m3, annual arithmetic mean. The 

secondary standard was revised by replacing it with 24-hour and annual standards identical in all 

respects to the primary standards. The revisions also included a new reference method for the 

measurement of PM10 in the ambient air and rules for determining attainment of the new 

standards. On judicial review, the revised standards were upheld in all respects. Natural 

Resources Defense Council v. EPA, 902 F. 2d 962 (D.C. Cir. 1990), cert. denied, 498 U.S. 1082 

(1991). 

 7

 Particulate matter is the generic term for a broad class of chemically and physically diverse substances that exist as 

discrete particles (liquid droplets or solids) over a wide range of sizes, such that the indicator for a PM NAAQS has 

historically been defined in terms of particle size ranges. 
 1-5 

Table 1-1. Summary of National Ambient Air Quality Standards Promulgated for 

Particulate Matter 1971-20068

Final Rule Indicator Averaging

Time Level Form

1971 

36 FR 8186 

April 30, 1971 

TSP

24-hour 

260 μg/m3 

(primary) 

150 μg/m3

(secondary) 

Not to be exceeded more than once per year

Annual 75 μg/m3

(primary) Annual average 

1987 

52 FR 24634 

July 1, 1987 

PM10

24-hour 150 μg/m3 Not to be exceeded more than once per year 

on average over a 3-year period 

Annual 50 μg/m3 Annual arithmetic mean, averaged over 3 

years 

1997 

62 FR 38652 

July 18, 1997 

PM2.5

24-hour 65 μg/m3 98th percentile, averaged over 3 years9

Annual 15 μg/m3 Annual arithmetic mean, averaged over 3 

years10,

11

PM10

24-hour 150 μg/m3

Initially promulgated 99th percentile, 

averaged over 3 years; when 1997 standards 

were vacated, the form of 1987 standards 

remained in place (not to be exceeded more 

than once per year on average over a 3-year 

period) 

Annual 50 μg/m3 Annual arithmetic mean, averaged over 3 

years 

2006 

71 FR 61144 

October 17, 2006 

PM2.5

24-hour 35 μg/m3 98th percentile, averaged over 3 years9

Annual 15 μg/m3 Annual arithmetic mean, averaged over 3 

years11,12

PM10 24-hour 150 μg/m3 Not to be exceeded more than once per year 

on average over a 3-year period 

 8

 When not specified, primary and secondary standards are identical. 9

 The 24-hour standard NAAQS metric is defined as an integer (zero decimal places) as determined by rounding. 

For example, a 3-year average 98th percentile concentration of 35.49 μg/m3

would round to 35 μg/m3and thus meet 

the 24-hour standard and a 3-year average of 35.50 μg/m3

would round to 36 μg/m3 and hence, violate the 24-hour 

standard (40 CFR part 50 Appendix N). 

10 The annual standard NAAQS metric is defined to one decimal place (i.e., 15.0 μg/m3

) as determined by rounding. 

For example, a 3-year average annual mean of 15.04 μg/m3

 would round to 15.0 μg/m3 and thus meet the annual 

standard and a 3-year average of 15.05 μg/m3

 would round to 15.1 μg/m3 and hence, violate the annual standard. 11 The level of the standard was to be compared to measurements made at sites that represent “community-wide air 

quality” recording the highest level, or, if specific constraints were met, measurements from multiple communitywide air quality monitoring sites could be averaged (“spatial averaging”). 

12 The constraints on the spatial averaging criteria were tightened by further limiting the conditions under which 

some areas may average measurements from multiple community-oriented monitors to determine compliance (see 

71 FR 61165-61167). 
 1-6 

In April 1994, EPA announced its plans for the second periodic review of the criteria and 

NAAQS for PM, and promulgated significant revisions to the NAAQS in 1997 (62 FR 38652, 

July 18, 1997). Most significantly, EPA determined that although the PM NAAQS should 

continue to focus on thoracic particles (PM10), the fine and coarse fractions of PM10 should be 

considered separately. New standards were added, using PM2.5 as the indicator for fine 

particles.13 The PM10 standards were retained for the purpose of regulating the coarse fraction of 

PM10 (referred to as thoracic coarse particles or PM10-2.5).14 The EPA established two new PM2.5

standards: an annual standard of 15 μg/m3, based on the 3-year average of annual arithmetic 

mean PM2.5 concentrations from single or multiple monitors sited to represent community-wide 

air quality15; and a 24-hour standard of 65 μg/m3, based on the 3-year average of the 98th

percentile of 24-hour PM2.5 concentrations at each population-oriented monitor16 within an area. 

Also, EPA established a new reference method for the measurement of PM2.5 in the ambient air 

and rules for determining attainment of the new standards. To continue to address thoracic 

coarse particles, the annual PM10 standard was retained, while the form, but not the level, of the 

24-hour PM10 standard was revised to be based on the 99th percentile of 24-hour PM10

concentrations at each monitor in an area. The EPA revised the secondary standards by making 

them identical in all respects to the primary standards. 

Following promulgation of the revised PM NAAQS in 1997, petitions for review were 

filed by a large number of parties, addressing a broad range of issues. In May 1998, a threejudge panel of the U.S. Court of Appeals for the District of Columbia Circuit issued an initial 

decision that upheld EPA’s decision to establish fine particle standards, holding that "the 

growing empirical evidence demonstrating a relationship between fine particle pollution and 

adverse health effects amply justifies establishment of new fine particle standards." American 

Trucking Associations v. EPA , 175 F. 3d 1027, 1055-56 (D.C. Cir. 1999), rehearing granted in 

part and denied in part, 195 F. 3d 4 (D.C. Cir. 1999), affirmed in part and reversed in part, 

Whitman v. American Trucking Associations, 531 U.S. 457 (2001). The panel also found "ample 

support" for EPA's decision to regulate coarse particle pollution, but vacated the 1997 PM10

standards, concluding, in part, that PM10 is a "poorly matched indicator for coarse particulate 

 13 PM2.5 includes particles with an aerodynamic diameter less than or equal to a nominal 2.5 μm. 14 See 40 CFR Parts 50, 53, and 58 for more information on reference and equivalent methods for measuring PM in 

ambient air. 

15 Monitoring stations sited to represent community-wide air quality will typically be at the neighborhood or urbanscale; however, where a population-oriented micro or middle-scale PM2.5 monitoring station represents many such 

locations throughout a metropolitan area, these smaller scales may also be considered to represent community-wide 

air quality (40 CFR Part 58, Appendix D, 4.7.1(b)). 

16 Population-oriented monitoring (or sites) means residential areas, commercial areas, recreational areas, industrial 

areas where workers from more than one company are located, and other areas where a substantial number of people 

may spend a significant fraction of their day. (40 CFR Part 58, §58.1) 
 1-7 

pollution" because it includes fine particles. Id. at 1053-55. Pursuant to the court’s decision, 

EPA removed the vacated 1997 PM10 standards from the Code of Federal Regulations (CFR) (69 

FR 45592, July 30, 2004) and deleted the regulatory provision [at 40 CFR section 50.6(d)] that 

controlled the transition from the pre-existing 1987 PM10 standards to the 1997 PM10 standards. 

The pre-existing 1987 PM10 standards remained in place (65 FR 80776, December 22, 2000). 

The court also upheld EPA’s determination not to establish more stringent secondary standards 

for fine particles to address effects on visibility (175 F. 3d at 1027). 

More generally, the panel held (over a strong dissent) that EPA’s approach to establishing 

the level of the standards in 1997, both for the PM and for the ozone (O3) NAAQS promulgated 

on the same day, effected “an unconstitutional delegation of legislative authority.” Id. at 1034-

40. Although the panel stated that “the factors EPA uses in determining the degree of public 

health concern associated with different levels of ozone and PM are reasonable,” it remanded the 

rule to EPA, stating that when EPA considers these factors for potential non-threshold pollutants 

“what EPA lacks is any determinate criterion for drawing lines” to determine where the 

standards should be set. Consistent with EPA’s long-standing interpretation and D.C. Circuit 

precedent, the panel also reaffirmed its prior holdings that in setting NAAQS EPA is “not 

permitted to consider the cost of implementing those standards” Id. at 1040-41. 

On EPA’s petition for rehearing, the panel adhered to its position on these points. 

American Trucking Associations v. EPA, 195 F. 3d 4 (D.C. Cir. 1999). The full Court of 

Appeals denied EPA’s request for rehearing en banc, with five judges dissenting. Id. at 13. Both 

sides filed cross appeals on these issues to the United States Supreme Court, which granted 

certiorari. In February 2001, the Supreme Court issued a unanimous decision upholding EPA’s 

position on both the constitutional and cost issues. Whitman v. American Trucking Associations, 

531 U.S. 457, 464, 475-76. On the constitutional issue, the Court held that the statutory 

requirement that NAAQS be “requisite” to protect public health with an adequate margin of 

safety sufficiently cabined EPA’s discretion, affirming EPA’s approach of setting standards that 

are neither more nor less stringent than necessary. The Supreme Court remanded the case to the 

Court of Appeals for resolution of any remaining issues that had not been addressed in that 

court’s earlier rulings. Id. at 475-76. In March 2002, the Court of Appeals rejected all 

remaining challenges to the standards, holding under the traditional standard of review that 

EPA’s PM2.5 standards were reasonably supported by the administrative record and were not 

“arbitrary and capricious.” American Trucking Associations v. EPA, 283 F. 3d 355, 369-72 

(D.C. Cir. 2002). 

In October 1997, EPA published its plans for the next periodic review of the air quality 

criteria and NAAQS for PM (62 FR 55201, October 23, 1997). After CASAC and public review 

of several drafts, EPA’s National Center for Environmental Assessment (NCEA) finalized the 
 1-8 

Air Quality Criteria Document for Particulate Matter (henceforth, AQCD or the "Criteria 

Document") in October 2004 (U.S. EPA, 2004) and OAQPS finalized an assessment document, 

Particulate Matter Health Risk Assessment for Selected Urban Areas (Abt Associates, 2005), 

and a “Staff Paper,” Review of the National Ambient Air Quality Standards for Particulate 

Matter: Policy Assessment of Scientific and Technical Information, in December 2005 (U.S. 

EPA, 2005). In conjunction with its review of the Staff Paper, CASAC provided advice to the 

Administrator on revisions to the PM NAAQS (Henderson, 2005a). In particular, most CASAC 

PM Panel members favored revising the level of the primary 24-hour PM2.5 standard in the range 

of 35 to 30 μg/m3 with a 98th percentile form, in concert with revising the level of the primary 

annual PM2.5 standard in the range of 14 to 13 μg/m3 (Henderson, 2005a, p.7). For thoracic 

coarse particles, the Panel had reservations in recommending a primary 24-hour PM10-2.5

standard, and agreed that there was a need for more research on the health effects of thoracic 

coarse particles (Henderson, 2005b). With regard to secondary standards, most Panel members 

strongly supported establishing a new, distinct secondary PM2.5 standard to protect urban 

visibility (Henderson, 2005a, p. 9). 

On January 17, 2006, EPA proposed to revise the primary and secondary NAAQS for PM 

(71 FR 2620) and solicited comment on a broad range of options. Proposed revisions included: 

revising the level of the primary 24-hour PM2.5 standard to 35 μg/m3

; revising the form, but not 

the level, of the primary annual PM2.5 standard by tightening the constraints on the use of spatial 

averaging; replacing the primary 24-hour PM10 standard with a 24-hour standard defined in 

terms of a new indicator, PM10-2.5

17 set at a level of 70 μg/m3

 based on the 3-year average of the 

98th percentile of 24-hour PM10-2.5 concentrations; revoking the primary annual PM10 standard; 

and revising the secondary standards by making them identical in all respects to the proposed 

suite of primary standards for fine and coarse particles.18 Subsequent to the proposal, CASAC 

provided additional advice to EPA in a letter to the Administrator requesting reconsideration of 

CASAC’s recommendations for both the primary and secondary PM2.5 standards as well as the 

standards for thoracic coarse particles (Henderson, 2006a). 

On October 17, 2006, EPA promulgated revisions to the PM NAAQS to provide 

increased protection of public health and welfare (71 FR 61144). With regard to the primary and 

secondary standards for fine particles, EPA revised the level of the primary 24-hour PM2.5

 17 This proposed indicator was qualified so as to include any ambient mix of PM10-2.5 dominated by particles 

generated by high-density traffic on paved roads, industrial sources, and construction sources, and to exclude any 

ambient mix of particles dominated by rural windblown dust and soils and agricultural and mining sources (71 FR 

2667 to 2668). 

18 In recognition of an alternative view expressed by most members of the CASAC PM Panel, the Agency also 

solicited comments on a subdaily (4- to 8-hour averaging time) secondary PM2.5 standard to address visibility 

impairment, considering alternative standard levels within a range of 20 to 30 μg/m3

 in conjunction with a form 

within a range of the 92nd to 98th percentile (71 FR 2685). 
 1-9 

standard to 35 μg/m3, retained the level of the primary annual PM2.5 standard at 15 μg/m3, and 

revised the form of the primary annual PM2.5 standard by adding further constraints on the 

optional use of spatial averaging. The EPA revised the secondary standards for fine particles by 

making them identical in all respects to the primary standards. With regard to the primary and 

secondary standards for thoracic coarse particles, EPA retained the level and form of the 24-hour 

PM10 standard (such that the standard remained at a level of 150 μg/m3 with a one-expected 

exceedance form), and revoked the annual PM10 standard. The EPA also established a new 

Federal Reference Method (FRM) for the measurement of PM10-2.5 in the ambient air (71 FR 

61212-13). Although the standards for thoracic coarse particles were not defined in terms of a 

PM10-2.5 indicator, the new FRM for PM10-2.5 was established to provide a basis for approving 

Federal Equivalent Methods (FEMs) and to promote gathering scientific data to support future 

reviews of the PM NAAQS. 

Following issuance of the final rule, CASAC articulated its concern that “EPA’s final 

rule on the NAAQS for PM does not reflect several important aspects of the CASAC’s advice” 

(Henderson et al., 2006b). With regard to the primary PM2.5 annual standard, CASAC expressed 

serious concerns regarding the decision to retain the level of the standard at 15 μg/m3. With 

regard to EPA’s final decision to retain the 24-hour PM10 standard for thoracic coarse particles, 

CASAC acknowledged concerns associated with retaining this standard while recognizing the 

need to have a standard in place to protect against effects associated with short-term exposures to 

thoracic coarse particles. With regard to EPA’s final decision to revise the secondary PM2.5

standards to be identical in all respects to the revised primary PM2.5 standards, CASAC 

expressed concernsthat its advice to establish a distinct secondary standard for fine particles to 

address visibility impairment was not followed. 

1.2.3 Litigation Related to the 2006 PM Standards 

Several parties filed petitions for review following promulgation of the revised PM 

NAAQS in 2006. These petitions addressed the following issues: (1) selecting the level of the 

primary annual PM2.5 standard; (2) retaining PM10 as the indicator of a standard for thoracic 

coarse particles, retaining the level and form of the 24-hour PM10 standard, and revoking the 

PM10 annual standard; and (3) setting the secondary PM2.5 standards identical to the primary 

standards. On February 24, 2009, the U.S. Court of Appeals for the District of Columbia Circuit 

issued its opinion in the case American Farm Bureau Federation v. EPA, 559 F. 3d 512 (D.C. 

Cir. 2009). The court remanded the primary annual PM2.5 NAAQS to EPA because EPA failed 

to adequately explain why the standard provided the requisite protection from both short- and 

long-term exposures to fine particles, including protection for at-risk populations. American 

Farm Bureau Federation v. EPA, 559 F. 3d 512, 520-27 (D.C. Cir. 2009). With regard to the 
 1-10 

standards for PM10, the court upheld EPA’s decisions to retain the 24-hour PM10 standard to 

provide protection from thoracic coarse particle exposures and to revoke the annual PM10

standard. American Farm Bureau Federation, 559 F. 2d at 533-38. With regard to the 

secondary PM2.5 standards, the court remanded the standards to EPA because the Agency failed 

to adequately explain why setting the secondary PM standards identical to the primary standards 

provided the required protection for public welfare, including protection from visibility 

impairment. American Farm Bureau Federation, 559 F. 2d at 528-32. 

The decisions of the court with regard to these three issues are discussed further in 

chapters 2, 3 and 4, respectively (see sections 2.1.2, 3.1.2, and 4.1.2). The EPA is responding to 

the court’s remands as part of the current review of the PM NAAQS. 

1.2.4 Current PM NAAQS Review 

The EPA initiated the current review of the air quality criteria for PM in June 2007 with a 

general call for information (72 FR 35462, June 28, 2007). In July 2007, EPA held two “kickoff” workshops on the primary and secondary PM NAAQS, respectively (72 FR 34003 and 

34004, June 20, 2007).19 These workshops provided an opportunity for a public discussion of 

the key policy-relevant issues around which EPA would structure this PM NAAQS review and 

the most meaningful new science that would be available to inform our understanding of these 

issues. 

Based in part on the workshop discussions, EPA developed a draft IRP outlining the 

schedule, process, and key policy-relevant questions that would guide the evaluation of the air 

quality criteria for PM and the review of the primary and secondary PM NAAQS (US EPA, 

2007). On November 30, 2007, EPA held a consultation with CASAC on the draft IRP (72 FR 

63177, November 8, 2007), which included the opportunity for public comment. The final IRP 

(US EPA, 2008a) incorporated comments from CASAC (Henderson, 2008) and the public on the 

draft plan as well as input from senior Agency managers.20

As part of the process of preparing the PM ISA, NCEA hosted a peer review workshop in 

June 2008 on preliminary drafts of key ISA chapters (73 FR 30391, May 27, 2008). The first 

external review draft ISA (US EPA, 2008b; 73 FR 77686, December 19, 2008) was reviewed by 

 19 See workshop materials available at: http://www.regulations.gov/search/Regs/home.html#home Docket ID 

numbers EPA-HQ-OAR-2007-0492-008; EPA-HQ-OAR-2007-0492-009; EPA-HQ-OAR-2007-0492-010; and 

EPA-HQ-OAR-2007-0492-012. 

20 The process followed in this review varies from the NAAQS review process described in section 1.1 of the IRP 

(US EPA, 2008a). On May 21, 2009, EPA Administrator Jackson called for key changes to the NAAQS review 

process including reinstating a policy assessment document that contains staff analyses of the scientific bases for 

alternative policy options for consideration by senior Agency management prior to rulemaking. In conjunction with 

this change, EPA will no longer issue a policy assessment in the form of an advance notice of proposed rulemaking 

(ANPR) as discussed in the IRP. For more information on the overall process followed in this review including a 

description of the major elements of the process for reviewing NAAQS see Jackson (2009). 
 1-11 

CASAC and the public at a meeting held in April 2009 (74 FR 2688, February 19, 2009). Based 

on CASAC and public comments, NCEA prepared a second draft ISA (US EPA, 2009b; 74 FR 

38185, July 31, 2009), which was reviewed by CASAC and the public at a meeting held on 

October 5-6, 2009 (74 FR 46586, September 10, 2009). Based on CASAC and public 

comments, NCEA prepared the final ISA (US EPA, 2009a; 74 FR 66353, December 15, 2009). 

In preparing the Risk and Exposure Assessment (REA) documents that build on the 

scientific evidence presented in the ISA, OAQPS released two planning documents: Particulate 

Matter National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and 

Exposure Assessment and Particulate Matter National Ambient Air Quality Standards: Scope 

and Methods Plan for Urban Visibility Impact Assessment (henceforth, Scope and Methods 

Plans, US EPA, 2009c,d; 74 FR 11580, March 18, 2009). These planning documents outlined 

the scope and approaches that staff planned to use in conducting quantitative assessments as well 

as key issues that would be addressed as part of the assessments. In designing and conducting 

the initial health risk and visibility impact assessments, we considered CASAC comments 

(Samet 2009a,b) on the Scope and Methods Plans made during an April 2009 consultation (74 

FR 7688, February 19, 2009) as well as public comments. Two draft assessment documents, 

Risk Assessment to Support the Review of the PM2.5 Primary National Ambient Air Quality 

Standards: External Review Draft - September 2009 (US EPA 2009e) and Particulate Matter 

Urban-Focused Visibility Assessment - External Review Draft - September 2009 (US EPA, 

2009f) were reviewed by CASAC and the public at a meeting held on October 5 - 6, 2009 (74 FR 

46586, September 10, 2009). Based on CASAC (Samet 2009c,d) and public comments, OAQPS 

staff revised these draft documents and released second draft assessment documents (US EPA, 

2010d,e) in January and February 2010 (75 FR 4067, January 26, 2010) for CASAC and public 

review at a meeting held on March 10-11, 2010 (75 FR 8062, February 23, 2010). Based on 

CASAC (Samet, 2010a,b) and public comments on the second draft assessment documents, we 

revised these documents and released final assessment documents in June and July 2010 (US 

EPA, 2010a,b; 75 FR 39252, July 8, 2010). 

A preliminary draft PA (US EPA, 2009g) was released in September 2009 for 

informational purposes and to facilitate discussion with CASAC at the October 5 - 6, 2009 

meeting on the overall structure, areas of focus, and level of detail to be included in the PA. 

CASAC’s comments on the preliminary draft PA encouraged the development of a document 

focused on the key policy-relevant issues that draws from and is not repetitive of information in 

the ISA and REAs. These comments were considered in developing a first draft PA (US EPA, 

2010c; 75 FR 4067, January 26, 2010) that built upon the information presented and assessed in 

the final ISA and second draft risk and visibility assessment documents. The EPA presented an 

overview of the first draft PA at a CASAC meeting on March 10, 2010 (75 FR 8062, February 
 1-12 

23, 2010). The first draft PA was reviewed by CASAC and the public and discussed during 

public teleconferences on April 8 - 9, 2010 (75 FR 8062, February 23, 2010) and May 7, 2010 

(75 FR 19971, April 16, 2010). 

The CASAC (Samet, 2010c) and public comments on the first draft PA were considered 

by EPA staff in developing a second draft PA (US EPA, 2010f; 75 FR 39253, July 8, 2010) 

which was reviewed by CASAC at a meeting on July 26 - 27, 2010 (75 FR 32763, June 9, 2010). 

CASAC (Samet, 2010d, see Appendix A) and public comments on the second draft PA were 

considered by EPA staff in preparing this final PA.21 This document includes final staff 

conclusions related to the adequacy of the current PM standards and the broadest range of 

alternative standards that are supported by the currently available scientific evidence and 

quantitative assessments. 

1.3 CURRENT AMBIENT MONITORING NETWORKS FOR PM 

In the U.S., state and local agencies operate the vast majority of PM monitors as part of 

the State and local air monitoring stations (SLAMS) network. The SLAMS network supports 

three major objectives: to provide air pollution data to the general public in a timely manner; to 

support compliance with ambient air quality standards and emissions strategy development; and 

to support air pollution research studies. PM monitors are deployed according to network design 

criteria described in Appendix D to 40 CFR Part 58. For comparison to the annual standard, 

PM2.5 monitoring sites are required to represent community-wide air quality. For most urban 

locations this will result in siting monitors at the neighborhood scale22 where PM2.5

concentrations are reasonably homogeneous throughout an entire urban sub-region. At least one 

monitoring station representing community-wide air quality is also to be sited in a populationoriented area of expected maximum concentration. Sites that represent relatively unique 

population-oriented microscale,23 or localized hot-spot, or unique population-oriented middle 

 21 All written comments submitted to the Agency are available in the docket for this PM NAAQS review (EPA-HQOAR-2007-0429). Transcripts of public meetings and teleconferences held in conjunction with CASAC’s reviews 

are also included in the docket. 

22 Neighborhood scale for PM2.5: Measurements in this category would represent conditions throughout some 

reasonably homogeneous urban sub-region with dimensions of a few kilometers and of generally more regular shape 

than the middle scale. Homogeneity refers to the particulate matter concentrations, as well as the land use and land 

surface characteristics. Much of the PM2.5 exposures are expected to be associated with this scale of measurement. 

In some cases, a location carefully chosen to provide neighborhood scale data would represent the immediate 

neighborhood as well as neighborhoods of the same type in other parts of the city. See 40 CRF Part 58 Appendix D, 

4.7.1(c)(3). 

23 Microscale for PM2.5: This scale would typify areas such as downtown street canyons and traffic corridors where 

the general public would be exposed to maximum concentrations from mobile sources. See 40 CFR Part 58 

Appendix D, 4.7.1(c)(1). 
 1-13 

scale24 impact sites are only eligible for comparison to the 24-hour PM2.5 NAAQS. For PM10, 

the network design criteria emphasize monitoring at middle25 and neighborhood26 scales to 

effectively characterize the emissions from both mobile and stationary sources, although not 

ruling out microscale monitoring in some instances (40 CFR Part 58 Appendix D, 4.6 (b)). 

The sections below briefly summarize the monitoring networks for PM including PM2.5 

mass, PM2.5 speciation, and PM10 mass, as well as the new PM10-2.5 mass network which will be 

part of the forthcoming National Core Multipollutant Monitoring Network (NCore).27 

Additional information and maps of monitoring sites are provided in Appendix B. 

1.3.1 PM2.5 Mass 

The PM2.5 monitoring requirements provide for monitors in Metropolitan Statistical 

Areas28 (MSAs) based on a combination of population and design value29 (see Table D-5, 40 

CFR Part 58) with higher populated locations having more polluted air required to have the most 

monitors. Background and transport monitors30 are also required of each state with options for 

utilizing IMPROVE31 and other PM2.5 data to provide for flexibility in meeting the monitoring 

requirements. 

 24 Middle scale for PM2.5: People moving through downtown areas, or living near major roadways, encounter 

particle concentrations that would be adequately characterized by this spatial scale. Thus, measurements of this type 

would be appropriate for the evaluation of possible effects associated with short-term exposures to PM2.5. See 40 

CFR Part 58 Appendix D, 4.7.1(c)(2). 

25 Middle scale for PM10: Much of the short-term public exposure to coarse fraction particles (using PM10 as the 

indicator) is characterized on this scale and on the neighborhood scale. People moving through downtown areas or 

living near major roadways or stationary sources, may encounter particulate matter that would be adequately 

characterized by measurements of this spatial scale. See 40 CFR Part 58 Appendix D, 4.6(b)(2). 26 Neighborhood scale for PM10: Measurements in this category represent conditions throughout some reasonably 

homogeneous urban subregion with dimensions of a few kilometers and of generally more regular shape than the 

middle scale. Homogeneity refers to the particulate matter concentrations, as well as the land use and land surface 

characteristics. In some cases, a location carefully chosen to provide neighborhood scale data would represent not 

only the immediate neighborhood but also neighborhoods of the same type in other parts of the city. See 40 CFR 

Part 58 Appendix D, 4.6(b)(3). 

27 The NCore network is a multi-pollutant network that includes measurements of particles, gases, and meteorology 

(71 FR 61236, October 17, 2006). The network is intended to support integrated air program management needs. 

The NCore monitoring network is expected to be fully operational by January 1, 2011. 

28 Metropolitan and micropolitan statistical areas (metro and micro areas) are geographic entities defined by the U.S. 

Office of Management and Budget (OMB) for use by Federal statistical agencies in collecting, tabulating, and 

publishing Federal statistics. The term "Core Based Statistical Area" (CBSA) is a collective term for both metro and 

micro areas. A metro area contains a core urban area of 50,000 or more population, and a micro area contains an 

urban core of at least 10,000 (but less than 50,000) population. Each metro or micro area consists of one or more 

counties and includes the counties containing the core urban area, as well as any adjacent counties that have a high 

degree of social and economic integration (as measured by commuting to work) with the urban core. 

29 Design values are the metrics (i.e., statistics) that are compared to the NAAQS levels to determine compliance. 

For example, for PM2.5, design values are calculated as shown in section 4.0 of Appendix N to 40 CFR Part 50. 30 Background and transport sites are used to provide regional background and transport data. When evaluated in 

combination with urban sites, these locations help determine the local urban contribution to PM. 

31 The Interagency Monitoring of Protected Visual Environment (IMPROVE) program was established in 1985 to 

aid the creation of federal and state implementation plans for the protection of visibility in Class 1 areas (i.e., 155 
 1-14 

The network of PM2.5 FRM monitors includes over 900 monitoring stations throughout 

the country. The FRM is a manually operated sampler that is programmed to operate for a 24-

hour period covering midnight to midnight local standard time. Although approximately 150 

FRM sites operate every day, most operate every third or sixth day according to a national 

schedule provided by EPA.32 The network of PM2.5 continuous monitors has grown to 

approximately 800 locations throughout the country of which about 115 locations have Class III 

FEMs33. The number of PM2.5 FRM monitors may decrease over the coming years as PM2.5

continuous FEMs replace FRMs. 

1.3.2 PM2.5 Speciation 

As part of the PM2.5 NAAQS review completed in 1997, EPA established a PM2.5

Chemical Speciation Network (CSN) to conduct routine speciation monitoring in primarily urban 

areas in the U.S. The PM2.5 CSN consists of about 50 Speciation Trends Network (STN) sites 

and about 150 SLAMS supplemental sites. All STN sites operate on a one-in-three day sample 

collection schedule. A majority of the SLAMS supplemental sites operate on a one-in-six day 

sample collection schedule. These sites collect aerosol samples over 24 hours on filters that are 

analyzed for PM2.5 mass, a number of trace elements, major ions (e.g., sulfate, nitrate, 

ammonium), and organic and elemental carbon. Similar to the CSN, the IMPROVE program34

also provides PM2.5 mass and speciation data for organic and elemental carbon, major ions, and 

trace elements; however, unlike CSN, IMPROVE also samples for PM10 mass. 

1.3.3 PM10 Mass 

PM10 monitoring stations have an urban focus and are required in MSAs according to 

Table D-4 of Appendix D to 40 CFR Part 58. Local considerations are a factor in determining 

the actual required number of monitoring sites. More stations are required in larger MSAs and 

areas with more evidence of poor air quality, while monitors are also required in “clean” MSAs 

of certain size. The network currently includes over 800 monitoring stations throughout the 

country with most metropolitan areas operating more PM10 monitors than the minimum required 

 

national parks and wilderness areas) as stipulated in the 1977 amendments to the CAA. The IMPROVE Network 

also supports goals set forth in the 1999 Regional Haze Rule (64 FR 35714. July 1, 1999). Additional information is 

available at http://www.epa.gov/visibility/program.html. 32 The national sampling schedule calendar is available on the web at: http://www.epa.gov/ttn/amtic/calendar.html. 33 Class III equivalent method means an equivalent method for PM2.5 that is an analyzer capable of providing 

ambient air measurements representative of one hour or less integrated concentrations as well as 24-hour 

measurements determined as, or equivalent to, the mean of 24 one-hour consecutive measurements. 34 IMPROVE is a cooperative measurement effort managed by a steering committee composed of representatives 

from federal, regional, and state organizations. See Appendix B and http://vista.cira.colostate.edu/improve/ for more 

information. 
 1-15 

by current monitoring rules. Many PM10 monitoring stations operate FRMs on a mix of daily, 

one-in-two day, one-in-three day, or one-in-six day sampling, based on the relative 

concentrations measured at a specific monitoring site with respect to the 24-hour standard. 

There are also FEMs that are operated continuously, providing hourly PM10 measurements. 

1.3.4 PM10-2.5 Mass 

Ambient measurements of PM10-2.5 concentrations are not routinely measured and 

reported at present (US EPA, 2009a, section 3.5.1.1). The EPA has required PM10-2.5 mass 

monitoring as part of the NCore network which began January 1, 2011 at approximately 80 

stations. Urban NCore stations are to be generally located at an urban scale35 or neighborhood 

scale to provide representative concentrations of exposure expected throughout the metropolitan 

area. Rural NCore stations are to be located, to the maximum extent practicable, at a regional36

or larger scale, away from any large local emission source, so that they represent ambient 

concentrations over an extensive area. 

1.4 GENERAL APPROACH AND ORGANIZATION OF THIS DOCUMENT 

This PA includes staff’s evaluation of the policy implications of the scientific assessment 

of the evidence presented and assessed in the ISA and the results of quantitative assessments 

based on that information presented and assessed in the REAs. Taken together, this information 

informs staff conclusions and the identification of policy options for consideration in addressing 

public health and welfare effects associated with exposure to ambient PM. 

Since the last review, much new information is now available on PM air quality and 

human health effects directly in terms of PM2.5 and, to a much more limited degree, PM10-2.5 and 

ultrafine particles (UFPs).37 Since the purpose of this review is to evaluate the adequacy of the 

current standards, which separately address fine and thoracic coarse particles, staff is focusing 

this policy assessment and associated quantitative analyses primarily on the evidence related 

directly to PM2.5 and PM10-2.5. In so doing, we are considering PM10-related evidence primarily 

to help inform our understanding of key issues and to help interpret and provide context for 

understanding the public health and welfare impacts of ambient fine and coarse particles. We are 

also considering the currently available evidence related to UFPs as well as PM2.5 components to 

 35 Urban scale: This class of measurement would be used to characterize PM concentrations over an entire 

metropolitan or rural area ranging in size from 4 to 50 kilometers. Such measurements would be useful for 

assessing trends in area-wide air quality, and hence, the effectiveness of large scale air pollution control strategies. 

Community-oriented PM2.5 sites may have this scale (see 40 CFR Part 58, Appendix D, section 4.7.1(c)(4)). 36 Regional scale: These measurements would characterize conditions over areas with dimensions as much as 

hundreds of kilometers. Such measurements provide information about PM emissions and atmospheric losses and 

transport (see 40 CFR Part 58, Appendix D, section 4.7.1(c)(5). 

37 Ultrafine particles generally include particles with a nominal serodynamic diameter less than or equal to 0.1 μm. 
 1-16 

aid in considering whether there is support to consider standards with a different size fraction 

and/or distinct standards focused on regulating a specific PM2.5 component or group of 

components associated with any source categories of fine particles. 

 Following this introductory chapter, this document is organized into two main parts: 

review of the primary PM NAAQS (chapters 2 and 3) and review of the secondary PM NAAQS 

(chapters 4 and 5). Chapters 2 and 3 present staff observations and conclusions related to review 

of the primary standards for fine and thoracic coarse particles, respectively. Each chapter 

includes background information on the rationale for previous reviews and the policy assessment 

approaches followed in the current review, focusing on evidence-based considerations and, as 

appropriate, quantitative risk-based considerations. Staff conclusions are presented with regard 

to the adequacy of the current primary standards and potential alternative primary standards for 

consideration, in terms of indicators, averaging times, forms, and levels. Chapter 4 focuses on 

PM-related visibility impairment, and presents staff observations and conclusions with regard to 

the adequacy of the current standards and potential distinct secondary standards for 

consideration, in terms of alternative indicators, averaging times, forms, and levels. Chapter 5 

focuses on other PM-related welfare effects, including effects on climate, ecological effects, and 

effects on materials, and presents staff observations and conclusions with regard to the adequacy 

of the current standards and the extent to which information is available to support consideration 

of alternative standards. 
 1-17 

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Risk and Exposure Assessment. Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC. EPA-452/P-09-002. February 2009. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html. 

US EPA (2009d). Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan for Urban 

Visibility Impact Assessment. Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC. EPA-452/P-09-001. February 2009. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html. 

US EPA (2009e). Risk Assessment to Support the Review of the PM Primary National Ambient Air Quality 

Standards - External Review Draft. Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC. EPA-452/P-09-006. September 2009. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

US EPA (2009f). Particulate Matter Urban-Focused Visibility Assessment – External Review Draft. Office of Air 

Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

EPA-452/P-09-005. September 2009. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

US EPA (2009g). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality 

Standards – Preliminary Draft. Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC. EPA-452/P-09-007. September 2009. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html. 
 1-20 

US EPA (2010a). Quantitative Health Risk Assessment for Particulate Matter – Final Report. Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/R10-005. June 2010. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

US EPA (2010b). Particulate Matter Urban-Focused Visibility Assessment – Final Report. Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/R10-004. July 2010. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

US EPA (2010c). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality 

Standards - First External Review Draft. Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC. EPA 452/P-10-003. March 2010. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html. 

US EPA (2010d). Quantitative Risk Assessment for Particulate Matter – Second External Review Draft. Office of 

Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

EPA-452/P-10-001. February 2010. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

US EPA (2010e). Particulate Matter Urban-Focused Visibility Assessment – Second External Review Draft. Office 

of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. EPA-452/P-10-002. January 2010. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

US EPA (2010f). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality 

Standards - Second External Review Draft. Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. EPA 452/P-10-007. June 2010. 

Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html. 
 2-1

2 REVIEW OF THE PRIMARY STANDARDS FOR FINE PARTICLES 

This chapter presents staff conclusions with regard to the adequacy of the current suite of 

primary PM2.5 standards and the alternative primary standards for fine particles that are 

appropriate for consideration in this review. Our assessment of these issues is framed by a series 

of key policy-relevant questions, which expand upon those presented at the outset of this review 

in the IRP (US EPA, 2008a). Answers to these questions will inform decisions by the 

Administrator on whether, and if so how, to revise the current suite of primary fine particle 

standards. 

Staff notes that final decisions regarding the primary standards must draw upon scientific 

information and analyses about health effects and risks, as well as judgments made about how to 

deal with the uncertainties that are inherent in the scientific evidence and analyses. Ultimately, 

the final decisions are largely public health policy judgments. Our approach to informing these 

judgments recognizes that the available health effects evidence generally reflects a continuum 

consisting of ambient levels at which scientists generally agree that health effects occur through 

lower levels at which the likelihood and magnitude of the response become appreciably more 

uncertain. 

Our approach for reviewing the primary standards for fine particles is presented in 

section 2.1. Staff conclusions regarding the adequacy of the current suite of primary PM2.5

standards are presented in section 2.2, focusing on both evidence-based and quantitative riskbased considerations. Section 2.3 presents our conclusions with respect to alternative fine 

particle standards that are appropriate to consider, addressing each of the basic elements of the 

standards: indicator (section 2.3.1), averaging time (section 2.3.2), form (section 2.3.3), and 

level (section 2.3.4). Section 2.4 summarizes staff conclusions on the primary fine particle 

standards. Key uncertainties and areas for future research and data collection efforts are 

included in section 2.5. 

2.1 APPROACH 

Staff’s general approach for reviewing the current primary PM2.5 standards, which 

involves translating scientific and technical information into the basis for addressing key policyrelevant questions, takes into consideration the approaches used in previous PM NAAQS 

reviews (section 2.1.1) and the court’s remand of the primary annual PM2.5 standard set in 2006 

(section 2.1.2). The past and current approaches described below are all based most 

fundamentally on using information from epidemiological studies to inform the selection of PM 

standards that, in the Administrator’s judgment, protect public health with an adequate margin of 

safety. Such information can be in the form of air quality distributions over which health effect 
 2-2

associations have been observed, or in the form of concentration-response (C-R) functions that 

support quantitative risk assessment. However, evidence- and risk-based approaches using 

information from epidemiological studies to inform decisions on PM standards are complicated 

by the recognition that no population threshold, below which it can be concluded with 

confidence that PM-related effects do not occur, can be discerned from the available evidence. 

As a result, any general approach to reaching decisions on what standards are appropriate 

necessarily requires judgments about how to translate the information available from the 

epidemiological studies into a basis for appropriate standards. This includes consideration of 

how to weigh the uncertainties in the reported associations across the distributions of PM 

concentrations in the studies and the uncertainties in quantitative estimates of risk. Such 

approaches are consistent with setting standards that are neither more nor less stringent than 

necessary, recognizing that a zero-risk standard is not required by the CAA. Our current 

approach for evaluating the primary PM2.5 standards using both evidence- and risk-based 

considerations is outlined in section 2.1.3. 

2.1.1 Approaches Used in Previous Reviews 

2.1.1.1 Review Completed in 1997 

In setting the 1997 primary PM2.5 annual and 24-hour standards, the Agency relied 

primarily on an evidence-based approach that focused on epidemiological evidence, especially 

from short-term exposure studies of fine particles judged to be the strongest evidence at that 

time. The EPA did not place much weight on quantitative risk estimates from the very limited 

risk assessment conducted, but did conclude that the risk assessment results confirmed the 

general conclusions drawn from the epidemiological evidence that a serious public health 

problem was associated with ambient PM levels allowed under the then current PM10 standards 

(62 FR 38665/1, July 18, 1997). 

The EPA considered the epidemiological evidence and data on air quality relationships to 

set an annual PM2.5 standard that was intended to be the “generally controlling” standard; i.e., the 

primary means of lowering both long- and short-term ambient concentrations of PM2.5.

1

 In 

conjunction with the annual standard, EPA also established a 24-hour PM2.5 standard to provide 

supplemental protection against days with high peak concentrations, localized “hotspots,” and 

 1

 In so doing, EPA noted that because an annual standard would focus control programs on annual average PM2.5

concentrations, it would not only control long-term exposure levels, but would also generally control the overall 

distribution of 24-hour exposure levels, resulting in fewer and lower 24-hour peak concentrations. Alternatively, a 

24-hour standard that focused controls on peak concentrations could also result in lower annual average 

concentrations. Thus, EPA recognized that either standard could provide some degree of protection from both shortand long-term exposures, with the other standard serving to address situations where the daily peaks and annual 

averages are not consistently correlated (62 FR 38669). 
 2-3

risks arising from seasonal emissions that might not be well controlled by a national annual 

standard (62 FR 38669/3). Recognizing that there are various ways to combine two standards to 

achieve an appropriate degree of public health protection, EPA evaluated but did not use an 

alternate approach that considered short- and long-term exposure evidence, analyses, and 

standards independently. The EPA concluded that the selected approach based on a generally 

controlling annual standard was the most effective and efficient approach. This conclusion was 

based in part on a key observation from the quantitative risk assessment that most of the 

aggregated annual risk associated with short-term PM2.5 exposures through a year results from 

the large number of days during which the 24-hour average concentrations are in the low- to 

mid-range, well below the peak 24-hour concentrations. As a result, lowering a wide range of 

ambient 24-hour PM2.5 concentrations by means of a generally controlling annual standard, as 

opposed to focusing on control of peak 24-hour concentrations, was determined to be the most 

effective and efficient way to reduce total population risk (62 FR 38668 to 38671). 

In setting the level of the annual standard in 1997, EPA first determined a level for the 

annual standard based on the short-term exposure studies, and then considered whether the key 

long-term exposure studies suggested the need for a lower level. While recognizing that health 

effects may occur over the full range of concentrations observed in the studies, EPA concluded 

that the strongest evidence for short-term PM2.5 exposure-related effects occurs at concentrations 

near the long-term (e.g., annual) average in the short-term exposure studies. The EPA selected a 

level for the annual standard at or below the long-term mean concentrations in studies providing 

evidence of associations with short-term exposures, placing greatest weight on those short-term 

exposure studies that reported clearly statistically significant associations with mortality and 

morbidity effects (62 FR 38676/1). Further consideration of the average PM2.5 concentrations 

across the cities in the key long-term exposure studies of mortality and respiratory effects in 

children did not provide a basis for establishing a lower annual standard level. Because the 

annual standard level selected was below the range of annual concentrations most strongly 

associated with both short- and long-term exposure effects at that time, and because even small 

changes in annual means in this concentration range could make a significant difference in 

overall risk reduction and total population exposures, EPA concluded that this standard would 

provide an adequate margin of safety against effects observed in these epidemiological studies 

(62 FR 68676/3). 

The selection of the level of the annual standard was done in conjunction with having 

first selected the form of the annual standard to be based on the concentration measured at a 
 2-4

single monitor sited to represent community-wide air quality2, or a value resulting from an 

average of measurements from multiple community-wide air quality monitoring sites that met 

specific criteria and constraints (i.e., “spatial averaging”; 62 FR 38672). This decision 

emphasized consistency with the types of air quality measurements that were used in the relevant 

epidemiological studies. In reaching this decision, EPA recognized the importance of ensuring 

that spatial averaging would not result in inequities in the level of protection provided by the 

PM2.5 standards in some areas. Because the annual standard, defined in terms of single or 

averaged community-wide air quality monitoring sites, could not be expected to offer an 

adequate margin of safety against the effects of all potential short-term exposures in areas with 

strong local or seasonal sources that could not be directly evaluated in the epidemiological 

studies, EPA set the level of the 24-hour standard to supplement the control afforded by the 

annual standard based on air quality relationships between annual and 24-hour concentrations. 

This approach was intended to provide an adequate margin of safety against infrequent or 

isolated peak concentrations that could occur in areas that attain the annual standard (62 FR 

38677). The selection of the level of the 24-hour standard was done in conjunction with having 

selected the form of the 24-hour standard to be based on the concentration measured at each 

population-oriented monitor3

 within an area (62 FR 38674). 

2.1.1.2 Review Completed in 2006 

In 2006, EPA used a different evidence-based approach to assess the appropriateness of 

the levels of the 24-hour and annual PM2.5 standards. Based on an expanded body of 

epidemiological evidence that was stronger and more robust, including both short- and long-term 

exposure studies, the Administrator decided that using evidence of effects associated with 

periods of exposure that were most closely matched to the averaging time of each standard was 

the most appropriate public health policy approach for evaluating the scientific evidence to 

inform selecting the level of each standard. Thus, the Administrator relied upon evidence from 

the short-term exposure studies as the principal basis for selecting the level of the 24-hour PM2.5

standard that would protect against effects associated with short-term exposures. The 

Administrator relied upon evidence from long-term exposure studies as the principal basis for 

 2

 As outlined in section 1.3, community-wide monitoring sites are considered beyond the zone of influence of a 

single source, and should have a neighborhood- to urban-scale zone of representation. The principal purpose of 

community-oriented monitoring sites is to approximate the long- and short-term exposures of large numbers of 

people where they live, work, and play. See 40 CFR part 58 for additional information. 

3

 As outlined in section 1.3, population-oriented monitoring sites represent residential areas, commercial areas, 

recreational areas, industrial areas where workers from more than one company are located, and other areas where a 

substantial number of people may spend a significant fraction of their day. See 40 CFR part 58, and especially the 

definitions in section 58.1 and the provisions of section 58.30, for additional information. 
 2-5

selecting the level of the annual PM2.5 standard that would protect against effects associated with 

long-term exposures. 

With respect to quantitative risk-based considerations, the Administrator determined that 

the estimates of risks likely to remain upon attainment of the 1997 suite of PM2.5 standards were 

indicative of risks that could be reasonably judged important from a public health perspective, 

and, thus, supported revision of the standards. However, the Administrator judged that the 

quantitative risk assessment had important limitations and did not provide an appropriate basis 

for selecting the levels of the revised standards (71 FR 61174/1-2). The Administrator more 

heavily weighed the implications of the uncertainties associated with the quantitative risk 

assessment than CASAC did in their comments on the proposed rulemaking. Specifically, in 

CASAC’s request for reconsideration,4

 they stated, “[w]hile the risk assessment is subject to 

uncertainties, most of the PM Panel found EPA’s risk assessment to be of sufficient quality to 

inform its recommendations...The risk analyses indicated that the uncertainties would increase 

rapidly below an annual level of 13 μg/m3 – and that was the basis for the PM Panel’s 

recommendation of 13 μg/m3

 as the lower bound for the annual PM2.5 standard level” 

(Henderson, 2006a, p.3). 

With regard to the primary annual PM2.5 standard, the Administrator placed the greatest 

weight on the long-term means of the concentrations associated with mortality effects in two key 

long-term exposure studies in the record, the American Cancer Society (ACS) and Harvard Six 

Cities studies (71 FR at 61172 to 61177). Important validation and reanalyses of the original 

studies provided “evidence of generally robust associations and provide[d] a basis for greater 

confidence in the reported associations than in the last review,” and the extended ACS study 

provided “new evidence of mortality related to lung cancer and further substantiate[d] the 

statistically significant associations with cardiorespiratory-related mortality observed in the 

original studies” (71 FR 61172/1-2). The Administrator also recognized the availability of longterm exposure studies that provided evidence of respiratory morbidity, including changes in lung 

function measurements and decreased growth in lung function as reported in the 24-Cities study 

and the Southern California Children’s Health Study (CHS), respectively (Dockery et al. 1996, 

Gauderman et al., 2002). In retaining the level of the annual standard at 15 μg/m3

, the 

Administrator selected a level that was “appreciably below” the long-term average 

concentrations reported in the long-term mortality studies the Administrator regarded as “key” 

and “basically at the same level” as the long-term average concentrations in the long-term 

respiratory morbidity studies. In the judgment of the Administrator, the two long-term 

 4

 As summarized in section 1.2.2, subsequent to the January 17, 2006 proposed rule (71 FR 2620), CASAC provided 

additional advice to EPA in a letter to the Administrator requesting reconsideration of CASAC’s recommendations, 

including its recommendation on the primary annual PM2.5 standard (Henderson, 2006a). 
 2-6

respiratory morbidity studies of children did “not warrant setting a lower level for the annual 

standard than the level warranted based on the key mortality studies” (71 FR 61176/3). 

In considering the form of the primary annual PM2.5 standard, the Administrator 

strengthened the standard by tightening the criteria for use of spatial averaging. Based on a 

much larger set of PM2.5 air quality data than was available in the 1997 review, analyses were 

conducted concerning the potential for disproportionate impacts on potentially vulnerable 

populations. These analyses suggested that “the highest concentrations in an area tend to be 

measured at monitors located in areas where the surrounding population [was] more likely to 

have lower education and income levels, and higher percentages of minority populations” (71 FR 

61166/2, see also US EPA, 2005, section 5.3.6.1; Schmidt et al., 2005, Attachment A/Analysis 

7).5

In revising the level of the 24-hour PM2.5 standard from 65 μg/m3 to 35 μg/m3, the 

Administrator placed greatest weight on the much expanded body of epidemiological evidence 

from U.S. and Canadian short-term PM2.5 exposure studies with more reliable air quality data 

that was reanalyzed to address statistical modeling issues. The Administrator recognized that 

these studies provided no evidence of clear effect thresholds or lowest-observed effect levels. 

Nonetheless, in focusing on the 98th percentile air quality values in these studies, the 

Administrator sought to establish a standard level that would require improvements in air quality 

generally in areas in which the distribution of daily short-term PM2.5 concentrations could 

reasonably be expected to be associated with serious health effects. The Administrator 

concluded that although future air quality improvement strategies in any particular area were not 

yet defined, most such strategies were likely to move a broad distribution of PM2.5 air quality 

values in an area lower, resulting in reductions in risk associated with exposures to PM2.5 levels 

across a wide range of concentrations and not just at the 98th percentile concentrations (71 FR 

61168/3). 

2.1.2 Remand of Primary Annual PM2.5 Standard 

As noted above in section 1.2.3, several parties filed petitions for review following 

promulgation of the revised PM NAAQS in 2006. These petitions challenged several aspects of 

the final rule including the selection of the level of the primary PM2.5 annual standard. More 

specifically, petitioners representing public health and environmental groups (American Lung 

 5

 As summarized in footnote 29 at 71 FR 61166/2, the 2004 AQCD noted that some epidemiologic studies, most 

notably the ACS study of associations between long-term PM2.5 exposure and mortality, reported larger effect 

estimates in the cohort subgroup with lower education levels (US EPA, 2004, p 8-103). The 2004 AQCD also noted 

that lower education level may be a marker for lower socioeconomic status (SES) that may be related to increased 

vulnerability to the effects of fine particle exposures, for example, as a result of greater exposure from proximity to 

sources such as roadways and industry, as well as other factors such as poorer health status and limited access to 

health care (US EPA, 2004, section 9.2.4.5). 
 2-7

Association, Environmental Defense, and the National Parks Conservation Association) and 

several states and state agencies argued that the decision to retain the level of the annual PM2.5

standard at 15 μg/m3 was “arbitrary, capricious, an abuse of discretion, or otherwise not in 

accordance with law.” 42 U.S.C. 7607(d)(9). The primary 24-hour PM2.5 standard was not 

challenged by any of the litigants and, thus, not considered in the court’s review and final 

decision. 

On judicial review, the Court of Appeals for the District of Columbia Circuit (D.C. 

Circuit) remanded the primary annual PM2.5 NAAQS to EPA because the Agency failed to 

adequately explain why the annual standard provided the requisite protection from both shortand long-term exposures to fine particles including protection for susceptible populations. 

American Farm Bureau Federation v. EPA, 559 F. 3d 512 (D.C. Cir. 2009). With respect to 

human health protection from short-term PM2.5 exposures, the court considered the different 

approaches used by EPA in the 1997 and 2006 PM NAAQS decisions, as summarized above. 

The court found that EPA failed to adequately explain why a 24-hour PM2.5 standard by itself 

would provide the protection needed from short-term exposures and remanded the primary 

annual PM2.5 standard to EPA “for further consideration of whether it is set at a level requisite to 

protect the public health while providing an adequate margin of safety from the risk of shortterm exposures to PM2.5.” American Farm Bureau Federation, 559 F. 3d at 520-24. In so 

holding, the court emphasized that the Administrator had failed to consider the short-term studies 

when setting the annual standard, and failed to provide a reasoned explanation of why it had 

done so. Id. at 521. 

With respect to protection from long-term exposure to fine particles, the court found that 

EPA failed to adequately explain how the primary annual PM2.5 standard provided an adequate 

margin of safety for children and other susceptible populations. The court found that EPA did 

not provide a reasonable explanation of why certain morbidity studies, including the study of 

children in Southern California showing lung damage associated with long-term PM2.5 exposure 

(Gauderman et.al, 2000) and a multi-city study (24-Cities Study) evaluating decreased lung 

function in children associated with long-term PM2.5 exposures (Raizenne et al., 1996), did not 

call for a more stringent annual PM2.5 standard. Id. at 522-23. Specifically, the court found that: 

EPA was unreasonably confident that, even though it relied solely upon long-term 

mortality studies, the revised standard would provide an adequate margin of safety with 

respect to morbidity among children. Notably absent from the final rule, moreover, is 

any indication of how the standard will adequately reduce risk to the elderly or to those 

with certain heart or lung diseases despite (a) the EPA’s determination in its proposed 

rule that those subpopulations are at greater risk from exposure to fine particles and (b) 

the evidence in the record supporting that determination. Id. at 525. 
 2-8

In remanding the primary annual PM2.5 standard for reconsideration, the court did not 

vacate the standard. Id. at 530. 

2.1.3 General Approach Used in Current Review 

This review is based on an assessment of a much expanded body of epidemiological 

evidence, more extensive air quality data and analyses, and a more comprehensive quantitative 

risk assessment relative to the information available in past reviews, as presented in the ISA and 

RA. As a result, staff’s general approach to reaching conclusions about the adequacy of the 

current suite of PM2.5 standards and potential alternative standards that are appropriate to 

consider is broader and more integrative than in past reviews. Our general approach also reflects 

consideration of the issues raised by the court in its remand of the primary annual PM2.5

standard, since decisions made in this review, and the rationales for those decisions, will 

comprise the Agency’s response to the remand. 

Our general approach takes into account both evidence-based and risk-based 

considerations, and the uncertainties related to both types of information, as well as advice from 

CASAC (Samet, 2010c,d) and public comments on the first and second draft PAs (US EPA, 

2010c,f). In so doing, we are seeking to provide as broad an array of policy options as is 

supportable by the available information, recognizing that the selection of a specific approach to 

reaching final decisions on the primary PM2.5 standards will reflect the judgments of the 

Administrator as to what weight to place on the various approaches and types of information 

presented in this document. 

We believe it is most appropriate to consider the protection against PM2.5-related 

mortality and morbidity effects, associated with both long- and short-term exposures, afforded by 

the annual and 24-hour standards taken together, as was done in the 1997 review, rather than to 

consider each standard separately, as was done in the 2006 review.6

 As EPA recognized in 1997, 

there are various ways to combine two standards to achieve an appropriate degree of public 

health protection. The extent to which these two standards are interrelated in any given area 

depends in large part on the relative levels of the standards, the peak-to-mean ratios that 

characterize air quality patterns in an area, and whether changes in air quality designed to meet a 

given suite of standards are likely to be of a more regional or more localized nature. 

 6

 By utilizing this approach, the Agency would also be responsive to the remand of the 2006 standard. As noted in 

section 2.1.2 above, the D.C. Circuit, in remanding the 2006 primary annual PM2.5 standard, concluded that the 

Administrator had failed to adequately explain why an annual standard was sufficiently protective in the absence of 

consideration of the long-term mean PM2.5 concentrations in short-term exposure studies as well, and likewise had 

failed to explain why a 24-hour standard was sufficiently protective in the absence of consideration of the effect of 

an annual standard on reducing the overall distribution of 24-hour average PM2.5 concentrations. 559 F. 3d at 520-

24. 
 2-9

In considering the combined effect of annual and 24-hour standards, we recognize that 

changes in PM2.5 air quality designed to meet an annual standard would likely result not only in 

lower annual average PM2.5 concentrations but also in fewer and lower peak 24-hour PM2.5

concentrations. We also recognize that changes designed to meet a 24-hour standard would 

result not only in fewer and lower peak 24-hour PM2.5 concentrations but also in lower annual 

average PM2.5 concentrations. Thus, either standard could be viewed as providing protection for 

both short- and long-term exposures, with the other standard serving to address situations where 

the daily peak and annual average concentrations are not consistently correlated. 

With respect to the currently available evidence, we note the short-term exposure studies 

are primarily drawn from epidemiological studies that associated variations in area-wide effects 

with monitor(s) that gauged the variation in daily PM2.5 concentrations over the course of several 

years. The strength of the associations in these data is demonstrably in the numerous “typical” 

days within the air quality distribution, not on the peak days. Furthermore, based on the 

quantitative risk assessments conducted for this and previous reviews, we recognize that much, if 

not most of the aggregated risk associated with short-term exposures results from the large 

number of days during which the 24-hour average concentrations are in the low-to mid-range, 

below the peak 24-hour concentrations (see section 2.2.2; US EPA, 2010a, section 3.1.2.2). In 

addition, there is no evidence suggesting that risks associated with long-term exposures are likely 

to be disproportionately driven by peak 24-hour concentrations.7

 For these reasons, strategies 

that focus primarily on reducing peak days are less likely to achieve reductions in PM2.5

concentrations that are most strongly associated with the observed health effects compared to an 

approach that concentrates on reducing the more typical part of the air quality distribution. 

Furthermore, a policy approach that focuses on reducing peak exposures would most likely result 

in more uneven public health protection across the U.S. by either providing inadequate 

protection in some areas or overprotecting in other areas (US EPA, 2010a, section 5.2.3). 

Staff concludes a policy goal of setting a “generally controlling” annual standard that will 

lower a wide range of ambient 24-hour PM2.5 concentrations, as opposed to focusing on control 

of peak 24-hour PM2.5 concentrations, is the most effective and efficient way to reduce total 

population risk and so provide appropriate protection. This approach would likely reduce 

aggregate risks associated with both long- and short-term exposures with more consistency than 

a generally controlling 24-hour standard and would likely avoid setting national standards that 

could result in relatively uneven protection across the country, due to setting standards that are 

either more or less stringent than necessary in different geographical areas. 

 7

 In confirmation, a number of studies that have presented analyses excluding higher PM concentration days 

reported a limited effect on the magnitude of the effect estimates or statistical significance of the association (e.g., 

Dominici, 2006b; Schwartz et al, 1996; Pope and Dockery, 1992). 
 2-10

However, we conclude that an annual standard intended to serve as the primary means for 

providing protection for effects associated with both long- and short-term PM2.5 exposures 

cannot be expected to offer an adequate margin of safety against the effects of all short-term 

PM2.5 exposures. As a result, in conjunction with a generally controlling annual standard, we 

conclude that it is appropriate to consider setting a 24-hour standard to provide supplemental 

protection, particularly for areas with high peak-to-mean ratios possibly associated with strong 

local or seasonal sources, or PM2.5-related effects that may be associated with shorter-than-daily 

exposure periods. 

Our consideration of the protection afforded by the current and alternative suites of 

standards focuses on PM2.5-related health effects associated with long-term exposures, for which 

the magnitude of quantitative estimates of risks to public health generated in the risk assessment 

is appreciably larger in terms of overall incidence and percent of total mortality or morbidity 

effects than for short-term PM2.5-related effects. We also consider effects and estimated risks 

associated with short-term exposures. In both cases, we place greatest weight on health effects 

that have been judged in the ISA to have a causal or likely causal relationship with PM2.5

exposures, while also considering health effects judged to be suggestive of a causal relationship

or that focus on specific susceptible populations. We focus on epidemiological studies 

conducted in the U.S. and Canada, as studies in other countries reflect air quality and exposure 

patterns that are not necessarily typical of the U.S.,8

 and place relatively greater weight on 

statistically significant associations that yield relatively more precise effect estimates and that are 

judged to be robust to confounding by other air pollutants. In the case of short-term exposure 

studies, we consider evidence from large multi-city studies, as well as single-city studies. 

In translating information from epidemiological studies into the basis for reaching staff 

conclusions on the adequacy of the current suite of standards (section 2.2.1), we consider a 

number of factors. As an initial matter, we consider the extent to which the currently available 

evidence and related uncertainties strengthens or calls into question conclusions from the last 

review regarding associations between fine particle exposures and health effects. We also 

consider evidence on susceptible populations and potential impacts on such populations. 

Further, we explore the extent to which PM2.5-related health effects have been observed in areas 

where air quality distributions extend to lower levels than previously reported or in areas that 

would likely have met the current suite of standards. 

In translating information from epidemiological studies into the basis for reaching staff 

conclusions on alternative standard levels for consideration (section 2.3.4), we first recognize the 

absence of discernible thresholds in the C-R functions from long- and short-term PM2.5 exposure 

 8

 Nonetheless, we recognize the importance of all studies, including international studies, in the ISA’s assessment of 

the weight of the evidence that informs causality determinations. 
 2-11

studies (US EPA, 2009a, section 2.4.3). In the absence of any discernible thresholds, our general 

approach for identifying appropriate standard levels for consideration involves characterizing the 

range of PM2.5 concentrations over which we have the most confidence in the associations 

reported in epidemiological studies. In so doing, we recognize that there is no single factor or 

criterion that comprises the “correct” approach, but rather there are various approaches that are 

reasonable to consider for characterizing the confidence in the associations and the limitations 

and uncertainties in the evidence. Identifying the implications of various approaches in reaching 

conclusions on the range of alternative standard levels that is appropriate to consider can help 

inform decisions that are made to either retain or revise the standards. Final decisions will 

necessarily also take into account public health policy judgments as to the degree of health 

protection that is to be achieved. 

In reaching staff conclusions on the range of annual standard levels that is appropriate to 

consider, we focus on identifying an annual standard that provides protection for effects 

associated with both long- and short-term exposures, as discussed above. In so doing, we 

explore different approaches for characterizing the range of PM2.5 concentrations over which our 

confidence in associations for both long- and short-term exposures is greatest, as well as the 

extent to which our confidence is reduced at lower PM2.5 concentrations. 

The approach that most directly addresses this issue considers studies that present 

confidence intervals around C-R relationships, and in particular, analyses that average across 

multiple C-R models rather than considering a single C-R model.9

 We explore the extent to 

which such analyses have been published for studies of health effects associated with long- or 

short-term PM2.5 exposures. Such analyses could potentially be used to characterize a 

concentration below which uncertainty in a C-R relationship substantially increases or is judged 

to be indicative of an unacceptable degree of uncertainty about the existence of a continuing C-R 

relationship. We believe that identifying this area of uncertainty in the C-R relationship can be 

used to inform identification of alternative standard levels that are appropriate to consider. 

We also take into account the general approach used in previous PM reviews which 

focused on consideration of alternative standard levels that were somewhat below the long-term 

mean PM2.5 concentrations reported in epidemiological studies. This approach recognizes that 

the strongest evidence of PM2.5-related associations occurs at concentrations near the long-term 

(i.e., annual) mean. In using this approach, we place greatest weight on those long- and short-

 

9 This is distinct from confidence intervals around C-R relationships that are related to the magnitude of effect 

estimates generated at specific PM2.5 concentrations (i.e., point-wise confidence intervals) and that are relevant to 

the precision of the effect estimate across the air quality distribution, rather than to our confidence in the existence 

of a continuing C-R relationship across the entire air quality distribution on which a reported association was based. 
 2-12

term exposure studies that reported statistically significant associations with mortality and 

morbidity effects. 

In extending this approach, we also consider information beyond a single statistical 

metric of PM2.5 concentrations (i.e., the mean) to the extent such information is available. In so 

doing, we employ distributional statistics (i.e., statistical characterization of an entire distribution 

of data) to identify the broader range of PM2.5 concentrations that were most influential in 

generating health effect estimates in epidemiological studies. Thus, we consider the range of 

PM2.5 concentrations where the data analyzed in the study (i.e., air quality and population-level 

data, as discussed below) are most concentrated, specifically, the range of PM2.5 concentrations 

around the long-term mean over which our confidence in the associations observed in the 

epidemiological studies is greatest. We then focus on the lower part of this range and seek to 

characterize where in the distributions the data become appreciably more sparse and, thus, where 

our understanding of the associations correspondingly becomes more uncertain. We recognize 

there is no one percentile value within a given distribution that is the most appropriate or 

“correct” way to characterize where our confidence in the associations becomes appreciably 

lower. We judge that the range from the 25th to 10th percentiles is a reasonable range to consider 

as a region where we have appreciably less confidence in the associations.10 

In considering distributional statistics from epidemiological studies, we first recognize 

that there are two types of population-level metrics that are useful to consider in identifying the 

PM2.5 concentrations most influential in generating the health effect estimates reported in the 

epidemiological studies. The most relevant information is the distribution of health events (e.g., 

deaths, hospitalizations) occurring within a study population in relation to the distribution of 

PM2.5 concentrations. However, in recognizing that access to health event data can be restricted, 

we also consider the number of study participants within each study area as a reasonable 

surrogate for health event data.11 

In the absence of data on the number of health events or study participants, we consider 

the distribution of PM2.5 concentrations across study areas in multi-city studies as representative 

of the PM2.5 concentrations likely experienced by study participants, which are integral to the 

generation of effect estimates. This approach is particularly relevant for identifying the range of 

 10 In the PM NAAQS review completed in 2006, staff recognized that the evidence of an association in any 

epidemiological study is “strongest at and around the long-term average where the data in the study are most 

concentrated. For example, the interquartile range of long-term average concentrations within a study [with a lower 

bound of the 25th percentile] or a range within one standard deviation around the study mean, may reasonably be 

used to characterize the range over which the evidence of association is strongest” (US EPA, 2005, p. 5-22). A 

range of one standard deviation around the mean represents approximately 68% of normally distributed data, and, 

below the mean falls between the 25th and 10th percentiles. 11 A limitation of applying population data as a substitute for the number of the health events occurring in a study 

area is the recognition that baseline incidence rates of health events will vary across study areas. 
 2-13

PM2.5 concentrations most influential in generating the effect estimates in short-term exposure 

studies (e.g., time-series studies). In short-term exposure studies, a similar number of events are 

likely occurring on a daily basis and, thus, information on the PM2.5 air quality distributions 

provides a better approximation of a study participant’s likely exposure in relation to the 

observed health effects as compared to long-term exposure studies where individual study 

participant’s exposures change over the course of the study follow-up period (Samet, 2010d, p. 

2). 

We recognize that an approach considering analyses of confidence intervals around C-R 

functions is intrinsically related to an approach that considers different distributional statistics. 

Both of these approaches can be employed to identify the range of PM2.5 concentrations over 

which we have the most confidence in the associations reported in epidemiological studies. 

In applying these approaches, we consider PM2.5 concentrations from long- and shortterm PM2.5 exposure studies using composite monitor distributions.

12 For multi-city studies, this 

distribution reflects concentrations averaged across ambient monitors within each area included 

in a given study and then averaged across study areas for an overall study mean PM2.5

concentration. Beyond considering air quality concentrations based on composite monitor 

distributions, in the second draft PA we also considered PM2.5 concentrations based on 

measurements at the monitor within each area that records the highest concentration (i.e., 

maximum monitor) (US EPA, 2010f, sections 2.1.3 and 2.3.4.1).13 Although consideration of 

alternative annual standard levels could be based on either composite or maximum monitor 

distributions, we conclude that it is reasonable to place more weight on an approach based on 

composite monitor distributions, which represent the PM2.5 concentrations typically presented 

and used in epidemiological analyses. Such composite monitor distributions provide a direct link 

between PM2.5 concentrations and the observed health effects reported in both long- and shortterm exposure studies. 

In reaching staff conclusions on alternative standard levels that are appropriate to 

consider, we have also included a broader consideration of the uncertainties related to the C-R 

relationships from multi-city long- and short-term exposure studies. Most notably, these 

uncertainties relate to our currently limited understanding of the heterogeneity of relative risk 

estimates in areas across the country, which may be attributed, in part, to the potential for 

 12 Using the term “composite monitor” does not imply we can identify one monitor that represents the air quality 

evaluated in a specific study area. 

13 The maximum monitor distribution is relevant because it is generally used to determine whether a given standard 

is met in an area and the extent to which ambient PM2.5 concentrations need to be reduced in order to bring an area 

into attainment with the standard. However, maximum monitor distributions represent a far less robust metric than 

composite monitor distributions for consideration of alternative annual standard levels because they are available for 

only a few epidemiological studies.
 2-14

different components within the mix of ambient fine particles to differentially contribute to 

health effects observed in the studies and to exposure-related factors. In addition, uncertainties 

remain in more fully understanding the role of PM2.5 in relationship to the roles of gaseous copollutants within complex ambient mixtures. 

We recognize the level of protection afforded by the NAAQS relies both on the level and 

the form of the standard. We conclude that a policy approach that uses data based on composite 

monitor distributions to identify alternative standard levels, and then compares those levels to 

concentrations at maximum monitors to determine if an area meets a given standard, 

directionally has the potential to build in some margin of safety.14 This conclusion is consistent 

with CASAC’s comments on the second draft PA, in which CASAC expressed its preference for 

focusing on an approach using composite monitor distributions “because of its stability, and for 

the additional margin of safety it provides” when “compared to the maximum monitor 

perspective” (Samet, et al., 2010d, pp. 2 to 3). 

In reaching staff conclusions on alternative 24-hour standard levels that are appropriate 

to consider for setting a 24-hour standard intended to supplement the protection afforded by a 

generally controlling annual standard, we consider currently available short-term PM2.5 exposure 

studies. The evidence from these studies informs our understanding of the protection afforded 

by the suite of standards against effects associated with short-term exposures. In considering the 

short-term exposure studies, we evaluate both the distributions of 24-hour PM2.5 concentrations, 

with a focus on the 98th percentile concentrations to match the form of the current 24-hour PM2.5

standard, to the extent such data were available, as well as the long-term mean PM2.5

concentrations reported in these studies. In addition to considering the epidemiological 

evidence, we also consider air quality information based on county-level 24-hour and annual 

design values to understand the policy implications of the alternative standard levels supported 

by the underlying science. In particular, we consider the extent to which different combinations 

of alternative annual and 24-hour standards would support the policy goal of focusing on a 

generally controlling annual standard in conjunction with a 24-hour standard that would provide 

supplemental protection. Based on the evidence-based considerations outlined above, we then 

develop integrated conclusions with regard to alternative suites of standards, including both 

annual and 24-hour standards that we conclude are appropriate to consider in this review based 

 14 Statistical metrics (e.g., means) based on composite monitor distributions may be identical to or below the same 

statistical metrics based on maximum monitor distributions. For example, some areas may have only one monitor, 

in which case the composite and maximum monitor distributions will be identical in those areas. Other areas may 

have multiple monitors that may be very close to the monitor measuring the highest concentrations, in which case 

the composite and maximum monitor distributions could be similar in those areas. Still other areas may have 

multiple monitors that may be separately impacted by local sources in which case the composite and maximum 

monitor distributions could be quite different and the composite monitor distributions may be well below the 

maximum monitor distributions. 
 2-15

on the currently available evidence and air quality information. In so doing, we discuss the roles 

that each standard might be expected to play in the protection afforded by alternative suites of 

standards. 

Beyond these evidence-based considerations, we also consider the quantitative risk 

estimates and the key observations presented in the RA. This assessment included an evaluation 

of 15 urban case study areas and estimated risk associated with a number of health endpoints 

associated with long- and short-term PM2.5 exposures (US EPA, 2010a). As part of our riskbased considerations, we consider estimates of the magnitude of PM2.5-related risks associated 

with recent air quality levels and air quality simulated to just meet the current and alternative 

suites of standards using alternative simulation approaches. We also characterize the risk 

reductions, relative to the risks remaining upon just meeting the current standards, associated 

with just meeting alternative suites of standards. In so doing, we recognize the uncertainties 

inherent in such risk estimates, and take such uncertainties into account by considering the 

sensitivity of the “core” risk estimates to alternative assumptions and methods likely to have 

substantial impact on the estimates. In addition, we consider additional analyses characterizing 

the representativeness of the urban study areas within a broader national context to understand 

the roles that the annual and 24-hour standards may play in affording protection against effects 

related to both long- and short-term PM2.5 exposures. 

Staff conclusions related to the primary PM2.5 standards reflect our understanding of both 

evidence-based and risk-based considerations to inform two overarching questions related to: (1) 

the adequacy of the current suite of PM2.5 standards and (2) potential alternative standards, if 

any, that are appropriate to consider in this review to protect against effects associated with both 

long- and short-term exposures to fine particles. In addressing these broad questions, we have 

organized the discussions below around a series of more specific questions reflecting different 

aspects of each overarching question. When evaluating the health protection afforded by the 

current or any alternative suites of standards considered, we have taken into account the four 

basic elements of the NAAQS: the indicator, averaging time, form, and level. 

Figure 2-1 provides an overview of the policy relevant questions that frame our review, 

as discussed more fully below. We believe that this general approach provides a comprehensive 

basis to help inform the judgments required of the Administrator in reaching decisions about the 

current and potential alternative primary fine particle standards and in responding to the remand 

of the 2006 primary annual PM2.5 standard. 
 2-16

Figure 2-1. Overview of Approach for Review of Primary PM2.5 Standards 
 2-17

2.2 ADEQUACY OF CURRENT STANDARDS 

In considering the adequacy of the current suite of PM2.5 standards, staff addresses the 

following overarching question: 

Does the currently available scientific evidence and risk-based information, as reflected in 

the ISA and RA, support or call into question the adequacy of the protection afforded by 

the current suite of fine particle standards? 

To inform our consideration of this broad question, we address a series of more specific 

questions to aid in considering the currently available scientific evidence (section 2.2.1) and the 

results of recent quantitative risk analyses (section 2.2.2) in a policy-relevant context, as 

discussed below. In considering the scientific and technical information, we reflect upon both 

the information available in the last review and information that is newly available since the last 

review as assessed and presented in the ISA and the RA (US EPA, 2009a; US EPA, 2010a). 

CASAC advice regarding the adequacy of the current suite of PM2.5 standards is summarized in 

section 2.2.3. Staff conclusions regarding the adequacy of the current suite of PM2.5 standards 

are presented in section 2.2.4. 

2.2.1 Evidence-based Considerations 

Our review of the adequacy of the current suite of primary PM2.5 standards begins by 

considering the strength of the evidence, susceptible populations, and the air quality distributions 

over which health effects associations have been reported. 

 To what extent does the currently available scientific evidence and related uncertainties 

strengthen or call into question evidence of associations between ambient fine particle 

exposures and health effects? 

In considering the strength of the associations between long- and short-term exposures to 

PM2.5 and health effects, we note that in the last review EPA concluded that there was “strong 

epidemiological evidence” for linking long-term PM2.5 exposures with cardiovascular-related 

and lung cancer mortality and respiratory-related morbidity and for linking short-term PM2.5

exposures with cardiovascular-related and respiratory-related mortality and morbidity (US EPA, 

2004, p. 9-46; US EPA, 2005, p. 5-4). Overall, the epidemiological evidence supported “likely 

causal associations” between PM2.5 and both mortality and morbidity from cardiovascular and 

respiratory diseases, based on “an assessment of strength, robustness, and consistency in results” 

(US EPA, 2004, p. 9-48). 

In looking across the extensive new scientific evidence available in this review, our 

overall understanding of health effects associated with fine particle exposures has been greatly 

expanded. The currently available evidence is stronger in comparison to evidence available in 

the last review because of its breadth and the substantiation of previously observed health effects 
 2-18

(US EPA, 2009a, section 2.3.1). A number of large multi-city epidemiological studies have been 

conducted throughout the U.S. including extended analyses of studies that were important to 

inform decision making in the last review. These studies have reported consistent increases in 

morbidity and/or mortality related to ambient PM2.5 concentrations, with the strongest evidence 

reported for cardiovascular-related effects. In addition, the findings of new toxicological and 

controlled human exposure studies greatly expand and provide stronger support for a number of 

potential biologic mechanisms or pathways for cardiovascular and respiratory effects associated 

with long- and short-term PM exposures (US EPA, 2009a, p 2-17; chapter 5; Figures 5-4 and 5-

5). 

With regard to causal inferences described in the ISA, we note that since the last review 

EPA has developed a more formal framework for reaching causal determinations that draws 

upon the assessment and integration of evidence from across epidemiological, controlled human 

exposure, and toxicological studies, and the related uncertainties, that ultimately influence our 

understanding of the evidence (US EPA, 2009a, section 1.5). This framework employs a fivelevel hierarchy that classifies the overall weight of evidence and causality using the following 

categorizations: causal relationship, likely to be a causal relationship, suggestive of a causal 

relationship, inadequate to infer a causal relationship, and not likely to be a causal relationship 

(US EPA, 2009a, Table 1-3).15 

Using this causal framework, the ISA concludes that the collective evidence is largely 

consistent with past studies and substantially strengthens what was known in the last review to 

reach the conclusion that a causal relationship exists between both long- and short-term 

exposures to PM2.5 and mortality and cardiovascular effects including cardiovascular-related 

mortality. The ISA also concludes that the collective evidence continues to support a likely 

causal relationship between long- and short-term PM2.5 exposures and respiratory effects, 

including respiratory-related mortality. Further, the ISA concludes that the currently available 

evidence is suggestive of a causal relationship between long-term PM2.5 exposures and other 

health effects, including developmental and reproductive effects (e.g., low birth weight) and 

carcinogenic, mutagenic, and genotoxic effects (e.g., lung cancer mortality). Table 2-1 

summarizes these causal determinations (US EPA, 2009a, sections 2.3.1 and 2.6). 

 15 Causal inferences, as discussed in the ISA, are based not only on the more expansive epidemiological evidence 

available in this review but also reflect consideration of important progress that has been made to advance our 

understanding of a number of potential biologic modes of action or pathways for PM-related cardiovascular and 

respiratory effects (US EPA 2009a, chapter 5). 
 2-19

Table 2-1. Summary of Causal Determinations for PM2.5

Exposure 

Duration Outcome Causality Determination 

Long-term 

Mortality Causal 

Cardiovascular Effects Causal 

Respiratory Effects Likely to be Causal 

Reproductive and Developmental Effects Suggestive 

Cancer, Mutagenicity, Genotoxicity Effects Suggestive 

Short-term 

Mortality Causal 

Cardiovascular Effects Causal 

Respiratory Effects Likely to be Causal 

Central Nervous System Effects Inadequate 

Source: adapted from US EPA, 2009a, Table 2-6. 

Health Effects Associated with Long-term PM2.5 Exposure

With regard to mortality, the ISA concludes that newly available evidence significantly 

strengthens the link between long-term exposure to PM2.5 and mortality, while providing 

indications that the magnitude of the PM2.5-mortality association may be larger than previously 

estimated (US EPA, 2009a, sections 7.2.10, 7.2.11, 7.6.1; Figures 7-6 and 7-7). A number of 

large U.S. cohort studies have been published since the last review, including extended analyses 

of the American Cancer Society (ACS) and Harvard Six Cities studies (US EPA, 2009a, pp 7-84 

to 7-85; Figure 7-6; Krewski et al., 2009; Pope et al., 2004; Jerrett et al., 2005; Laden et al., 

2006). In addition, new long-term PM2.5 exposure studies evaluating mortality impacts in 

additional cohorts are now available (US EPA, 2009a, section 7.6). For example, the Women’s 

Health Initiative (WHI) study reported effects of PM2.5 on cardiovascular-related mortality in 

post-menopausal women with no previous history of cardiac disease (Miller et al., 2007), based 

on one year of air quality data and multiple years of health event data. Additionally, multiple 

studies observed PM2.5-associated mortality among older adults using Medicare data (Eftim et 

al., 2008; Zeger et al., 2007, 2008). Collectively, these new studies, along with evidence 

available in the last review, provide us with consistent and stronger evidence of associations 

between long-term exposure to PM2.5 and mortality (U.S. EPA, 2009a, sections 2.3.1 and 7.6). 

The strength of the causal relationship between long-term PM2.5 exposure and mortality is 

also built upon new studies providing evidence of improvement in community health following 

reductions in ambient fine particles. Pope et al. (2009) documented the population health 
 2-20

benefits of reducing ambient air pollution by correlating past reductions in ambient PM2.5

concentrations with increased life expectancy. These investigators reported that reductions in 

ambient fine particles during the 1980s and 1990s account for as much as 15 percent of the 

overall improvement in life expectancy in 51 U.S. metropolitan areas, with the fine particle 

reductions reported to be associated with an estimated increase in mean life expectancy across 

the entire population of approximately 5 to 9 months (US EPA, 2009a, p. 7-95; Pope et al., 

2009). An extended analysis of the Harvard Six Cities study found that as cities cleaned up their 

air, locations with the largest reductions in PM2.5 saw the largest improvements in reduced 

mortality rates, while those with the smallest decreases in PM2.5 concentrations saw the smallest 

improvements (Laden et al., 2006). Another extended follow-up to the Harvard Six Cities study 

investigated the delay between changes in ambient PM2.5 concentrations and changes in mortality 

(Schwartz et al., 2008) and reported that the effects of changes in PM2.5 were seen within the two 

years prior to death (US EPA, 2009a, p. 7-92; Figure 7-9). Looking more broadly across studies, 

the ISA concludes, “Generally, these results indicate a developing coherence of the air pollution 

mortality literature, suggesting that the health benefits from reducing air pollution do not require 

a long latency period and would be expected within a few years of intervention” (US EPA, 

2009a, p. 7-95). 

With regard to cardiovascular effects, several new studies have examined the association 

between cardiovascular effects and long-term PM2.5 exposures in multi-city studies conducted in 

the U.S. and Europe. The ISA concludes that the strongest evidence comes from recent studies 

investigating cardiovascular-related mortality. This includes evidence from a number of large, 

multi-city U.S. long-term cohort studies including extended follow-up analyses of the ACS and 

Harvard Six Cities studies, as well as the WHI study (US EPA, 2009a, sections 7.2.10 and 7.6.1; 

Krewski et al., 2009; Pope et al., 2004; Laden et al., 2006; Miller et al., 2007). Pope et al. (2004) 

reported a positive association between mortality and long-term PM2.5 exposure for a number of 

specific cardiovascular diseases, including ischemic heart disease (IHD), dysrhythmia, heart 

failure, and cardiac arrest (US EPA, 2009a, p. 7-84; Figure 7-7). Krewski et al. (2009) provides 

further evidence for IHD-related mortality associated with long-term PM2.5 exposure (US EPA, 

2009a, p. 7-84, Figure 7-7). 

With regard to cardiovascular-related morbidity associated with long-term PM2.5

exposures, studies were not available in the last review. Recent studies, however, have provided 

new evidence linking long-term exposure to PM2.5 with cardiovascular outcomes that has 

“expanded upon the continuum of effects ranging from the more subtle subclinical measures to 

cardiopulmonary mortality” (US EPA, 2009a, p. 2-17). In the current review, studies are now 

available that evaluated a number of endpoints ranging from subtle indicators of cardiovascular 

health to serious clinical events associated with coronary heart disease (CHD) and cardiovascular 
 2-21

disease (CVD) and cerebrovascular disease (CBVD), including myocardial infarction (MI), 

coronary artery revascularization (e.g., bypass graft, angioplasty, stent, atherectomy), congestive 

heart failure (CHF), and stroke. The most important new evidence comes from the WHI study 

which provides evidence of nonfatal cardiovascular events including both coronary and 

cerebrovascular events (Miller et al., 2007; US EPA, 2009a, sections 7.2.9 and 7.6.1). 

Toxicological studies provide supportive evidence that the cardiovascular morbidity effects 

observed in long-term exposure epidemiological studies are biologically plausible and coherent 

with studies of cardiovascular-related mortality as well as with studies of cardiovascular-related 

effects associated with short-term exposures to PM2.5, as described below (US EPA, 2009a, p 7-

19). 

With regard to respiratory effects, the ISA concludes that extended analyses of studies 

available in the last review as well as new epidemiological studies conducted in the U.S. and 

abroad provide stronger evidence of respiratory-related morbidity associated with long-term 

PM2.5 exposure. The strongest evidence for respiratory-related effects available in this review is 

from studies that evaluated decrements in lung function growth, increased respiratory symptoms, 

and asthma development (U.S. EPA, 2009a, sections 2.3.1.2, 7.3.1.1, and 7.3.2.1).16 

Specifically, extended analyses of the Southern California CHS provided evidence that clinically 

important deficits in lung function17 associated with children’s long-term exposure to PM2.5

persisted into early adulthood (U.S., EPA, 2009a, p. 7-27; Gauderman et al., 2004). Additional 

analyses of the CHS cohort reported an association between long-term PM2.5 exposure and 

bronchitic symptoms (US EPA, 2009a, p. 7-24; McConnell et al., 2003) and a strong modifying 

effect of PM2.5 on the association between lung function and asthma incidence (US EPA, 2009, 

7-24; Islam et al., 2007). The outcomes observed in these more recent reports from the Southern 

California CHS, including evaluation of a broader range of endpoints and longer follow-up 

periods, were larger in magnitude and more precise than previously reported. Supporting these 

results are new longitudinal cohort studies conducted by other researchers in varying locations 

using different methods (U.S. EPA, 2009a, section 7.3.9.1). New evidence from a U.S. cohort of 

cystic fibrosis (CF) patients provided evidence of association between long-term PM2.5

exposures and exacerbations of respiratory symptoms resulting in hospital admissions or use of 

home intravenous antibiotics (US EPA, 2009a, p. 7-25; Goss et al., 2004). 

 16 Supporting evidence comes from studies “that observed associations between long-term exposure to PM10 and an 

increase in respiratory symptoms and reductions in lung function growth in areas where PM10 is dominated by 

PM2.5” (US EPA, 2009a, p. 2-12). 17 Clinical significance was defined as a FEV1 below 80% of the predicted value, a criterion commonly used in 

clinical settings to identify persons at increased risk for adverse respiratory conditions (US EPA, 2009a, pp. 7-29 to 

7-30). The primary NAAQS for SO2 also includes this interpretation for FEV1 (75 FR 35525, June 22, 2010). 
 2-22

Toxicological studies provide coherence and biological plausibility for the respiratory 

effects observed in epidemiological studies (US EPA, 2009a, p. 7-42). For example, pre- and 

postnatal exposure to ambient levels of urban particles has been found to affect lung 

development in an animal model (US EPA, 2009a, section 7.3.2.2; p. 7-43). This finding is 

important because impaired lung development is one mechanism by which PM exposure may 

decrease lung function growth in children (US EPA, 2009a, p. 2-12; section 7.3). 

With regard to respiratory-related mortality associated with long-term PM2.5 exposure, 

the ISA concludes that the evidence is “limited and inconclusive” (US EPA, 2009a, p. 7-41). 

The extended follow-up of the Harvard Six Cities study reported a positive but statistically 

nonsignificant association between long-term PM2.5 exposure and respiratory-related mortality 

(Laden et al., 2006), whereas Pope et al. (2004) found no association in the ACS cohort (US 

EPA, 2009a, p. 7-84). There is emerging but limited evidence for an association between longterm PM2.5 exposure and respiratory mortality in post-neonatal infants where long-term exposure 

was considered as approximately one month to one year (US EPA, 2009a, pp. 7-54 to 7-55). 

Emerging evidence of short- and long-term exposure to PM2.5 and respiratory morbidity and 

infant mortality provide some support for the weak respiratory-related mortality effects observed. 

Beyond effects considered to have causal or likely causal relationships with long-term 

PM2.5 exposure as discussed above, the following health outcomes are classified as having 

evidence suggestive of a causal relationship with long-term PM2.5 exposure: (1) reproductive 

and developmental effects and (2) cancer, mutagenicity, and genotoxicity effects (US EPA, 

2009a, Table 2-6). With regard to reproductive and developmental effects, the ISA notes, 

“[e]vidence is accumulating for PM2.5-related effects on low birth weight and infant mortality, 

especially due to respiratory causes during the post-neonatal period” (US EPA, 2009a, section 

2.3.1.2). New evidence available in this review reports a significant association between 

exposure to PM2.5 during pregnancy and lower birth weight, pre-term birth, and intrauterine 

growth restriction, and a significant association between postnatal exposure to PM2.5 and an 

increased risk of infant mortality (US EPA, 2009a, section 7.4). The ISA further notes that 

“[i]nfants and fetal development processes may be particularly vulnerable to PM exposure, and 

although the physical mechanisms are not fully understood, several hypotheses have been 

proposed involving direct effects on fetal health, altered placenta function, or indirect effects on 

the mother’s health” (US EPA, 2009a, section 7.4.1). While toxicological studies provide some 

evidence that supports an association between long-term PM2.5 exposure and adverse 

reproductive and developmental outcomes, there is “little mechanistic information or biological 

plausibility for an association between long-term PM exposure and adverse birth outcomes (e.g., 

low birth weight, infant mortality)” (US EPA, 2009a, p. 2-13). 
 2-23

With regard to cancer, mutagenic and genotoxic effects, “[m]ultiple epidemiologic 

studies have shown a consistent positive association between PM2.5 and lung cancer mortality, 

but studies have generally not reported associations between PM2.5 and lung cancer incidence” 

(US EPA, 2009a, p. 2-13 and sections 2.3.1.2 and 7.5; Table 7-7; Figures 7-6 and 7-7). The 

extended follow-up to the ACS study reported an association between long-term PM2.5 exposure 

and lung cancer mortality (US EPA, 2009a, p. 7-71; Krewski et al., 2009) as did the extended 

follow-up to the Harvard Six Cities study when considering the entire 25-year follow-up period 

(Laden et al., 2006). There is some evidence, primarily from in vitro studies, providing 

biological plausibility for the PM-lung cancer relationships observed in epidemiological studies 

(US EPA, 2009a, p. 7-80), although toxicological studies of carcinogenicity, mutagenicity, and 

genotoxicity generally reported mixed results (US EPA, 2009a, section 7.5).

Health Effects Associated with Short-term PM2.5 Exposure

In considering effects associated with short-term PM2.5 exposure, the body of currently 

available scientific evidence has been expanded greatly by the publication of a number of new 

multi-city time-series studies that have used uniform methodologies to investigate the effects of 

short-term fine particle exposures on public health. This body of evidence provides a more 

expansive data base and considers multiple locations representing varying regions and seasons 

that provide evidence of the influence of local or regional climate and air pollution mixes on 

PM2.5-associated health effects. These studies provide more precise estimates of the magnitude 

of effects associated with short-term PM2.5 exposure than most smaller-scale single-city studies 

that were more commonly available in the last review (U.S. EPA 2009a, chapter 6). 

With regard to mortality, new U.S. and Canadian multi-city and single-city PM2.5

exposure studies have found generally consistent positive associations between short-term PM2.5

exposures and cardiovascular- and respiratory-related mortality as well as all-cause (nonaccidental) mortality (US EPA, 2009a, sections 2.3.1.1, 6.2.11 and 6.5.2.2; Figures 6-26, 6-27, 

and 6-28). In an analysis of the National Morbidity, Mortality, and Air Pollution Study 

(NMMAPS) data, Dominici et al. (2007a) reported associations between fine particle exposures 

and all-cause and cardiopulmonary-related mortality (US EPA, 2009a, p. 6-175, Figure 6-26). In 

a study of 112 U.S. cities, Zanobetti and Schwartz (2009) reported positive associations (in 99% 

of the cities) and frequently statistically significant associations (in 55% of the cities) between 

short-term PM2.5 exposure and total (non-accidental) mortality (US EPA, 2009a, pp 6-176 to 6-

179; Figures 6-23 and 6-24).18 A Canadian 12-city study (Burnett et al., 2004) is generally 

 18 Single-city Bayes-adjusted effect estimates for the 112 cities analyzed in Zanobetti and Schwartz (2009) were 

provided by the study author (personal communication with Dr. Antonella Zanobetti, 2009; see also US EPA, 

2009a, Figure 6-24). 
 2-24

consistent with an earlier Canadian 8-city study (Burnett and Goldberg, 2003). Both studies 

reported a positive and statistically significant association between short-term PM2.5 exposure 

and nonaccidental mortality (US EPA, 2009a, p 6-182, Figure 2-1), although the influence of 

NO2 and limited PM2.5 data for several years during the study period somewhat diminished the 

findings reported in the 12-city study. Collectively, these studies provide generally consistent 

and much stronger evidence for PM2.5-associated mortality than the evidence available in the last 

review. 

With regard to cardiovascular effects, new multi-city as well as single-city short-term 

PM2.5 exposure studies conducted since the last review support a largely positive and frequently 

statistically significant association between short-term exposure to PM2.5 and cardiovascularrelated morbidity and mortality, substantiating prior findings. For example, among a multi-city 

cohort of older adults participating in the Medicare Air Pollution Study (MCAPS), investigators 

reported evidence of a positive association between short-term PM2.5 exposures and hospital 

admissions related to cardiovascular outcomes (US EPA, 2009a, pp. 6-57 to 58; Dominici et al, 

2006a; Bell et al, 2008). The strongest evidence for cardiovascular effects has been observed 

predominantly for hospital admissions and emergency department visits for IHD and CHF and 

cardiovascular-related mortality (US EPA, 2009a, Figure 2-1, p. 6-79, sections 6.2.10.3, 6.2.10.5, 

and 6.2.11; Bell et al., 2008; Dominici et al., 2006a; Tolbert et al., 2007; Zanobetti and Schwartz, 

2009). Furthermore, these findings are supported by a recent study of a multi-city cohort of 

women participating in the WHI study that reported a positive but statistically nonsignificant 

association between short-term exposure to PM2.5 and electrocardiogram (ECG) measures of 

myocardial ischemia (Zhang et al., 2009). 

In focusing on respiratory effects, the strongest evidence from short-term PM2.5 exposure 

studies has been observed for respiratory-related emergency department visits and hospital 

admissions for chronic obstructive pulmonary disease (COPD) and respiratory infections (U.S. 

EPA, 2009a, sections 2.3.1.1 and 6.3.8.3; Figures 2-1 and 6-13; Dominici et al., 2006a). 

Evidence for PM2.5-related respiratory effects has also been observed in panel studies, which 

indicate associations with respiratory symptoms, pulmonary function, and pulmonary 

inflammation among asthmatic children. Although not consistently observed, some controlled 

human exposure studies have reported small decrements in various measures of pulmonary 

function following controlled exposures to PM2.5 (US EPA, 2009a, p. 2-10). Furthermore, the 

comparatively larger body of toxicological evidence since the last review is coherent with the 

evidence from epidemiological and controlled human exposure studies that examined short-term 

exposures to PM2.5 and respiratory effects (US EPA 2009a, section 6.3.10.1). 
 2-25

Uncertainties in the Evidence

With respect to understanding the nature and magnitude of PM2.5-related risks, as 

discussed above, we recognize that epidemiological studies evaluating health effects associated 

with long- and short-term PM2.5 exposures have reported heterogeneity in responses both within 

and between cities and geographic regions across the U.S. This heterogeneity may be attributed, 

in part, to differences in the fine particle composition. However, the currently available evidence 

and limited availability of city-specific PM2.5 speciation data does not allow conclusions to be 

drawn regarding the relative toxicity of PM2.5 components, or group of components associated 

with any source categories of fine particles in different locations. Overall, the ISA concludes 

“that many constituents of PM2.5 can be linked with multiple health effects, and the evidence is 

not yet sufficient to allow differentiation of those constituents or sources that are more closely 

related to specific health outcomes” (US EPA, 2009a, p. 2-17). 

Exposure measurement error is also an important source of uncertainty (US EPA, 2009a, 

section 3.8.6). Variability in the associations observed across PM2.5 epidemiological studies may 

be due in part to exposure error related to measurement-related issues, the use of central fixedsite monitors to represent population exposure to PM2.5, models used in lieu of or to supplement 

ambient measurements, and our limited understanding of factors that may influence exposures 

(e.g., topography, the built environment, climate, source characteristics, ventilation usage, 

personal activity patterns, photochemistry). As noted in the ISA, exposure measurement error 

can introduce bias and increased uncertainty in associated health effect estimates (US EPA, 

2009a, p. 2-17). 

In particular, we note that there are challenges with interpreting differences in health 

effects observed in the eastern versus western parts of the U.S, including evaluating effects 

stratified by seasons. As noted in section 2.3.2 of the ISA, western U.S. counties tend to be 

larger and more topographically diverse than eastern U.S. counties. These characteristics may 

contribute to a higher likelihood of exposure measurement error influencing health effect 

estimates. Seasonal differences in effects may be related to PM2.5 composition as well as 

regional differences in climate and infrastructure that may affect time spent outdoors or indoors, 

housing characteristics including air conditioning usage, and differences in baseline incidence 

rates (US EPA, 2009a, p. 3-182). In consideration of these differences, however, the ISA notes 

“...the available evidence and the limited amount of city-specific speciated PM2.5 data does not 

allow conclusions to be drawn that specifically differentiate effects of PM in different locations” 

(US EPA 2009a, p. 2-17). Therefore, we recognize that important uncertainties remain in this 

review related to understanding the temporal and spatial variability in PM2.5 concentrations, 

including PM2.5 components, and associated health impacts across different geographic areas and 

seasons. 
 2-26

In addition, where PM2.5 and other pollutants (e.g., O3, NO2, CO) are correlated, it can be 

difficult to distinguish the effects of the various pollutants in the ambient mixture (i.e, copollutant confounding).19 The use of multipollutant models is generally used in air pollution 

epidemiological studies to provide insights into the potential for confounding or interaction 

among pollutants (US EPA, 2009a, p. 1-16). A number of research groups have found the 

effects of various indicators of PM, including PM2.5, and gaseous copollutants to be independent 

of one another (US EPA, 2009a, Figures 6-9 and 6-15). Although many recent US multi-city 

studies did not analyze multipollutant models, evidence from single-city studies available in the 

last review suggest that gaseous copollutants do not confound the PM2.5-related mortality 

association, which is further supported by studies that examined the PM10-mortality relationship 

(US EPA, 2009a, p. 6-182 and 6-201). 

Summary

In considering the extent to which newly available scientific evidence strengthens or calls 

into question evidence of associations identified in the last review between ambient fine particle 

exposures and health effects, we recognize that much progress has been made in assessing some 

key uncertainties related to our understanding of health effects associated with long- and shortterm exposure to PM2.5. As briefly discussed above as well as in the more complete discussion 

of the evidence as assessed in the ISA, we note that the newly available information combined 

with information available in the last review provides substantially stronger confidence in a

causal relationship between long- and short-term exposures to PM2.5 and mortality and 

cardiovascular effects. In addition, the newly available evidence reinforces and expands the 

evidence supporting the likely causal relationship between long- and short-term exposure to 

PM2.5 and respiratory effects. With respect to evidence suggestive of a causal relationship for a 

broader range of effects, the body of scientific evidence is somewhat expanded but is still limited 

with respect to associations between long-term PM2.5 exposures and developmental and 

reproductive effects as well as cancer, mutagenic, and genotoxic effects. Thus, we conclude that 

there is stronger and more consistent and coherent support for associations between long- and 

short-term PM2.5 exposure and a broader range of health outcomes than was available in the last 

review, providing the basis for fine particle standards at least as protective as the current PM2.5

standards. 

Having reached this initial conclusion, we then consider how the new evidence informs 

our understanding of susceptible populations by addressing the following question: 

 19 A copollutant meets the criteria for potential confounding in PM-health associations if: (1) it is a potential risk 

factor for the health effect under study; (2) it is correlated with PM; and (3) it does not act as an intermediate step in 

the pathway between PM exposure and the health effect under study (US EPA, 2004, p. 8-10). 
 2-27

 To what extent does the currently available scientific evidence expand our 

understanding of susceptible populations? 

Specific groups within the general population, referred to as susceptible populations, are 

at increased risk for experiencing adverse health effects related to PM exposures. These groups 

could exhibit a greater risk of PM-related health effects than the general population for a number 

of reasons including, being affected by lower concentrations of PM, experiencing a larger health 

impact at a given PM concentration, and/or being exposed to higher PM concentrations than the 

general population. Given the heterogeneity of individual responses to PM exposures, the 

severity of the health effects experienced by a susceptible population may be much greater than 

that experienced by the population at large. 

As summarized below, the currently available epidemiological and controlled human 

exposure evidence expands our understanding of previously identified susceptible populations 

(i.e., children, older adults, and individuals with pre-existing heart and lung disease) and supports 

the identification of additional susceptible populations (e.g., persons with lower socioeconomic 

status (SES), genetic differences) (US EPA, 2009a, section 2.4.1, Table 8-2). In addition, 

toxicological studies provide underlying support for the biological mechanisms that potentially 

lead to increased susceptibility to PM-related health effects. 

Lifestages: Children and Older Adults 

Two different lifestages have been associated with increased susceptibility to PM-related 

health effects: childhood (i.e., less than 18 years of age) and older adulthood (i.e., 65 years of 

age and older). Childhood represents a lifestage where susceptibility to PM exposures may be 

related to the following observations: children spend more time outdoors; children have greater 

activity levels than adults; children have exposures resulting in higher doses per body weight and 

lung surface area; and the developing lung is prone to damage, including irreversible effects, 

from environmental pollutants as it continues to develop through adolescence (US EPA, 2009a, 

section 8.1.1.2). Older adults represent a lifestage where susceptibility to PM-associated health 

effects may be related to the higher prevalence of pre-existing cardiovascular and respiratory 

diseases found in this age group compared to younger age groups as well as the gradual decline 

in physiological processes that occur as part of the aging process (US EPA, 2009a, section 

8.1.1.1). 

With regard to mortality, recent epidemiological studies have continued to find that older 

adults are at greater risk of all-cause (nonaccidental) mortality associated with short-term 

exposure to both PM2.5 and PM10, providing consistent and stronger evidence of effects in this 

susceptible population compared to the last review. Epidemiological studies that examined the 

association between mortality and long-term exposure to PM2.5 that stratified the results by age 
 2-28

(i.e., less than 65 years of age compared to aged 65 and older; different age groups within the 

aged 65 and older population) reported results that are generally consistent with the findings of 

these short-term exposure studies and also provided some evidence that the increased risk 

declined with increasing age starting at age 60, such that there was no evidence of an association 

among persons 85 years and older (US EPA, 2009a, Figure 7-7, section 8.1.1.1, Zeger et al., 

2008). 

With regard to morbidity effects, currently available studies provide evidence that older 

adults have heightened responses, especially for cardiovascular-related effects, and children have 

heightened responses for respiratory-related effects associated with long- and short-term PM2.5

exposures. With regard to older adults, epidemiological studies provide evidence of increases in 

PM2.5-related risk of MI, coronary revascularization,20 and their combination with CHD-related 

death for participants free of CVD at baseline (Miller et al., 2007) as well as cardiovascularrelated hospitalization (Dominici et al., 2006a; Bell et al., 2008). Further, dosimetry studies have 

shown a depression of fine and coarse PM clearance in all regions of the respiratory tract with 

increasing age beyond young adulthood, suggesting that older adults are at greater risk of PMrelated respiratory health effects (US EPA, 2009a, section 8.1.1). 

With regard to respiratory-related effects in children associated with long-term PM 

exposures, our understanding of effects on lung development has been strengthened based on 

newly available evidence that is consistent and coherent across different study designs, locations, 

and research groups. For example, the strongest evidence comes from the extended follow-up 

for the Southern California CHS which includes several new studies that report positive 

associations between long-term exposure to PM2.5 and respiratory morbidity, particularly for 

such endpoints as lung function growth persisting to young adulthood, respiratory symptoms 

(e.g., bronchitic symptoms), and respiratory disease incidence (US EPA, 2009a, section 7.3; 

McConnell et al, 2003; Gauderman et al., 2004; Islam et al., 2007). These analyses provide 

evidence that PM2.5–related effects persist into early adulthood and are more robust and larger in 

magnitude than previously reported. 

With regard to respiratory effects in children associated with short-term exposures to PM, 

currently available studies provide stronger evidence of respiratory-related hospitalizations with 

larger effect estimates observed among children. In addition, reductions in lung function (i.e., 

FEV1) and increases in respiratory symptoms and medication use associated with PM exposures 

have been reported among asthmatic children (US EPA, 2009a, sections 6.3.1, 6.3.2.1, 8.4.9). 

In addition, accumulating evidence suggests that the developing fetus may also represent 

an additional lifestage that is susceptible to PM exposures. The ISA notes that “[i]nfants and 

 20 Coronary revascularization includes percutaneous coronary interventions, such as angioplasty. 
 2-29

fetal development processes may be particularly vulnerable to PM exposure, and although the 

physical mechanisms are not fully understood, several hypotheses have been proposed involving 

direct effects on fetal health, altered placenta function, or indirect effects on the mother’s health” 

(US EPA, 2009a, section 7.4.1). Evidence is accumulating for PM2.5-related effects on low 

birthweight and infant mortality, especially due to respiratory causes during the post-neonatal 

period (US EPA, 2009a, sections 2.3.1.2 and 7.4). 

Pre-existing Diseases/Health Conditions

A number of health conditions have been found to put individuals at greater risk for 

adverse effects following exposure to PM. The currently available evidence confirms and 

strengthens evidence in the last review that individuals with underlying cardiovascular and 

respiratory diseases are more susceptible to PM exposures. 

The majority of the epidemiological studies that examined associations between shortterm PM exposures and cardiovascular outcomes focused on cardiovascular-related hospital 

admissions and emergency department visits (US EPA, section 8.1.6.1). There is some new 

evidence that individuals with pre-existing IHD are at greater risk of PM-associated hospital 

admissions and emergency department visits related to cardiovascular effects. Additional studies 

have focused on hypertension and on the effects of PM on cardiac function in individuals with 

dysrhythmia with mixed results. One epidemiological study (US EPA, 2009a, section 7.2.9; 

Zanobetti and Schwartz, 2007) investigated associations between long-term exposure to PM10

and the progression of disease or reduced survival in a 21-city study of people discharged 

following an acute MI, finding significant associations for mortality, CHF, and new 

hospitalization for MI. 

With regard to individuals with pre-existing respiratory illnesses (e.g., asthma, COPD), 

the ISA presents and assesses a number of studies that evaluate a broad range of health outcomes 

(e.g., mortality, asthma symptoms) in response to PM exposures (US EPA, 2009a, section 

8.1.6.2). Evidence in asthmatics is stronger and more consistent than previously reported, while 

studies of persons with COPD have reported mixed results. Epidemiological studies have 

examined the effect of short-term exposure to PM in asthmatics finding an increase in 

medication use, asthma attacks, and respiratory symptoms (i.e., cough, shortness of breath, chest 

tightness). Controlled human exposure studies reported healthy and asthmatic subjects exposed 

to fine, coarse, and ultrafine concentrated ambient particles (CAPs), exhibited similar respiratory 

responses, although these studies excluded moderate and severe asthmatics that would be 

expected to show increased susceptibility to PM exposure. Toxicological studies using diesel 

exhaust particles (DEPs) provide mechanistic support that PM exposure results in allergic 

sensitization, and individuals with allergic airway conditions are at greater risk of adverse effects 
 2-30

upon exposure to PM2.5 (US EPA, 2009a, section 6.3.6.3). Further, there is emerging but limited 

evidence which suggests that non-allergic respiratory morbidities may also increase the 

susceptibility of an individual to PM-related respiratory effects (US EPA, 2009a, p. 8-12). 

There is also emerging evidence that suggests the influence of additional pre-existing 

diseases or health conditions, including diabetes and obesity, on the manifestation of PM-related 

health effects. The ISA notes that additional research exploring the effect of PM exposures on 

obese individuals and identifying the biological pathway(s) that could increase the susceptibility 

of diabetic and obese individuals to PM could improve our understanding of these potentially 

susceptible populations (US EPA 2009a, pp. 2-23-2-24). 

Socioeconomic Status

Stronger evidence is available in this review indicating that people from lower 

socioeconomic strata are a susceptible population relative to PM exposures (US EPA, 2009a, 

section 8.1.7). Persons with lower socioeconomic status (SES)21 have been generally found to 

have a higher prevalence of pre-existing diseases; limited access to medical treatment; and 

increased nutritional deficiencies, which can increase this population’s risk to PM-related effects. 

Evidence available in the last review from the ACS and Harvard Six Cities cohort studies 

indicated increased mortality risk with long-term exposure to PM2.5 in the cohort subgroups with 

lower education levels (US EPA 2004, section 9.2.4.5). In this review, additional support is 

available to identify persons with lower SES as a susceptible population. For example, Krewski 

et al. (2009) found moderate evidence for increased lung cancer mortality in individuals with a 

high school education or less in response to long-term exposure to PM2.5. However, IHD-related 

mortality associated with long-term PM2.5 exposures was most strongly associated with 

individuals with higher education levels (US EPA, 2009a, p. 8-15). 

Genetic Factors 

Investigation of potential genetic susceptibility has provided evidence that individuals 

with null alleles or polymorphisms in genes that mediate the antioxidant response to oxidative 

stress (e.g., GSTM1), regulate enzyme activity (i.e., MTHFR and cSHMT), or regulate levels of 

procoagulants (i.e., fibrinogen) are more susceptible to PM-related effects. However, some 

evidence suggests that polymorphisms in genes (e.g., HFE) may provide protection for PMrelated effects. Emerging evidence also suggests that PM exposure can impart epigenetic effects 

(i.e., DNA methylation) (US EPA, 2009a, p. 8-16). More research is needed to better understand 

the relationship between genetic effects and potential susceptibility to PM-related effects. 

 21 SES is a composite measure that usually consists of economic status, measured by income; social status measured 

by education; and work status measured by occupation (US EPA, 2009a, p. 8-14). 
 2-31

Summary

In summary, we conclude that there are several susceptible populations that are likely to 

be at increased risk of PM-related effects, including the lifestages of childhood and older 

adulthood, those with preexisting heart and lung diseases, and those of lower SES. Evidence for 

PM-related effects in these susceptible populations has expanded and is stronger than previously 

observed. We also recognize that there is emerging, though still limited evidence for additional 

potentially susceptible populations, such as those with diabetes, people who are obese, or those 

with specific genetic factors. We note that the available evidence does not generally allow 

distinctions to be drawn between the PM indicators in terms of whether populations are more 

susceptible to a particular size fraction (i.e., PM2.5 and PM10-2.5). 

 To what extent does the currently available scientific evidence report associations that 

extend to air quality concentrations that are lower than had previously been observed 

or that are observed in areas that would likely meet the current suite of PM2.5

standards? 

In focusing our attention on whether the available evidence supports consideration of 

standards that are more protective than the current suite of PM2.5 standards, we first recognize 

that the ISA concludes there is no evidence to support the existence of a discernible population 

threshold below which effects would not occur (US EPA, 2009a, section 2.4.3). Next, we 

consider whether the evidence provides information for health effects associated with air quality 

concentrations that are lower than had previously been observed, or if epidemiological studies 

have reported effects in areas that would likely have met the current suite of PM2.5 standards. 

Associations with Long-term PM2.5 Exposure

Extended follow-up analyses of the ACS and Harvard Six Cities studies provide 

consistent and stronger evidence of an association with mortality at lower air quality 

distributions than had previously been observed. The original and reanalysis of the ACS study 

reported positive and statistically significant effects associated with a long-term mean PM2.5

concentration of 18.2 μg/m3

 across 50 metropolitan areas for 1979-1983 (Pope et al., 1995; 

Krewski et al., 2000).22 In extended analyses, positive and statistically significant effects of 

approximately similar magnitude were associated with declining PM2.5 concentrations, from an 

aggregate long-term mean in 58 metropolitan areas of 21.2 μg/m3 in the original monitoring 

period (1979-1983) to 14.0 μg/m3 for 116 metropolitan areas in the most recent years evaluated 

(1999-2000), with an overall average across the two study periods in 51 metropolitan areas of 

17.7 μg/m3

 (Pope et al., 2002; Krewski et al., 2009). With regard to the Harvard Six Cities 

 22 The study periods referred to in this document reflect the years of air quality data that were included in the 

analyses, whereas the study periods identified in the ISA reflect the years of health status data that were included. 
 2-32

Study, the original and reanalysis reported positive and statistically significant effects associated 

with a long-term mean PM2.5 concentration of 18.0 μg/m3 for 1980-1985 (Dockery et al., 1993; 

Krewski et al., 2000). In an extended follow-up of this study, the aggregate long-term mean 

concentration across all years evaluated was 16.4 μg/m3

 for 1980-198823 (Laden et al., 2006). In 

an additional analysis of the extended follow-up of the Harvard Six Cities Study, investigators 

reported the C-R relationship was linear and “clearly continuing below the level” of the current 

annual standard (US EPA, 2009a, p. 7-92; Schwartz et al., 2008). 

We then consider new cohort studies that provide evidence of mortality associated with 

air quality distributions that are generally lower than those reported in the ACS and Harvard Six 

Cities studies, with effect estimates that were similar or greater in magnitude. The WHI study 

reported positive and most often statistically significant associations between long-term PM2.5

exposure and cardiovascular-related mortality, with much larger relative risk estimates than in 

the ACS and Harvard Six Cities studies, as well as morbidity effects at an aggregate long-term 

mean PM2.5 concentration of 12.9 μg/m3 for 2000 (Miller et al., 2007).24 Using the Medicare 

cohort, Eftim et al. (2008) reported somewhat higher effect estimates than in the ACS and 

Harvard Six Cities studies with aggregate long-term mean concentrations of 13.6 μg/m3

 and 14.1 

μg/m3, respectively, for 2000-2002. The MCAPS reported associations between long-term PM2.5

exposure and mortality for the eastern region of the U.S. at an aggregated long-term PM2.5

median concentration of 14.0 μg/m3

, although no association was reported for the western region 

with an aggregated long-term PM2.5 median concentration of 13.1 μg/m3 (US EPA, 2009a, p. 7-

88; Zeger et al., 2008).25 Premature mortality in children reported in a national infant mortality 

study as well as mortality in a cystic fibrosis cohort including both children and adults reported 

positive but statistically nonsignificant effects associated with long-term aggregate mean 

 23 Aggregate mean concentration provided by study author (personal communication from Dr. Francine Laden, 

2009). 

24 Miller et al. (2007) reported a long-term mean PM2.5 concentration across study areas of 13.5 μg/m3

. This 

concentration was presented in the ISA (US EPA, 2009a) and discussed in the second draft PA (US EPA, 2010f). In 

response to a request from EPA for additional information on the air quality data used in selected epidemiological 

studies (Hassett-Sipple and Stanek, 2009), study investigators provided updated air quality data for the study period. 

The updated long-term mean PM2.5 concentration provided by the study authors was 12.9 μg/m3

 (personal 

communication from Cynthia Curl, 2009; Stanek et al., 2010). We note that this updated long-term mean 

concentration matches the composite monitor approach annual mean calculated by EPA for the year of air quality 

data (i.e., 2000) considered by the study investigators (Hassett-Sipple et al., 2010, Attachment A, p. 6). Staff 

concludes it is most appropriate to include the updated air quality data in this final document. 

25 Zeger et al. (2008) also reported positive and statistically significant effects for the central region, with an 

aggregate long-term mean PM2.5 concentration of 10.7 μg/m3

. However, in contrast to the eastern and western risk 

estimates, the central risk estimate increased with adjustment for COPD (used as a proxy for smoking status). Due 

to the potential for confounding bias influencing the risk estimate for the central region, we have not focused on the 

results reported in the central region to inform the adequacy of the current suite of standards or alternative annual 

standard levels discussed in section 2.3.4.1. 
 2-33

concentrations of 14.8 μg/m3 and 13.7 μg/m3, respectively (Woodruff et al., 2008; Goss et al., 

2004). 

With respect to respiratory morbidity effects associated with long-term PM2.5 exposure, 

the across-city mean of 2-week average PM2.5 concentrations reported in the initial Southern 

California CHS was approximately 15.1 μg/m3

 (Peters et al., 1999). These results were found to 

be consistent with results of cross-sectional analyses of the 24-Cities Study (Dockery et al., 

1996; Raizenne et al., 1996), which reported a long-term cross-city mean PM2.5 concentration of 

14.5 μg/m3. In this review, extended analysis of the Southern California CHS provided stronger 

evidence of PM2.5-related respiratory effects, at lower air quality concentrations than had 

previously been reported, with a four-year aggregate mean concentration of 13.8 μg/m3 across 

the 12 study communities (McConnell et al., 2003; Gauderman et al., 2004, US EPA, 2009a, 

Figure 7-4). 

Broadening our consideration to effects for which evidence is suggestive of a causal 

relationship, we note a limited number of birth outcome studies that reported positive and 

statistically significant effects related to aggregate long-term mean PM2.5 concentrations of 

approximately 12 μg/m3 (US EPA, 2009a, Table 7-5; Bell et al., 2007; Liu et al., 2007; Parker et 

al, 2005). In contrast, Parker and Woodruff (2008) reported no overall association with birth 

weight with an aggregate long-term mean concentration of 13.5 μg/m3 (US EPA, 2009a, section 

7.4.1.1). 

Collectively, the currently available evidence provides support for associations between 

long-term PM2.5 exposure and mortality and morbidity effects that extend to air quality 

concentrations that are lower than had previously been observed, with aggregate long-term mean 

PM2.5 concentrations extending to well below the level of the current annual standard. These 

studies evaluated a broader range of health outcomes in the general population and in susceptible 

populations than were considered in the last review, and include extended follow-up for 

prospective epidemiological studies that were important in the last review as well as additional 

evidence in important new cohorts. 

Associations with Short-term PM2.5 Exposure

In considering long-term average ambient concentrations from multi-city, short-term 

PM2.5 exposure studies, a 12-cities Canadian study (Burnett et al 2004; aggregate long-term 

mean PM2.5 concentration of 12.8 μg/m3

) provided evidence of an association between shortterm PM2.5 exposure and mortality at lower air quality distributions than had previously been 

observed in an 8-cities Canadian study (Burnett and Goldberg, 2003; aggregate long-term mean 

PM2.5 concentration of 13.3 μg/m3

). In a multi-city time-series analysis of 112 U.S. cities, 

Zanobetti and Schwartz (2009) reported a positive and statistically significant association with 
 2-34

all-cause, cardiovascular-related (e.g., MI, stroke), and respiratory-related mortality and shortterm PM2.5 exposure, in which the aggregate long-term mean PM2.5 concentration was 13.2 

μg/m3 (US EPA, 2009a, Figure 6-24). Furthermore, city-specific effect estimates in Zanobetti 

and Schwartz (2009) indicate the association between short-term exposure to PM2.5 and total 

mortality and cardiovascular- and respiratory-related mortality is consistently positive for an 

overwhelming majority (99%) of the 112 cities across a wide range of air quality concentrations 

(ranging from 6.6 μg/m3

to 24.7 μg/m3; US EPA, 2009a, Figure 6-24, p. 6-178 to 179). We note 

that for all-cause mortality, city-specific effect estimates were statistically significant for 55% of 

the 112 cities, with long-term city-mean PM2.5 concentrations ranging from 7.8 μg/m3 to 18.7 

μg/m3 and 24-hour PM2.5 city-mean 98th percentile concentrations ranging from 18.4 to 64.9 

μg/m3

 (personal communication with Dr. Antonella Zanobetti, 2009).26 

With regard to cardiovascular and respiratory morbidity effects, in the first analysis of the 

MCAPS cohort conducted by Dominici et al. (2006a) across 204 U.S. counties, investigators 

reported a statistically significant association with hospitalizations for cardiovascular and 

respiratory diseases and short-term PM2.5 exposure, in which the aggregate long-term mean 

PM2.5 concentration was 13.4 μg/m3. Furthermore, a sub-analysis restricted to days with 24-hour 

average concentrations of PM2.5 at or below 35 μg/m3 indicated that, in spite of a reduced 

statistical power from a smaller number of study days, statistically significant associations were 

still observed between short-term exposure to PM2.5 and hospital admissions for cardiovascular 

and respiratory diseases (Dominici, 2006b).27 In an extended analysis of the MCAPS study, Bell 

et al. (2008) reported a positive and statistically significant increase in cardiovascular 

hospitalizations associated with short-term PM2.5 exposure, in which the aggregate long-term 

mean PM2.5 concentration was 12.9 μg/m3. These results, along with the observation that 

approximately 50% of the 204 county-specific mean 98th percentile PM2.5 concentrations in 

Dominici et al. (2006a) aggregated across all years were below the 24-hour standard of 35 

μg/m3,suggests that the overall health effects observed across the U.S. are not primarily driven 

by the higher end of the PM2.5 air quality distribution (Bell, 2009, personal communication from 

Dr. Michelle Bell regarding air quality data for Bell et al., 2008 and Dominici et al., 2006a). 

In considering single-city short-term PM2.5 exposure studies that were conducted in areas 

that would likely have met the current suite of standards, the following studies reported positive 

and statistically significant associations: three studies noted in the last review, Mar et al. (2003) 

 26 Single-city Bayes-adjusted effect estimates for the 112 cities analyzed in Zanobetti and Schwartz (2009) were 

provided by the study authors (personal communication with Dr. Antonella Zanobetti, 2009; see also US EPA, 

2009a, Figure 6-24). 

27 This sub-analysis was not included in the original publication (Dominici et al., 2006a). Authors provided subanalysis results for the Administrator’s consideration as a letter to the docket following publication of the proposed 

rule in January 2006 (personal communication with Dr. Francesca Dominici, 2006b). 
 2-35

reported a positive and statistically significant association for premature mortality in Phoenix and 

Peters et al. (2001) and Delfino et al. (1997) reported an association with short-term exposure to 

PM2.5 and MI- and respiratory-related hospital admissions in Boston and Montreal, Canada, 

respectively, and one more recent study, Mar et al. (2004) reported an association for short-term 

PM2.5 exposures in relation to respiratory symptoms among children in Spokane. Single-city 

studies that reported positive but statistically non-significant associations for cardiovascular and 

respiratory endpoints include a number of studies conducted in Saint John (Steib et al., 2000), 

Phoenix (Wilson et al., 2007), Denver (Rabinovitch et al., 2006), Edmonton (Villeneuve et al., 

2006), and Nueces County, TX (Lisabeth et al., 2008). Other single-city short-term PM2.5

exposure analyses reported null findings for cardiovascular and respiratory morbidity effects in 

association with short-term exposure to PM2.5 in areas that would likely have met the current 

suite of standards, including Spokane (i.e., respiratory symptoms in adults, Mar et al., 2004; 

Slaughter et al. 2005), Denver (Rabinovitch et al., 2004), and Edmonton (Villeneuve et al., 

2006). In light of the mixed findings reported in single-city studies, particularly for studies 

conducted in areas such as Phoenix, Denver, and Edmonton that report both positive and null 

findings, we place comparatively greater weight on the results from multi-city studies in 

considering the adequacy of the current suite of standards. 

Collectively, the findings from multi-city and single-city short-term PM2.5 exposure 

studies provide evidence of PM2.5-associated health effects occurring in areas that would likely 

have met the current suite of PM2.5 standards. These findings are further bolstered by evidence 

of statistically significant PM2.5 associated health effects occurring in analyses restricted to days 

in which 24-hour average PM2.5 concentrations were below 35 μg/m3 (Dominici, 2006b). 

Summary 

In evaluating the currently available scientific evidence, we conclude that the evidence 

from long and short-term PM2.5 exposure studies clearly calls into question whether the current 

suite of primary PM2.5 standards protects public health with an adequate margin of safety from 

effects associated with long- and short-term exposures to PM2.5. We also conclude that this 

evidence provides strong support for considering fine particle standards that would afford 

increased protection beyond that afforded by the current annual and 24-hour PM2.5 standards. 

More protective standards would reflect the substantially stronger and broader body of evidence 

for mortality and cardiovascular-related and respiratory-related morbidity effects now available 

in this review both at lower concentrations of air quality than had previously been observed and 

at concentrations allowed by the current suite of PM2.5 standards.
 2-36

2.2.2 Risk-based Considerations 

Looking beyond evidence-based considerations, staff also has considered the extent to 

which health risks estimated to occur upon just meeting the current suite of PM2.5 standards may 

be judged to be important from a public health perspective. For this review, we have estimated 

risk for a set of health effect endpoints based on a number of selection criteria (US EPA, 2010a, 

section 3.3.1). Specifically, we have estimated risks for (1) all-cause, IHD-related, 

cardiopulmonary- and lung cancer-related mortality associated with long-term PM2.5 exposure, 

(2) non-accidental, cardiovascular-related, and respiratory-related mortality associated with 

short-term PM2.5 exposure, and (3) cardiovascular-related and respiratory-related hospital 

admissions and asthma-related emergency department visits associated with short-term PM2.5

exposure. In the discussion below, we focus on cardiovascular-related endpoints, since the 

causal relationship for these endpoints based on the currently available scientific evidence as 

assessed in the ISA is the strongest of the endpoints considered. The estimated risks for the 

broader set of health effect endpoints modeled are included in the RA (US EPA, 2010a). 

As discussed below, three factors figure prominently in the interpretation of the risk 

estimates associated with simulating just meeting the current suite of standards, including: (1) 

the importance of changes in annual mean PM2.5 concentrations for a specific study area in 

estimating changes in risks related to both long- and short-term exposures associated with recent 

air quality conditions and air quality simulated to just meet the current suite of PM2.5 standards; 

(2) the ratio of peak- to-mean ambient PM2.5 concentrations in a study area; and (3) the spatial 

pattern of ambient PM2.5 reductions that result from using different approaches to simulate just 

meeting the current standard levels (i.e., rollback approaches). The latter two factors are 

interrelated and influence the degree of risk reduction estimated under the current suite of 

standards. 

The magnitude of both long- and short-term exposure-related risk estimated to remain 

upon just meeting the current suite of standards is strongly associated with the simulated change 

in annual mean PM2.5 concentrations. The role of annual mean PM2.5 concentrations in driving 

long-term exposure-related risk estimates is intuitive given that risks are modeled using the 

annual mean air quality metric.28 The fact that short-term exposure-related risk estimates are 

also driven by changes in long-term mean PM2.5 concentrations is less intuitive, since changes in 

 28 As noted in section 3.2.1 of the RA (U.S.EPA, 2010a), estimates of long-term exposure-related mortality are 

actually based on an annual mean PM2.5 concentration that is the average across monitors in a study area (i.e., based 

on the composite monitor distribution). Therefore, in considering changes in long-term exposure-related mortality, 

it is most appropriate to compare composite monitor estimates generated for a study area under each alternative suite 

of standards considered. The annual mean at the highest reporting monitor (i.e., based on the maximum monitor 

distribution) for a study area is the annual design value. The annual design value is used to determine the percent 

reduction in PM2.5 concentrations required to meet a particular standard. Both types of air quality estimates are 

provided in Table 3-4 of the RA and both are referenced in this discussion of core risk estimates, as appropriate. 
 2-37

mean 24-hour PM2.5 concentrations are used to estimate changes in risk for this time period.29 

Analyses show that short-term exposure-related risks are not primarily driven by the small 

number of days with PM2.5 concentrations in the upper tail of the air quality distribution, but 

rather by the large number of days with PM2.5 concentrations at and around the mean of the 

distribution (US EPA, 2010a, section 3.1.2.2). Consequently, changes in annual mean PM2.5

concentrations are related, to a large extent, to changes in short-term exposure-related risk. 

Therefore, we focus on changes in annual mean PM2.5 concentrations to inform our 

understanding of patterns of both long- and short-term exposure-related risk estimates across the 

set of urban study areas evaluated in the quantitative risk assessment. 

The ratio of peak-to-mean ambient PM2.5 concentrations within a study area informs the 

type of rollback approach used to simulate just meeting the current suite of standards to 

determine the magnitude of the reduction in annual mean PM2.5 concentrations for that study area 

and consequently the degree of risk reduction.30 For example, study areas with relatively high 

peak-to-mean ratios are likely to have greater estimated risk reductions for the current suite of 

standards (depending on the combination of 24-hour and annual design values), and such 

locations can be especially sensitive to the type of rollback approach used, with the proportional 

rollback approach resulting in notably greater estimated risk reduction compared with the 

locally-focused rollback approach. In contrast, study areas with lower peak-to-mean ratios 

typically experience greater simulated risk reductions when simulating just meeting the current 

annual-standard level than with simulating just meeting the current 24-hour standard level (again 

depending on the combination of 24-hour and annual design values). In addition, the type of 

rollback approach used will tend to have less of an impact on the magnitude of risk reductions 

for study areas with lower peak-to-mean ratios. Rigorous consideration of these two factors, 

allowed us to better understand the nature and pattern of estimated risk reductions and risk 

remaining upon simulation of just meeting the current suite of standards across the urban study 

areas (see U.S.EPA, 2010a, section 5.2.1). 

We have considered a series of questions to inform our understanding of the adequacy of 

the current suite of fine particle standards based on insights obtained from the quantitative risk 

assessment. We begin by considering the overall confidence associated with the quantitative risk 

assessment and the degree to which the set of urban study areas analyzed is representative of 

 29 Estimates of short-term PM2.5 exposure-related mortality and morbidity are based on composite monitor 24-hour 

PM2.5 concentrations. However, similar to the case with long-term exposure-related mortality, under the current 

rules, it is the 98th percentile 24-hour concentration estimated at the maximum monitor (the 24-hour design value) 

that will determine the degree of reduction required to meet a given 24-hour standard. 

30 The peak-to-mean ratio of ambient PM2.5 concentrations also has a direct bearing on whether the 24-hour or 

annual standard will be the controlling standard for a particular study area, with higher peak-to-mean ratios 

generally being associated with locations where the 24-hour standard is likely the controlling standard. 
 2-38

urban areas across the U.S. We then consider the nature and magnitude of risk estimated to 

remain based on simulating just meeting the current suite of standards. 

 What is the level of confidence associated with risk estimates generated for simulating 

just meeting the current suite of PM2.5 standards? 

A number of design elements were included in the quantitative risk assessment to 

increase the overall confidence in the risk estimates generated for the 15 urban study areas. 

These elements included: (1) use of a deliberative process for specifying components of the risk 

model that reflects consideration of the latest research on PM2.5 exposure and risk (US EPA, 

2010a, section 5.1.1), (2) integration of key sources of variability into the design as well as the 

interpretation of risk estimates (U.S.EPA, 2010a, section 5.1.2), (3) assessment of the degree to 

which the urban study areas are representative of areas in the U.S. experiencing higher PM2.5-

related risk (U.S.EPA, 2010a, section 5.1.3), and (4) identification and assessment of important 

sources of uncertainty and the impact of these uncertainties on the core risk estimates (U.S.EPA, 

2010a, section 5.1.4).31 Two additional analyses examined potential bias and overall confidence 

in the risk estimates. The first analysis explored potential bias in the core risk estimates by 

considering a set of alternative reasonable risk estimates generated as part of a sensitivity 

analysis. The second analysis compared the annual mean PM2.5 concentrations associated with 

simulating just meeting the current suite of standards with the air quality distribution used in 

deriving the C-R functions applied in modeling mortality risk.32 Greater confidence is associated 

with risk estimates based on simulated annual mean PM2.5 concentrations that are within the 

region of the air quality distribution used in deriving the C-R functions where the bulk of the 

data reside (e.g., within one standard deviation (SD) around the mean). Each of the design 

elements listed above together with the two additional analyses is discussed below. 

Staff used a deliberative process to specify each of the key analytical elements 

comprising the core risk model, including: selection of urban study areas; selection of health 

endpoints, including specification of the C-R functions to use in modeling those endpoints; and 

choice of rollback approach used to simulate just meeting the current suite of standards. This 

deliberative process involved rigorous review of the currently available literature addressing both 

PM2.5 exposure and risk combined with the application of a formal set of criteria to guide 

development of each of the key analytical elements in the quantitative risk assessment (US EPA, 

2010a, section 5.1.1).33 The application of this deliberative process increases our overall 

 31 The “core” risk estimates produced in this assessment refer to those generated using the combination of modeling 

elements and input data sets in which we had the highest confidence. Alternative modeling elements were included 

as part of the sensitivity analyses. 

32 This analysis also considered simulations of alternative standard levels as discussed in section 2.3.4.2. 

33 In addition, as discussed in section 1.2.4, the quantitative risk assessment design reflects consideration of CASAC 

and public comments on the Scope and Methods Plan and two draft assessment documents. 
 2-39

confidence in the risk estimates by insuring that the estimates are based on the best available 

science and data characterizing PM2.5 exposure and risk, and that they reflect consideration of 

input from external experts on PM exposure and risk through CASAC and public reviews. 

We considered key sources of variability that can impact the nature and magnitude of 

risks associated with the current standard levels across the urban study areas. These sources of 

uncertainty include those that contribute to differences in risk across urban study areas, but do 

not directly affect the degree of risk reduction associated with the simulation of the current 

standard levels (e.g., differences in baseline incidence rates, demographics and population 

behavior). We also focused on factors that not only introduce variability into risk estimates 

across study areas, but also play an important role in determining the magnitude of risk 

reductions upon simulation of current standard levels (e.g., peak-to-mean ratios of ambient PM2.5

concentrations within individual urban study areas and the nature of the rollback approach used 

to simulate just meeting the current standards – see earlier discussion). 

Single and multi-factor sensitivity analyses were combined with a qualitative analysis to 

assess the impact of potential sources of uncertainty on the core risk estimates. The qualitative 

uncertainty analysis supplemented the quantitative sensitivity analyses by allowing coverage for 

sources of uncertainty that could not be readily included in the sensitivity analysis (US EPA, 

2010a, section 3.5.3). The quantitative sensitivity analyses informed our understanding of 

sources of uncertainty that may have a moderate to large impact on the core risk estimates. With 

respect to the long-term exposure-related mortality risk estimates, the most important sources of 

uncertainty identified in the quantitative sensitivity analyses included: selection of C-R 

functions;34 modeling risk down to policy-relevant background (PRB) versus lowest measured 

level (LML); and the choice of rollback approach used. With regard to the qualitative analysis of 

uncertainty, the following sources were identified as potentially having a large impact on core 

risk estimates for the long-term exposure-related mortality: characterization of inter-urban 

population exposures; impact of historical air quality; and potential variation in effect estimates 

reflecting differences in PM2.5 composition. Together, the qualitative analysis of uncertainty and 

quantitative sensitivity analyses provided us with a comprehensive understanding of which 

sources of uncertainty could have a significant impact on the core risk estimates. This 

 34 In the case of long-term exposure-related mortality, we considered both alternative C-R functions from the 

epidemiological study providing the C-R function used in the core analysis (i.e., alternative functions obtained from 

the Krewski et al. (2009) study involving the ACS dataset) as well as alternative C-R functions identified in other 

studies (i.e., C-R functions obtained from Krewski et al. (2000) based on a reanalysis of the Harvard Six Cities 

study). 
 2-40

information proved useful in interpreting core risk estimates and increased our overall 

confidence in the analysis.35

In addition to identifying sources of uncertainty with a moderate to large impact on the 

core risk estimates, the single and multi-element sensitivity analyses also produced a set of 

reasonable alternative risk estimates that allowed us to place the results of the core analysis in 

context with regard to uncertainty and potential bias.36 Most of the alternative model 

specifications supported by the currently available scientific information produced risk estimates 

that are higher (by up to a factor of 2 to 3) than the core risk estimates. This was not unexpected. 

The C-R functions used in the core analysis for estimating mortality risks associated with longterm PM2.5 exposures were selected from the extended analysis of the ACS study (Krewski et al., 

2009). The C-R functions used in the sensitivity analysis were from the reanalysis and validation 

of the Harvard Six Cities study (Krewski et al., 2000). In generalizing the results of the extended 

analyses of the ACS and Harvard Six Cities studies across the broader national population, we 

recognize differences in the underlying populations enrolled in these long-term cohort studies, 

specifically related to SES, a factor in considering impacts on susceptible populations. As noted 

in the last review, the ACS study population has a higher SES status (e.g., educational status) 

relative to the Harvard Six Cities study population (12% versus 28% of the cohort had less than a 

high school education, respectively) (US EPA, 2004a, p. 8-118). The Harvard Six Cities cohort 

may provide a more representative sample of the broader national population than the ACS 

cohort. 

As discussed above, lower SES groups have been identified as a susceptible population. 

Therefore, use of effect estimates reported in the ACS study which does not provide 

representative coverage for lower-SES groups, may result in risk estimates that are biased low. 

In contrast, risk estimates developed in the sensitivity analysis based on the Harvard Six Cities 

study data set provide better coverage for lower SES populations but give greater weight to 

eastern and Midwestern populations and, therefore, result in higher risk estimates (US EPA, 

2010a, section 5.1.5).

While being mindful that the use of C-R functions from Krewski et al. (2009) introduces 

potential for low bias in the core risk estimates, we also recognize many strengths of this study 

 35 Given increased emphasis placed in this analysis on long-term exposure-related mortality, the uncertainty analyses 

completed for this health endpoint category were more comprehensive than those conducted for analyses of shortterm exposure-related mortality and morbidity. This reflects, to some extent, limitations in the epidemiological data 

available for addressing uncertainty in the latter categories (U.S.EPA 2010a, section 3.5.4.2). 36 The alternative set of reasonable risk estimates were based on alternative model specifications compared to the 

core risk model. These alternative reasonable risk estimates were only generated for long-term exposure-related 

mortality and not for any of the short-term exposure-related mortality or morbidity endpoints. Consequently, 

consideration of overall confidence (and potential bias) in the core risk estimates based on consideration for these 

alternative risk estimates is limited to estimates of mortality associated with long-term PM2.5 exposures. 
 2-41

and reasons for its continued use for generating the core risk estimates, including: consideration 

of a large number of metropolitan statistical areas (MSAs), inclusion of two time periods for the 

air quality data which allowed us to consider different exposure windows; and analysis of a wide 

range of C-R function models. Therefore, we concluded that C-R functions obtained from this 

study had the greatest overall support and should be used in the core risk model. Consideration 

of the alternative set of reasonable risk estimates provided several observations relevant to the 

interpretation of the core risk estimates including: (a) the core estimates are unlikely to 

underestimate risk and (b) the degree of potential bias in the core risk estimates could range up to 

at least a factor of 2-3 higher.37

In considering the overall confidence in the core risk estimates, we have compared the 

PM2.5 concentrations simulated under the current suite of standard levels across the urban study 

areas to the distribution of PM2.5 concentrations used in deriving the C-R functions used for 

long-term exposure-related mortality (as presented in Krewski et al., 2009). Specifically, this 

assessment compared the composite monitor annual mean PM2.5 concentrations used in modeling 

long-term exposure-related mortality risk in the core analysis to the distribution of annual mean 

PM2.5 concentrations from the 1999-2000 ACS exposure period.38 Generally, when composite 

monitor annual mean concentrations were within one SD of the long-term mean concentration 

for the most recent years of air quality considered in the ACS dataset (i.e., in the range of 14+/-3 

μg/m3

), we had relatively high confidence in the risk estimates, since they were based on PM2.5

concentrations that roughly matched those used in deriving the C-R functions. However, as 

composite monitor annual mean PM2.5 concentrations extend below this range, our confidence in 

the risk estimates decreased, with our confidence being significantly reduced when composite 

monitor annual mean concentrations approached the LML of the ACS data set (i.e., 5.8 μg/m3). 

 37 We note that these findings regarding potential bias in the core risk estimates were based on modeling PM2.5-

attributable IHD and all-cause mortality associated with long-term PM2.5 exposure for the current suite of standards. 

However, we would expect these observations regarding overall confidence in the core risk estimates to hold for 

other long-term exposure-related mortality endpoints modeled in the quantitative risk assessment for both the 

alternative annual and 24-hour standard levels considered in section 2.3.4.2. Furthermore, given increased emphasis 

placed in this analysis on long-term exposure-related mortality, as noted earlier, the uncertainty analyses completed 

for this health endpoint category are more comprehensive than those conducted for short-term exposure-related 

mortality and morbidity effects, which to some extent reflects limitations in study data available for addressing 

uncertainty in the latter category. Therefore, an alternative set of reasonable risk estimates was not generated to 

supplement core risk estimates generated for short-term PM2.5 exposure. 38 As discussed in sections 3.3.3 and 4.0 of the RA (U.S.EPA, 2010a), each category of long-term exposure-related 

mortality was estimated using separate C-R functions derived from the 1979-1983 and 1999-2000 ACS monitoring 

periods. For purposes of comparing composite monitor annual mean PM2.5 concentrations to the ACS data sets used 

in deriving the C-R functions, we focused on the later monitoring period (1999-2000), since ambient PM2.5

concentrations from this period more closely matched those associated with the study areas in our consideration of 

recent air quality conditions (2005-2007). The 1999-2000 ACS monitoring period had a mean PM2.5 concentration 

of 14.0 μg/m3

, a SD of 3.0 μg/m3 and an LML of 5.8 μg/m3 (see Table 1 in Krewski et al., 2009). 
 2-42

 How representative is the set of urban study areas for the broader set of urban areas in 

the U.S. expected to experience elevated risk from ambient PM2.5 exposure? 

The goal in selecting urban study areas was to provide coverage for the range of larger 

urban areas in the U.S. expected to experience relatively elevated risk due to ambient PM2.5

exposure and other factors associated with PM2.5-related risk (e.g., elevated baseline incidence 

rates for relevant health endpoints, relatively larger susceptible populations). As part of 

considering our overall confidence in the quantitative risk assessment, we assessed the 

representativeness of the 15 urban study areas in the broader national context. Three separate 

analyses were used to explore representativeness: 

 A comparison of PM2.5-risk-related attributes of the 15 urban study areas against 

national distributions of these same attributes suggested that the urban study areas 

likely reflect the distribution of risk for the nation, with the potential for better 

characterization at the higher end of that distribution (US EPA, 2010a, section 

4.4.1).39 

 An analysis of the distribution of U.S. counties included in a national-scale mortality 

analysis suggested that counties associated with the 15 urban study areas are likely to 

experience elevated PM2.5-related risk (US EPA, 2010a, section 4.4.2). 

 An evaluation of the mix of design values across the 15 urban study areas as 

contrasted with design values for the broader set of urban study areas in the U.S. This 

analysis suggested that (a) the 15 urban study areas reasonably captured the key 

groupings of urban areas in the U.S. likely to experience elevated risk due to PM2.5

exposures and (b) we have included study areas likely to experience relatively greater 

degrees of PM2.5-related risk (US EPA, 2010a, section 4.5.1). 

Based on these analyses, we conclude that these study areas are generally representative 

of urban areas in the U.S. likely to experience relatively elevated levels of risk related to ambient 

PM2.5 exposure. 

 What is the nature and magnitude of the long-term and short-term exposure-related 

risks remaining upon just meeting the current suite of PM2.5 standards? 

In considering PM2.5-related risks likely to remain upon just meeting the current PM2.5

annual and 24-hour standards in the 15 urban study areas included in the quantitative RA, we 

focus on the 13 areas that would likely not have met the current standards based on recent air 

quality (2005-2007). These 13 areas have annual and/or 24-hour design values that are above the 

 39 This representativeness analysis also showed that the urban study areas do not capture areas with the highest 

baseline morality risks or the oldest populations (both of which can result in higher PM2.5-related mortality 

estimates). However, some of the areas with the highest values for these attributes have relatively low PM2.5

concentrations (e.g., urban areas in Florida) and consequently failure to include these areas in the set of urban study 

areas is unlikely to exclude high PM2.5-risk locations. 
 2-43

levels of the current standards (see Tables 2-2 and 2-3).40 Based on the core risk estimates for 

these areas, using the proportional rollback approach, we make the following key observations 

regarding the magnitude of risk remaining upon simulation of just meeting the current suite of 

standards: 

 Long-term exposure-related mortality risk remaining: IHD-related mortality 

attributable to long-term PM2.5 exposure was estimated to range from less than 100 to 

approximately 2,000 cases per year. The variability in these estimates reflects, to a 

great extent, differences in the size of study area populations. These estimates 

represent from 4 to 17% of all IHD-related mortality in a given year for the urban 

study areas, representing a measure of risk that takes into account differences in 

population size and baseline mortality rates (see Table 2-2). 

 Short-term exposure-related mortality risk remaining: Cardiovascular (CV)-related 

mortality associated with short-term PM2.5 exposure was estimated to range from less 

than 10 to 500 cases per year. These estimates represent approximately 1 to 2% of 

total CV-related mortality in a given year for the urban study areas (see Table 2-3). 

 Short-term exposure-related morbidity risk remaining: CV-related hospitalizations 

were estimated to range from approximately 10 to 800 cases per year across the study 

areas, which is approximately equivalent to less than 1% of total CV-related 

hospitalizations (see Table 2-3). 

Although long- and short-term exposure-related mortality rates have similar patterns in 

terms of the subset of urban study areas experiencing risk reductions for the current suite of 

standard levels, the magnitude of risk remaining is substantially lower for short-term exposurerelated mortality. These findings were expected, since, as noted earlier, changes in annual mean 

PM2.5 concentrations were expected to drive both long- and short-term exposure-related risk, 

resulting in similar overall patterns in risk reduction for both exposure periods (in terms of the 

subset of urban study areas experiencing risk reductions). We note, however, that the variability 

in the effect estimates used to model short-term exposure-related health endpoints across urban 

study areas introduced additional variation into the pattern of risk reduction across study areas. 

 40 Of the 15 study areas, only Dallas and Phoenix have both annual and 24-hour design values below the levels of 

the current standards based on 2005-2007 air quality data. 
 2-44

Table 2-2. Estimated Incidence and Percent of Total Annual Incidence Associated with 

Long-term PM2.5 Exposure Based on Simulation of the Current Suite of Standards (for 

IHD Mortality based on 2007 PM2.5 Concentrations)

Urban Study Area 

Incidence of Ischemic Heart Disease 

Mortality Associated with Long-term 

Exposure to PM2.53 

Percent of Incidence of Ischemic Heart 

Disease Mortality Associated with Longterm Exposure to PM2.53

Exposure Period: 

1979-1983 

Exposure Period: 

1999-2000 

Exposure Period: 

1979-1983 

Exposure Period: 

1999-2000 

Atlanta, GA 220 277 13.2% 16.7% 

(180 - 258) (227 - 324) (10.9% - 15.5%) (13.7% - 19.5%) 

Baltimore, MD 297 374 11.7% 14.7% 

(243 - 349) (307 - 440) (9.6% - 13.7%) (12.1% - 17.3%) 

Birmingham, AL 131 165 10.9% 13.8% 

(107 - 154) (135 - 194) (8.9% - 12.9%) (11.3% - 16.2%) 

Dallas, TX 195 247 9% 11.4% 

(159 - 230) (202 - 291) (7.3% - 10.6%) (9.3% - 13.4%) 

Detroit, MI 377 478 9.1% 11.5% 

(308 - 445) (390 - 563) (7.4% - 10.7%) (9.4% - 13.5%) 

Fresno, CA 77 98 6.7% 8.5% 

(63 - 92) (80 - 116) (5.5% - 8%) (7% - 10.1%) 

Houston, TX 344 434 10.7% 13.6% 

(281 - 405) (355 - 511) (8.8% - 12.6%) (11.1% - 16%) 

Los Angeles, CA 860 1094 6.1% 7.7% 

(701 - 1018) (890 - 1296) (4.9% - 7.2%) (6.3% - 9.1%) 

New York, NY 1755 2222 9.3% 11.8% 

(1435 - 2070) (1814 - 2620) (7.6% - 11%) (9.6% - 13.9%) 

Philadelphia, PA 261 330 10.5% 13.2% 

(214 - 308) (270 - 389) (8.6% - 12.3%) (10.8% - 15.6%) 

Phoenix, AZ 317 402 6.7% 8.5% 

(258 - 374) (327 - 476) (5.5% - 7.9%) (6.9% - 10.1%) 

Pittsburgh, PA 256 324 9.3% 11.8% 

(209 - 302) (264 - 382) (7.6% - 11%) (9.6% - 13.9%) 

Salt Lake City, UT 15 19 2.9% 3.7% 

(12 - 18) (16 - 23) (2.4% - 3.4%) (3% - 4.4%) 

St. Louis, MO 446 563 11.2% 14.2% 

(365 - 525) (461 - 662) (9.2% - 13.2%) (11.6% - 16.7%) 

Tacoma, WA 38 49 3.7% 4.7% 

(31 - 46) (40 - 58) (3% - 4.4%) (3.8% - 5.6%) 

1

 The current primary PM2.5 standards include an annual standard set at 15 μg/m3 and a 24-hour standard set at 

35 μg/m3

. 2 Numbers rounded to the nearest whole number. Numbers in parentheses are 95% confidence or credible 

intervals based on statistical uncertainty surrounding the PM coefficient. 

3

 Estimates based on Krewski et al. (2009), using ambient PM2.5 concentrations from 1979 - 1983 and from 1999-

2000, respectively. Estimated incidence is presented for 30 years of age and older within each urban study area. 
 2-45

Table 2-3. Estimated Incidence and Percent of Total Annual Incidence Associated with 

Short-term PM2.5 Exposure Based on Simulation of the Current Suite of Standards (CV 

mortality and hospital admissions based on 2007 PM2.5 concentrations) 

Urban Study Area 

Incidence of 

Cardiovascular 

Mortality 

Associated with 

Short-term 

Exposure to PM2.53 

Incidence of 

Cardiovascular 

Hospitalizations 

Associated with 

Short-term 

Exposure to PM2.54 

Percent of 

Incidence of 

Cardiovascular 

Mortality 

Associated with 

Short-term 

Exposure to PM2.53 

Percent of 

Incidence of 

Cardiovascular 

Hospital 

Admissions 

Associated with 

Short-term 

Exposure to PM2.54 

Atlanta, GA 32 41 0.8% 0.4% 

(-33 - 95) (-27 - 109) (-0.8% - 2.4%) (-0.2% - 1%) 

Baltimore, MD 62 216 1.6% 1.3% 

(-4 - 126) (159 - 273) (-0.1% - 3.2%) (1% - 1.7%) 

Birmingham, AL -1 16 0% 0.3% 

(-42 - 40) (-11 - 43) (-1.5% - 1.5%) (-0.2% - 0.9%) 

Dallas, TX 29 28 0.8% 0.3% 

(-19 - 76) (-18 - 73) (-0.5% - 2.2%) (-0.2% - 0.7%) 

Detroit, MI 60 233 1% 1.1% 

(-8 - 127) (171 - 295) (-0.1% - 2.2%) (0.8% - 1.4%) 

Fresno, CA 12 23 0.7% 0.5% 

(-9 - 33) (0 - 46) (-0.5% - 2%) (0% - 0.9%) 

Houston, TX 46 56 0.9% 0.3% 

(-31 - 122) (-37 - 149) (-0.6% - 2.4%) (-0.2% - 0.8%) 

Los Angeles, CA -30 258 -0.2% 0.5% 

(-132 - 72) (3 - 511) (-0.7% - 0.4%) (0% - 0.9%) 

New York, NY 473 752 2.1% 1.2% 

(276 - 668) (552 - 951) (1.2% - 3%) (0.8% - 1.5%) 

Philadelphia, PA 84 203 2.1% 1.3% 

(22 - 145) (149 - 257) (0.5% - 3.6%) (0.9% - 1.6%) 

Phoenix, AZ 84 108 1.3% 0.5% 

(-4 - 170) (1 - 215) (-0.1% - 2.7%) (0% - 1%) 

Pittsburgh, PA 43 140 1.1% 1.1% 

(-9 - 93) (103 - 177) (-0.2% - 2.3%) (0.8% - 1.4%) 

Salt Lake City, UT 9 9 0.8% 0.4% 

(-2 - 20) (0 - 18) (-0.2% - 1.7%) (0% - 0.7%) 

St. Louis, MO 106 178 1.9% 1.3% 

(24 - 187) (131 - 225) (0.4% - 3.3%) (0.9% - 1.6%) 

Tacoma, WA 11 19 0.7% 0.5% 

(-6 - 27) (-46 - 82) (-0.4% - 1.8%) (-1.3% - 2.3%) 1

 The current primary PM2.5 standards include an annual standard set at 15 μg/m3 and a 24-hour standard set at 

35 μg/m3

. 2

 Percents rounded to the nearest tenth. Numbers in parentheses are 95% confidence or credible intervals based on 

statistical uncertainty surrounding the PM coefficient. 

3

 Based on location-specific single pollutant concentration-response function estimates from Zanobetti and Schwartz 

(2009) that have been "shrunken" towards the appropriate regional means. "Shrunken" coefficient estimates and 

their standard errors were provided to EPA by the study authors (personal communication with Dr. Antonella 

Zanobetti, 2009). 

4 Incidence estimates were calculated using the appropriate regional concentration-response function estimates 

reported in Table 2 of Bell et al. (2008). Location-specific C-R function estimates were not available from this 

study. 
 2-46

 Substantial variability exists in the magnitude of risk remaining across urban study 

areas: Estimated risks remaining upon just meeting the current suite of standards 

vary substantially across study areas, even when considering risks normalized for 

differences in population size and baseline incidence rates. This variability is a 

consequence of the substantial differences in the annual mean PM2.5 concentrations 

across study areas that result from simulating just meeting the current standards. This 

is important because, as discussed above, annual mean concentrations are highly 

correlated with both long- and short-term exposure-related risk. The variability in 

annual mean PM2.5 concentrations occurred primarily in those study areas in which 

the 24-hour standard was the “controlling” standard. In such areas, the variability in 

estimated risks across study areas was largest when regional patterns of reductions in 

PM2.5 concentrations were simulated, using the proportional rollback approach, as 

was done in the core analyses. Less variability was observed when more localized 

patterns of PM2.5 reductions were simulated using the locally-focused rollback 

approach, as was done in a sensitivity analysis. When simulations were done using 

the locally-focused rollback approach, estimated risks remaining upon just meeting 

the current suite of standards were appreciably larger than those estimated in the core 

analysis (US EPA, 2010a, section 4.3.1.1). 

 Simulation of just meeting the current suite of standards results in annual mean PM2.5

concentrations well below the current standard for some study areas: In simulating 

just meeting the current suite of standards, the resulting composite monitor annual 

mean PM2.5 concentrations ranged from about 15 μg/m3

 (for those study areas in 

which the annual standard was controlling) down to as low as about 8 μg/m3 (for 

those study areas in which the 24-hour standard was controlling or the annual mean 

concentration was well below 15 μg/m3 based on recent air quality). As discussed 

above, as the composite monitor annual mean PM2.5 concentrations used in generating 

risk estimates extend below 11.0 μg/m3

 (one SD below the mean for the 1999-2000 

ACS monitoring period, Krewski et al., 2009) we have increasingly less confidence in 

the risk estimates, with confidence decreasing significantly as composite monitor 

concentrations approach the LML for the ACS dataset (5.8 μg/m3). Typically, for the 

15 urban study areas assessed, the locations where the 24-hour standard was the 

controlling standard were simulated to have the lowest composite monitor annual 

mean PM2.5 concentrations. We observe that all four of the urban study areas with 

simulated composite monitor annual mean PM2.5 concentrations below 11 μg/m3 are 

areas where the 24-hour standard is generally controlling (U.S.EPA, 2010a, Table 3-

4). While such locations often are estimated to have the greatest risk reductions, there 

is also reduced confidence associated with these risk estimates.

 To what extent are the risks remaining upon simulation of the current suite of 

standards important from a public health perspective? 

Estimates of long-term exposure-related IHD mortality risk associated with simulating 

just meeting the current suite of standards range from less than 100 deaths per year for the urban 

study area with the lowest risk to approximately 2,000 deaths per year for the urban study areas 

with the greatest risk. Estimates of risk for the urban areas included in the quantitative risk 
 2-47

assessment suggest that IHD-related mortality associated with long-term PM2.5 exposure would 

likely be in a range of thousands of deaths per year on a national scale. Based on these risk 

estimates for IHD-related mortality alone, we conclude that risks estimated to remain upon 

simulation of just meeting the current suite of standards are important from a public health 

standpoint. This reflects consideration of both the severity of the effect and the magnitude of the 

effect. We have also estimated risks for long-term exposure related mortality risk related to 

cardiopulmonary effects and lung cancer, which increase the total annual incidence of mortality 

attributable to long-term PM2.5 exposure (see U.S. EPA, 2010a, section 4.2.1). 

In addition to long-term exposure-related mortality, we estimated cardiovascular-related 

and respiratory-related mortality risk associated with short-term PM2.5 exposure. We note that 

these mortality estimates are up to an order of magnitude smaller than estimates related to longterm exposure-related mortality.41 As part of the quantitative risk assessment, we also estimated 

respiratory and cardiovascular-related hospital admissions as well as asthma-related emergency 

department visits associated with short-term exposure to PM2.5, with estimates of cardiovascularrelated and respiratory-related hospital admissions together ranging up to approximately 1,000 

admissions per year across the urban study areas, with the estimated incidence of asthma-related 

emergency department visits being several fold higher. Further, as discussed in section 2.2.1, we 

recognize that the currently available scientific information includes evidence for a broader range 

of health endpoints and susceptible populations beyond those included in the quantitative risk 

assessment, including lung function growth and respiratory symptoms in children and 

reproductive and developmental effects. Taken together, the set of quantitative risk estimates 

related to long- and short-term PM2.5 exposure, together with consideration of the health 

endpoints which could not be quantified, further strengthen the conclusion that risks estimated to 

remain following simulation of just meeting the current suite of PM2.5 standards are important 

from a public health perspective, both in terms of severity and magnitude. 

2.2.3 CASAC Advice 

In our consideration of the adequacy of the current suite of PM2.5 standards, in addition to 

the evidence and risk-based information discussed above, we have also considered the advice of 

CASAC, based on their review of drafts of the ISA, the RA, and this document, as well as 

comments from the public on earlier drafts of this document and the RA. In their comments on 

the second draft PA, CASAC stated agreement with EPA staff’s conclusion that the “currently 

 41 Estimates of short-term exposure-related and long-term exposure-related mortality should not be added because 

there is the potential for overlap (i.e., the long-term exposure-related mortality estimate may include some of the 

short-term exposure-related signal on a daily basis, aggregated over the year). 
 2-48

available information clearly calls into question the adequacy of the current standards” (Samet, 

2010d, p. i). Further, in an earlier draft of this document, CASAC noted: 

With regard to the integration of evidence-based and risk-based considerations, CASAC 

concurs with EPA’s conclusion that the new data strengthens the evidence available on 

associations previously considered in the last round of the assessment of the PM2.5 standard. 

CASAC also agrees that there are significant public health consequences at the current levels 

of the standard that justify consideration of lowering the PM2.5 NAAQS further (Samet, 

2010c, p.12). 

2.2.4 Staff Conclusions on Adequacy of Current Standards 

Collectively, taking into consideration the responses to specific questions focused on 

different ways to address the adequacy of the current suite of PM2.5 standards, we revisit the 

overarching policy question: does the currently available scientific evidence and risk-based 

information support or call into question the adequacy of the protection afforded by the current 

suite of fine particle standards? 

With respect to evidence-based considerations, the currently available evidence provides 

stronger evidence beyond what was available in the last review, that associations between shortand long-term PM2.5 exposures and a broad range of adverse health effects exist. The currently 

available information strengthens the associations between PM2.5 and mortality and 

cardiovascular-related and respiratory-related morbidity effects observed in the last review. This 

information also expands our understanding of a broader range of health outcomes as well as our 

understanding of effects in susceptible populations. The currently available evidence provides 

support for associations that extend to lower concentrations than what had been observed in the 

last review, including at ambient concentrations below the levels of the current standards 

providing the basis for consideration of alternative standards that would provide increased 

protection beyond that afforded by the current suite of PM2.5 standards. 

In relation to risk-based considerations for informing our understanding of the adequacy 

of the current fine particle standards, we focus on the estimates of PM2.5-related mortality and 

morbidity effects likely to remain upon simulations of just meeting the current standards in a 

number of example urban areas. In considering the core risk estimates together with our 

understanding of the uncertainties in these estimates, based upon extensive sensitivity analyses, 

we conclude that the risks estimated to be associated with just meeting the current standards can 

reasonably be judged to be important from a public health perspective. We further conclude that 

these estimated risks provide strong support for consideration of alternative standards that would 

provide increased protection beyond that afforded by the current PM2.5 standards. 

We recognize that important uncertainties and research questions remain when 

considering both evidence- and risk-based approaches. Nonetheless, we note that much progress 
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has been made in reducing some key uncertainties since the last review, including important 

progress in advancing our understanding of potential mechanisms by which ambient PM2.5 is 

causally linked with mortality and cardiovascular-related and respiratory-related effects observed 

in epidemiological and toxicological studies. Additional information continues to emerge for a 

broader range of health effects including reproductive and development effects and more 

information is available to understand effects in susceptible populations including children, older 

adults, individuals with pre-existing cardiovascular and respiratory disease, persons at lower 

SES, and persons with genetic susceptibility. 

As was true in the last review, we recognize that as the body of available evidence has 

expanded, it has added greatly both to our knowledge of health effects associated with fine 

particle exposures, as well as to the complexities inherent in interpreting the evidence in a 

policy-relevant context as a basis for setting appropriate standards. In evaluating both evidencebased and risk-based considerations, along with associated limitations and uncertainties, we 

reach the conclusion that the available information clearly calls into question the adequacy of the 

current suite of PM2.5 standards and provides strong support for giving consideration to revising 

the current suite of standards to provide increased public health protection. 

2.3 CONSIDERATION OF ALTERNATIVE STANDARDS 

Having reached the conclusion that the currently available scientific evidence calls into 

question the adequacy of the current suite of PM2.5 standards, staff considers a second 

overarching question: 

What alternative suites of fine particle standards are supported by the currently available 

scientific evidence and risk-based information, as reflected in the ISA and RA? 

To address this overarching question, we have posed a series of more specific questions 

to inform decisions regarding the basic elements of the NAAQS: indicator (section 2.3.1), 

averaging time (section 2.3.2), form (section 2.3.3), and level (section 2.3.4). These elements are 

considered collectively in evaluating the health protection afforded by alternative suites of 

standards under consideration. In taking into account the currently available scientific and 

technical information, we consider both the information available in the last review and 

information that is newly available since the last review as assessed and presented in the ISA and 

RA prepared for this review (US EPA, 2009a; US EPA, 2010a). 

2.3.1 Indicator 

In initially setting standards for fine particles in 1997, EPA concluded it was more 

appropriate to control fine particles as a group, rather than singling out any particular component 

or class of fine particles for which only very limited evidence was available. In establishing a 
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size-based indicator to distinguish fine particles from particles in the coarse mode, EPA noted 

that the available epidemiological studies of fine particles were based largely on PM2.5 and also 

considered monitoring technology that was generally available. The selection of a 2.5 μm size 

cut reflected the regulatory importance of defining an indicator that would more completely 

capture fine particles under all conditions likely to be encountered across the U.S., especially 

when fine particle concentrations and humidity are likely to be high, while recognizing that some 

small coarse particles would also be captured by current methods to monitor PM2.5 (62 FR 38666 

to 38668, July, 18, 1997). In the last review, based on the same considerations, EPA again 

recognized that the available information supported retaining the PM2.5 indicator and remained 

too limited to support a distinct standard for any specific PM2.5 component or group of 

components associated with any source categories of fine particles (71 FR 61162 to 61164, 

October 17, 2006). 

 Does the currently available information provide support for the continued use of a 

PM2.5 mass-based indicator for fine particles? 

In this review, epidemiological studies linking cardiovascular and respiratory effects as 

well as mortality with long- and short-term fine particle exposures continue to be largely indexed 

by PM2.5. Based on the same considerations that informed the last two reviews, summarized 

above in section 2.1.1, we again conclude that it is appropriate to retain a PM2.5 indicator to 

provide protection associated with long- and short-term exposure to fine particles. 

We also look to the expanded body of evidence available in this review to consider 

whether there is sufficient evidence to support a separate standard for ultrafine particles (UFPs)42

and whether there is sufficient evidence to establish distinct standards focused on regulating 

specific PM2.5 components or group of components associated with any source categories of fine 

particles, as addressed below. 

 To what extent does the currently available information provide support for 

considering a separate indicator for UFPs? 

A number of studies available in this review have evaluated potential health effects 

associated with short-term exposures to UFPs. As noted in the ISA, the enormous number and 

larger, collective surface area of UFPs are important considerations for focusing on this particle 

size fraction in assessing potential public health impacts (US EPA, 2009a, p. 6-83).43 Per unit 

mass, UFPs may have more opportunity to interact with cell surfaces due to their greater surface 

 42 Ultrafine particles, generally including particles with a mobility diameter less than or equal to 0.1 μm, are emitted 

directly to the atmosphere or are formed by nucleation of gaseous constituents in the atmosphere (US EPA, 2009a, 

p. 3-3). 

43 Particle number is most highly concentrated in the UFP fraction with volume (or mass) most concentrated in the 

larger size fractions (US EPA, 2009a, p. 3-2, Figure 3-1). 
 2-51

area and their greater particle number compared with larger particles (US EPA, 2009a, p. 5-3). 

Greater surface area also increases the potential for soluble components (e.g., transition metals, 

organics) to adsorb to UFPs and potentially cross cell membranes and epithelial barriers (US 

EPA, 2009a, p. 6-83). In addition, evidence available in this review suggests that the ability of 

particles to enhance allergic sensitization is associated more strongly with particle number and 

surface area than with particle mass (US EPA, 2009a, p. 6-127).44 

New evidence, primarily from controlled human exposure and toxicological studies, 

expands our understanding of UFP-related cardiovascular and respiratory effects. However, this 

evidence is still very limited and largely focused on exposure to diesel exhaust (DE), for which 

the ISA concludes it is unclear if the effects observed are due to UFPs, larger particles within the 

PM2.5 mixture, or the gaseous components of DE (US EPA, 2009a, p. 2-22). In addition, the ISA 

notes uncertainties associated with the controlled human exposure studies as CAP systems have 

been shown to modify the composition of UFPs (US EPA, 2009a, p. 2-22, see also section 1.5.3). 

Relatively few epidemiological studies have examined potential cardiovascular and respiratory 

effects associated with short-term exposures to UFPs. These studies have reported inconsistent 

and mixed results (US EPA, 2009a, section 2.3.5). 

Collectively, in considering the body of scientific evidence available in this review, the 

ISA concludes that the currently available evidence is suggestive of a causal relationship

between short-term exposures to UFPs and cardiovascular and respiratory effects. Furthermore, 

the ISA concludes that evidence is inadequate to infer a causal relationship between short-term 

exposure to UFPs and mortality as well as long-term exposure to UFPs and all outcomes 

evaluated (US EPA, 2009a, sections 2.3.5, 6.2.12.3, 6.3.10.3, 6.5.3.3, 7.2.11.3, 7.3.9, 7.4.3.3, 

7.5.4.3, and 7.6.5.3; Table 2-6). 

With respect to our understanding of ambient UFP concentrations, at present, there is no 

national network of UFP samplers; thus, only episodic and/or site-specific data sets exist (US 

EPA, 2009a, p. 2-2).45 Therefore, a national characterization of concentrations, temporal and 

spatial patterns, and trends is not possible, and the availability of ambient UFP measurements to 

support health studies is extremely limited. In general, measurements of UFPs are highly 

dependent on monitor location and, therefore, more subject to exposure error than accumulation 

mode particles (US EPA, 2009a, p. 2-22). In addition, UFPs are often monitored based on 

numerical concentrations (i.e., particle counts), rather than mass concentrations, as UFP mass in 

ambient air is typically very low (US EPA, 2009a, section 3.4.1.4). The UFP number 

 44 More information on possible modes of action for effects associated with UFP exposures is discussed in sections 

5.1 and 5.4 of the ISA (US EPA, 2009a). 

45 The ISA contains a review of the current scientific information related to measurements of UFPs (US EPA, 

2009a, sections 3.5.1 and 3.5.2). 
 2-52

concentrations fall off sharply downwind from sources, as UFPs may grow into the accumulation 

mode by coagulation or condensation (US EPA, 2009a, p. 3-89). Limited studies of UFP 

ambient measurements suggest these particles exhibit a high degree of spatial and temporal 

heterogeneity driven primarily by differences in nearby source characteristics (US EPA, 2009a, 

p. 3-84). Internal combustion engines and, therefore, roadways are a notable source of UFPs, so 

concentrations of UFPs near roadways are generally expected to be elevated (US EPA, 2009a, p. 

2-3). Concentrations of UFPs have been reported to drop off much more quickly with distance 

from roadways than fine particles (US EPA, 2009a, p. 3-84). 

In considering both the currently available health effects evidence and the air quality data 

for UFPs, we conclude that this information is still too limited to provide support for 

consideration of a distinct PM standard for UFPs. 

 To what extent does the currently available information provide support for 

considering a separate indicator for a specific PM2.5 component or group of components 

associated with any source categories of fine particles? Conversely, to what extent does 

the currently available information provide support for eliminating any component or 

group of components associated with any source categories from the mix of fine 

particles included in the PM2.5 indicator? 

In addressing the issue of particle composition, the ISA concludes that, “[f]rom a 

mechanistic perspective, it is highly plausible that the chemical composition of PM would be a 

better predictor of health effects than particle size” (US EPA, 2009a, p. 6-202). Heterogeneity of 

ambient concentrations of PM2.5 constituents (e.g., elemental carbon (EC), organic carbon (OC), 

sulfates, nitrates) observed in different geographical regions as well as regional heterogeneity in 

PM2.5-related health effects reported in a number of epidemiological studies are consistent with 

this hypothesis (US EPA, 2009a, section 6.6). 

With respect to the availability of ambient measurement data for fine particle components 

in this review, there are now more extensive ambient PM2.5 speciation measurement data 

available through the CSN than in previous reviews (see section 1.3.2 and Appendix B, section 

B.1.3). Data from the CSN provide further evidence of spatial and seasonal variation in both 

PM2.5 mass and composition among cities/regions (US EPA, 2009a, pp. 3-50 to 3-60; Figures 3-

12 to 3-18; Figure 3-47). Some of this variation may be related to regional differences in 

meteorology, sources, and topography (US EPA, 2009a, p. 2-3). 

The currently available epidemiological, toxicological, and controlled human exposure 

studies have evaluated the health effects associated with ambient PM2.5 constituents and 

categories of fine particle sources, using a variety of quantitative methods applied to a broad set 

of PM2.5 constituents, rather than selecting a few constituents a priori (US EPA, 2009a, p. 2-26). 

Epidemiological studies have used measured ambient PM2.5 speciation data, including 

monitoring data from the CSN, while all of the controlled human exposure and most of the 
 2-53

toxicological studies have used CAPs and analyzed the constituents therein (US EPA, 2009a, p. 

6-203).46 The CSN provides PM2.5 speciation measurements generally on a one-in-three or onein-six day sampling schedule and, thus, do not capture data every day at most sites. To expand 

our understanding of the role of specific PM2.5 components and sources with respect to the 

observed health effects, researchers have expressed a strong interest in having access to PM2.5

speciation measurements collected more frequently.47 

With respect to epidemiological studies evaluating short-term exposures to fine particle 

constituents, several new multi-city studies are now available. These studies continue to show an 

association between mortality and cardiovascular and/or respiratory morbidity effects and shortterm exposures to various PM2.5 components including nickel (Ni), vanadium (V), EC, OC, and 

sulfates (US EPA, 2009a, sections 6.5.2.5 and 6.6). Lippmann et al. (2006) and Dominici et al. 

(2007) evaluated the heterogeneity in the PM10–mortality association as evaluated in the 

NMMAPS data by analyzing the PM2.5 speciation data. Nickel and V were identified as 

significant predictors of variation in PM10-related mortality across cities, with Ni levels in New 

York City being reported as particularly high (US EPA, 2009a, section 6.5.2.5; Figure 6-31).48 

Bell et al. (2009) and Peng et al. (2009) conducted similar analyses focusing on the variation in 

PM2.5-related cardiovascular and respiratory hospital admissions in older adults. Peng et al. 

(2009) focused on the components that make up the majority of PM2.5 mass and using multipollutant models reported only EC and OC were significantly associated with risk of 

hospitalization for cardiovascular disease. Bell et al. (2009) used data from twenty PM2.5

components and found that EC, Ni, and V were most positively and significantly associated with 

the risk of PM2.5-related hospitalizations suggesting that the observed associations between PM2.5

and hospitalizations may be primarily due to particles from oil combustion and traffic (US EPA, 

 46 Most studies considered between 7 to 20 ambient PM2.5 constituents, with EC, OC, sulfates, nitrates, and metals 

most commonly measured. Many of the studies grouped the constituents with various factorization or source 

apportionment techniques to examine the relationship between the grouped constituents and various health effects. 

However, not all studies labeled the constituent groupings according to their presumed source and a small number of 

controlled human exposure and toxicological studies did not use any constituent grouping. These differences across 

studies substantially limit any integrative interpretation of these studies (US EPA, 2009a, p. 6-203). 47 As outlined in section 6.6.2.11 of the ISA, some investigators have circumvented the issue of less than daily 

speciation data by using the PM2.5 chemical species data in a second stage regression to explain the heterogeneity in 

PM10 or PM2.5 mortality risk estimates across cities and assuming that the relative contributions of PM2.5 have 

remained the same over time (US EPA, 2009a, p. 6-206). In April 2008, EPA co-sponsored a workshop to discuss 

modifications to the current ambient air quality monitoring networks that would advance our understanding of the 

impacts of PM exposures on public health/welfare in the most meaningful way, including improving our 

understanding of fine particle components. A summary of the workshop recommendations, including 

recommendations for daily PM2.5 speciation measurements in large urban areas, is available at 

www.epa.gov/ORD/npd/pdfs/FINAL-April-2008-AQ-Health-Research-Workshop-Summary-Dec-2008.pdf. 48 However, as noted in the ISA, in a sensitivity analysis when selectively removing cities from the overall estimate, 

the significant association between the PM10 mortality risk estimate and the PM2.5 Ni fraction was diminished upon 

removing New York City from the analysis, which is consistent with the results presented by Dominici et al. (2007) 

(US EPA, 2009a, section 6.5.2.5; Figure 6-32). 
 2-54

2009a, section 6.2.10.1). In a study of 25 U.S. cities, Franklin et al. (2008) focused on a timeseries regression of mortality related to PM2.5 mass by season and also examined effect 

modification due to various PM2.5 species. They concluded that Al, As, Ni, Si and sulfates were 

significant effect modifiers of PM2.5 mortality risk estimates, and “simultaneously including Al, 

Ni, and sulfates together or Al, Ni, and As together further increased explanatory power. Of the 

species examined, Al and Ni explained the most residual heterogeneity” (US EPA, 2009a, p. 6-

194; Table 6-17).49 Furthermore, Ostro et al (2007) examined associations between PM2.5

components and mortality in six California counties and found an association between mortality, 

especially cardiovascular-related mortality and several PM2.5 components including EC, OC, 

nitrates, iron (Fe), potassium (K), and titanium (Ti) at various lags (US EPA, 2009a, p. 6-195).

Limited evidence is available to evaluate the health effects associated with long-term 

exposures to PM2.5 components (US EPA, 2009a, section 7.6.2). The most significant new 

evidence is provided by a study that evaluated multiple PM2.5 components and an indicator of 

traffic density in an assessment of health effects related to long-term exposure to PM2.5 (Lipfert 

et al., 2006). Using health data from a cohort of U.S. military veterans and PM2.5 measurement 

data from the CSN, Lipfert et al. (2006) reported positive associations between mortality and 

long-term exposures to nitrates, EC, Ni and V as well as traffic density and peak O3

concentrations. Additional evidence from a long-term exposure study conducted in a Dutch 

cohort provides supportive evidence that long-term exposure to traffic-related particles is 

associated with increased mortality (Beelen et al., 2008). 

With respect to source categories of fine particles associated with a range of health 

endpoints, the ISA reports that the currently available evidence suggests associations between 

cardiovascular effects and a number of specific PM2.5–related source categories, specifically oil 

combustion, wood or biomass burning, motor vehicle emissions, and crustal or road dust sources 

(US EPA, 2009a, section 6.6; Table 6-18). In addition, a few studies have evaluated associations 

between PM2.5–related source categories and mortality. These studies included a study that 

reported an association between mortality and a PM2.5 coal combustion factor (Laden et al., 

2000), while other studies linked mortality to a secondary sulfate long-range transport PM2.5

source (Ito et al., 2006; Mar et al., 2006) (US EPA, 2009a, section 6.6.2.1). There is less 

consistency in associations observed between sources of fine particles and respiratory health 

effects, which may be partially due to the fact that fewer studies have evaluated respiratoryrelated outcomes and measures. However, there is some evidence for PM2.5-related associations 

with secondary sulfate and decrements in lung function in asthmatic and healthy adults (US 

EPA, 2009a, p. 6-211; Gong et al., 2005; Lanki et al., 2006). Respiratory effects relating to the 

 49 We note that New York City was not included in the 25 cities examined by Franklin et al. (2008). 
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crustal/soil/road dust and traffic sources of PM have been observed in asthmatic children and 

adults (US EPA, 2009a, p. 6-205; Gent et al., 2009; Penttinen et al., 2006). 

Recent studies have shown that source apportionment methods have the potential to add 

useful insights into which sources and/or PM constituents may contribute to different health 

effects. Of particular interest are several epidemiological studies that compared source 

apportionment methods and reported consistent results across research groups (US EPA, 2009a, 

p. 6-211; Hopke et al., 2006; Ito et al., 2006; Mar et al., 2006; Thurston et al., 2005). These 

studies reported associations between total mortality and secondary sulfate in two cities for two 

different lag times. The sulfate effect was stronger for total mortality in Washington D.C. and 

for cardiovascular-related morality in Phoenix (US EPA, 2009a, p. 6-204). These studies also 

found some evidence for associations with mortality and a number of source categories (e.g., 

biomass/wood combustion, traffic, copper smelter, coal combustion, sea salt) at various lag times 

(US EPA, 2009a, p. 6-204). Sarnat et al. (2008) compared three different source apportionment 

methods and reported consistent associations between emergency department visits for 

cardiovascular diseases with mobile sources and biomass combustion as well as increased 

respiratory-related emergency department visits associated with secondary sulfate (US EPA, 

2009a, pp. 6-204 and 6-211). 

Collectively, in considering the currently available evidence for health effects associated 

with specific PM2.5 components or groups of components associated with any source categories 

of fine particles as presented in the ISA, we conclude that additional information available in this 

review continues to provide evidence that many different constituents of the fine particle mixture 

as well as groups of components associated with specific source categories of fine particles are 

linked to adverse health effects. However, as noted in the ISA, while “[t]here is some evidence 

for trends and patterns that link particular ambient PM constituents or sources with specific 

health outcomes...there is insufficient evidence to determine whether these patterns are 

consistent or robust” (US EPA, 2009a, p. 6-210). Furthermore, the ISA concludes that “the 

evidence is not yet sufficient to allow differentiation of those constituents or sources that are 

more closely related to specific health outcomes” (US EPA, 2009a, pp. 2-26 and 6-212). 

Therefore, we conclude that the currently available evidence is not sufficient to support 

consideration of a separate indicator for a specific PM2.5 component or group of components 

associated with any source category of fine particles. We also conclude that the evidence is not 

sufficient to support eliminating any component or group of components associated with any 

source categories of fine particles from the mix of fine particles included in the PM2.5 indicator.
 2-56

Summary 

In considering whether the currently available evidence provides support for retaining, 

revising, or supplementing the current PM2.5 mass-based indicator, we first conclude that it is 

appropriate to consider retaining PM2.5 as the indicator for fine particles. Secondly, we conclude 

that the currently available evidence does not provide a sufficient basis for supplementing the 

mass-based PM2.5 indicator by considering a separate indicator for ultrafine particles. We also 

conclude that the currently available evidence is too limited to provide support for considering a 

separate indicator for a specific PM2.5 component or group of components associated with any 

source categories of fine particles or for eliminating any individual component or group of 

components associated with any source categories from the mix of fine particles included in the 

PM2.5 mass-based indicator. 

In their review of the first draft PA, CASAC agreed with staff’s conclusion that it is 

appropriate to consider retaining PM2.5 as the indicator for fine particles and further asserted, 

“There [is] insufficient peer-reviewed literature to support any other indicator at this time” 

(Samet, 2010c, p. 12). CASAC expressed a strong desire for EPA to “look ahead to future 

review cycles and reinvigorate support for the development of evidence that might lead to newer 

indicators that may correlate better with the health effects associated with ambient air 

concentrations of PM ...” (Samet, 2010c, p 2). We further conclude that consideration of 

alternative indicators (e.g., taking into account new evidence for UFP or PM2.5 composition) in 

future reviews is desirable and could be informed by additional research and data collection 

efforts, as described in section 2.5 below. 

2.3.2 Averaging Times 

In 1997, EPA initially set both an annual standard, to provide protection from health 

effects associated with both long- and short-term exposures to PM2.5, and a 24-hour standard to 

supplement the protection afforded by the annual standard (62 FR 38667 to 38668, July, 18, 

1997). In the last review, EPA retained both annual and 24-hour averaging times (71 FR 61164, 

October 17, 2006). 

In this review, we consider whether the currently available information provides support 

for maintaining standards with annual and 24-hour averaging times and whether there is 

sufficient evidence to support setting standards with other averaging times to address sub-daily 

or seasonal exposures. 

 To what extent does the currently available information continue to provide support for 

annual and 24-hour averaging times? 

The overwhelming majority of studies conducted since the last review continue to utilize 

annual (or multi-year) and 24-hour averaging times, reflecting the averaging times of the current 
 2-57

PM2.5 standards. These studies continue to provide evidence that health effects are associated 

with annual and 24-hour averaging times. Therefore, we conclude it is appropriate to retain the 

current annual and 24-hour averaging times to provide protection for effects associated with both 

long-term (from one year to several years) and short-term (from less than one day to up to 

several days) PM2.5 exposures. 

 To what extent does the currently available scientific evidence provide support for 

considering a standard with an averaging time less than 24 hours to address health 

effects associated with sub-daily fine particle exposures? 

Relative to information available in the last review, recent studies provide additional 

evidence for cardiovascular effects associated with sub-daily (e.g., one to several hours) 

exposure to PM, especially effects related to cardiac ischemia, vasomotor function, and more 

subtle changes in markers of systemic inflammation, hemostasis, thrombosis and coagulation 

(US EPA, 2009a, section 6.2).50 Because these studies have used different indicators (e.g., 

PM2.5, PM10, PM10-2.5, UFPs), averaging times (e.g., 1, 2, and 4 hours), and health outcomes, it is 

difficult to draw conclusions about cardiovascular effects associated specifically with sub-daily 

exposures to PM2.5. 

With regard to respiratory effects associated with sub-daily PM2.5 exposures, the 

currently available evidence is much sparser than for cardiovascular effects and continues to be 

very limited. The ISA concludes that for several studies of hospital admissions or medical visits 

for respiratory diseases, the strongest associations were observed with 24-hour average or longer 

exposures, not with less than 24-hour exposures (US EPA, 2009a, section 6.3). 

We conclude that this information, when viewed as a whole, is too unclear, with respect 

to the indicator, averaging time and health outcome, to serve as a basis for consideration of 

establishing a primary PM2.5 standard with an averaging time shorter than 24-hours at this time. 

 To what extent does the currently available scientific evidence provide support for 

considering separate standards with distinct averaging times to address effects 

associated with seasonal fine particle exposures? 

With regard to health effects associated with PM2.5 exposure across varying seasons in 

this review, Bell et al. (2008) reported higher PM2.5 risk estimates for hospitalization for 

cardiovascular and respiratory diseases in the winter compared to other seasons. In comparison 

to the winter season, smaller statistically significant associations were also reported between 

PM2.5 and cardiovascular morbidity for spring and autumn, and a positive, but statistically non-

 50 A limited number of additional studies have also provided evidence of reported ECG changes typically 

representative of cardiac ischemia (S-T segment depression) or reported changes in heart rate variability (HRV) (US 

EPA, 2009a, sections 6.2.1.2 and 6.2.12.1), however, these changes are often variable and difficult to interpret the 

PM2.5 etiologically relevant mechanism underlying the observed effects. 
 2-58

significant association was observed for the summer months. In the case of mortality, Zanobetti 

and Schwartz (2009) reported a 4-fold higher effect estimate for PM2.5 associated mortality for 

the spring as compared to the winter. Taken together, these results provide emerging evidence 

that individuals may be at greater risk of dying from higher exposures to PM2.5 in the warmer 

months and may be at greater risk of PM2.5-associated hospitalization for cardiovascular and 

respiratory diseases during colder months of the year. 

Overall, we observe that there are few studies presently available to deduce a general 

pattern in PM2.5-related risk across seasons. In addition, these studies utilized 24-hour exposure 

periods within each season to assess the PM2.5 associated health effects, and do not provide 

information on health effects associated with a season-long exposure to PM2.5. Due to these 

limitations in the currently available evidence, we conclude that there is no basis to consider a 

seasonal averaging time separate from a 24-hour averaging time. 

Summary

We recognize that the currently available evidence informs our understanding of 

exposure durations of concern and continues to provide strong support for standards that provide 

protection for both long- and short- term exposures. In considering the possibility of effects 

associated with sub-daily PM2.5 exposures (i.e., less than 24-hour exposures), we recognize that 

there is additional evidence available in this review, primarily focused on cardiovascular effects 

with more limited evidence for respiratory effects. However, because these studies have used 

different indicators of PM exposure (e.g., PM2.5, PM10, UFPs), averaging times, and a broad 

range of health outcomes, it is difficult to use this evidence to serve as a basis for establishing a 

national standard with a shorter-than-24-hour averaging time. With respect to seasonal effects, 

while we recognize there is some new evidence for PM2.5- related effects differentiated by 

season, we conclude that this evidence is too limited to use as a basis for establishing a PM2.5

standard with a seasonal averaging time. Based on the above considerations, we conclude that 

the currently available information provides strong support for consideration for retaining the 

current annual and 24-hour averaging times but does not provide support for considering 

alternative averaging times. CASAC agreed with these conclusions and further noted, “[t]o the 

extent that EPA supplements or replaces 24-hour fine PM samplers with continuous monitors, it 

may become possible to conduct studies that demonstrate that other averaging times for acute 

responses may be more appropriate than 24-hour averages, for at least some effects” (Samet, 

2010c, p. 13). 

2.3.3 Forms 

The “form” of a standard defines the air quality statistic that is to be compared to the 

level of the standard in determining whether an area attains the standard. In this review, we 
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consider whether currently available information supports consideration of alternative forms for 

the annual or 24-hour PM2.5 standards. 

2.3.3.1 Form of the Annual Standard 

In 1997, EPA established the form of the annual PM2.5 standard as an annual arithmetic 

mean, averaged over 3 years, from single or multiple community-oriented monitors. This form 

was intended to represent a relatively stable measure of air quality and to characterize area-wide 

PM2.5 concentrations. The level of the standard was to be compared to measurements made at 

the community-oriented monitoring site recording the highest concentration, or, if specific 

constraints were met, measurements from multiple community-oriented monitoring sites could 

be averaged51 (62 FR 38671 to 38672, July 18, 1997). The constraints were intended to ensure 

that spatial averaging would not result in inequities in the level of protection provided by the 

standard (62 FR 38672). This approach was consistent with the epidemiological studies on 

which the PM2.5 standard was primarily based, in which air quality data were generally averaged 

across multiple monitors in an area or were taken from a single monitor that was selected to 

represent community-wide exposures, not localized “hot spots.” 

In the last review, EPA tightened the criteria for use of spatial averaging to provide 

increased protection for vulnerable populations exposed to PM2.5.

52 This change was based in 

part on an analysis of the potential for disproportionate impacts on potentially vulnerable 

populations, which found that the highest concentrations in an area tend to be measured at 

monitors located in areas where the surrounding population is more likely to have lower 

education and income levels, and higher percentages of minority populations (71 FR 61166/2; 

US EPA, 2005, section 5.3.6.1). 

In this review, we again consider the potential impact of allowing for spatial averaging, 

noting that persons from lower socioeconomic strata have been identified as an additional 

susceptible population (section 2.2.1). 

 Does the currently available evidence provide support for the continued use of spatial 

averaging as part of the form of the annual standard? 

In considering the potential for disproportionate impacts on potentially susceptible 

populations, we updated an air quality analysis conducted for the last review. This analysis 

focused on determining if the spatial averaging provisions, as modified in 2006, could introduce 

 51 The original criteria for spatial averaging included: (1) the annual mean concentration at each site shall be within 

20 percent of the spatially averaged annual mean, and (2) the daily values for each monitoring site pair shall yield a 

correlation coefficient of at least 0.6 for each calendar quarter (62 FR 38671 to 38672, July 18, 1997). 52 The current criteria for spatial averaging include: (1) the annual mean concentration at each site shall be within 10 

percent of the spatially averaged annual mean, and (2) the daily values for each monitoring site pair shall yield a 

correlation coefficient of at least 0.9 for each calendar quarter (71 FR 61167/2-3, October 17, 2006). 
 2-60

inequities in protection for susceptible populations exposed to PM2.5. Specifically, we evaluated 

whether persons with a lower SES (a susceptible population discussed in US EPA,2009a, section 

8.1.7) are more likely than the general population to live in areas in which the monitors recording 

the highest air quality values in an area are located. Data used in this analysis included 

demographic parameters measured at the Census Block or Census Block Group level, including 

percent minority population, percent minority subgroup population, percent of persons living 

below the poverty level, percent of persons 18 years of age or older, and percent of persons 65 

years of age and older. In each candidate geographic area, data from the Census Block(s) or 

Census Block Group(s) surrounding the location of the monitoring site (as delineated by radii 

buffers of 0.5, 1.0, 2.0, and 3.0 miles) in which the highest air quality value was monitored were 

compared to the area-wide average value in the area. This analysis looked beyond areas that 

would meet the current spatial averaging criteria and considered all urban areas (i.e., CBSAs) 

with at least two valid annual DV monitors (Schmidt et al., 2011a, Analysis A). Recognizing the 

limitations of such cross-sectional analyses, we observe that the highest concentrations in an area 

tend to be measured at monitors located in areas where the surrounding populations are more 

likely to live below the poverty line and to have higher percentage of minorities. 

Based upon the analysis described above, staff concludes that the existing constraints on 

spatial averaging, as modified in 2006, may not be adequate to avoid substantially greater 

exposures in some areas, potentially resulting in disproportionate impacts on susceptible 

populations of persons with lower SES levels and minorities. Therefore, we conclude that it is 

appropriate to consider revising the form of the annual PM2.5 standard such that it does not allow 

for the use of spatial averaging across monitors. In doing so, the level of the annual PM2.5

standard would be compared to measurements made at the monitoring site that represents 

community-wide air quality recording the highest PM2.5 concentrations. CASAC agreed with 

staff conclusions that it is “reasonable” for EPA to eliminate the spatial averaging provisions 

(Samet, 2010d, p. 2). Further, in CASAC’s comments on the first draft PA, they noted, “Given 

mounting evidence showing that persons with lower SES levels are a susceptible group for PMrelated health risks, CASAC recommends that the provisions that allow for spatial averaging 

across monitors be eliminated for the reasons cited in the (first draft) Policy Assessment” (Samet, 

2010c, p. 13).

2.3.3.2 Form of the 24-Hour Standard 

In 1997, EPA established the form of the 24-hour PM2.5 standard as the 98th percentile of 

24-hour concentrations at each population-oriented monitor within an area, averaged over three 

years (62 FR at 38671 to 38674, July 18, 1997). The Agency selected the 98th percentile as an 

appropriate balance between adequately limiting the occurrence of peak concentrations and 
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providing increased stability which, when averaged over 3 years, facilitated effective health 

protection through the development of more stable implementation programs. By basing the 

form of the standard on concentrations measured at population-oriented monitoring sites, EPA 

intended to provide protection for people residing in or near localized areas of elevated 

concentrations. In the last review, in conjunction with lowering the level of the 24-hour 

standard, EPA retained this form based in part on a comparison with the 99th percentile form.53 

In this review, we have again considered the relative stability of the 98th and 99th

percentile forms. 

 Does the currently available evidence provide support for the continued use of the 98th

percentile form of the 24-hour standard? 

We recognize that the selection of the appropriate form of the 24-hour standard includes 

maintaining adequate protection against peak 24-hour concentrations while also providing a 

stable target for risk management programs, which serves to provide for the most effective 

public health protection in the long run.54 As in previous reviews, we recognize that a 

concentration-based form, compared to an exceedance-based form, is more reflective of the 

health risks posed by elevated pollutant concentrations because such a form gives proportionally 

greater weight to days when concentrations are well above the level of the standard than to days 

when the concentrations are just above the level of the standard. Further, staff concludes that a 

concentration-based form, when averaged over three years, provides an appropriate balance 

between limiting peak pollutant concentrations and providing a stable regulatory target, thus, 

facilitating the development of more stable implementation programs. 

In revisiting the stability of a 98th versus 99th percentile form for a 24-hour standard 

intended to provide supplemental protection for a generally controlling annual standard, we 

consider air quality data reported in 2000 to 2008 to update our understanding of the ratio 

between peak-to-mean PM2.5 concentrations.55 As illustrated in Figure 2-2, the 98th percentile 

value is a more stable metric than the 99th percentile. 

On this basis, we conclude that it is appropriate to consider retaining the current 98th

percentile form of the 24-hour standard as it represents an appropriate balance between 

 53 In reaching this final decision, EPA recognized a technical problem associated with a potential bias in the method 

used to calculate the 98th percentile concentration for this form. The EPA adjusted the sampling frequency 

requirement in order to reduce this bias. Accordingly, the Agency modified the final monitoring requirements such 

that areas that are within 5 percent of the standards are required to increase the sampling frequency to every day (71 

FR 61164 to 61165, October 17, 2006). 

54 See ATA III, 283 F.3d at 374-375 which concludes it is legitimate for EPA to consider overall stability of the 

standard and its resulting promotion of overall effectiveness of NAAQS implementation programs in setting a 

standard that is requisite to protect the public health. 

55 We consider a coefficient of variation instead of simply the standard deviation because the 99th percentile values 

have higher concentration levels and dividing by the mean normalizes the data. In focusing on three years of recent 

air quality (2006 to 2008), we see a similar pattern of peak-to-mean ratios (Schmidt et al., 2010, Analysis 3). 
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adequately limiting the occurrence of peak concentrations and providing increased stability 

relative to an alternative 99th percentile form. In addition, by basing the form of the standard on 

concentrations measured at population-oriented monitoring sites, the standard would continue to 

focus on providing protection for people residing in or near localized areas of elevated 

concentrations. 

Figure 2-2. Distribution of Site-level Variation in 98th and 99th Percentile Concentrations, 

as Measured by Coefficient of Variation (SD/Mean) Computed by Site Across Years, 2000-

2008 

Coefficient of variation x 100

98th percentile 

concentrations

99th percentile 

concentrations

16.13

18.76

Notes: The box indicates the 

interquartile range of the 

distribution, the line through the 

box denotes the distribution 

median, the whisker caps mark 

the 5th and 95th percentile values, 

and the star identifies the 

distribution mean.

Source: Schmidt et al., 2010, Analysis 3 

2.3.4 Alternative Levels 

In reaching staff conclusions for alternative standard levels that are appropriate to 

consider, we take into account both evidence-based (section 2.3.4.1) and risk-based 

considerations (section 2.3.4.2) as well as the related limitations and uncertainties associated 

with this information as presented and discussed more fully in the ISA and RA (US EPA, 2009a; 

US EPA, 2010a). CASAC advice on alternative standard levels is summarized in section 2.3.4.3. 

Staff conclusions based on the integration of the evidence-based and risk-based approaches as 

well as consideration of CASAC advice and public comments on the first and second draft PAs 

are presented in section 2.3.4.4.

Alternative levels are discussed in conjunction with staff conclusions on other elements 

of the standard presented above, notably, retaining PM2.5 as the indicator for fine particles 

(section 2.3.1); retaining the current annual and 24-hour averaging times (section 2.3.2); 

modifying the current form of the annual standard to eliminate spatial averaging (section 2.3.3.1) 
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and retaining the current form of the 24-hour standard (section 2.3.3.2). Specifically, we address 

the following overarching question: 

What alternative standard levels are appropriate to consider to provide requisite 

protection for health effects associated with long- and short-term PM2.5 exposures? 

2.3.4.1 Evidence-based Considerations 

In translating information from epidemiological studies into the basis for reaching staff 

conclusions on alternative standard levels that are appropriate for consideration, we apply the 

policy framework outlined in section 2.1.3. In doing so, we focus on identifying levels for an 

annual standard and a 24-hour standard that, in combination, provide protection for health effects 

associated with both long- and short-term PM2.5 exposures. We also consider the extent to which 

various combinations of annual and 24-hour standards reflect setting a generally controlling 

annual standard with a 24-hour standard providing supplemental protection. We conclude this 

policy goal results in the most efficient and effective way to provide appropriate public health 

protection. 

As discussed in section 2.1.3, we recognize that there is no single factor or criterion that 

comprises the “correct” approach for reaching staff conclusions on alternative standard levels for 

consideration, but rather there are various approaches that are reasonable to consider. In 

reaching staff conclusions on the ranges of standard levels that are appropriate to consider, we 

address a series of specific questions beginning with consideration of the relative weight to place 

on different evidence. 

In recognizing the absence of a discernible population threshold below which effects 

would not occur, our general approach for identifying alternative annual standard levels that are 

appropriate to consider focuses on characterizing the range of PM2.5 concentrations over which 

we have the most confidence in the associations reported in the epidemiological studies, and 

conversely where our confidence in the association becomes appreciably lower. The most direct 

approach to address this issue is to consider epidemiological studies reporting confidence 

intervals around C-R relationships. We also explore other approaches that consider different 

statistical metrics to identify ranges of long-term mean PM2.5 concentrations that were most 

influential in generating health effect estimates in long- and short-term epidemiological studies, 

placing greatest weight on those studies that reported positive and statistically significant 

associations. 

With regard to identifying alternative 24-hour standard levels that are appropriate to 

consider, we look at the distributions of 24-hour PM2.5 concentrations reported in short-term 

exposure studies, focusing on the 98th percentile concentrations to match the form of the 24-hour 

standard (as discussed in section 2.3.3.2). In recognizing that the annual and 24-hour standards 
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work together to provide protection for effects associated with short-term PM2.5 exposures, we 

also consider information on the long-term mean PM2.5 concentrations from these studies. In 

addition to considering the epidemiological evidence, we also consider air quality information, 

specifically peak-to-mean ratios using county-level 24-hour and annual design values, to 

characterize air quality patterns in areas possibly associated with strong local or seasonal 

sources. These patterns help us understand the extent to which different combinations of annual 

and 24-hour standards would be consistent with the policy goal of setting a generally controlling 

annual standard with a 24-hour standard that provides supplemental protection. 

 What factors do we weigh in placing emphasis on epidemiological evidence to translate 

this information into staff conclusions on alternative standard levels? 

As discussed in section 2.1.3, we initially focus on long- and short-term PM2.5 exposure 

studies conducted in the U.S. and Canada and place the greatest weight on health outcomes that 

have been judged in the ISA as having evidence to support a causal or likely causal relationship. 

We also consider the evidence for a broader range of health outcomes judged in the ISA to have 

evidence suggestive of a causal relationship, specifically studies that focus on effects in 

susceptible populations, to evaluate whether this evidence provides support for considering lower 

alternative standard levels. 

We take several factors into account in placing relative weight on the body of available 

epidemiological studies, for example, study characteristics, including study design (e.g., time 

period of air quality monitoring, control for potential confounders); strength of the study (in 

terms of statistical significance and precision of results); and availability of population-level and 

air quality distribution data. We place greatest weight on information from multi-city 

epidemiological studies to inform staff conclusions regarding alternative annual standard levels. 

These studies have a number of advantages compared to single-city studies56 that include 

providing representation of ambient PM2.5 concentrations and potential health impacts across a 

range of diverse locations providing spatial coverage for different regions across the country, 

reflecting differences in PM2.5 sources, composition, and potentially other exposure-related 

factors which might impact PM2.5-related risks; lack of ‘publication bias’ (US EPA, 2004, p. 8-

30); and consideration of larger study populations that afford the possibility of generalizing to 

the broader national population and provide higher statistical power than single-city studies to 

 56 As discussed in section 2.2.1, we recognize that single-city studies provide ancillary evidence to multi-city studies 

in support of calling into question the adequacy of the current suite of standards. However, in light of the mixed 

findings reported in single-city short-term PM2.5 exposure studies, and the likelihood that these results are influenced 

by localized events and not representative of air quality across the country, we place comparatively greater weight 

on the results from multi-city studies in considering alternative annual standard levels. 
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detect potentially statistically significant associations with relatively more precise effect 

estimates. 

In reaching staff conclusions regarding alternative 24-hour standard levels that are 

appropriate to consider, we also take into account relevant information from single-city shortterm PM2.5 exposure studies. Although, as discussed above, multi-city studies have greater 

power to detect associations and provide broader geographic coverage in comparison to singlecity studies, the extent to which effects reported in multi-city short-term PM2.5 exposure studies 

are associated with the specific short-term air quality in any particular location is unclear, 

especially when considering short-term concentrations at the upper end of the air quality 

distribution (i.e., at the 98th percentile value) for a given study area. In contrast, single-city 

studies are more limited in terms of power and geographic coverage but the link between 

reported health effects and the air quality in a given study area is more straightforward to 

establish. Therefore, we consider the results of both multi-city and single-city short-term 

exposure studies to inform staff conclusions regarding alternative levels that are appropriate to 

consider for a 24-hour standard that is intended to provide supplemental protection in areas 

where the annual standard may not offer appropriate protection against the effects of all shortterm exposures. 

 To what extent have confidence intervals around concentration-response relationships 

reported in epidemiological studies been characterized and to what extent do they 

inform the identification of alternative standard levels? 

Based on a thorough search of the available evidence, we identified three long-term PM2.5

exposure studies reporting confidence intervals around C-R functions (i.e., Schwartz et al., 2008; 

Pope et al., 2002; Miller et al., 2007; see Figure 2-3).57 In our assessment of these studies, we 

placed greater weight on analyses that averaged across multiple C-R models since this approach 

represents a more robust examination of the underlying C-R relationship than analyses 

considering a single C-R model. Although epidemiological studies reporting C-R functions and 

associated 95% confidence intervals provide information on the precision of the effect estimates 

at specific concentrations in the air quality distribution (i.e., point-wise confidence intervals), the 

concentration below which the confidence intervals for effect estimates becomes notably wider is 

intrinsically related to data density (an issue that is explored in the subsequent question), and not 

necessarily indicative of lower confidence in the underlying C-R relationship. Therefore, for the 

purposes of this discussion, we are interested in additional information that confidence intervals 

associated with C-R functions can provide, specifically as to the extent to which the confidence 

 57 Subsequent to a thorough exploration of the published evidence, we were unable to identify any short-term PM2.5

exposure studies that characterized confidence intervals around C-R relationships. 
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intervals around non-parametrically estimated C-R functions indicate appreciably less 

confidence that an association continues to exist at lower PM2.5 concentrations. 

Based on our review of the evidence and on our discussions with the investigators of the 

aforementioned three studies, we determined that these studies analyzed the C-R function 

primarily to determine if a linear curve most appropriately represented the C-R relationship, and 

not to characterize the confidence intervals associated with the underlying C-R relationship. 

Nonetheless, we consider what additional information beyond the shape of the C-R relationship 

these non-parametric curves can provide to inform our confidence in the association at lower 

PM2.5 concentrations. 

In the first study, Schwartz et al. (2008) used a variety of statistical methods to analyze the shape 

of the C-R relationship associated with long-term PM2.5 exposure, and to investigate whether a 

mortality effect threshold exists among participants of the Harvard Six Cities cohort. The 

authors conducted a Bayesian Modeling Averaging (BMA) analysis that included 32 distinct 

models, some with the option for a threshold at 10 μg/m3 and higher. Based on the BMA 

analysis, the C-R function reported by Schwartz et al. (2008) was found to be linear, with 

“....little evidence for a threshold in the association between exposure to fine particles and the 

risk of death...”. Moreover, based on the BMA analysis (Figure 2-3a), EPA staff observed a 

widening of the confidence intervals around the C-R function for PM2.5-related mortality. This 

widening of the confidence intervals occurred around the long-term mean concentration of 

approximately 10 μg/m3

 with continued broadening of the confidence intervals around the C-R 

function at lower PM2.5 concentrations. This broadening is likely due in part to the comparative 

lack of data at the lower end of the air quality distribution considered in this analysis (i.e., 10.7 

μg/m3 is one standard deviation below the long-term mean concentration and the lower bound of 

the range of air quality considered was 8 μg/m3

). Consequently, we observe from this analysis 

that the widening of confidence intervals around C-R relationships at lower concentrations is 

due, in part, to the density of air quality data, reinforcing the view that one has most confidence 

in study results over the range of concentrations where the bulk of the data exist. In a separate 

analysis, Schwartz et al. (2008) also included point-wise confidence intervals for the association 

between PM2.5 and mortality in a smoothing plot of a single nonparametric model (Figure 2-3b). 

However, since these point-wise confidence intervals relate specifically to the magnitude of 

effect estimates generated at specific PM2.5 concentrations from a single model, we conclude that 

this analysis is not informative for characterizing confidence intervals associated with the 

underlying C-R relationship. 
 2-67

 Figure 2-3. Confidence Intervals Around PM2.5 Concentration-Response Relationships – 

Information from Multi-city Epidemiological Studies 

a. 

b. 

Source: Schwartz et al., 2008
 2-68

Figure 2-3. Confidence Intervals Around PM2.5 Concentration-Response Relationships 

from Multi-City Epidemiological Studies (cont.) 

c. 

Source: Pope et al., 2002 

d.

Source: Miller et al., 2007
 2-69

In the second long-term PM2.5 exposure study, Pope et al. (2002) utilized a nonparametric 

smoothing model to depict the shape of the C-R function and associated confidence intervals 

using mortality data from the ACS cohort. This exploratory analysis was conducted primarily to 

examine departures from linearity and to support the utilization of log-linear regression models. 

Goodness-of-fit testing indicated these data supported a log-linear C-R relationship. We observe 

(and observed in the last review) that there was an appreciable widening of point-wise 

confidence intervals on the smoothing plot for all-cause mortality beginning at approximately 13 

to 12 μg/m3 (Figure 2-3c; US EPA, 2005, p. 3-56; Figure 3-4) which is somewhat below where 

the bulk of the air quality distribution in the study occurs (i.e., 14 μg/m3 is one standard deviation 

below the long-term mean concentration of 17.7 μg/m3). However, similar to the single-model 

analysis presented in the discussion of the Schwartz et al. (2008) study (Figure 2-3b), this 

analysis was also generated from a single model, which resulted in point-wise confidence 

intervals that relate to the magnitude of effect estimates generated at specific PM2.5

concentrations. Moreover, the widening of the confidence intervals in the smoothing plot is 

likely a consequence of the comparative lack of air quality data at lower PM2.5 concentrations in 

this study, and does not suggest the possibility that an effects threshold may exist at lower PM2.5

concentrations. It also remains unclear how representative this single model is of the underlying 

C-R relationship. Therefore, a potentially more robust approach to characterizing confidence 

intervals associated with C-R relationships may involve the generation of multiple C-R functions 

as was conducted by Schwartz et al. (2008), as just discussed. 

The third long-term PM2.5 exposure study provides information on the shape of the C-R 

function and associated confidence intervals using cardiovascular-related mortality and 

morbidity data from the WHI cohort (Miller et al., 2007; Figure 2-3d). This analysis of 

cardiovascular events in relation to long-term PM2.5 exposure is indicative of a continued 

reduction in risk at lower concentrations of PM2.5, with no evidence of a discernible effects 

threshold, and provides additional support for log-linear C-R relationships (US EPA, 2009a, 

section 2.4.3). We also recognize that the relative number of events occurring at the lower 

quintiles of exposure is similar to the number of events at the higher quintiles, indicating that 

there is statistical power to detect an association between PM2.5 and mortality at the lower PM2.5

concentrations. However, since the reference value for the C-R function in this analysis is 

11μg/m3

, and effect estimates across the entire distribution are being compared to effect 

estimates at this concentration, this analysis does not identify a PM2.5 concentration where there 

is an appreciable widening of confidence intervals and reduced confidence in the underlying C-R 

relationship in this study. 

In summary, we recognize there are important differences in the statistical approaches 

utilized to create the C-R functions and associated confidence intervals reported in Schwartz et 
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al. (2008), Pope et al. (2002), and Miller et al. (2007) that inform our interpretation of these 

studies. Although these analyses of long-term exposure to PM2.5 provide information on the lack 

of any discernible population threshold, only Schwartz et al. (2008) conducted a multi-model 

analysis to characterize confidence intervals around the estimated C-R relationship that can help 

inform at what PM2.5 concentrations we have appreciably less confidence in the nature of the 

underlying C-R relationship. Although analyses of confidence intervals associated with C-R 

relationships can help inform consideration of alternative standard levels, we conclude that the 

single relevant analysis now available is too limited to serve as the principal basis for identifying 

alternative standard levels in this review. 

 How do we consider different statistical metrics for identifying alternative levels that 

are appropriate to consider for an annual standard? 

As outlined above, we recognize that health effects may occur over the full range of 

concentrations observed in the long- and short-term epidemiological studies and that the ISA 

concluded no discernible population threshold for any effects can be identified based on the 

currently available evidence (US EPA 2009a, section 2.4.3). In identifying alternative standard 

levels that are appropriate to consider, we first take into account the statistical metric used in 

previous reviews. This approach recognizes that the strongest evidence of associations occurs at 

concentrations around the long-term mean concentration. Thus, in earlier reviews, we focused 

on identifying standard levels that were somewhat below the long-term mean concentrations 

reported in PM2.5 exposure studies. As outlined in section 2.1.3, the long-term mean 

concentrations represent air quality data typically used in epidemiological analyses and provide a 

direct link between PM2.5 concentrations and the observed health effects. Further, these data are 

available for all long- and short-term exposure studies analyzed and, therefore, represent the data 

set available for the broadest set of epidemiological studies. 

Consistent with CASAC’s comments on the second draft PA, we also explore ways to 

take into account additional information from epidemiological studies, when available (Samet, 

2010d, p. 2). We do this by evaluating different statistical metrics, beyond the long-term mean 

concentration, to characterize the range of PM2.5 concentrations down through which we 

continue to have confidence in the associations observed in epidemiological studies and below 

which there is a comparative lack of data such that our confidence in the relationship is 

appreciably less. This would also be the range of PM2.5 concentrations which have the most 

influence on generating the health effect estimates reported in epidemiological studies. As 

discussed in section 2.1.3, we recognize there is no one percentile value within a given 

distribution that is the most appropriate or “correct” way to characterize where our confidence in 

the associations becomes appreciably lower. We conclude that focusing on concentrations 

within the lower quartile of a distribution, such as the range from the 25th to the 10th percentile, is 
 2-71

reasonable to consider as a region within which we begin to have appreciably less confidence in 

the associations observed in epidemiological studies.58 In staff’s view, considering lower PM2.5

concentrations, down to the lowest concentration observed in a study, would be a highly 

uncertain basis for selecting alternative standard levels. 

As outlined in 2.1.3, we recognize that there are two types of population-level 

information to consider in identifying the range of PM2.5 concentrations which have the most 

influence on generating the health effect estimates reported in epidemiological studies. The most 

relevant information to consider is the number of health events (e.g., deaths, hospitalizations) 

occurring within a study population in relation to the distribution of PM2.5 concentrations likely 

experienced by study participants. Although not as directly relevant, the number of participants 

within each study area can be used as a surrogate for health event data in relation to the 

distribution of PM2.5 concentrations. 

In applying this approach, we focus on identifying the broader range of PM2.5

concentrations which had the most influence on generating health effect estimates in 

epidemiological studies. As discussed below, in working with study investigators, we were able 

to obtain health event data for three large multi-city studies (Krewski et al., 2009; Zanobetti and 

Schwartz, 2009; Bell et al., 2008) 59 and population data for the same three studies and one 

additional long-term PM2.5 exposure study (Miller et al., 2007); as documented in a staff 

memorandum (Rajan et al., 2011). In the absence of health event and population data, the 

distribution of PM2.5 concentrations across study areas could be used to represent the PM2.5

concentrations likely experienced by study participants. However, we were unable to determine 

from the methodologies published for these studies how the air quality was statistically weighted 

to account for variations in the availability of daily PM2.5 measurements by study area. 

Consequently, we are unable to consider this type of information as part of our effort to identify 

the broader range of PM2.5 concentrations that were most influential in generating the health 

effect estimates in epidemiological studies. 

Figures 2-4 through 2-7 summarize policy-relevant epidemiological evidence. Long- and 

short-term exposure studies that evaluated endpoints classified in the ISA as having evidence of 

 58 In the last review, staff believed it was appropriate to consider a level for an annual PM2.5 standard that was 

somewhat below the averages of the long-term concentrations across the cities in each of the key long-term 

exposures studies, recognizing that the evidence of an association in any such study was strongest at and around the 

long-term average where the data in the study are most concentrated. For example, the interquartile range of longterm average concentrations within a study and a range within one standard deviation around the study mean were 

considered reasonable approaches for characterizing the range over which the evidence of association is strongest 

(US EPA, 2005, p. 5-22). In this review, we note the interrelatedness of the distributional statistics and a range of 

one standard deviation around the mean which contains approximately 68% of normally distributed data, in that one 

standard deviation below the mean falls between the 25th and 10th percentiles. 59 Effect estimates from Krewski et al. (2009), Zanobetti and Schwartz (2009), and Bell et al. (2008) were used to 

generate the core quantitative risk assessment results presented in the RA (US EPA, 2010a). 
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a causal or likely causal relationship (including studies with long-term mean PM2.5

concentrations below 17 μg/m3) are summarized in Figures 2-4 through 2-6.60 Figures 2-4 and 

2-6 provide summaries of long- and short-term exposure studies that evaluated mortality 

(evidence of a causal relationship); cardiovascular effects (evidence of a causal relationship); 

and respiratory effects (evidence of a likely causal relationship) in the general population, as 

well as older adults, a susceptible population. Figure 2-5 provides a summary of studies that 

evaluated respiratory effects (evidence of a likely causal relationship) in children, a susceptible 

population, as well as long-term exposure studies of children that evaluated developmental 

effects that are classified as having evidence suggestive of a causal relationship. Figure 2-7 

presents cumulative frequency plots of the health events or number of study participants and 

corresponding long-term mean PM2.5 concentrations for the four studies for which we were able 

to obtain such data (Krewski et al., 2009; Zanobetti and Schwartz, 2009; Bell et al., 2008; Miller 

et al., 2007). This figure highlights the range of PM2.5 concentrations between the 25th to 10th

percentiles of the distribution of health events or number of study participants. In general, we 

note that the long-term mean PM2.5 concentrations based upon the distributions of health event 

and study population data are similar, and provide support for the utilization of data on the 

number of study participants as a surrogate for health event data (Rajan et al., 2011). 

As outlined above in section 2.1.3, epidemiological studies typically report 

concentrations based upon a composite monitor distribution; that is, concentrations averaged 

across ambient monitors within each area included in a given study are averaged across study 

areas to calculate an overall study mean concentration. As noted above, a policy approach that 

uses data based on composite monitor distributions to identify alternative standard levels, and 

then compares those levels to concentrations at appropriate maximum monitors to determine if 

an area meets a given standard, directionally has the potential to build in some margin of 

safety.61

 60 We note that additional studies presented and assessed in the ISA report effects at higher long-term mean PM2.5

concentrations. 

61 In analyses conducted by EPA staff based on selected long- and short-term exposure studies, we note that the 

differences between the maximum and composite distributions were greater for studies with fewer years of air 

quality data (i.e., 1 to 3 years) and smaller numbers of study areas (i.e., 36 to 51 study areas). The differences in the 

maximum and composite monitor distribution were much smaller for studies with more years of air quality data (i.e., 

up to 6 years) and larger numbers of study areas (i.e., 112 to 204 study areas) (Hassett-Sipple et al., 2010; US EPA, 

2010f, section 2.3.4.1). Therefore, any margin of safety that may be provided by a policy approach that uses data 

based on composite monitor distributions to identify alternative standard levels, and then compares those levels to 

concentrations at appropriate maximum monitors to determine if an area meets a given standard, will vary 

depending upon the number of monitors and air quality distributions within a given area. See also footnote 14 in 

section 2.1.3 and associated text. 
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Figure 2-4. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for Multi-City, 

Exposure Studies of the General Population and Older Adults 
2-74

Figure 2-5. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for Multi-City, 

Exposure Studies of Children 
2-75

Figure 2-6. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for Multi-City, 

Exposure Studies of the General Population and Older Adults 
2-76

Figure 2-7. Distribution of Population-Level Data and Corresponding PM2.5 Concentrations for Sele

Epidemiological Studies 
 2-77

We initially consider distributional statistics for both long- and short-term exposure 

studies that evaluated mortality (causal relationship), cardiovascular effects (causal relationship) 

or respiratory effects (likely causal relationship). In looking first at the long-term mean 

concentrations in the long-term exposure studies, we observe positive and often statistically 

significant associations at long-term mean PM2.5 concentrations ranging from 16.4 to 12.9 μg/m3

(Laden et al., 2006; Lipfert et al., 2006; Krewski et al., 2009; Goss et al., 2004; Miller et al.; 

2007; Zeger et al., 2008; Eftim et al., 2008; Dockery et al., 1996; McConnell et al., 2003; see 

Figures 2-4 and 2-5). In considering the one long-term PM2.5 exposure study for which we have 

health event data (Krewski et al., 2009), we observe that the long-term mean PM2.5

concentrations corresponding with study areas contributing to the 25th and 10th percentiles of the 

distribution of mortality data are 12.0 μg/m3

 and 10.2 μg/m3, respectively. As identified above, 

although less directly relevant than event data, the number of participants within each study area 

can be used as a surrogate for health event data in relation to the distribution of PM2.5

concentrations. The long-term mean PM2.5 concentrations corresponding with study areas 

contributing to the 25th and 10th percentiles of the distribution of study participants for Miller et 

al. (2007) were 11.2 μg/m3 and 9.7 μg/m3

, respectively (Rajan et al., 2011). 

In then considering information from multi-city, short-term exposure studies reporting 

positive and statistically significant associations with these same broad health effect categories, 

we observe positive and statistically significant associations at long-term mean PM2.5

concentrations in a similar range of 15.6 to 12.8 μg/m3 (Franklin et al., 2007, 2008; Klemm and 

Mason, 2003; Burnett and Goldberg, 2003; Zanobetti and Schwartz, 2009; Burnett et al., 2005; 

Bell et al., 2008; Dominici et al., 2006a; see Figure 2-6).62 In considering the two multi-city, 

short-term PM2.5 exposure studies for which we have health event data, we observe that the longterm mean PM2.5 concentrations corresponding with study areas contributing to the 25th and 10th

percentiles of the distribution of deaths and cardiovascular-related hospitalizations are 12.5 

μg/m3 and 10.3 μg/m3, respectively,for Zanobetti and Schwartz (2009), and 11.5 μg/m3 and 9.8 

μg/m3

, respectively,for Bell et al. (2008) (Rajan et al., 2011). 

Taking into consideration additional studies of specific susceptible lifestages (i.e., 

childhood), we expand our evaluation of the long-term exposure studies to include a broader 

range of health outcomes judged in the ISA to have evidence suggestive of a causal relationship. 

This evidence is taken into account to evaluate whether it provides support for considering lower 

alternative levels than if weight were only placed on studies for which health effects have been 

 62 When integrating evidence from short-term exposure studies reporting positive and statistically significant 

associations with mortality, cardiovascular, and respiratory effects, the ISA concluded that these associations are 

generally consistent and precise at long-term mean PM2.5 concentrations of 12.8 μg/m3 and above (US EPA, 2009a, 

pp. 2-10 to 2-11) 
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judged in the ISA to evidence supporting a causal or likely causal relationship. We make note of 

a limited number of studies that provide emerging evidence for PM2.5-related low birth weight 

and infant mortality, especially related to respiratory causes during the post-neonatal period. 

This more limited body of evidence indicates positive and often statistically significant effects 

associated with long-term PM2.5 mean concentrations in the range of 14.9 to 11.9 μg/m3

(Woodruff et al., 2008; Liu et al., 2007; Bell et al., 2007; see Figure 2-5). As illustrated in 

Figure 2-5, although Parker and Woodruff (2008) did not observe an association between 

quarterly estimates of exposure to PM2.5 and low birth weight in a multi-city U.S. study, other 

U.S. and Canadian studies did report positive and statistically significant associations between 

PM2.5 and low birth weight at lower ambient concentrations (Bell et al., 2007; Liu et al., 2007).63 

There remain significant limitations (e.g., identifying the etiologically relevant time period) in 

the evaluation of evidence on the relationship between PM2.5 exposures and birth outcomes (US 

EPA, 2009a, pp. 7-48 and 7-56) which should be taken into consideration in reaching judgments 

about how to weigh these studies of potential impacts on specific susceptible populations in 

considering alternative standard levels that provide protection with an appropriate margin of 

safety. 

With respect to carcinogenicity, mutagenicity, and genotoxicity (evidence suggestive of a 

causal relationship), the strongest evidence currently available is from long-term prospective 

cohort studies that report positive associations between PM2.5 and lung cancer mortality. At this 

time, the PM2.5 concentrations reported in studies evaluating these effects generally included 

ambient concentrations that are equal to or greater than ambient concentrations observed in 

studies that reported mortality and cardiovascular and respiratory effects (US EPA, 2009a, 

section 7.5). Therefore, in selecting alternative levels, we note that in providing protection for 

mortality and cardiovascular and respiratory effects, it is reasonable to anticipate that protection 

will also be provided for carcinogenicity, mutagenicity, and genotoxicity effects. 

Summary of Evidence-based Considerations to Inform Annual Standard Level

In considering the currently available evidence and air quality information within the 

context of identifying potential alternative annual standard levels for consideration, we again 

note that the ISA concludes there is no evidence of a discernible population threshold below 

which effects would not occur. Thus, health effects may occur over the full range of 

concentrations observed in the epidemiological studies. In the absence of any discernible 

thresholds, our general approach for identifying alternative standard levels that would provide 

 63 As noted in section 7.4 of the ISA, Parker et al. (2005) reported that over a 9 month exposure period (mean PM2.5

concentration of 15.4 μg/m3

) a significant decrease in birth weight was associated with infants in the highest quartile 

of PM2.5 exposure as compared to infants exposed in the lowest quartile. 
 2-79

appropriate protection against effects observed in epidemiological studies has focused on the 

central question of identifying the range of PM2.5 concentrations below the long-term mean 

concentrations where we continue to have confidence in the associations observed in 

epidemiological studies. 

In considering the evidence, we recognize that NAAQS are standards set so as to provide 

requisite protection, neither more nor less stringent than necessary to protect public health with 

an adequate margin of safety. This judgment, ultimately made by the Administrator, involves 

weighing the strength of the evidence and the inherent uncertainties and limitations of that 

evidence. Therefore, depending on the weight placed on different aspects of the evidence and 

inherent uncertainties, considerations of different alternative standard levels could be supported. 

As discussed in section 2.1.3, by applying the general policy approach used in previous 

reviews, one could focus on identifying alternative standard levels for the annual standard that 

are somewhat below the long-term mean PM2.5 concentrations reported in the long- and shortterm exposure studies. By expanding this approach to consider additional population-level 

information from epidemiological studies, one could also focus on the range of PM2.5

concentrations below the long-term mean concentrations over which we continue to have 

confidence in the associations observed in epidemiological studies, as well as the extent to which 

our confidence in the associations is appreciably less at lower concentrations, to identify 

alternative annual standard levels. Figure 2-8 provides a summary of the long-term mean PM2.5

concentrations for the long- and short-term exposure studies presented in Figures 2-4 through 2-6 

as well as the range of PM2.5 concentrations corresponding with the 25th to 10th percentiles of 

health event or number of study participants data from the four multi-city studies, for which 

distributional statistics are available and presented in Figure 2-7. 

Given the currently available evidence and considering the various approaches discussed 

above, as an initial matter we conclude it is appropriate to focus on an annual standard level 

within a range of about 12 to 11 μg/m3

. As illustrated in Figure 2-8, a standard level of 12 

μg/m3

, at the upper end of this range, is somewhat below the long-term mean PM2.5

concentrations reported in all the multi-city long- and short-term exposure studies that provide 

evidence of positive and statistically significant associations with health effects classified as 

having evidence of a causal or likely causal relationship, including premature mortality and 

hospitalizations and emergency department visits for cardiovascular and respiratory effects as 

well as respiratory effects in children. Further, a level of 12 μg/m3

 would reflect consideration 

of additional population-level information from such epidemiological studies in that it is the 

PM2.5 concentration generally corresponding with the 25th percentile of the available 

distributions of health events data, which we consider to be at the high end of the range of PM2.5

concentrations within which the data become appreciably more sparse and, thus, where our 
2-80

Figure 2-8. Translating Epidemiological Evidence from Multi-City Exposure Studies into Annual P
 2-81

confidence in the associations observed in epidemiological studies starts to become appreciably 

less. In addition, a level of 12 μg/m3 would reflect some consideration of studies that provide 

more limited evidence of reproductive and developmental effects, which are suggestive of a 

causal relationship, in that it is about at the same level as the lowest long-term mean PM2.5

concentrations reported in such studies (see Figure 2-8). 

Alternatively, an annual standard level of 11 μg/m3

, at the lower end of this range, is well 

below the lowest long-term mean PM2.5 concentrations reported in all multi-city long- and shortterm exposure studies that provide evidence of positive and statistically significant associations 

with health effects classified as having evidence of a causal or likely causal relationship. A level 

of 11 μg/m3 would reflect placing more weight on the distributions of health event and 

population data, in that this level is within the range of PM2.5 concentrations corresponding to the 

25th and 10th percentiles of all the available distributions of such data. In addition, a level of 11 

μg/m3 is somewhat below the lowest long-term mean PM2.5 concentrations reported in 

reproductive and developmental effects studies that are suggestive of a causal relationship. 

Thus, a level of 11 μg/m3 would reflect an approach to translating the available evidence that 

places relatively more emphasis on margin of safety considerations than would a standard set at a 

higher level. Such a policy approach would tend to weigh uncertainties in the evidence in such a 

way as to avoid potentially underestimating PM2.5-related risks to public health. Further, 

recognizing the uncertainties inherent in identifying any particular point at which our confidence 

in reported associations becomes appreciably less, we conclude that the available evidence does 

not provide a sufficient basis to consider alternative annual standard levels below 11 μg/m3

. 

We have also considered the extent to which the available evidence provides a basis for 

considering alternative annual standard levels above 12 μg/m3. As discussed below, we 

conclude that it could be reasonable to consider a standard level up to 13 μg/m3

 based on a 

policy approach that tends to weigh uncertainties in the evidence in such a way as to avoid 

potentially overestimating PM2.5-related risks to public health, especially to the extent that 

primary emphasis is placed on long-term exposure studies as a basis for an annual standard level. 

A level of 13 μg/m3 is somewhat below the long-term mean PM2.5 concentrations reported in all 

but one of the long-term exposure studies providing evidence of positive and statistically 

significant associations with PM2.5-related health effects classified as having a causal or likely 

causal relationship. As shown in Figure 2-8, the one long-term exposure study with a long-term 

mean PM2.5 concentration just below 13 μg/m3

 is the WHI study (Miller et al., 2007).64 We note 

that in comparison to other long-term exposure studies, the WHI study was more limited in that 

 64 As noted in section 2.2.1 and Table 2-4, WHI study investigators provided EPA with updated air quality data 

indicating the long-term mean PM2.5 concentration for this study was 12.9 μg/m3

, not 13.5 μg/m3 as originally 

reported (Curl, 2009). 
 2-82

it was based on only one year of air quality data. Thus, to the extent that less weight is placed on 

the WHI study than on other long-term exposure studies with more robust air quality data, a level 

of 13 μg/m3 could be considered as being protective of long-term exposure related effects 

classified as having a causal or likely causal relationship. In also considering short-term 

exposure studies, we note that a level of 13 μg/m3 is below the long-term mean PM2.5

concentrations reported in most such studies, but is above the long-term means of 12.8 and 12.9 

μg/m3 reported in Burnett et al. (2004) and Bell et al. (2008), respectively. In considering these 

studies, we find no basis to conclude that these two studies are any more limited or uncertain 

than the other short-term studies shown in Figure 2-8, noting in particular that Bell et al. (2008) 

is a large study of older adults, a susceptible population. On this basis, as discussed below, we 

conclude that consideration of an annual standard level of 13 μg/m3

 would have implications for 

the degree of protection that would need to be provided by the 24-hour standard, such that taken 

together the suite of PM2.5 standards would provide appropriate protection from effects on public 

health related to short-term exposure to PM2.5. 

We also note that a standard level of 13 μg/m3

 would reflect a judgment that the 

uncertainties in the epidemiological evidence in general, including uncertainties related to the 

heterogeneity observed in the epidemiological studies in the eastern versus western parts of the 

U.S., the relative toxicity of PM2.5 components, and the potential role of co-pollutants, are too 

great to warrant placing any weight on distributions of health event and population data that 

extend down below the mean into the lower quartile of the data. This level would also reflect a 

judgment that the evidence from reproductive and developmental effects studies that are 

suggestive of a causal relationship is too uncertain to support consideration of any lower level. 

Taken together, staff concludes that consideration of alternative annual standard levels in 

the range of 13 to 11 μg/m3

 may be appropriate, although we also conclude that the currently 

available evidence most strongly supports consideration of an alternative annual standard level in 

the range of 12 to 11 μg/m3. We conclude that an alternative level within the range of 12 to 11 

μg/m3

 would more fully take into consideration the available information from all long- and 

short-term PM2.5 exposure studies, including studies of susceptible populations, than would a 

higher level. This range would also reflect placing weight on information from studies that helps 

to characterize the range of PM2.5 concentrations over which we continue to have confidence in 

the associations observed in epidemiological studies, as well as the extent to which our 

confidence in the associations is appreciably less at lower concentrations. 

 What alternative standard levels are appropriate to consider for a 24-hour standard 

intended to supplement the protection afforded by an annual standard? 

As recognized in section 2.1.3, an annual standard intended to serve as the primary means 

for providing protection for effects associated with both long- and short-term PM2.5 exposures is 
 2-83

not expected to provide appropriate protection against the effects of all short-term PM2.5

exposures. Of particular concern are areas with high peak-to-mean ratios possibly associated 

with strong local or seasonal sources, or PM2.5-related effects that may be associated with 

shorter-than-daily exposure periods. As a result, we believe it is appropriate to consider 

alternative 24-hour PM2.5 standard levels that would supplement the protection provided by an 

annual standard. 

As outlined in section 2.1.3, we consider the available evidence from short-term PM2.5

exposure studies, as well as the uncertainties and limitations in that evidence, to assess the 

degree to which alternative annual and 24-hour PM2.5 standards can be expected to reduce the 

estimated risks attributed to short-term fine particle exposures. In considering the available 

epidemiological evidence, we take into account information from multi-city studies as well as 

single-city studies. We look both at the distributions of 24-hour PM2.5 concentrations, focusing 

on the 98th percentile air quality values to match the form of the 24-hour standard (section 

2.3.3.2), to the extent such data are available, as well as the long-term mean PM2.5

concentrations. 

In considering the information provided by the short-term exposure studies, we recognize 

that to the extent these studies were conducted in areas that likely did not meet one or both of the 

current standards, such studies do not help inform the characterization of the potential public 

health improvements of alternative standards set at lower levels.65 Therefore, in considering the 

short-term exposure studies to inform staff conclusions regarding levels of the 24-hour standard 

that are appropriate to consider, we place greatest weight on studies conducted in areas that 

likely met both the current annual and 24-hour standards. 

With regard to multi-city studies that evaluated effects associated with short-term PM2.5

exposures, as summarized in Figure 2-6, we observe an overall pattern of positive and 

statistically significant associations in studies with 98th percentile values averaged across study 

areas in the range of 45.8 to 34.2 μg/m3 (Burnett et al., 2004; Zanobetti and Schwartz, 2009; Bell 

et al., 2008; Dominici et al., 2006a, Burnett and Goldberg, 2003; Franklin et al., 2008).66 We 

 65 We recognize that in considering studies conducted in areas that likely did not meet the current annual standard, 

by reducing the long-term mean PM2.5 concentrations in such areas to just meet the current annual standard, one 

could reasonably anticipate additional public health protection from short-term exposure (as well as long-term 

exposure). Further, we recognize that in considering studies conducted in areas that likely did not meet the current 

24-hour standard, by reducing the upper end of the air quality distributions in order to meet the current 24-hour 

standard, one could similarly reasonably anticipate additional public health protection. 

66 We note that Figure 2-6 also includes one multi-city study conducted in areas that likely did not meet either the 

annual or 24-hour standards (Franklin et al., 2007). To the extent that changes in air quality designed to meet the 

current annual and/or 24-hour standard are undertaken, one could reasonably anticipate additional public health 

protection will occur in these study areas. Figure 2-6 also includes one multi-city study for which the ISA reports a 

long-term mean PM2.5 concentration but does not report information on the 98th percentile value (Klemm and 

Mason, 2003). We do not know whether this study was conducted in areas that likely would have met the current 
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note that, to the extent air quality distributions were reduced to reflect just meeting the current 

24-hour standard, additional protection would be anticipated for the effects observed in the three 

multi-city studies with 98th percentile values greater than 35 μg/m3 (Burnett et al., 2004; Burnett 

and Goldberg, 2003; Franklin et al., 2008). In the three additional studies with 98th percentile 

values below 35 μg/m3

, specifically 98th percentile concentrations of 34.2, 34.3, and 34.8 μg/m3, 

we note that these studies reported long-term mean PM2.5 concentrations of 12.9, 13.2, and 13.4 

μg/m3, respectively (Bell et al., 2008; Zanobetti and Schwartz, 2009; Dominici et al., 2006a). To 

the extent that consideration is given to revising the level of the annual standard, as discussed 

above in section 2.3.4.1, we recognize that potential changes associated with meeting such an 

alternative annual standard would result in lowering risks associated with both long- and shortterm PM2.5 exposures. Consequently, in considering a 24-hour standard that would work in 

conjunction with an annual standard to provide appropriate public health protection, we note that 

to the extent that the level of the annual standard is revised to within a range of 13 to 11 μg/m3

, 

in particular in the range of 12 to 11 μg/m3

, as discussed above, additional protection would be 

provided for the effects observed in these multi-city studies. 

Taken together, staff concludes that the multi-city, short-term exposure studies generally 

provide support for retaining the 24-hour standard level at 35 μg/m3

, specifically, in conjunction 

with an annual standard level revised to within a range of 12 to 11 μg/m3. Alternatively, in 

conjunction with an annual standard level of 13 μg/m3

, we conclude that the multi-city studies 

provide limited support for revising the 24-hour standard level somewhat below 35 μg/m3, such 

as down to 30 μg/m3, based on one study (Bell et al., 2008) that reported positive and statistically 

significant effects with an overall 98th percentile value below the level of the current 24-hour 

standard in conjunction with an overall long-term mean concentration slightly less than 13 

μg/m3

. 

In reaching staff conclusions regarding alternative 24-hour standard levels that are 

appropriate to consider, we also take into account relevant information from single-city studies

that evaluated effects associated with short-term PM2.5 exposures. We recognize that these 

studies may provide additional insights regarding impacts on susceptible populations and/or on 

areas with isolated peak concentrations. Although, as discussed above, multi-city studies have 

advantages over single-city studies in terms of statistical power to detect associations and 

 

24-hour PM2.5 standard and, therefore, this study does not inform staff conclusions regarding alternative standard 

levels that are appropriate to consider. Further, we note one study reported no association between PM2.5 and 

respiratory symptoms in children in areas that likely would have met the current annual standard but not the current 

24-hour standard (O’Connor et al., 2008). This study reported effects on lung function in asthmatic children 

associated with 5-day average PM2.5 concentrations but not associated with 1-day average PM2.5 concentrations (US 

EPA, 2009a, section 6.3.2.1). 
 2-85

broader geographic coverage as well as other factors such as less likelihood of publication bias, 

reflecting differences in PM2.5 sources, composition, and potentially other factors that could 

impact PM2.5-related effects, multi-city studies often present overall effect estimates rather than 

single-city effect estimates. Since short-term air quality can vary considerably across cities, the 

extent to which effects reported in multi-city studies are associated with short-term air quality in 

any particular location is uncertain, especially when considering short-term concentrations at the 

upper end of the distribution of daily PM2.5 concentrations (i.e., at the 98th percentile value). In 

contrast, single-city studies are more limited in terms of power and geographic coverage but the 

link between reported health effects and the air quality in a given study area is more 

straightforward to establish. Therefore, we also consider evidence from single-city, short-term 

exposure studies to inform staff conclusions regarding alternative levels that are appropriate to 

consider for a 24-hour standard that is intended to provide supplemental protection in areas 

where the annual standard may not provide an adequate margin of safety against the effects of all 

short-term PM2.5 exposures. 

As discussed above for the multi-city studies, we look both at the 24-hour PM2.5

concentrations in the single-city studies, focusing on the 98th percentile air quality values, as well 

as the long-term mean PM2.5 concentrations. We consider single-city studies conducted in areas 

that would likely have met the current suite of PM2.5 standards as most useful for informing staff 

conclusions related to the level of the 24-hour standard, as summarized in Figure 2-9.67 We note 

that additional single-city studies summarized in Figure 2-9 were conducted in areas that would 

likely have met one but not both of the current PM2.5 standards.

68 To the extent changes in air 

quality designed to just meet the current suite of PM2.5 standards are undertaken, one could 

reasonably anticipate additional public health protection will occur in these study areas. 

 67 We note there are additional single-city studies discussed in the ISA beyond the short-term exposure studies 

discussed in this section that were conducted in areas that would likely not have met both the current annual and 24-

hour standards (e.g., Ito et al., 2007; Sheppard, 2003; Burnett, 1997). To the extent that changes in air quality 

designed to just meet the current annual and/or 24-hour standard are undertaken, one could reasonably anticipate 

additional public health protection will occur in these study areas For another group of studies, we have no 

information on the distribution of air quality concentrations, specifically information on the upper end of the 

distribution (i.e., the 98th percentile values) (e.g., NYDOH, 2006; Sullivan et al., 2005; Zanobetti and Schwartz, 

2006; Pope et al., 2006; Dockery et al., 2005; Rich et al., 2005; Villeneuve et al, 2003; Fung et al., 2006; Chen et al, 

2004; Chen et al., 2005 Slaughter et al, 2003; Chimonas and Gessner, 2007). Therefore, we do not know whether 

these studies were conducted in areas that would likely have met the current 24-hour PM2.5 standard. Collectively, 

staff concludes these two groups of studies are not helpful to inform staff conclusions regarding alternative standard 

levels that are appropriate to consider. 68 This group of studies included two studies conducted in areas that would likely have met the current annual 

standard but not the current 24-hour standard (Santa Clara, California and the Wasatch Front, UT) that reported 

positive and statistically significant associations between short-term PM2.5 exposures and mortality and 

cardiovascular-related hospitalizations, respectively (Fairley, 2003; Pope et al., 2008) and one study conducted in an 

area that would likely have met the current 24-hour standard but not the current annual standard (Coachella Valley, 

California) that reported no association between short-term PM2.5 exposures and cardiovascular –related mortality 

(Ostro et al., 2003). 
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Figure 2-9. Summary of Effect Estimates (per 10 μg/m3) and Air Quality Distributions for Single-City, 

Exposure Studies
 2-87

Therefore, staff concludes that these studies are not helpful to inform staff conclusions regarding 

alternative standard levels that are appropriate to consider. 

With regard to single-city studies that were conducted in areas that would likely have met 

both the current 24-hour and annual standards, we first look to studies that reported positive and 

statistically significant associations with short-term PM2.5 exposures. In considering this group 

of studies, we note Mar et al. (2003) reported a positive and statistically significant association 

for premature mortality in Phoenix with a long-term mean concentration of 13.5 μg/m3 in 

conjunction with a 98th percentile value of 32.2 μg/m3. To the extent that consideration is given 

to revising the level of the annual standard, within a range of 13 to 11 μg/m3, as discussed above, 

additional protection would be provided for the effects observed in this study. 

Four additional studies reported positive and statistically significant associations with 

98th percentile values within a range of 31.2 to 25.8 μg/m3 and long-term mean concentrations 

within a range of 12.1 to 8.5 μg/m3 (Delfino et al., 1997; Peters et al., 2001; Stieb et al., 2000; 

and Mar et al., 2004). Delfino et al. (1997) reported statistically significant associations between 

PM2.5 and respiratory emergency department visits for older adults (greater than 64 years old) but 

not young children (less than 2 years old), in one part of the study period (summer 1993) but not 

the other (summer 1992). Peters et al. (2001) reported a positive and statistically significant 

association between short-term exposure to PM2.5 (2-hour and 24-hour averaging times) and 

onset of acute MI in Boston.69 Stieb et al. (2000) reported positive and statistically significant 

associations with cardiovascular- and respiratory-related emergency department visits in Saint 

John, Canada, in single pollutant models but not in multi-pollutant models (US EPA, 2004, pp 8-

154 and 8-252 to 8-253). Mar et al. (2004) reported a positive and statistically significant 

association for short-term PM2.5 exposures in relation to respiratory symptoms among children 

but not adults in Spokane, however, this study had very limited statistical power because of the 

small number of children and adults evaluated. 

We then consider short-term single-city PM2.5 exposure studies that reported positive but 

nonstatistically significant associations for cardiovascular and respiratory endpoints in areas that 

would likely have met both the current 24-hour and annual standards. The 98th percentile values 

reported in these studies ranged from 31.6 μg/m3

 to 17.2 μg/m3 and the long-term mean 

concentrations ranged from 13.0 to 7.0 μg/m3. These studies included consideration of 

cardiovascular-related mortality effects in Phoenix (Wilson et al., 2007), asthma medication use 

in children in Denver (Rabinovitch et al., 2006), hospital admissions for hemorrhagic and 

 69 A King County, WA (Seattle) study using similar methods to Peters et al. (2001) reported no association with 

acute onset MI and PM2.5 concentration with a long-term mean concentration of 12.8 μg/m3

 (Sullivan et al., 2005). 

We do not have information on the 98th percentile value for the Seattle study. It is unclear whether the differences 

observed in these two studies were due to regional differences in population characteristics and/or air pollution 

sources/PM2.5 components (US EPA, 2009a, section 6.2.10.4). 
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ischemic stroke in Edmonton, Canada (Villeneuve et al., 2006), and hospital admissions for 

ischemic stroke/transient ischemic attack in Nueces County, TX (Lisabeth et al., 2008). 

Lastly, we consider single-city studies conducted in areas that would likely have met both 

the current 24-hour and annual standards that reported null findings. The 98th percentile values 

reported in these studies ranged from 29.6 μg/m3

 to 24.0 μg/m3 and the long-term mean 

concentrations ranged from 10.8 to 8.5 μg/m3. These studies reported no associations with shortterm PM2.5 exposures and cardiovascular-related hospital admissions and respiratory-related 

emergency department visits (Slaughter et al., 2005) and cardiovascular-related emergency 

department visits (Schreuder et al., 2006) in Spokane; asthma exacerbation in children in Denver 

(Rabinovitch et al., 2004); and hospital admissions for transient ischemic attack in Edmonton, 

Canada (Villeneuve et al., 2006). 

Viewing the evidence as a whole, we observe a limited number of single-city studies that 

reported positive and statistically significant associations for a range of health endpoints related 

to short-term PM2.5 concentrations in areas that would likely have met the current suite of PM2.5

standards. Many of these studies had significant limitations (e.g., limited statistical power, 

limited exposure data) as briefly identified above and discussed in more detail in the ISA (US 

EPA, 2009a, chapter 6). Other studies reported positive but not statistically significant results or 

null associations also in areas that would likely have met the current suite of PM2.5 standards. In 

addition, still other studies (e.g., those conducted in Phoenix, Denver, and Edmonton) reported 

mixed results within the same study areas. Overall, the entire body of results from these singlecity studies is mixed, particularly as 24-hour 98th percentile concentrations go below 35 μg/m3

. 

Although a number of single-city studies report effects at appreciably lower PM2.5

concentrations than multi-city short-term exposure studies, the uncertainties and limitations 

associated with the single-city studies were greater and, thus, we have less confidence in using 

these studies as a basis for setting the level of a standard. Therefore, we conclude that the multicity short-term exposure studies provide the strongest evidence to inform decisions on the level 

of the 24-hour standard, and the single city studies do not warrant consideration of 24-hour levels 

different from those supported by the multi-city studies. 

In addition to considering the epidemiological evidence, we look to air quality 

information based on county-level 24-hour and annual design values to understand the 

implications of the alternative standard levels supported by the currently available scientific 

evidence, as discussed above. As outlined in section 2.1.3, we recognize that changes in PM2.5

air quality designed to meet an annual standard would likely result not only in lower annual 

mean concentrations but also in fewer and lower peak 24-hour concentrations. We also 

recognize that changes designed to meet a 24-hour standard would result not only in fewer and 

lower peak 24-hour concentrations (especially when coupled with a high percentile-based form, 
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such as the 98th percentile) but also in lower annual mean concentrations. As discussed in 

section 2.1.3, we conclude that a policy goal which includes setting the annual standard to be the 

“generally controlling” standard in conjunction with setting the 24-hour standard to provide 

supplemental protection to the extent that additional protection is warranted, is the most effective 

and efficient way to reduce total population risk associated with both long- and short-term PM2.5

exposures, resulting in more uniform protection across the U.S than the alternative of setting the 

24-hour standard to be the controlling standard. Therefore, we consider the extent to which 

different combinations of alternative annual and 24-hour standard levels based on the evidence 

would support this policy goal. 

Figure 2-10 presents a scatter plot of annual and 24-hour design values in urban areas 

across the U.S., color-coded by geographic region. This figure provides a visual perspective of 

whether the annual or 24-hour standard is likely to be the controlling standard for various 

combinations of standards. In Figure 2-10, the horizontal lines represent alternative 24-hour 

standard levels (i.e., 35 or 30 μg/m3

) with a 98th percentile form, averaged over three years, while 

the vertical lines represent alternative annual standard levels (i.e., 13, 12, or 11 μg/m3

), using an 

annual arithmetic mean averaged over three years. To understand this figure, we note that there 

are four quadrants that distinguish counties that are likely to have met or not met one or both of 

the PM2.5 standards. The lower left quadrant characterizes counties that would likely meet both

the annual and 24-hour standards. The lower right quadrant characterizes counties that would 

likely meet the 24-hour standard but not the annual standard, representing counties where the 

annual standard would be the controlling standard. The upper left quadrant characterizes 

counties that would likely exceed the 24-hour standard but not the annual standard, representing 

counties where the 24-hour standard would be the controlling standard. Finally, the upper right 

quadrant characterizes counties that would likely exceed both the annual and 24-hour standards. 

In this quadrant, the diagonal lines that intercept the origin and the intersection of a suite of 

alternative standard levels (e.g., the “11/35” line) represents the point of demarcation between 

those counties for which the 24-hour standard would be controlling (to the left of the diagonal 

line) and those for which the annual standard would be controlling (to the right of the diagonal 

line). More specifically, for counties to the left of the diagonal line, 24-hour concentrations 

would need to be reduced by a greater proportion to meet the 24-hour standard than the 

proportional reduction needed in the annual average concentrations to meet the annual standard. 
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Figure 2-10. County-level 24-hour DVs versus Annual DVs, 2007-2009 

Source: S
 2-91

Using information on the relationship of the 24-hour and annual design values, we 

examine the implications of three alternative suites of PM2.5 standards identified as appropriate 

to consider based on the currently available scientific evidence, as discussed above. As seen in 

Figure 2-10, an alternative suite of PM2.5 standards that would include an annual standard level 

of 11 or 12 μg/m3

and a 24-hour standard with a level of 35 μg/m3 (i.e., 11/35 or 12/35), would 

result in the 24-hour standard as the generally controlling standard in the Northwest (i.e., 

counties identified as light blue dots in Figure 2-10) but not generally in other regions across the 

country. These Northwest counties generally represent areas where the annual mean PM2.5

concentrations have historically been low but where relatively high 24-hour concentrations 

occur, often related to seasonal wood smoke emissions. Alternatively, combining an alternative 

annual standard of 13 μg/m3

 with a 24-hour standard of 30 μg/m3 would result in many more 

areas across the country in which the 24-hour standard would likely become the controlling 

standard than if an alternative annual standard of 12 or 11 μg/m3

 were paired with the current 

level of the 24-hour standard (i.e., 35 μg/m3

). This can be seen by comparing the area to the left 

of the “13/30” line to the areas to the left of the “12/35” and “11/35” lines, in particular in the 

upper right quadrant in Figure 2-10, which represents the regions on the graph in which the 24-

hour standard would likely be controlling. 

Summary of Evidence-based Considerations to Inform the 24-Hour Standard Level

In considering the currently available scientific information and air quality information 

within the context of identifying potential alternative standard levels for consideration we have 

identified a range of alternative 24-hour standard levels that is appropriate to consider based on 

the general approach outlined in section 2.1.3. Our general approach for identifying alternative 

24-hour standard levels that would protect against effects observed in epidemiological studies 

has focused on setting a level somewhat below the 98th percentile values where associations have 

been observed in epidemiological studies, particularly in areas that would meet alternative 

annual standard levels discussed above. In considering the available evidence, staff concludes it 

is appropriate to consider either retaining the level of the current 24-hour standard at 35 μg/m3 or 

revising the level to somewhat below 35 μg/m3, such as, down to 30 μg/m3. 

Consideration for retaining the level at 35 μg/m3

 would reflect placing greatest weight on 

evidence from multi-city studies that reported positive and statistically significant associations 

with health effects classified as having a causal or likely causal relationship. In conjunction 

with lowering the annual standard level, especially within a range of 12 to 11 μg/m3, this 

alternative would recognize additional public health protection for effects associated with shortterm PM2.5 exposures would be provided by lowering the annual standard such that revision to 

the 24-hour standard would not be warranted. In addition, such combinations of 24-hour and 
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annual standards levels (i.e., 11/35 and 12/35) would be expected to result in the annual standard 

being the generally controlling standard, except in areas with particularly high peak-to-mean 

ratios (e.g., Northwest), with the annual standard providing protection against PM2.5-related 

health effects associated with long- and short-term exposures, in conjunction with the 24-hour 

standard providing appropriate supplemental protection. These combinations would likely result 

in providing more uniform protection across the U.S than if the 24-hour standard were the 

generally controlling standard. 

An alternative approach to considering the evidence would recognize that the currently 

available information provides some support for revising the level below 35 μg/m3, perhaps as 

low as 30 μg/m3. This alternative 24-hour standard level would be more compatible with an 

alternative annual standard of 13 μg/m3

 based on placing greater weight on one multi-city shortterm exposure study (Bell et al., 2008) that reported positive and statistically significant effects at 

a 98th percentile value less than 35 μg/m3

 (i.e., 34.2 μg/m3) in conjunction with a long-term mean 

concentration less than 13 μg/m3

 (i.e., 12.9 μg/m3). However, in considering the implications of 

this combination (i.e., 13/30), staff recognizes that this option would likely result in the 24-hour 

standard being the controlling standard in a large number of areas in many geographic regions, 

potentially resulting in more uneven public health protection across the U.S. than if the annual 

standard was the generally controlling standard. 

Taken together, while we consider it appropriate to consider an alternative 24-hour 

standard level within a range of 35 to 30 μg/m3

, staff concludes that the currently available 

evidence most strongly supports consideration for retaining the current 24-hour standard level at 

35 μg/m3 in conjunction with lowering the level of the annual standard within a range of 12 to 11 

μg/m3. 

2.3.4.2 Risk-based Considerations 

Beyond looking directly at the relevant epidemiologic evidence, staff has also considered 

the extent to which specific levels of alternative PM2.5 standards are likely to reduce both longterm exposure-related mortality risk and short-term exposure-related mortality and morbidity 

risk. In addition to considering the nature and magnitude of PM2.5-attributable risk remaining 

under each set of alternative standards, we have also considered the nature and magnitude of risk 

reductions under the alternative standards considered. These risk estimates for the set of 

alternative standard levels considered are based on the same methodology used in estimating risk 

for the current suite of standard levels as discussed above in section 2.2.2. 

The quantitative risk assessment initially included analyses of alternative annual standard 

levels of 14, 13, and 12 μg/m3 paired with either the current 24-hour standard level of 35 μg/m3

or with alternative 24-hour standard levels of 30 and 25 μg/m3. The specific combinations of 
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alternative standard levels assessed in the quantitative risk assessment included: (a) alternative 

suites of standards focusing on alternative annual standard levels in conjunction with the current 

24-hour standard including combinations denoted by 14/35, 13/25, and 12/35and (b) alternative 

suites of standards reflecting combinations of alternative annual and 24-hour standard levels 

including combinations denoted by 13/30 and 12/25. This set of alternative annual and 24-hour 

standard levels was chosen prior to completion of the first draft RA and reflects consideration for 

evidence related to potential PM2.5-related health effects as presented in the Integrated Science 

Assessment for Particulate Matter: Second External Review Draft (2nd draft ISA) released in 

July, 2009 (US EPA, 2009b). The presentation of that evidence was refined in subsequent 

iterations of the PM ISA, culminating in the Final ISA released in December 2009 (US EPA, 

2009a). The range of alternative standard levels discussed in section 2.3.4.1 above (i.e., annual 

standard levels within a range of 13 to 11 μg/m3 and 24-hour standard levels within a range of 35 

to 30 μg/m3), reflects consideration of evidence as presented in the Final ISA and consequently 

differs somewhat from the set of alternative standard levels originally selected for modeling in 

the quantitative risk assessment. Subsequent to the release of the second draft RA (US EPA, 

2010d), we expanded the range of alternative annual standard levels evaluated in the final RA to 

include an alternative annual standard level of 10 μg/m3 and developed risk estimates for two 

additional combinations of alternative standards – 10/35 and 10/25 (US EPA, 2010a, Appendix 

J). 

In simulating ambient PM2.5 levels associated with these alternative standard levels, we 

included a more regional spatial pattern of reductions (reflected in the use of a proportional 

rollback approach) as well as more localized spatial patterns of reductions (reflected in the use of 

a hybrid approach and to an even greater extent in the use of a locally focused rollback approach) 

(see U.S. EPA, 2010a, section 3.2.3). While the proportional rollback approach was used in 

generating the core risk estimates, the other two more localized rollback approaches were 

considered as part of sensitivity analyses. 

Results for the alternative suites of standards considered are presented in Figures 2-11 and 2-12, 

which depict patterns in risk reduction for long-term exposure-related risk (Figure 2-11) and 

short-term exposure-related risk (Figure 2-12) using different combinations of alternative 

standard levels relative to the estimated risks related to simulating just meeting the current 

standards. These figures include results for each of the 15 urban study areas, thereby allowing 

patterns in risk reduction across alternative standard levels and urban study areas to be 

considered together.70 The discussion below of the magnitude of risk remaining under simulated 

 70 Patterns of risk reduction across alternative annual standard levels (in terms of percent change relative to risk for 

the current annual standard level) are similar for all health endpoints modeled for a particular exposure duration (i.e., 

patterns of percent risk reduction will be similar for long-term exposure related all-cause, IHD-related and 
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attainment of the alternative standard levels is based on risk estimates presented in U.S.EPA, 

2010a, section 4.2.2. The figures also depict the level of confidence, also discussed below, 

associated with risk estimates generated for the current suite of standard levels as well as 

alternative standard levels considered. 

 What is the nature and magnitude of risk associated with just meeting the alternative 

annual PM2.5 standards considered? 

In characterizing estimates of PM2.5-related risk associated with simulation of the 

alternative annual standards combined with the current 24-hour standard level (i.e., 14/35, 13/35 

and 12/35), we estimated both the magnitude of risk reductions (relative to risk remaining upon 

just meeting the current suite of standards), as well as the risk estimated to remain upon just 

meeting the alternative annual standard levels.71 We have greater overall confidence in our 

estimates of risk reduction with simulation of just meeting an alternative standard level (relative 

to risk associated with simulation of just meeting the current standards) than for estimates of 

absolute risk remaining upon simulation of just meeting that alternative standard level. In the 

context of long-term exposure-related mortality, this greater confidence primarily reflects the 

fact that we modeled risk down to the LML of the epidemiological study providing C-R 

functions (Krewski et al., 2009).72 While this introduces the potential for underestimating the 

absolute risk remaining upon simulation of just meeting an alternative standard level (since the 

portion or exposure associated with ambient PM2.5 concentrations below the LML is not 

translated into risk), it has little to no effect on the simulation of risk reduction for that alternative 

standard level, relative to risk estimated for the current standards (since this calculation involves 

ambient PM2.5 concentrations that are typically above the LML and are therefore translated into 

risk). In the context of short-term exposure-related mortality and morbidity, while we modeled 

risk down to PRB, we still have greater uncertainty in estimating absolute risk remaining since 

this requires extrapolation of the C-R function to cover lower ambient PM2.5 concentrations, 

 

cardiopulmonary-related mortality). This reflects the fact that the C-R functions used in the quantitative risk 

assessment are close to linear across the range of ambient air concentrations evaluated. However, estimated 

incidence will vary by health endpoint. 71 Our analysis also included the assessment of risk associated with an alternative annual standard level of 10 

μg/m3

.US EPA, 2010a, Appendix J). 72 As discussed in section 3.1.1 of the RA (U.S. EPA, 2010a), we did not model long-term exposure-related risk 

below the LML due to concerns over uncertainty in extrapolating the C-R functions below the range of PM2.5

concentrations reflected in the epidemiological study providing those C-R functions. However, as stated in the ISA, 

there is no evidence for a discernible threshold (US EPA, 2009a, section 2.4.3) and our decision not to estimate risk 

below the LML should not be construed as support for the existence of a threshold. Therefore, the decision not to 

estimate risk below the LML, while reasonable given our desire to generate higher-confidence estimates of risk, 

does introduce low-bias into the estimate of absolute risk remaining upon simulation of each of the alternative 

standard levels considered, since evidence suggests that risk does extend to long-term PM2.5 concentrations below 

the LML. 
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Figure 2-11 Percent reduction in long-term exposure-related mortality risk (alternative standards relative to the 

 (Note: inset shows PM2.5 related incidence and percent of total incidence for IHD mortality under the current suite of 

*Based on Krewski et al. (2009), exposure period from 1999 – 2000. The legend contains, for each urban area, the incidence estimate (and 95% CI) antotal incidence (and 95% CI) under the current standards. While incidence and percent of total incidence estimates are provided specifically for IHD-r

reduction plots provided in the figure apply to all long-term exposure-related mortality categories assessed – see text. **The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a dai

n/m in this figure. Note, that the percent reductions for Salt Lake City and Tacoma at the 12/25 standard are 100% and 93%, respectively. 

***Level of confidence reflects consideration for how the composite monitor annual mean PM2.5 concentrations used in generating the risk estimates c

ambient PM2.5 concentrations considered in fitting the C-R functions used (see accompanying text for additional detail). 
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Figure 2-12. Percent Reduction in Short-term Exposure-related Mortality and Morbidity Risk (alternative sta

current standards) (Note: inset shows PM2.5 related incidence and percent of total incidence for CV mortality under th

standards*) 

*Based on Zanobetti and Schwartz (2009). The legend contains, for each urban area, the incidence estimate (and 95% CI) and the estimate of percent CI) under the current standards. While incidence and percent of total incidence estimates are provided specifically for CV-related mortality, the perce

the figure apply to all short-term exposure-related mortality and morbidity categories assessed – see text. **The current standards consist of an annual standard of 15 μg/m3 and a daily standard of 35 μg/m3. Combinations of an annual standard (n) and a 24-

denoted n/m in this figure. *** Although short-term exposure-related risk estimates differ from long-term exposure-related risk estimates in that the former are estimated down to 

general observations regarding the level of confidence in risk estimates related to the composite monitor PM2.5 levels involved in risk estimation (i.e., 

associated with lower composite monitor values) generally apply in the case of short-term exposure-related mortality and morbidity estimates. 
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relative to the estimation of risk reduction, which involves assessing risk for higher ambient 

PM2.5 concentrations. 

Because we have greater confidence in our estimates of the magnitude of risk reduction 

associated with alternative standard levels, we emphasize this risk metric in discussing risk 

estimates below. While we do present estimates of absolute risk remaining for each of the 

alternative standard levels, this is done with the caveat that, particularly in the context of longterm exposure-related mortality, these estimates may be biased low. 

In discussing the estimated risks, we focus on the set of urban study areas experiencing 

risk reductions under each alternative annual standard modeled. Key policy-relevant 

observations associated with these risk estimates include: 

 Magnitude of estimated reductions in long-term exposure-related mortality risk: 

Upon simulation of just meeting the alternative annual standard levels considered (14, 

13, and 12 μg/m3) in conjunction with the current 24-hour standard (35 μg/m3, 

denoted as alternative suites of standards of 14/35, 13/35 and 12/35), the core analysis 

estimated reductions in long-term exposure-related mortality for 12 of the 15 urban 

study areas, with the degree of estimated risk reduction increasing incrementally 

across the alternative standard levels (both in terms of the number of study areas 

experiencing risk reduction and the magnitude of those reductions). For the 

alternative annual standard level of 12 μg/m3

 (in conjunction with the current 24-hour 

standard), the core analysis estimates that these study areas have reductions in risk 

(relative to risk remaining upon just meeting the current suite of standards) ranging 

from about 11 to 35%. 

For some of those areas in which the 24-hour standard is the generally controlling 

standard, larger risk reductions would have been estimated in this case (i.e., 12/35) if 

the locally-focused rollback approach had been used to simulate just meeting the 

current suite of standards. This result would be expected since the magnitude of risk 

remaining upon just meeting the current suite of standards would have been higher 

than that estimated based on the proportional rollback approach used in the core 

analysis. Therefore, while the absolute risks would not change, the percentage 

difference would have been greater if we had started with higher risks related to 

simulation of just meeting the current annual standard. 

 Long-term exposure-related mortality risk remaining: For an annual standard level 

of 14 μg/m3, the percent of total incidence of long-term exposure-related IHD 

mortality attributable to PM2.5 (i.e., risk remaining) in the 5 urban study areas 

experiencing risk reductions ranged from an estimate of 9 to 15%. For an alternative 

annual standard of 12 μg/m3

, estimated risk remaining in the 12 urban study areas 

experiencing risk reductions ranged from 6 to 11% in terms of PM2.5-attributable 

long-term exposure-related mortality. This translates into estimates of between 90 

and 300 cases per year attributable to long-term PM2.5 exposure for those study areas 

experiencing the greatest reductions in risk under the lowest alternative annual 

standard level simulated. 
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 Short-term exposure-related mortality and morbidity risk: For the alternative annual 

standard level of 12 μg/m3 (in conjunction with the current 24-hour standard, 12/35), 

the core analysis estimated that reductions in both short-term exposure-related 

cardiovascular-related mortality and morbidity risk ranged from 5 to 23%.73 In terms 

of risk remaining upon simulation of 12 μg/m3 (in conjunction with the current 24-

hour standard), the urban study areas with the greatest percent reduction have CVrelated mortality estimates ranging from 25 to 50 deaths per year. 

 Simulation of risks for an alternative annual standard level below 12 μg/m3

: 

Simulation of risks for an alternative annual standard of 10 μg/m3

 suggested that 

additional risk reductions could be expected with alternative annual standards below 

12 μg/m3. However, we recognize that there is potentially greater uncertainty 

associated with these risk estimates compared with estimates generated for the higher 

alternative annual standards considered in the quantitative risk assessment, since 

these estimates require simulation of relatively greater reductions in ambient PM2.5

concentrations. As lower ambient PM2.5 concentrations are simulated (i.e., ambient 

concentrations further from recent conditions), potential variability in such factors as 

the spatial pattern of ambient PM2.5 reductions (rollback) increases, thereby 

introducing greater uncertainty into the simulation of composite monitor annual mean 

PM2.5 concentrations and, consequently, risk estimates (US EPA, 2010a, Appendix J). 

 Substantial variability in magnitude of estimated risk reduction across urban study 

areas: While there is a consistent pattern of estimated risk reduction across the 

alternative annual standards with lower alternative standard levels resulting in more 

urban study areas experiencing increasingly larger risk reductions, there is 

considerable variability in the magnitude of these reductions across study areas for a 

given alternative annual standard level. This variability reflects differing degrees of 

reduction in annual mean concentrations across the study areas, which results, in part, 

because the study areas began with varying annual mean PM2.5 concentrations after 

simulating just meeting the current suite of standards. Therefore, even if study areas 

have similar “ending” annual mean PM2.5 concentrations after simulation of just 

meeting a specific alternative annual standard, because the starting point in the 

calculation (the annual mean PM2.5 concentrations upon just meeting the current suite 

of standards) can be variable, the overall reduction in annual mean PM2.5

concentrations across the standards can also be variable. This translates into variation 

in reductions in long-term exposure-related risk upon just meeting alternative annual 

standard levels across the study areas. 

 The nature of the spatial pattern in PM2.5 reductions (reflected in the rollback 

approach used) can impact the magnitude of estimated risk reductions: The 

sensitivity analysis involving application of the locally focused rollback approach 

revealed that the pattern of reductions in ambient PM2.5 concentrations upon just 

meeting the current suite of standards can impact the magnitude of additional risk 

reductions estimated for just meeting alternative (lower) annual standard levels. 

 73 Because the same air quality metric (annual distributions of 24-hour PM2.5 concentrations) was used in generating 

short-term exposure-related mortality and morbidity endpoints, patterns of risk reduction (as a percent of risk under 

the current suite of standards) are similar for both sets of endpoints (see US EPA, 2010a, section 4.2.2). 
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Specifically, for those study areas with higher peak-to-mean PM2.5 ratios, application 

of the locally focused rollback approach resulted in higher annual mean PM2.5

concentrations remaining upon just meeting the current suite of standards. If a 

proportional rollback approach was then used to simulate just meeting alternative 

annual standard levels, a greater degree of reduction in composite monitor annual 

mean PM2.5 concentrations will result, since the starting point for the calculation 

(annual mean PM2.5 concentrations upon just meeting the current suite of standards) 

would be higher. These findings highlight the important role played by variability in 

the spatial pattern of ambient PM2.5 concentrations in influencing the magnitude of 

risk reductions under alternative annual standard levels. 

 Based on consideration of the composite monitor annual mean PM2.5 concentrations 

involved in estimating long-term exposure-related mortality, we have varying levels 

of confidence in risk estimates generated for the alternative annual standard levels 

considered: With the exception of one study area, those study areas estimated to have 

risk reductions under the alternative annual standards of 14 and 13 μg/m3 had 

simulated composite monitor annual mean PM2.5 concentrations ranging from just 

below 10.6 to over 13.3 μg/m3

 (see US EPA, 2010a, Table 3-4). In other words, 

these composite monitor annual mean PM2.5 concentrations generally fell well within 

the range of ambient PM2.5 concentrations considered in fitting the C-R functions 

used (i.e., within one SD of the mean PM2.5 concentration from 1999-2000 ACS 

dataset, Krewski et al., 2009). The urban study areas estimated to have risk 

reductions under the lower alternative annual standard level of 12 μg/m3 had lower 

composite monitor annual mean values ranging from 9.0 to over 11.4 μg/m3. These 

values generally extend to below one SD of the mean of the ACS dataset and 

therefore, we have somewhat lower confidence in these risk estimates, relative to 

those generated for the higher alternative annual standards. By contrast, urban study 

areas estimated to have risk reductions under the alternative standard level of 10 

μg/m3 (paired with the current 24-hour standard) had simulated composite monitor 

annual estimates ranging from 7.6 to 8.9 μg/m3 (see US EPA, 2010a, Appendix J). 

These concentrations are towards the lower end of the range of ACS data used in 

fitting the C-R functions (in some cases approaching the LML) and, therefore, we 

have substantially less confidence in these risk estimates, compared with those for the 

higher alternative annual standards assessed. The levels of confidence associated 

with risk estimates generated for the suites of alternative standard levels considered in 

the quantitative risk assessment are depicted in Figure 2-11. These confidence levels 

are repeated in presenting risk estimates for short-term exposure-related mortality and 

morbidity in Figure 2-12, since the general observation that confidence in risk 

estimates decreases as we consider lower composite monitor annual mean values also 

holds for the short-term exposure-related health endpoints. 

 What is the nature and magnitude of risk associated with simulating different 

combinations of alternative annual and 24-hour PM2.5 standards? 

In characterizing PM2.5-related risks associated with simulation of alternative annual 

standards combined with alternative 24-hour standards (i.e., 13/30 and 12/25), we estimated both 

the magnitude of risk remaining upon just meeting these alternative standards, as well as the 
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magnitude of risk reductions (relative to risk remaining upon just meeting the current suite of 

standards). While the alternative 24-hour standard levels considered did result in estimated risk 

reductions, we have substantially lower confidence in these risk estimates because of the 

relatively low annual-average PM2.5 concentrations associated with simulation of these 

alternative standard levels. 

Of the 11 urban study areas estimated to have risk reductions under the alternative 24-

hour standard of 30 μg/m3 (with the 24-hour standard controlling – see US EPA 2010a, Table 3-

4), composite monitor annual mean PM2.5 concentrations ranged from 6.6 to 11.3 μg/m3 with 

most of the urban study areas having concentrations in the 8 to 10 μg/m3 range. These 

concentrations extend into the lower range of PM2.5 concentrations considered in the ACS study 

to fit the C-R functions and therefore, we have substantially lower confidence in these estimates. 

When we consider composite monitor concentrations for urban study areas assessed to 

have risk reductions under the alternative 24-hour standard level of 25 μg/m3 (again, where the 

24-hour standard is controlling), we observed composite monitor annual mean PM2.5

concentrations that are even lower, ranging from 5.6 to 11.2 μg/m3

 with most study areas having 

concentrations in the range of 7 to 9 μg/m3. Because this range extends well into the lower range 

of PM2.5 concentrations considered in the ACS study to fit the C-R functions (in some cases 

extending below the LML), we have substantially lower confidence in these risk estimates. 

Furthermore, we find that those urban study areas with the greatest degree of estimated risk 

reduction under these alternative 24-hour standard levels also had the lowest composite monitor 

annual average PM2.5 levels, and therefore we have the lowest overall confidence in these results. 

2.3.4.3 CASAC Advice 

Based on its review of the second draft PA, CASAC concluded that the levels presented 

in that draft document (i.e., alternative annual standard levels within a range of 13 to 11 μg/m3

and alternative 24-hour standard levels within a range of 35 to 30 μg/m3) “are supported by the 

epidemiological and toxicological evidence, as well as by the risk and air quality information 

compiled” in the ISA, RA, and second draft PA. CASAC further noted that “[a]lthough there is 

increasing uncertainty at lower levels, there is no evidence of a threshold (i.e., a level below 

which there is no risk for adverse health effects)” (Samet, 2010d, p. ii). 

Although CASAC supported the alternative standard level ranges presented in the second 

draft PA, they encouraged EPA to develop a clearer rationale for staff conclusions regarding 

annual and 24-hour standards that are appropriate to consider, including consideration of the 

combination of these standards supported by the available information. Specifically, CASAC 

encouraged staff to focus on information related to the concentrations that were most influential 

in generating the health effect estimates in individual studies to inform alternative annual 
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standard levels (Samet, 2010d, p. 2). CASAC also commented that the approach presented in the 

second draft PA to identify alternative 24-hour standard levels which focused on peak-to-mean 

ratios was not relevant for informing the actual level (Samet 2010d, p. 4). Further, they 

expressed the concern that the combinations of annual/24-hour standard levels discussed in the 

second draft PA (i.e., in the range of 13 to 11 μg/m3 for the annual standard, in conjunction with 

retaining the current 24-hour PM2.5 standard level of 35 μg/m3; alternatively, revising the level of 

the 24-hour standard to 30 μg/m3 in conjunction with an annual standard level of 11 μg/m3) 

“may not be adequately inclusive” and encouraged EPA to more clearly explain its rationale for 

identifying the 24-hour/annual combinations that are appropriate for consideration (Samet 

2010d, p. ii). 

In considering CASAC’s advice, we note that staff conclusions in this final PA regarding 

alternative standard levels that are appropriate to consider differ somewhat from the alternative 

standard levels discussed in the second draft PA (US EPA, 2010f, section 2.3.4), upon which 

CASAC based its advice. In commenting on draft staff conclusions in the second draft PA, 

CASAC did not have an opportunity to review the analyses of distributional statistics conducted 

by staff to identify the broader range of PM2.5 concentrations that were most influential in 

generating health effect estimates in epidemiological studies (Rajan et al., 2011), as presented in 

section 2.3.4.1 above. In addition, CASAC was not aware of the revised long-term mean 

concentration in the WHI study as discussed in section 2.2.1, specifically, footnote 24. 

2.3.4.4 Staff Conclusions on Alternative Standard Levels 

In considering the epidemiological evidence, estimates of risk reductions associated with 

just meeting alternative annual and/or 24-hour standards, air quality analyses, and related 

limitations and uncertainties, staff concludes that there is clear support for considering revisions 

to the suite of current PM2.5 standards to provide additional protection against health effects 

associated with long- and short-term exposures. We recognize that health effects may occur over 

the full range of concentrations observed in the long- and short-term epidemiological studies and 

that no discernible threshold for any effects can be identified based on the currently available 

evidence. In reaching staff conclusions regarding appropriate alternative standard levels to 

consider, we have examined where the evidence of associations is strongest and, conversely, 

where we have appreciably less confidence in the associations and in quantitative estimates of 

risk. 

Based upon the currently available evidence, we conclude alternative annual standard 

levels in the range of 13 to 11 μg/m3

 are appropriate to consider. We further conclude that the 

evidence most strongly supports consideration of an alternative annual standard level in the 

range of 12 to 11 μg/m3. An alternative level within the range of 12 to 11 μg/m3 would more 
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fully take into consideration the available information from all long- and short-term PM2.5

exposure studies, including studies of susceptible populations, than would a higher level. This 

range would also reflect placing weight on information from studies that helps to characterize the 

range of PM2.5 concentrations over which we continue to have confidence in the associations 

observed in epidemiological studies, as well as the extent to which our confidence in the 

associations is appreciably less at lower concentrations. 

In considering how the annual and 24-hour standards work together to provide 

appropriate public health protection, we conclude it is appropriate to consider retaining the 

current 24-hour standard level at 35 μg/m3 in conjunction with an annual standard in the range of 

12 to 11 μg/m3, as well as to consider an alternative 24-hour standard level of 30 μg/m3, 

particularly in conjunction with an annual standard level of 13 μg/m3

. 

These conclusions reflect the much stronger body of scientific evidence available in this 

review supporting a causal relationship between long- and short-term PM2.5 exposures and 

mortality and cardiovascular effects and a likely causal relationship between long- and shortterm PM2.5 exposures and respiratory effects, as well as evidence that is suggestive of a causal 

relationship with other health outcomes such as low reproductive and development effects (e.g., 

birth weight and infant mortality) and cancer, mutagenicity, and genotoxicity effects. In 

addition, we reflect upon the broad range of long- and short-term exposure studies that reported 

PM2.5-related effects in areas that would likely have met the current suite of PM2.5 standards, and 

specifically consider available information on the range of concentrations that were most 

influential in generating the health effect estimates in epidemiological studies. 

Beyond evidence-based considerations, we have also considered the extent to which the 

quantitative risk assessment supports consideration of these alternative standard levels or 

provides support for lower levels. We first conclude that risks estimated to remain upon 

simulation of just meeting the current suite of standards are important from a public health 

perspective, considering both the severity and estimated magnitude of effects. In considering 

simulations of just meeting alternative annual standard levels within the range of 13 to 11 μg/m3

in conjunction with the current 24-hour standard level of 35 μg/m3, we conclude that important 

public health improvements are associated with risk reductions estimated for standard levels of 

13 and 12 μg/m3

, noting that the level of 11 μg/m3 was not included in the quantitative risk 

assessment. Our overall confidence in the quantitative risk estimates is strongest for the 

alternative annual standard level of 13 μg/m3

. We have somewhat lower confidence in risk 

estimates for the alternative annual standard level of 12 μg/m3

. We also estimated risks likely to 

remain upon just meeting an annual standard level of 10 μg/m3

, although we have substantially 

lower confidence in those estimates. With regard to level of the 24-hour standard, our overall 

confidence in the quantitative risk estimates is strongest for the current standard level of 35 
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μg/m3. We have somewhat lower confidence in our risk estimates for an alternative 24-hour 

standard level of 30 μg/m3, and substantially lower confidence in our estimates of risks for an 

alternative 24-hour standard level of 25 μg/m3. 

Based on the above considerations, we conclude that the quantitative risk assessment 

provides support for considering an alternative annual standard within a range of 13 to 11 μg/m3, 

in conjunction with a 24-hour standard of 35 or 30 μg/m3, but does not provide strong support 

for considering lower alternative levels. 

In evaluating this range of alternative annual standard levels, staff has taken into 

consideration the importance of balancing the strength of the currently available evidence and 

risk-based information with the remaining uncertainties and limitations associated with this 

information. The upper end of the range of alternative annual standard levels (13 μg/m3

) reflects 

placing appreciably more weight on the uncertainties and limitations in the information which 

would serve to reduce the potential to overestimate public health risks and protection likely to be 

associated with just meeting a standard set at this level. This policy option would reflect placing 

greater weight on the remaining uncertainties in the evidence, including uncertainties associated 

with understanding the heterogeneity observed in the epidemiological studies such as those 

associated with the role of specific components, sources, and subfractions (e.g., UFPs) within the 

current PM2.5 mass-based indicator; the role of fine particles and co-pollutants within the broader 

ambient mixture; and exposure-related factors that influence the magnitude and duration of fine 

particle exposures. The lower end of this range (11 μg/m3

) reflects placing much less weight on 

uncertainties and limitations in the information which would serve to reduce the potential to 

underestimate public health risks and protection likely to be associated with just meeting a 

standard set at this level. This policy option would reflect placing considerably more weight on 

limited evidence of serious effects in susceptible populations such as potential developmental 

effects, while recognizing that significant limitations remain in assessing the relationship 

between PM2.5 exposures and these effects, specifically, understanding the nature of the 

association and exposure windows of concern. We recognize that air quality changes designed 

to meet alternative annual standards are an effective and efficient way to reduce not only longterm exposure-related mortality, but also short-term exposure-related risk. Thus, we judge it is 

appropriate to consider a policy goal of focusing on establishing a generally controlling annual 

standard intended to serve as the primary means for providing protection for effects associated 

with both long- and short-term PM2.5 exposures in conjunction with a 24-hour standard that 

provides supplemental protection. 

The alternative suites of PM2.5 standards supported by the currently available evidence 

and quantitative risk assessment discussed above might reasonably be judged to provide 

appropriate public health protection. However, some combinations of standards are more likely 
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to provide more consistent protection, whereas other combinations are more likely to provide 

less uniform protection. Combining a need to provide requisite protection with a policy goal of 

providing such protection as uniformly as possible in areas across the U.S., we conclude it is 

most appropriate to consider revising the annual standard level within a range of 12 to 11 μg/m3 

in conjunction with retaining the 24-hour standard level at 35 μg/m3. Such combinations of 24-

hour and annual standards levels (i.e., 12/35 and 11/35) would be expected to result in the annual 

standard being the generally controlling standard, except in areas with particular high peak-tomean ratios. 

Alternatively, we also reach the conclusion that there is limited support for considering 

revising the annual standard level to 13 μg/m3 in conjunction with revising the 24-hour standard 

level to somewhat below 35 μg/m3

, such as, down to 30 μg/m3 (i.e., 13/30). In considering the 

implication of this alternative suite of standards, we conclude this combination would result in a 

large number of areas in many geographic areas where the 24-hour standard would likely 

become the controlling standard. Staff judges that this approach would likely provide less 

uniform protection in areas across the U.S. 

To provide some perspective on the implications of applying various combinations of 

alternative annual and 24-hour standards, staff assessed (based on 2007 to 2009 air quality data) 

the percentage of counties, and the population in those counties, that would not likely attain 

various alternative suites of PM2.5 standards (Schmidt, 2011a, Analysis C). This assessment, 

shown in Appendix C, Table C-1, was not considered as a basis for the above staff conclusions. 

2.4 SUMMARY OF STAFF CONCLUSIONS ON PRIMARY FINE PARTICLE 

STANDARDS 

In reaching conclusions on the adequacy of the current suite of PM2.5 standards and 

potential alternative suites of standards to provide the appropriate protection for health effects 

associated with long- and short-term fine particle exposures, staff has considered these standards 

in terms of the basic elements of the NAAQS: indicator, averaging time, form, and level 

(sections 2.3.1 to 2.3.4). In considering the scientific and technical information, we reflect upon 

the information available in the last review integrated with information that is newly available as 

assessed and presented in the ISA and RA (US EPA, 2009a; US EPA, 2010a) and as summarized 

in sections 2.2 and 2.3. We also consider the issues raised by the court in its remand of the 

primary annual PM2.5 standard as discussed in section 2.1.2. 

As outlined in section 2.1.3, our approach to reaching conclusions about the adequacy of 

the current suite of PM2.5 standards and potential alternative standards that are appropriate for 

consideration is broader and more integrative than approaches used in past reviews. Our 

approach integrates a much expanded body of health effects evidence, more extensive air quality 
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data and analyses, and a more comprehensive quantitative risk assessment, and considers the 

combined protection against PM2.5-related mortality and morbidity effects associated with both 

long- and short-term exposures afforded by the suite of annual and 24-hour standards. 

We recognize that selecting from among alternative suites of standards will necessarily 

reflect consideration of the qualitative and quantitative uncertainties inherent in the relevant 

evidence and in the assumptions that underlie the quantitative risk assessment. In reaching staff 

conclusions on alternative suites of standards that are appropriate to consider, we are mindful 

that the CAA requires primary standards to be set that are requisite to protect public health with 

an adequate margin of safety, such that the standards are to be neither more nor less stringent 

than necessary. Thus, the CAA does not require that the NAAQS be set at zero-risk levels, but 

rather at levels that reduce risk sufficiently so as to protect public health with an adequate margin 

of safety (section 1.2.1). 

Based on the currently available scientific evidence and other information, staff reaches 

the following conclusions regarding the primary fine particle standards: 

(1) Consideration should be given to revising the current suite of primary PM2.5 standards to 

provide increased public health protection from the effects of both long- and short-term 

exposures to fine particles in the ambient air. This conclusion is based, in general, on the 

evaluation in the ISA of the currently available epidemiological, toxicological, dosimetric, 

and exposure-related evidence, and on air quality information and analyses related to the 

epidemiological evidence, together with judgments as to the public health significance of the 

estimated incidence of effects remaining upon just meeting the current suite of standards. 

(2) It is appropriate to retain PM2.5 as the indicator for fine particles. Staff concludes that the 

available evidence does not provide a sufficient basis for replacing or supplementing the 

PM2.5 indicator with any other indicator(s) defined in terms alternative size fractions (i.e., 

UFPs) or for any specific fine particle component or group of components associated with 

any source categories of fine particles, nor does it provide a basis for excluding any 

component or group of components associated with any source categories from the mix of 

particles included in the PM2.5 indicator. 

(3) With regard to averaging times for the PM2.5 standards, it is appropriate to retain annual and 

24-hour averaging times to provide protection against health effects associated with longterm (seasons to years) and short-term (hours to days) exposure periods. The available 

evidence does not provide a sufficient basis for consideration of other averaging times, 

including an averaging time less than 24 hours to address health effects associated with subdaily exposures or an averaging time to address effects associated with seasonal exposures, 

given the relatively small amount of relevant information available. 

(4) It is appropriate to consider revising the form of the annual standard to one based on the 

highest appropriate monitor in an area rather than a form that allows averaging across 

monitors (i.e., spatial averaging) to provide increased protection for susceptible populations. 

Further, it is appropriate to retain the 98th percentile form of the current 24-hour standard. 
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(5) Consideration should be given to revising the suite of PM2.5 standards to provide increased 

protection against effects associated with both long- and short-term exposures, taking into 

account both evidence-based and risk-based considerations, with a particular focus on 

revising the annual standard level to provide protection for effects associated with both 

exposure periods. An emphasis on the annual standard would be consistent with the policy 

approach of setting a “generally controlling” annual standard to provide protection for both 

long- and short-term PM2.5 exposures in conjunction with a 24-hour standard that provides 

supplemental protection against days with high peak concentrations. This would limit peak 

concentrations in areas with high peak-to-mean ratios, possibly associated with strong local 

or seasonal sources. This would also provide supplemental protection for potential PM2.5-

related effects that may be associated with shorter-than-daily exposure periods. Staff 

concludes that this policy goal is the most effective and efficient way to reduce total 

population risk associated with both long- and short-term exposures, and would provide 

relatively more uniform protection in areas across the country. 

(a) Taken together, staff concludes that the currently available evidence and 

information from a quantitative risk assessment and air quality analyses provide 

support for considering revision of the level of the annual standard to within a 

range of 13 to 11 μg/m3

. Staff further concludes that the evidence most strongly 

supports consideration of an alternative annual standard level in the range of 12 to 

11 μg/m3

. 

(b) In conjunction with consideration of an annual standard level in the range of 12 to 

11 μg/m3, staff concludes it is appropriate to consider retaining the current 24-

hour standard level at 35 μg/m3

. 

(c) In conjunction with consideration of an annual standard level of 13 μg/m3, staff 

concludes that there is limited support to consider revising the 24-hour standard 

level to somewhat below 35 μg/m3, such as down to 30 μg/m3. 

2.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

COLLECTION 

The uncertainties and limitations that remain in the review of the primary fine particle 

standards are primarily related to understanding the range of ambient concentrations over which 

we continue to have confidence in the health effects observed in the epidemiological studies, as 

well as the extent to which the heterogeneity observed in the epidemiological evidence is related 

to differences in the ambient fine particle mixture and/or exposure-related factors. In addition, 

uncertainties remain in more fully understanding the role of PM2.5 in relationship to the roles of 

gaseous co-pollutants within complex ambient mixtures. 

In this section, we highlight areas for future health-related research, model development, 

and data collection activities to address these uncertainties and limitations in the current body of 

scientific evidence. These efforts, if undertaken, could provide important evidence for informing 

future PM NAAQS reviews and, in particular, consideration of possible alternative indicators, 

averaging times, and/or levels. In some cases, research in these areas can go beyond aiding 
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standard setting to informing the development of more efficient and effective control strategies. 

We note, however, that a full set of research recommendations to meet standards implementation 

and strategy development needs is beyond the scope of this discussion. 

As has been presented and discussed in the PM ISA, particularly in Chapters 4 through 8, 

the scientific body of evidence informing our understanding of health effects associated with 

long- and short-term exposures to fine particles has been broadened and strengthened since the 

last review. In reviewing the adequacy of the current suite of primary PM2.5 standards and in 

evaluating alternative health-based fine particle standards appropriate for consideration, we 

identify the following key uncertainties and areas for future research and data collection efforts 

that have been highlighted in this review. We recognize that some research could be available to 

inform the next PM NAAQS review, while other research may require longer-term efforts. 

Interpretation of Epidemiological Evidence

Additional research focused on identifying the most important factors contributing to the 

observed heterogeneity in the epidemiological evidence could provide insights for interpreting 

these studies. We encourage research and data collection efforts directed at improving our 

understanding of the nature of the exposures contributing to the observed health effects, for 

example, the role of specific components, sources, and different size fractions (e.g., UFPs) 

within the current PM2.5 mass-based indicator and the role of fine particles and co-pollutants 

within the broader ambient mixture, as well as improving our understanding of exposure-related 

factors that influence the magnitude and duration of fine particle exposures. Much of this 

research may depend on the availability of increased monitoring data, as discussed below. 

 Components/Sources. The currently available scientific evidence continues to be largely 

indexed by aggregate PM2.5 mass-based concentrations which vary in composition both 

regionally and seasonally. Source characterization, exposure, epidemiological, and 

toxicological research could focus on improving our understanding of the relative toxicity of 

different fine particle components, properties, and sources that may be more closely linked 

with various health effects. Critical to this better understanding of the impacts of PM2.5

components and their associated sources are data that refines the temporal and spatial 

variability of the fine particle mixture. This research would reduce the uncertainties in 

estimating risks. It could also inform consideration of alternative indicators in future PM 

NAAQS reviews as well as aid in the development of efficient and effective source control 

strategies for reducing health risks. 

 Ultrafine Particles (UFPs). Additional monitoring methods development work, health 

research, and ambient monitoring data collection efforts are needed to expand the currently 

available scientific data base for UFPs. UFP measurements should include surface area as 

well as number, mass and composition. It would be most useful for an UFPs monitoring 

network to be designed to inform our understanding of the spatial and temporal variability of 

these particles, including in near-roadway environments. This information would improve 
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our ability to explore consideration of a separate indicator for UFPs in future PM NAAQS 

reviews. 

 Co-pollutant Exposures. Research focused on furthering our understanding of the extent to 

which an association between fine particles and specific health effects can be modified by 

one or more co-pollutants would inform our ability to discern the role of PM in the complex 

ambient mixture. For example, does the magnitude of a PM2.5-related effect estimate differ 

on days when O3 concentrations are higher compared to days when O3 concentrations are 

lower? 

 Factors Influencing Exposures. Additional research and analyses would be useful to provide 

insights on population exposures, specifically in improving our understanding of intra-city 

and inter-city differences related to various PM2.5 components, source contributions and 

personal and building-related factors that may enhance our interpretation of the 

epidemiological evidence. This could include time-activity data to support probabilistic 

scenario-based exposure models, such as additional activity diary data to incorporate into the 

Consolidated Human Activity Database (CHAD); air conditioning use; residence near 

roadways; and penetration rates to better characterize ambient PM2.5 impacts on indoor 

microenvironments. This research could focus on different size fractions in PM2.5 (i.e., 

UFPs) as well as components. Coordination between exposure and health studies could 

advance our understanding of exposure-related factors. For example, epidemiological panel 

studies might use various exposure measurements to explore differences in personal 

exposures related to (1) indoor generated fine particles, (2) fine particle exposures measured 

by community monitors, and (3) fine particle exposures not captured by community monitors 

(i.e., personal exposures during commuting). 

Health Outcomes, Exposure Durations of Concern, and Susceptible Populations

New information available in this review reinforces and expands the evidence of 

associations between long- and short-term PM2.5 exposures and mortality and a number of 

cardiovascular and respiratory effects. Less evidence is available to understand other health 

effects (e.g., developmental/reproductive effects; central nervous system effects). Additional 

research could expand our understanding of the associations between PM2.5 and a broader range 

of health outcomes; reduce uncertainties associated with our current understanding of 

concentration-response relationships; improve our understanding of exposure durations of 

concern; and improve our understanding of the potential public health impacts of fine particle 

exposures in susceptible populations. Toxicological studies could provide additional evidence of 

coherence and biological plausibility for the effects observed in epidemiological studies as well 

as additional insights on possible mechanisms of action. 

 Health Effects. Research on a broader range of cardiovascular and respiratory endpoints 

could improve our understanding of the mechanisms by which these effects occur. In 

addition, future research could expand the scientific data base for health effects that are 

currently less understood including effects categorized within the ISA as having evidence 

suggestive of a causal relationship or for which currently available evidence is inadequate to 

support a quantitative risk analysis. To the extent that research supports a link between fine 
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particles and adverse effects on the nervous system, reproduction, development, or other 

endpoints, such effects could play an increased role for informing future PM NAAQS 

reviews including expanding the health endpoints that could potentially be evaluated in 

future quantitative risk assessments. 

 Concentration-Response Relationships. Research focused on improving our understanding 

of the shape of the C-R relationships, especially at lower ambient fine particle 

concentrations, as well as the confidence intervals around these C-R relationships, could 

reduce uncertainties associated with estimating and characterizing risks throughout the full 

range of air quality distributions. As more information becomes available on fine particle 

components and sources, it will be important to understand the C-R relationships for key 

constituents of the fine particle mixture, as well. 

 Exposure Durations of Concern. Research should be directed at broadening the scientific 

data base to improve our understanding of health effects associated with short-term, peak 

exposures, such as those related to traffic-related sources, wildfires, agricultural burning, or 

other episodic events, as well as to improve our understanding of health effects associated 

with seasonal-length exposures, such as those related to wintertime wood-burning emissions. 

Additional quantitative measures of exposure might take into account factors including the 

magnitude and duration of sub-daily and seasonal length PM2.5 exposures and the frequency 

of health impacts associated with repeated peak exposures. More research is needed to better 

understand effects that occur at longer lag times than have historically been studied (e.g., 0 to 

2 day lags). 

 Susceptible Populations. Improving our understanding of the populations that are more 

likely to experience adverse health effects related to fine particle exposures and the 

concentrations at which these effects may occur is important for informing future PM 

NAAQS reviews and for developing programs to reduce related public health risks. This 

evidence may also provide insights into the biologic modes of action for toxicity. 

o Pre-existing Health Conditions. While currently identified susceptible populations 

include persons with pre-existing cardiovascular and respiratory disease, evidence 

continues to emerge related to additional health conditions that may increase 

susceptibility to fine particle exposures (e.g., diabetes, obesity, neurological 

disorders). Research to replicate or extend these findings would enhance our 

understanding of these and other potentially susceptible populations. 

o Children. Epidemiological and toxicological studies provide evidence that children 

are more susceptible to PM exposures, primarily for respiratory-related effects. 

Evidence of developmental effects associated with PM exposures continues to 

emerge. Additional research exploring issues to better understand key windows of 

development impacted by PM exposures could enhance our understanding of this 

important susceptible lifestage. 

o Genetic Susceptibility. Research to expand our understanding of genetic 

susceptibility could inform our understanding of potentially susceptible populations 

and provide additional information for identifying the specific pathways and 

mechanisms of action by which PM initiates health effects. 
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o Socioeconomic status (SES). Additional research is needed to identity what factors 

(e.g., general health status, diet, medication, stress, unmeasured pollution) cause SES 

differences in response to pollution measured in communities. 

Data Collection Needs and Methods Development Activities

Additional research and data collection efforts focused on expanding current monitoring 

methods and networks as well as continued development of exposure models to expand data 

available for health studies could improve our understanding of potential alternative indicators, 

averaging times, and levels to consider in future PM NAAQS reviews. In particular, staff 

encourages work to enhance our understanding of the temporal and spatial variability of PM2.5, 

PM2.5 components, and different size fractions (e.g., UFPs). 

 Monitoring Measurements. In order to improve our understanding of the association 

between fine particles and health effects, more frequent measurement data could be collected. 

This would provide information that could inform our understanding of alternative lags. 

o PM2.5 Components. With respect to improving our understanding of the impacts of 

PM2.5 components, enhancements to the CSN, including more frequent measurement 

schedules and the development and deployment of continuous monitoring methods 

for specific fine particle components (e.g., EC/OC, sulfates), could enhance our 

understanding of the temporal and spatial variability of specific components. 

Furthermore, identifying chemical species within the mix of organic aerosols would 

improve our understanding of the artifacts associated with semi-volatile PM 

components and aid in designing toxicological experiments. 

o Ultrafine Particles. In order to improve our understanding of the public health 

impacts of UFPs, consideration should be given to establishing an FRM for UFPs and 

establishing a national UFP monitoring network. 

o Source Apportionment. Composition data with better time resolution (e.g., 1 to 6 

hour) and better size resolution (e.g., UFPs, accumulation mode particles, coarse 

particles in PM2.5 and PM10-2.5) could provide more precise and accurate information 

on sources of fine particles to inform health research as well as development of more 

efficient and effective control strategies. 

o Spatial Variability. Some portion of the required PM2.5 monitoring network could be 

dedicated to improving our ability to characterize spatial variability across urban 

areas including both at localized and area-wide scales. 

 Model Development. Continuing work to improve models for estimating PM2.5 mass and 

composition in areas with only every third or sixth day measurements, and by space where 

measurements are not available could enhance our understanding of the temporal and spatial 

variability of fine particles. Refinement of these models to finer spatial scales may improve 

exposure estimates in epidemiological studies as well as in quantitative risk and exposure 

assessments. 

 Air Quality Distributions Reported in Epidemiological Studies. Most epidemiological 

studies provide some information on the distribution of ambient measurement data evaluated, 

however, published information is often generally limited in scope and the descriptive 
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statistics reported vary from one study to another. Understanding the air quality distributions 

at which effects have been observed is important for informing consideration of the adequacy 

of the current NAAQS as well as potential alternative indicators, averaging times, and levels 

to consider. Working with intramural and extramural research groups, we plan to encourage 

a more comprehensive and more consistent reporting of population-level and air quality data. 
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3-1 

3 REVIEW OF THE PRIMARY STANDARD FOR THORACIC COARSE 

PARTICLES 

This chapter presents staff conclusions with regard to the adequacy of the current primary 

PM10 standard, which is intended to protect public health against exposures to thoracic coarse 

particles (PM10-2.5), and potential alternative primary standards for consideration in this review. 

Our assessment of these issues is framed by a series of key policy-relevant questions, which 

expand upon those presented in the IRP (US EPA, 2008a). The answers to these questions will 

inform decisions on whether, and if so how, to revise the current PM10 standard. 

Our approach for reviewing the primary PM10 standard is presented in section 3.1. Our 

considerations and conclusions regarding the adequacy of the current PM10 standard are 

presented in section 3.2. Section 3.3 presents our considerations and conclusions with respect to 

potential alternative standards, focusing on each of the basic elements of the standards: pollutant 

indicator (section 3.3.1), averaging time (section 3.3.2), form (section 3.3.3), and level (section 

3.3.4). Section 3.4 summarizes staff conclusions on the current and potential alternative 

standards. Section 3.5 discusses key uncertainties and suggested future research areas and data 

collection efforts. 

3.1 APPROACH 

Staff’s approach for reviewing the current primary PM10 standard builds upon the 

approaches used in previous PM NAAQS reviews. The past and current approaches described 

below are all based most fundamentally on using information from epidemiological studies to 

inform the selection of PM standards that, in the Administrator’s judgment, protect public health 

with an adequate margin of safety. Evidence-based approaches to using information from 

epidemiological studies to inform decisions on PM standards are complicated by the recognition 

that no population threshold, below which it can be concluded with confidence that PM-related 

effects do not occur, can be discerned from the available evidence (US EPA, 2009a, section 

2.4.3). As a result, any approach to reaching decisions on what standards are appropriate 

requires judgments about how to translate the information available from the epidemiological 

studies into a basis for appropriate standards, which includes consideration of how to weigh the 

uncertainties in reported associations across the distributions of PM concentrations in the studies. 

Our approach to informing these decisions, discussed more fully below, recognizes that the 

available health effects evidence reflects a continuum consisting of ambient levels at which 

scientists generally agree that health effects are likely to occur through lower levels at which the 

likelihood and magnitude of the response become increasingly uncertain. Such an approach is 
3-2 

consistent with setting standards that are neither more nor less stringent than necessary, 

recognizing that a zero-risk standard is not required by the CAA. 

3.1.1 Approaches Used in Previous Reviews 

3.1.1.1 Reviews Completed in 1987 and 1997 

The PM NAAQS have always included some type of a primary standard to protect 

against effects associated with exposures to thoracic coarse particles. In 1987, when EPA first 

revised the PM NAAQS, EPA changed the indicator for PM from Total Suspended Particles 

(essentially applicable to particles smaller than 25-45 micrometers) to focus on inhalable 

particles, those which can penetrate into the trachea, bronchi, and deep lungs (52 FR 24634). 

The EPA changed the PM indicator to PM10 based on evidence that the risk of adverse health 

effects associated with particles with a nominal mean aerodynamic diameter less than or equal to 

10 μm was significantly greater than risks associated with larger particles (52 FR at 24639). 

In the 1997 review, in conjunction with establishing new fine particle (i.e., PM2.5)

standards (discussed above in sections 1.2.2, 2.1.1.1), EPA concluded that continued protection 

was warranted against potential effects associated with thoracic coarse particles in the size range 

of 2.5 to 10 μm. This conclusion was based on particle dosimetry, toxicological information, 

and on limited epidemiological evidence from studies that measured PM10 in areas where coarse 

particles were likely to dominate the distribution (62 FR 38677, July 18, 1997). Thus, EPA 

concluded that the existing PM10 standards would provide requisite protection against effects 

associated with particles in the size range of 2.5 to 10 μm. Although EPA considered a more 

narrowly defined indicator for thoracic coarse particles in that review (i.e., PM10-2.5), EPA 

concluded that it was more appropriate, based on existing evidence, to continue to use PM10 as 

the indicator. This decision was based, in part, on the recognition that the only studies of clear 

quantitative relevance to health effects most likely associated with thoracic coarse particles used 

PM10 in areas where the coarse fraction was the dominant fraction of PM10, namely two studies 

conducted in areas that substantially exceeded the 24-hour PM10 standard (62 FR 38679). In 

addition, there were only very limited ambient air quality data then available specifically for 

PM10-2.5, in contrast to the extensive monitoring network already in place for PM10, Therefore, it 

was judged more administratively feasible to use PM10 as an indicator. The EPA also stated that 

the PM10 standards would work in conjunction with the PM2.5 standards by regulating the portion 

of particulate pollution not regulated by the PM2.5 standards. 

As explained in chapter 1, in May 1998, a three-judge panel of the U.S. Court of Appeals 

for the District of Columbia Circuit found "ample support" for EPA's decision to regulate coarse 

particle pollution, but vacated the 1997 PM10 standards, concluding that EPA had failed to 

adequately explain its choice of PM10 as the indicator for thoracic coarse particles pointing to the 
3-3 

lack of reasoned explanation for the variable level of allowable concentrations of thoracic coarse 

particles (varying by levels of PM2.5) and the consequent double regulation of PM2.5. American 

Trucking Associations v. EPA , 175 F. 3d 1027, 1054-56 (D.C. Cir. 1999). The court also 

rejected considerations of administrative feasibility as justification for use of PM10 as the 

indicator for thoracic coarse PM, since NAAQS (and their elements) are to be based exclusively 

on health and welfare considerations. Id. at 1054. Pursuant to the court’s decision, EPA 

removed the vacated 1997 PM10 standards from the Code of Federal Regulations (CFR) (69 FR 

45592, July 30, 2004) and deleted the regulatory provision (at 40 CFR section 50.6(d)) that 

controlled the transition from the pre-existing 1987 PM10 standards to the 1997 PM10 standards 

(65 FR 80776, December 22, 2000). The pre-existing 1987 PM10 standards remained in place. Id. 

at 80777. 

3.1.1.2 Review Completed in 2006 

In the review of the PM NAAQS that concluded in 2006, EPA considered the growing, 

but still limited, body of evidence supporting associations between health effects and thoracic 

coarse particles measured as PM10-2.5.

1

 The new studies available in the 2006 review included 

epidemiological studies that reported associations with health effects using direct measurements 

of PM10-2.5, as well as dosimetric and toxicological studies. In considering this growing body of 

PM10-2.5 evidence, as well as evidence from studies that measured PM10 in locations where the 

majority of PM10 was in the PM10-2.5 fraction (US EPA, 2005, section 5.4.1), staff concluded that 

that the level of protection afforded by the existing 1987 PM10 standard remained appropriate 

(US EPA, 2005, p. 5-67), but recommended that the indicator for the standard be revised. 

Specifically, staff recommended replacing the PM10 indicator with an indicator of urban thoracic 

coarse particles in the size range of 10-2.5 μm (US EPA, 2005, pp. 5-70 to 5-71). The agency 

proposed to retain a standard for a subset of thoracic coarse particles, proposing a qualified 

PM10-2.5 indicator to focus on the mix of thoracic coarse particles generally present in urban 

environments. More specifically, the proposed revised thoracic coarse particle standard would 

have applied only to an ambient mix of PM10-2.5 dominated by resuspended dust from highdensity traffic on paved roads and/or by industrial and construction sources. The proposed 

revised standard would not have applied to any ambient mix of PM10-2.5 dominated by rural 

windblown dust and soils. In addition, agricultural sources, mining sources, and other similar 

sources of crustal material would not have been subject to control in meeting the standard (71 FR 

2667 to 2668, January 17, 2006). 

 1

The PM Staff Paper (US EPA, 2005) also presented results of a quantitative assessment of health risks for PM10-2.5

(see also Abt Associates, 2005). However, staff concluded that the nature and magnitude of the uncertainties and 

concerns associated with this risk assessment weighed against its use as a basis for recommending specific levels for 

a thoracic coarse particle standard (US EPA, 2005, p.5-69). 
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The Agency received a large number of comments overwhelmingly opposed to the 

proposed qualified PM10-2.5 indicator (71 FR 61188 to 61197). After careful consideration of the 

scientific evidence and the recommendations contained in the 2005 Staff Paper, the advice and 

recommendations from CASAC, the public comments received regarding the appropriate 

indicator for coarse particles, and after extensive evaluation of the alternatives available to the 

Agency, the Administrator decided it would not be appropriate to adopt a qualified PM10-2.5

indicator. Underlying this determination was the decision that it was requisite to provide 

protection from exposure to all thoracic coarse PM, regardless of its origin, rejecting arguments 

that there are no health effects from community-level exposures to coarse PM in non-urban areas 

(71 FR 61189). The EPA concluded that dosimetric, toxicological, occupational and 

epidemiological evidence supported retention of a primary standard for short-term exposures that 

included all thoracic coarse particles (i.e., particles of both urban and non-urban origin), 

consistent with the Act’s requirement that primary NAAQS provide an adequate margin of 

safety. At the same time, the Agency concluded that the standard should target protection 

toward urban areas, where the evidence of health effects from exposure to PM10-2.5 was strongest 

(71 FR at 61193, 61197). The proposed indicator was not suitable for that purpose. Not only did 

it inappropriately provide no protection at all to many areas, but it failed to identify many areas 

where the ambient mix was dominated by coarse particles contaminated with urban/industrial 

types of coarse particles for which evidence of health effects was strongest (71 FR 61193). 

The Agency ultimately concluded that the existing indicator, PM10, was most consistent 

with the evidence. Although PM10 includes both coarse and fine PM, the Agency concluded that 

it remained an appropriate indicator for thoracic coarse particles because, as reported by Schmidt 

et al. (2005), fine particle levels are generally higher in urban areas and, therefore, a PM10

standard set at a single unvarying level will generally result in lower allowable concentrations of 

thoracic coarse particles in urban areas than in non-urban areas. The EPA considered this to be 

an appropriate targeting of protection given that the strongest evidence for effects associated 

with thoracic coarse particles came from epidemiological studies conducted in urban areas and 

that elevated fine particle concentrations in urban areas could result in increased contamination 

of coarse fraction particles by PM2.5, potentially increasing the toxicity of thoracic coarse 

particles in urban areas (71 FR 61195-96). Given the evidence that the existing PM10 standard 

afforded requisite protection with an adequate margin of safety, the Agency retained the level 

and form of the 24-hour PM10 standard.2

 

The Agency also revoked the annual PM10 standard, in light of the conclusion in the PM 

Criteria Document (US EPA, 2004, p. 9-79) that the available evidence does not suggest an 

 

2

Thus, the standard is met when a 24-hour average PM10 concentration of 150 g/m3 is not exceeded more than one 

day per year, on average over a three-year period. 
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association with long-term exposure to PM10-2.5 and the conclusion in the Staff Paper (US EPA, 

2005, p. 5-61) that there is no quantitative evidence that directly supports an annual standard. 

In the same rulemaking, EPA also included a new FRM for the measurement of PM10-2.5 

in the ambient air (71 FR 61212 to 61213). Although the standard for thoracic coarse particles 

does not use a PM10-2.5 indicator, the new FRM for PM10-2.5 was established to provide a basis for 

approving FEMs and to promote the gathering of scientific data to support future reviews of the 

PM NAAQS (section 1.3.4). 

3.1.2 Litigation of 2006 Final Rule for Thoracic Coarse Particles 

A number of groups filed suit in response to the final decisions made in the 2006 review. 

See American Farm Bureau Federation and the National Pork Producers Council v. EPA (DC 

Cir. 2009). Among the petitions for review were challenges from industry groups on the 

decision to retain the PM10 indicator and the level of the PM10 standard and from environmental 

and public health groups on the decision to revoke the annual PM10 standard. The court upheld 

both the decision to retain the 24-hour PM10 standard and the decision to revoke the annual 

standard. 

First, the court upheld EPA’s decision for a standard to encompass all thoracic coarse 

PM, both of urban and non-urban origin. The court rejected arguments that the evidence showed 

there are no risks from exposure to non-urban coarse PM. The court further found that EPA had 

a reasonable basis not to set separate standards for urban and non-urban coarse PM, namely the 

inability to reasonably define what ambient mixes would be included under either ‘urban’ or 

‘non-urban;’ and the evidence in the record that supported EPA’s appropriately cautious decision 

to provide “some protection from exposure to thoracic coarse particles... in all areas.” 559 F. 3d 

at 532-33. Specifically, the court stated, 

Although the evidence of danger from coarse PM is, as EPA recognizes, 

“inconclusive,” (71 FR 61193, October 17, 2006), the agency need not wait for 

conclusive findings before regulating a pollutant it reasonably believes may pose 

a significant risk to public health. The evidence in the record supports the EPA’s 

cautious decision that “some protection from exposure to thoracic coarse particles 

is warranted in all areas.” Id. As the court has consistently reaffirmed, the CAA 

permits the Administrator to “err on the side of caution” in setting NAAQS.

559 F. 3d at 533. 

The court also upheld EPA’s decision to retain the level of the standard at 150 μg/m3 and 

to use PM10 as the indicator for thoracic coarse particles. In upholding the level of the standard, 

the court referred to the conclusion in the Staff Paper that there is “little basis for concluding that 

the degree of protection afforded by the current PM10 standards in urban areas is greater than 
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warranted, since potential mortality effects have been associated with air quality levels not 

allowed by the current 24-hour standard, but have not been associated with air quality levels that 

would generally meet that standard, and morbidity effects have been associated with air quality 

levels that exceeded the current 24-hour standard only a few times.” 559 F. 3d at 534. The court 

also rejected arguments that a PM10 standard established at an unvarying level will result in 

arbitrarily varying levels of protection given that the level of coarse PM would vary based on the 

amount of fine PM present. The court agreed that the variation in allowable coarse PM accorded 

with the strength of the evidence: typically less coarse PM would be allowed in urban areas 

(where levels of fine PM are typically higher), in accord with the strongest evidence of health 

effects from coarse particles. 559 F. 3d at 535-36. In addition, such regulation would not 

impermissibly double regulate fine particles, since any additional control of fine particles 

(beyond that afforded by the primary PM2.5 standard) would be for a different purpose: to prevent 

contamination of coarse particles by fine particles. 559 F. 3d at 535, 536. These same 

explanations justified the choice of PM10 as an indicator, and provided the reasoned explanation 

for that choice lacking in the record for the 1997 standard. 559 F. 3d at 536. 

With regard to the challenge from environmental and public health groups, the court 

upheld EPA’s decision to revoke the annual PM10 standard. Specifically, the court stated the 

following: 

The EPA reasonably decided that an annual coarse PM standard is not necessary 

because, as the Criteria Document and the Staff Paper make clear, the latest 

scientific data do not indicate that long-term exposure to coarse particles poses a 

health risk. The CASAC also agreed that an annual coarse PM standard is 

unnecessary. 559 F. 3d at 538-39. 

3.1.3 General Approach Used in Current Review 

Our approach relies most heavily on the health evidence, primarily the epidemiological 

evidence, assessed in the ISA (US EPA, 2009a) and on available PM air quality information. As 

discussed in more detail in the Quantitative Health Risk Assessment for Particulate Matter – 

Final (RA, US EPA, 2010a), we have not conducted a quantitative assessment of health risks 

associated with PM10-2.5. Staff concluded that limitations in the monitoring network and in the 

health studies that rely on that monitoring network, which would be the basis for estimating 

PM10-2.5 health risks, would introduce significant uncertainty into a PM10-2.5 risk assessment such 

that the risk estimates generated would be of limited value in informing review of the standard. 

Therefore, staff concluded in the RA that a quantitative risk assessment for PM10-2.5 is not 

supportable at this time (US EPA, 2010a, p. 2-6). 

For purposes of this policy assessment, we seek to provide as broad an array of options 

for consideration as is supportable by the available evidence and air quality information, 
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recognizing that the final decisions on the primary PM10 standard will reflect the judgments of 

the Administrator. In developing these options for consideration, we consider the available 

evidence and air quality information that informs overarching questions related to: (1) the 

adequacy of the current 24-hour PM10 standard to protect against effects associated with 

exposures to thoracic coarse particles and (2) what potential alternative standard(s), if any, 

should be considered in this review. In addressing these broad questions, we have organized the 

discussions below around a series of more specific questions reflecting different aspects of each 

overarching question. When evaluating the health protection afforded by the current or potential 

alternative standards, we have taken into account the four basic elements of the NAAQS: 

indicator, averaging time, form, and level. 

Figure 3-1 provides an overview of the policy-relevant questions that frame our review, 

as discussed more fully below. We believe that this general approach provides a comprehensive 

basis to help inform the judgments required of the Administrator in reaching decisions about the 

current and potential alternative primary standards meant to protect public health against 

exposures to thoracic coarse particles.
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Figure 3-1. Overview of Approach for Review of Primary PM10 Standard 
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3.2 ADEQUACY OF THE CURRENT PM10 STANDARD 

In considering the adequacy of the current 24-hour PM10 standard to protect against 

effects associated with exposures to thoracic coarse particles, we address the following 

overarching question: 

Does the available scientific evidence, as reflected in the ISA, support or call into question 

the adequacy of the protection afforded by the current 24-hour PM10 standard against 

effects associated with exposures to thoracic coarse particles? 

To inform our consideration of this overarching question, we consider the scientific 

evidence for associations between PM10-2.5 and mortality and morbidity, evidence linking 

PM10-2.5 toxicity to specific sources/locations, uncertainties in the evidence, and available PM10

air quality concentrations in PM10-2.5 study locations (section 3.2.1). Evidence for populations 

that are particularly susceptible to PM exposures is discussed in detail in section 2.2.1 above, and 

is not repeated here. Staff conclusions regarding the adequacy of the current standard are 

presented in section 3.2.2. 

3.2.1 Evidence-Based Considerations 

In considering the currently available body of scientific evidence for health effects of 

thoracic coarse particles, we consider the following question: 

 To what extent does the currently available scientific evidence, including associated 

uncertainties, strengthen or call into question evidence of associations between ambient 

thoracic coarse particle exposures and adverse health effects? 

Since the conclusion of the last review, the Agency has developed a more formal 

framework for reaching causal inferences from the body of scientific evidence. As discussed 

above in section 2.2.1, this framework uses a five-level hierarchy that classifies the overall 

weight of evidence using the following categorizations: causal relationship, likely to be a causal 

relationship, suggestive of a causal relationship, inadequate to infer a causal relationship, and not 

likely to be a causal relationship (US EPA 2009a, section 1.5, Table 1-3). Applying this 

framework to thoracic coarse particles, the ISA concludes that the existing evidence is suggestive 

of a causal relationship between short-term PM10-2.5 exposures and mortality, cardiovascular 

effects, and respiratory effects (US EPA, 2009a, section 2.3.3; see Table 3-1 below). In contrast, 

the ISA concludes that available evidence is inadequate to infer a causal relationship between 

long-term PM10-2.5 exposures and various health effects (US EPA, 2009a, section 2.3; Table 3-1 

below). Similar to the judgment made in the AQCD regarding long-term exposures (US EPA, 

2004), the ISA states, “To date, a sufficient amount of evidence does not exist in order to draw 

conclusions regarding the health effects and outcomes associated with long-term exposure to 
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PM10-2.5” (US EPA, 2009a, section 2.3.4). Given these weight of evidence conclusions in the 

ISA, our evidence-based considerations regarding the adequacy of the current 24-hour PM10

standard focus on effects that have been linked with short-term exposures to PM10-2.5. 

Table 3-1. Summary of Causality Determinations for PM10-2.5 

Exposure Duration Outcome Causal Determination

Short-term

Mortality Suggestive 

Cardiovascular Effects Suggestive 

Respiratory Effects Suggestive 

Central Nervous System Effects Inadequate 

Long-term 

Mortality Inadequate 

Cardiovascular Effects Inadequate 

Respiratory Effects Inadequate 

Reproductive and Developmental Effects Inadequate 

Cancer Mutagenicity, Genotoxicity Effects Inadequate 

Source: adapted from US EPA, 2009a; Table 2-6 

As noted above, in the last review of the PM NAAQS, PM10 studies conducted in 

locations where PM10 is comprised predominantly of PM10-2.5 were also considered (US EPA, 

2005, pp. 5-49 to 5-50). However, PM10 studies are difficult to interpret within the context of a 

standard meant to protect against exposures to PM10-2.5 because PM10 is comprised of both fine 

and coarse particles, even in locations with the highest concentrations of PM10-2.5 (see below). In 

light of the considerable uncertainty in the extent to which PM10 effect estimates reflect 

associations with PM10-2.5 versus PM2.5, together with the availability in this review of several 

studies that evaluated associations with PM10-2.5 and the fact that the ISA weight of evidence 

conclusions for thoracic coarse particles were based on studies of PM10-2.5, we focus in this PA 

on studies that have specifically evaluated PM10-2.5. The evidence supporting a link between 

short-term thoracic coarse particle exposures and adverse health effects is discussed in detail in 

the ISA (US EPA, 2009a, Chapter 6) and is summarized briefly below for mortality, 

cardiovascular effects, and respiratory effects. 

Short-Term PM10-2.5 and Mortality 

The ISA assesses a number of multi-city and single-city epidemiological studies that have 

evaluated associations between mortality and short-term PM10-2.5 concentrations (US EPA, 

2009a, Figure 6-30 presents PM10-2.5 mortality studies assessed in the last review and the current 
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review). Different studies have used different approaches to estimate ambient PM10-2.5. Some 

studies have used the difference between PM10 and PM2.5 mass, either measured at co-located 

monitors (e.g., Lipfert et al., 2000; Mar et al., 2003; Ostro et al., 2003; Sheppard et al., 2003; 

Wilson et al., 2007) or as the difference in county-wide average concentrations (Zanobetti and 

Schwartz, 2009), while other studies have measured PM10-2.5 directly with dichotomous samplers 

(e.g., Burnett and Goldberg, 2003; Fairley et al., 2003; Burnett et al., 2004; Klemm et al., 2004). 

Despite differences in the approaches used to estimate ambient PM10-2.5 concentrations, the 

majority of multi- and single-city studies have reported positive associations between PM10-2.5

and mortality, though most of these associations were not statistically significant (US EPA, 

2009a, Figure 6-30). When considered as a whole, the ISA concluded that epidemiological 

studies have reported consistent, positive associations between short-term PM10-2.5 and mortality 

(US EPA, 2009a, section 6.5.2.3). 

In considering specific mortality studies, we note that the U.S. multi-city study by 

Zanobetti and Schwartz (2009) reported positive and statistically significant associations with 

PM10-2.5 for all-cause, cardiovascular-related, and respiratory-related mortality (US EPA, 2009a, 

section 6.5.2.3) while other multi-city studies have reported positive, but not statistically 

significant, PM10-2.5 effect estimates for mortality (US EPA, 2009a, Figure 6-30, Burnett and 

Goldberg, 2003; Klemm et al., 2003; Burnett et al., 2004). In the study by Zanobetti and 

Schwartz, the effect estimates for all-cause and respiratory-related mortality remained 

statistically significant in co-pollutant models that included PM2.5, while the effect estimate for 

cardiovascular-related mortality remained positive but not statistically significant. When risk 

estimates in this study were evaluated by climatic region (US EPA, 2009a, Figure 6-28), the “dry 

continental” region, which included areas with relatively high PM10-2.5 concentrations such as 

Salt Lake City, Provo, and Denver, showed the largest risk estimates (see US EPA, 2009a, 

Figure 6-29; Schmidt and Jenkins, 2010; and discussion of regional differences in PM10-2.5 

concentrations below). However, the “dry” region, which included Phoenix and Albuquerque, 

two locations that also have relatively high PM10-2.5 concentrations, did not show positive 

associations with all-cause or respiratory-related mortality and only a relatively small positive 

association for cardiovascular-related mortality. In addition, the “Mediterranean” region (which 

included cities in California, Oregon, and Washington) did not show positive associations while 

the other three regions (i.e., “hot summer, continental,” “warm summer, continental,” and 

“humid, subtropical and maritime”), which included cities that correspond to the mid-west, 

northeast, and southeast geographic regions, all showed positive associations (US EPA, 2009a, 

Figure 6-28). 

The ISA also presents single-city empirical Bayes-adjusted effect estimates (Le Tertre et 

al., 2005) for the 47 cities evaluated by Zanobetti and Schwartz (US EPA, 2009a, Figure 6-29). 
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City-specific estimates were positive, though generally not statistically significant, for 

cardiovascular-related mortality in all 47 cities evaluated. Effect estimates were positive for allcause and respiratory-related mortality in all cities except Los Angeles (negative association for 

all-cause and respiratory-related mortality) and Phoenix (negative association for respiratoryrespiratory-related mortality) (US EPA, 2009a, Figure 6-29). In addition, positive and 

statistically significant associations between mortality (all-cause, cardiovascular-related, and/or 

respiratory-related) and PM10-2.5 were reported for six locations (i.e., St. Louis, MO; Salt Lake 

City, UT; Chicago, IL; Pittsburgh, PA; Detroit, MI; and Birmingham, AL). 

In considering single-city PM10-2.5 mortality studies, we note that all of the studies 

included in Figure 6-30 of the ISA (US EPA, 2009a) reported positive PM10-2.5 effect estimates, 

with three single-city studies reporting effect estimates that were statistically significant (Mar et 

al., 2003; Ostro et al., 2003; Wilson et al., 2007). One study reported a negative PM10-2.5 effect 

estimate for respiratory-related mortality (Villeneuve et al., 2003), though effect estimates for 

all-cause and cardiovascular-related mortality were positive in this study (US EPA, 2009a, 

Figure 6-30).   

Short-Term PM10-2.5 and Cardiovascular Effects 

The ISA assesses a number of studies that have evaluated the link between short-term 

ambient concentrations of thoracic coarse particles and cardiovascular effects. In considering the 

available epidemiological evidence, the ISA concludes that single- and multi-city 

epidemiological studies generally report positive associations between short-term PM10-2.5

concentrations and hospital admissions or emergency department visits for cardiovascular causes 

(US EPA, 2009a, section 2.3.3, 6.2.12.2). Some of these studies have reported positive and 

statistically significant PM10-2.5 effect estimates in co-pollutant models while others report that 

PM10-2.5 effect estimates remain positive, but not statistically significant (US EPA, 2009a, Figure 

6-5). 

These studies include a recent U.S. multi-city study evaluating hospital admissions and 

emergency department visits for cardiovascular disease in Medicare patients (MCAPS, Peng et 

al., 2008). In this study of older adults, the authors reported a positive and statistically 

significant association between 24-hour PM10-2.5 concentrations and cardiovascular disease 

hospitalizations in a single pollutant model using air quality data for 108 U.S. counties with colocated PM10 and PM2.5 monitors. The magnitude of this effect estimate was larger in counties 

with higher degrees of urbanization (Peng et al., 2008). The effect estimate was reduced only 

slightly in a two-pollutant model that included PM2.5, but it was no longer statistically significant 

(US EPA, 2009a, sections 2.3.3, 6.2.10.9). County-specific analyses were not reported for the 

locations evaluated by Peng and, therefore, it is not possible to consider differences in PM10-2.5
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effect estimates in specific locations. Effect estimates for PM10-2.5 were larger in the eastern U.S. 

than the western U.S., though this difference was not statistically significant (Peng et al., 2008). 

In addition to this U.S. multi-city study, positive associations reported for short-term PM10-2.5 and 

cardiovascular-related morbidity reached statistical significance in a multi-city study in France 

(Host et al., 2007) and single-city studies in Detroit (Ito, 2003) and Toronto (Burnett et al., 1999) 

(US EPA, 2009a, Figures 6-2, 6-3). In contrast, associations were positive but not statistically 

significant in single-city studies conducted in Atlanta (Metzger et al., 2004; Tolbert et al., 2007; ) 

and Boston (Peters et al., 2001) (and for some endpoints in Detroit) (US EPA, 2009a, Figures 6-1 

to 6-3, 6-5). 

The plausibility of the positive associations reported for PM10-2.5 and cardiovascularrelated hospital admissions and emergency department visits is supported by a small number of 

controlled human exposure studies that have reported alterations in heart rate variability 

following short-term exposure to PM10-2.5 (Gong et al., 2004; Graff et al., 2009); by short-term 

PM10-2.5 epidemiological studies reporting positive associations with cardiovascular-related 

mortality (see discussion above); by a small number of recent epidemiological studies that have 

examined dust storm events and reported increases in cardiovascular-related emergency 

department visits and hospital admissions (see below); and by associations with other 

cardiovascular effects including heart rhythm disturbances and changes in heart rate variability 

(US EPA, 2009a, sections 2.3.3, 6.2.12.2). The few toxicological studies that examined the 

effect of PM10-2.5 on cardiovascular health effects used intratracheal instillation and, as a result, 

provide only limited evidence on the biological plausibility of PM10-2.5 induced cardiovascular 

effects (US EPA, 2009a, sections 2.3.3, 6.2.12.2). 

Short-Term PM10-2.5 and Respiratory Effects

The ISA also assesses a number of studies that have evaluated the link between shortterm ambient concentrations of thoracic coarse particles and respiratory effects. This includes 

recent studies conducted in the U.S., Canada, and France (US EPA, 2009a, section 6.3.8), 

including the U.S. multi-city study of Medicare patients by Peng et al. (2009). As discussed 

above, Peng estimated PM10-2.5 concentrations as the difference between PM10 and PM2.5

concentrations measured by co-located monitors. The authors reported a positive, but not 

statistically significant, PM10-2.5 effect estimate for respiratory-related hospital admissions. 

Single-city studies have reported positive, and in some cases statistically significant, PM10-2.5 

effect estimates for respiratory-related hospital admissions and emergency department visits (Lin 

et al., 2002; Ito, 2003; Sheppard et al., 2003; Chen et al., 2004; Yang et al., 2004; Chen et al., 

2005; Lin et al., 2005; Peel et al., 2005; Slaughter et al., 2005; Fung et al., 2006; NYS DOH, 

2006; Tolbert et al., 2007) (US EPA, 2009a, Figures 6-10 to 6-15). Some of these PM10-2.5
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respiratory morbidity studies have reported positive and statistically significant PM10-2.5 effect 

estimates in co-pollutant models that included gaseous pollutants while others reported that 

PM10-2.5 effect estimates remain positive, but not statistically significant, in such co-pollutant 

models (US EPA, 2009a, Figure 6-15). 

A limited number of epidemiological studies have focused on specific respiratory 

morbidity outcomes and reported both positive and negative, but generally not statistically 

significant, associations between PM10-2.5 and lower respiratory symptoms, wheeze, and 

medication use (US EPA, 2009a, sections 2.3.3.1 and 6.3.1.1; Figures 6-7, 6-8, 6-9). Although 

controlled human exposure studies have not observed an effect on lung function or respiratory 

symptoms in healthy or asthmatic adults in response to short-term exposure to PM10-2.5, healthy 

volunteers have exhibited an increase in markers of pulmonary inflammation. Toxicological 

studies using inhalation exposures are still lacking, but pulmonary injury and inflammation has 

been reported in animals after intratracheal instillation exposure (US EPA, 2009a, section 

6.3.5.3) and, in some cases, PM10-2.5 was found to be more potent than PM2.5. 

PM10-2.5 Toxicity: Impacts of Sources and Composition 

As discussed above, positive, and in some cases statistically significant, associations 

between short-term PM10-2.5 concentrations and mortality and morbidity have been reported in a 

number of different locations. Little is known about how PM10-2.5 composition varies across 

these locations and how that variation could affect particle toxicity (US EPA, 2009a, sections 

2.3.3, 2.3.4, 2.4.4). However, the limited available evidence suggests that specific components 

of thoracic coarse particles tend to comprise different fractions of PM10-2.5 mass in different 

environments (e.g., urban versus rural environments) (US EPA, 2009a, section 3.5.1.1; Schmidt 

et al., 2005; Edgerton et al., 2009). It is possible that such differences in particle composition 

affect particle toxicity, though the ISA concludes that currently available evidence is insufficient 

to draw distinctions in toxicity based on composition and notes that recent studies have reported 

that PM (both PM2.5 and PM10-2.5) from different sources, including crustal sources, is associated 

with adverse health effects (US EPA, 2009a, section 2.4.4). The evidence for associations with 

particles originating from different types of sources and in different locations is discussed briefly 

below. 

As discussed above, most PM10-2.5 epidemiological studies have been conducted in urban 

locations in the U.S., Canada, and Europe while a small number of studies have examined the 

health impacts of dust storm events (US EPA, 2009a, sections 6.2.10.1, 6.5.2.3). Although these 

dust storm studies do not link specific particle constituents to health effects, it is useful to 

consider them within the context of the toxicity of particles of non-urban crustal origin. Several 
3-15 

studies have reported positive and statistically significant associations between dust storm events 

and morbidity or mortality, including the following: 

 Middleton et al. (2008) reported that dust storms in Cyprus were associated with a 

statistically significant increase in risk of hospitalization for all causes and a nonsignificant increase in hospitalizations for cardiovascular disease. 

 Chan et al. (2008) studied the effects of Asian dust storms on cardiovascular-related 

hospital admissions in Taipei, Taiwan and reported a statistically significant increase 

associated with 39 Asian dust events. Evaluating the same data, Bell et al. (2008) also 

reported positive and statistically significant associations between hospitalization for 

ischemic heart disease and PM10-2.5. 

 Perez et al. (2008) tested the hypothesis that outbreaks of Saharan dust exacerbate the 

effects of PM10-2.5 on daily mortality in Spain. During Saharan dust days, the PM10-2.5 

effect estimate was larger than on non-dust days and it became statistically significant, 

whereas it was not statistically significant on non-dust days. 

In contrast to the studies noted above, some dust storm studies have reported associations 

that were not statistically significant. Specifically, Bennett et al. (2006) reported on a dust storm 

in the Gobi desert that transported PM across the Pacific Ocean, reaching western North America 

in the spring of 1998. The authors reported no excess risk of cardiovascular-related or 

respiratory-related hospital admissions associated with the dust storm in the population of British 

Columbia’s Lower Fraser Valley (Bennett et al., 2006). In addition, Yang et al. (2009) reported 

that hospitalizations for congestive heart failure were elevated during or immediately following 

54 Asian dust storm events, though effect estimates were not statistically significant. The 

implications of these studies for the current review, specifically for consideration of potential 

alternative indicators, are discussed below in section 3.3.1. 

Next we consider uncertainties associated with the evidence by addressing the following 

question: 

 What are the important uncertainties associated with the currently available scientific 

evidence that should be considered in evaluating the adequacy of the current PM10 

standard? 

The majority of the health evidence supporting the link between short-term thoracic 

coarse particle exposures and mortality and morbidity comes from epidemiological studies. 

Although new studies have become available since the last review and have expanded our 

understanding of the association between PM10-2.5 and adverse health effects (see above and U.S. 

EPA, 2009a, Chapter 6), important uncertainties remain. These uncertainties, and their 

implications for interpreting the scientific evidence, are discussed below. 
3-16 

The ISA (sections 2.3.3, 2.3.4) concludes that an important uncertainty in the PM10-2.5

epidemiological literature is related to the air quality estimates used in these studies. 

Specifically, the ISA concludes that there is greater error in estimating ambient exposures to 

PM10-2.5 than to PM2.5 and that such uncertainty is a particularly relevant consideration when 

interpreting PM10-2.5 epidemiological studies. Contributing to this uncertainty is the relatively 

limited spatial coverage provided by the existing PM10-2.5 monitoring network (discussed in 

section 1.3.4 above; US EPA, 2009a, sections 2.2.3, 2.3.3, 2.3.4, 3.5.1.1). As discussed above, a 

national monitoring network for PM10-2.5 is not in place, limiting the spatial area over which 

PM10-2.5 concentrations are measured. In addition, based on the limited available evidence, the 

ISA concluded that “there is greater spatial variability in PM10-2.5 concentrations than PM2.5

concentrations, resulting in increased exposure error for the larger size fraction” (US EPA, 

2009a, p. 2-8) and that available measurements do not provide sufficient information to 

adequately characterize the spatial distribution of PM10-2.5 concentrations (US EPA, 2009a, 

section 3.5.1.1). The net effect of these uncertainties on epidemiological studies of PM10-2.5 is to 

bias the results of such studies toward the null hypothesis. That is, as noted in the ISA, these 

limitations in estimates of ambient PM10-2.5 concentrations “would tend to increase uncertainty 

and make it more difficult to detect effects of PM10-2.5 in epidemiologic studies” (US EPA 2009a, 

p. 2-21). 

Given these limitations in the available PM10-2.5 monitoring data, different 

epidemiological studies have employed different approaches for estimating PM10-2.5

concentrations, further contributing to uncertainty in interpreting these studies. For example, as 

discussed above, the multi-city study by Peng et al. (2008) estimated PM10-2.5 by taking the 

difference between collocated PM10 and PM2.5 monitors while the study by Zanobetti and 

Schwartz (2009) used the difference between county average PM10 and PM2.5 concentrations. A 

small number of studies have directly measured PM10-2.5 concentrations with dichotomous 

samplers (e.g., Burnett et al., 2004; Villeneuve et al., 2003; Klemm et al., 2004). It is not clear 

how computed PM10-2.5 measurements, such as those used by Zanobetti and Schwartz, compare 

with the PM10-2.5 concentrations obtained in other studies either by direct measurement with a 

dichotomous sampler or by calculating the difference using co-located samplers (US EPA, 

2009a, section 6.5.2.3).3

 Given the use of these different approaches to estimating PM10-2.5

 3

In addition, several sources of uncertainty can be specifically associated with PM10-2.5 concentrations that are 

estimated based on co-located monitors. For example, the potential for differences among operational flow rates 

and temperatures for PM10 and PM2.5 monitors add to the potential for exposure misclassification. As discussed in 

Appendix B, PM10 data are often reported at standard temperature and pressure (STP) while PM2.5 is reported at 

local conditions (LC). In these cases, the PM10 data should be adjusted to local conditions when estimating PM10-2.5 

concentrations. In many of the epidemiological studies that estimated PM10-2.5 concentrations based on co-located 
3-17 

concentrations across studies, and their inherent limitations, the distributions of thoracic coarse 

particle concentrations over which reported health outcomes occur remain highly uncertain. 

The ISA also notes that the potential for confounding by co-occurring pollutants, 

particularly PM2.5, has been addressed in only a relatively small number of PM10-2.5

epidemiological studies, introducing additional uncertainty into the interpretation of these studies 

(US EPA, 2009a, section 2.3.3). This is a particularly important consideration given the 

relatively limited body of experimental evidence available to support the plausibility of 

associations between PM10-2.5 itself and health effects reported in epidemiological studies. As 

discussed above, many epidemiological studies that have evaluated co-pollutant models have 

reported that PM10-2.5 effect estimates remain positive, but lose precision and are not statistically 

significant in these models (US EPA, 2009a, Figures 6-5, 6-9, 6-15). The net effect of this 

limitation in the number of epidemiological studies that have evaluated co-pollutant models, 

combined with the limited number of supporting experimental studies, is to increase the 

uncertainty associated with estimates of the extent to which PM10-2.5 itself, rather than one or 

more co-occurring pollutants, is responsible for the reported health effects. 

Another uncertainty results from the relative lack of information on the chemical and 

biological composition of PM10-2.5, and the effects associated with the various components (US 

EPA, 2009a, section 2.3.4). As discussed above, a few recent studies have evaluated 

associations between health effects and particles of non-urban, crustal origin by evaluating the 

health impacts of dust storm events. Though these studies provide some information on the 

health effects of particles that likely differ in composition from the particles of urban origin that 

are typically studied, without more information on the chemical speciation of PM10-2.5, the 

apparent variability in associations with health effects across locations is difficult to characterize 

(US EPA, 2009a, section 6.5.2.3). 

As discussed above, a 24-hour PM10 standard is in place to protect the public health 

against exposures to thoracic coarse particles. Therefore, in further considering the adequacy of 

the current PM10 standard, we ask the following question: 

 To what extent does the available scientific evidence report associations between 

PM10-2.5 and morbidity and mortality in areas that would likely meet the current PM10

standard? 

In addressing this question, we have used EPA’s AQS4

 to characterize PM10

concentrations in U.S. locations where both single-city and multi-city PM10-2.5 studies have been 

 

monitors, it is not made explicitly clear whether this adjustment was made, adding to the overall uncertainty in the 

PM10-2.5 concentrations that are associated with health effects. 4

 Accessible at http://www.epa.gov/ttn/airs/airsaqs/ 
3-18 

conducted (see U.S. EPA, 2009a, Figures 6-1 to 6-30 for studies). When compared to single-city 

studies, we note that multi-city studies assess PM10-2.5-associated health effects among larger 

study populations, providing enhanced power to detect PM10-2.5-associated health effects. In 

addition, multi-city studies often provide spatial coverage for different regions across the 

country, reflecting differences in PM10-2.5 sources, composition, and potentially other factors that 

could impact PM10-2.5-related effects. These factors make multi-city studies particularly 

important when drawing conclusions about health effect associations. However, multi-city 

studies often present overall effect estimates rather than single-city effect estimates, while shortterm air quality can vary considerably across cities. Therefore, the extent to which effects 

reported in multi-city studies are associated with the short-term air quality in any particular 

location is uncertain, especially when considering short-term concentrations at the upper end of 

the distribution of daily concentrations for pollutants with relatively heterogeneous spatial 

distributions such as PM10-2.5 and PM10 (US EPA, 2009a, section 2.1.1.2). In contrast, single-city 

studies are more limited in terms of power and geographic coverage but the link between 

reported health effects and the short-term air quality in a given city is more straightforward to 

establish. As a result, in considering 24-hour PM10 concentrations in locations of 

epidemiological studies, we have focused below primarily on single-city studies (Figures 3-2 and 

3-3) and single-city analyses of the locations evaluated in the multi-city study by Zanobetti and 

Schwartz (US EPA, 2009a, Figure 6-29). 

The current PM10 standard has a form of one-expected-exceedance per year, averaged 

over 3 years.5 In order to compare PM10 concentrations in study locations to the level of the 

current standard, we have identified the PM10 3-year expected exceedance concentrationequivalent design value for each study period (labeled “DV” in Figures 3-2 and 3-3 below) using 

the protocol specified in the PM10 State Implementation Plan (SIP) Development Guidelines (US 

EPA, 1987).6 Some studies (indicated by a * in Figure 3-3) covered time periods of less than 

three years. For these study areas, to characterize ambient PM10 concentrations relative to 

concentrations allowed under the current PM10 standard, we averaged the second highest 24-hour 

 5

The one-expected-exceedance form implies that the standard level is not to be exceeded more than once per year, 

on average over 3 years. Therefore, in areas that report 24-hour PM10 concentrations every day, the 4th highest 24-

hour PM10 concentration measured during a three year period is compared to the standard level. In contrast, in areas 

that monitor PM10 every six days or every three days, the PM10 concentrations that are comparable to the standard 

level are, respectively, the highest and 2nd highest 24-hour PM10 concentrations measured during a three year period. 

6

Specifically, the PM10 3-year expected exceedance concentration-equivalent design value is identified as the highest 

24-hour average concentration (i.e., from a single monitor in the study area) over a 3-year period when there are 347 

or fewer samples reported for that time-frame, the second highest 24-hour average concentration when there are 348 

to 695 samples in the 3-year period, the third highest 24-hour average concentration when there are 696 to 1042 

samples in the 3-year period, and the fourth highest 24-hour average concentration when there are 1043 or more 

samples reported over the 3-year period. Concentration-equivalent design values were not identified for study 

periods less than 3 years. 
3-19

Figure 3-2. PM10 Air Quality and PM10-2.5 Effect Estimates in Locations of U.S. Single-City PM10-2.5 M

++Studies in Figures 3-2 and 3-3 are a combination of those assessed in the last review and those assessed in the ISA in the current revie

figures are ordered by increasing PM10 concentration-equivalent design values.
3-20

Figure 3-3. PM10 Air Quality PM10-2.5 Effect Estimates in Locations of U.S. Single-City PM10-2.5 Mo

* Concentration-equivalent design values were not identified for study periods less than three years. For study periods of less than thresecond highest 24-hour PM10 concentrations for each year of the study (i.e., second highest concentration measured by the single monit

recording the highest such concentration). 
3-21

PM10 concentrations for each year of the study (i.e., second highest concentration measured at 

the single monitor in the study area recording the highest such concentration). The identification 

of concentration-equivalent design values and second highest PM10 concentrations for each study 

area are described in more detail in Schmidt and Jenkins (2010) and Jenkins (2011). 

In addition to the single-city studies included in Figures 3-2 and 3-3 above, multi-city 

averages of the 3-year expected exceedance concentration-equivalent design values for U.S. 

multi-city studies were 110 μg/m3, for the locations evaluated by Zanobetti and Schwartz (2009) 

(see Jenkins, 2011 for PM10 air quality concentrations), and 100 μg/m3, for the locations 

evaluated by Peng et al. (2008) (see Schmidt and Jenkins, 2010 for PM10 air quality 

concentrations). As discussed above, the extent to which overall PM10-2.5 effect estimates 

reported in multi-city studies are associated with the air quality in any particular location is 

uncertain. However, the ISA also presents single-city Bayes-adjusted effect estimates for each 

of the cities evaluated by Zanobetti and Schwartz (US EPA, 2009a, Figure 6-29), providing the 

opportunity to consider associations between PM10-2.5 and mortality, and to consider the PM10 air 

quality, in each of the individual cities evaluated in this study. 

As discussed above, in single-city analyses in the locations evaluated by Zanobetti and 

Schwartz, PM10-2.5 effect estimates for mortality were generally positive but not statistically 

significant, and most were similar in magnitude and precision, particularly for cardiovascularrelated mortality, across a wide range of estimated PM10-2.5 concentrations (US EPA, 2009a, 

Figure 6-29). Three-year PM10 expected exceedance concentration-equivalent design values in 

these cities ranged from 50 μg/m3 (Davie, FL) to 283 μg/m3 (Salt Lake City, UT). In most of the 

cities evaluated (37 of the 45 for which concentration-equivalent design values could be 

identified), concentration-equivalent design values were below 150 μg/m3 (Jenkins, 2011). In 

the six cities where positive and statistically significant PM10-2.5 mortality effect estimates were 

reported (see above), concentration-equivalent design values were as follows (Jenkins, 2011): 

 Chicago: 113 μg/m3 

 Pittsburgh: 139 μg/m3

 Birmingham7

: 154 μg/m3

 Detroit: 165 μg/m3

 St. Louis: 165 μg/m3

 Salt Lake City: 283 μg/m3 

Therefore, while PM10-2.5 effect estimates in single-city analyses were not statistically significant 

for most locations evaluated by Zanobetti and Schwartz, including some locations with PM10

 

7

According to rounding convention for the PM10 standard, a 24-hour PM10 concentration of 154 g/m3 would round 

to 150 g/m3

 (71 FR 61144). Therefore, based on the PM10 one-expected-exceedance concentration-equivalent 

design value, Birmingham would have been expected to just meet the current PM10 standard during the study period. 
3-22

concentrations well above those allowed by the current 24-hour PM10 standard, positive and 

statistically significant PM10-2.5 effect estimates were reported in two locations (Chicago, 

Pittsburgh) with concentration-equivalent design values below 150 μg/m3.

7

In considering PM10-2.5 epidemiological studies conducted in Canada and elsewhere 

outside the U.S., we note that we generally do not have access to PM10 air quality information 

beyond that published by the study authors. Many of these studies report PM concentrations 

averaged across monitors, rather than from the highest monitor in the study area, and/or report 

only mean or median concentrations. Lin et al. (2002) reported positive and statistically 

significant associations between PM10-2.5 and asthma hospital admissions in children in Toronto 

(US EPA, 2009a; Figures 6-12, 6-15). The authors reported a maximum PM10 concentration 

measured at a single monitor in the study area of 116 μg/m3

, indicating that the PM10 air quality 

in Toronto during this study would have been allowed by the current 24-hour PM10 standard. In 

contrast Middleton et al. (2008), who reported that dust storms in Cyprus were associated with a 

statistically significant increase in risk of hospitalization for all causes and a non-significant 

increase in hospitalizations for cardiovascular diseases, reported a maximum 24-hour PM10

concentration of 1,371 μg/m3. Thus, the dust storm-associated increases in hospitalizations 

reported in this study occurred in an area with PM10 concentrations that were likely well above 

those allowed by the current standard. Other dust storm studies did not report maximum 24-hour 

PM10 concentrations from individual monitors, though the studies by Chan et al. (2008) and Bell 

et al. (2008), which reported positive and statistically significant associations between dust storm 

metrics and cardiovascular-related hospital admissions, reported that 24-hour PM10

concentrations, averaged across monitors, exceeded 200 μg/m3. It is likely that peak 

concentrations measured at individual monitors in these studies were much higher and, therefore, 

24-hour PM10 concentrations in these study areas were likely above those allowed by the current 

standard. 

Summary of Evidence-Based Considerations

New evidence supporting an association between PM10-2.5 and mortality and morbidity 

has become available since the last review of the PM NAAQS. The available evidence was 

judged in the ISA to be suggestive of a causal relationship between short-term PM10-2.5 

exposures and mortality, cardiovascular effects, and respiratory effects while the evidence was 

judged inadequate to infer a causal relationship with long-term PM10-2.5 exposures for these 

same broad health effect categories as well as other effects considered in the ISA (US EPA, 

2009a, section 2.3.3; see Table 3-1 above). The evidence supporting a link between short-term 

thoracic coarse particle exposures and adverse health effects comes primarily from 

epidemiological studies, with limited supporting evidence from controlled human exposure 
3-23

studies and, to a lesser extent, animal instillation studies. This evidence includes several recent 

(i.e., published since the last review of the PM NAAQS) multi-city epidemiological studies 

conducted in the U.S., Canada, and Europe and a small number of recent studies of particles of 

non-urban origin. In general, epidemiological studies have reported positive, and in some cases 

statistically significant, PM10-2.5 effect estimates. In the limited number of studies that have 

evaluated co-pollutant models that include either gaseous pollutants or fine particles, PM10-2.5 

effect estimates generally remained positive, and in a few cases statistically significant. 

Positive associations between PM10-2.5 and mortality and morbidity have been reported in 

a number of locations across the U.S. with a wide range of PM10-2.5 and PM10 concentrations. 

Among single-city analyses, PM10-2.5 effect estimates were positive and statistically significant in 

a few U.S. cities and at least one Canadian city with ambient PM10 concentrations that would be 

allowed by the current 24-hour PM10 standard. In addition, multi-city average PM10 oneexpected-exceeded concentration-equivalent design values were below the level of the current 

PM10 standard when averaged across U.S. cities that have been evaluated in multi-city studies 

reporting positive, and in some cases statistically significant, associations between PM10-2.5 and 

mortality and morbidity. However, most PM10-2.5 effect estimates, even those reported in 

locations with PM10 concentrations above the concentrations allowed by the current standard, 

were not statistically significant. 

3.2.2 CASAC Conclusions and Recommendations 

Following their review of the first and second draft PAs, CASAC provided advice and 

recommendations regarding the current and potential alternative standards for thoracic coarse 

particles (Samet, 2010c; Samet, 2010d). With regard to the current PM10 standard, CASAC 

concluded that “the current data, while limited, is sufficient to call into question the level of 

protection afforded the American people by the current standard” (Samet, 2010d, p. 7). In 

drawing this conclusion, CASAC noted the positive associations in multi-city and single-city 

studies, including in locations with PM10 concentrations below those allowed by the current 

standard. In addition, CASAC gave “significant weight to studies that have generally reported 

that PM10-2.5 effect estimates remain positive when evaluated in co-pollutant models” and 

concluded that “controlled human exposure PM10-2.5 studies showing decreases in heart rate 

variability and increases in markers of pulmonary inflammation are deemed adequate to support 

the plausibility of the associations reported in epidemiologic studies” (Samet, 2010d, p. 7). 

Given all of the above conclusions CASAC recommended that “the primary standard for PM10

should be revised” in order to increase public health protection (Samet, 2010d, p. ii and p. 7). 
3-24

3.2.3 Staff Conclusions on Adequacy of Current PM10 Standard 

In light of the available PM10-2.5 health evidence and the PM10 air quality concentrations 

in study locations, as discussed above, we revisit the overarching question: Does the currently 

available scientific evidence, as reflected in the ISA, and air quality information support or call 

into question the adequacy of the protection afforded by the current 24-hour PM10 standard 

against effects associated with exposures to thoracic coarse particles? 

In considering the evidence and information as they relate to the adequacy of the current 

24-hour PM10 standard we note that, as discussed above, this standard is meant to protect the 

public health against effects associated with short-term exposures to PM10-2.5. In the last review, 

it was judged appropriate to maintain such a standard given the “growing body of evidence 

suggesting causal associations between short-term exposure to thoracic coarse particles and 

morbidity effects, such as respiratory symptoms and hospital admissions for respiratory diseases, 

and possibly mortality” (71 FR 61185, October 17, 2006). Given the expanded body of evidence 

available in the current review, discussed in detail in the ISA (US EPA, 2009a, Chapter 6) and 

summarized above, we conclude that the evidence continues to support the appropriateness of a 

standard to protect the public health against effects associated with short-term exposures to 

PM10-2.5. In addition, when considering the evidence for associations with PM10-2.5 from different 

types of sources and in different locations (e.g., thoracic coarse particles of urban/industrial 

origin as well as windblown dust of non-urban origin), we conclude that it remains appropriate to 

provide some measure of protection against exposures to all thoracic coarse particles. 

In considering the evidence, we note that a decision on the adequacy of the public health 

protection provided by the current PM10 standard will be a public health policy judgment in 

which the Administrator weighs that evidence and its inherent uncertainties. Therefore, 

depending on the emphasis placed on different aspects of the evidence and uncertainties, 

consideration of different conclusions on adequacy could be supported. 

For example, one approach to considering the evidence and its associated uncertainties 

would be to place emphasis on the following: 

 While important uncertainties are associated with the health evidence, several multi-city 

epidemiological studies conducted in the U.S., Canada, and Europe, as well as a number 

of single-city studies, have reported generally positive, and in some cases statistically 

significant, associations between short-term PM10-2.5 concentrations and adverse health 

endpoints including mortality and cardiovascular-related and respiratory-related hospital 

admissions and emergency department visits. 

 Both single-city and multi-city analyses, using different approaches to estimate ambient 

PM10-2.5 concentrations, have reported positive PM10-2.5 effect estimates in locations that 

would likely have met the current 24-hour PM10 standard. In some cases, these PM10-2.5 

effect estimates were statistically significant. 
3-25

 While limited in number, studies that have evaluated co-pollutant models have generally 

reported that PM10-2.5 effect estimates remain positive, and in a few cases statistically 

significant, when these models include gaseous pollutants or fine particles. 

 Support for the plausibility of the associations reported in epidemiological studies is 

provided by a small number of controlled human exposure studies reporting that shortterm (i.e., 2-hour) exposures to PM10-2.5 decrease heart rate variability and increase 

markers of pulmonary inflammation. 

Such an approach to considering the evidence would place substantial weight on the generally 

positive PM10-2.5 effect estimates that have been reported for mortality and morbidity, even those 

effect estimates that are not statistically significant. This could be judged appropriate given that 

consistent results have been reported across multiple studies using different approaches to 

estimate ambient PM10-2.5 concentrations and that exposure measurement error, which is likely to 

be larger for PM10-2.5 than for PM2.5, tends to bias the results of epidemiological studies toward 

the null hypothesis, making it less likely that associations will be detected. In contrast, such an 

approach would place relatively little weight on the uncertainties in the evidence that resulted in 

the ISA conclusions that the evidence is only “suggestive” of a causal relationship, rather than 

“likely causal” or “causal.” To the extent that a decision on the adequacy of the current 24-hour 

PM10 standard were to place emphasis on the considerations noted above, it could be judged that 

the current 24-hour PM10 standard does not protect public health with an adequate margin of 

safety and that it should be revised in order to increase protection against effects associated with 

short-term exposures to thoracic coarse particles. 

Another approach to considering the evidence and its uncertainties would be to place 

emphasis on the following: 

 While most of PM10-2.5 effect estimates reported for mortality and morbidity were 

positive, many were not statistically significant, even in single-pollutant models. This 

includes effect estimates reported in study locations with PM10 concentrations above 

those allowed by the current 24-hour PM10 standard. 

 The number of epidemiological studies that have employed co-pollutant models to 

address the potential for confounding, particularly by PM2.5, remains limited. 

Therefore, the extent to which PM10-2.5 itself, rather than one or more co-pollutants, 

contributes to reported health effects remains uncertain. 

 Only a limited number of experimental studies provide support for the associations 

reported in epidemiological studies, resulting in further uncertainty regarding the 

plausibility of the associations between PM10-2.5 and mortality and morbidity reported 

in epidemiological studies. 
3-26

 Limitations in PM10-2.5 monitoring data and the different approaches used to estimate 

PM10-2.5 concentrations across epidemiological studies result in uncertainty in the 

ambient PM10-2.5 concentrations at which the reported effects occur. 

 The chemical and biological composition of PM10-2.5, and the effects associated with 

the various components, remains uncertain. Without more information on the chemical 

speciation of PM10-2.5, the apparent variability in associations across locations is 

difficult to characterize. 

 In considering the available evidence and its associated uncertainties, the ISA 

concluded that the evidence is “suggestive” of a causal relationship between short-term 

PM10-2.5 exposures and mortality, cardiovascular effects, and respiratory effects. These 

weight-of-evidence conclusions contrast with those for the relationships between PM2.5

exposures and adverse health effects, which were judged in the ISA to be either 

“causal” or “likely causal” for mortality, cardiovascular effects, and respiratory effects. 

To the extent that a decision on the adequacy of the current 24-hour PM10 standard were to place 

emphasis on the considerations noted above, it could be judged that, while it remains appropriate 

to maintain a standard to protect against short-term exposures to thoracic coarse particles, the 

available evidence suggests that the current 24-hour PM10 standard appropriately protects public 

health and that it provides an adequate margin of safety against effects that have been associated 

with PM10-2.5. While this approach to considering the evidence would recognize the positive, and 

in some cases statistically significant, associations between PM10-2.5 and mortality and morbidity 

by maintaining a standard to protect against exposures to thoracic coarse particles, it would place 

relatively greater emphasis on the limitations and uncertainties noted above, which tend to 

complicate the interpretation of that evidence. 

Given all of the above, we conclude that it would be appropriate to consider either 

retaining or revising the current 24-hour PM10 standard, depending on the approach taken to 

considering the available evidence and information. Therefore, we judge that it is appropriate in 

this PA to consider what potential alternative standards, if any, could be supported by the 

available scientific evidence in order to increase public health protection against exposures to 

PM10-2.5. 
3-27

3.3 CONSIDERATION OF POTENTIAL ALTERNATIVE STANDARDS 

Staff next considers the following overarching question: 

What potential alternative standard(s) could be supported by the currently available 

scientific evidence and air quality information? 

In addressing this overarching question, we consider how the currently available 

scientific evidence and air quality information could inform decisions regarding the basic 

elements of the NAAQS: indicator (section 3.3.1), averaging time (section 3.3.2), form (section 

3.3.3), and level (section 3.3.4). These elements are considered collectively in evaluating the 

health protection afforded by potential alternative standards under consideration. 

3.3.1 Indicator 

As discussed above, PM10 includes both PM10-2.5 and PM2.5, with the relative contribution 

of each to PM10 mass varying across locations (see below). In the most recent review completed 

in 2006, EPA concluded that the PM10 indicator remained appropriate because a PM10 standard 

would be expected to provide appropriate protection against effects associated with exposures to 

PM10-2.5. In particular, a PM10 indicator would be expected to target protection to urban areas, 

where the evidence of effects from exposure to coarse PM is the strongest (71 FR at 61196). In 

considering potential alternative standards in the current review, we have considered the 

following question with regard to indicator: 

 To what extent does the available evidence and/or air quality information provide 

support for retaining or revising the current PM10 indicator?

In addressing this question, we focus on the following considerations: 

 The extent to which PM10 is comprised of PM10-2.5 

 The appropriateness of a standard that would be expected to allow lower PM10-2.5 

concentrations in areas with higher fine particle concentrations (i.e., urban areas) than 

areas with lower fine particle concentrations (i.e., rural areas)8

As an initial matter, we consider the proportion of PM10 mass in different regions of the 

U.S. that is PM10-2.5 (see US EPA, 2009a, section 3.5.1.1; Schmidt and Jenkins, 2010). Schmidt 

and Jenkins (2010) divided the U.S. into climatic regions using the same approach as used in the 

multi-city epidemiological study by Zanobetti and Schwartz (2009) (see above).9 Consistent 

 8

For comparisons of PM2.5 mass in urban and rural areas see Schmidt (2005, outputs for Attachment D). 

9

The Mediterranean region includes CA, OR, WA. The dry region includes NM, AZ, NV. The dry continental 

region includes MT, ID, WY, UT, CO. The hot summer continental region includes SD, NE, IA, IL, IN, OH. The 

warm summer continental region includes ND, MN, WI, MI, PA, NY, CT, RI, MA, VT, NH, ME. The humid 
3-28

with the air quality analyses in the ISA (US EPA, 2009a, section 3.5.1.1) and the concentration 

estimates of Zanobetti and Schwartz (2009), PM10-2.5 concentrations were higher in the dry, 

mediterranean, and dry continental regions, with the highest concentrations in the dry region, 

which included the southwestern U.S. (data not shown, from Schmidt and Jenkins, 2010). On 

average, ratios of PM10-2.5 concentrations to PM10 concentrations were also higher in these 

regions than in the remainder of the U.S., with the dry region having the highest ratios. 

Consistent results were reported in the ISA analyses of PM air quality (US EPA, 2009a, compare 

Tables 3-9 and 3-10). While the same general pattern persisted when the analysis was restricted 

to days with PM10 concentrations at the high end of the distribution of 24-hour concentrations 

(i.e., at or above the 95th percentile values), ratios of PM10-2.5 to PM10 on these days tended to be 

somewhat higher, on average, across most regions of the U.S. (see right-hand columns in Figure 

3-4). 

Figure 3-4. Site-Level Ratio of 24-Hour PM10-2.5 to 24-Hour PM10 Concentrations* 

*Blue stars represent mean concentrations, horizontal lines represent median concentrations, boxes 

represent 75% confidence intervals, and error bars represent 95% confidence intervals. N values 

equal the number of site years of monitoring data for each region. 

Thus, on average across the U.S., PM10-2.5 comprises a larger portion of PM10 on days with 

relatively high PM10 concentrations than on days with more typical PM10 concentrations. Given 

this, a PM10 standard that focuses on the upper end of the distribution of daily PM10

concentrations could effectively control ambient PM10-2.5 concentrations. 

 

subtropical and maritime region includes FL, LA, TX, GA, AL, MS, AR, OK, KS, MO, TN, SC, NC, VA, WV, KY, 

NJ, DE, DC, MD. 
3-29

Given the above conclusion, in further considering the issue of indicator we note that 

most of the evidence for positive associations between PM10-2.5 and morbidity and mortality, 

particularly evidence for these associations at relatively low concentrations of PM10-2.5, continues 

to come from studies conducted in locations where the PM10-2.5 is expected to be largely of urban 

origin. While some studies have reported positive associations between relatively high 

concentrations of particles of non-urban origin (i.e., crustal material from windblown dust in 

non-urban areas, see above) and mortality and morbidity, we note that the extent to which these 

associations would remain at the lower particle concentrations more typical of U.S. and 

Canadian urban study locations remains uncertain.10 Given these considerations, and given the 

increased potential for coarse particles in urban areas to become contaminated by toxic 

components of fine particles from urban/industrial sources (US EPA, 2004), we conclude that it 

remains appropriate to maintain a standard that allows lower ambient thoracic coarse particle 

concentrations in urban areas than in non-urban areas. 

Given this conclusion, we note that it would be reasonable to consider an indicator that 

targets control on areas with the types of ambient mixes generally present in urban areas. Such 

an indicator would focus control on areas with ambient mixes known with greater certainty to be 

associated with adverse health effects and, therefore, would provide public health benefits with 

the greatest degree of certainty. As noted in the last review of the PM NAAQS, a PM10 standard 

would allow lower concentrations of PM10-2.5 in areas with higher fine particle concentrations, 

which tend to be urban locations, than areas with lower fine particle concentrations, which tend 

to be rural locations (section 3.1.1.2 above). Therefore, as in the last review, we reach the 

conclusion that a PM10 indicator would appropriately target protection to those locations where 

the evidence is strongest for associations between adverse health effects and exposures to 

thoracic coarse particles. In contrast, we note that a PM10-2.5 indicator, for a standard set at a 

single unvarying level, would not achieve this targeting, given that allowable thoracic coarse 

particle concentrations would be the same regardless of the location or the likely sources of PM. 

Therefore, given the currently available evidence, one possible result of using a PM10-2.5 indicator 

would be a standard that is overprotective in rural areas and/or underprotective in urban areas. 

In addition, while we note that administrative feasibility is not an appropriate basis for 

informing decisions on the NAAQS or its elements (see above), we also note that PM10-2.5 

concentrations are not routinely measured and reported at present (US EPA, 2009a, section 

 10Other than the dust storm studies, we note that the study in Coachella Valley by Ostro et al. (2003) reported 

statistically significant associations in a location where thoracic coarse particles are expected to be largely due to 

windblown dust. Specifically, we note the CASAC conclusion in the last review that “studies from Ostro et al. 

showed significant adverse health effects, primarily involving exposures to coarse-mode particles arising from 

crustal sources” (Henderson, 2005b). In considering this study, we also note the relatively high PM10 concentrations 

in the study area (see Figure 3-2 above). 
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3.5.1.1). In the last review of the PM NAAQS, EPA required monitoring of PM10-2.5 mass and 

we expect that approximately 80 stations will be reporting mass concentrations as part of the 

National Core (NCore) network (http://www.epa.gov/ttnamti1/ncore/index.html) (section 1.3.3 

and Appendix B, section B.4). However, data from those monitors are not available for locations 

and time periods of existing PM10-2.5 health studies. 

In their review of the second draft PA, CASAC agreed with staff’s conclusions that the 

available evidence supports consideration in the current review of a PM10 indicator for a standard 

that protects against exposures to thoracic coarse particles. Specifically, CASAC concluded that 

“[w]hile it would be preferable to use an indicator that reflects the coarse PM directly linked to 

health risks (PM10-2.5), CASAC recognizes that there is not yet sufficient data to permit a change 

in the indicator from PM10 to one that directly measures thoracic coarse particles” (Samet, 

2010d, p. ii). In addition, CASAC “vigorously recommends the implementation of plans for the 

deployment of a network of PM10-2.5 sampling systems so that future epidemiological studies will 

be able to more thoroughly explore the use of PM10-2.5 as a more appropriate indicator for 

thoracic coarse particles” (Samet, 2010d, p. 7). 

Given all of the above considerations, as in the draft Policy Assessment, we conclude that 

the available evidence supports consideration in the current review of a PM10 indicator for a 

standard that protects against exposures to thoracic coarse particles. We further conclude that 

consideration of alternative indicators (e.g., PM10-2.5) in future reviews is desirable and could be 

informed by additional research, as described below (section 3.5). 

3.3.2 Averaging Time 

Based primarily on epidemiological studies that reported positive associations between 

short-term (24-hour) PM10–2.5 concentrations and mortality and morbidity, the Administrator 

concluded in the last review that the available evidence supported a 24-hour averaging time for a 

standard intended to control thoracic coarse particles. In contrast, given the relative lack of 

studies supporting a link between long-term exposures to thoracic coarse particles and morbidity 

or mortality (US EPA, 2004a, Chapter 9), the Administrator further concluded that an annual 

coarse particle standard was not warranted at that time (71 FR 61198-61199). 

In the current review, we consider the extent to which the available evidence provides 

information relevant for decisions on averaging time by considering the following question: 

 To what extent does the available evidence continue to support a 24-hour averaging 

time for a standard meant to protect against effects associated with exposures to 

PM10-2.5? 

With regard to this question, we note the conclusions from the ISA regarding the weight 

of evidence for short-term and long-term PM10-2.5 exposures as well as the studies on which those 
3-31

conclusions are based. Specifically, as discussed above (see Table 3-1 above), the ISA 

concludes that the existing evidence is suggestive of a causal relationship between short-term 

PM10-2.5 exposures and mortality, cardiovascular effects, and respiratory effects (ISA, section 

2.3.3). This conclusion is based largely on epidemiological studies which have primarily 

evaluated associations between 24-hour PM10-2.5 concentrations and morbidity and mortality 

(e.g., see ISA, Figure 2-3), though a small number of controlled human exposure studies have 

reported effects following shorter exposures (i.e., 2-hours) to PM10-2.5 (e.g., see ISA, sections 

6.2.1.2, 6.3.3.2). In contrast, with respect to long-term exposures, the ISA concludes that 

available evidence is inadequate to infer a causal relationship with all health outcomes 

evaluated (US EPA, 2009a, section 2.3). Specifically, the ISA states, “To date, a sufficient 

amount of evidence does not exist in order to draw conclusions regarding the health effects and 

outcomes associated with long-term exposure to PM10-2.5” (US EPA, 2009a, section 2.3.4; see 

Table 3-1 above). 

In considering these weight of evidence determinations in the ISA, we conclude that, at a 

minimum, they suggest the importance of maintaining a standard that protects against short-term 

exposures to thoracic coarse particles. Given that the majority of the evidence supporting the 

link between short-term PM10-2.5 and morbidity and mortality is based on 24-hour average 

thoracic coarse particle concentrations, we conclude that the evidence available in this review 

continues to support consideration of a 24-hour averaging time for a PM10 standard meant to 

protect against effects associated with short-term exposures to PM10-2.5. We further conclude that 

the available evidence does not support consideration of an annual thoracic coarse particle 

standard at this time. In reaching this conclusion, we also note that, to the extent a short-term 

standard requires areas to reduce their 24-hour ambient particle concentrations, long-term 

concentrations would also be expected to decrease. Therefore, a 24-hour standard meant to 

protect against short-term exposures to thoracic coarse particles would also be expected to 

provide some protection against any potential effects associated with long-term exposures to 

ambient concentrations. CASAC agreed with these conclusions (Samet, 2010c). 

3.3.3 Form 

The “form” of a standard defines the air quality statistic that is to be compared to the 

level of the standard in determining whether an area attains that standard. In identifying a single 

statistic for the form, we note that although future air quality improvement strategies in any 

particular area are not defined until after a standard is promulgated, many such strategies are 

likely to affect a broad distribution of PM air quality concentrations in an area. Therefore, 

although the form of the standard defines a single statistic, any reductions in health risks that are 
3-32

likely to result from strategies designed to meet a specific standard are likely to occur across a 

wide range of concentrations. 

As discussed above, in the last review the Administrator retained the one-expected 

exceedance form of the primary 24-hour PM10 standard. This decision was linked to the overall 

conclusion that “the level of protection from coarse particles provided by the current 24-hour 

PM10 standard remains requisite to protect public health with an adequate margin of safety” (71 

FR 61202). Because revising either the level or the form of the standard would have altered the 

protection provided, it was concluded that such changes “would not be appropriate based on the 

scientific evidence available at this time” (71 FR 21202). Therefore, the decision in the last 

review to retain the one-expected-exceedance form was part of the broader decision that the 

existing 24-hour standard provided requisite public health protection. 

In the current review, we are also considering the form of the standard within the context 

of the overall decision on whether, and if so how, to revise the current 24-hour PM10 standard. 

Given the conclusions above regarding the appropriate indicator and averaging time for 

consideration for potential alternative standards, we consider potential alternative forms for a 24-

hour PM10 standard. To frame our consideration of this issue, we pose the following question: 

 To what extent does available evidence and information support consideration of an 

alternative form for a 24-hour PM10 standard? 

Although the selection of a specific form must be made within the context of decisions on 

the other elements of the standard, EPA generally favors concentration-based forms for shortterm standards. In 1997 EPA established a 98th percentile form for the 24-hour PM2.5 standard 

and in 2010 EPA established a 98th percentile form for the 1-hour NO2 standard (62 FR 38671; 

75 FR 6474) and a 99th percentile form for the 1-hour SO2 standard (75 FR 35541). In making 

these decisions, EPA noted that, compared to an exceedance-based form, a concentration-based 

form is more reflective of the health risks posed by elevated pollutant concentrations because 

such a form gives proportionally greater weight to days when concentrations are well above the 

level of the standard than to days when the concentrations are just above the level of the 

standard. In addition, when averaged over three years, these concentration-based forms were 

judged to provide an appropriate balance between limiting peak pollutant concentrations and 

providing a stable regulatory target, facilitating the development of stable implementation 

programs. 

These considerations are also relevant in the current review of the 24-hour PM10 standard. 

Specifically, we conclude that it is appropriate to consider concentration-based forms that would 

provide a balance between limiting peak pollutant concentrations and providing a stable 

regulatory target. To accomplish this, it would be appropriate to consider forms from the upper 
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end of the annual distribution of 24-hour PM10 concentrations.11 However, given the potential 

for local sources to have important impacts on monitored PM10 concentrations (US EPA, 2009a, 

section 2.1.1.2), we also note that it would be appropriate to consider forms that, when averaged 

over three years, would be expected to promote the stability of local implementation programs.12 

In considering these issues in the most recent review of the NO2 primary NAAQS, we note that a 

98th percentile form was adopted, rather than a 99th percentile form, due to the potential for 

“instability in the higher percentile concentrations” near local sources (75 FR 6493).13 

In considering the potential appropriateness of a 98th percentile form in the current 

review, we note that, compared to the current PM10 standard, attainment status for a PM10

standard with a 98th percentile form would be based on a more stable air quality statistic and 

would be expected to be less influenced by relatively rare events that can cause elevations in 

PM10 concentrations over short-periods of time (Schmidt, 2011b). Specifically, we note that in 

areas that monitor PM10 every six days, every three days, or every day the one-expectedexceedance concentrations that are comparable to the current standard level are, respectively, the 

highest, 2nd highest, or 4th highest 24-hour PM10 concentrations measured during a three year 

period. In contrast, for the same monitoring frequencies, the PM10 concentrations that would be 

comparable to the level of a standard with a 98th percentile form would be the three-year average 

of the 2nd highest, 3rd highest, or 7th/8th highest 24-hour PM10 concentrations measured during a 

single year. 

In further considering this issue we note that, compared to the current expectedexceedance form, a concentration-based form specified as a percentile of the annual distribution 

of PM10 concentrations (e.g., such as a 98th percentile form) would be expected to better 

compensate for missing data and less-than-daily monitoring (Davidson and Hopke, 1984). This 

is a particularly important consideration in the case of PM10 because, depending largely on 

ambient concentrations, the frequency of PM10 monitoring differs across locations (i.e., either 

daily, 1 in 2 days, 1 in 3 days, or 1 in 6 days) (Section 1.3.10 above and Appendix B). With a 

98th percentile form, attainment status would be determined based on PM10 concentrations from 

 11With regard to this conclusion, we also note that PM10-2.5 is likely to make a larger contribution to PM10 mass on 

days with relatively high PM10 concentrations than on days with more typical PM10 concentrations (see above). 12Stability of implementation programs has been held to be a legitimate consideration in determining a NAAQS 

(American Trucking Assn’s v. EPA, 283 F. 3d at 374-75). 

13See also, ATA III, 283 F. 3d at 374-75 (upholding 98th percentile form since “otherwise States would have to 

design their pollution control programs around single high exposure events that may be due to unusual 

meteorological conditions alone, rendering the programs less stable – and hence, we assume, less effective – than 

programs designed to address longer-term average conditions.”). In contrast, in the recently completed review of the 

SO2 primary NAAQS, a 99th percentile form was adopted. However, in the case of SO2, the standard was intended 

to limit 5-minute exposures and a 99th percentile form was markedly more effective at doing so than a 98th percentile 

form. 75 FR at 35540, 41. 
3-34

the same part of the annual distribution of 24-hour PM10 concentrations, regardless of the 

frequency of PM10 monitoring. 

In light of all of the above considerations, we conclude that, to the extent it is judged 

appropriate to revise the current 24-hour PM10 standard, it would be appropriate to consider 

revising the form to the 3-year average of the 98th percentile of the annual distribution of 24-hour 

PM10 concentrations.14 CASAC agreed, noting that they “felt strongly that it is appropriate to 

change the statistical form of the PM10 standard to a 98th percentile form” (Samet, 2010d). In 

reaching this conclusion, CASAC noted that “[p]ublished work has shown that the percentile 

form has greater power to identify non-attainment and a smaller probability of misclassification 

relative to the expected exceedance form of the standard” (Samet, 2010d). 

However, CASAC also noted that such a change in form “will lead to changes in levels 

of stringency across the country” and recommended that this issue be explored further. In 

considering this issue, we acknowledge that, given differences in PM10 air quality distributions 

across locations (US EPA, 2009a, Table 3-10), a revised standard with a 98th percentile form 

would likely target public health protection to some different locations than does the current 

standard with its one-expected-exceedance form. Given this, we note that a further consideration 

with regard to the appropriateness of revising the form of the current PM10 standard is the extent 

to which, when compared with the current standard, a revised standard with a 98th percentile 

form would be expected to target public health protection to areas where we have more 

confidence that ambient PM10-2.5 is associated with adverse health effects. 

In giving initial consideration to this issue, we have used recent PM10 air quality 

concentrations (i.e., from 2007-2009) to identify counties that would meet, and counties that 

would violate, the current PM10 standard as well as potential alternative standards with 98th

percentile forms (Schmidt, 2011b).15, 16 In some cases, counties that would violate the current 

standard do so because of a small number of “outlier” days (e.g., as few as one such day in three 

years) with PM10 concentrations well-above more typical concentrations (Schmidt, 2011b). Mean 

and 98th percentile PM10 and PM10-2.5 concentrations were higher in counties that met the current 

standard, but would have violated a revised standard with a 98th percentile form,17 than in 

 14As noted above, local sources can have important impacts on monitored PM10 concentrations. In the recent review 

of the NO2 primary NAAQS, where this was also an important consideration, a 98th percentile form was adopted, 

rather than a 99th percentile form, due to the potential for “instability in the higher percentile concentrations” near 

local sources (75 FR 6493). A similar conclusion in the current review has led us to focus on the 98th percentile 

rather than the 99th percentile. 

15Section 3.3.4 discusses potential alternative standard levels that would be appropriate to consider in conjunction 

with a revised standard with a 98th percentile form. 

16The memo by Schmidt (2011b) identifies specific counties that are expected to meet, and counties that are 

expected to violate, the current standard as well as potential alternative standards with 98th percentile forms. 

17This analysis considered a revised PM10 standard with a 98th percentile form and a level from the middle of the 

range discussed in section 3.3.4 (i.e., 75 g/m3

). 
3-35

counties that violated the current standard, but would have met a revised standard with a 98th

percentile form (Schmidt, 2011b). This suggests that, to the extent a revised PM10 standard with a 

98th percentile form could target public health protection to different areas than the current 

standard, those areas preferentially targeted by a revised standard generally have higher ambient 

concentrations of thoracic coarse particles. The issue of targeting public health protection is 

considered further in section 3.3.4, within the context of considering specific potential alternative 

standard levels for a 24-hour PM10 standard with a 98th percentile form. 

3.3.4 Level 

As noted above, to the extent it is judged in the current review that the 24-hour PM10

standard does not provide adequate public health protection against exposures to thoracic coarse 

particles, potential alternative standard levels could be considered. Given the conclusions 

described above for indicator, averaging time, and form, we conclude that it would be 

appropriate to consider potential alternative levels for a 24-hour PM10 standard with a 98th

percentile form. To inform our consideration of this issue, we have considered the following 

question: 

 To what extent does available evidence and air quality information support 

consideration of alternative standard levels for a 24-hour PM10 standard with a 98th

percentile form? 

Evidence-based Considerations

In considering the evidence as it relates to potential alternative standard levels, we first 

consider the relative weight to place on specific epidemiological studies, including the weight to 

place on the uncertainties associated with those studies. We have considered several factors in 

placing weight on specific epidemiological studies including the extent to which studies report 

statistically significant associations with PM10-2.5 and the extent to which the reported 

associations are robust to co-pollutant confounding. 

In addition, we consider the extent to which associations with PM10-2.5 can be linked to 

the air quality in a specific location. With regard to this, we place greatest weight on information 

from single-city analyses. Although, as discussed above, multi-city studies have advantages in 

terms of power to detect associations and geographic coverage, the extent to which effects 

reported in multi-city studies are associated with the short-term air quality in any particular 

location is highly uncertain, especially when considering short-term concentrations at the upper 

end of the distribution of daily concentrations for pollutants with relatively heterogeneous spatial 

distributions such as PM10 (US EPA, 2009a, section 2.1.1.2). In contrast, single-city studies are 

more limited in terms of power and geographic coverage but the link between reported health 

effects and the air quality in a given city is more straightforward to establish (US EPA, 2009a, 
3-36

section 2.1.1.2). Given this, in considering PM10 concentrations in locations of epidemiological 

studies, we place the most weight on single-city studies (Figures 3-2 and 3-3) and single-city 

analyses of the locations evaluated in the multi-city study by Zanobetti and Schwartz (US EPA, 

2009a, Figure 6-29). 

In considering PM air quality in study locations, we also note that the available evidence 

does not support the existence of thresholds, or lowest-observed-effects levels, in terms of 24-

hour average concentrations (US EPA, 2009a, section 2.4.3). In the absence of an apparent 

threshold, for purposes of identifying a range of standard levels potentially supported by the 

health evidence, we focus on the range of PM10 concentrations that have been measured in 

locations where U.S. epidemiological studies have reported associations with PM10-2.5 (see U.S. 

EPA, 2009a, Figures 6-1 to 6-30 for studies). In characterizing PM10 air quality in PM10-2.5 study 

locations, we have used EPA’s AQS to identify the highest 98th percentile 24-hour PM10

concentrations for each year in each study location (i.e., from the monitor in the study area 

recording the highest 98th percentile concentration), as described in Schmidt and Jenkins (2010) 

and Jenkins (2011). The 98th percentile concentrations from each study year were averaged 

together and these averages are presented below in Figure 3-5 (PM10-2.5 mortality studies) and 

Figure 3-6 (PM10-2.5 morbidity studies) for locations of single-city studies. 
3-37

Figure 3-5. 98th Percentile PM10 Concentrations in Locations of U.S. Single-City PM10-2.5 Morta

*These studies are a combination of those assessed in the last review and those assessed in the ISA in the current review. Studies are o

percentile PM10 concentrations. See 2005 Staff Paper (US EPA, 2005, pp. 5-65 to 5-66) describing the levels from the two separate stu

et al. (2003).
3-38

Figure 3-6. 98th Percentile PM10 Concentrations in Locations of U.S. Single-City PM10-2.5 Morbid

*These studies are a combination of those assessed in the last review and those assessed in the ISA in the current review. Studies are o

percentile PM10 concentrations. See 2005 Staff Paper (US EPA, 2005, pp. 5-63 to 5-66) describing measurement uncertainties associat

levels in Ito (2003). 
3-39

In addition to the single-city study locations in Figures 3-5 and 3-6, 98th percentile PM10

concentrations averaged across the study locations evaluated in the multi-city studies by 

Zanobetti and Schwartz (2009) and Peng et al. (2008) were 77 g/m3

 (Jenkins, 2011) and 68 

g/m3

 (Schmidt and Jenkins, 2010), respectively. Multi-city effect estimates remained positive, 

and in some cases (i.e., Zanobetti and Schwartz for all-cause and respiratory mortality) 

statistically significant, in co-pollutant models that included fine particles. 

Bayes-adjusted single-city effect estimates for the 47 cities evaluated by Zanobetti and 

Schwartz (US EPA, 2009a, Figure 6-29), which were generally positive but statistically 

significant in only six cities, can provide some additional insight into the PM10 concentrations in 

specific locations where associations between PM10-2.5 and mortality have been reported. The 

98th percentile PM10 concentrations in these 47 cities ranged from 39 g/m3

 (Davie, FL) to 187 

g/m3

 (Phoenix, AZ), and in the 6 cities where positive and statistically significant PM10-2.5

mortality effect estimates were reported, 98th percentile PM10 concentrations were as follows 

(Schmidt and Jenkins, 2010): 

 Chicago: 91 g/m3 

 Salt Lake City: 98 g/m3 

 Detroit: 105 g/m3 

 Pittsburgh: 112 g/m3

 

 Birmingham: 122 g/m3

 

 St. Louis: 138 g/m3 

Thus, in the single-city mortality studies in Figure 3-5 above, as well as the Bayes-adjusted 

single-city analyses of the locations evaluated by Zanobetti and Schwartz, positive and 

statistically significant PM10-2.5 effect estimates were reported in some locations with 98th

percentile PM10 concentrations ranging from 200 g/m3

 to 91 g/m3 (i.e., locations evaluated by 

Mar et al., 2003; Ostro et al., 2003; Wilson et al., 2007; and the cities listed above, US EPA 

2009a, Figure 6-29). Among the U.S. morbidity studies, Ito (2003) reported a positive and 

statistically significant PM10-2.5 effect estimate for hospital admissions for ischemic heart disease 

in Detroit, where the 98th percentile PM10 concentration (102 g/m3

) was also within this range. 

PM10-2.5 effect estimates in this study remained positive, and in some cases statistically 

significant, in co-pollutant models with gaseous pollutants (US EPA, 2009a, Figures 6-5 and 6-

15). Other morbidity studies generally did not report statistically significant PM10-2.5 effect 

estimates. 
3-40

Air Quality-based Considerations

In addition to the evidence-based considerations described above, we have estimated the 

level of a 24-hour PM10 standard with a 98th percentile form that would approximate the degree 

of protection, on average across the country, provided by the current 24-hour PM10 standard with 

its one-expected-exceedance form. Our initial approach to estimating this “generally equivalent” 

98th percentile PM10 concentration was to use EPA’s AQS as the basis for regressing 98th

percentile PM10 concentrations onto one-expected-exceedance concentration equivalent design 

values (Schmidt and Jenkins, 2010). Based on this approach, and using monitoring data from 

1988 to 2008, a 98th percentile PM10 concentration of 87 g/m3

 is, on average, generally 

equivalent to the current standard level (Figure 3-7 below and Schmidt and Jenkins, 2010). 

However, as indicated in Figure 3-7, the range of equivalent concentrations varies considerably 

across monitoring sites and over the time period evaluated (95% confidence interval ranges from 

63 to 111 g/m3

) (Schmidt and Jenkins, 2010). As a consequence, we note that in some 

locations a 98th percentile standard with a level of 87 g/m3 would likely be more protective than 

the current standard while in other locations it would likely be less protective than the current 

standard. 

Regional differences in the relationship between 98th percentile PM10 concentrations and 

one-expected-exceedance concentration equivalent design values are illustrated in Figure 3-8, 

based on air quality data from 1988 to 2008. The 98th percentile PM10 concentrations that are, on 

average, generally equivalent to the current standard level ranged from just below 87 g/m3 in 

the southeast, southwest, upper Midwest, and outlying areas (i.e., generally equivalent 98th

percentile PM10 concentrations ranged from 82 to 85 g/m3

 in these regions) to just above 87 

g/m3

 in the northeast, industrial Midwest, and southern California (i.e., generally equivalent 

98th percentile PM10 concentrations ranged from 88 to 93 g/m3

 in these regions) (Schmidt, 

2011b). However, even within these regions there is considerable variability in the “generally 

equivalent” 98th percentile PM10 concentration across monitoring sites (Figure 3-8). 
3-41

Figure 3-7. Composite 3-year PM10 98th Percentile 24-Hour Average concentration versus 

the PM10 Expected Exceedance Concentration-equivalent Design Value (1988-2008) 
3-42

Figure 3-8. Regional 3-year PM10 98th Percentile 24-Hour Average Concentrations Versus 

the PM10 Expected Exceedance Concentration-equivalent Design Values (1988-2008)* 

*Bold regression lines reflect region-specific relationships between 24-hour average 98th percentile PM10

concentrations and PM10 expected exceedance concentration-equivalent design values. Non-bolded regression lines 

reflect the overall relationship across all regions, as illustrated above in Figure 3-7. In some areas (i.e., southeast, 

upper Midwest, northwest, southwest, and southern California), the two regression lines are almost 

indistinguishable. Vertical lines mark the level of the current standard and horizontal lines mark a 98th percentile 

concentration of 87 g/m3

. 
3-43

Given the spatial and temporal variability in the relationship between one-expectedexceedance concentration-equivalent design values and 98th percentile PM10 concentrations, no 

single 98th percentile PM10 standard level would provide public health protection equivalent to 

that provided by the current standard, consistently over time and across locations.18 Therefore, 

to provide a broader perspective on the relationship between the current standard and potential 

98th percentile standards, we have also compared the size of the populations living in counties 

with PM10 one-expected-exceedance concentration-equivalent design values greater than the 

current standard level to the size of the populations living in counties with 98th percentile PM10

concentrations above different potential alternative standard levels (based on air quality data 

from 2007 to 2009). Such comparisons can be considered as surrogates for comparisons of the 

breadth of public health protection provided by the current and potential alternative standards, as 

discussed below. The results are presented in Table 3-2 below.19 

Based on comparisons of total population counts across all regions of the U.S. for the 

years 2007 to 2009, a 98th percentile PM10 standard with a level between 75 and 80 g/m3 would 

be most closely equivalent to the current standard. However, as with the regression analysis 

described above, there is considerable variability across locations in the “generally equivalent” 

98th percentile PM10 concentration (see Table 3-2). 

 18The “generally equivalent” concentration also differs depending on the years of monitoring data used. For 

example, when this analysis was restricted to only the most recent years available (i.e., 2007 to 2009), the “generally 

equivalent” 98th percentile PM10 concentration was 78 g/m3

. Given the temporal variability in the relationship 

between the current standard level and 98th percentile PM10 concentrations, and the potential for the “generally 

equivalent” 98th percentile concentration to vary year-to-year, staff concluded that it remains appropriate to consider 

the regression analyses that use the broader range of available monitoring years (i.e., 1998-2008), as these analyses 

are likely to be more robust than analyses based on a shorter period of time. 

19Table 3-2 presents counts of counties that would not meet the current and potential alternative standards (see also 

Appendix D). These county counts reflect the net number of counties in each region. However, some counties that 

meet the current standard would violate one or more of the alternative standards while some counties that violate the 

current standard would meet one or more of the alternatives. Therefore, the net county counts presented in Table 3-

2 do not reflect the total number of counties that could potentially change attainment status if the PM10 standard 

were to be revised. The specific counties that would violate the current standard and/or potential alternative 

standards with 98th percentile forms are listed in the memo by Schmidt (2011b). 
3-44

Table 3-2. Predicted Counts of Counties, and Population (x 1,000) within those Counties, Not Likely to 

PM10 Standard and Potential Alternative PM10 Standards with 98th Percentile Forms (based on air qual

20The information in Table 3-2 (and presented in more detail in Schmidt, 2011b and Appendix D) is based on monitoring data from the 

is an update to the information presented in the memo from Schmidt and Jenkins (2010), and presented in draft versions of this Policy Abased on the years 2006 to 2008. 
3-45

One consequence of this variability, as noted above in the discussion of the form of the 

standard, would be that a 24-hour PM10 standard with a 98th percentile form and a revised level 

would likely target public health protection to some different locations than does the current 

standard. Therefore, in further considering the appropriateness of revising the form and level of 

the current PM10 standard, we have considered the following question: 

 To what extent, when compared with the current standard, would a revised PM10

standard be expected to target public health protection to areas where we have more 

confidence that PM10-2.5 is associated with adverse health effects? 

 To address this question, we considered the potential impact of revising the form and level of 

the PM10 standard in locations where health studies have reported associations with PM10-2.5. 

We initially considered U.S. study locations that would likely have met the current PM10

standard during the study period and where positive and statistically significant associations with 

PM10-2.5 were reported (PM10 air quality concentrations in study locations are presented above 

and in Schmidt and Jenkins, 2010; Jenkins, 2011). Only Birmingham, Chicago, Pittsburgh, and 

Detroit21 met these criteria. As shown in Table 3-3, these areas where positive and statistically 

significant associations with PM10-2.5 were reported would likely have met the current PM10

standard during the study periods. However, none of these areas would likely have met a 98th

percentile 24-hour PM10 standard with a level at or below 87 g/m3. 

Table 3-3. PM10 Concentrations in Locations that Met Current PM10 Standard and where 

Positive and Statistically Significant Associations with PM10-2.5 have been Reported 

Study Location PM10 ConcentrationEquivalent Design Value PM10 98th Percentile 

Birmingham22 154 g/m3

 122 g/m3

Chicago 113 g/m3

 91 g/m3

Pittsburgh 139 g/m3

 112 g/m3

Detroit 123 g/m3

 102 g/m3

We next broadened our consideration of study locations to include U.S. locations where 

health studies have reported positive, though not necessarily statistically significant, associations 

between PM10-2.5 and mortality or morbidity. Such positive associations were reported in 47 

 21Positive and statistically significant PM10-2.5 effect estimates for Birmingham, Chicago, and Pittsburgh are reported 

in the ISA (U.S. EPA, 2009a, figure 6-29; from cities evaluated by Zanobetti and Schwartz, 2009). Effect estimates 

for Detroit are reported by Ito et al. (2003) 

22According to rounding convention for the PM10 standard, a 24-hour PM10 concentration of 154 g/m3

 would round 

to 150 g/m3

 (71 FR 61144). Therefore, based on the PM10 one-expected-exceedance concentration-equivalent 

design value, Birmingham would have been expected to just meet the current PM10 standard during the study period. 
3-46

locations that would likely have met the current PM10 standard during the study period.23 Of 

these 47 locations, 13 would likely not have met a 98th percentile 24-hour PM10 standard with a 

level at 87 g/m3

, 20 would likely not have met a 98th percentile 24-hour PM10 standard with a 

level of 75 g/m3

, and 31 would likely not have met a 98th percentile 24-hour PM10 standard with 

a level of 65 g/m3

 (Schmidt and Jenkins, 2010; Jenkins, 2011; Figures 3-2, 3-3, 3-5, 3-6 above). 

In addition to the above analyses, we have also considered locations where health studies 

reported positive associations with PM10-2.5 and where ambient PM10 concentrations were likely 

to have exceeded those allowed under the current PM10 standard during the study period. Nine 

locations met these criteria.24 Of these locations, all would also likely have exceeded a 98th

percentile PM10 standard with a level at or below 87 g/m3

 (Schmidt and Jenkins, 2010; Jenkins, 

2011; Figures 3-2, 3-3, 3-5, 3-6 above). 

Therefore, among U.S. study locations where PM10-2.5-associated health effects have been 

reported, some areas that met the current standard would likely not have met a 98th percentile 

PM10 standard with a level at or below 87 g/m3. In contrast, of the locations that did not meet 

the current standard during the study period, none would likely have met a 98th percentile PM10

standard with a level at or below 87 g/m3

. Given this, we conclude that, compared to the 

current PM10 standard, a 24-hour PM10 standard with a 98th percentile form could potentially 

better target public health protection to locations where we have more confidence that ambient 

PM10-2.5 concentrations are associated with mortality and/or morbidity. 

Integration of evidence- and air quality-based considerations

In considering the evidence and air quality information within the context of identifying 

potential alternative standard levels for consideration, we first note the following: 

 Linear regression analysis suggests that a 98th percentile 24-hour PM10 concentration as 

high as 87 g/m3

 could be considered “generally equivalent” to the current PM10

standard, over time and across the country. 

 A 98th percentile 24-hour PM10 standard with a level at or below 87 g/m3

 would be 

expected to maintain PM10 and PM10-2.5 concentrations below those present in U.S. 

locations where single-city studies have reported PM10-2.5 effect estimates that are 

positive and statistically significant (lowest concentration in such a location was 91 

g/m3

). While some single-city studies have reported positive PM10-2.5 effect estimates in 

locations with 98th percentile PM10 concentrations below 87 g/m3, these effect estimates 

were not statistically significant. 

 23Philadelphia (Lipfert et al., 2000), Detroit (Ito et al., 2003), Santa Clara (CA) (Fairley et al., 2003), Seattle 

(Sheppard et al., 2003), Atlanta (Klemm et al., 2004), Spokane (Slaughter et al., 2005), Bronx and Manhattan (NYS 

DOH, 2006), and 39 of the cities evaluated by Zanobetti and Schwartz (2009) (US EPA, 2009a, Figure 6-29) 24Pittsburgh (Chock et al., 2000), Coachella Valley (CA) (Ostro et al., 2003), Phoenix (Mar et al., 2007; Wilson et 

al., 2007), and 6 of the cities evaluated by Zanobetti and Schwartz (2009) (US EPA, 2009a, Figure 6-29). 
3-47

 Multi-city average 98th percentile PM10 concentrations were below 87 g/m3 for U.S. 

multi-city studies, which have reported positive and statistically significant PM10-2.5 effect 

estimates. However, the extent to which effects reported in multi-city studies are 

associated with the short-term air quality in any particular location is highly uncertain, 

especially when considering short-term concentrations at the upper end of the distribution 

of daily PM10 concentrations. 

 Epidemiological studies have reported positive, and in a few instances statistically 

significant, associations with PM10-2.5 in some locations likely to have met the current 

PM10 standard but not a PM10 standard with a 98th percentile form and a level at or below 

87 g/m3

. 

To the extent the above considerations are emphasized, we note that a standard level as 

high as about 85 g/m3

, for a 24-hour PM10 standard with a 98th percentile form, could be 

supported. Such a standard level would be expected to maintain PM10 and PM10-2.5

concentrations below those present in U.S. locations of single-city studies where PM10-2.5 effect 

estimates have been reported to be positive and statistically significant and below those present 

in some U.S. locations where single-city studies reported PM10-2.5 effect estimates that were 

positive, but not statistically significant. These include some locations likely to have met the 

current PM10 standard during the study periods. Given this, when compared to the current 

standard, a 24-hour PM10 standard with a 98th percentile form and a level at or below 85 g/m3

could have the effect of focusing public health protection on locations where we have more 

confidence that PM10-2.5 is associated with mortality and/or morbidity. 

Given the above, we conclude that a 98th percentile standard with a level as high as 85 

g/m3

 could be considered to the extent that more weight is placed on the appropriateness of 

focusing public health protection in areas where positive and statistically significant associations 

with PM10-2.5 have been reported, and to the extent less weight is placed on PM10-2.5 effect 

estimates that are not statistically significant and/or that reflect estimates across multiple cities. 

It could be judged appropriate to place less weight on PM10-2.5 effect estimates that are not 

statistically significant given the relatively large amount of uncertainty that is associated with the 

broader body of PM10-2.5 health evidence, including uncertainty in the extent to which health 

effects evaluated in epidemiological studies result from exposures to PM10-2.5 itself, rather than 

one or more co-occurring pollutants. This uncertainty, as well as other uncertainties discussed in 

section 3.2.1 above, are reflected in the ISA conclusions that the evidence is “suggestive” of a 

causal relationship (i.e., rather than “causal” or “likely causal”) between short-term PM10-2.5 and 

mortality, respiratory effects, and cardiovascular effects. In addition, it could be judged 

appropriate to place less weight on 98th percentile PM10 concentrations averaged across multiple 
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cities, given the uncertainty in linking multi-city effect estimates with the air quality in any 

particular location. 

However, we also note that, overall across the U.S. based on recent air quality 

information (i.e., 2007-2009), fewer people live in counties with 98th percentile 24-hour PM10

concentrations above 85 g/m3

 than in counties likely to exceed the current PM10 standard. 

These results could be interpreted to suggest that a 98th percentile standard with a level of 85 

g/m3

 might decrease overall public health protection compared to the current standard. Based 

on this analysis, a 98th percentile 24-hour PM10 standard with a level between 75 and 80 g/m3

would provide a level of public health protection that is generally equivalent, on average across 

the U.S., to that provided by the current standard. To the extent these population counts are 

emphasized in comparing the public health protection provided by the current and potential 

alternative standards, and to the extent it is judged appropriate to set a standard that provides at 

least the level of public health protection that is provided by the current standard, it would be 

appropriate to consider standard levels in the range of approximately 75 to 80 g/m3

. 

Alternative approaches to considering the evidence could also lead to consideration of 

standard levels below 75 g/m3

. For example, a number of single-city epidemiological studies 

have reported positive, though not statistically significant, PM10-2.5 effect estimates in locations 

with 98th percentile PM10 concentrations below 75 g/m3. Given that exposure error is 

particularly important for PM10-2.5 epidemiological studies and can bias the results of these 

studies toward the null hypothesis, it could be judged appropriate to place more weight on 

positive associations reported in these epidemiological studies, even when those associations are 

not statistically significant. In addition, the multi-city averages of 98th percentile PM10

concentrations in the locations evaluated by Zanobetti and Schwartz (2009) and Peng et al. 

(2008) were 77 and 68 g/m3

, respectively. Both of these multi-city studies reported positive 

and statistically significant PM10-2.5 effect estimates that remained positive in co-pollutant models 

that included PM2.5, though only Zanobetti and Schwartz (2009) reported PM10-2.5 effect 

estimates that remained statistically significant in such co-pollutant models. Despite 

uncertainties in the extent to which effects reported in these multi-city studies are associated with 

the short-term air quality in any particular location, emphasis could be placed on these multi-city 

associations. We conclude that, to the extent more weight is placed on single-city studies 

reporting positive, but not statistically significant, PM10-2.5 effect estimates and on multi-city 

studies, it could be appropriate to consider standard levels as low as 65 g/m3

. A standard level 

of 65 g/m3

 would be expected to provide a substantial margin of safety against health effects 

that have been associated with PM10-2.5 and, as discussed above, could better focus (compared to 
3-49

the current standard) public health protection on areas where health studies have reported 

associations with PM10-2.5. 

In considering potential alternative standard levels below 65 g/m3

, we note that, as 

discussed above, the overall body of PM10-2.5 health evidence is relatively uncertain, with 

somewhat stronger support in U.S. studies for associations with PM10-2.5 in locations with 98th

percentile PM10 concentrations above 85 g/m3

 than in locations with 98th percentile PM10

concentrations below 65 g/m3. Specifically, we note the following: 

 Epidemiological studies, either single-city or multi-city, have not reported positive and 

statistically significant PM10-2.5 effect estimates in locations with 98th percentile PM10

concentrations (multi-city average 98th percentile concentrations in the case of multi-city 

studies) at or below 65 g/m3

. 

 Although some single-city morbidity studies have reported positive, but not statistically 

significant, associations with PM10-2.5 in locations with 98th percentile PM10

concentrations below 65 g/m3

, the results of U.S. morbidity studies were generally less 

consistent than those of mortality studies, with some PM10-2.5 effect estimates being 

positive while others were negative (i.e., negative effect estimates were reported in 

several studies conducted in Atlanta, where the 98th percentile PM10 concentrations 

ranged from 67 g/m3

 to 71 g/m3). 

 Although Bayes-adjusted single-city PM10-2.5 effect estimates were positive, but not 

statistically significant, in some locations with PM10 concentrations below 65 g/m3, 

these effect estimates were based on the difference between community-wide PM10 and 

PM2.5 concentrations. As discussed above, it is not clear how these estimates of PM10-2.5 

concentrations compare to those more typically used in other studies to calculate PM10-2.5 

effect estimates. At present, few corroborating studies are available that use other 

approaches (i.e., co-located monitors, dichotomous samplers) to estimate/measure 

PM10-2.5 in locations with 98th percentile PM10 concentrations below 65 g/m3

. 

In light of these limitations in the evidence for a relationship between PM10-2.5 and 

adverse health effects in locations with relatively low PM10 concentrations, along with the 

overall uncertainties in the body of PM10-2.5 health evidence as described above and in the ISA, 

we conclude that while it could be judged appropriate to consider standard levels as low as 65 

g/m3

, it is not appropriate, based on the currently available body of evidence, to consider 

standard levels below 65 g/m3. 

In its review of the draft Policy Assessment, CASAC concluded that “alternative standard 

levels of 85 and 65 g/m3 (based on consideration of 98th percentile PM10 concentration) could 

be justified” (Samet, 2010d). However, in considering the evidence and uncertainties, CASAC 

recommended a standard level from the lower part of the range, recommending a level 

“somewhere in the range of 75 – 65 g/m3

” (Samet, 2010d). In making this recommendation, 
3-50

CASAC noted that the number of people living in counties with air quality not meeting the 

current standard is approximately equal to the number living in counties that would not meet a 

98th percentile standard with a level between 75 and 80 g/m3

. CASAC used this information as 

the basis for their conclusion that a 98th percentile standard between 75 and 80 g/m3

 would be 

“comparable to the current standard” (Samet, 2010d). 

3.4 SUMMARY OF STAFF CONCLUSIONS ON PRIMARY THORACIC COARSE 

PARTICLE STANDARD 

In reaching conclusions on the adequacy of the current PM10 standard and potential 

alternative standards to provide requisite protection for health effects associated with short-term 

exposures to thoracic coarse particles, staff has considered the basic elements of the NAAQS: 

indicator, averaging time, form, and level (section 3.3.1 to 3.3.4 above). In considering available 

scientific evidence and air quality information, we reflect upon the evidence and information 

available in the last review integrated with evidence and information that has become available 

since that review as assessed and presented in the ISA (US EPA, 2009a) and summarized above 

in sections 3.2 and 3.3. 

We recognize that selecting from among potential alternative standards will necessarily 

reflect consideration of the evidence as well as the uncertainties inherent in that evidence. In 

considering the current PM10 standard and identifying potential alternative primary standards for 

consideration, we are mindful that the Clean Air Act requires standards to be set that are 

requisite to protect public health with an adequate margin of safety, such that the standards are to 

be neither more nor less stringent than necessary. Thus, the Act does not require that the 

NAAQS be set at zero-risk levels, but rather at levels that avoid unacceptable risks to public 

health. 

Based on the currently available scientific evidence, staff reaches the following 

conclusions regarding the primary PM10 standard: 

(1) It would be appropriate to consider either retaining or revising the current 24-hour PM10

primary standard, depending on the relative weight placed on the evidence supporting 

associations with PM10-2.5, the uncertainties associated with this evidence, and the ISA 

conclusions that the evidence is only “suggestive” of a causal relationship (i.e., rather than 

“causal” or “likely causal”) between short-term PM10-2.5 and mortality, respiratory effects, 

and cardiovascular effects. 

(2) It is appropriate to retain PM10 as the indicator for thoracic coarse particles. This 

conclusion is based on our assessment of the evidence for effects related to particles of 

urban and non-urban origins. We also conclude that research should be targeted so as to 

inform consideration of different indicators in future reviews. 

(3) It is appropriate to retain a 24-hour averaging time for a PM10 standard meant to protect 

against short-term exposures to thoracic coarse particles. This conclusion reflects the body 
3-51

of epidemiological studies, which are most often based on 24-hour average PM10-2.5 

concentrations. 

(4) To the extent consideration is given to revising the current standard: 

(a) Consideration should be given to a 98th percentile form for a 24-hour PM10

standard. This conclusion is based on consideration of providing a balance between 

limiting peak concentrations and providing a stable regulatory target, compensating 

for differences in monitoring frequency across locations, focusing the standard on 

days when PM10-2.5 is likely to make a relatively larger contribution to PM10 mass, 

and focusing public health protection to areas where we have greater confidence 

that mortality and morbidity are associated with ambient PM10-2.5. 

(b) In conjunction with considering a 98th percentile form, it is appropriate to consider 

PM10 standard levels in the range of 85 g/m3 down to about 65 g/m3. This range 

of levels is based on consideration of 98th percentile PM10 concentrations in U.S. 

study locations where PM10-2.5 epidemiological studies have been conducted and of 

the relationship between the current one-expected-exceedance form and a potential 

alternative 98th percentile form. Staff concludes that standard levels in the upper 

part of this range are supported by the strongest evidence and would reflect greater 

emphasis on positive and statistically significant PM10-2.5 effect estimates reported 

in single-city studies. Standard levels in the lower part of this range would reflect 

greater emphasis on PM10-2.5 effect estimates that are positive, though not 

necessarily statistically significant, and on multi-city effect estimates. 

3.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

COLLECTION 

As discussed above (see section 3.2), a number of key uncertainties and limitations in the 

health evidence have been considered in this review. These include uncertainties and limitations 

in the air quality estimates used in PM10-2.5 epidemiological studies; in the extent to which PM10-

2.5 air quality concentrations reflect exposures to PM10-2.5; in the extent to which PM10-2.5 itself is 

responsible for health effects reported in epidemiological studies; and in the extent to which the 

chemical and/or biological composition of PM10-2.5 affects particle toxicity. In this section, we 

highlight areas for future health-related research, model development, and data collection 

activities to address these uncertainties and limitations in the current body of evidence. These 

efforts, if undertaken, could provide important evidence for informing future PM NAAQS 

reviews and, in particular, consideration of possible alternative indicators, averaging times, 

forms, and/or levels. In some cases, research in these areas can go beyond aiding standard 

setting to informing the development of more efficient and effective control strategies. 

As an initial matter, we note that many of the research needs identified for fine particles 

(see above, section 2.5) are also relevant for thoracic coarse particles. This includes research in 

the following areas: 
3-52

 Sources and components of coarse particles, including source apportionment modeling; 

monitoring of components; linking specific sources/components to health outcomes; 

linking sources/components to intra- and inter-city differences in health effects; linking 

sources/components to population exposures; and evaluating different size cut-points 

 Understanding the extent to which an association between thoracic coarse particles and 

specific health effects can be modified by co-pollutants 

 Understanding associations with a broad range of cardiovascular and respiratory 

endpoints as well as adverse effects in the nervous system, on reproduction, and/or on 

development 

 Understanding C-R relationships and the confidence bounds around these relationships, 

especially at lower ambient thoracic coarse particle concentrations 

 Understanding air quality distributions in locations of epidemiological studies 

 Identifying populations susceptible to PM10-2.5-related health effects 

 Modeling to estimate PM10-2.5 mass and composition in areas with less-than-daily 

monitoring 

These uncertainties and areas for future research are discussed above in section 2.5 and 

that discussion will not be repeated here. In addition to the above, there are several areas for 

future research that are particularly relevant for thoracic coarse particles. These include the 

following: 

 The body of experimental inhalation studies (e.g., controlled human exposure and animal 

toxicology studies) is currently relatively sparse. Additional well-conducted experimental 

studies could play an important role in weight of evidence judgments in future ISAs. 

Therefore, experimental evaluation of effects (e.g., vasomotor function, airways 

responsiveness, pulmonary function/inflammation) of concentrated ambient PM10-2.5 from 

specific sources (e.g., traffic, industrial, non-industrial) would be useful, particularly if 

exposure-response relationships are evaluated. 

 Exposure error is of particular concern for thoracic coarse particles, given the relative lack of 

monitoring and its less homogeneous atmospheric distribution compared to fine particles (US 

EPA, 2009a, section 2.1.1.2). Therefore, short-term studies with well-characterized personal 

exposures to PM10-2.5 (e.g., panel studies) would be useful. Such studies could examine 

indicators of cardiovascular and respiratory morbidity (e.g., arrhythmia, ischemia, vasomotor 

function, respiratory symptoms, pulmonary inflammation, pulmonary function, pulmonary 

injury) and would be particularly useful if they evaluated concentration-response 

relationships and/or effects of repeated peak exposures. 

 Epidemiological studies currently use a variety of approaches to measure/estimate PM10-2.5 

concentrations. It is important that we better understand the relationship between results 

from studies that estimate PM10-2.5 concentrations using either (1) difference method of colocated monitors, (2) difference method of county-wide averages of PM10 and PM2.5, or (3) 

direct measurement of PM10-2.5 using a dichotomous sampler. In addition, as described 
3-53

above, PM10-2.5 monitoring will be required at NCORE sites by 2011. It could be useful for 

future epidemiological studies to make use of these new PM10-2.5 monitoring sites. 

 Very little information is available to inform weight of evidence conclusions for endpoints 

associated with long-term PM10-2.5 exposures. Epidemiological and animal toxicological 

studies of long-term exposures (i.e., months to years) to PM10-2.5 would be helpful, though 

limitations in the extent to which coarse particles penetrate rodent respiratory systems could 

add uncertainty to the interpretation of rodent inhalation studies. Long-term studies could 

evaluate links with cardiovascular and respiratory morbidity, reproductive and developmental 

outcomes, cancer, and mortality. 
3-54

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 4-1

4 REVIEW OF THE SECONDARY STANDARDS FOR VISIBILITYRELATED EFFECTS 

This chapter presents staff conclusions with regard to the adequacy of the current suite of 

secondary PM2.5 standards to protect against PM-related visibility impairment as well as 

alternative secondary PM standards that are appropriate for consideration in this review. Our 

assessment of these issues is framed by a series of key policy-relevant questions, which expand 

upon those presented at the outset of this review in the IRP (US EPA, 2008). The answers to 

these questions will inform decisions on whether, and if so how, to revise the current suite of 

secondary PM2.5 standards for the purpose of providing appropriate protection from PM-related 

visibility impairment. 

In presenting staff conclusions on a range of alternative secondary standards that are 

appropriate for consideration, we note that the final decision is largely a public welfare policy 

judgment. A final decision must draw upon scientific information and analyses about PMrelated visibility impairment and related impacts on public welfare, as well as taking into 

consideration the range of uncertainties that are inherent in the scientific evidence and analyses. 

Our approach to informing these judgments is discussed more fully below. 

Information on the approaches used to set the secondary PM2.5 standards in past reviews 

as well as our current approach for this review are presented in section 4.1. Our conclusions 

regarding the adequacy of the current suite of secondary PM2.5 standards to protect against PMrelated visibility impairment are presented in section 4.2. Section 4.3 presents our conclusions 

with respect to alternative PM2.5 standards by focusing on each of the basic elements of the 

standards: pollutant indicator (section 4.3.1), averaging time (section 4.3.2), and level and form 

(section 4.3.3). The performance of alternative standards, with a focus on the uniformity of 

protection from visibility impairment afforded by the alternative standards, is evaluated in 

section 4.3.4. Section 4.4 summarizes all staff conclusions on the secondary PM2.5 standards for 

visibility protection. This chapter concludes with an overview of areas of key uncertainties and 

suggested future research areas and data collection efforts (section 4.5). 

4.1 APPROACH 

Staff’s approach for reviewing the current suite of secondary PM2.5 standards builds upon 

and broadens the approaches used in previous PM NAAQS reviews. We first present a brief 

summary of the approaches used to review and establish secondary PM standards in the last two 

reviews of the PM NAAQS (section 4.1.1). Recent litigation on the 2006 standards has resulted 

in the remand of the secondary annual and 24-hour PM2.5 NAAQS to EPA as discussed in 
 4-2

section 4.1.2. Our current approach for evaluating the secondary PM2.5 standards using both 

evidence- and impact assessment-based considerations is outlined in section 4.1.3. 

4.1.1 Approaches Used in Previous Reviews 

The original suite of secondary PM2.5 standards was established in 1997 and revisions to 

those standards were made in 2006. The approaches used in making final decisions on 

secondary standards in those reviews, as well as the current review, utilize different ways to 

consider the underlying body of scientific evidence. They also reflect an evolution in our 

understanding of the nature of the effect on public welfare from visibility impairment, from an 

approach focusing only on Class I area visibility impacts to a more multifaceted approach that 

also considers PM-related impacts on non-Class I area visibility, such as in urban areas. This 

evolution has occurred in conjunction with the expansion of available PM data and information 

from associated studies of public perception, valuation, and personal comfort and well-being. 

4.1.1.1 Review Completed in 1997 

In 1997, EPA revised the identical primary and secondary PM NAAQS in part by 

establishing new identical primary and secondary PM2.5 standards. In revising the secondary 

standards, EPA recognized that PM produces adverse effects on visibility and that impairment of 

visibility was being experienced throughout the U.S., in multi-state regions, urban areas, and 

remote mandatory Class I Federal areas alike. However, in considering an appropriate level for a 

secondary standard to address adverse effects of PM2.5 on visibility, EPA concluded that the 

determination of a single national level was complicated by regional differences. These 

differences included several factors that influence visibility such as background and current 

levels of PM2.5, composition of PM2.5, and average relative humidity. Variations in these factors 

across regions could thus result in situations where attaining an appropriately protective 

concentration of fine particles in one region might or might not provide adequate protection in a 

different region. EPA also determined that there was insufficient information at that time to 

establish a level for a national secondary standard that would represent a threshold above which 

visibility conditions would always be adverse and below which visibility conditions would 

always be acceptable. 

Based on these considerations, EPA assessed potential visibility improvements in urban 

areas and on a regional scale that would result from attainment of the new primary standards for 

PM2.5. The agency concluded that the spatially averaged form of the annual PM2.5 standard was 

well suited to the protection of visibility, which involves effects of PM2.5 throughout an extended 

viewing distance across an urban area. Based on air quality data available at that time, many 

urban areas in the Northeast, Midwest, and Southeast, as well as Los Angeles, were expected to 

see perceptible improvement in visibility if the annual PM2.5 primary standard were attained. 
 4-3

The EPA also concluded that attainment of the 24-hour PM2.5 standard in some areas would be 

expected to reduce, to some degree, the number and intensity of ‘‘bad visibility’’ days, resulting 

in improvement in the 20% of days having the greatest impairment over the course of a year. 

Having concluded that attainment of the annual and 24-hour PM2.5 primary standards 

would lead to visibility improvements in many eastern and some western urban areas, EPA also 

considered whether these standards could provide potential improvements to visibility on a 

regional scale. Based on information available at the time, EPA concluded that attainment of 

PM2.5 secondary standards set identical to the primary standards would be expected to result in 

visibility improvements in the eastern U.S. at both urban and regional scales, but little or no 

change in the western U.S., except in and near certain urban areas. 

The EPA then considered the potential effectiveness of a regional haze program, required 

by sections 169A and 169B of the Act1 to address those effects of PM on visibility that would 

not be addressed through attainment of the primary PM2.5 standards. The regional haze program 

would be designed to address the widespread, regionally uniform type of haze caused by a 

multitude of sources. The structure and requirements of sections 169A and 169B of the Act 

provide for visibility protection programs that can be more responsive to the factors contributing 

to regional differences in visibility than can programs addressing a nationally applicable 

secondary NAAQS. The regional haze visibility goal is more protective than a secondary 

NAAQS since the goal addresses any anthropogenic impairment rather than just impairment at 

levels determined to be adverse to public welfare. Thus, an important factor considered in the 

1997 review was whether a regional haze program, in conjunction with secondary standards set 

identical to the suite of PM2.5 primary standards, would provide appropriate protection for 

visibility in non-Class I areas. The EPA concluded that the two programs and associated control 

strategies should provide such protection due to the regional approaches needed to manage 

emissions of pollutants that impair visibility in many of these areas. 

For these reasons, EPA concluded that a national regional haze program, combined with 

a nationally applicable level of protection achieved through secondary PM2.5 standards set 

identical to the primary PM2.5 standards, would be more effective for addressing regional 

variations in the adverse effects of PM2.5 on visibility than would be national secondary 

standards for PM with levels lower than the primary PM2.5 standards. The EPA further 

recognized that people living in certain urban areas may place a high value on unique scenic 

resources in or near these areas, and as a result might experience visibility problems attributable 

 1

 In 1977, Congress established as a national goal ‘‘the prevention of any future, and the remedying of any existing, 

impairment of visibility in mandatory Class I Federal areas which impairment results from manmade air pollution’’, 

section 169A(a)(1) of the Act. The EPA is required by section 169A(a)(4) of the Act to promulgate regulations to 

ensure that ‘‘reasonable progress’’ is achieved toward meeting the national goal.
 4-4

to sources that would not necessarily be addressed by the combined effects of a regional haze 

program and PM2.5 secondary standards. The EPA concluded that in such cases, state or local 

regulatory approaches, such as past action in Colorado to establish a local visibility standard for 

the City of Denver, would be more appropriate and effective in addressing these special 

situations because of the localized and unique characteristics of the problems involved. 

Visibility in an urban area located near a mandatory Class I Federal area could also be improved 

through state implementation of the then-current visibility regulations, by which emission 

limitations can be imposed on a source or group of sources found to be contributing to 

‘‘reasonably attributable’’ impairment in the mandatory Class I Federal area. 

Based on these considerations, EPA set secondary PM2.5 standards identical to the 

primary PM2.5 standards, in conjunction with a regional haze program under sections 169A and 

169B of the Act, as the most appropriate and effective means of addressing the public welfare 

effects associated with visibility impairment. Together, the two programs and associated control 

strategies were expected to provide appropriate protection against PM-related visibility 

impairment and enable all regions of the country to make reasonable progress toward the 

national visibility goal. 

4.1.1.2 Review Completed in 2006 

In 2006, EPA revised the secondary PM2.5 standards to address visibility impairment by 

making them identical to the revised primary standards. The EPA’s decision regarding the need 

to revise the secondary PM2.5 standards reflected a number of new developments and sources of 

information that had occurred and/or become available following the 1997 review. First, EPA 

promulgated a regional haze program in 1999 (65 FR 35713) which required states to establish 

goals for improving visibility in Class I areas and to adopt control strategies to achieve these 

goals. Second, extensive new information from visibility and fine particle monitoring networks 

had become available, allowing for updated characterizations of visibility trends and PM levels 

in urban areas, as well as Class I areas. These new data allowed EPA to better characterize 

visibility impairment in urban areas and the relationship between visibility and PM2.5

concentrations. Finally, additional studies in the U.S. and abroad provided the basis for the 

establishment of standards and programs to address specific visibility concerns in a number of 

local areas. These studies (e.g., in Denver, Phoenix, British Columbia) utilized photographic 

representations of visibility impairment and produced reasonably consistent results in terms of 

the visual ranges found to be generally acceptable by study participants. The EPA considered 

the information generated by these studies useful in characterizing the nature of particle-induced 

haze and for informing judgments about the acceptability of various levels of visual air quality in 

urban areas across the U.S. Based largely on this information, the Administrator concluded that 
 4-5

it was appropriate to revise the secondary PM standards to provide increased protection from 

visibility impairment principally in urban areas, in conjunction with the regional haze program 

for protection of visual air quality in Class I areas. 

In so doing, the Administrator recognized that PM-related visibility impairment is 

principally related to fine particle levels and that perception of visibility impairment is most 

directly related to short-term, nearly instantaneous levels of visual air quality. Thus, in 

considering whether the current suite of secondary standards would provide the appropriate 

degree of protection, he concluded that it was appropriate to focus on just the 24-hour secondary 

PM2.5 standard to provide requisite protection. 

The Administrator then considered whether PM2.5 remained the appropriate indicator for 

a secondary standard to protect visibility, primarily in urban areas. The Administrator noted that 

PM-related visibility impairment is principally related to fine particle levels. Hygroscopic 

components of fine particles, in particular sulfates and nitrates, contribute disproportionately to 

visibility impairment under high humidity conditions. Particles in the coarse mode generally 

contribute only marginally to visibility impairment in urban areas. With the substantial addition 

to the air quality and visibility data made possible by the national urban PM2.5 monitoring 

networks, an analysis conducted for the 2006 review found that, in urban areas, visibility levels 

showed far less difference between eastern and western regions on a 24-hour or shorter time 

basis than implied by the largely non-urban data available in the 1997 review. In analyzing how 

well PM2.5 concentrations correlated with visibility in urban locations across the U.S., the 2005 

Staff Paper (US EPA, 2005) concluded that clear correlations existed between 24-hour average 

PM2.5 concentrations and calculated (i.e., reconstructed) light extinction, which is directly related 

to visual range. These correlations were similar in the eastern and western regions of the U.S. 

These correlations were less influenced by relative humidity and more consistent across regions 

when PM2.5 concentrations are averaged over shorter, daylight time periods (e.g., 4 to 8 hours) 

when relative humidity was generally lower and less variable. The 2005 Staff Paper noted that a 

standard set at any specific PM2.5 concentration would necessarily result in visual ranges that 

vary somewhat in urban areas across the country, reflecting the variability in the correlations 

between PM2.5 concentrations and light extinction. The 2005 Staff Paper concluded that it was 

appropriate to use PM2.5 as an indicator for standards to address visibility impairment in urban 

areas, especially when the indicator is defined for a relatively short period (e.g., 4 to 8 hours) of 

daylight hours. Based on their review of the Staff Paper, most CASAC Panel members also 

endorsed such a PM2.5 indicator for a secondary standard to address visibility impairment 

(Henderson, 2005a). Based on the above considerations, the Administrator concluded that PM2.5 

should be retained as the indicator for fine particles as part of a secondary standard to address 

visibility protection, in conjunction with averaging times from 4 to 24 hours. 
 4-6

In considering what level of protection against PM-related visibility impairment would be 

appropriate, the Administrator took into account the results of the public perception and attitude 

surveys regarding the acceptability of various degrees of visibility impairment in the U.S. and 

Canada, state and local visibility standards within the U.S., and visual inspection of photographic 

representations of several urban areas across the U.S. In the Administrator’s judgment, these 

sources provided useful but still quite limited information on the range of levels appropriate for 

consideration in setting a national visibility standard primarily for urban areas, given the 

generally subjective nature of the public welfare effect involved. Based on photographic 

representations of varying levels of visual air quality, public perception studies, and local and 

state visibility standards, the 2005 Staff Paper had concluded that 30 to 20 μg/m3 PM2.5

represented a reasonable range for a national visibility standard primarily for urban areas, based 

on a sub-daily averaging time. The upper end of this range was below the levels at which 

illustrative scenic views are significantly obscured, and the lower end was around the level at 

which visual air quality generally appeared to be good based on observation of the illustrative 

views. This concentration range generally corresponded to median visual ranges in urban areas 

within regions across the U.S. of approximately 25 to 35 km, a range that was bounded above by 

the visual range targets selected in specific areas where state or local agencies placed particular 

emphasis on protecting visual air quality. In considering a reasonable range of forms for a PM2.5

standard within this range of levels, the 2005 Staff Paper had concluded that a concentrationbased percentile form was appropriate, and that the upper end of the range of concentration 

percentiles should be consistent with the 98th percentile used for the primary standard and that 

the lower end of the range should be the 92nd percentile, which represented the mean of the 

distribution of the 20% most impaired days, as targeted in the regional haze program. While 

recognizing that it was difficult to select any specific level and form based on then-currently 

available information (Henderson, 2005a), the CASAC Panel was generally in agreement with 

the ranges of levels and forms presented in the 2005 Staff Paper. 

The Administrator also considered the level of protection that would be afforded by the 

proposed suite of primary PM2.5 standards (71 FR 2681), on the basis that although significantly 

more information was available than in the 1997 review concerning the relationship between fine 

PM levels and visibility across the country, there was still little available information for use in 

making the relatively subjective value judgment needed in selecting the appropriate degree of 

protection to be afforded by such a standard. In so doing, the Administrator compared the extent 

to which the proposed suite of primary standards would require areas across the country to 

improve visual air quality with the extent of increased protection likely to be afforded by a 

standard based on a sub-daily averaging time. Based on such an analysis, the Administrator 

observed that the predicted percent of counties with monitors not likely to meet the proposed 
 4-7

suite of primary PM2.5 standards was actually somewhat greater than the predicted percent of 

counties with monitors not likely to meet a sub-daily secondary standard with an averaging time 

of 4 daylight hours, a level toward the upper end of the range recommended in the 2005 Staff 

Paper, and a form within the recommended range. Based on this comparison, the Administrator 

tentatively concluded that revising the secondary 24-hour PM2.5 standard to be identical to the 

proposed revised primary PM2.5 standard (and retaining the current annual secondary PM2.5

standard) was a reasonable policy approach to addressing visibility protection primarily in urban 

areas. In proposing this approach, the Administrator also solicited comment on a sub-daily (4- to 

8-hour averaging time) secondary PM2.5 standard (71 FR 2675-2781). 

In commenting on the proposed decision, the CASAC requested that a sub-daily standard 

to protect visibility be favorably reconsidered (Henderson, 2006a). The CASAC noted three 

cautions regarding the proposed reliance on a secondary PM2.5 standard identical to the proposed 

24-hour primary PM2.5 standard: (1) PM2.5 mass measurement is a better indicator of visibility 

impairment during daylight hours, when relative humidity is generally low; the sub-daily 

standard more clearly matches the nature of visibility impairment, whose adverse effects are 

most evident during the daylight hours; using a 24- hour PM2.5 standard as a proxy introduces 

error and uncertainty in protecting visibility; and sub-daily standards are used for other NAAQS 

and should be the focus for visibility; (2) CASAC and its monitoring subcommittees have 

repeatedly commended EPA’s initiatives promoting the introduction of continuous and nearcontinuous PM monitoring, and expanded deployment of continuous PM2.5 monitors is consistent 

with setting a sub-daily standard to protect visibility; and (3) The analysis showing a similarity 

between percentages of counties not likely to meet what the CASAC Panel considered to be a 

lenient 4- to 8-hour secondary standard and a secondary standard identical to the proposed 24-

hour primary standard was a numerical coincidence that was not indicative of any fundamental 

relationship between visibility and health. The CASAC Panel further stated that “visual air 

quality is substantially impaired at PM2.5 concentrations of 35 μg/m3

” and that “it is not 

reasonable to have the visibility standard tied to the health standard, which may change in ways 

that make it even less appropriate for visibility concerns.” 

In reaching a final decision, the Administrator focused on the relative protection provided 

by the proposed primary standards based on the above-mentioned similarities in percentages of 

counties meeting alternative standards, and on the limitations in the information available 

concerning studies of public perception and attitudes regarding the acceptability of various 

degrees of visibility impairment in urban areas, as well as on the subjective nature of the 

judgment required. In so doing, the Administrator concluded that caution was warranted in 

establishing a distinct secondary standard for visibility impairment and that the available 

information did not warrant adopting a secondary standard that would provide either more or less 
 4-8

protection against visibility impairment in urban areas than would be provided by secondary 

standards set equal to the proposed primary PM2.5 standards. 

4.1.2 Remand of Secondary PM2.5 Standards 

Several parties filed petitions for review challenging EPA’s decision to set the secondary 

NAAQS for fine PM at the same level as the primary NAAQS. On judicial review, the D.C. 

Circuit remanded to EPA for reconsideration the secondary NAAQS for fine PM because the 

Agency’s decision was unreasonable and contrary to the requirements of section 109(b)(2). 

American Farm Bureau Federation v. EPA, 559 F. 3d 512 (D.C. Cir., 2009). 

The petitioners argued that EPA’s decision lacked a reasoned basis. First, they asserted 

that EPA never determined what level of visibility was “requisite to protect the public welfare”. 

They argued that EPA unreasonably rejected the target level of protection recommended by its 

staff, while failing to provide a target level of its own. The court agreed, stating that “the EPA’s 

failure to identify such a level when deciding where to set the level of air quality required by the 

revised secondary fine PM NAAQS is contrary to the statute and therefore unlawful. 

Furthermore, the failure to set any target level of visibility protection deprived the EPA’s 

decision-making of a reasoned basis.” 559 F. 3d at 530. 

Second, the petitioners challenged EPA’s method of comparing the protection expected 

from potential standards. They contended that the EPA relied on a meaningless numerical 

comparison, ignored the effect of humidity on the usefulness of a standard using a daily 

averaging time, and unreasonably concluded that the primary standards would achieve a level of 

visibility roughly equivalent to the level the EPA staff and CASAC deemed “requisite to protect 

the public welfare.” Again, the court found that EPA’s equivalency analysis based on the 

percentages of counties exceeding alternative standards “failed on its own terms”. The same 

table showing the percentages of counties exceeding alternative secondary standards, used for 

comparison to the percentages of counties exceeding alternative primary standards to show 

equivalency, also included six other alternative secondary standards within the recommended 

CASAC range that would be more “protective” under EPA’s definition than the adopted primary 

standards. Two-thirds of the potential secondary standards within the CASAC’s recommended 

range would be substantially more protective than the adopted primary standards. The court 

found that EPA failed to explain why it looked only at one of the few potential secondary 

standards that would be less protective and only slightly less so than the primary standards. 

More fundamentally, however, the court found that EPA’s equivalency analysis based on 

percentages of counties demonstrated nothing about the relative protection offered by the 

different standards, and that the tables offered no valid information about the relative visibility 

protection provided by the standards. 559 F. 3d at 530-31. 
 4-9

Finally, the Staff Paper had made clear that a visibility standard using PM2.5 mass as the 

indicator in conjunction with a daily averaging time would be confounded by regional 

differences in humidity. The court noted that EPA acknowledged this problem, yet did not 

address this issue in concluding that the primary standards would be sufficiently protective of 

visibility. 559 F. 3d at 530. Therefore, the court granted the petition for review and remanded 

for reconsideration the secondary PM2.5 NAAQS. 

4.1.3 General Approach Used in Current Review 

The staff’s approach in this review broadens the general approaches used in the last two 

PM NAAQS reviews by utilizing, to the extent available, enhanced tools, methods, and data to 

more comprehensively characterize visibility impacts. As such, we are taking into account 

considerations based on both the scientific evidence (“evidence-based”) and a quantitative 

analysis of PM-related impacts on visibility (“impact-based”) to inform our conclusions related 

to the adequacy of the current secondary PM2.5 standards and alternative standards that are 

appropriate for consideration in this review. As in past reviews, we are also considering 

secondary NAAQS to address PM-related visibility impairment in conjunction with the Regional 

Haze Program, such that the secondary NAAQS would focus on protection from visibility 

impairment principally in urban areas in conjunction with the Regional Haze Program that is 

focused on improving visibility in Class I areas. We again recognize that such an approach is the 

most appropriate and effective means of addressing the public welfare effects associated with 

visibility impairment in areas across the country. We are seeking to provide as broad an array of 

options as is supportable by the available information, recognizing that the selection of a specific 

approach to reaching final decisions on the secondary PM2.5 standards will reflect the judgments 

of the Administrator. 

In preparing this final PA, staff has drawn from the qualitative evaluation of all studies 

discussed in the ISA (US EPA, 2009a). We consider the extensive new air quality and source 

apportionment information available from the regional planning organizations, long-standing 

evidence of PM effects on visibility, and public preference studies from four urban areas (ISA 

chapter 9), as well as the integration of evidence across disciplines (ISA chapter 2). In addition, 

limited information that has become available regarding the characterization of public 

preferences in urban areas has provided some new perspectives on the usefulness of this 

information in informing the selection of target levels of urban visibility protection. On these 

bases, we are again focusing our assessments in this review on visibility conditions in urban 

areas. 

Our conclusions reflect our understanding of both evidence-based and impact-based 

considerations to inform two overarching questions related to: (1) the adequacy of the current 
 4-10

suite of PM2.5 standards and (2) what potential alternative standards, if any, should be considered 

in this review to provide appropriate protection from PM-related visibility impairment. In 

addressing these broad questions, we have organized the discussions below around a series of 

more specific questions reflecting different aspects of each overarching question, as summarized 

in Figure 4-1. When evaluating the visibility protection afforded by the current or any 

alternative standards considered, we have taken into account the four basic elements of the 

NAAQS: indicator, averaging time, level, and form. 
 4-11

Figure 4-1. Overview of Approach for Review of Secondary PM2.5 Standards 
 4-12

4.2 ADEQUACY OF CURRENT STANDARDS 

In considering the adequacy of the current suite of PM2.5 standards, staff addresses the 

following overarching question: 

Does the currently available scientific evidence and visibility impact information, as 

reflected in the ISA and UFVA, support or call into question the adequacy of the visibility 

protection afforded by the current suite of fine particle standards? 

To inform the answer to this overarching question, we have posed a series of more 

specific questions to aid in considering the currently available scientific evidence and the results 

of recent quantitative visibility impact analyses in a policy-relevant context, as discussed below. 

In considering the scientific and technical information, we reflect upon both the information 

available in the last review and information that is newly available since the last review as 

assessed and presented in the ISA and UFVA (US EPA, 2009a; US EPA, 2010b). 

4.2.1 Evidence-based and Impact-Based Considerations 

In reviewing the adequacy of the current suite of PM2.5 standards, we have taken into 

account evidence-based considerations, primarily as presented in the ISA, and impact-based 

considerations as presented in the UFVA, by considering causal inference, impacts on 

susceptible populations, and the nature and degree of PM-related visibility effects that would be 

expected to exist in urban areas when meeting the current standards. 

 To what extent do the newly available scientific evidence and other information 

strengthen or call into question evidence of associations between ambient fine particle 

exposures and visibility effects? 

New research conducted by regional planning organizations in support of the Regional 

Haze Rule, as discussed in chapter 9 of the ISA, continues to support and refine our 

understanding of the nature of the PM visibility effect and the source contributions to that effect 

in rural and remote locations. Additional by-products of this research include new insights 

regarding the regional source contributions to urban visibility and better characterization of the 

increment in PM concentrations and visibility impairment that occur in many cities (i.e., the 

urban excess) relative to conditions in the surrounding rural areas (i.e., regional background). 

Ongoing urban PM2.5 speciated and mass monitoring has produced new information that has 

allowed for updated characterization of current visibility levels in urban areas. Information from 

both of these sources of PM data, while useful, has not however changed the fundamental and 

long understood science characterizing the contribution of PM, especially fine particles, to 

visibility impairment. This science, briefly summarized below, provides the basis for the ISA 

designation of the relationship between PM and visibility impairment as causal. 
 4-13

Visibility impairment is caused by the scattering and absorption of light by suspended 

particles and gases in the atmosphere. The combined effect of light scattering and absorption by 

both particles and gases is characterized as light extinction, i.e., the fraction of light that is 

scattered or absorbed per unit of distance in the atmosphere. Light extinction is measured in 

units of 1/distance, which is often expressed in the technical literature as 1/(1 million meters) or 

inverse megameters (abbreviated Mm-1). When PM is present in the air, its contribution to light 

extinction typically greatly exceeds that of gases. 

The amount of light extinction contributed by PM depends on the particle size 

distribution and composition, as well as its concentration. If details of the ambient particle size 

distribution and composition (including the mixing of components) are known, Mie theory can 

be used to accurately calculate PM light extinction (US EPA, 2009a, chapter 9). However, 

routine monitoring rarely includes measurements of particle size and composition information 

with sufficient detail for such calculations. To make estimation of light extinction more 

practical, visibility scientists have developed a much simpler algorithm, known as the IMPROVE 

algorithm,2

 to estimate light extinction using routinely monitored fine particle (PM2.5) speciation 

and coarse particle mass (PM10–2.5) data. In addition, relative humidity information is needed to 

estimate the contribution by liquid water that is in solution with hygroscopic PM components 

(US EPA, 2009a, section 9.2.2.2; US EPA, 2010b, chapter 3). There is both an original and a 

revised version of the IMPROVE algorithm. The revised version was developed to address 

observed biases in the predictions using the original algorithm under very low and very high 

light extinction conditions.3

 These IMPROVE algorithms are routinely used to calculate light 

extinction levels on a 24-hour basis in Class I areas under the Regional Haze Program. 

In either version of the IMPROVE algorithm, the concentration of each of the major 

aerosol components is multiplied by a dry extinction efficiency value and, for the hygroscopic 

components (i.e., ammoniated sulfate and ammonium nitrate), also multiplied by an additional 

factor to account for the water growth to estimate these components’ contribution to light 

extinction. Both the dry extinction efficiency and water growth terms have been developed by a 

combination of empirical assessment and theoretical calculation using typical particle size 

distributions associated with each of the major aerosol components. They have been evaluated 

by comparing the algorithm estimates of light extinction with coincident optical measurements. 

 2

 The algorithm is referred to as the IMPROVE algorithm because it was developed specifically to use the aerosol 

monitoring data generated at network sites and with equipment specifically designed to support the IMPROVE 

program and was evaluated using IMPROVE optical measurements at the subset of sites that make those 

measurements (Malm et al., 1994). 

3

 These biases were detected by comparing light extinction estimates generated from the IMPROVE algorithm to 

direct optical measurements in a number of rural Class I areas. 
 4-14

Summing the contribution of each component gives the estimate of total light extinction (denoted 

as bext), as shown below for the original IMPROVE algorithm. 

bext ≈ 3 x f(RH) x [Sulfate] 

 + 3 x f(RH) x [Nitrate] 

 + 4 x [Organic Mass] 

 + 10 x [Elemental Carbon] 

 + 1 x [Fine Soil] 

 + 0.6 x [Coarse Mass] 

 + 10 

Light extinction (bext) is in units of Mm-1, the mass concentrations of the components 

indicated in brackets are in units of μg/m3, and f(RH) is the unitless water growth term that 

depends on relative humidity. The final term of 10 Mm-1 is known as the Rayleigh scattering 

term and accounts for light scattering by the natural gases in unpolluted air. The dry extinction 

efficiency for particulate organic mass is larger than those for particulate sulfate and nitrate 

principally because the density of the dry inorganic compounds is higher than that assumed for 

the PM organic mass components. Since IMPROVE does not include ammonium ion 

monitoring, the assumption is made that all sulfate is fully neutralized ammonium sulfate and all 

nitrate is assumed to be ammonium nitrate. Though often reasonable, neither assumption is 

always true (see US EPA, 2009a, section 9.2.3.1). In the eastern U.S. during the summer there is 

insufficient ammonia in the atmosphere to neutralize the sulfate fully. Fine particle nitrates can 

include sodium or calcium nitrate, which are the fine particle fraction of generally much coarser 

particles due to nitric acid interactions with sea salt at near-coastal areas (sodium nitrate) or nitric 

acid interactions with calcium carbonate in crustal aerosol (calcium nitrate). Despite the 

simplicity of the algorithm, it performs reasonably well and permits the contributions to light 

extinction from each of the major components (including the water associated with the sulfate 

and nitrate compounds) to be separately approximated. 

The f(RH) term reflects the increase in light scattering caused by particulate sulfate and 

nitrate under conditions of high relative humidity. For relative humidity below 40% the f(RH) 

value is 1, but it increases to 2 at ~66%, 3 at ~83%, 4 at ~90%, 5 at ~93%, and 6 at ~95% 

relative humidity. The result is that both particulate sulfate and nitrate are more efficient per unit 

mass than any other aerosol component for relative humidity above ~85% where its total light 

extinction efficiency exceeds the 10 m2

/g associated with elemental carbon (EC). Based on this 

algorithm, particulate sulfate and nitrate are estimated to have comparable light extinction 

efficiencies (i.e., the same dry extinction efficiency and f(RH) water growth terms), so on a per 

unit mass concentration basis at any specific relative humidity they are treated as equally 

effective contributors to visibility effects. 
 4-15

Inspection of the PM component-specific terms in the simple original IMPROVE 

algorithm shows that most of the PM2.5 components contribute 5 times or more light extinction 

than a similar concentration of PM10–2.5. We also know that particles with hygroscopic 

components (e.g., particulate sulfate and nitrate) contribute more light extinction at higher 

relative humidity than at lower relative humidity because they change size in the atmosphere in 

response to ambient relative humidity conditions. PM containing elemental or black carbon 

absorbs light as well as scattering it, making it the component with the greatest light extinction 

contributions per unit of mass concentration, except for the hygroscopic components under high 

relative humidity conditions.4

Subsequent to the development of the original IMPROVE algorithm, an alternative 

algorithm (variously referred to as the “revised algorithm” or the “new algorithm” in the 

literature) has been developed that employs a more complex split component mass extinction 

efficiency to correct biases believed to be related to particle size distributions, a sea salt term that 

can be important for remote coastal areas, a different multiplier for organic carbon for purposes 

of estimating organic carbonaceous material, and site-specific Rayleigh light scattering terms in 

place of a universal Rayleigh light scattering value. These features of the revised IMPROVE 

algorithm are described in section 9.2.3.1 of the ISA, which also presents a comparison of the 

estimates produced by the two algorithms for rural areas. Compared to the original algorithm, 

the revised IMPROVE algorithm can yield higher estimates of current light extinction levels in 

urban areas on days with relatively poor visibility (Pitchford, 2010). This difference is primarily 

attributable to the split component mass extinction efficiency treatment in the revised algorithm 

rather than to the inclusion of a sea salt term or the use of site-specific Rayleigh scattering 

values. 

As mentioned above, particles are not the only contributor to ambient visibility 

conditions. Light scattering by gases also occurs in ambient air. Under pristine atmospheric 

conditions, naturally occurring gases such as N2 and O2 cause what is known as Rayleigh 

scattering. Rayleigh scattering depends on the density of air as a function primarily of the 

elevation above sea level, and can be treated as a site-dependent constant. The Rayleigh 

scattering contribution to light extinction is only significant under pristine conditions. The only 

other commonly occurring atmospheric gas to appreciably absorb light in the visible spectrum is 

NO2. NO2 forms in the atmosphere from NO emissions associated with combustion processes. 

These combustion processes also emit PM at levels that generally contribute much higher light 

extinction than the NO2 (i.e., NO2 absorption is generally less than approximately 5% of the light 

extinction, except where emission controls remove most of the PM prior to releasing the 

 4

 The IMPROVE algorithm does not explicitly separate the light-scattering and light-absorbing effects of elemental 

carbon. 
 4-16

remaining gases to the atmosphere). The remainder of this chapter focuses on the contribution of 

PM, which is typically much greater than that of gases, to ambient light extinction, unless 

otherwise specified. 

In the following discussions, visual air quality is characterized both in terms of light 

extinction, as discussed above, and deciviews (dv). Deciview refers to an alternative scale for 

characterizing visibility that is defined directly in terms of light extinction (expressed in units of 

Mm-1) by the following equation5: 

Deciview (dv) = 10 ln (bext / 10 Mm-1) 

The deciview scale is frequently used in the scientific and regulatory literature on visibility, as 

well as in the Regional Haze Program. In particular, the deciview scale is used in the public 

perception studies that were considered in the past and current reviews to inform judgments 

about an appropriate degree of protection to be provided by a secondary NAAQS. 

 To what extent does the available evidence inform our understanding of the temporal 

nature of the PM visibility effect, including relevant exposure periods, associated 

atmospheric conditions, and diurnal patterns of exposure? 

Temporal Variations of Light Extinction

PM concentrations and light extinction in urban environments vary from hour-to-hour 

throughout the 24-hour day due to a combination of diurnal changes in meteorological conditions 

and systematic changes in emissions activity (e.g., rush hour traffic). Generally, low mixing 

heights at night and during the early morning hours tend to trap locally produced emissions, 

which are diluted as the mixing height increases due to heating during the day. Low 

temperatures and high relative humidity at night are conducive to the presence of ammonium 

nitrate particles and water growth by hygroscopic particles compared with the generally higher 

temperatures and lower relative humidity later in the day. These combine to make early morning 

the most likely time for peak urban light extinction. Superimposed on such systematic time-ofday variations are the effects of synoptic meteorology (i.e., those associated with changing 

weather) and regional-scale air quality that can generate peak light extinction impacts any time 

of day. The net effects of the systematic urban- and larger-scale variations are that peak daytime 

PM light extinction levels can occur any time of day, although in many areas they most often 

occur in early morning hours (USEPA, 2010b, sections 3.4.2 and 3.4.3; Figures 3-9, 3-10, and 3-

12). 

 5

 As used in the Regional Haze Program, the term bext refers to light extinction due to PM2.5, PM10-2.5, and “clean” 

atmospheric gases. In this chapter, in focusing on light extinction due to PM2.5, the deciview values include only the 

effects of PM2.5 and the gases. The “Rayleigh” term associated with clean atmospheric gases is represented by the 

constant value of 10 Mm-1. Omission of the Rayleigh term would create the possibility of a negative deciview 

values when the PM2.5 concentration is very low. 
 4-17

 This temporal pattern in urban areas contrasts with the general lack of a strong diurnal 

pattern in PM concentrations and light extinction in most Class I areas, reflective of a relative 

lack of local sources as compared to urban areas. The use in the Regional Haze Program of 24-

hour average concentrations in the IMPROVE algorithm is consistent with this general lack of a 

strong diurnal pattern in Class I areas. 

Periods during the Day of Interest for Assessment of Visibility

Typically, we think of visibility associated with daytime periods because we are outside 

more during the day than at night and there are more viewable scenes at a distance during the day 

than at night. We recognize, however, that PM light extinction behaves the same at night as 

during the day, enhancing the scattering of anthropogenic light, contributing to the “skyglow” 

within and over populated areas, adding to the total sky brightness, and contributing to the 

reduction in contrast of stars against the background. These effects produce the visual result of a 

reduction in the number of visible stars and the disappearance of diffuse or subtle phenomena 

such as the Milky Way. The extinction of starlight is a secondary and minor effect also caused 

by increased PM scattering and absorption. 

However, there are significant and important differences between daytime and nighttime 

visual environments with regard to how light extinction per se relates to visual air quality (or 

visibility) and public welfare. First, daytime visibility has dominated the attention of those who 

have studied the visibility effects of air pollution, particularly in urban areas. As a result, little 

research has been conducted on nighttime visibility and the state of the science is not yet 

comparable to that associated with daytime visibility impairment. We are not aware of urbanfocused preference or valuation studies providing information on public preferences for 

nighttime visual air quality. Second, in addition to air pollution, nighttime visibility is affected 

by the addition of light into the sight path from numerous sources, including anthropogenic light 

sources in urban environments such as artificial outdoor lighting, which varies dramatically 

across space, and natural sources including the moon, planets, and stars. Light sources and 

ambient conditions are typically five to seven orders of magnitude dimmer at night than in 

sunlight. Moonlight, like sunlight, introduces light throughout an observer’s sight path at a 

constant angle. On the other hand, dim starlight emanates from all over the celestial hemisphere 

while artificial lights are concentrated in cities and illuminate the atmosphere from below. These 

different light sources will yield variable changes in visibility as compared to what has been 

established for the daytime scenario, in which a single source, the sun, is by far the brightest 

source of light. Third, the human psychophysical response (e.g., how the human eye sees and 

processes visual stimuli) at night is expected to differ (US EPA, 2009a, section 9.2.2).
 4-18

Given the above, we do not believe that the science is available at this time to support 

adequate characterization specifically of nighttime PM light extinction conditions and the related 

effects on public welfare. Thus, we have focused our assessments of PM visibility impacts in 

urban areas on daylight hours. For simplicity, and because perceptions and welfare effects from 

light extinction-related visual effects during the minutes of actual sunrise and sunset have not 

been explored, we have defined daylight hours as those hours entirely after the local sunrise time 

and before the local sunset time. 

In so doing, we note that the 24-hour averaging time used in the Regional Haze Program 

includes nighttime conditions. We also note, however, that the goal of the Regional Haze 

Program is to address any manmade impairment of visibility without regard to distinctions 

between daylight and nighttime conditions. Moreover, because of the lack of strong diurnal 

patterns, both nighttime and daylight visibility are strongly correlated with 24-hour average 

visibility conditions, so a 24-hour averaging period is suitable for driving both daylight and 

nighttime visibility towards their natural conditions. Also, the focus on 24-hour average 

visibility allows the Regional Haze Program to make use of more practically obtained ambient 

PM measurements of adequate accuracy than if a shorter averaging period were used, an 

important consideration given the remoteness of many Class I area monitoring sites and given 

the low PM concentrations that must be measured accurately in such areas. 

In addition, when natural conditions such as fog and rain cause poor visibility, it can be 

reasonably assumed that the light extinction properties of the air that are attributable to air 

pollution are not important from a public welfare perspective. Thus, it is appropriate to give 

special treatment to such periods when considering whether current PM2.5 standards adequately 

protect public welfare from PM-related visibility impairment. In evaluating alternative sub-daily 

standards, we have addressed this issue by screening out hours with particularly high relative 

humidity. As discussed further below (section 4.3.2.1), we used a relative humidity screen of 

90% on the basis that it serves as a reasonable surrogate for excluding hours affected by fog and 

rain. 

Exposure Durations of Interest

The roles that exposure duration and variations in visual air quality within any given 

exposure period play in determining the acceptability or unacceptability of a given level of visual 

air quality has not been investigated via preference studies. In the preferences studies available 

for this review, subjects were simply asked to rate the acceptability or unacceptability of each 

image of a haze-obscured scene, without being provided any suggestion of assumed duration or 

of assumed conditions before or after the occurrence of the scene presented. We do know from 

preference and/or valuation studies that atmospheric visibility conditions can be quickly assessed 
 4-19

and preferences determined. These studies show that a momentary glance at an image of a scene 

(i.e., less than a minute) is enough for study participants to judge the acceptability or 

unacceptability of the viewed visual air quality conditions. Moreover, individual participants in 

general consistently judge the acceptability of same-scene images that differed only with respect 

to light extinction levels when these images were presented repeatedly for such short periods. 

That is, individuals generally did not say that a higher-light extinction image was acceptable 

while saying a lower-light extinction, same-scene image was unacceptable, even though they 

could not compare images side-to-side. However, we do not have information about what 

assumptions, if any, the participants may have made about the duration of exposure in 

determining the acceptability of the images. We are unaware of any studies that characterize the 

extent to which different frequencies and durations of exposure to visibility conditions contribute 

to the degree of public welfare impact that occurs. 

In the absence of such studies, we considered a variety of circumstances that are 

commonly expected to occur in evaluating the potential impact of visibility impairment on the 

public welfare based on the information we do have. In some circumstances, such as infrequent 

visits to scenic vistas in natural or urban environments, people are motivated specifically to take 

the opportunity to view a valued scene and are likely to do so for many minutes to hours to 

appreciate various aspects of the vista they choose to view. In such circumstances, the viewer 

may consciously evaluate how the visual air quality at that time either enhances or diminishes 

the experience or view. However, the public also has many more opportunities to notice 

visibility conditions on a daily basis in settings associated with performing daily routines (e.g., 

during commutes and while working, exercising, or recreating outdoors). These scenes, whether 

iconic or generic, may not be consciously viewed for their scenic value and may not even be 

noticed for periods comparable to what would be the case during purposeful visits to scenic 

visits, but their visual air quality may still affect a person’s sense of wellbeing. Research has 

demonstrated that people are emotionally affected by low visual air quality, that perception of 

pollution is correlated with stress, annoyance, and symptoms of depression, and that visual air 

quality is deeply intertwined with a “sense of place,” affecting people’s sense of the desirability 

of a neighborhood (US EPA, 2009a, section 9.2.4). Though we do not know the extent to which 

these emotional effects are linked to different periods of exposure to poor visual air quality, 

providing additional protection against short-term exposures to levels of visual air quality 

considered unacceptable by subjects in the context of the preference studies would be expected 

to provide some degree of protection against the risk of loss in the public’s “sense of wellbeing.” 

Some people have mostly intermittent opportunities on a daily basis (e.g., during morning 

and/or afternoon commutes) to experience ambient visibility conditions because they spend 

much of their time indoors without access to windows. For such people a view of poor visual air 
 4-20

quality during their morning commute may provide their perception of the day’s visibility 

conditions until the next time they venture outside during daylight hours later or perhaps the next 

day. Other people have exposure to visibility conditions throughout the day, conditions that may 

differ from hour to hour. A day with multiple hours of visibility impairment would likely be 

judged as having a greater impact on their wellbeing than a day with just one such hour followed 

by clearer conditions. 

We have no information or studies on the fraction of the public that has only one or a few 

opportunities to experience visibility during the day, or information or studies on the duration of 

the effect on wellbeing from exposure to different durations of poor visual air quality conditions. 

However, it is logical to conclude that people with limited opportunities to experience visibility 

conditions on a daily basis would receive the entire impact of the day’s visual air quality based 

on the visibility conditions that occur during the short time period when they can see it. Since 

this group could be affected on the basis of observing visual air quality conditions for periods as 

short as one hour or less, and because during each daylight hour there are some people outdoors, 

commuting, or near windows, we judge that it would be appropriate to use the maximum hourly 

value of PM light extinction during daylight hours for each day for purposes of evaluating the 

adequacy of the current suite of secondary standards. This approach would recognize that at 

least some but not all of the population of an area will actually be exposed to this worst hour and 

that some that people who are exposed to this worst hour may not have an opportunity to observe 

clearer conditions in other hours if they were to occur. Moreover, because visibility conditions 

and people’s daily activities on work/school days both tend to follow the same diurnal pattern 

day after day, those who are exposed only to the worst hour will tend to have this experience day 

after day. 

For another group of observers, those who have access to visibility conditions often or 

continuously throughout the day, the impact of the day’s visibility conditions on their welfare 

may be based on the varying visibility conditions they observe throughout the day. For this 

group, it might be that an hour with poor or “unacceptable” visibility can be offset by one or 

more other hours with clearer conditions. Based on these considerations, we judge that it would 

also be appropriate to use the maximum multi-hour daylight period for evaluating the adequacy 

of the current suite of secondary standards. 

The above discussion is based on what people see, which is determined by the extinction 

of light along the paths between observers and various objects that are looked at. A related but 

separate issue is what measurement period is relevant, if (as expected) what will be measured is 

the light extinction property or the PM concentration of the local air at a fixed site. Light 

extinction conditions at a fixed site can change quickly (i.e., in less than a minute). Sub-hourly 

variations in light extinction determined at any point in the atmosphere are likely the result of 
 4-21

small-scale spatial pollution features (i.e., high concentration plumes) just generated in the 

immediate vicinity due to local sources or being transported by the wind across that point. These 

small-scale pockets of air causing short periods of higher light extinction at the fixed site likely 

do not determine the visual effect for scenes with longer sight paths. In contrast, atmospheric 

sight path-averaged light extinction which is pertinent to visibility impacts generally changes 

more slowly (i.e., tens of minutes generally), because a larger air mass must be affected by a 

broader set of emission sources or the air mass must be replaced by a cleaner or dirtier air mass 

due to the wind operating over time. At typical wind speeds found in U.S. cities, an hour 

corresponds to a few tens of kilometers of air flowing past a point, which is similar to sight path 

lengths of interest in urban areas. Based on the above considerations, hourly average light 

extinction would generally be reasonably representative of the net visibility effect of the spatial 

pattern of light extinction levels, especially along site paths that generally align with the wind 

direction. 

 Based on currently available information, what range of levels of visibility impairment 

is reasonable to consider in reaching judgments about the adequacy of the current 

NAAQS? 

In order to identify levels of visibility impairment appropriate for consideration in setting 

secondary PM NAAQS to protect the public welfare, we comprehensively examined information 

that was available in this review regarding people’s stated preferences for acceptable and 

unacceptable visual air quality. 

Light extinction is an atmospheric property that by itself does not directly translate into a 

public welfare effect. Instead, light extinction becomes meaningful in the context of the impact 

of differences in visibility on the human observer. This has been studied in terms of the 

acceptability or unacceptability expressed for the visibility impact of a given level of light 

extinction by a human observer. The perception of the visibility impact of a given level of light 

extinction occurs in conjunction with the associated characteristics and lighting conditions of the 

viewed scene.6

 Thus, a given level of light extinction may be perceived differently by observers 

looking at different scenes or the same scene with different lighting characteristics. Likewise, 

different observers looking at the same scene with the same lighting may have different 

preferences regarding the associated visual air quality. When scene and lighting characteristics 

are held constant, the perceived appearance of a scene (i.e., how well the scenic features can be 

seen and the amount of visible haze) depends only on changes in light extinction. This has been 

 6

 By “characteristics of the scene” we mean the distance(s) between the viewer and the object(s) of interest, the 

shapes and colors of the objects, the contrast between objects and the sky or other background, and the inherent 

interest of the objects to the viewer. Distance is particularly important because at a given value of light extinction, 

which is a property of air at a given point(s) in space, more light is actually absorbed and scattered when light passes 

through more air between the object and the viewer. 
 4-22

demonstrated using the WinHaze model (Molenar et al., 1994) that uses image processing 

technology to apply user-specified changes in light extinction values to the same base 

photograph with set scene and lighting characteristics. 

Much of what we know about the acceptability of levels of visibility comes from survey 

studies in which participants were asked questions about their preference or the value they place 

on various visibility levels as displayed to them in scenic photographs and/or WinHaze images 

with a range of known light extinction levels. Urban visibility preference studies for four urban 

areas were reviewed in the UFVA (chapter 2) to assess the light extinction levels judged by the 

participant to have acceptable visibility for those particular scenes. While the results differed 

among the four urban areas, results from a rating exercise showed that within each preference 

study, individual survey participants consistently distinguish between photos or images 

representing different levels of light extinction, and that more participants rate as acceptable 

images representing lower levels of light extinction than do images representing higher levels. 

The reanalysis of urban preference studies conducted for this review included three 

completed urban visibility preference survey studies plus a pair of smaller focus studies designed 

to explore and further develop urban visibility survey instruments. The three western studies 

included one in Denver, Colorado (Ely et al., 1991), one in the lower Fraser River valley near 

Vancouver, British Columbia (BC), Canada (Pryor, 1996), and one in Phoenix, Arizona (BBC 

Research & Consulting, 2003). A pilot focus group study was also conducted for Washington, 

DC (Abt Associates Inc., 2001). In response to an EPA request for public comment on the Scope 

and Methods Plan (74 FR 11580, March 18, 2009), we received comments (Smith, 2009) about 

the results of a new Washington, DC focus group study that had been conducted using methods 

and approaches similar to the method and approach employed in the EPA pilot study (Smith and 

Howell, 2009). When taken together, these studies from the four different urban areas included a 

total of 852 individuals, with each individual responding to a series of questions answered while 

viewing a set of images of various urban visual air quality conditions. 

The approaches used in the four studies are similar and are all derived from the method 

first developed for the Denver urban visibility study. In particular, the studies all used a similar 

group interview type of survey to investigate the level of visibility impairment that participants 

described as “acceptable.” While each study asked the basic question, “What level of visibility 

degradation is acceptable?”, the term “acceptable” was not defined, so that each person’s 

response was based on his/her own values and preferences for visual air quality. Given the 

similarities in the approaches used, it is reasonable to compare the results to identify overall 

trends in the study findings and that this comparison can usefully inform the selection of a range 

of levels for use in further analyses. However, variations in the specific materials and methods 

used in each study introduce uncertainties that should also be considered when interpreting the 
 4-23

results of these comparisons. Key differences between the studies include: 1) scene 

characteristics; 2) image presentation methods (e.g., projected slides of actual photos, projected 

images generated using WinHaze (a significant technical advance in the method of presenting 

visual air quality conditions), or use of a computer monitor screen; 3) number of participants in 

each study; 4) participant representativeness of the general population of the relevant 

metropolitan area; and 5) specific wording used to frame the questions used in the group 

interview process. 

In the UFVA, each study was evaluated separately and figures developed to display the 

percentage of participants that rated the visual air quality depicted in each photograph as 

“acceptable.” Ely et al. (1991) introduced a “50% acceptability” criterion analysis of the Denver 

preference study results. The 50% acceptability criterion is designed to identify the visual air 

quality level (defined in terms of deciviews or light extinction) that best divides the photographs 

into two groups: those with a visual air quality rated as acceptable by the majority of the 

participants, and those rated not acceptable by the majority of participants. We adopted the 

criterion as a useful index for comparison between studies. The results of each individual 

analysis were then combined graphically to allow for visual comparison. Figure 4-2 (Figure 2-

16 in UFVA) presents the graphical summary of the results of the studies in the four cities and 

draws on results previously presented in Figures 2-3, 2-5, 2-7, and 2-11 of chapter 2 in the 

UFVA. Figure 4-2 also contains lines at 20 dv and 30 dv that generally identify a range where 

the 50% acceptance criteria occur across all four of the urban preference studies. Out of the 114 

data points shown in Figure 4-2, only one photograph (or image) with a visual air quality below 

20 dv was rated as acceptable by less than 50% of the participants who rated that photograph.7

 

Similarly, only one image with a visual air quality above 30 dv was rated acceptable by more 

than 50% of the participants who viewed it.8

 

 7

 Only 47% of the BC participants rated a 19.2 dv photograph as acceptable. 8

 In the 2001 Washington, D.C. study, a 30.9 dv image was used as a repeated slide. The first time it was shown 

56% of the participants rated it as acceptable, but only 11% rated it as acceptable the second time it was shown. The 

same VAQ level was rated as acceptable by 42% of the participants in the 2009 study (Test 1). All three points are 

shown in Figure 4-2.
 4-24

Figure 4-2. Summary of Results of Urban Visibility Studies in Four Cities, Showing the 

Identified Range of the 50% Acceptance Criteria9

As can be seen in the figure, each urban area has a separate and unique response curve 

that appears to indicate that it is distinct from the others.10 These curves are the result of a 

logistical regression analysis using a logit model of the greater than 19,000 ratings of haze 

images as acceptable or unacceptable. The model results can be used to estimate the visual air 

quality in terms of deciview values where the estimated response functions cross the 50% 

acceptability level, as well as any alternative criteria levels. Selected examples of these are 

shown in Table 4-1 (US EPA, 2010b, Table 2-4). These results show that the logit model results 

also supports the upper and lower ends of the range of 50th percentile acceptability values (e.g., 

near 20 dv for Denver and near 30 dv for Washington, DC) already identified in Figure 4-2. 

 9

 Top scale shows light extinction in inverse megameter units; bottom scale in deciviews. Logit analysis estimated 

response functions are shown as the color-coded curved lines for each of the four urban areas. 10 Note that the Washington, DC results (black dots) shown in Figure 4-2 include values from two separate studies 

individually, so there are at least two points at each haze level (x-axis). There is a third point for some haze levels 

because an image was sometimes presented twice. Since one of the two studies had only 9 participants, the percent 

of participants’ acceptable ratings are limited to multiples of 1/9, so that a single participant rating can move a point 

by about 11%. A composite of the results from the two studies (similar to US EPA, 2010b, Figure 2-15) would 

show much less scatter among the points, but obscures the fact of it being data from differing studies. 
 4-25

Table 4-1. Logit Model-estimated Visual Air Quality Values (in deciviews) Corresponding 

to Various Percent Acceptability Values for the Four Cities 

Acceptability 

Criteria Denver British 

Columbia Phoenix Washington, DC 

90% Acceptability 14.2 16.8 19.4 23.0 

75% Acceptability 17.1 19.6 21.8 26.0 

50% Acceptability 19.9 22.5 24.2 29.0 

25% Acceptability 22.7 25.3 26.5 32.0 

10% Acceptability 25.6 28.1 28.9 35.0 

Based on the composite results and the effective range of 50th percentile acceptability 

across the four urban preference studies shown in Figure 4-2 and Table 4-1, benchmark levels of 

(total) light extinction have been selected in a range from 20 dv to 30 dv (75 to 200 Mm-1)

11 for 

the purpose of provisionally assessing whether visibility conditions would be considered 

acceptable (i.e., less than the low end of the range), unacceptable (i.e., greater than the high end 

of the range), or potentially acceptable (within the range). A midpoint of 25 dv (120 Mm-1) was 

also selected for use in the assessment. This level is also very near to the 50th percentile criterion 

value from the Phoenix study (i.e., 24.2 dv), which is by far the best of the four studies in terms 

of least noisy preference results and the most representative selection of participants. Based on 

the currently available information, we conclude that the use of 25 dv to represent the middle of 

the distribution of results seems well supported. 

These three benchmark values provide a low, middle, and high set of light extinction 

conditions that are used to provisionally define daylight hours with urban haze conditions that 

have been judged unacceptable by the participants of these preference studies. As discussed 

above, PM light extinction is taken to be (total) light extinction minus the Rayleigh scatter (i.e., 

light scattering by atmospheric gases which is on average about 10 Mm-1), so the low, middle, 

and high levels correspond to PM light extinction levels of about 65 Mm-1, 110 Mm-1, and 190 

Mm-1. In the UFVA, these three light extinction levels were called Candidate Protection Levels 

 11 These values were rounded from 74 Mm-1 and 201 Mm-1 to avoid an implication of greater precision than is 

warranted. Note that the middle value of 25 dv when converted to light extinction is 122 Mm-1 is rounded to 120 

Mm-1 for the same reason. Assessments conducted for the UFVA and the first and second draft PAs used the 

unrounded values. The EPA staff considers the results of assessment using unrounded values to be sufficiently 

representative of what would result if the rounded values were used that it was unnecessary to redo the assessments. 

That is why some tables and figures in this document reflect the unrounded values. 
 4-26

(CPLs). We continue to use this term in this document. However, it is important to note that the 

degree of protection provided by a secondary NAAQS is not determined solely by any one 

component of the standard but by all the components (i.e., indicator, averaging time, form, and 

level) being applied together. Therefore, the reader should keep in mind that the term CPL is 

meant only to indicate target levels of visibility within a range that we feel is appropriate for 

consideration that could, in conjunction with other elements of the standard, including indicator, 

averaging time, and form, provide an appropriate degree of visibility protection. 

In characterizing our degree of confidence in each CPL and across the range, a number of 

issues were considered. Looking first at the two studies that define the upper and lower bounds 

of the range, we considered whether they represent a true regional distinction in preferences for 

urban visibility conditions between western and eastern U.S. There is little information available 

to help evaluate this, especially given that we have preference studies in only one eastern urban 

area. Smith and Howell (2009) found little difference in preference response to Washington, DC 

haze photographs between the study participants from Washington, DC and those from Houston, 

TX.12 This provides some limited evidence that the value judgment of the public in different 

areas of the country may not be an important factor in explaining the differences in these study 

results. 

In further considering what factors could explain the observed differences in preferences 

across the four urban areas, we noted that the urban scenes used in each study had different 

characteristics. For example, each of the western urban visibility preference study scenes 

included mountains in the background while the single eastern urban study did not. It is also true 

that each of the western scenes included objects at greater distances from the camera location 

than in the Washington, DC study. There is no question that objects at a greater distance have a 

greater sensitivity to perceived visibility changes as light extinction is changed compared to 

otherwise similar scenes with objects at a shorter range. This alone might explain the difference 

between the results of the Washington, DC study and those from the Western urban studies. 

Also, it seems likely that people value the views of mountains in the background more than 

generic distant buildings in the foreground of the western scenes; just as it seems likely that the 

Capital Mall and Washington Monument were the likely objects of greatest interest for the 

Washington, DC study base photograph. Having scenes with the object of greatest intrinsic 

value nearer and hence less sensitive for Washington compared with more distant objects of 

 12 The first preference study using WinHaze images of a scenic vista from Washington, DC was conducted in 2001 

using subjects who were residents of Washington, DC. More recently, Smith and Howell (2009) interviewed 

additional subjects using the same images and interview procedure. The additional subjects included some residents 

of the Washington, DC area and some residents of the Houston, TX area. 
 4-27

greatest intrinsic value in the western urban areas could further explain the difference in 

preference results. 

Another question that we considered was whether the high CPL value that is based on the 

Washington, DC preference results is likely to be generally representative of urban areas that do 

not have associated mountains or other valued objects visible in the distant background. Such 

areas would include the middle of the country and many areas in the eastern U.S., and possibly 

some areas in the western U.S. as well. In order to examine this issue, an effort would have to be 

made to see if scenes in such areas could be found that would be generally comparable to the 

western scenes (e.g., scenes that contain valued scenic elements at more sensitive distances than 

that used in the Washington, DC study). This is only one of a family of issues concerning how 

exposure to urban scenes of varying sensitivity affects public perception for which no preference 

study information is currently available. Based on the currently available information, we 

conclude that the high end of the CPL range (30 dv) is an appropriate level to consider. 

With respect to the low end of the range, we considered factors that might further refine 

our understanding of the robustness of this level. We concluded that additional urban preference 

studies, especially with a greater variety in types of scenes, could help evaluate whether the 

lower CPL value of 20 dv is generally supportable. Further, the reason for the noisiness in data 

points around the curves apparent in both the Denver and British Columbia results compared to 

the smoother curve fit of Phoenix study results could be explored. One possible explanation that 

we identified is that these older studies used photographs taken at different times of day and on 

different days to capture the range of light extinction levels needed for the preference studies. In 

contrast, the use of WinHaze in the Phoenix (and Washington, DC) study reduced variations in 

scene appearance that affects preference rating and avoided the uncertainty inherent in using 

ambient measurements to represent sight path-averaged light extinction values. Reducing these 

sources of noisiness and uncertainty in the results of future studies of sensitive urban scenes 

could provide more confidence in the selection of a low CPL value. 

Based on the above considerations, and recognizing the limitations in the currently 

available information, staff concludes that it is reasonable to consider a range of CPL values 

including a high value of 30 dv, a mid-range value of 25 dv, and a low value of 20 dv. Based on 

its review of the second draft PA, CASAC supported this set of CPLs for consideration by the 

EPA in this review. CASAC noted that these CPL values were based on all available visibility 

preference data and that they bound the study results as represented by the 50% acceptability 

criteria. CASAC concluded that this range of levels is “adequately supported by the evidence 

presented” (Samet, 2010d, p. iii). 
 4-28

 To what extent does the available information demonstrate or suggest that PM-related 

visibility impairment within the range of CPLs is occurring at current ambient 

conditions, or that such impairment would occur under ambient PM2.5 levels that would 

meet the current standards? 

Current Visibility Levels

Current visibility conditions have been characterized in terms of PM light extinction 

levels for the 15 urban areas13 that were selected for analysis in the UFVA. We have analyzed 

hourly average PM-related light extinction in terms of both PM10 and PM2.5 light extinction.14 

These current visibility conditions were then compared to the CPLs identified above. 

As an initial matter, as noted above, we recognize that visibility impairment occurs 

during periods with fog or precipitation irrespective of the presence or absence of PM. While it 

is a popular notion that areas with many foggy or rainy days are “dreary” places to live compared 

to areas with more sunny days per year, we have no basis for taking into account how the 

occurrence of such days might modify the effect of pollution-induced hazy days on public 

welfare. It is logical that periods with naturally impaired visibility due to fog or precipitation 

should not be treated as having PM-impaired visibility. Moreover, depending on the specific 

indicator, averaging time, and measurement approach used for the NAAQS, foggy conditions 

might result in measured or calculated indicator values that are higher than the light extinction 

actually caused by PM.15 Therefore, in order to avoid precipitation and fog confounding 

estimates of PM visibility impairment, and as advised by CASAC as part of its comments on the 

first draft UFVA, we restricted our assessment of visibility conditions to daylight hours with 

relative humidity less than or equal to 90% when evaluating sub-daily alternative standards (US 

EPA, 2010b, section 3.3.5, and US EPA, 2010b, Appendix G). However, not all periods with 

 13 Comments on the second draft UFVA from those familiar with the monitoring sites in St. Louis indicated that the 

site selected to provide continuous PM10 monitoring, although less than a mile from the site of the PM2.5 data, is not 

representative of the urban area and resulted in unrealistically large PM10-2.5 values. The EPA staff considers these 

comments credible and has set aside the St. Louis assessment results for PM10 light extinction. Thus, results and 

statements in this PA regarding PM10 light extinction apply to only the other 14 areas. However, results regarding 

PM2.5 light extinction in most cases apply to all 15 study areas because the St. Louis estimates for PM2.5 light 

extinction were not affected by the PM10 monitoring issue. 14 PM-related light extinction is used here to refer to the light extinction caused by PM regardless of particle size; 

PM10 light extinction refers to the contribution by particles sampled through an inlet with a particle size 50% 

cutpoint of 10 m diameter; and PM2.5 light extinction refers to the contribution by particles sampled through an 

inlet with a particle size 50% cutpoint of 2.5 m diameter. 15 One example of an indicator and measurement approach for which indicator values could be higher than true PM 

light extinction as a result of fog would be a light extinction indicator measured in part by an unheated 

nephelometer, which is an optical instrument for measuring PM light scattering from an air sample as it flows 

through a measurement chamber. Rain drops would be removed by the initial size-selective inlet device, although 

some particles associated with fog may be small enough that they might pass through the inlet and enter the 

measurement chamber of the instrument. This would result in a reported scattering coefficient that does not 

correspond to true PM light extinction. Direct measurement of light extinction using an open-path instrument would 

be even more affected by both fog and precipitation. 
 4-29

relative humidity above 90% have fog or precipitation. Removing those hours from application 

of a secondary PM standard involves a tradeoff between the benefits of not including many of 

the hours with meteorological causes of visibility impacts and the loss of public welfare 

protection of not including some hours with high relative humidity without fog or precipitation, 

where the growth of hygroscopic PM into large solution droplets results in enhanced PM 

visibility impacts. For the 15 urban areas included in the assessment for which meteorological 

data were obtained to allow an examination of the co-occurrence of high relative humidity and 

fog or precipitation, a 90% relative humidity cutoff criterion is effective in that on average less 

than 6% of the daylight hours are removed from consideration, yet those hours have on average 

ten times the likelihood of rain, six time the likelihood of snow/sleet, and 34 times the likelihood 

of fog compared with hours with 90% or lower relative humidity. Based on these findings, we 

conclude that it is appropriate that a sub-daily standard intended to protect against PM-related 

visibility impairment would be defined in such a way as to exclude hours with relative humidity 

greater than approximately 90%, regardless of measured values of light extinction or PM. 

The UFVA analyses were done in terms of PM10 light extinction. Figure 4-3 (Figure 3-8 

in UFVA) presents box-and-whisker plots to illustrate the distributions of the estimates of the 

daily maximum daylight 1-hour calculated PM10 light extinction levels in each area (excluding 

St. Louis) based on data from the 2005-2007 time period. The horizontal dashed lines in the 

plots represent the low, middle, and high CPLs for PM10 light extinction of 65, 110, and 190 

Mm-1, corresponding to the benchmark visual air quality values of 20, 25, and 30 dv, as 

discussed above. Table 4-2 (Table 3-7 in UFVA) provides the percentages of days (across all of 

2005-2007, not seasonally weighted) in which the daily maximum daylight 1-hour PM10 light 

extinction level was greater than each of the three CPLs (excluding hours with relative humidity 

greater than 90%). 

From these UFVA-based displays it can be seen that among these 14 urban areas, those in 

the East and in California tend to have a higher frequency of visibility conditions estimated to be 

above the high CPL compared with those in the western U.S. Both Figure 4-3 and Table 4-2 

indicate that all 14 urban areas have daily maximum hourly PM10 light extinctions that are 

estimated to exceed even the highest CPL some of the days. Except for the two Texas areas and 

the non-California western urban areas, all of the other urban areas are estimated to exceed the 

high CPL from about 20% to over 60% of the days. We also note that all 14 of the urban areas 

are estimated to exceed the low CPL from about 40% to over 90% of the days. 

Since the completion of the UFVA, EPA staff has repeated the UFVA-type modeling 

based on PM2.5 light extinction and data from the 2007-2009 time period for the same 15 study 

areas (including St. Louis), as described in Appendix F. Figure 4-4 and Table 4-3 present the 

same type of information as do Figure 4-3 and Table 4-2, respectively, except for the PM2.5 vs. 
 4-30

PM10 distinction and the use of data from a more recent time period. While the estimates of the 

percentage of daily maximum hourly PM2.5 light extinction values exceeding the CPLs are 

somewhat lower than for PM10 light extinction, the patterns of these estimates across the study 

areas are similar. More specifically, except for the two Texas and the non-California western 

urban areas, all of the other urban areas are estimated to exceed the high CPL from about 10% up 

to about 50% of the days based on PM2.5 light extinction, while all 15 areas are estimated to 

exceed the low CPL from over 10% to over 90% of the days. 
 4-31

Figure 4-3. Distribution of Estimated Daily Maximum Daylight 1-hour PM10 Light 

Extinction Across the 2005-2007 Period, by Study Area (excluding hours with relative 

humidity >90%). (Adapted from US EPA, 2010b, Figure 3-8)* 

* In the box-and-whisker plot, the box represents the 25th - 75th percentile range; the whiskers represent the 10th and 90th

percentiles; individual data points below the 10th percentile and above the 90th percentile are graphed as small circles. The 

three dashed horizontal lines represent the three CPL levels of 65, 110, and 190 Mm-1(i.e., 20, 25, and 30 dv).

Table 4-2. Percentage of 2005-2007 Daily Maximum Daylight Hourly Values of PM10 Light 

Extinction Exceeding CPLs (excluding hours with relative humidity >90%) 

 (Adapted from Table 3-7 in UFVA) 

Study Area Number of Days with 

Estimates

Candidate Protection Level

20 dv 25 dv 30 dv

Percentage of Daily Maximum Hourly Values Exceeding CPL

Tacoma 109 52 22 4 

Fresno 324 75 52 30 

Los Angeles 300 90 83 62 

Phoenix 86 42 7 1 

Salt Lake City 306 44 17 8 

Dallas 273 80 41 10 

Houston 148 79 45 11 

Birmingham 349 89 65 34 

Atlanta 279 91 75 31 

Detroit 141 87 68 43 

Pittsburgh 277 85 57 26 

Baltimore 181 80 50 23 

Philadelphia 143 86 64 31 

New York 225 83 59 28 
 4-32

Figure 4-4. Distribution of Estimated Daily Maximum Daylight 1-hour PM2.5 Light 

Extinction Across the 2007-2009 Period, by Study Area (excluding hours with relative 

humidity >90%).* 

* In the box-and-whisker plot, the box represents the 25th - 75th percentile range; the whiskers represent the 10th and 90th

percentiles; individual data points below the 10th percentile and above the 90th percentile are graphed as small circles. The 

three dashed horizontal lines represent the three CPL levels of 65, 110, and 190 Mm-1(i.e., 20, 25, and 30 dv).

Table 4-3. Percentage of 2007-2009 Daily Maximum Daylight Hourly Values of PM2.5

Light Extinction Exceeding CPLs (excluding hours with relative humidity >90%) 

Study Area Number of Days 

with Estimates

Candidate Protection Level

20 dv 25 dv 30 dv

Percentage of Daily Maximum Hourly Values Exceeding CPL

Tacoma 150 40 14 1 

Fresno 325 66 43 22 

Los Angeles 161 89 78 48 

Phoenix 84 14 2 0 

Salt Lake City 276 41 15 8 

Dallas 257 72 30 5 

Houston 144 70 25 2 

St. Louis 287 78 49 17 

Birmingham 330 79 51 21 

Atlanta 258 85 49 12 

Detroit 133 71 49 27 

Pittsburgh 264 77 43 15 

Baltimore 140 72 33 9 

Philadelphia 98 92 71 34 

New York 145 82 50 23 
 4-33

Visibility Levels That Just Meet Current Standards

We have also conducted analyses to assess the likelihood that PM-related visibility 

impairment would exceed CPLs for a scenario based on simulating just meeting the current suite 

of PM2.5 secondary standards: 15 μg/m3

 annual average PM2.5 concentration and 35 μg/m3 24-

hour average PM2.5 concentration with a 98th percentile form, averaged over three years. As 

describe in the UFVA, The steps needed to model this scenario involve explicit consideration of 

changes in PM2.5 components. First, we applied proportional rollback to all the PM2.5 monitoring 

sites in each study area, taking into account policy-relevant background (PRB) PM2.5 mass, to 

“just meet” the NAAQS scenario for the area as a whole, not just at the visibility assessment 

study site. The quantitative health risk assessment document (US EPA, 2010a) describes this air 

quality roll-back procedure in detail. The degree of rollback (i.e., the percentage reduction in 

non-PRB PM2.5 mass) is controlled by the highest annual or 24-hour design value, which in most 

study areas is from a site other than the site used in this visibility assessment. The relevant result 

from this analysis is the percentage reduction in non-PRB PM2.5 mass needed to “just meet” the 

NAAQS scenario, for each study area. These percentage reductions are shown in Table 4-4 of 

the UFVA. Note that Phoenix and Salt Lake City meet the current PM2.5 NAAQS under current 

conditions and require no reduction. PM2.5 levels in these two cities were not “rolled up.” 

Second, for each day and hour for each PM2.5 component, we subtracted the PRB concentration 

from the current conditions concentration to determine the non-PRB portion of the current 

conditions concentration. Third, we applied the same percentage reduction from the first step to 

the non-PRB portion of each of the five PM2.5 components and added back the PRB portion of 

the component. Finally, we applied the IMPROVE algorithm, using the reduced PM2.5

component concentrations, the current conditions PM10-2.5 concentration for the day and hour, 

and relative humidity for the day and hour to calculate the PM10 light extinction. 

In these analyses, we have estimated both PM10 and PM2.5 light extinction in terms of 

both daily maximum 1-hour average values and multi-hour (i.e., 4-hour) average values for 

daylight hours. Figure 4-5 and Table 4-4 display the results of the rollback procedure as a box 

and whisker plot of daily maximum daylight 1-hour PM10 light extinction and the percentage of 

daily maximum hourly PM10 light extinction values estimated to exceed the CPLs when just 

meeting the current suite of PM2.5 secondary standards (excluding hours with relative humidity 

greater than 90%). These displays show that the daily maximum 1-hour average values in all of 

the study areas other than the three western non-California areas are estimated to exceed the high 

CPL from 10% up to about 40% of the days and the middle CPL from 30% up to about 70% of 

the days, while all 14 areas are estimated to exceed the low CPL from over 20% to about 90% of 

the days. 
 4-34

Figure 4-5. Distribution of Daily Maximum Daylight 1-hour PM10 Light Extinction when 

Rolled Back to Just Meet Current PM2.5 NAAQS, by Study Area * (excluding hours with 

relative humidity >90%) 

 * In the box-and-whisker plot, the box represents the 25th - 75th percentile range; the whiskers represent the 10th and 90th

percentiles; individual data points below the 10th percentile and above the 90th percentile are graphed as small circles. The 

three dashed horizontal lines represent the three CPL levels of 65, 110, and 190 Mm-1(i.e., 20, 25, and 30 dv).

Table 4-4. Percentage of Daily Maximum Daylight Hourly Values of PM10 Light Extinction 

Exceeding CPLs When Just Meeting the Current PM2.5 NAAQS (excluding hours with 

relative humidity >90%). (Adapted from Table 4-7 in UFVA)

Study Area Number of Days 

with Estimates

Candidate Protection Level

20 dv 25 dv 30 dv

Percentage of Daily Maximum Hourly Values Exceeding CPL

Tacoma 109 40 12 1 

Fresno 324 53 30 10 

Los Angeles 300 85 70 39 

Phoenix 98 44 7 1 

Salt Lake City 306 23 10 4 

Dallas 273 79 43 10 

Houston 158 74 41 11 

Birmingham 349 83 55 24 

Atlanta 279 89 71 25 

Detroit 141 80 61 33 

Pittsburgh 277 77 49 17 

Baltimore 181 76 49 20 

Philadelphia 143 84 61 29 

New York 225 76 49 19 
 4-35

Figure 4-6 and Table 4-5 show the same type of results for PM10 based on daily 

maximum 4-hour average values. While the estimates of the percentage of values exceeding the 

CPLs are lower than for 1-hour average light extinction, the patterns of these estimates across the 

study areas are similar. More specifically, the daily maximum 4-hour average values in all of the 

study areas other than the three western non-California areas and the two areas in Texas are 

estimated to exceed the high CPL from about 7% up to about 20% of the days and the middle 

CPL from about 20% up to over 50% of the days, while all 14 areas are estimated to exceed the 

low CPL from 15% to about 80% of the days. 

A similar set of figures and tables have been developed in terms of PM2.5 light extinction 

for all 15 areas considered in the UFVA (including St. Louis), as shown below. Figure 4-7 and 

Table 4-6 present results for PM2.5 light extinction based on daily maximum 1-hour average 

values, and Figure 4-8 and Table 4-7 present results based on daily maximum 4-hour average 

values. These displays show that the daily maximum 1-hour average PM2.5 light extinction 

values in all of the study areas other than the three western non-California areas are estimated to 

exceed the high CPL from about 8% up to over 30% of the days and the middle CPL from about 

30% up to about 70% of the days, while all areas except Phoenix are estimated to exceed the low 

CPL from over 15% to about 90% of the days. Further, the daily maximum 4-hour average 

PM2.5 light extinction values in all of the study areas other than the three western non-California 

areas and the two areas in Texas are estimated to exceed the high CPL from about 4% up to over 

15% of the days and the middle CPL from about 15% up to about 45% of the days, while all 

areas except Phoenix are estimated to exceed the low CPL from over 10% to about 75% of the 

days. 
 4-36

Figure 4-6. Distribution of Daily Maximum Daylight 4-hour Average PM10 Light 

Extinction when Rolled Back to Just Meet Current PM2.5 NAAQS, by Study Area* 

(excluding hours with relative humidity >90%) 

* In the box-and-whisker plot, the box represents the 25th - 75th percentile range; the whiskers represent the 10th and 90th

percentiles; individual data points below the 10th percentile and above the 90th percentile are graphed as small circles. The 

three dashed horizontal lines represent the three CPL levels of 65, 110, and 190 Mm-1(i.e., 20, 25, and 30 dv).

Table 4-5. Percentage of Daily Maximum Daylight 4-Hour Average Values of PM10 Light 

Extinction Exceeding CPLs when Just Meeting the Current PM2.5 NAAQS (excluding 

hours with relative humidity >90%) 

Study Area Number of Days 

with Estimates

Candidate Protection Level

20 dv 25 dv 30 dv

Percentage of Daily Maximum Hourly Values Exceeding CPL

Tacoma 107 22 3 1 

Fresno 320 41 19 7 

Los Angeles 298 77 53 20 

Phoenix 98 30 2 0 

Salt Lake City 302 15 6 2 

Dallas 272 59 22 4 

Houston 156 55 15 1 

Birmingham 346 68 35 11 

Atlanta 274 80 50 7 

Detroit 140 76 51 14 

Pittsburgh 273 63 34 8 

Baltimore 179 67 36 13 

Philadelphia 138 73 46 12 

New York 220 63 35 13 
 4-37

Figure 4-7. Distribution of Daily Maximum Daylight 1-hour Average PM2.5 Light 

Extinction when Rolled Back to Just Meet Current PM2.5 NAAQS, by Study Area* 

(excluding hours with relative humidity >90%) 

* In the box-and-whisker plot, the box represents the 25th - 75th percentile range; the whiskers represent the 10th and 90th

percentiles; individual data points below the 10th percentile and above the 90th percentile are graphed as small circles. The 

three dashed horizontal lines represent the three CPL levels of 65, 110, and 190 Mm-1(i.e., 20, 25, and 30 dv).

Table 4-6. Percentage of Daily Maximum Daylight 1-Hour Average Values of PM2.5

Light Extinction Exceeding CPLs when Just Meeting the Current PM2.5 NAAQS 

(excluding hours with relative humidity >90%) 

Study Area Number of Days 

with Estimates

Candidate Protection Level

20 dv 25 dv 30 dv

Percentage of Daily Maximum Hourly Values Exceeding CPL

Tacoma 107 15 6 1 

Fresno 320 37 28 9 

Los Angeles 298 72 61 33 

Phoenix 98 2 1 0 

Salt Lake City 302 13 8 3 

Dallas 272 54 38 8 

Houston 156 45 35 8 

St. Louis 285 61 48 17 

Birmingham 346 62 48 17 

Atlanta 274 76 68 20 

Detroit 140 65 53 26 

Pittsburgh 273 61 45 14 

Baltimore 179 62 45 18 

Philadelphia 138 67 57 25 

New York 220 59 48 19 
 4-38

Figure 4-8. Distribution of Daily Maximum Daylight 4-hour Average PM2.5 Light 

Extinction when Rolled Back to Just Meet Current PM2.5 NAAQS, by Study Area* 

(excluding hours with relative humidity >90%) 

* In the box-and-whisker plot, the box represents the 25th - 75th percentile range; the whiskers represent the 10th and 90th

percentiles; individual data points below the 10th percentile and above the 90th percentile are graphed as small circles. The 

three dashed horizontal lines represent the three CPL levels of 65, 110, and 190 Mm-1(i.e., 20, 25, and 30 dv).

Table 4-7. Percentage of Daily Maximum Daylight 4-Hour Average Values of PM2.5 Light 

Extinction Exceeding CPLs when Just Meeting the Current PM2.5 NAAQS (excluding 

hours with relative humidity >90%) 

Study Area Number of Days 

with Estimates

Candidate Protection Level

20 dv 25 dv 30 dv

Percentage of Daily Maximum Hourly Values Exceeding CPL

Tacoma 107 15 1 1 

Fresno 320 37 16 6 

Los Angeles 298 72 45 16 

Phoenix 98 2 0 0 

Salt Lake City 302 13 6 2 

Dallas 272 54 18 3 

Houston 156 45 8 1 

St. Louis 285 61 30 7 

Birmingham 346 62 28 7 

Atlanta 274 76 42 4 

Detroit 140 65 45 10 

Pittsburgh 273 61 30 6 

Baltimore 179 62 31 10 

Philadelphia 138 67 39 10 

New York 220 59 35 12 
 4-39

4.2.2 CASAC Advice 

In our consideration of the adequacy of the current suite of PM2.5 secondary standards, in 

addition to the evidence- and impact-based information discussed above, we have also 

considered the advice of CASAC, based on its review of drafts of the ISA and the UFVA, and 

drafts of this document, as well as comments from the public on earlier drafts of this document 

and the UFVA. In its comments on the second draft PA, CASAC stated agreement with EPA 

staff’s conclusion that the “currently available information clearly calls into question the 

adequacy of the current standards and that consideration should be given to revising the suite of 

standards to provide increased public welfare protection” (Samet, 2010d, p. iii). CASAC noted 

that the detailed estimates of hourly PM light extinction associated with just meeting the current 

standards “clearly demonstrate that current standards do not protect against levels of visual air 

quality which have been judged to be unacceptable in all of the available urban visibility 

preference studies.” Further, CASAC stated, with respect to the current suite of PM2.5 secondary 

standards, that “[T]he levels are too high, the averaging times are too long, and the PM2.5 mass 

indicator could be improved to correspond more closely to the light scattering and absorption 

properties of suspended particles in the ambient air” (Samet, 2010d, p. 9). 

4.2.3 Staff Conclusions on Adequacy of Current Standards 

Taking into account the above considerations, EPA staff concludes that the available 

information in this review, as described above and in the UFVA and ISA, clearly calls into 

question the adequacy of the current suite of PM2.5 standards in the context of public welfare 

protection from visibility impairment, primarily in urban areas, and supports consideration of 

alternative standards to provide appropriate protection. 

This conclusion is based in part on the large percentage of days, in many urban areas, that 

exceed the range of CPLs identified for consideration under simulations of conditions that would 

just meet the current suite of PM2.5 secondary standards. In particular, for air quality that is 

simulated to just meet the current standards, for 9 of the 14 or 15 urban areas greater than 10% of 

the days are estimated to exceed the highest, least protective CPL of 30 dv in terms of PM10 and 

PM2.5 light extinction, respectively, based on 1-hour average values, and would thus likely fail to 

meet a 90th percentile-based standard at that level. For these areas, the percent of days estimated 

to exceed the highest CPL ranges from approximately 10% to 40%. Similarly, when the middle 

CPL of 25 dv is considered, for 11 of the 14 or 15 urban areas greater than 30% up to over 70% 

of the days are estimated to exceed that CPL in terms of PM10 and PM2.5 light extinction, 

respectively, based on 1-hour average values. Based on a 4-hour averaging time, 5 or 6 of the 

areas were estimated to have at least 10% of the days exceeding the highest CPL in terms of 

PM2.5 and PM10 light extinction, respectively, and 8 or 11 of the areas were estimated to have 
 4-40

greater than 30% of the days exceeding the middle CPL in terms of PM2.5 and PM10 light 

extinction, respectively. For the lowest CPL of 20 dv, the percentages of days estimated to 

exceed that CPL are even higher for all cases considered. Based on all of the above, we 

conclude that PM light extinction estimated to be associated with just meeting the current suite 

of PM2.5 secondary standards in many areas across the country exceeds levels and percentages of 

days that could reasonably be considered to be important from a public welfare perspective. 

Further, we conclude that use of the current indicator of PM2.5 mass, in conjunction with 

the current 24-hour and annual averaging times, is clearly called into question for a national 

standard intended to protect public welfare from PM-related visibility impairment. This is 

because such a standard is inherently confounded by regional differences in relative humidity 

and species composition of PM2.5, which are critical factors in the relationship between the mix 

of fine particles in the ambient air and the associated impairment of visibility. We note that this 

concern was one of the important elements in the court’s decision to remand the PM2.5 secondary 

standards set in 2006 to the Agency, as discussed above in section 4.1.2. 

Thus, beyond concluding that the available information clearly calls into question the 

adequacy of the protection against PM-related visibility impairment afforded by the current suite 

of PM2.5 standards, we also conclude that it clearly calls into question the appropriateness of each 

of the current standard elements: indicator, averaging time, form, and level. Section 4.3 below 

discusses considerations related to each of these elements in its discussion of alternative 

standards for consideration. 

4.3 CONSIDERATION OF ALTERNATIVE STANDARDS 

Having reached the conclusion that the available information clearly calls into question 

the adequacy and appropriateness of the current suite of PM2.5 standards, this section considers a 

second overarching question: 

What alternative fine particle standards are supported by the currently available scientific 

evidence and impact-based information, as reflected in the ISA and UFVA? 

In addressing this question, we have posed a series of more specific questions to inform 

decisions regarding the basic elements of the NAAQS: indicator (section 4.3.1), averaging time 

(section 4.3.2), form (section 4.3.3), and level (section 4.3.4). These elements are considered 

collectively in evaluating the welfare protection afforded by alternative standards under 

consideration. 
 4-41

4.3.1 Indicator 

4.3.1.1 Evidence-based and Impact-based Considerations 

 To what extent does currently available information provide support for considering a 

different indicator for PM to replace the current PM2.5 mass indicator? 

As described below, EPA staff has considered three indicators: the current PM2.5 mass 

indicator and two alternative indicators, including directly measured PM2.5 light extinction and 

calculated PM2.5 light extinction.16 Directly measured PM2.5 light extinction is a measurement 

(or combination of measurements) of the light absorption and scattering caused by PM2.5 under 

ambient conditions. Calculated PM2.5 light extinction uses the IMPROVE algorithm to calculate 

PM2.5 light extinction using measured PM2.5 mass and/or measured PM components and relative 

humidity.17 

We believe that consideration of the use of either directly measured PM2.5 light extinction 

or calculated PM2.5 light extinction as an indicator is justified because light extinction is a 

physically meaningful measure of the ambient PM2.5 characteristic that is most relevant and 

directly related to PM-related visibility effects. Further, as noted in section 4.2.1 above, PM2.5 is 

the component of PM responsible for most of the visibility impairment in most urban areas. In 

these areas, the contribution of PM10–2.5 is a minor contributor to visibility impairment most of 

the time, although at some locations (see UFVA Figure 3-13 for Phoenix) PM10–2.5 can be a 

major contributor to urban visibility effects. Few urban areas conduct continuous PM10-2.5

monitoring. For example, among the 15 urban areas featured in the UFVA, only four areas had 

collocated continuous PM10 data allowing calculation of hourly PM10-2.5 data for 2005-2007. In 

the absence of PM10-2.5 air quality information from a much larger number of urban areas across 

the country, it is not possible at this time to know in how many urban areas PM10–2.5 is a major 

contributor to urban visibility effects, though it is reasonable to assume that other urban areas in 

the desert southwestern region of the country may have conditions similar to the conditions 

shown for Phoenix. PM10-2.5 is generally less homogenous in urban areas than PM2.5, making it 

more challenging to select sites that would adequately represent urban visibility conditions. 

While it would be possible to include a PM10– 2.5 light extinction term in a calculated light 

extinction indicator, as was done in the UFVA, there is insufficient information available at this 

 16 In the second draft PA, this indicator was referred to as speciated PM2.5 mass calculated light extinction. 

17 In 2009, the D.C. Circuit remanded the secondary PM2.5 standards to the Agency in part because EPA did not 

address the problem that a PM2.5 mass-based standard using a daily averaging time would be confounded by regional 

differences in relative humidity, although EPA had acknowledged this problem (as discussed above in section 4.1.2). 

We note that both of the light extinction indicators considered in this assessment explicitly take into account 

differences in relative humidity in areas across the country. 
 4-42

time to assess the impact and effectiveness of such a refinement in providing public welfare 

protection in areas across the country. 

The basis for considering each of these three indicators is discussed below. The 

discussion also addresses monitoring data requirements for directly measured PM2.5 light 

extinction and for calculated PM2.5 light extinction. The following discussion also takes into 

consideration different averaging times since the combination of indicator and averaging time is 

relevant to understanding the monitoring data requirements. Consideration of alternative 

averaging times is addressed more specifically in the next section (section 4.3.2) on averaging 

time. 

PM2.5 Mass Indicator

PM2.5 mass monitoring methods are in widespread use, including the Federal Reference 

Method (FRM) involving the collection of periodic (usually 1-day-in-6 or 1-day-in-3) 24-hour 

filter samples. Blank and loaded filters are weighed to determine 24-hour PM2.5 mass. 

Continuous PM2.5 monitoring produces hourly average mass concentrations and is conducted at 

about 900 locations. About 180 of these locations employ newer model continuous instruments 

that have been approved by EPA as federal equivalent methods (FEM), although we note that 

FEM approval has been based only on 24-hour average, not hourly, PM2.5 mass. These routine 

monitoring activities do not include measurement of the full water content of the ambient PM2.5

that contributes, often significantly, to visibility impacts.18 Further, the PM2.5 mass concentration 

monitors do not provide information on the composition of the ambient PM2.5, which plays a 

central role in the relationship between PM-related visibility impairment and ambient PM2.5 mass 

concentrations.19

The overall performance of 1-hour average PM2.5 mass as a predictor of PM-related 

visibility impairment as indicated by PM10 light extinction can be seen in scatter plots shown in 

Figure 4-9 for two illustrative urban areas, Pittsburgh and Philadelphia, PA. (Similar plots for all 

14 urban areas are in Appendix D, Figure D-2 of the UFVA). These illustrative examples 

demonstrate the large variations in hourly PM10 light extinction corresponding to any specific 

level of hourly PM2.5 mass concentration as well as differences in the statistical average 

relationships (depicted as the best fit lines) between cities. This poor correlation between hourly 

 18 FRM filters are stabilized in a laboratory at fixed temperature and relative humidity levels, which alters whatever 

water content was present on the filter when removed from the sampler. FEM instruments are designed to meet 

performance criteria compared to FRM measurements, and accordingly typically manage temperature and/or 

humidity at the point of measurement to levels that are not the same as ambient conditions. 

19 As discussed below, 24-hour average PM2.5 chemical component mass is measured at about 200 sites in the 

EPA/state/local Chemical Speciation Network.
 4-43

PM10 light extinction and hourly PM2.5 mass is not due to any great extent to the contribution of 

PM10-2.5 to light extinction, but rather is principally due to the impact of the water content of the 

particles on light extinction, which depends on both the composition of the PM2.5 and the 

ambient relative humidity. Both composition and especially relative humidity vary during a 

single day, as well as from day to day, at any site and time of year. This contributes to the 

noisiness of the relationship at any site and time of year. Also, there are systematic regional and 

seasonal differences in the distribution of ambient humidity and PM2.5 composition conditions 

that make it impossible to select a PM2.5 concentration that generally would correspond to the 

same PM-related light extinction levels across all areas of the nation. 

Figure 4-9. Relationship between Daylight 1-hour PM2.5 Mass Concentration vs. SameHour PM10 Calculated Light Extinction for Two Cities 

(from UFVA Appendix D, Figure D2) 

As part of the UFVA, we conducted an assessment that estimated PM10 light extinction 

levels that may prevail if areas were simulated to just meet a range of alternative secondary 

standards based on hourly PM2.5 mass as the indicator. Appendix E contains the results of this 

rollback-based assessment. This assessment quantifies the projected uneven protection, noted 

qualitatively above, that would result from the use of 1-hour average PM2.5 mass as the indicator. 

Directly Measured PM2.5 Light Extinction Indicator

PM light extinction is the major contributor to light extinction, which is the property of 

the atmosphere that is most directly related to visibility effects. It differs from light extinction by 

the nearly constant contributions for Rayleigh (or clean air) light scattering and the minor 

contributions by NO2 light absorption. The net result is that PM light extinction has a nearly 
 4-44

one-to-one relationship to light extinction, unlike PM2.5 mass concentration. As explained 

above, PM2.5 is the component responsible for the large majority of PM10 light extinction in most 

places and times. PM2.5 light extinction can be directly measured. Direct measurement of PM2.5 

light extinction can be accomplished using several instrumental methods, some of which have 

been used for decades to routinely monitor the two components of PM2.5 light extinction (light 

scattering and absorption) or to jointly measure both as total light extinction (from which 

Rayleigh scattering is subtracted to get PM2.5 light extinction). There are a number of 

advantages to direct measurements of light extinction for use in a secondary standard relative to 

estimates of PM2.5 light extinction calculated using PM2.5 mass and speciation data, as was done 

to generate hourly light extinction values for the UFVA and as would be done for the calculated 

PM2.5 light extinction indicator approach discussed below. These include greater accuracy of 

direct measurements with shorter averaging times and overall greater simplicity when compared 

to the need for measurements of multiple parameters to calculate PM light extinction. 

As part of the UFVA, we conducted an assessment that estimated PM10 light extinction 

levels that may prevail in 14 urban study areas if the areas were simulated to just meet a 

secondary standard based on directly measured hourly PM10 light extinction as the indicator (see 

section 4.3 of the UFVA). 20 As would be expected, this assessment indicated that a secondary 

standard based on a directly measured PM10 light extinction indicator would provide the same 

percentage of days having indicator values above the level of the standard in each of the areas, 

with the percentage being dependent on the statistical form of the standard. We consider this 

assessment reasonably informative for a directly measured PM2.5 light extinction indicator as 

well, because in most of the UFVA study areas PM10 light extinction is dominated by PM2.5 light 

extinction. 

In evaluating whether direct measurement of PM2.5 or PM10 light extinction is appropriate 

to consider in the context of this PM NAAQS review, EPA produced a White Paper on 

Particulate Matter (PM) Light Extinction Measurements (US EPA, 2010h), and solicited 

comment on the White Paper from the Ambient Air Monitoring and Methods Subcommittee 

(AAMMS) of CASAC. In its review of the White Paper (Russell and Samet, 2010), the 

AAMMS made the recommendation to EPA that consideration of direct measurement should be 

limited to PM2.5 light extinction as this can be accomplished by a number of commercially 

available instruments and because PM2.5 is generally responsible for most of the PM visibility 

impairment in urban areas. The AAMMS indicated that it is technically more challenging at this 

time to accurately measure the PM10–2.5 component of light extinction. 

 20 This assessment was conducted prior to staff’s decision to focus on PM2.5 light extinction indicators in this PA. 
 4-45

The AAMMS not only endorsed the concept of direct measurement of PM2.5 light 

extinction, but also commented on the capabilities of currently available instruments, and 

expressed optimism regarding the near-term development of even better instruments for such 

measurement than are now commercially available. The AAMMS advised against choosing any 

currently available commercial instrument, or even a general measurement approach, as a 

Federal Reference Method because to do so could discourage development of other potentially 

superior approaches. Instead, the AAMMS recommended that EPA develop performance-based 

approval criteria for direct measurement methods in order to put all approaches on a level 

playing field. Such criteria would necessarily include procedures and pass/fail requirements for 

demonstrating that the performance criteria have been met. For example, instruments might be 

required to demonstrate their performance in a wind tunnel, where the concentration of PM2.5

components, and thus of PM2.5 light extinction, could be controlled to known values. It might 

also be possible to devise approval testing procedures based on operation in ambient air, 

although knowing the true light extinction level (without in effect treating some particular 

instrument as if it were the FRM) would be more challenging. At the present time, EPA has not 

undertaken to develop and test such performance-base approval criteria. The EPA anticipates 

that if an effort were begun it would take at least several years before such criteria would be 

ready for regulatory use. 

Calculated PM2.5 Light Extinction Indicator

As discussed above in section 4.2.1, PM2.5 light extinction can be calculated from PM2.5

mass speciation data plus relative humidity data, as is presently routinely done on a 24-hour 

average basis under the Regional Haze Program using data from the rural IMPROVE monitoring 

network. This same calculation procedure, using a 24-hour average basis, could also be used for 

a NAAQS focused on protecting against PM-related visibility impairment primarily in urban 

areas using the type of data that is routinely collected from the urban CSN.21 This calculation 

procedure, using the light extinction equations presented above in section 4.2.1 on a 24-hour 

basis, does not require PM2.5 mass concentration measurements. 

Alternatively, a conceptually similar approach could be applied in urban areas on an 

hourly or multi-hour basis. Applying this conceptual approach on a sub-daily basis would 

involve translating 24-hour speciation data into hourly estimates of species concentrations, using 

24-hour average species concentrations in conjunction with hourly PM2.5 mass concentrations. 

 21 About 200 sites in the EPA/state/local CSN routinely measure 24-hour average PM2.5 chemical components using 

filter-based samplers and chemical analysis in a laboratory, on either a 1-day-in-3 or 1-day-in-6 schedule, see 

Appendix B, section B.1.3.. 
 4-46

This translation can be made using more or less complex alternative approaches, as discussed 

below. 

The approach used to generate hourly PM10 light extinction for the UFVA was a 

relatively more complex method for implementing such a conceptual approach. It involved the 

use of the original IMPROVE algorithm22 with estimates of hourly PM2.5 components derived 

from day-specific 24-hour and hourly measurements of PM2.5 mass, 24-hour measurements of 

PM2.5 composition, and (for some but not all study sites) hourly PM10-2.5 mass, along with hourly 

relative humidity information (UFVA, Section 3.3). The UFVA approach also involved the use 

of output from a chemical transport modeling run to provide initial estimates of diurnal profiles 

for PM2.5 components at particular sites. The UFVA approach entailed numerous and complex 

data processing steps to generate hourly PM2.5 composition information from these less timeresolved data, including application of a mass-closure approach, referred to as the SANDWICH 

approach (Frank, 2006), to adjust for nitrate retention differences between FRM and CSN filters, 

which is a required step for consistency with the IMPROVE algorithm and for estimating organic 

carbonaceous material via mass balance.23 The EPA staff employed complex custom software to 

do these data processing steps. While the complexity of the approach used in the UFVA was 

reasonable for assessment purposes at 14 urban areas, we recognize that a relatively more simple 

approach would be more straightforward and have greater transparency, and thus should be 

considered for purposes of a national standard.24 Therefore, we evaluated the degree to which 

simpler approaches would correlate with the results of the highly complex method used in the 

UFVA. This evaluation of two specific simpler approaches (described briefly below and in more 

detail in Appendix F, especially Table F-1) demonstrated that the PM2.5 portions of the PM10

light extinction values developed for the UFVA can be well approximated using the same 

IMPROVE algorithm applied to hourly PM2.5 composition values that were much more simply 

generated than with the method used in the UFVA. 

The simplified approaches we examined are aimed at calculating hourly PM2.5 light 

extinction using the original IMPROVE algorithm (see section 4.2.1 above) excluding the 

Rayleigh term for light scattering by atmospheric gases and the term for PM10-2.5.

25 Initially, this 

 22 The original IMPROVE algorithm was selected for the described analysis in the UFVA because of its simplicity 

relative to the revised algorithm. 

23 Daily temperature data were also used as part of the SANDWICH method. 

24 The sheer size of the ambient air quality, meteorological, and chemical transport modeling data files involved with 

the UFVA approach would make it very difficult for state agencies or any interested party to consistently apply such 

an approach on a routine basis for the purpose of implementing a national standard defined in terms of the UFVA 

approach. 

25 The original IMPROVE algorithm was the basis for the approaches considered here to maintain comparability to 

the estimates developed in the UFVA. This allowed the effects of other simplifications relative to the UFVA 

approach to be better discerned. 
 4-47

leaves five PM2.5 species concentration terms (i.e., sulfate, nitrate, organic carbonaceous mass, 

elemental carbon, and fine soil/crustal), plus relative humidity that need to be determined on an 

hourly basis from monitoring data. For a given time period, this algorithm with its five species 

concentration terms can be algebraically rearranged to a two-term algorithm (Pitchford, 2010), as 

shown in the equation below. The rearranged algorithm relates PM2.5 light extinction to the 

product of PM2.5 mass concentration and the sum of dry light extinction efficiency (DLEE) plus 

moist light extinction efficiency (MLEE). As its name suggests, DLEE accounts for light 

extinction (per microgram per cubic meter of air) that would occur if relative humidity were very 

low. MLEE accounts for the increment of light extinction associated with the water content of 

the PM2.5 and thus is a function of both species composition and ambient relative humidity. 

PM2.5 light extinction ≈ PM2.5 X (DLEE + MLEE) 

where DLEE is dependent only on the relative amounts of dry PM2.5 components as measured in 

CSN-type sampling, and MLEE is 3 times the hygroscopic fraction (HF) of the PM2.5 (the sulfate 

plus nitrate component concentrations divided by the sum of all components) times a non-linear 

function of relative humidity. The non-linear function of relative humidity is simply f(RH)-1, 

where f(RH) represents the humidity adjustment factor of the original IMPROVE algorithm. 

MLEE = 3 X HF X [f(RH)-1] 

MLEE is zero for relative humidity below 40% and increases with increasing relative humidity 

to be comparable to or greater than DLEE as relative humidity approaches 90%, depending on 

the HF. 

 Given this algorithm structure and the goal of estimating PM2.5 light extinction for as 

many 1-hour periods as possible, we focused on two alternative approaches to determining 

values for DLEE and HF for 1-hour periods that are based on using hourly measured relative 

humidity. One approach (designated as “T” in Appendix F) is based on calculating 24-hour 

values of DLEE and HF for each day of speciated sampling, averaging these together within each 

calendar month to allow for seasonal variability, and applying the results to each daylight hour of 

the respective month for which hourly PM2.5 mass data are available. A feature of this approach 

is that values of DLEE and HF are available for use with hourly PM2.5 mass measurements taken 

on days that were not speciated sampling days. Thus, this approach would allow the full history 

of daylight hourly PM2.5 mass concentrations and relatively humidity levels to be used in 

calculating PM2.5-related light extinction. 

 A second approach (designated as “W” in Appendix F) is based on calculating 24-hour 

values of DLEE and HF for each day of speciated sampling and applying the results only to 
 4-48

daylight hours within the respective speciated sampling day. No estimates of hourly PM2.5 light 

extinction would be generated on days without speciated sampling. By recognizing that DLEE 

and HF can vary from day to day within a month, this approach may provide somewhat more 

accurate estimates of hourly PM2.5 light extinction for those hours for which estimates are made, 

although calculations of PM2.5-related light extinction would be limited to air quality data 

gathered on only a fraction of the days in each year. 

A more detailed description of the sources of the data and steps required to determine 

calculated PM2.5 light extinction by approaches T and W is contained in Appendix F (see Table 

F-2). Also, Table F-1 of Appendix F compares/contrasts each of these approaches with the 

UFVA approach and with each other. 

The PM2.5 light extinction values generated by using either approach T or approach W are 

comparable to those developed for use in the UFVA as indicated by the regression statistics for 

scatter plots of the paired data (i.e., slopes of the regression equation and R2

 values are near 1 as 

show in Tables F-3 and F-4 in Appendix F). Because the UFVA had no way of providing 

estimates of PM2.5 light extinction on days that were not CSN sampling days, we have not 

assessed how well approach T estimates PM2.5 light extinction on such days. Also, Appendix F 

notes that approaches T and W both underestimate PM2.5 light extinction on some days in a few 

study areas, which we attribute to the occurrence of very high nitrate concentrations and the 

failure of the FRM-correlated/adjusted FEM instrument to report the entire nitrate mass. 

Nevertheless, we believe that each of these simplified approaches provides reasonably good 

estimates of PM2.5 light extinction and each is appropriate to consider as the indicator for a new 

secondary standard. In addition, there are variations of approaches T and W that may also be 

appropriate to consider. For example, some variations that may improve the correlation with 

actual ambient light extinction in certain areas of the country include the use of the split mass 

term approach from the revised IMPROVE algorithm,26 the use of more refined value(s) for the 

organic carbon multiplier (see Appendix F), and the use of the reconstructed 24-hour PM2.5 mass 

(i.e., the sum of the five PM2.5 components from speciated monitoring) as a normalization value 

for the hourly measurements from the PM2.5 instrument as a way of better reflecting ambient 

nitrate concentrations. Other variations may serve to simplify the calculation of PM2.5 light 

extinction values, such as those suggested by CASAC for consideration, including the use of 

historical monthly or seasonal speciation averages as well as speciation estimates on a regional 

basis (Samet, 2010d, p. 11). 

 26 If the revised IMPROVE algorithm were used to define the speciated PM2.5 mass-based indicator, it would not be 

possible to algebraically reduce the revised algorithm to a two-factor version as described above and in Appendix F 

for approaches T and W. Instead, five component fractions would be determined from each day of speciated 

sampling, and then either applied to hourly measurements of PM2.5 mass on the same day (as in approach W) or 

averaged across a month and then applied to measurements of PM2.5 mass on each day of the month. 
 4-49

As mentioned above, as part of the UFVA, we conducted an assessment of PM10 light 

extinction levels that would prevail if areas met a standard based on directly measured hourly 

PM10 light extinction as the indicator. This assessment indicated that a standard based on a 

directly measured PM10 light extinction indicator would provide the same percentage of days 

having indicator values above the level of the standard across areas, with the percentage being 

dependent on the statistical form of the standard. This assessment was based on the more 

complex UFVA approach to estimating PM10 light extinction, rather than the simpler T and W 

approaches for estimating PM2.5 light extinction. Nevertheless, the generally close 

correspondence between UFVA-consistent design values for PM2.5 light extinction and design 

values based on approaches T and W (see Figure F-5 in Appendix F) suggest that the findings 

regarding the protection offered by alternative PM10 light extinction standards using a directly 

measured light extinction would also hold quite well for standards based on the T or W 

indicators.27 Thus, we conclude that the use of a calculated PM2.5 light extinction indicator 

would provide a much higher degree of uniformity in terms of the visibility levels across the 

country than is possible using PM2.5 mass as the indicator. This is due to the fact that the PM2.5

mass indicator does not account for the effects of humidity and PM2.5 composition differences 

between various regions, while a calculated PM2.5 light extinction indicator directly incorporates 

those effects. 

The inputs that would be necessary to use either approach T or W to calculate a sub-daily 

PM2.5 light extinction indicator (e.g., 1- or 4-hour averaging time) include PM2.5 chemical 

speciation, relative humidity, and hourly PM2.5 mass measurements. In defining a standard in 

terms of such an indicator, the criteria for allowable protocols for these inputs would need to be 

specified. It would be appropriate to base these criteria on the protocols utilized in the 

IMPROVE28 and CSN networks, as well as sampling and analysis protocols for ambient relative 

humidity sensors, and approved FEM mass monitors for PM2.5. Any approach to approving 

methods for use in calculating a light extinction indicator should take advantage of the existing 

inventory of monitoring and analysis methods. 

The CSN measurements have a strong history of being reviewed by CASAC technical 

committees, both during their initial deployment about ten years ago,29 and during the more 

 27 The degree of emission reduction needed to meet a standard is tightly tied to the degree to which the design value 

exceeds the level of the standard. 

28 Several monitoring agencies utilize IMPROVE in urban areas to meet their chemical speciation monitoring needs. 

These sites are known as IMPROVE-protocol stations. 29 Notification of a Consultation on the PM2.5 Chemical Speciation Network & Supersites Plan, EPA-SAB-CASACCON-99-007; Advisory on the PM2.5 Monitoring Program, EPA-SAB-CASAC-ADV-99-002; CASAC Advisory on 

the PM2.5 Monitoring Network, EPA-SAB-CASAC-ADV-00-006 
 4-50

recent transition to carbon sampling that is consistent with the IMPROVE protocols.30 Because 

the methods for the CSN are well documented in a nationally implemented Quality Assurance 

Project Plan (QAPP) and accompanying SOPs, are validated through independent performance 

testing, and are used to meet multiple data objectives (e.g., source apportionment, trends, and as 

an input to health studies), consideration should be given to an approach that utilizes the existing 

methods as the basis for criteria for allowable sampling and analysis protocols for purposes of a 

calculated light extinction indicator. Such an approach of basing criteria on the current CSN and 

IMPROVE methods provides a nationally consistent way to provide the chemical species data 

used in the light extinction calculation, while preserving the opportunity for improved methods 

for measuring the chemical species. For relative humidity, consideration should be given to 

simply using criteria based on available relative humidity sensors such as already utilized by the 

National Oceanic and Atmospheric Administration (NOAA) at routine weather stations. These 

relative humidity sensors are already widely used by a number of monitoring agencies and can 

be easily compared to other relative humidity measurements. Finally, approaches T and W 

depend on having values of hourly PM2.5 mass, as discussed below. 

Since 2008, EPA has approved several PM2.5 continuous mass monitoring methods as 

FEMs.31 These methods have several advantages over filter-based FRMs, such as producing 

hourly data and the ability to report air quality information in near real-time. However, initial 

assessments of the data quality as operated by State and local monitoring agencies have mixed 

results. A recent assessment of continuous FEMs and collocated FRMs conducted by EPA staff 

(Hanley and Reff, 2011) found some sites and continuous FEM instruments to have an 

acceptable degree of comparability of 24-hour average PM2.5 mass values derived from 

continuous FEMs and filter-based FRMs, while others had poor data quality that would not meet 

current data quality objectives. The EPA is working closely with the monitoring committee of 

the National Association of Clean Air Agencies, instrument manufacturers, and monitoring 

agencies to document and communicate best practices on these methods to improve quality and 

consistency of resulting data. It should be noted that performance testing submitted to EPA for 

purposes of designating the PM2.5 continuous methods as FEMs, and the recent assessment of 

collocated FRMs and continuous FEMs, are both based on 24-hour sample periods. Therefore, 

we do not have similar performance data for continuous PM2.5 FEMs for 1-hour or 4-hour 

averaging periods, nor is there an accepted practice to generate performance standards for these 

time periods. Until issues regarding the comparability of 24-hour PM2.5 mass values derived 

 30 EPA’s Final Draft National Ambient Air Monitoring Strategy - An Advisory by the Ambient Air Monitoring and 

Methods Subcommittee of the EPA Clean Air Scientific Advisory Committee, EPA-SAB-CASAC-05-006 31 EPA maintains a list of designated Reference and Equivalent Methods on the web at: 

http://www.epa.gov/ttn/amtic/files/ambient/criteria/reference-equivalent-methods-list.pdf 
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from continuous FEMs and filter-based FRMs are resolved, there is reason to be cautious about 

relying on a calculation procedure that uses hourly PM2.5 mass values reported by continuous 

FEMs and speciated mass values from 24-hour filter-based samplers. Section 4.3.2.1 discusses 

another reason for such caution, based on a preliminary assessment of hourly data from 

continuous FEMs. 

This section has addressed the types of measurements that would be necessary to support a 

calculated PM2.5 light extinction indicator for either 24-hour or sub-daily (e.g., 1-hour and 4-

hour) averaging periods. Considerations related specifically to each of these alternative 

averaging times, in conjunction with a standard defined in terms of a calculated PM2.5 light 

extinction indicator, are discussed below in section 4.3.2. 

4.3.1.2 CASAC Advice on Indicator 

Based on its review of the second draft PA, CASAC stated that it “ overwhelmingly . . . 

would prefer the direct measurement of light extinction,” recognizing it as the property of the 

atmosphere that most directly relates to visibility effects (Samet, 2010d, p. iii). CASAC noted 

that “[I]t has the advantage of relating directly to the demonstrated harmful welfare effect of 

ambient PM on human visual perception.” However, based on CASAC’s understanding of the 

time that would be required to develop an FRM for this indicator, CASAC agreed with the staff 

preference presented in the second draft PA for a calculated PM2.5 light extinction indicator. 

CASAC noted that “[I]ts reliance on procedures that have already been implemented in the CSN 

and routinely collected continuous PM2.5 data suggest that it could be implemented much sooner 

than a directly measured indicator” (Samet, 2010d, p. iii). 

4.3.1.3 Staff Conclusions on Indicator 

Taking the above considerations and CASAC’s advice into account, EPA staff concludes 

that consideration should be given to establishing a new calculated PM2.5 light extinction 

indicator. This conclusion takes into consideration the available evidence that demonstrates a 

strong correspondence between calculated PM2.5 light extinction and PM-related visibility 

impairment, as well as the significant degree of variability in visibility protection across the U.S. 

allowed by a PM2.5 mass indicator. While a secondary standard that uses a PM2.5 mass indicator 

could be set to provide additional protection from PM2.5-related visibility impairment, we 

conclude that the advantages of using a calculated PM2.5 light extinction indicator make it the 

preferred choice. In addition, We recognize that while in the future it would be appropriate to 

consider a direct measurement of PM2.5 light extinction, or the sum of separate measurements of 

light scattering and light absorption, as the indicator for the secondary PM2.5 standard, we 

conclude this is not an appropriate option in this review because a suitable specification of the 
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equipment and associated performance verification procedures cannot be developed in the time 

frame for this review. 

Further, we conclude that consideration could be given to defining a calculated PM2.5

light extinction indicator on either a 24-hour or a sub-daily basis, as discussed more fully below 

in section 4.3.2. In either case, it would be appropriate to base criteria for allowable monitoring 

and analysis protocols to obtain PM2.5 speciation measurements on the protocols utilized in the 

IMPROVE and CSN networks. Further, in the case of a calculated PM2.5 light extinction 

indicator defined on a sub-daily basis, it would be appropriate to consider using approaches T or 

W, or some variations on these approaches. In reaching this conclusion, as discussed above, we 

note that while it is possible to utilize data from PM2.5 continuous FEMs on a 1-hour or multihour (e.g., 4-hour) basis, the mixed results of data quality assessments on a 24-hour basis, as well 

as the near absence of performance data for sub-daily averaging periods, increases the 

uncertainty of utilizing continuous methods to support 1-hour or 4-hour PM2.5 mass 

measurements as an input to the light extinction calculation. 

 4.3.2 Averaging Time and Related Considerations 

Consideration of appropriate averaging times for use in conjunction with a calculated 

PM2.5 light extinction indicator was informed by information related to the nature of PM 

visibility effects, as discussed above in section 4.2.1, and the nature of inputs to the calculation 

of PM2.5 light extinction, as discussed above in section 4.3.1. Based on this information, we 

have considered both sub-daily (1- and 4-hour averaging times) and 24-hour averaging times, as 

discussed below. In considering sub-daily averaging times, we have also addressed what diurnal 

periods and ambient relative humidity conditions would be appropriate to consider in 

conjunction with such an averaging time. 

4.3.2.1 Sub-daily Averaging Times 

 To what extent does the available information provide support for alternative sub-daily 

averaging times in conjunction with a calculated PM2.5 light extinction indicator?

As an initial matter, in considering sub-daily averaging times, we took into account what 

we know from available studies concerning how quickly people experience and judge visibility 

conditions, the possibility that some fraction of the public experience infrequent or short periods 

of exposure to ambient visibility conditions, and the typical rate of change of the path averaged 

PM light extinction over urban areas. While perception of change in visibility can occur in less 

than a minute, meaningful changes to path-averaged light extinction occur more slowly. As 

discussed above in section 4.2.1, one hour is a short enough averaging period to result in 

indicator values that are close to the maximum one- or few-minute visibility impact that an 

observer could be exposed to within the hour. Further, a 1-hour averaging time could reasonably 
 4-53

characterize the visibility effects experienced by the segment of the population that experiences 

infrequent short-term exposures during peak visibility impairment periods in each area/site. 

Based on the above considerations, the initial analyses we conducted as part of the UFVA to 

support consideration of alternative standards focused on a 1-hour averaging time. 

In its review of the first draft PA, CASAC agreed that a 1-hour averaging time would be 

appropriate to consider, noting that PM effects on visibility can vary widely and rapidly over the 

course of a day and such changes are almost instantaneously perceptible to human observers 

(Samet, 2010c, p. 19). We note that this view related specifically to a standard defined in terms 

of a directly measured PM light extinction indicator, in that CASAC also noted that a 1-hour 

averaging time is well within the instrument response times of the various currently available and 

developing optical monitoring methods. CASAC also advised that if a PM2.5 mass indicator 

were to be used, it would be appropriate to consider “somewhat longer averaging times – 2 to 4 

hours – to assure a more stable instrumental response” (Samet, 2010c, p. 19). In considering this 

advice, we conclude that since a calculated PM2.5 light extinction indicator relies in part on 

measured PM2.5 mass, as discussed above in section 4.3.1, it is also appropriate to consider a 

multi-hour averaging time in conjunction with such an indicator. 

Thus, in preparing this final PA, we have also considered multi-hour averaging times, on 

the order of a few hours as illustrated by a 4-hour averaging time. Such averaging times might 

reasonably characterize the visibility effects experienced by the segment of the population who 

have access to visibility conditions often or continuously throughout the day. For this segment 

of the population, it may be that their perception of visual air quality reflects some degree of 

offsetting an hour with poor visual air quality with one or more hours of clearer visual 

conditions. Further, we recognize that a multi-hour averaging time would have the effect of 

averaging away peak hourly visibility impairment, which can change significantly from one hour 

to the next (see UFVA Figure 3-12). In considering either 1-hour or multi-hour averaging times, 

we recognize that no data are available with regard to how the duration and variation of time a 

person spends outdoors during the daytime impacts his or her judgment of the acceptability of 

different degrees of visibility impairment. As a consequence, it is not clear to what degree, if at 

all, the protection levels found to be acceptable in the public preference studies would change for 

a multi-hour averaging time as compared to a 1-hour averaging time. Thus, we conclude that it 

is appropriate to consider a 1-hour or multi-hour (e.g., 4-hour) averaging time as the basis for a 

sub-daily standard defined in terms of a calculated PM2.5 light extinction indicator. 

Additionally, as part of the review of data from all continuous FEM PM2.5 instruments 

operating at state/local monitoring sites, as discussed above in section 4.3.1, we have recently 

become aware of the occurrence of questionable outliers in 1-hour data submitted to AQS from 

continuous FEM PM2.5 instruments at some of these sites (Evangelista, 2011). Some of these 
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outliers are questionable simply by virtue of their extreme magnitude, as high as 985 μg/m3, 

whereas other values are questionable because they are isolated to single hours with much lower 

values before and after, a pattern that is much less plausible than if the high concentrations were 

more sustained.32 The nature and frequency of questionable 1-hour FEM data collected in the 

past two years are being investigated by EPA. At this time, we note that any current data quality 

problems might be resolved in the normal course of monitoring program evolution as operators 

become more adept at instrument operation and maintenance and data validation or by improving 

the approval criteria and testing requirements for continuous instruments. Regardless, we note 

that multi-hour averaging of FEM data could serve to reduce the effects of such outliers relative 

to the use of a 1-hour averaging time. 

 What is an appropriate diurnal period to consider in conjunction with a sub-daily 

averaging time? 

In considering an appropriate diurnal period for use in conjunction with a sub-daily 

averaging time, we recognize that nighttime visibility impacts, described in the ISA (section 

9.2.2) are significantly different from daytime impacts and are not sufficiently well understood to 

be included at this time. As a result, consistent with CASAC advice (Samet, 2010c, p. 4), we 

conclude that it would be appropriate to define a sub-daily standard in terms of only daylight 

hours at this time. In the UFVA, daylight hours were defined to be those morning hours having 

no minutes prior to local sunrise and afternoon hours having no minutes after local sunset. This 

definition ensures the exclusion of periods of time where the sun is not the primary outdoor 

source of light to illuminate scenic features. 

 What ambient relative humidity conditions are appropriate to consider in conjunction 

with a sub-daily averaging time? 

In considering the well-known interaction of PM with ambient relative humidity 

conditions, we recognize that PM is not generally the primary source of visibility impairment 

during periods with fog or precipitation. In order to reduce the probability that hours with a high 

degree of visibility impairment caused by fog or precipitation are unintentionally used for 

purposes of determining compliance with a standard, staff determined that a relative humidity 

screen that excludes daylight hours with average relative humidity above approximately 90% is 

appropriate (UFVA section 3.3.5 and UFVA Appendix G). For example, for the 15 urban 

 32 Similarly questionable hourly data were not observed in the 2005-2007 continuous PM2.5 data used in the UFVA, 

all of which came from early-generation continuous instruments that had not been approved as FEMs. However, 

only 15 sites and instruments were involved in the UFVA analyses, versus about 180 currently operating FEM 

instruments submitting data to AQS. Therefore, there were more opportunities for very infrequent measurement 

errors to be observed in the larger FEM data set. Also, the instruments providing data used in the UFVA were a mix 

of measurement approaches (e.g., TEOM® or beta attenuation), while the currently operating FEMs are mostly of 

one model (beta attenuation). 
 4-55

areas33 included in the UFVA, a 90% relative humidity cutoff criterion proved effective in that 

on average less than 6% of the daylight hours were removed from consideration, yet those same 

hours had on average 10 times the likelihood of rain, 6 times the likelihood of snow/sleet, and 34 

times the likelihood of fog compared with hours with 90% or lower relative humidity. However, 

not all periods with relative humidity above 90% have fog or precipitation. We recognize that 

removing those hours from consideration involves a tradeoff between the benefits of avoiding 

many of the hours with meteorological causes of visibility impacts and not counting some hours 

without fog or precipitation in which high humidity levels (> 90%) lead to the growth of 

hygroscopic PM to large solution droplets resulting in enhanced PM visibility impacts. 

4.3.2.2 24-Hour Averaging Time 

 To what extent does the available information provide support for a 24-hour averaging 

time in conjunction with a calculated PM2.5 light extinction indicator?

 As discussed in section 4.3.1 and below, there are significant reasons to consider using 

PM2.5 light extinction calculated on a 24-hour basis to reduce the various data quality concerns 

over relying on continuous PM2.5 monitoring data. However, we recognize that 24 hours is far 

longer than the hourly or multi-hour time periods that might reasonably characterize the visibility 

effects experienced by various segments of the population, including those who have access to 

visibility conditions often or continuously throughout the day, as discussed above in section 

4.3.2.1. Thus, consideration of a 24-hour averaging time depends upon the extent to which PMrelated light extinction calculated on a 24-hour average basis would be a reasonable and 

appropriate surrogate for PM-related light extinction calculated on a sub-daily basis, as 

discussed below in this section. Further, since a 24-hour averaging time combines daytime and 

nighttime periods, we recognize that the public preference studies do not directly provide a basis 

for identifying an appropriate level of protection, in terms of 24-hour average light extinction, 

based on judgments of acceptable daytime visual air quality obtained in those studies. Thus, 

consideration of a 24-hour averaging time also depends upon developing an approach to translate 

the candidate levels of protection derived from the public preference studies, which we have 

interpreted on an hourly or multi-hour basis, to a candidate level of protection defined in terms of 

a 24-hour average calculated light extinction, as discussed below in section 4.3.4 on level. 

 To determine whether PM2.5 light extinction calculated on a 24-hour basis is a reasonable 

and appropriate surrogate to PM2.5 light extinction calculated on a sub-daily basis, we have done 

comparative analyses of 24-hour and 4-hour averaging times in conjunction with a calculated 

 33 The 90% relative humidity cap assessment was conducted as part of the UFVA on all 15 of the urban areas, 

including St. Louis. 
 4-56

PM2.5 indicator.34 These analyses are presented and discussed in Appendix G, section G.4. For 

these analyses, 4-hour average PM2.5 light extinction was calculated based on using the UFVA 

approach. The 24-hour average PM2.5 light extinction calculations used the original IMPROVE 

algorithm and long-term (1988 - 1997) average relative humidity conditions, to calculate 

monthly average values of the relative humidity term in the IMPROVE algorithm, consistent 

with the approach used for the Regional Haze Program. Based on these analyses, scatter plots 

comparing 24-hour and 4-hour calculated PM2.5 light extinction are shown for each of the 15 

cities included in the UFVA and for all 15 cities pooled together (Figures G-4 and G-5, 

respectively). It can be seen, as expected, that there is some scatter around the regression line for 

each city, because the calculated 4-hour light extinction includes day-specific and hour-specific 

influences that are not captured by the simpler 24-hour approach. We note that this scatter could 

be reduced by the use of same-day hourly relative humidity data to calculate a 24-hour average 

value of the relative humidity term in the IMPROVE algorithm. Scatter plots are also shown for 

the annual 90th percentile values, based on data from 2007 – 2009, for 4-hour and 24-hour 

calculated PM2.5 light extinction across all 15 cities (Figure G-7) and for the 3-year design values 

across all 15 cities (Figure G-8). We judge that these analyses shown good correlation between 

24-hour and 4-hour average PM2.5 light extinction, as evidenced by reasonably high city-specific 

and pooled R-squared values, generally in the range of over 0.635 to over 0.8. 

4.3.2.3 CASAC Advice on Averaging Time 

 As noted above, in its review of the first draft PA, CASAC agreed with the staff 

conclusion that PM effects on visibility can vary widely and rapidly over the course of a day and 

such changes are almost instantaneously perceptible to human observers (Samet, 2010c, p. 19). 

Based in part on this consideration, CASAC agreed that a 1-hour averaging time would be 

appropriate to consider in conjunction with a directly measured PM light extinction indicator, 

noting that a 1-hour averaging time is well within the instrument response times of the various 

currently available and developing optical monitoring methods. At that time, CASAC also 

advised that if a PM2.5 mass indicator were to be used, it would be appropriate to consider 

“somewhat longer averaging times – 2 to 4 hours – to assure a more stable instrumental 

response” (Samet, 2010c, p. 19). Thus, CASAC’s advice on averaging times that would be 

appropriate for consideration were predicated in part on the capabilities of monitoring methods 

that were available for the alternative indicators discussed in the draft PA. CASAC’s views on a 

multi-hour averaging time would also apply to the calculated PM2.5 light extinction indicator 

 34 These analyses are also based on the use of a 90th percentile form, averaged over 3 years, as discussed below in 

section 4.3.3 on form. 

35 We note that the R-squared value (0.44) for Houston was notably lower than for the other cities. 
 4-57

since PM2.5 mass measurements are also required for this indicator when calculated on a subdaily basis. 

In considering this advice, we first note that CASAC did not have the benefit of EPA’s 

recent assessment of the data quality issues associated with the use of continuous FEMs as the 

basis for hourly PM2.5 mass measurements. We also note that since earlier drafts of this PA did 

not include discussion of a calculated PM2.5 indicator based on a 24-hour averaging time, 

CASAC did not have a basis to offer advice regarding a 24-hour averaging time. In addition, the 

24-hour averaging time is not based on consideration of 24-hours as a relevant exposure period, 

but rather as a surrogate for a sub-daily period of 4 hours, which is consistent with CASAC’s 

advice concerning an averaging time associated with the use of a PM2.5 mass indicator. 

4.3.2.4 Staff Conclusions on Averaging Time 

 Taking the above considerations and CASAC’s advice into account, EPA staff concludes 

that it is appropriate to consider in this review a 24-hour averaging time, in conjunction with a 

calculated PM2.5 light extinction indicator and an appropriately specified standard level. This 

conclusion reflects the judgment that PM2.5 light extinction calculated on a 24-hour basis is a 

reasonable and appropriate surrogate for sub-daily PM2.5 light extinction calculated on a 4-hour 

average basis. This conclusion is also predicated on consideration of a 24-hour average standard 

level, as discussed below in section 4.3.4, that is appropriately translated from the CPLs derived 

from the public preference studies, which we have interpreted as providing information on the 

acceptability of daytime visual air quality over an hourly or multi-hour exposure period. 

A 24-hour average calculated PM2.5 light extinction indicator would avoid data quality 

uncertainties that have recently been associated with currently available instruments for 

measurement of hourly PM2.5 mass. The particular 24-hour indicator considered by staff uses the 

original IMPROVE algorithm and long-term relative humidity conditions to calculate PM2.5 light 

extinction. By using site-specific daily data on PM2.5 composition and site-specific long-term 

relative humidity conditions, this 24-hour average indicator would provide more consistent 

protection from PM2.5-related visibility impairment than would a secondary PM2.5 NAAQS based 

only on 24-hour or annual average PM2.5 mass. In particular, this approach would account for 

the systematic difference in humidity conditions between most eastern states and most western 

states. Further, we have identified for consideration possible variations in the method used to 

calculate PM2.5 light extinction on a 24-hour average basis that would be expected to provide 

somewhat more consistent protection in areas across the country, including the use of the revised 

IMPROVE algorithm and the use of same-day relative humidity data, either at the visibility

monitoring site or another site in the same area. 
 4-58

Further, staff concludes that it would also be appropriate to consider a multi-hour, subdaily averaging time, for example a period of 4 hours, in conjunction with a calculated PM2.5

light extinction indicator and with further consideration of the data quality issues that have been 

raised by the recent EPA study of continuous FEMs. Such an averaging time, to the extent that 

data quality issues can be appropriately addressed, would be more directly related to the shortterm nature of the perception of visibility impairment, short-term variability in PM-related visual 

air quality, and the short-term nature (hourly to multiple hours) of relevant exposure periods for 

segments of the viewing public. Such an averaging time would also result in an indicator that is 

less sensitive than a 1-hour averaging time to short-term instrument variability with respect to 

PM2.5 mass measurement. In conjunction with consideration of a multi-hour, sub-daily 

averaging time, we conclude that consideration should be given to including daylight hours only 

and to applying a relative humidity screen of approximately 90% to remove hours in which fog 

or precipitation is much more likely to contribute to the observed visibility impairment. 

Recognizing that a 1-hour averaging time would be even more sensitive to data quality issues, 

including short-term variability in hourly data from currently available continuous monitoring 

methods, we conclude that it would not be appropriate to consider a 1-hour averaging time in 

conjunction with a calculated PM2.5 light extinction indicator in this review. 

4.3.3 Form 

The “form” of a standard defines the air quality statistic that is to be compared to the 

level of the standard in determining whether the standard is achieved. The form of the current 

24-hour PM2.5 NAAQS is such that the level of the standard is compared to the 3-year average of 

the annual 98th percentile of the measured indicator. The purpose in averaging for three years is 

to provide stability from the occasional effects of inter-annual meteorological variability that can 

result in unusually high pollution levels for a particular year that is otherwise typical. The use of 

a multi-year percentile form, among other things, makes the standard less subject to the 

possibility of transient violations caused by statistically unusual indicator values, thereby 

providing stability to the air quality management process that may enhance the practical 

effectiveness of efforts to implement the NAAQS. Also, a percentile form can be used to take 

into account the number of times an exposure might occur as part of the judgment on 

protectiveness in setting a NAAQS. For all of these reasons, we conclude it is appropriate to 

consider defining the form of a new secondary standard in terms of a 3-year average of a 

specified percentile air quality statistic. 
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 To what extent does available information support consideration of an alternative 

percentile form for a secondary standard defined in terms of a calculated PM2.5 light 

extinction indicator?

The urban visibility preference studies that provided results leading to the range of CPLs 

being considered in this document offer no information that addresses the frequency of time that 

visibility levels should be below those values. Given this lack of information, and recognizing 

that the nature of the public welfare effect is one of aesthetics and/or feelings of well-being, it is 

our view that it would not be appropriate to consider eliminating all exposures above the level of 

the standard and that allowing some number of hours/days with reduced visibility can reasonably 

be considered. In the UFVA, 90th, 95th, and 98th percentile forms were assessed for alternative 

PM light extinction standards (chapter 4, UFVA). In considering these alternative percentiles, 

we note that the Regional Haze Program targets the 20% most impaired days for improvements 

in visual air quality in Class I areas. If improvement in the 20% most impaired days were 

similarly judged to be appropriate for protecting visual air quality in urban areas, a percentile 

well above the 80th percentile would be appropriate to increase the likelihood that all days in this 

range would be improved by control strategies intended to attain the standard. A focus on 

improving the 20% most impaired days suggests to staff that the 90th percentile, which represents 

the median of the distribution of the 20% worst days, would be an appropriate form to consider. 

Strategies that are implemented so that 90% of days have visual air quality that is at or below the 

level of the standard would reasonably be expected to lead to improvements in visual air quality 

for the 20% most impaired days. Higher percentile values within the range assessed could have 

the effect of limiting the occurrence of days with peak PM-related light extinction in urban areas 

to a greater degree. In considering the limited information available from the public preference 

studies, we find no basis to conclude that it would be appropriate to consider limiting the 

occurrence of days with peak PM-related light extinction in urban areas to a greater degree. 

Another aspect of the form that was considered in the UFVA for a sub-daily (i.e., 1-hour) 

averaging time is whether to include all daylight hours or only the maximum daily daylight hour. 

This consideration would also be relevant for a multi-hour (e.g., 4-hour) averaging time, 

although such an analysis was not included in the UFVA. The maximum daily daylight 1-hour 

or multi-hour form is most directly protective of the welfare of people who have limited, 

infrequent or intermittent exposure to visibility during the day (e.g., during commutes), but 

spend most of their time without an outdoor view. For such people a view of poor visibility 

during their morning commute may represent their perception of the day’s visibility conditions 

until the next time they venture outside during daylight, which may be hours later or perhaps the 

next day. Other people have exposure to visibility conditions throughout the day. For those 
 4-60

people, it might be more appropriate to include every daylight hour in assessing compliance with 

a standard, since it is more likely that each daylight hour could affect their welfare. 

We do not have information regarding the fraction of the public that has only one or a 

few opportunities to experience visibility during the day, nor do we have information on the role 

the duration of the observed visibility conditions has on wellbeing effects associated with those 

visibility conditions. However, it is logical to conclude that people with limited opportunities to 

experience visibility conditions on a daily basis would experience the entire impact associated 

with visibility based on their short-term exposure. The impact of visibility for those who have 

access to visibility conditions often or continuously during the day may be based on varying 

conditions throughout the day. 

In light of these considerations, the UFVA assessment included both the maximum daily 

hour form and the all daylight hours form. We observed a close correspondence between the 

level of protection afforded for all 15 urban areas in the assessment by the maximum daily 

daylight 1-hour approach using the 90th percentile form and the all daylight hours approach 

combined with the 98th percentile form (UFVA section 4.1.4). On this basis, we note that the 

reductions in visibility impairment required to meet either form of the standard would provide 

protection to both fractions of the public (i.e., those with limited opportunities and those with 

greater opportunities to view PM-related visibility conditions). We note that CASAC generally 

supported consideration of both types of forms without expressing a preference based on its 

review of information presented in the second draft PA (Samet, 2010d, p11). 

Staff Conclusions on Form

In conjunction with a calculated PM2.5 light extinction indicator and alternative 24-hour 

or sub-daily (e.g., 4-hour) averaging times, based on the above considerations, and given the lack 

of information on and the high degree of uncertainty over the impact on public welfare of the 

number of days with visibility impairment over a year, we conclude that it is appropriate to give 

primary consideration to a 90th percentile form, averaged over three years. Further, in the case of 

a multi-hour, sub-daily alternative standard, we conclude that it is appropriate to give primary 

consideration to a form based on the maximum daily multi-hour period in conjunction with the 

90th percentile form. This sub-daily form would be expected to provide appropriate protection 

for various segments of the population, including those with limited opportunities during a day 

and those with more extended opportunities over the daylight hours to experience PM-related 

visual air quality. 

4.3.4 Level 

In considering alternative levels for a new standard that would provide requisite 

protection against PM-related visibility impairment primarily in urban areas, staff has taken into 
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account the evidence- and impact-based considerations discussed above in section 4.2.1, with a 

focus on the results of public perception and attitude surveys related to the acceptability of 

various levels of visual air quality and on the important limitations in the design and scope of 

such available studies. We consider this information in the context of a standard defined in 

terms of a calculated PM2.5 light extinction indicator, discussed above in section 4.3.1; with 

alternative averaging times of 24-hours or multi-hour, sub-daily periods (e.g., 4-hours), discussed 

above in section 4.3.2; and a 90th percentile-based form, discussed above in section 4.3.3. 

As part of our assessment of the adequacy of the current standards (section 4.2.1), we 

interpreted the results from the visibility preferences studies conducted in four urban areas to 

define a range of low, middle, and high CPLs for a sub-daily standard (e.g., 1- to 4-hour 

averaging time) of 20, 25, and 30 dv, which are approximately equivalent to PM2,5 light 

extinction of values of 65, 110, and 190 Mm-1. We note that CASAC agreed that this was an 

appropriate range of levels to consider for such a standard (Samet, 2010d, p. 11).36 We also 

recognize that to define a range of alternative levels that would be appropriate to consider for a 

24-hour calculated PM2.5 light extinction standard, some adjustment to these CPLs is appropriate 

since these preference studies cannot be directly interpreted as applying to a 24-hour exposure 

period (as noted above in section 4.3.1). Such adjustments are more specifically discussed 

below. 

As an initial matter, in considering alternative levels for a sub-daily standard based 

directly on the four preference study results, we note that the individual low and high CPLs are 

in fact reflective of the results from the Denver and Washington, DC studies respectively, and 

the middle CPL is very near to the 50th percentile criteria result from the Phoenix study. As 

discussed above in section 4.2.1, we note that the Phoenix study was by far the best of the 

studies, providing somewhat more support for the middle CPL. In considering the results from 

these studies, we recognize that the available studies are limited in that they were conducted in 

only four areas, three in the U.S. and one in Canada. Further, we recognize that available studies 

provide no information on how the duration and variation of time a person spends outdoors 

during the daytime may impact their judgment of the acceptability of different degrees of 

visibility impairment. As such, there is a relatively high degree of uncertainty associated with 

using the results of these studies to inform consideration of a national standard. Nonetheless, we 

conclude, as did CASAC, that these studies are appropriate to use for this purpose. 

 36 In 2009, the D.C. Circuit remanded the secondary PM2.5 standards to EPA in part because the Agency failed to 

identify a target level of protection, even though EPA staff and CASAC had identified a range of target levels of 

protection that were appropriate for consideration. The court determined that the Agency’s failure to identify a 

target level of protection as part of its final decision was contrary to the statute and therefore unlawful, and that it 

deprived EPA’s decision-making of a reasoned basis (as discussed above in section 4.1.2). 
 4-62

In identifying alternative levels for a 24-hour standard, we have explored various 

approaches to adjusting the CPLs derived directly from the preference studies, as presented and 

discussed in Appendix G, especially section G-5. These regression analyses have focused on 

determining adjusted CPLs for a 24-hour standard that would provide generally equivalent 

protection as that provided by a 4-hour standard with CPLs of 20, 25, and 30 dv. Some of these 

approaches focused on comparing 24-hour and 4-hour light extinction values in each of the 15 

urban areas assessed in the UFVA, whereas other approaches focused on comparisons based on 

using aggregated data across the urban areas. Two of these approaches, which used regressions 

of city-specific annual 90th percentile light extinction values or 3-year light extinction design 

values, gave nearly identical results and were considered most appropriate to use as the basis for 

identifying generally equivalent 24-hour adjusted CPLs. These approaches (shown in Figures G7 and G-8) were preferred based on the high R-squared values of the regressions and because the 

regressions were determined by data from days with PM2.5 light extinction conditions in the 

range of 20 to 40 dv. This contrasted with the other approaches that were highly influenced by 

PM2.5 light extinction conditions well below this range. Based on these analyses and staff 

conclusions presented in Appendix G, we have identified adjusted 24-hour CPLs of 21, 25, and 

28 dv as being generally equivalent to 4-hour CPLs of 20, 25, and 30 dv.37 To provide some 

perspective in considering these results (shown in Table G-6), we note that 1 deciview is about 

the amount that persons can distinguish when viewing scenic vistas, and that a difference of 1 

deciview is equivalent to about a 10% difference in light extinction expressed in Mm-1. 

In more broadly considering alternative standard levels that would be appropriate for a 

nationally applicable secondary standard focused on protection from PM-related urban visibility 

impairment based on either a 24-hour or multi-hour, sub-daily (e.g., 4-hour) averaging time, we 

are mindful of the important limitations in the available evidence from public preference studies. 

We are also mindful that the scenic vistas available on a daily basis in many urban areas across 

the country generally do not have the inherent visual interest or the distance between viewer and 

object of greatest intrinsic value as in the Denver and Phoenix preference studies. 

As in past reviews, we are considering a national visibility standard in conjunction with 

the Regional Haze Program as a means of achieving appropriate levels of protection against PMrelated visibility impairment in urban, non-urban, and Class I areas across the country. We 

 37.As discussed in more detail in Appendix G, some days have higher values for 24-hour average light extinction 

than for daily maximum 4-hour daylight light extinction, and consequently an adjusted "equivalent" 24-hour CPL 

can be greater than the original 4-hour CPL. This can happen for two reasons. First, the use of monthly average 

historical RH data will lead to cases in which the f(RH) values used for the calculation of 24-hour average light 

extinction are higher than all or some of the four hourly values of f(RH) used to determine daily maximum 4-hour 

daylight light extinction on the same day. Second, PM2.5 concentrations may be greater during non-daylight periods 

than during daylight hours. 
 4-63

recognize that programs implemented to meet a national standard focused primarily on the 

visibility problems in urban areas can be expected to improve visual air quality in surrounding 

non-urban areas as well, as would programs now being developed to address the requirements of 

the Regional Haze Program established for protection of visual air quality in Class I areas. We 

also believe that the development of local programs, such as those in Denver and Phoenix, can 

continue to be an effective and appropriate approach to provide additional protection, beyond 

that afforded by a national standard, for unique scenic resources in and around certain urban 

areas that are particularly highly valued by people living in those areas. 

Staff Conclusions on Level

Based on the above considerations, we conclude that it is appropriate to give primary 

consideration to alternative standard levels toward the upper end of the ranges identified above 

for 24-hour and sub-daily standards, respectively. Thus, we conclude it is appropriate to 

consider the following alternative levels: a level of 28 dv or somewhat below, down to 25 dv, 

for a standard defined in terms of a calculated PM2.5 light extinction indicator, a 90th percentile 

form, and a 24-hour averaging time; and a standard level of 30 dv or somewhat below, down to 

25 dv, for a similar standard but with a 4-hour averaging time. We judge that such standards 

would provide appropriate protection against PM-related visibility impairment primarily in urban 

areas. We note that support for consideration of the upper part of the range of the CPLs derived 

from the public preference studies was expressed by some CASAC Panel members during the 

public meeting on the second draft PA. We conclude that such a standard would be appropriate 

in conjunction with the Regional Haze Program to achieve appropriate levels of protection 

against PM-related visibility impairment in areas across the country. 

To provide some perspective on the implications of alternative averaging times, forms, 

and levels for a new secondary standard, staff assessed the percentage of counties that would not 

likely meet alternative 24-hour standards with a 90th percentile form and a range of levels from 

25 dv to 28 dv, as well as alternative 4-hour standards with a 90th percentile form and a range of 

levels from 25 dv to 30 dv. This assessment, shown in Appendix H, Tables H-1 and H-2, was 

not considered as a basis for the above staff conclusions. It should be noted that the geographic 

coverage of this assessment was much more constrained by the availability of suitable data than 

the similar assessments for the primary PM standards in Appendices C and D. 

4.4 SUMMARY OF STAFF CONCLUSIONS ON SECONDARY STANDARDS FOR 

VISIBILITY-RELATED EFFECTS 

In reaching conclusions on the adequacy of the current suite of PM2.5 secondary standards 

and potential alternative standards to provide requisite protection of PM-related visibility 
 4-64

impairment, staff has considered these standards in terms of the basic elements of the NAAQS: 

indicator, averaging time, form, and level (sections 4.3.1 to 4.3.4 above). In considering the 

scientific and technical information, we reflect on the information available in the last review 

integrated with information that is newly available as assessed and presented in the ISA (US 

EPA, 2009a) and UFVA (US EPA, 2010a) and as summarized above and in and Appendices E,F, 

and G. We also consider issues raised by the court in its remand of the secondary PM2.5

standards as discussed above in section 4.1.2. 

As outlined in section 4.1.3, we emphasize a policy approach that broadens the general 

approaches used in the last two PM NAAQS reviews by utilizing, to the extent available, 

enhanced tools, methods, and data to more comprehensively characterize visibility impacts. As 

such, we have taken into account both evidence-based and impact assessment-based 

considerations to inform our conclusions related to the adequacy of the current PM2.5 secondary 

standards and alternative standards that are appropriate for consideration in this review. 

We recognize that selecting from among alternative standards will necessarily reflect 

consideration of the qualitative and quantitative uncertainties inherent in the relevant evidence 

and in the assumptions that underlie the quantitative visibility impact assessment. In reaching 

staff conclusions on alternative suites of standards and ranges of levels that are appropriate to 

consider, we are mindful that the CAA requires secondary standards to be set that are requisite to 

protect public welfare from known and anticipated adverse effects, such that the standards are to 

be neither more nor less stringent than necessary. 

Based on the currently available information, staff reaches the following conclusions 

regarding the secondary PM2.5 standards for protecting against PM2.5-related visibility 

impairment: 

(1) Consideration should be given to revising the current suite of PM2.5 secondary standards to 

provide increased public welfare protection from PM2.5-related visibility impairment, 

primarily in urban areas. This conclusion is based in part on the relatively large number of 

days in which PM-related light extinction is estimated to exceed levels that can reasonably be 

judged to be important from a public welfare perspective under simulations of conditions 

associated with just meeting the current suite of PM2.5 standards. This conclusion is also 

based on information that indicates that the current PM2.5 mass indicator is not appropriate 

for a national standard intended to protect public welfare from PM-related visibility 

impairment since such a standard is inherently confounded by regional differences in relative 

humidity and species composition of PM2.5, which are critical factors in the relationship 

between the mix of fine particles in the ambient air and the associated impairment of 

visibility. 

(2) Consideration should be given to establishing a new calculated PM2.5 light extinction 

indicator. This conclusion takes into consideration the available evidence that demonstrates 

a strong correspondence between PM2.5 light extinction as calculated based on the 
 4-65

IMPROVE algorithm used in the Regional Haze Program and PM-related visibility 

impairment, as well as the significant degree of variability in visibility protection across the 

U.S. allowed by a PM2.5 mass indicator that does not take into account relative humidity and 

PM2.5 species composition. 

(a) While a secondary standard that uses a PM 2.5 mass indicator could be set to provide 

additional protection from PM2.5-related visibility impairment, we conclude that the 

advantages of using a calculated PM2.5 light extinction indicator make it the preferred 

choice. 

(b) While in the future it may be appropriate to consider a direct measurement of PMrelated light extinction, or the sum of separate measurements of light scattering and 

light absorption, as the indicator for the secondary PM2.5 standard, we conclude this is 

not an appropriate option in this review because a suitable specification of the 

equipment and associated performance verification procedures, or suitable alternative 

to these, cannot be developed in the time frame for this review. 

(3) Consideration should be given to a 24-hour averaging time, in conjunction with a calculated 

PM2.5 light extinction indicator and an appropriately specified standard level. This 

conclusion reflects the judgment that PM2.5 light extinction calculated on a 24-hour basis is a 

reasonable and appropriate surrogate for light extinction calculated on a sub-daily basis (e.g. 

4 hours) that is more directly related to public perception of visual air quality. Such a 

standard would avoid data quality uncertainties that have recently been associated with 

currently available instruments for the measurement of hourly PM2.5 mass. 

It would also be appropriate to consider a multi-hour, sub-daily averaging period, for 

example a period of four hours, to the extent that data quality issues about continuous FEMs 

that have recently been raised can be appropriately addressed. Such a multi-hour averaging 

period would be more directly related to the short-term nature of the perception of visibility 

impairment, short-term variability in PM-related visual air quality, and the short-term nature 

(hourly to multiple hours) of relevant exposure periods for segments of the viewing public. It 

would also be less sensitive than a 1-hour averaging time to short-term variability in PM2.5

mass measurement. In conjunction with consideration of a multi-hour, sub-daily averaging 

time, we conclude that consideration should be given to including daylight hours only and to 

applying a relative humidity screen of approximately 90% to remove hours in which fog or 

precipitation is much more likely to contribute to the observed visibility impairment. 

(4) With regard to form, in conjunction with a calculated PM2.5 light extinction indicator and 

alternative 24-hour or multi-hour, sub-daily (e.g., 4-hour) averaging times, primary 

consideration should be given to a 90th percentile form, averaged over three years. This form 

recognizes the high degree of uncertainty over the impact on public welfare of the number of 

days with visibility impairment over a year. In the case of a sub-daily alternative standard, 

we conclude that it is appropriate to give primary consideration to a maximum daily value 

form. 

(5) With regard to level, primary consideration should be given to alternative levels toward the 

upper end of the ranges identified above for 24-hour and multi-hour, sub-daily standards, 

respectively, including: a level of 28 dv or somewhat below, down to 25 dv, for a standard 

defined in terms of a calculated PM2.5 light extinction indicator, a 90th percentile form, and a 

24-hour averaging time; and a level of 30 dv or somewhat below, down to 25 dv, for a 
 4-66

similar standard but with a 4-hour averaging time. Staff concludes that such a standard 

would be appropriate in conjunction with the Regional Haze Program to achieve appropriate 

levels of protection against PM-related visibility impairment in areas across the country. 

4.5 KEY UNCERTAINTIES AND AREAS FOR FUTURE RESEARCH AND DATA 

COLLECTION 

This section will be organized into two overarching topic areas: refining current 

understanding of visibility preferences and characterization of ambient urban visibility 

conditions. The first deals principally with how the public reacts to and values visibility 

conditions, while the second is more concerned with determining ambient visibility conditions 

and the relationships between PM component concentrations and light extinction. 

Visibility Preferences 

 Levels: The results of the reanalysis of the four urban preference studies (UFVA Chapter 2) 

demonstrated well defined though significant statistical differences in visibility impairment 

levels that divide participant decisions on acceptable from unacceptable conditions across the 

study areas. A number of hypotheses concerning why the results differed for each area are 

discussed in chapter 2 of the UFVA, but the current state of knowledge does not support a 

definitive explanation for the range of results. A better understanding of the reasons for the 

differences in preference response among the studies of the four urban areas could influence 

the design of future visibility preference survey studies and the interpretation of their results 

ultimately leading to a better defined range of CPLs for the next PM NAAQS review. 

 Averaging Times/Forms: Additional information would also be helpful in deciding among 

the various forms and averaging times to develop an effective visibility-based secondary PM 

NAAQS and to assess the overall benefits of visibility improvements. Our current 

understanding of urban visibility effects does not provide insights concerning: 

o relative importance of degree of visibility impairment (i.e., light extinction level) 

versus frequency of visibility impairment; 

o strength of preference for different distributions of visibility conditions; and 

o public exposure patterns. 

Future research to address these deficiencies should include designing and conducting 

additional preference, valuation and exposure studies to: 

o expand the number and geographic coverage of urban area preference results; 

o evaluate the sensitivity of results to the differences in survey study methodology; 

o apply consistent methodology at multiple urban areas to better understand reasons for 

preference difference among results in different urban areas; 

o develop information on the strength of preference and relative importance of intensity 

versus frequency of visibility impairment; 

o identify the types of scenic elements that are most influential for informing public 

visibility impact awareness; and 
 4-67

o provide insights concerning visibility impact exposure duration, intensity, and timing 

and their relationship to the degree and longevity of public welfare effects. 

As part of the planning for additional preference and valuation survey studies, a literature review 

of recent social science literature could usefully be conducted to assess the state of knowledge of 

view exposure mechanisms and the psychological and behavioral effects associated with viewed 

stimuli. 

Urban Visibility Conditions 

In this review, the paucity of light extinction monitoring data for urban areas led to the 

use of the original IMPROVE algorithm to calculate hourly light extinction from continuous PM 

mass and 24-hour PM2.5 component and relative humidity data (UFVA Chapter 3). The steps 

used to temporally apportion 24-hour PM2.5 components in order to calculate hourly-averaged 

values used monthly-averaged diurnal PM2.5 component variations from chemical transport air 

quality modeling. The mass balance method used to estimate organic carbonaceous material 

concentration and the loss of nitrate is reasonable but not likely to be precise. The original 

IMPROVE algorithm was originally developed for remote area application to estimate 24-hour 

light extinction and it was not verified for use in generating urban hourly estimates. The revised 

IMPROVE algorithm would notably increase estimates of PM2.5 light extinction at the high end 

of the range of light extinction conditions. Nevertheless, the resulting hourly PM2.5 light 

extinction data set is thought by EPA staff to be sufficiently representative of the hourly PM2.5

light extinction levels in the study areas to allow an assessment of whether the current suite of 

secondary standards is adequately protective, although these data may be biased low at high light 

extinction levels, and are certainly less accurate than would be data from direct measurements. 

A pilot PM2.5 light extinction monitoring program could usefully be designed and 

deployed at some number of locations selected to cover a range of PM2.5 air quality conditions 

with emphasis given to locations with continuous PM2.5 mass and speciation monitoring as well 

as 24-hour mass and speciation sampling. Information from such a pilot monitoring program 

could be used to: 

o evaluate the performance of PM2.5 light extinction monitoring methods that could 

ultimately be use as an FRM; 

o evaluate and refine approaches for apportioning 24-hour PM2.5 species to hourly 

values (needed for sites without continuous PM2.5 speciation monitoring); 

o evaluate and refine light extinction calculation algorithms for use in urban settings; 

and 

o conduct the visibility effects assessment for the next PM secondary NAAQS. 
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4.6 REFERENCES 

Abt Associates Inc. (2001). Assessing Public Opinions on Visibility Impairment Due to Air Pollution: Summary 

Report. 

BBC Research & Consulting. (2003). Phoenix Area Visibility Survey. Draft Report. Available: 

http://www.azdeq.gov/environ/air/download/vis_021903f.pdf. Accessed 9/16/2008

Edgerton, E. et al. (2006). The Southeastern Aerosol Research and Characterization Study, Part 3: Continuous 

Measurements of Fine Particulate Matter Mass and Composition. J. Air Waste Manage. Assoc., 56, 1325-

1341. 

Evangelista, M. (2011). Investigation of 1-hour PM2.5 Mass Concentration Data from EPA-Approved Continuous 

Federal Equivalent Method Analyzers. Memorandum to PM NAAQS review docket EPA-HQ-OAR-2007-

0492. April 5, 2011. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 

Frank, N. (2006). Retained Nitrate, Hydrated Sulfates, and Carbonaceous Mass in Federal Reference Method Fine 

Particulate Matter for Six Eastern U.S. Cities. J. Air Waste Manage. Assoc., 56, 500-511. 

Hanley, T.and Reff, A (2011). Assessment of PM2.5 FEMs compared to collocated FRMs. Memorandum to PM 

NAAQS review docket EPA-HQ-OAR-2007-0492. April 7, 2011. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 

Henderson, R. (2005a). Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee to 

Honorable Stephen L. Johnson, Administrator, US EPA. CASAC PM Review Panel’s Peer Review of the 

Agency’s Review of the National Ambient Air Quality Standards for Particulate Matter: Policy Assessment 

of Scientific and Technical Information (Second Draft PM Staff Paper, January 2005). June 6, 2005. 

Available at: http://www.epa.gov/sab/pdf/casac-05-007.pdf. 

Henderson, R. (2006a). Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee to the 

Honorable Stephen L. Johnson, Administrator, US EPA. Clean Air Scientific Advisory Committee 

Recommendations Concerning the Proposed National Ambient Air Quality Standards for Particulate 

Matter. March 21, 2006. Available: http://www.epa.gov/sab/pdf/casac-ltr-06-002.pdf

Ely DW; Leary JT; Stewart TR; Ross DM (1991). The establishment of the Denver Visibility Standard. Presented at 

Presented at: 84th annual meeting & exhibition of the Air & Waste Management Association; June; 

Vancouver, British Columbia. Pittsburgh, PA: Air & Waste Management Association; paper no. 91-48.4. 

Lorang, P. (2010). Simplified Approaches for Calculation of Hourly PM2.5 Light Extinction Values from Hourly 

PM2.5 Mass and Relative Humidity Data and 24-hour PM2.5 Composition Data. . Memorandum to PM 

NAAQS review docket EPA-HQ-OAR-2007-0492.November 17, 2010. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 

 Malm, W.C., Sisler, J.F., Huffman, D., Eldred, R.A., Cahill, T.A. (1994) Spatial and Seasonal Trends in Particle 

Concentration and Optical Extinction in the United States, Journal of Geophysical Research (Atmospheres) 

99:1347-1370. 

Molenar, J.V., Malm, W.C., Johnson, C.E. (1994) Visual air quality simulation techniques. Atmos Environ, 28(5): 

1055-1063. 

Pitchford , M. (2010). Assessment of the Use of Speciated PM2.5 Mass-Calculated Light Extinction as a Secondary 

PM NAAQS Indicator of Visibility. . Memorandum to PM NAAQS review docket EPA-HQ-OAR-2007-

0492. November 17, 2010. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 
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Pryor SC (1996). Assessing public perception of visibility for standard setting exercises. Atmos Environ, 30: 2705-

2716. 

Russell, A; Samet, J.M. (2010). Letter from the Clean Air Science Advisory Committee (CASAC) Ambient Air 

Monitoring and Methods Subcommittee (AAMMS) to the Honorable Lisa P, Jackson, Administrator, US 

EPA. Review of the White Paper on Particulate Matter (PM) Light Extinction Measurements. April 29, 

2010, Available at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/92C9F5AA09A76A938

52577150004A782/$File/EPA-CASAC-10-010-unsigned.pdf

Samet J (2010c). Letter from Dr. Jonathan M. Samet, Chair, Clean Air Scientific Advisory Committee to the 

Honorable Lisa P. Jackson, Administrator, US EPA. CASAC Review of Policy Assessment for the Review 

of the PM NAAQS – First External Revew Draft (March 2010). May 17, 2010. Available at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/E504EE3276D87A9E8

525772700647AFB/$File/EPA-CASAC-10-011-unsigned.pdf/. 

Samet J (2010d). Letter from Dr. Jonathan M. Samet, Chair, Clean Air Scientific Advisory Committee to the 

Honorable Lisa P. Jackson, Administrator, US EPA. CASAC Review of Policy Assessment for the Review 

of the PM NAAQS – Second External Revew Draft (June 2010). September 10, 2010. Available at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/CCF9F4C0500C500F8

525779D0073C593/$File/EPA-CASAC-10-015-unsigned.pdf

Smith, A. (2009). Comments on the First External Review Draft of EPA’s “Integrated Science Assessment for 

Particulate Matter.” CRA International. Washington, DC. March 30. Prepared for the Utility Air 

Regulatory Group. Submitted as public comments to the public meeting of the EPA Clean Air Science 

Advisory Council. April 2. 

Smith AE; Howell S (2009). An assessment of the robustness of visual air quality preference study results. CRA 

International. Washington, DC. 

http://yosemite.epa.gov/sab/sabproduct.nsf/B55911DF9796E5E385257592006FB737/$File/CRA+VAQ+Pr

ef+Robustness+Study+3+30+09+final.pdf

US EPA (2005). Review of the National Ambient Air Quality Standards for Particulate Matter: Policy Assessment 

of Scientific and Technical Information, OAQPS Staff Paper. Research Triangle Park, NC 27711: Office 

of Air Quality Planning and Standards; report no. EPA EPA-452/R-05-005a. December 2005. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html

US EPA (2008). Integrated Review Plan for the National Ambient Air Quality Standards for Particulate Matter. 

National Center for Environmental Assessment and Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. Report No. EPA 452/R-08-004. March 

2008. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pd.html. 

US EPA (2009a). Integrated Science Assessment for Particulate Matter: Final Report. National Center for 

Environmental Assessment-RTP Division, Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. EPA/600/R-08/139F. December 2009. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html. 

US EPA (2010 a). Quantitative Risk Assessment for Particulate Matter – Final Report. Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/R10-005. June 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html 

US EPA (2010b). Particulate Matter Urban-Focused Visibility Assessment – Final Report. Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/R10-004. June 2010. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 
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US EPA (2010c). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality 

Standards - First External Review Draft. Office of Air Quality Planning and Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC. EPA 452/P-10-003. March 2010. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html. 

US EPA (2010f). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality 

Standards - Second External Review Draft. Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. EPA 452/P-10-007. June 2010. 

Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_pa.html. 

US EPA (2010h). White Paper on PM Light Extinction Measurements. Office of Air Quality Planning and 

Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. January 2010. Available 

at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/823a6c8842610e76852

5764900659b22!OpenDocument
 

 

5-1

5 REVIEW OF THE SECONDARY STANDARDS FOR OTHER 

WELFARE EFFECTS 

This chapter presents staff conclusions with regard to the current suite of secondary PM

standards to protect against PM-related welfare effects other than visibility impairment. 

Specifically, staff has assessed the relevant information related to effects of atmospheric PM on 

the environment, including effects on climate, ecological effects, and effects on materials. Our 

assessment is framed by a series of key policy-relevant questions, which expand upon those 

presented in the Integrated Review Plan (IRP) (US EPA, 2008a, section 3.2). The answers to 

these questions will inform decisions on whether to retain or revise the current suite of secondary 

PM standards. 

In presenting staff conclusions with regard to the current secondary standards relative to 

PM-related effects on climate, ecological effects, and materials, we note that the final decision is 

largely a public welfare policy judgment. A final decision must draw upon scientific information 

and analyses about non-visibility PM-related effects and related impacts on public welfare, as 

well as judgments about how to deal with the range of uncertainties that are inherent in the 

scientific evidence and analyses. Our approach to informing these judgments is discussed more 

fully below. This approach is consistent with the requirements of the NAAQS provisions of the 

Act and with how EPA and the courts have historically interpreted the Act. These provisions 

require the Administrator to establish secondary standards that, in the Administrator’s judgment, 

are requisite to protect public welfare from any known or anticipated adverse effects associated 

with the presence of the pollutant in the ambient air. In so doing, the Administrator seeks to 

establish standards that are neither more nor less stringent than necessary for this purpose. The 

Act does not require that secondary standards be set at a zero-risk level, but rather at a level that 

avoids unacceptable public welfare impacts. 

Information on the approaches used to set the secondary PM standards in past reviews as 

well as our current approach for this review are presented in section 5.1. A discussion of the 

scope of the review as related to non-visibility welfare effects of PM is included in section 5.1.2. 

This chapter considers each of the non-visibility welfare effects separately. The discussion of 

PM-associated effects on climate (section 5.2), ecological effects (section 5.3), and materials 

(section 5.4) are each followed by a consideration of key uncertainties and areas for future 

research and data collection. 

5.1 APPROACH 

Background information on the approaches used to establish the PM secondary standards 

in 1997 and revisions to those standards in 2006 are summarized below. This section also 
 

 

5-2

includes a discussion of the ongoing joint review of ecological effects of oxides of nitrogen and 

sulfur (NOx/SOx secondary NAAQS review) for clarity, since depositional effects of PM 

components of NOx and SOx to ecosystems were historically considered as a component of the 

PM secondary review. Lastly, there is a discussion of the current approach for evaluating the 

effects of PM on climate, ecosystems, and materials using evidence-based considerations to 

inform our understanding of the key policy-relevant issues. 

5.1.1 Approaches Used in Previous Reviews 

5.1.1.1 Review Completed in 1997 

In the 1997 review, as discussed in section 2.1.1.1, EPA determined that for the primary 

standard the fine and coarse fractions of PM10 should be considered separately and added a suite 

of new primary standards, using PM2.5, as the indicator for fine particles, and retaining PM10 as 

the indicator for regulating thoracic coarse particles. The EPA established two new PM2.5

standards: an annual standard of 15 μg/m3, based on the 3-year average of annual arithmetic 

mean PM2.5 concentrations from single or multiple community-oriented monitors; and a 24-hour 

standard of 65 μg/m3, based on the 3-year average of the 98th percentile of 24-hour PM2.5

concentrations at each population-oriented monitor within an area (62 FR 38652, July 18, 1997). 

With respect to the secondary PM standards, EPA concluded in 1997 that the available 

evidence on effects of PM on non-visibility welfare endpoints was not sufficient to warrant a 

separate secondary standard. Therefore, the secondary standards were set equal to the primary 

PM2.5 and PM10 standards in the final rule to provide protection against effects on visibility as 

well as materials damage and soiling effects related to fine and coarse particles (62 FR 38683). 

5.1.1.2 Review Completed in 2006 

In 2006, the Administrator concluded that there was insufficient information to consider a 

distinct secondary standard based on PM-related impacts to ecosystems, materials damage and 

soiling, and climatic and radiative processes (71 FR 61144, October 17, 2006). Specifically, 

there was a lack of evidence linking various non-visibility welfare effects to specific levels of 

ambient PM. To provide a level of protection for welfare-related effects, the secondary 

standards were set equal to the revised primary standards to directionally improve the level of 

protection afforded vegetation, ecosystems and materials (71 FR 61210). 

In the last review, the 2004 AQCD concluded that regardless of size fraction, particles 

containing nitrates and sulfates have the greatest potential for widespread environmental 

significance (US EPA, 2004, sections 4.2.2 and 4.2.3.1). Considerable supporting evidence was 

available that indicated a significant role of NOx, SOx, and transformation products in 
 

 

5-3

acidification and nutrient enrichment of terrestrial and aquatic ecosystems (71 FR 61209). The 

recognition of these ecological effects, coupled with other considerations detailed below, led 

EPA to initiate a joint review of the NO2 and SO2 secondary NAAQS that will consider the 

gaseous and particulate species of NOx and SOx with respect to the ecosystem-related welfare 

effects that result from the deposition of these pollutants and transformation products. 

5.1.2 Scope of Current NAAQS Reviews 

Non-visibility welfare-based effects of oxides of nitrogen and sulfur are divided between 

two NAAQS reviews; (1) the PM NAAQS review and, (2) the joint NOx/SOx secondary 

NAAQS review. The scope of each document and the components of nitrogen (N) and sulfur (S) 

considered in each review are detailed in this section and summarized in Table 5-1. 

5.1.2.1 Scope of the Current Secondary PM NAAQS Review 

In reviewing the current suite of secondary PM standards to address visibility impairment 

(chapter 4), climate forcing effects (section 5.2), and other welfare-related effects (sections 5.3 

and 5.4), all PM-related effects that are not being covered in the NOx/SOx review are 

considered. With regard to the materials section (5.4), the discussion has been expanded to 

include particles and gases that are associated with the presence of ambient NOx and SOx, as 

well as NOy, NH3 and NHX for completeness. By excluding the effects associated with 

deposited particulate matter components of NOx and SOx and their transformation products 

which are addressed fully in the NOx/SOx secondary review, as outlined below, the discussion 

of ecological effects of PM has been narrowed to focus on effects associated with the deposition 

of metals and, to a lesser extent, organics (section 5.3). 
5-4

Table 5-1. Scope of the Current Secondary PM NAAQS Review and Current NOx/SOx Secondary N

NOx/SOx Secondary Review 

PM Secondary Review

 Materials 

Welfare Effect 

Acidifying deposition, nutrient enrichment 

Direct effects of gas-phase NOx/SOx on vegetation 

Visibility impairment 

Climate Forcing effects 

Ecological effects Damage 

Documents  ISA NOx/SOx NOx/SOx PM PM PM 

PM and NOx/SOx Annex E 

 REA NOx/SOx NOx/SOx 

PM (Urban focused visibility assessment) 

 PA NOx/SOx NOx/SOx PM PM PM PM Components 

Deposited particulate and gaseous forms of oxides of nitrogen and sulfur and related N and S containing compounds. 

Gaseous forms of oxides of nitrogen and sulfur and related N and S containing compounds in the ambient air. 

All particles 10 microns or smaller in the ambient air. 

Climate-related particles (aerosols) in the ambient air. 

Deposited components of PM, including metals and organics but not N and S containing compounds. 

Particles and gases associated with ambient NOx and SOx including NOy, NH3 and NHx. 
 

 

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5.1.2.2 Scope of the Current NOx/SOx Secondary NAAQS Review 

This is the first time since the NAAQS were established in 1971 that a joint review of the 

secondary NAAQS for NOx and SOx has been conducted. This review is being conducted 

because the atmospheric chemistry and environmental effects of NOx, SOx, and their associated 

transformation products are linked, and because the National Research Council (NRC) has 

recommended that EPA consider multiple pollutants, as appropriate, in forming the scientific 

basis for the NAAQS. The NOx/SOx secondary review focuses on the welfare effects associated 

with exposures from deposited particulate and gaseous forms of oxides of nitrogen and sulfur 

and related N and S containing compounds and transformation products on ecosystem receptors. 

An assessment of the complex ecological effects associated with N deposition requires 

consideration of multiple forms of N. These include evaluation of data on inorganic reduced 

forms of N (e.g., ammonia [NH3] and ammonium ion [NH4+]), inorganic oxidized forms (e.g., 

NOx, nitric acid [HNO3], nitrous oxide [N2O], nitrate [NO3−

]), and organic N compounds (e.g., 

urea, amines, proteins, nucleic acids). In addition to acidification and N-nutrient enrichment, 

other welfare effects related to deposition of N-and S-containing compounds are discussed, such 

as SOx interactions with mercury (Hg) methylation. In addition, the NOx/SOx secondary review 

includes evidence related to direct ecological effects of gas-phase NOx and SOx since the direct 

effects of gas-phase SOx on vegetation formed a primary basis for the initial establishment of the 

secondary NAAQS for SO2. 

Effects of acidifying deposition associated with particulate N and S are covered in the 

recent Integrated Science Assessment for Oxides of Nitrogen and Sulfur-Ecological Criteria

(Final Report, US EPA, 2008c). The Risk and Exposure Assessment for Review of the 

Secondary National Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur 

(Final)(NOx/SOx REA) (US EPA, 2009h) considers four main targeted ecosystem effects 

considered in the review of secondary effects of NOx and SOx: (1) aquatic acidification due to N 

and S, (2) terrestrial acidification due to N and S, (3) aquatic nutrient enrichment, including 

eutrophication and (4) terrestrial nutrient enrichment. In the Policy Assessment for Review of the 

Secondary National Ambient Air Quality Standards for Oxides of Nitrogen and Oxides of Sulfur

(US EPA, 2011) an acidification index is being considered. This index provides potential 

ecosystem protection from deposition related to atmospheric concentrations. 

5.1.3 General Approach Used in Current Review 

The remainder of this chapter summarizes and highlights key aspects of the policy 

relevant information from the ISA to help inform the Administrator’s judgments regarding the 

adequacy of the current suite of secondary PM NAAQS in relation to climate processes, 
 

 

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ecological effects, and materials damage. The ISA uses a five-level hierarchy that classifies the 

weight of evidence for causation, not just association, into a qualitative statement about the 

overall weight of evidence and causality (US EPA, 2009a, section 1.5.5, Table 1-3): causal 

relationship; likely to be a causal relationship; suggestive of a causal relationship; inadequate to 

infer a causal relationship; not likely to be a causal relationship (see US EPA, 2009a, Table 1-3). 

Staff is evaluating evidence-based considerations primarily by assessing the evidence of 

associations identified in the ISA. All relationships between PM and climate, ecological effects, 

and materials damage effects identified in the ISA are considered to be either “likely causal” or 

“causal”. The staff’s approach in this review of non-visibility welfare effects of PM is to 

consider information regarding particulate matter effects on climate, ecological endpoints and 

materials. This includes new literature available since the last review as well as existing, 

relevant information as presented in the ISA (US EPA 2009a). 

5.2 CLIMATE 

5.2.1 Scope 

 Information and conclusions about what is currently known about the role of PM in 

climate is summarized in Chapter 9 of the PM ISA (US EPA, 2009a). The ISA concludes; “that 

a causal relationship exists between PM and effects on climate, including both direct effects on 

radiative forcing and indirect effects that involve cloud feedbacks that influence precipitation 

formation and cloud lifetimes” (US EPA, 2009a, section 9.3.10). Material from the climate 

section of the ISA is principally drawn from the U.S. Climate Change Science Program 

Synthesis and Assessment Product 2.3, Atmospheric Aerosol Properties and Climate Impacts, by 

Chin et al., (CCSP 2009) and Chapter 2, Changes in Atmospheric Constituents and in Radiative 

Forcing,(Forster et al., 2007) in the comprehensive Working Group I report in the Fourth 

Assessment Report (AR4) from the Intergovernmental Panel on Climate Change (IPCC), Climate 

Change 2007: The Physical Science Basis. Sections 9.3.7 (Fire as a Special Source of PM 

Welfare Effects), 9.3.9 (Other Special Sources and Effects), 9.3.9.1 (Glaciers and Snowpack) 

and 9.3.9.3 (Effects on Local and Regional Climate) of the ISA were written by NCEA staff. 

This section of the PA summarizes and synthesizes the policy-relevant science in the ISA for the 

purpose of helping to inform consideration of climate aspects in the review of the secondary PM 

NAAQS. 

Atmospheric PM (referred to as aerosols1

 in the remainder of this section to be consistent 

with the ISA) affects multiple aspects of climate. These include absorbing and scattering of 

 

1 In the sections of the ISA included from IPCC AR4 and CCSP SAP2.3, ‘aerosols’ is more frequently used than 

“PM” and that word is retained. 
 

 

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incoming solar radiation, alterations in terrestrial radiation, effects on the hydrological cycle, and 

changes in cloud properties (US EPA, 2009a, section 9.3.1). Major aerosol components that 

contribute to climate processes include black carbon (BC), organic carbon (OC), sulfates, nitrates 

and mineral dusts. There is a considerable ongoing research effort focused on understanding 

aerosol contributions to changes in global mean temperature and precipitation patterns. The 

Climate Change Research Initiative identified research on atmospheric concentrations and effects 

of aerosols as a high research priority (National Research Council, 2001) and the IPCC 2007 

Summary for Policymakers states that anthropogenic contributions to aerosols remain the 

dominant uncertainty in radiative forcing (IPCC 2007). The current state of the science of 

climate alterations attributed to PM is in flux as a result of continually updated information. 

5.2.2 Adequacy of the Current Standards 

In considering the adequacy of the suite of secondary standards, staff addresses the 

following overarching question: 

Does currently available scientific information, as reflected in the ISA, support or call into 

question the adequacy of the protection for climate effects afforded by the current suite of 

secondary PM standards? 

To inform the answer to this overarching question, staff has posed specific questions to 

aid in assessing the available scientific evidence as related to climate effects attributed to 

aerosols. In considering the currently available scientific and technical information, we included 

both the information available from the last review and information that is newly available since 

the last review synthesized in Chapter 9 of the ISA (US EPA, 2009a). 

 What new techniques are available to improve our understanding of climate effects of 

aerosols? 

Global climate change has increasingly been the focus of intense international research 

endeavors. Major efforts are underway to understand the complexities inherent in atmospheric 

aerosol interactions and to decrease uncertainties associated with climate estimations. Two recent 

reports, the US CCSP Product 2.3 and sections of the IPCC AR4 were combined to form the 

climate discussion in the ISA (CCSP 2009; Forster et al., 2007). A review of the most recently 

available techniques for assessing climate-aerosol relationships is presented in the ISA. Aerosol 

measurement capabilities reviewed in the ISA include a discussion of the increasingly 

sophisticated instrumentation and techniques available for quantifying aerosols, the enhanced 

sensing capabilities of satellites, development of remote sensing networks and synergy of 

 

 
 

 

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measurements with model simulations (US EPA, 2009a, section 9.3.2). Advances in measured 

aerosol properties as related to modeling as well as outstanding issues remaining in these 

measurement-based studies are elaborated in the ISA (US EPA, 2009a, sections 9.3.3 and 9.3.4). 

Section 9.3.6 of the ISA, “Global Aerosol Modeling” considers the capabilities of climate 

modeling that have developed over the last decade and limitations of the techniques currently in 

use (US EPA, 2009a). 

 To what extent does newly available evidence improve our understanding of the nature 

and magnitude of climate responses to PM (aerosols)? 

Aerosols have direct and indirect effects on climate processes. The direct effects of 

aerosols on climate result mainly from particles scattering light away from earth into space, 

directly altering the radiative balance of the Earth-atmosphere system. This reflection of solar 

radiation back to space decreases the transmission of visible radiation to the surface of the earth 

and results in a decrease in the heating rate of the surface and the lower atmosphere. At the same 

time, absorption of either incoming solar radiation or outgoing terrestrial radiation by particles, 

primarily BC, results in an increased heating rate in the lower atmosphere. Global estimates of 

aerosol direct radiative forcing (RF) were recently summarized using a combined model-based 

estimate (Forster et al., 2007). The overall, model-derived aerosol direct RF was estimated in the 

IPCC AR4 as -0.5 (-0.9 to -0.1) watts per square meter (W/m2

), with an overall level of scientific 

understanding of this effect as “medium low” (Forster et al., 2007), indicating a net cooling 

effect in contrast to greenhouse gases (GHGs) which have a warming effect. 

The contribution of individual aerosol components to total aerosol direct radiative forcing 

is more uncertain than the global average (US EPA, 2009a, section 9.3.6.6). The direct effect of 

radiative scattering by atmospheric particles exerts an overall net cooling of the atmosphere, 

while particle absorption of solar radiation leads to warming. For example, the presence of OC 

and sulfates decrease warming from sunlight by scattering shortwave radiation back into space. 

Such a perturbation of incoming radiation by anthropogenic aerosols is designated as aerosol 

climate forcing, which is distinguished from the aerosol radiative effect of the total aerosol 

(natural plus anthropogenic). The aerosol climate forcing and radiative effect are characterized 

by large spatial and temporal heterogeneities due to the wide variety of aerosol sources, the 

spatial non-uniformity and intermittency of these sources, the short atmospheric lifetime of 

aerosols (relative to that of the greenhouse gases), and processing (chemical and microphysical) 

that occurs in the atmosphere. For example, OC can be warming (positive forcer) when 

deposited on or suspended over a highly reflective surface such as snow or ice but, on a global 

average, is a negative forcer in the atmosphere. 

More information has also become available on indirect effects of aerosols. Particles in 

the atmosphere indirectly affect both cloud albedo (reflectivity) and cloud lifetime by modifying 
 

 

5-9

the cloud amount, and microphysical and radiative properties (US EPA, 2009a, section 9.3.6.4). 

The RF due to these indirect effects (cloud albedo effect) of aerosols is estimated in the IPCC 

AR4 to be -0.7(-1.8 to -0.3) W/m2 with the level of scientific understanding of this effect as 

“low” (Forster et al., 2007). Aerosols act as cloud condensation nuclei (CCN) for cloud 

formation. Increased particulates in the atmosphere available as CCN with no change in 

moisture content of the clouds have resulted in an increase in the number and decrease in the size 

of cloud droplets in certain clouds that can increase the albedo of the clouds (the Twomey 

effect). Smaller particles slow the onset of precipitation and prolong cloud lifetime. This effect, 

coupled with changes in cloud albedo, increases the reflection of solar radiation back into space. 

The altitude of clouds also affects cloud radiative forcing. Low clouds reflect incoming sunlight 

back to space but do not effectively trap outgoing radiation, thus cooling the planet, while higher 

elevation clouds reflect some sunlight but more effectively can trap outgoing radiation and act to 

warm the planet (US EPA, 2009a, section 9.3.3.5). 

The total negative RF due to direct and indirect effects of aerosols computed from the top 

of the atmosphere, on a global average, is estimated at -1.3 (-2.2 to -0.5) W/m2

 in contrast to the 

positive RF of +2.9 (+3.2 to +2.6) W/m2 for anthropogenic GHGs (IPCC 2007, pg. 200). 

The understanding of the magnitude of aerosol effects on climate has increased 

substantially in the last decade. Data on the atmospheric transport and deposition of aerosols 

indicate a significant role for PM components in multiple aspects of climate. Aerosols can 

impact glaciers, snowpack, regional water supplies, precipitation and climate patterns (US EPA, 

2009a, section 9.3.9). Aerosols deposited on ice or snow can lead to melting and subsequent 

decrease of surface albedo (US EPA 2009a, section 9.3.9.2). Aerosols are potentially important 

agents of climate warming in the Arctic and other locations (US EPA, 2009a, section 9.3.9). 

Incidental fires and biomass burning are being recognized as having a significant impact on 

PM2.5 concentrations and climate forcing. Intermittent fires can occur at large enough scales to 

affect hemispheric aerosol concentrations (US EPA 2009a, section 9.3.7). 

A series of studies available since the last review examine the role of aerosols on local 

and regional scale climate processes (US EPA, 2009a, section 9.3.9.3). Studies on the South 

Coast Air Basin (SCAB) in California indicate aerosols may reduce near-surface wind speeds, 

which, in turn reduce evaporation rates and increase cloud lifetimes. The overall impact can be a 

reduction in local precipitation (Jacobson and Kaufmann, 2006). Conditions in the SCAB impact 

ecologically sensitive areas including the Sierra Nevadas. Precipitation suppression due to 

aerosols in California (Givati and Rosenfield, 2004) and other similar studies in Utah and 

Colorado found that orographic precipitation decreased by 15-30% downwind of pollution 

sources. Evidence of regional-scale impacts of aerosols on meteorological conditions in other 

regions of the U.S. is lacking. 
 

 

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 To what extent does the currently available information provide evidence of association 

between specific PM constituents (i.e. BC, OC, sulfates) and climate-related effects? 

Advances in the understanding of aerosol components and how they contribute to climate 

change have enabled refined global forcing estimates of individual PM constituents. The global 

mean radiative effect from individual components of aerosols was estimated for the first time in 

the IPCC AR4 where they were reported to be (all in W/m2 units): -0.4 (+0.2) for sulfate, -0.05 

(+0.05) for fossil fuel-derived OC, +0.2 (+0.15) for fossil fuel derived BC, +0.03 (+0.12) for 

biomass burning, -0.1 (+0.1) for nitrates, and -0.1 (+0.2) for mineral dust (US EPA, 2009a, 

section 9.3.10). Sulfate and fossil fuel-derived OC cause negative forcing whereas BC causes 

positive forcing because of its highly absorbing nature (US EPA, 2009a, 9.3.6.3). Although BC 

comprises only a small fraction of anthropogenic aerosol mass load and aerosol optical depth 

(AOD), its forcing efficiency (with respect to either AOD or mass) is an order of magnitude 

stronger than sulfate and particulate organic matter (POM), so its positive shortwave forcing 

largely offsets the negative direct forcing from sulfate and POM (IPCC, 2007; US EPA 2009a, 

9.3.6.3). Global loadings for nitrates and anthropogenic dust remain very difficult to estimate, 

making the radiative forcing estimates for these constituents particularly uncertain (US EPA, 

2009a, section 9.3.7). 

Improved estimates of anthropogenic emissions of some aerosols, especially BC and OC, 

have promoted the development of improved global emissions inventories and source-specific 

emissions factors useful in climate modeling (Bond et al. 2004). Recent data suggests that BC is 

one of the largest individual warming agents after carbon dioxide (CO2) and perhaps methane 

(CH4) (Jacobson 2000; Sato et al., 2003; Bond and Sun 2005). There are several studies 

modeling BC effects on climate and/or considering emission reduction measures on 

anthropogenic warming detailed in section 9.3.9 of the ISA. In the U.S., most of the warming 

aerosols are emitted by biomass burning and internal engine combustion and much of the cooling 

aerosols are formed in the atmosphere by oxidation of SO2 or VOC’s. (US EPA, 2009a, section 

3.3). Fires release large amounts of BC, CO2, CH4 and OC (US EPA, 2009a, section 9.3.7). 

5.2.3 Staff Conclusions 

Aerosols alter climate processes directly through radiative forcing and by indirect effects 

on cloud brightness, changes in precipitation and possible changes in cloud lifetimes. 

 Individual components of aerosols differ in their reflective properties, and direction of 

climate forcing. Overall, based on current estimates of aerosol radiative forcing, 

aerosols have a net climate cooling effect. 
 

 

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 Most climate model simulations are based on global scale scenarios. These models 

may fail to consider the local variations in climate forcing due to emissions sources 

and local meteorological patterns. 

 Most of the warming aerosols in the U.S. are emitted by biomass burning and internal 

engine combustion. Much of the cooling aerosols are formed in the atmosphere by 

oxidation of SO2 or VOC’s. The relative mix and sources of warming and cooling 

components will vary in areas across the U.S. and over time. Thus, a set of controls 

to reduce warming PM would not necessarily reduce cooling PM and vice versa. 

Collectively taking into consideration the responses to specific questions regarding the 

adequacy of the current secondary PM standards for climate effects, we revisit the overarching 

question: “does available scientific information, as reflected in the ISA, support or call into 

question the adequacy of the protection for climate effects afforded by the current suite of 

secondary PM standards?” As an initial matter, we considered the appropriateness of the current 

secondary standard defined in terms of PM2.5 and PM10 indicators, for providing protection 

against potential climate effects of aerosols. Newly available scientific information on climateaerosol relationships has improved our understanding of direct and indirect effects of aerosols 

and aerosol properties. The major aerosol components that contribute to climate processes 

include BC, OC, sulfate, nitrate and mineral dusts. These components vary in their reflectivity, 

forcing efficiencies and even in the direction of climate forcing. The current standards that are 

defined in terms of aggregate size mass cannot be expected to appropriately target controls on 

components of fine and coarse particles that are related to climate forcing effects. Thus, the 

current mass-based PM2.5 and PM10 secondary standards are not an appropriate or effective 

means of focusing protection against PM-associated climate effects due to these differences in 

components. 

Overall, there is a net climate cooling associated with aerosols in the global atmosphere 

(US EPA, 2009a, section 9.2.10). Staff recognizes that some individual aerosol components, 

such as BC, are positive climate forcers, whereas others, such as OC and sulfates, are negative 

climate forcers. The relative mix of components will vary in areas across the U.S. and over time. 

Due to the spatial and temporal heterogeneity of PM components that contribute to climate 

forcing, uncertainties in the measurement of aerosol components, inadequate consideration of 

aerosol impacts in climate modeling, insufficient data on local and regional microclimate 

variations and heterogeneity of cloud formations, it is not currently feasible to conduct a 

quantitative analysis for the purpose of informing revisions of the current NAAQS PM standard 

based on climate. Based on these considerations, we conclude that there is insufficient 
 

 

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information at this time to base a national ambient standard on climate impacts associated with 

current ambient concentrations of PM or its constituents.2 

5.2.4 Key Uncertainties and Areas for Future Research and Data Collection 

Although considerable progress is being made in estimating aerosol contributions to 

radiative forcing and climate fluctuations, significant uncertainties remain that preclude 

consideration of climate effects as a basis for establishing a separate NAAQS secondary 

standard. Further research into the effects of aerosols on climate could provide important 

information to reduce these uncertainties. 

A major impediment at this time to establishing a secondary standard for PM based on 

climate is the lack of accurate measurement of aerosol contributions, specifically quantification 

of aerosol absorption and inability to separate the anthropogenic component from total aerosol 

forcing. Section 9.3.4 of the ISA details the current limitations in aerosol measurement. Most 

measurement studies focus on the sum of natural and anthropogenic contributions under clear 

sky conditions, however, this scenario is simplistic when effects of cloud cover and differing 

reflective properties of land and ocean are considered. Satellite measurements do not currently 

have the capability to distinguish anthropogenic from natural aerosols. Due to a lack of data on 

the vertical distribution of aerosols, above-cloud aerosols and profiles of atmospheric radiative 

heating are poorly understood (US EPA, 2009a, section 9.3.4). 

Another uncertainty in considering climate effects of PM in the NAAQS review is the 

spatial and temporal heterogeneity of aerosols. In regions having high concentrations of 

anthropogenic aerosols, aerosol forcing is greater than the global average, and can exceed 

warming by GHGs, locally reversing the sign of the forcing (US EPA, 2009a, section 9.3.1). 

The contributions of policy-relevant background (PRB) concentrations to aerosol climate forcing 

are not sufficiently characterized (US EPA, 2009a, section 3.7). Emissions of carbonaceous 

aerosols from intermittent fires and volcanic activity can further complicate regional climate 

forcing estimates (US EPA, 2009a, sections 9.3.7 and 9.3.8). Individual components of aerosols 

may either be positive or negative climate forcers. Airborne PM components may be directly 

emitted or undergo a variety of physical and chemical interactions and transformations. These 

result in changes in particle size, structure and composition which alter aerosol reflective 

properties. Aerosols can grow in size in the atmosphere because ambient water vapor condenses 

on individual particles, a phenomenon known as hygroscopic growth (US EPA, 2009a, section 

 2

 Given the reasons discussed above, this conclusion would apply for both the secondary (welfare based) and the 

primary (health based) standards. 
 

 

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9.3.6.2). Atmospheric lifetimes of individual aerosol components vary greatly confounding 

tracking source receptor relationships. 

Improved representation of aerosols in climate models is essential to more accurately 

predict the role of PM in climate forcing (US EPA, 2009a, section 9.3.6.7). The influence of 

aerosols on climate is not yet adequately taken into account in computer predictions although 

considerable progress in being made in this area. For example, PM components 

underrepresented or missing from many models include nitrate aerosols and anthropogenic 

secondary aerosols (US EPA, 2009a, section 9.3.6.7). The modeling of aerosol indirect effects 

and absorption is difficult due to the high level of uncertainty associated with these climate 

factors. 

The interaction of PM with clouds remains a large source of uncertainty in climate 

estimates. The interactions of aerosols with clouds and linkages between clouds and the overall 

climate system are complex and limit the feasibility of conducting quantitative analysis for the

purpose of establishing a secondary PM standard based on welfare effects on climate processes. 

There are uncertainties associated with the potential effects of the alternative standards 

for visibility discussed in Chapter 4 on regional radiative forcing and climate. A secondary 

standard for visibility based on light extinction would result in reduced emissions that affect PM 

in areas where monitoring shows exceedance of the standard. The extinction budget work 

conducted for the UFVA (Figure 3-13, U.S. EPA, 2010b) and second draft PA (US EPA, 2010f, 

Appendix B) indicates that most of the current visibility impact contributions on worst days 

comes from light scattering particles (e.g., nitrates, sulfates) that are negative climate forcers, and 

a smaller portion comes from absorbing aerosols (e.g., black carbon) that are positive climate 

forcers. The relative proportions of scattering and absorbing particles vary by location and some 

major contributing emission sources contribute to both scattering and absorbing PM, so it is 

unclear how the ratio of scattering to absorption might change in response to a secondary 

standard for visibility affects. However, since the prevailing mixture of aerosol is thought to 

have a net cooling effect on regional climate, reducing PM and light scattering aerosols could 

lead to increased radiative forcing and regional climate warming while having a beneficial effect 

on visibility. 

5.3 ECOLOGICAL EFFECTS 

5.3.1 Scope 

Information on what is currently known about ecological effects of PM is summarized in 

Chapter 9 of the ISA (US EPA, 2009a). Four main categories of ecological effects are identified 

in the ISA: direct effects, effects of PM-altered radiative flux, indirect effects of trace metals and 

indirect effects of organics. Exposure to PM for direct effects occur via deposition (e.g. wet, dry 
 

 

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or occult) to vegetation surfaces, while indirect effects occur via deposition to ecosystem soils or 

surface waters where the deposited constituents of PM then interacts with biological organisms. 

Both fine and coarse-mode particles may affect plants and other organisms; however, PM size 

classes do not necessarily relate to ecological effects (U.S. EPA, 1996). More often the chemical 

constituents drive the ecosystem response to PM (Grantz et al., 2003). The trace metal 

constituents of PM considered in the ecological effects section of the ISA are cadmium (Cd), 

copper (Cu), chromium (Cr), mercury (Hg), nickel (Ni) and zinc (Zn). Ecological effects of lead 

(Pb) in particulate form are covered in the Air Quality Criteria Document for Lead (US EPA, 

2006). The organics included in the ecological effects section of the ISA are persistent organic 

pollutants (POPs), polyaromatic hydrocarbons (PAHs) and polybromiated diphenyl ethers 

(PBDEs). 

Ecological effects of PM include direct effects to metabolic processes of plant foliage; 

contribution to total metal loading resulting in alteration of soil biogeochemistry and 

microbiology, plant and animal growth and reproduction; and contribution to total organics 

loading resulting in bioaccumulation and biomagnification across trophic levels. It is important 

to emphasize that the metal and organic constituents of PM contribute to total metal and organic 

loads in ecosystems. 

The ISA states that overall, ecological evidence is sufficient to conclude that a causal 

relationship is likely to exist between deposition of PM and a variety of effects on individual 

organisms and ecosystems based on information from the previous review and limited new 

findings in this review (US EPA, 2009a, sections 2.5.3 and 9.4.7). However the ISA also finds, 

in many cases, it is difficult to characterize the nature and magnitude of effects and to quantify 

relationships between ambient concentrations of PM and ecosystem response due to significant 

data gaps and uncertainties as well as considerable variability that exists in the components of 

PM and their various ecological effects. 

Ecological effects of PM must then be evaluated to determine if they are known or 

anticipated to have an adverse impact on public welfare. Characterizing a known or anticipated 

adverse effect to public welfare is an important component of developing any secondary 

NAAQS. The most recent secondary NAAQS reviews have assessed changes in ecosystem 

structure or processes using a weight-of-evidence approach that uses both quantitative and 

qualitative data. For example, the 2008 ozone (O3) final rule and 2010 O3 proposal conclude that 

a determination of what constitutes an “adverse” welfare effect in the context of secondary 

NAAQS review can appropriately occur by considering effects at higher ecological levels 

(populations, communities, ecosystems) as supported by recent literature. In the 2008 

rulemaking and current ozone proposal, the interpretation of what constitutes an adverse effect 

on vegetation can vary depending on the location and intended use of the plant. The degree to 
 

 

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which O3-related effects are considered adverse depends on the intended use of the vegetation 

and its significance to public welfare (73 FR 16496, March 27, 2008; 75 FR 2938, January 19, 

2010). Therefore, effects (e.g. biomass loss, foliar injury) may be judged to have a different 

degree of impact on public welfare depending, for example, on whether that effect occurs in a 

Class I area, a city park, commercial cropland or private land. 

A paradigm useful in evaluating ecological adversity is the concept of ecosystem 

services. Ecosystem services identify the varied and numerous ways that ecosystems are 

important to human welfare. Ecosystems provide many goods and services that are of vital 

importance for the functioning of the biosphere and provide the basis for the delivery of tangible 

benefits to human society. An EPA initiative to consider how ecosystem structure and function 

can be interpreted through an ecosystem services approach has resulted in the inclusion of 

ecosystem services in the NOx/SOx REA (US EPA, 2009h). The Millennium Ecosystem 

Assessment (MEA) defines these to include supporting, provisioning, regulating and cultural 

services (Hassan et al., 2005): 

 Supporting services are necessary for the production of all other ecosystem services. 

Some examples include biomass production, production of atmospheric O2, soil 

formation and retention, nutrient cycling, water cycling, and provisioning of habitat. 

Biodiversity is a supporting service that is increasingly recognized to sustain many of 

the goods and services that humans enjoy from ecosystems. These provide a basis for 

three higher-level categories of services. 

 Provisioning services, such as products (Gitay et al., 2001) i.e., food (including game, 

roots, seeds, nuts, and other fruit, spices, fodder), fiber (including wood, textiles), and 

medicinal and cosmetic products (including aromatic plants, pigments). 

 Regulating services that are of paramount importance for human society such as (a) C 

sequestration, (b) climate and water regulation, (c) protection from natural hazards 

such as floods, avalanches, or rock-fall, (d) water and air purification, and (e) disease 

and pest regulation. 

 Cultural services that satisfy human spiritual and aesthetic appreciation of ecosystems 

and their components. 

An important consideration in evaluating biologically adverse effects of PM and linkages 

to ecosystem services is that many of the MEA categories overlap and any one pollutant may 

impact multiple services. For example, deposited PM may alter the composition of soilassociated microbial communities, which may affect supporting services such as nutrient 

cycling. Changes in available soil nutrients could result in alterations to provisioning services 

such as timber yield and regulating services such as climate regulation. If enough information is 
 

 

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available, these alterations can be quantified based upon economic approaches for estimating the 

value of ecosystem services. Valuation may be important from a policy perspective because it 

can be used to compare the benefits of altering versus maintaining an ecosystem. Knowledge 

about the relationships linking ambient concentrations and ecosystem services can be used to 

inform a policy judgment on a known or anticipated adverse public welfare effect. 

This policy assessment seeks to build upon and focus this body of science using the 

concept of ecosystem services to qualitatively evaluate linkages between biologically adverse 

effects and particulate deposition. This approach is similar to that taken in the NOx/SOx REA in 

which the relationship between air quality indicators, deposition of N and S, ecologically 

relevant indicators and effects on sensitive receptors are linked to changes in ecosystem structure 

and services (US EPA, 2009h). This approach considers the benefits received from the resources 

and processes that are supplied by ecosystems. Ecosystem components (e.g. plants, soils, water, 

wildlife) are impacted by PM air pollution, which may alter the services provided by the 

ecosystems in question. The goals of this policy assessment are: (1) to identify ecological effects 

associated with PM deposition that can be linked to ecosystem services and (2) to qualitatively 

evaluate ecological endpoints when possible. Keeping these goals and guidelines in mind, 

limited new data on PM effects on plants, soil and nutrient cycling, wildlife and water are 

evaluated in the context of ecosystem services to qualitatively evaluate linkages between 

biologically adverse effects and particulate deposition for the purpose of evaluating the adequacy 

of the current standard. 

5.3.2 Adequacy of the Current Standards 

In considering the adequacy of the suite of secondary standards, staff addresses the 

following overarching question: 

Does available scientific information, as reflected in the ISA support or call into question 

the adequacy of the protection afforded by the current suite of secondary PM standards for 

vegetation and ecosystems from the effects of deposited particulate metals and organics? 

To inform the answer to this overarching question, staff has posed specific questions to 

aid in assessing the available scientific evidence as related to ecosystem effects attributed to PM 

deposition as presented in the ISA (US EPA, 2009a). 

 To what extent has key scientific evidence become available to improve our 

understanding of the nature and magnitude of ecosystem responses, the variability 

associated with these responses, and the impact of PM on ecosystem services? 

Key scientific evidence regarding PM effects on plants, soil and nutrient cycling, wildlife 

and water available since the last review is summarized below to evaluate how this information 

has improved our understanding of ecosystem responses to PM. 
 

 

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Plants

As primary producers, plants play a pivotal role in energy flow through ecosystems. 

Ecosystem services derived from plants include all of the categories (supporting, provisioning, 

regulating, cultural) identified in the MEA (Hassan et al., 2005). Vegetation supports other 

ecosystem processes by cycling nutrients through food webs and serving as a source of organic 

material for soil formation and enrichment. Trees and plants provide food, wood, fiber, and fuel 

for human consumption. Flora help to regulate climate by sequestering CO2, control flooding by 

stabilizing soils and cycling water via uptake and evapotranspiration. Plants are significant in 

aesthetic, spiritual and recreational aspects of human interactions. 

Particulate matter can adversely impact plants and ecosystem services provided by plants 

by deposition to vegetative surfaces (US EPA, 2009a, section 9.4.3). Particulates deposited on 

the surfaces of leaves and needles can block light, altering the radiation received by the plant. 

PM deposition can obstruct stomata limiting gas exchange, damage leaf cuticles and increase 

plant temperatures. This level of PM accumulation is typically observed near sources of heavy 

deposition such as smelters and mining operations (US EPA, 2009a, section 9.4.3). Plants 

growing on roadsides exhibit impact damage from near-road PM deposition, having higher levels 

of organics and heavy metals, and accumulate salt from road de-icing during winter months (US 

EPA, 2009a, sections 9.4.3.1 and 9.4.5.7). 

In addition to damage to plant surfaces, deposited PM can be taken up by plants from soil 

or foliage. The ability of vegetation to take up heavy metals and organics is dependent upon the 

amount, solubility and chemical composition of the deposited PM. Uptake of PM by plants from 

soils and vegetative surfaces can disrupt photosynthesis, alter pigments and mineral content, 

reduce plant vigor, decrease frost hardiness and impair root development. The ISA indicates that 

there are little or no effects on foliar processes at ambient levels of PM (US EPA, 2009a, 

sections 9.4.3 and 9.4.7) however, damage due to atmospheric pollution can occur near pointsources or under conditions where plants are subjected to multiple stressors. 

Though all heavy metals can be directly toxic at sufficiently high concentrations, only 

Cu, Ni, and Zn have been documented as being frequently toxic to plants (U.S. EPA, 2004), 

while toxicity due to Cd, Co, and Pb has been observed less frequently (Smith, 1990; US EPA 

2009a, section 9.4.5.3). In general, plant growth is negatively correlated with trace metal and 

heavy metal concentration in soils and plant tissue (Audet and Charest, 2007). Trace metals, 

particularly heavy metals, can influence forest growth. Growth suppression of foliar microflora 

has been shown to result from Fe, Al, and Zn. These three metals can also inhibit fungal spore 

formation, as can Cd, Cr, Mg, and Ni (see Smith, 1990). Metals cause stress and decreased 

photosynthesis (Kucera et al., 2008) and disrupt numerous enzymes and metabolic pathways 

(Strydom et al., 2006). Excessive concentrations of metals result in phytotoxicity through: (i) 
 

 

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changes in the permeability of the cell membrane; (ii) reactions of sulfydryl (-SH) groups with 

cations; (iii) affinity for reacting with phosphate groups and active groups of ADP or ATP; and 

(iv) replacement of essential ions (Patra et al., 2004). 

New information since the last review provides additional evidence of plant uptake of 

organics (US EPA, 2009a, section 9.4.6). An area of active study is the impact of PAHs on 

provisioning ecosystem services due to the potential for human and other animal exposure via 

food consumption (US EPA, 2009a, section 9.4.6 page 9-190). The uptake of PAHs depends on 

the plant species, site of deposition, physical and chemical properties of the organic compound 

and prevailing environmental conditions. It has been established that most bioaccumulation of 

PAHs by plants occurs via leaf uptake, and to a lesser extent, through roots. Differences 

between species in uptake of PAHs confound attempts to quantify impacts to ecosystem 

provisioning services. For example, zucchini (Cucurbita pepo) accumulated significantly more 

PAHs than related plant species (Parrish et al., 2006). 

Plants as ecosystem regulators can serve as passive monitors of pollution (US EPA, 

2009a, section 9.4.2.3). Lichens and mosses are sensitive to pollutants associated with PM and 

have been used with limited success to show spatial and temporal patterns of atmospheric 

deposition of metals (US EPA, 2009a, section 9.4.2.3). For example, the presence or absence of 

a specific species of lichen can be used as a bioindicator of metal or organics contamination. 

PBDEs detected in moss and lichens in Antarctica indicate long-range transport of PM 

components (Yogui and Sericano 2008). In the U.S. Blue Ridge Mountains, a study linked metal 

concentrations in mosses to elevation and tree canopy species at some sites but not with 

concentrations of metals in the O horizon of soil (Schilling, 2002). A limitation to employing 

mosses and lichens to detect for the presence of air pollutants is the difference in uptake 

efficiencies of metals between species. The European Moss Biomonitoring Network has been 

shown to be useful in Europe for estimating general trends in metal concentrations and 

identification of some sources of trace contaminants. However, quantification of ecological 

effects is not possible due to the variability of species responses (US EPA, 2009a, section 

9.4.2.3). 

A potentially important regulating ecosystem service of plants is their capacity to 

sequester contaminants (US EPA, 2009a, section 9.4.5.3). Ongoing research on the application 

of plants to environmental remediation efforts are yielding some success in removing heavy 

metals and organics from contaminated sites (phytoremediation) with tolerant plants such as the 

willow tree (Salix spp.) and members of the family Brassicaceae (US EPA, 2009a, section 

9.4.5.3). Tree canopies can be used in urban locations to capture particulates and improve air 

quality (Freer-Smith et al., 2004). Plant foliage is a sink for Hg and other metals and this 

regulating ecosystem service may be impacted by atmospheric deposition of trace metals. 
 

 

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An ecological endpoint (phytochelatin concentration) associated with presence of metals 

in the environment has been correlated with the ecological effect of tree mortality (Grantz et al., 

2003). Metal stress may be contributing to tree injury and forest decline in the Northeastern U.S. 

where red spruce populations are declining with increasing elevation. Quantitative assessment of 

PM damage to forests potentially could be conducted by overlaying PM sampling data and 

elevated phytochelatin levels. However, limited data on phytochelatin levels in other species 

currently hinders use of this peptide as a general biomarker for PM. 

The presence of PM in the atmosphere affects ambient radiation as discussed in the ISA 

which can impact the amount of sunlight received by plants (US EPA, 2009a, section 9.4.4). 

Atmospheric PM can change the radiation reaching leaf surfaces through attenuation and by 

converting direct radiation to diffuse radiation. Diffuse radiation is more uniformly distributed 

in a tree canopy, allowing radiation to reach lower leaves. The net effect of PM on 

photosynthesis depends on the reduction of photosynthetically active radiation (PAR) and the 

increase in the diffuse fraction of PAR. Decreases in crop yields (provisioning ecosystem 

service) have been attributed to regional scale air pollution, however, global models suggest that 

the diffuse light fraction of PAR can increase growth (US EPA, 2009a, section 9.4.4). 

Soil and Nutrient Cycling

Many of the major indirect plant responses to PM deposition are chiefly soil-mediated 

and depend on the chemical composition of individual components of deposited PM. Major 

ecosystem services impacted by PM deposition to soils include support services such as nutrient 

cycling, products such as crops and regulating flooding and water quality. Upon entering the soil 

environment, PM pollutants can alter ecological processes of energy flow and nutrient cycling, 

inhibit nutrient uptake to plants, change microbial community structure and, affect biodiversity. 

Accumulation of heavy metals in soils depends on factors such as local soil characteristics, 

geologic origin of parent soils, and metal bioavailability. It can be difficult to assess the extent 

to which observed heavy metal concentrations in soil are of anthropogenic origin (US EPA, 

2009a, section 9.4.5.1). Trace element concentrations are higher in some soils that are remote 

from air pollution sources due to parent material and local geomorphology. 

Heavy metals such as Zn, Cu, and Cd and some pesticides can interfere with 

microorganisms that are responsible for decomposition of soil litter, an important regulating 

ecosystem service that serves as a source of soil nutrients (US EPA, 2009a, sections 9.4.5.1 and 

9.4.5.2). Surface litter decomposition is reduced in soils having high metal concentrations. Soil 

communities have associated bacteria, fungi, and invertebrates that are essential to soil nutrient 

cycling processes. Changes to the relative species abundance and community composition can 

be quantified to measure impacts of deposited PM to soil biota. A mutualistic relationship exists 
 

 

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in the rhizophere (plant root zone) between plant roots, fungi, and microbes. Fungi in 

association with plant roots form mycorrhizae that are essential for nutrient uptake by plants. 

The role of mychorrizal fungi in plant uptake of metals from soils and effects of deposited PM 

on soil microbes is discussed in section 9.4.5.2 of the ISA. 

Wildlife

Animals play a significant role in ecosystem function including nutrient cycling and crop 

production (supporting ecosystem service), and as a source of food (provisioning ecosystem 

service). Cultural ecosystem services provided by wildlife include bird and animal watching, 

recreational hunting and fishing. Impacts on these services are dependent upon the 

bioavailability of deposited metals and organics and their respective toxicities to ecosystem 

receptors. Pathways of PM exposure to fauna include ingestion, absorption and trophic transfer. 

Bioindicator species (known as sentinel organisms) can provide evidence of contamination due 

to atmospheric pollutants. Use of sentinel species can be of particular value because chemical 

constituents of deposited PM are difficult to characterize and have varying bioavailability (US 

EPA, 2009a, section 9.4.5.5). Snails readily bioaccumulate contaminants, such as PAHs and 

trace metals. These organisms have been deployed as biomonitors for urban pollution and have 

quantifiable biomarkers of exposure including growth inhibition, impairment of reproduction, 

peroxidomal proliferation and induction of metal detoxifying proteins (metallothioneins) 

(Gomet-de Vaufleury, 2002; Regoli, 2006). Earthworms have also been used as sensitive 

indicators of soil metal contamination. 

Evidence of deposited PM effects on animals is limited (US EPA, 2009a, section 9.4.5.5). 

Trophic transfer of pollutants of atmospheric origin has been demonstrated in limited studies. 

PM may also be transferred between aquatic and terrestrial compartments. There is limited 

evidence for biomagnifications of heavy metals up the food chain except for Hg which is well 

known to move readily through environmental compartments (US EPA, 2009a, section 9.4.5.6). 

Bioconcentration of POPs and PBDEs in the Arctic and deep-water oceanic food webs indicates 

the global transport of particle-associated organics (US EPA, 2009a, section 9.4.6). Salmon 

migrations are contributing to metal accumulation in inland aquatic systems, potentially 

impacting the provisioning and cultural ecosystem service of fishing (US EPA, 2009a, section 

9.4.6). Stable isotope analysis can be applied to establish linkages between PM exposure and 

impacts to food webs, however, the use of this evaluation tool is limited for this ecological 

endpoint due to the complexity of most trophic interactions (US EPA 2009a, section 9.4.5.6). 

Foraging cattle have been used to assess atmospheric deposition and subsequent bioaccumulation 

of Hg and trace metals and their impacts on provisioning services (US EPA, 2009a, section 

9.4.2.3). 
 

 

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Water

New limited information on impacts of deposited PM on receiving water bodies indicate 

that the ecosystem services of primary production, provision of fresh water, regulation of climate 

and floods, recreational fishing and water purification are adversely impacted by atmospheric 

inputs of metals and organics (US EPA, 2009a, sections 9.4.2.3 and 9.4.5.4). Deposition of PM 

to surfaces in urban settings increases the metal and organic component of storm water runoff 

(US EPA, 2009a, sections 9.4.2.3). This atmospherically-associated pollutant burden can then be 

toxic to aquatic biota. 

Atmospheric deposition can be the primary source of some organics and metals to 

watersheds. The contribution of atmospherically deposited PAHs to aquatic food webs was 

demonstrated in high elevation mountain lakes with no other anthropogenic contaminant sources 

(US EPA, 2009a, section 9.4.6). Metals associated with PM deposition limit phytoplankton 

growth, impacting aquatic trophic structure. Long-range atmospheric transport of 47 pesticides 

and degradation products to the snowpack in seven national parks in the Western U.S. was 

recently quantified indicating PM-associated contaminant inputs to receiving waters during 

spring snowmelt (Hageman et al., 2006). 

 What new techniques are available to improve our understanding of ecosystem effects 

associated with metal and organic components of PM? 

The recently completed Western Airborne Contaminants Assessment Project (WACAP) 

is the most comprehensive database on contaminant transport and PM depositional effects on 

sensitive ecosystems in the U.S. In this project, the transport, fate, and ecological impacts of 

anthropogenic contaminants from atmospheric sources were assessed from 2002 to 2007 in seven 

ecosystem components (air, snow, water, sediment, lichen, conifer needles and fish) in eight core 

national parks (Landers et al., 2008). The goals of the study were to identify where the 

pollutants were accumulating, identify ecological indicators for those pollutants causing 

ecological harm, and to determine the source of the air masses most likely to have transported 

the contaminants to the parks (US EPA, 2009a, section 9.4.6). Collected data were analyzed to 

identify probable local, regional and/or global sources of deposited PM components and their 

concurrent effects on ecological receptors. The study concluded that bioaccumulation of semivolatile organic compounds (VOCs) was observed throughout park ecosystems (Landers et al., 

2008). Findings from this study included the observation of an elevational gradient in PM 

deposition with greater accumulation at higher altitude areas of the parks. Furthermore, specific 

ecological indicators were identified in the WACAP that can be useful in assessing 

contamination on larger spatial scales. For example, quantification of concentrations of selected 
 

 

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pesticides in second-year conifer needles served as a method for regional-scale comparison of 

pollutant distribution (Landers et al., 2008). 

In the WACAP study, bioaccumulation and biomagnification of airborne contaminants 

were demonstrated on a regional scale in remote ecosystems in the Western United States. 

Contaminants were shown to accumulate geographically based on proximity to individual 

sources or source areas, primarily agriculture and industry (Landers et al., 2008). This finding 

was counter to the original working hypothesis that most of the contaminants found in western 

parks would originate from Eastern Europe and Asia (Landers et al., 2008 p 6-8). The WACAP 

study represents an experimental design in which ecological effects could be correlated to 

ambient pollutant levels on a regional scale. Although this assessment focuses on chemical 

species that are components of PM, it does not specifically assess the effects of particulates 

versus gas-phase forms; therefore, in most cases it is difficult to apply the results to this 

assessment based on particulate concentration and size fraction (US EPA, 2009a, section 9.4.6). 

There is a need for ecological modeling of PM components in different environmental 

compartments to further elucidate links between PM and ecological indicators. 

Europe and other countries are using the critical load approach to assess pollutant effects 

at the level of the ecosystem. This type of assessment requires site-specific data and information 

on individual species responses to PM. In respect to trace metals and organics, there are 

insufficient data for the vast majority of U.S. ecosystems to calculate critical loads. However, a 

methodology is being presented in the NOx/SOx Secondary REA (US EPA, 2009g) to calculate 

atmospheric concentrations from deposition that may be applicable to other environmental 

contaminants. 

 Is there currently available information on ambient levels of PM that cause adverse 

effects on ecosystem components? 

As reviewed above, there is considerable data on impacts of PM on ecological receptors, 

but few studies that link ambient PM levels to observed effect. This is due, in part, to the nature, 

deposition, transport and fate of PM in ecosystems. PM is not a single pollutant, but a 

heterogeneous mixture of particles differing in size, origin and chemical composition (US EPA, 

2009a, section 9.4.1). The heterogeneity of PM exists not only within individual particles or 

samples from individual sites, but to even a greater extent, between samples from different sites. 

Since vegetation and other ecosystem components are affected more by particulate chemistry 

than size fraction, exposure to a given mass concentration of airborne PM may lead to widely 

differing plant or ecosystem responses, depending on the particular mix of deposited particles. 

Many of the PM components bioaccumulate over time in organisms or plants making 

correlations to ambient levels of PM difficult. For example, in the WACAP study, SOC 
 

 

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accumulation in vegetation and air showed different patterns, possibly because each medium 

absorbs different types of SOCs with varying efficiencies (Landers et al., 2008). 

Bioindicator organisms demonstrated biological effects including growth inhibition, 

metallothionein induction and reproductive impairment when exposed to complex mixtures of 

ambient air pollutants (US EPA, 2009a, section 9.4.5.5). Other studies quantify uptake of metals 

and organics by plants or animals. However, due to the difficulty in correlating individual PM 

components to a specific physiological response, these studies are limited. Furthermore, there 

may be differences in uptake between species such as differing responses to metal uptake 

observed in mosses and lichens (US EPA 2009a, section 9.4.2.3). PM may also biomagnify 

across trophic levels confounding efforts to link atmospheric concentrations to physiological 

endpoints (US EPA, 2009a, section 9.4.5.6). 

Evidence of PM effects that are linked to a specific ecological endpoint can be observed 

when ambient levels are exceeded. Most direct ecosystem effects associated with particulate 

pollution occur in severely polluted areas near industrial point sources (quarries, cement kilns, 

metal smelting) (US EPA, 2009a, sections 9.4.3 and 9.4.5.7). Extensive research on biota near 

point sources provide some of the best evidence of ecosystem function impacts and demonstrates 

that deposited PM has the potential to alter species composition over long time scales. 

Ecological field studies conducted in proximity to Cu-Ni smelter in Harjavalta, Finland indicated 

ecological structure and community composition are altered in response to PM and these effects 

decrease with increasing distance from the point source (US EPA, 2009a, section 9.4.5.7). The 

ISA indicates at 4 km distance, species composition of vegetation, insects, birds, and soil 

microbiota changed, and within 1 km only the most resistant organisms were surviving (US 

EPA, 2009a, section 9.4.5.7). Heavy metal concentrations were quantified in understory plant 

species growing at varying distance from the Harjavalta smelter (Salemaa et al., 2004). Heavy 

metal concentrations were highest in bryophytes, followed by lichens and were lowest in 

vascular plants. At the Harjavalta smelter there are clear links between PM deposition levels, 

ecological endpoints and compromised ecosystem structure. However, these conditions are not 

reflective of ambient concentrations of PM in the majority of US ecosystems (US EPA, 2009a, 

section 9.4.7). 

5.3.3 Staff Conclusions 

 A number of significant environmental effects that either have already occurred or are 

currently occurring are linked to deposition of chemical constituents found in ambient 

PM. 

 Ecosystem services can be adversely impacted by PM in the environment, including 

supporting, provisioning, regulating and cultural services. 
 

 

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 The lack of sufficient information to relate specific ambient concentrations of 

particulate metals and organics to a degree of impairment of a specific ecological 

endpoint hinders our ability to identify a range of appropriate indicators, levels, forms 

and averaging times of a distinct secondary standard to protect against associated 

effects. 

 Data from regionally-based ecological studies can be used to establish probable local, 

regional and/or global sources of deposited PM components and their concurrent 

effects on ecological receptors. 

Taking into consideration the responses to specific questions regarding the adequacy of 

the current secondary PM standards for ecological effects, we revisit the overarching question: 

“does available scientific information, as reflected in the ISA, support or call into question the 

adequacy of the protection for ecosystems afforded by the current suite of secondary PM 

standards?” Staff concludes that the available information is insufficient to assess the adequacy 

of the protection for ecosystems afforded by the current suite of PM secondary standards. 

Ecosystem effects linked to PM are difficult to determine because the changes may not be 

observed until pollutant deposition has occurred for many decades. Because the high levels 

necessary to cause injury occur only near a few limited point sources and/or on a very local 

scale, protection against these effects alone may not provide sufficient basis for considering a 

separate secondary NAAQS based on the ecological effects of particulate metals and organics. 

Data on ecological responses clearly linked with atmospheric PM is not abundant enough to 

perform a quantitative analysis although the WACAP study may represent an opportunity for 

quantification at a regional scale. At this time, we conclude that available evidence is not 

sufficient for establishing a distinct national standard for ambient PM based on ecosystem effects 

of particulates not addressed in the NOx/SOx secondary review (e.g. metals, organics). 

Staff considered the appropriateness of continuing to use the PM2.5 and PM10 size 

fractions as the indicators for protection of ecological effects of PM. The chemical constitution 

of individual particles can be strongly correlated with size, and the relationship between particle 

size and particle composition can be quite complex, making it difficult in most cases to use 

particle size as a surrogate for chemistry. At this time it remains to be determined as to what 

extent PM secondary standards focused on a given size fraction would result in reductions of the 

ecologically relevant constituents of PM for any given area. Nonetheless, in the absence of 

information that provides a basis for specific standards in terms of particle composition, 

observations continue to support retaining an appropriate degree of control on both fine and 

coarse particles to help address effects to ecosystems and ecosystem components associated with 

PM. 
 

 

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5.3.4 Key Uncertainties and Areas for Future Research and Data Collection 

The above discussions identify linkages between ecological effects of deposited PM and 

potential impacts to ecosystem services. Unfortunately, our ability to relate ambient 

concentrations of PM to ecosystem response is hampered by a number of significant data gaps 

and uncertainties. These limitations include the presence of multiple ecological stressors 

confounding attempts to link specific ecosystem responses to PM deposition. These stressors 

can be anthropogenic (e.g. habitat destruction, eutrophication, other pollutants) or natural (e.g. 

drought, fire, disease). Deposited PM interacts with other stressors to affect ecosystem patterns 

and processes. Furthermore, the environmental effects of deposited PM are decoupled in space 

and time from the point of emission confounding efforts to identify ecological perturbations 

attributed to PM deposition. 

A second source of uncertainty lies in predicting the amount of PM deposited to sensitive 

receptors from measured concentrations of PM in the ambient air. This makes it difficult to 

relate a given air concentration to a receptor response, an important factor in being able to set a 

national ambient air quality standard. A multitude of factors such as the mode of deposition 

(wet, dry and occult), wind speed, surface roughness or stickiness, elevation, particle 

characteristics (e.g. size, shape, chemical composition), and relative humidity exert varying 

degrees of influence on the deposition velocities for different PM components in any point in 

time. Composition of ambient PM varies in time and space and the particulate mixture may have 

synergistic, antagonistic or additive effects on ecological receptors depending upon the chemical 

species present. Furthermore, presence of co-occurring pollutants make it difficult to attribute 

observed effects to ecological receptors to PM alone or one component of deposited PM. 

Third, each ecosystem has developed within a context framed by the topography, 

underlying bedrock, soils, climate, meteorology, hydrologic regime, natural and land use history, 

and species composition that make it unique from all others. Sensitivity of ecosystem response 

is highly variable in space and time. Because of this variety and lack of sufficient baseline data 

on each of these features for most ecosystems, it is currently not possible to extrapolate with 

confidence any effect from one ecosystem to another. Further research is needed to decrease the 

uncertainties associated with ambient PM effects on ecosystems and ecosystem components. 

5.4 MATERIALS 

5.4.1 Scope 

Welfare effects on materials associated with deposition of PM include both physical 

damage (materials damage effects) and impaired aesthetic qualities (soiling effects). Because the 

effects of PM are exacerbated by the presence of acidic gases and can be additive or synergistic 

due to the complex mixture of pollutants in the air and surface characteristics of the material, this 
 

 

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discussion will also include those particles and gases that are associated with the presence of 

ambient NOx and SOx, as well as NH3 and NHX for completeness. Building upon the 

information presented in the last Staff Paper (US EPA, 2005), and including the limited new 

information presented in Chapter 9 of the PM ISA (US EPA, 2009a) and Annex E. Effects of 

NOy, NHx, and SOx on Structures and Materials of the Integrated Science Assessment for 

Oxides of Nitrogen and Sulfur-Ecological Criteria (NOx/SOx ISA) (US EPA, 2008c) the 

following sections consider the policy-relevant aspects of physical damage and aesthetic soiling 

effects of PM on materials including metal and stone. 

The ISA concludes that evidence is sufficient to support a causal relationship between 

PM and effects on materials (US EPA, 2009a, sections 2.5.4 and 9.5.4). The deposition of PM 

can physically affect materials, adding to the effects of natural weathering processes, by 

potentially promoting or accelerating the corrosion of metals, by degrading paints and by 

deteriorating building materials such as stone, concrete and marble (US EPA, 2009a, section 

9.5). Particles contribute to these physical effects because of their electrolytic, hygroscopic and 

acidic properties, and their ability to sorb corrosive gases (principally SO2). In addition, the 

deposition of ambient PM can reduce the aesthetic appeal of buildings and objects through 

soiling. Particles consisting primarily of carbonaceous compounds cause soiling of commonly 

used building materials and culturally important items such as statues and works of art. Soiling 

is the deposition of particles on surfaces by impingement, and the accumulation of particles on 

the surface of an exposed material results in degradation of its appearance (US EPA, 2009a, 

section 9.5). Soiling can be remedied by cleaning or washing, and depending on the soiled 

material, repainting. 

5.4.2 Adequacy of the Current Standards 

In considering the adequacy of the suite of secondary standards, staff addresses the 

following overarching question: 

Does available scientific information, as reflected in the ISA support or call into question 

the adequacy of the protection for materials afforded by the current suite of secondary PM 

standards? 

To inform the answer to this overarching question, staff has posed a specific question to 

aid in assessing the available scientific evidence as related to materials damage and soiling 

attributed to PM deposition as presented in the ISA (US EPA, 2009a).

 What new evidence is available to improve our understanding of effects of PM on 

materials and linking ambient concentrations to materials damage? 

The majority of available new studies on materials effects of PM are from outside the 

U.S., however, they provide limited new data for consideration of the secondary standard. 
 

 

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Metal and stone are susceptible to damage by ambient PM. Considerable research has 

been conducted on the effects of air pollutants on metal surfaces due to the economic importance 

of these materials, especially steel, zinc, aluminum, and copper. Chapter 9 of the PM ISA and 

Annex E of the NOx/SOx ISA summarize the results of a number of studies on the corrosion of 

metals (US EPA, 2009a; US EPA, 2008c). Moisture is the single greatest factor promoting metal 

corrosion, however, deposited PM can have additive, antagonistic or synergistic effects. In 

general, SO2 is more corrosive than NOx although mixtures of NOx, SO2 and other particulate 

matter corrode some metals at a faster rate than either pollutant alone (US EPA, 2008c, Annex 

E.5.2). Information from both the PM ISA and NOx/SOx ISA suggest that the extent of damage 

to metals due to ambient PM is variable and dependent upon the type of metal, prevailing 

environmental conditions, rate of natural weathering and presence or absence of other pollutants. 

The PM ISA and NOx/SOx ISA summarize the results of a number of studies on PM and 

stone surfaces. While it is clear from the available information that gaseous air pollutants, in 

particular SO2, will promote the deterioration of some types of stones under specific conditions, 

carbonaceous particles (non-carbonate carbon) and particles containing metal oxides may help to 

promote the decay process. Studies on metal and stone summarized in the ISA do not show an 

association between particle size, chemical composition and frequency of repair. 

A limited number of new studies available on materials damage effects of PM since the 

last review consider the relationship between pollutants and biodeterioration of structures 

associated with microbial communities that colonize monuments and buildings (US EPA 2009a, 

section 9.5). Presence of air pollutants may synergistically enhance microbial deterioration 

processes. The role of heterotrophic bacteria, fungi and cyanobacteria in biodeterioration varied 

by local meterological conditions and pollutant components. In a comparative study of 

biodeterioration processes on monuments in Latin America, limestone deterioration at the Mayan 

site of Uxmal was enhanced by biosolubilization by metabolic acids from bacteria and fungi 

while destruction of the Cathedral of La Plata was attributed primarily to atmospheric pollutants 

(Herrera and Videla, 2004). 

PM deposition onto surfaces such as metal, glass, stone and paint can lead to soiling. 

Soiling results when PM accumulates on an object and alters the optical characteristics 

(appearance). The reflectivity of a surface may be changed or presence of particulates may alter 

light transmission. These effects can impact the aesthetic value of a structure or result in 

reversible or irreversible damage to statues, artwork and architecturally or culturally significant 

buildings. Due to soiling of building surfaces by PM, the frequency and duration of cleaning 

may be increased. Soiling affects the aesthetic appeal of painted surfaces. In addition to natural 

factors, exposure to PM may give painted surfaces a dirty appearance. Pigments in works of art 
 

 

5-28

can be degraded or discolored by atmospheric pollutants, especially sulfates (US EPA, 2008c, 

Annex E-15). 

Formation of black crusts due to carbonaceous compounds and buildup of microbial 

biofilms results in discoloration of surfaces. Black crust includes a carbonate component derived 

from building material and organic carbon (OC) and elemental carbon (EC). In limited new 

studies quantifying the OC and EC contribution to soiling by black crust, OC predominated over 

EC at almost all locations (Bonazza et al., 2005). Limited new studies suggest that traffic is the 

major source of carbon associated with black crust formation (Putaud et al., 2004) and that 

soiling of structures in Oxford, UK showed a relationship with traffic and NO2 concentrations 

(Viles and Gorbushina, 2003). These findings attempt to link atmospheric concentrations of PM 

to observed damage. However, no data on rates of damage are available and all studies were 

conducted outside of the U.S. 

5.4.3 Staff Conclusions 

Available evidence in regards to materials damage and soiling supports the following 

observations: 

 Materials damage and soiling that occur through natural weathering processes are 

enhanced by exposure to atmospheric pollutants, most notably SO2 and particulate 

sulfates. 

 While ambient particles play a role in the corrosion of metals and in the weathering of 

materials, no quantitative relationships between ambient particle concentrations and 

rates of damage have been established. 

 While soiling associated with fine and course particles can result in increased 

cleaning frequency and repainting of surfaces, no quantitative relationships between 

particle characteristics and the frequency of cleaning or repainting have been 

established. 

 Limited new data on the role of microbial colonizers in biodeterioration processes and 

contributions of black crust to soiling are not sufficient for quantitative analysis. 

 While several studies in the PM ISA and NOx/SOx ISA suggest that particles can 

promote corrosion of metals there remains insufficient evidence to relate corrosive 

effects to specific particulate levels or to establish a quantitative relationship between 

ambient PM and metal degradation. With respect to damage to calcareous stone, 

numerous studies suggest that wet or dry deposition of particles and dry deposition of 

gypsum particles can enhance natural weathering processes. 
 

 

5-29

Revisiting the overarching policy question as to whether the available scientific evidence 

supports or calls into question the adequacy of the protection for materials afforded by the 

current suite of secondary PM standards, we conclude that no new evidence in this review calls 

into question the adequacy of the protection for materials afforded by the current standard. PM 

effects on materials can play no quantitative role in considering whether any revisions of the 

secondary PM NAAQS are appropriate at this time. Nonetheless, in the absence of information 

that provides a basis for establishing a different level of control, observations continue to support 

retaining an appropriate degree of control on both fine and coarse particles to help address 

materials damage and soiling associated with PM. 

5.4.4 Key Uncertainties and Areas for Future Research and Data Collection 

Quantitative relationships are needed between particle size, concentration, chemical 

concentrations and frequency of repainting and repair. Deposition rates of airborne PM to 

surfaces would provide an indication of rate and degree of damage to surfaces. There is 

considerable uncertainty with regard to interaction of co-pollutants in regards to materials 

damage and soiling processes. 

 
 

 

5-30

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Parrish ZD; White JC; Isleyen M; Gent MPN; Iannucci-Berger W; Eitzer BD; Kelsey JW; Mattina MI. (2006). 

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genotoxic effects on plant systems and the development of genetic tolerance. Environ Exp Bot, 52: 199-

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ten Brink H; Torseth K; Wiedensohler A. (2004). A European aerosol phenomenology--2: chemical 

characteristics of particulate matter at kerbside, urban, rural and background sites in Europe. Atmos 

Environ, 38: 2579-2595. 

Regoli F; Gorbi S; Fattorini D; Tedesco S; Notti A; Machella N; Bocchetti R; Benedetti M; Piva F. (2006). Use of 

the land snail Helix aspersa sentinel organism for monitoring ecotoxicologic effects of urban pollution: An 

integrated approach. Comp Biochem Physiol A Mol Integr Physiol, 114:63-69. 

Salemaa M; Derome J; Helmisaari HS; Nieminen T; Vanha-Majamaa I. (2004). Element accumulation in boreal 

bryophytes,lichens and vascular plants exposed to heavy metal and sulfur deposition in Finland. Sci Total 

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Sato M; Hansen J; Koch D; Lucis A; Ruedy R; Dubovik O; Holben B; Chin M; Novakov T. (2003). Global 

atmospheric black carbon inferred from AAEONET. Presented at Proceedings of the National Academy of 

Science. 

Schilling JS; Lehman ME. (2002). Bioindication of atmospheric heavy metal deposition in the Southeastern US 

using the moss Thuidium delicatulum. Atmos Environ, 36: 1611-1618. 

Smith WH (1990). Forest nutrient cycling: toxic ions. In Air pollution and forests: interactions between air 

contaminants and forest ecosystems. New York, NY: Springer-Verlag. 

Strydom C; Robinson C; Pretorius E; Whitcutt JM; Marx J; Bornman MS (2006). The effect of selected metals on 

the central metabolic pathways in biology: A review. WaterSA, 32: 543-554. 

U.S. EPA (1996). Air Quality Criteria for Particulate Matter. U.S. Environmental Protection Agency. Research 

Triangle Park, NC. EPA/600/P-95/001aF-cF. November 2004. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_cd.html
 

 

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US EPA (2004). Air Quality Criteria for Particulate Matter. National Center for Environmental Assessment, Office 

of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; 

report no. EPA/600/P-99/002aF and EPA/600/P-99/002bF. October 2004. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_cd.html

US EPA (2005). Review of the National Ambient Air Quality Standards for Particulate Matter: Policy Assessment 

of Scientific and Technical Information, OAQPS Staff Paper. Research Triangle Park, NC 27711: Office 

of Air Quality Planning and Standards; report no. EPA EPA-452/R-05-005a. December 2005. Available 

at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html

U.S. EPA. (2006). Air Quality Criteria for Lead Final Report. U.S. Environmental Protection Agency, Washington, 

DC, EPA/600/R-05/144aF-bF, October 2006. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pb/s_pb_cr_cd.html 

US EPA (2008a). Integrated Review Plan for the National Ambient Air Quality Standards for Particulate Matter. 

National Center for Environmental Assessment and Office of Air Quality Planning and Standards, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. Report No. EPA 452/R-08-004. March 

2008. Available at: 

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US EPA (2008c). U.S. EPA. Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur Ecological 

Criteria (Final Report). U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-08/082F, 

December 2008. Available at: http://www.epa.gov/ttn/naaqs/standards/no2so2sec/cr_isi.html 

US EPA (2009a). U.S. EPA. Integrated Science Assessment for Particulate Matter (Final Report). U.S. 

Environmental Protection Agency, Washington, DC, EPA/600/R-08/139F, December 2009. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_isa.html 

US EPA (2009h) Risk and Exposure Assessment for Review of the Secondary National Ambient Air Quality 

Standards for Oxides of Nitrogen and Oxides of Sulfur (Final Report). US Environmental Protection 

Agency, Research Triangle Park, NC, EPA-452/R-09-008a. 

US EPA (2010b). Particulate Matter Urban-Focused Visibility Assessment – Final Report. Office of Air Quality 

Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA-452/R10-004. June 2010. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 

US EPA (2011) Policy Assessment for the Review of the Secondary National Ambient Air Quality Standards for 

Oxides of Nitrogen and Oxides of Sulfur. U.S. Environmental Protection Agency, Research Triangle Park, 

NC, EPA-452/R-11-005a,b. February 2011. Available at: 

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Viles HA; Gorbushina AA (2003). Soiling and microbial colonisation on urban roadside limestone: A three year 

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George Island, maritime Antarctica. Chemosphere, 73: 1589-1593. 
APPENDICES 

A. Clean Air Scientific Advisory Committee Letter on Second Draft Policy Assessment, 

September 10, 2010.................................................................................A-1 

B. Ambient PM Monitoring Networks...............................................................B-1 

C. Predicted Percent of Counties with Monitors Not Likely to Meet Current and Alternative 

Primary Annual and 24-hour PM2.5 Standards..................................................C-1 

D. Predicted Percent of Counties with Monitors Not Likely to Meet Current and Alternative 

Primary 24-hour PM10 Standards.................................................................D-1 

E. Information Regarding a 1-hour PM2.5 Mass Indicator..........................................E-1 

F. Two Simplified Approaches to Calculate Hourly PM2.5 Light Extinction Values from 

Hourly PM2.5 Mass and Relative Humidity Data Plus 24-hour Mean PM2.5 Composition 

Data....................................................................................................F-1 

G. Calculated 24-hour Average PM2.5 Light Extinction and Adjusted Candidate Protection 

Levels.................................................................................................G-1 

H. Predicted Percent of Counties with Monitors Not Likely to Meet Current and Alternative 

Secondary PM2.5 Standards........................................................................H-1 
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A-1 

APPENDIX A 

Clean Air Scientific Advisory Committee (CASAC) Letter on 

Second Draft Policy Assessment 

 September 10, 2010 
 UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

WASHINGTON D.C. 20460 

 

OFFICE OF THE ADMINISTRATOR 

 SCIENCE ADVISORY BOARD 

September 10, 2010 

EPA-CASAC-10-015 

The Honorable Lisa P. Jackson 

Administrator 

U.S. Environmental Protection Agency 

1200 Pennsylvania Avenue, N.W. 

Washington, D.C. 20460 

Subject: CASAC Review of Policy Assessment for the Review of the PM NAAQS – Second 

External Review Draft (June 2010) 

Dear Administrator Jackson: 

The Clean Air Scientific Advisory Committee (CASAC) Particulate Matter (PM) Review 

Panel met on July 26 – 27, 2010 and on August 25, 2010 in a public teleconference to review the 

Policy Assessment for the Review of the PM NAAQS - Second External Review Draft (June 2010). 

This letter highlights CASAC’s main comments on this document, followed by consensus 

responses to the charge questions and comments of individual Panel members. 

This review of the Second Draft Policy Assessment completes the first cycle through the 

revised suite of NAAQS review documents and thus represents a major milestone. CASAC 

commends EPA staff for developing an ordered and transparent basis for decision-making 

throughout the NAAQS review process from the Integrated Science Assessment (ISA) to the 

Quantitative Health Risk Assessment and Urban Focused Visibility Assessment and then to the 

Policy Assessment. The Second Draft Policy Assessment was notably responsive to CASAC’s 

comments on the first draft. At CASAC’s request, the current draft sets out the underlying 

decision-making algorithms, greatly enhancing the transparency and readability of the document. 

EPA’s approach to reviewing the standard is explicitly articulated throughout the document, as are 

the key decision-making points and the evidence considered. CASAC’s major concerns, as 

expressed in our letter of May 17, 2010, have been addressed. EPA staff are to be congratulated 

for building on CASAC’s suggestions and developing an assessment that provides a scientifically 

sound basis for making decisions on the primary and secondary PM standards. 

Primary Standards for Fine Particles 

CASAC supports the EPA staff’s conclusion in the Second Draft Policy Assessment that 

“currently available information clearly calls into question the adequacy of the current standards”. 

For PM2.5, the current 24-hour primary standard is 35 μg/m3

 and the annual standard is 15 μg/m3. 

EPA staff also conclude that consideration should be given to alternative annual PM2.5 standard 

levels in the range of 13 – 11 μg/m3

, in conjunction with retaining the current 24-hour PM2.5 
standard level of 35 μg/m3

, and that consideration could also be given to an alternative 24-hour 

PM2.5 standard level of 30 μg/m3

 in conjunction with an annual standard level of 11μg/m3. 

CASAC concludes that the levels under consideration are supported by the epidemiological and 

toxicological evidence, as well as by the risk and air quality information compiled in the Integrated 

Science Assessment (December 2009), Quantitative Health Risk Assessment for Particular Matter

(June 2010) and summarized in the Second Draft Policy Assessment. Although there is increasing 

uncertainty at lower levels, there is no evidence of a threshold (i.e., a level below which there is no 

risk for adverse health effects). In addition, these combinations of annual/daily levels may not be 

adequately inclusive. It was not clear why, for example, a daily standard of 30 μg/m3 should only 

be considered in combination with an annual level of 11 μg/m3

. The rationale for the 24-

hour/annual combinations proposed for the Administrator’s consideration (and the exclusion of 

other combinations within the ranges contemplated) should be more clearly explained. 

Primary Standard for Thoracic Coarse Particles 

 

 CASAC recommends that the primary standard for PM10 should be revised downwards. 

While current evidence is limited, it is sufficient to call into question the level of protection 

afforded by the current standard (a 24-hour standard of 150 μg/m3). 

 

 CASAC supports the EPA staff conclusion that it is appropriate to change the PM10 

standard to a 98th percentile form because of its higher rate of identifying areas in nonattainment 

while reducing the rate of misclassification. We do not agree that the available scientific evidence 

strongly supports the proposed upper bound standard level of 85 μg/m3. The Second Draft Policy 

Assessment demonstrates that a 98th percentile level of 85 μg/m3 would be less stringent as 

compared to the current standard, protecting a smaller fraction of the population. In fact, on a 

population basis, results in the Second Draft Policy Assessment demonstrate that a 98th percentile 

level between 75 and 80 μg/m3 is comparable in the degree of protection afforded to the current 

PM10 standard. The change in form will lead to changes in levels of stringency across the country, 

a topic needing further exploration. While recognizing scientific uncertainties, CASAC supports a 

lower level to provide enhanced protection, somewhere in the range of 75 – 65 μg/m3. We 

recognize that the Administrator will need to apply the Clean Air Act’s requirement for a “margin 

of safety” in a context of uncertainty with respect to the health effects of thoracic coarse particles. 

 The Second Draft Policy Assessment concludes that PM10 should continue to be the 

indicator for thoracic coarse particles. While it would be preferable to use an indicator that reflects 

the coarse PM directly linked to health risks (PM10-2.5), CASAC recognizes that there is not yet 

sufficient data to permit a change in the indicator from PM10 to one that directly measures thoracic 

coarse particles. To improve EPA’s scientific basis for the next NAAQS review, we recommend 

the deployment of a network of PM10-2.5 sampling systems so that future studies will be able to 

expand the evidence base on this indicator and facilitate assessment of whether PM10-2.5 should be 

used as an appropriate indicator for thoracic coarse particles. In concluding this letter, we elaborate 

further on the urgency of research on certain aspects of PM and health. 

ii
Secondary Standard for PM-Related Visibility Impairment 

CASAC supports the EPA staff conclusion that “currently available information clearly 

calls into question the adequacy of the current standards and that consideration should be given to 

revising the suite of standards to provide increased public welfare protection.” The current 

secondary standards are identical to the current primary standards for fine and thoracic coarse 

particles. The detailed estimates of hourly PM light extinction under current conditions (and for 

assumed scenarios of meeting current standards) clearly demonstrate that current standards do not 

protect against levels of visual air quality which have been judged to be unacceptable in all of the 

available urban visibility preference studies. EPA staff’s approach for translating and presenting 

the technical evidence and assessment results is logically conceived and clearly presented. The 20-

30 deciview range of levels chosen by EPA staff as “Candidate Protection Levels” is adequately 

supported by the evidence presented. 

 While the evidence shows that the current standard does not adequately protect visibility, 

the choice of indicator for such protection was a subject of considerable discussion among CASAC 

panelists. The Second Draft Policy Assessment discusses three potential indicators: a PM2.5 Mass 

Indicator, a Speciated PM2.5 Mass-calculated Light Extinction Indicator, and a Directly Measured 

PM2.5 Light Extinction Indicator. Overwhelmingly, CASAC would prefer the direct measurement 

of light extinction, the property of the atmosphere that most directly relates to visibility effects. It 

has the advantage of relating directly to the demonstrated harmful welfare effect of ambient PM on 

human visual perception. However, in discussing the Directly Measured PM2.5 Light Extinction 

Indicator with EPA staff, we learned that the time required to develop an official Federal Reference 

Method (FRM) for this indicator would postpone its implementation for years. Given the time lag 

associated with implementing the Directly Measured Indicator, CASAC agrees with EPA staff’s 

preference for a Speciated PM2.5 Mass-calculated Light Extinction Indicator. Its reliance on 

procedures that have already been implemented in the Chemical Speciation Network (CSN) and 

routinely collected continuous PM2.5 data suggest that it could be implemented much sooner than a 

directly measured indicator. 

Areas for Future Research 

 

 The Second Draft Policy Assessment has identified scientific issues that will need to be 

addressed in order to improve EPA’s scientific basis for promulgating PM standards in the future. 

As stated in our letter of May 17, 2010, CASAC urges the Agency to reinvigorate research that 

might lead to new indicators that may be more directly linked to the health and welfare effects 

associated with ambient concentrations of PM. CASAC also suggests the ongoing collection of 

more comprehensive PM monitoring data, including expanding the range of sizes to provide 

information in the ultrafine particle range, and adding measurements of numbers, chemistry, 

species, and related emissions characteristics of particles. CASAC strongly urges EPA to pursue 

research to develop a Federal Reference Method for a Directly Measured PM2.5 Light Extinction 

Indicator and to develop baseline light extinction data so that it will be available for the next 5 year 

review cycle. CASAC is available to provide advice on priorities for PM-related research. 

 

iii
Thank you for the opportunity to comment on the Second Draft Policy Assessment. We 

look forward to receiving your response. 

 Sincerely, 

 /Signed/ 

 Dr. Jonathan M. Samet, Chair 

 Clean Air Scientific Advisory Committee 

iv
NOTICE 

This report has been written as part of the activities of the EPA's Clean Air Scientific Advisory 

Committee (CASAC), a federal advisory committee independently chartered to provide extramural 

scientific information and advice to the Administrator and other officials of the EPA. CASAC 

provides balanced, expert assessment of scientific matters related to issues and problems facing the 

Agency. This report has not been reviewed for approval by the Agency and, hence, the contents of 

this report do not necessarily represent the views and policies of the EPA, nor of other agencies 

within the Executive Branch of the federal government. In addition, any mention of trade names or 

commercial products does not constitute a recommendation for use. CASAC reports are posted on 

the EPA Web site at: http://www.epa.gov/casac. 

v
Clean Air Scientific Advisory Committee 

Particulate Matter Review Panel 

CHAIR 

Dr. Jonathan M. Samet, Professor and Chair, Department of Preventive Medicine, University of 

Southern California, Los Angeles, CA 

CASAC MEMBERS 

Dr. Joseph Brain, Philip Drinker Professor of Environmental Physiology, Department of 

Environmental Health, Harvard School of Public Health, Harvard University, Boston, MA 

Dr. H. Christopher Frey, Professor, Department of Civil, Construction and Environmental 

Engineering, College of Engineering, North Carolina State University, Raleigh, NC 

Dr. Donna Kenski,1

 Data Analysis Director, Lake Michigan Air Directors Consortium, Rosemont, 

IL 

Dr. Armistead (Ted) Russell, Professor, Department of Civil and Environmental Engineering, 

Georgia Institute of Technology, Atlanta, GA 

Dr. Helen Suh, Associate Professor, Harvard School of Public Health, Harvard University, Boston, 

MA 

Dr. Kathleen Weathers, Senior Scientist, Cary Institute of Ecosystem Studies, Millbrook, NY 

CONSULTANTS 

Dr. Lowell Ashbaugh, Associate Research Ecologist, Crocker Nuclear Lab, University of 

California, Davis, Davis, CA 

Prof. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine, University of Southern 

California, Los Angeles, CA 

Dr. Wayne Cascio, Professor, Medicine, Cardiology, Brody School of Medicine at East Carolina 

University, Greenville, NC 

Dr. David Grantz, Director, Botany and Plant Sciences and Air Pollution Research Center, 

Riverside Campus and Kearney Agricultural Center, University of California, Parlier, CA 

Dr. Joseph Helble, Dean and Professor, Thayer School of Engineering, Dartmouth College, 

Hanover, NH 

Dr. Rogene Henderson, Senior Scientist Emeritus, Lovelace Respiratory Research Institute, 

Albuquerque, NM 

vi

 

1

 / Did not participate in this review. 
vii

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department of Chemical 

Engineering, Clarkson University, Potsdam, NY 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York 

University School of Medicine, Tuxedo, NY 

Dr. William Malm, Research Physicist, National Park Service Air Resources Division, 

Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 

Mr. Charles Thomas (Tom) Moore, Jr., Air Quality Program Manager, Western Governors' 

Association, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort 

Collins, CO 

Dr. Robert F. Phalen, Professor, Department of Community & Environmental Medicine; Director, 

Air Pollution Health Effects Laboratory; Professor of Occupational & Environmental Health, 

Center for Occupation & Environment Health, College of Medicine, University of California 

Irvine, Irvine, CA 

Dr. Kent Pinkerton, Professor, Regents of the University of California, Center for Health and the 

Environment, University of California, Davis, CA 

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT 

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard Medical 

School, Boston, MA 

Dr. Sverre Vedal, Professor, Department of Environmental and Occupational Health Sciences, 

School of Public Health and Community Medicine, University of Washington, Seattle, WA 

SCIENCE ADVISORY BOARD STAFF 

Dr. Holly Stallworth, Designated Federal Officer, EPA Science Advisory Board Staff Office 
CASAC Responses to Charge Questions on the Second Draft Policy Assessment for the 

Review of the Particulate Matter NAAQS 

Primary Standards for Fine Particles

1. Current Approach (Section 2.1.3): 

a. What are CASAC’s views on the staff’s approach to translating the available 

epidemiological evidence, risk information, and air quality information into the 

basis for reaching conclusions on the adequacy of the current standards and on 

alternative standards for consideration? 

CASAC agrees with the approach as described in Section 2.1.3 and appreciates the clarity with 

which the approach is detailed. The overview of the approach presented in Figure 2-1 is wellorganized, logical, and clear. CASAC agrees that it is appropriate to return to the strategy used in 

1997 that considers the annual and the short-term standards together, with the annual standard as 

the controlling standard, and the short-term standard supplementing the protection afforded by the 

annual standard. CASAC commends the expansion of the discussion on evidence of risk across life 

stages as well as of specific susceptibility risk factors and the use of empirical evidence and risk 

assessment findings together. CASAC considers it appropriate to place the greatest emphasis on 

health effects judged to be causal or likely causal in the analysis presented in the ISA. Finally, the 

statement that the data “call into question” the adequacy of the current standard could be more 

forcefully stated by concluding that the current standard is not protective. 

b. Has staff appropriately applied this approach in reviewing the adequacy of the 

current standards (Section 2.2) and potential alternative standards (Section 2.3)? 

The staff has carefully followed this approach in reviewing the adequacy of the current standards 

and in considering potential alternative standards. The outline of the text of Section 2.3 follows the 

outline presented in the overview of the approach given in Figure 2-1. 

2. Form of the Annual Standard (Section 2.3.3.1): 

a. What are CASAC’s views on the additional analyses conducted to characterize the 

potential for disproportionate impacts on susceptible populations, including low 

income groups and minorities associated with spatial averaging allowed by the 

current annual standard? 

b. In light of these analyses, what are CASAC’s views on staff’s conclusion that the 

form of the annual standard should be revised to eliminate spatial averaging? 

CASAC found the additional analyses provided in the 2nd draft PA to be helpful in understanding 

how spatial averaging differs relative to the highest average value from a single community site. 

This latter approach helps to ensure adequate protection of populations living in lower 

socioeconomic areas and contributes an additional margin of safety for other populations. 

Although much of the epidemiological research has been conducted using community-wide 

averages, several key studies reference the nearest measurement site, so that some risk estimates 

are not necessarily biased by the averaging process. Further, the number of such studies is likely to 

1
expand in the future. CASAC concludes that it is reasonable for EPA to eliminate the spatial 

averaging in the new PM2.5 annual average standard. 

3. Alternative Levels (Section 2.3.4): What are CASAC’s views on the following: 

a. The insights that can be gained into potential alternative standard levels by 

considering: 

i. Confidence bounds on concentration-response relationships? 

CASAC commends the progress made in attempting to use confidence bounds in considering 

alternative levels of the standard, but also finds unresolved complexities. First, staff apparently 

made a comprehensive effort to identify relevant studies for which bounds were reported on 

concentration-response (C-R) relationships; this should be explicitly stated. Second, the statement 

made in reference to what these bounds do not indicate (“these analyses do not provide evidence of 

a concentration below which the confidence interval becomes notably wider and uncertainty in a 

C-R relationship substantially increases” [p.2-57]) is contradictory to what they, in fact, do 

indicate. The confidence bounds widen at lower concentrations because there are fewer data at 

such concentrations, as acknowledged by staff. This widening is of interest in characterizing 

precision of estimates as one source of uncertainty. Third, CASAC does not agree with the 

conclusion that these bounds cannot be used in considering alternative levels of the PM NAAQS, 

even with the limited C-R functions shown. EPA Staff should be encouraged to integrate the 

information available on relevant C-R confidence bounds with that on study concentration 

distributions in arriving at a range of levels for consideration. 

For the future, findings of epidemiological studies might be used in several ways in considering a 

range of levels for a NAAQS. It would be preferable to have information on the concentrations 

that were most influential in generating the health effect estimates in individual studies. Less ideal, 

but still useful, would be information on the distribution of concentrations experienced by 

participants in the studies. For time-series studies, because of the similar number of events (e.g., 

deaths) per day, this is likely to be the same as the PM concentration distribution; the situation is 

more complex for cohort studies in which exposures of individuals change over time. Least 

preferable is using PM concentration distribution metrics, such as those used by EPA Staff in 

arriving at a range of levels for consideration. An attempt should be made, to the extent possible, 

to integrate this latter approach with aspects of the first two approaches, realizing that the reported 

study findings and data needed to accomplish this goal may not be readily available, and that 

interactions with investigators may be needed. 

ii. Different statistical metrics that characterize air quality distributions from 

multi-city epidemiological studies? 

2

The Second Draft Policy Assessment provides two alternatives, referred to as the composite 

monitor and the maximum monitor. On the top of page 2-61, the text appears to be stating that, for 

the same air quality domain, the composite monitor concentrations are less than those based on the 

maximum monitor approach, and an argument is made that an approach based on composite 

monitors has a “margin of safety” compared to the maximum monitor perspective. However, a 

judgment is made that data should be selected from the epidemiological studies for which the C-R 

relationships are “strongest,” and that concentrations not more than one standard deviation below 
the long-term mean concentration should be used. The judgment, while not unreasonable, is not 

explained. 

 

It is not clear why the lower bound to be considered is a range from the 10th to 25th percentiles, as 

opposed to, say, the 10th percentile alone. In Figure 2-7, for long-term exposure studies, in the 

upper panel, the 10th percentile annual mean concentrations range from approximately 9 to 11 

μg/m3

. The population-weighted values are 10 to 13 μg/m3. In both cases, the upper bounds of 

these ranges are for the high site, and the lower bounds are for the composite monitor. 

In summary, this section of the report lacks clarity and focus on the key consideration of 

identifying ambient concentrations at which adverse effects are observed, in anticipation of 

supporting a range of concentrations that take into account the statutory mandate to provide an 

adequate margin of safety. 

b. Potential alternative annual standard levels based on composite monitor 

distributions versus maximum monitor distributions? 

The composite monitor approach is preferable because of its stability, and for the additional margin 

of safety it provides. The NAAQS should provide health protection for both long-term and shortterm health effects. It is not clear, for example, as to why the 24-hour level should be at least 2.5 

times higher than the annual standard. Such a statement seems to be independent of consideration 

of health effects. A statement is made on page 2-73, lines 26-27 that “based on this consideration” 

consideration should be given to retaining the 35 μg/m3 24-hr level in conjunction with annual 

standards of 13 to 11 μg/m3. Setting aside the math problem here (e.g., 11*2.5 = 27.5, not 35), the 

rationale for the 2.5 times factor appears arbitrary and not based on health considerations. 

c. Use of risk information in informing staff conclusions on alternative annual and 

24-hour standard levels, including approaches used to assess overall confidence 

and potential bias in the risk estimates? 

The risk information provides valuable insights, and should be used in drawing conclusions.

However, there is not symmetry between the evidence-based section and the risk-based section. . 

The “evidence-based” section reaches the conclusion that alternative levels to be considered should 

be 11 to 13 μg/m3

 for the annual standard and 35 ug/m3 for the 24-hour standard, and also a 

combination of 11/30 μg/m3 for the annual/24-hour levels. However, the risk-based analysis does 

not systematically evaluate these combinations, omitting the 11/35 μg/m3 and 11/30 μg/m3

combinations. Furthermore, the text implies that a 10/35 ug/m3

 case was analyzed, but no results 

were reported. This difference between the ranges from the two sections reflects in part the 

scenarios considered in the risk assessment. While the Administrator’s consideration should not be 

limited to those combinations that were analyzed quantitatively, the final policy assessment should 

be systematic and emphatic about providing conclusions regarding combinations of annual and 

daily levels that were not analyzed quantitatively but that are recommended for consideration. 

3

The results of the risk assessments are presented mainly in terms of percentage risk reduction 

compared to the current standard, in Figures 2-11 and 2-12 for long-term and short-term effects, 

respectively. While this is useful information, it is not directly relevant to the setting of a NAAQS, 
given the goal of a NAAQS--to protect public health with an adequate margin of safety. 

Additionally, the information on risk reduction might be better presented as the absolute numbers 

of deaths avoided rather than the percentage reduction under the various scenarios. The text should 

be rewritten to better reflect the utility and relevance of the information on reduction of disease 

burden for determining the NAAQS. 

This section should not only focus on the best estimate of risk, but the confidence intervals and 

non-quantified sources of bias, such as the role of socio-economic status (SES). See also Page 2-

35, lines 10-12, which indicates that sensitivity analysis of model specification used in the risk 

assessment produce risk estimates that are a factor of 2 to 3 higher than the core risk estimates. 

d. Staff’s conclusion that alternative annual standard levels in the range of 13 to 11 

μg/m3 are most strongly supported by the available evidence and risk-based 

information? 

The rationale for the conclusion was well developed, but could use further justification, particularly 

in regard to the pairing of the 24-hour and annual standards. The risk assessment did not explore all 

the combinations considered in the Policy Assessment. While CASAC agrees with the range of 13 

to 11 ug/m3

, it finds less justification for the pairings proposed. 

e. Staff’s approach of focusing on peak-to-mean ratios to inform the level of a 24-

hour standard that would provide supplemental protection to a generally 

controlling annual standard? 

The peak-to-mean ratio merits consideration in providing insight as to whether the annual or 24-

hour standard would be controlling in a particular area. It is not relevant to informing the actual 

level to be selected for the 24-hour standard. 

f. Staff’s conclusion that consideration should be given to retaining the current 24-

hour standard level of 35 μg/m3 in conjunction with annual standard levels in the 

range of 13 to 11 μg/m3

, and that consideration could also be given to an 

alternative 24-hour standard level of 30 μg/m3

 particularly in conjunction with an 

annual standard level of 11 μg/m3

? 

The conclusions are reasonable in relation to the criteria established by the Clean Air Act (CAA), 

and those developed by the OAQPS Staff that have been endorsed by CASAC. The choices within 

these options will need to be based on the Administrator's interpretation of the CAA’s requirement 

for an adequate margin-of-safety. In other words, in the absence of thresholds in the dose-response 

relationships for the health outcomes of concern, how much public health impact resulting from 

exposure to ambient air PM2.5 is acceptable under the CAA. 

 

4

The least protective option (35-13 μg/m3

) would provide significant additional public health 

benefits in most of the U.S., in comparison to the current limits (35-15 μg/m3). The most protective 

option (30-11 μg/m3

) would provide significant additional public health benefits to a larger part of 

the U.S. population in comparison to the current limits (35-15 μg/m3

) and any of the intermediate 

options, but would not prevent at least some adverse health effects among the most susceptible 
segments of the population, given our current understanding of dose-response relationships. 

4. Key Uncertainties and Areas for Future Research and Data Collection (Section 2.5): 

What are CASAC’s views on the areas for future research and data collection outlined in 

this section, on relative priorities for research in these areas, and on any other areas that 

ought to be identified? 

 

The key uncertainties and areas for future research and data collection are well summarized in 

Section 2.5. The acknowledgement (at the top of page 2-87) that “Much of this research may 

depend on the availability of increased monitoring data” is apt and appreciated. The opportunities 

for epidemiological research to effectively address the knowledge gaps on the effects, and 

concentration-response relationships, of PM components and source-related mixtures cannot be 

achieved without additional monitoring data to provide PM speciation and better temporal and 

spatial resolution. Only the EPA can provide the impetus and support for such an enhancement in 

air quality monitoring. 

The research needs to address uncertainties in health outcomes, exposure durations of concern, and 

susceptible populations that are also very nicely outlined are well targeted, and can be effectively 

studied in human populations. Such studies, to be most productive, will need the enhanced 

monitoring data, as recognized by EPA staff. 

This section, as written, has more to do with future research priorities than with uncertainties that 

influence impending decisions on revisions to the PM2.5 NAAQS. The section outlines a very 

broad and ambitious research agenda. It would help to begin this section with a prioritized review 

of key uncertainties in order to help establish priorities among the suggested research topics. 

Obviously the key uncertainty is the range of concentrations that are causing the observed health 

effects in the epidemiological studies, and the degree of certainty in effects at the lower 

concentrations along the C-R relationship. This uncertainty has necessitated using the 

distributional measures of concentrations from the epidemiology studies in attempting to make the 

link between the epidemiological findings and consideration of alternative concentrations for the 

PM NAAQS. While this uncertainty is reflected in two (p.2-88 and 2-90) of the many 

recommendations for future research that C-R functions include confidence bounds, this 

uncertainty should be highlighted. We urge careful attention to priorities in relation to future 

revisions of the PM NAAQS, rather than a lengthy list of research topics. 

CASAC finds the list to be appropriate, but also suggests consideration of the following: 

• Generating time-activity data to support probabilistic scenario-based exposure models, 

such as additional activity diary data to incorporate into the Consolidated Human 

Activity Database (CHAD). 

5

• Characterizing indoor exposures to PM of ambient origin. For example, the penetration 

of ambient PM2.5 and PM10 into indoor microenvironments (home, work, school, 

restaurant, bar, vehicle) should be better characterized, particularly taking into account 

differences in penetration with respect to particle size and composition. Given the 

greater amount of time we spend in indoor vs. outdoor environments, the need for these 

data is compelling. 
• Addressing the bidirectional linkages between climate change and concentration, size 

distribution and composition of PM in the PM10, PM2.5, and ultrafine particle (UFP) 

fractions. This would include assessing the relative effects of climate cooling due to 

aerosols (e.g., sulfate) vs. climate warming due to elemental carbon. Effects of 

increased wildfires, windblown dust and pollen seasonality are also of interest. 

• Continuing support of toxicological research in terms of chemical components, sources 

and subfractions (to include UFP). Toxicological studies will address biological 

plausibility and give insights as to possible mechanisms. Although C-R relationships 

are a challenge to extrapolate from animal to human, animal studies do provide an 

effective means to conduct controlled and well-characterized exposure scenarios to 

examine C-R relationships. 

 

Primary Standard for Coarse Particles

5. Current Approach (sections 3.1.4, 3.2, 3.3): 

a. What are CASAC’s views on the approach to translating the available evidence 

and air quality information into the basis for reviewing the coarse particle 

standard? 

CASAC finds the second draft superior to the first draft reviewed earlier; it demonstrates 

considerable progress and responsiveness to CASAC's suggestions. The document is grounded on 

explicit data and clearly stated arguments. EPA staff has done its best to take the available 

evidence relating to exposure and health effects and to use them as the basis for reviewing the 

coarse particle standard. 

 

There are inherent deficiencies which persist because of lack of data. Concentrations of the coarse 

particle fraction--particles between 2.5 and 10 microns—are usually estimated by subtraction and 

not measured directly. Moreover, given the limited data on coarse particles, much of the evidence 

on health effects comes from interpreting studies using PM10 and assessing the extent to which the 

health effects observed relate to the entire size range collected [including PM2.5] or to only the 

coarse particle fraction. 

b. Has staff appropriately applied this approach in reviewing the adequacy of the 

current standard (section 3.2) and potential alternative standards (section 3.3)? 

CASAC responds affirmatively to this question. The staff have noted the limitations of the data and 

used them in light of these limitations to address the question of whether current standards are 

adequate. CASAC also finds that staff has adequately discussed alternative standards and the 

consequences of applying them. 

 

In toto, Chapter 3 reads well and is much improved. EPA staff has done its best to describe an 

evidence-based approach for applying the limited amount of health effects evidence and air quality 

information in different US regions as a basis for reviewing the adequacy of the current coarse 

particle standard. 

 

6
6. Adequacy of the Current PM10 Standard (section 3.2): What are CASAC’s views on the 

alternative approaches presented for considering the evidence and its uncertainties as 

they relate to the adequacy of the current standard? 

The general consensus of CASAC is that consideration should be given to revising the current 24-

hour PM10 standard. The rationale for this recommendation emerges from the judgment that the 

current data, while limited, is sufficient to call into question the level of protection afforded the 

American people by the current standard. The opinion hinges on the strength of associations in 

multi-city studies and positive trends in single city studies linking PM10 exposure and health 

endpoints, and moreover that these health effects can occur below the current standard. This 

approach gives significant weight to studies that have generally reported that PM10-2.5 effect 

estimates remain positive when evaluated in co-pollutant models. Likewise controlled human 

exposure PM10-2.5 studies showing decreases in heart rate variability and increases in markers of 

pulmonary inflammation are deemed adequate to support the plausibility of the associations 

reported in epidemiologic studies. 

7. Indicator (Section 3.3.1): What are CASAC’s views on the approach taken to 

considering standard indicator and on staff’s conclusion that PM10 remains an 

appropriate indicator in this review? 

The majority of CASAC determined that there was insufficient evidence currently available to 

support a change in the indicator from PM10 to PM10-2.5. However, CASAC vigorously 

recommends the implementation of plans for the deployment of a network of PM10-2.5 sampling 

systems so that future epidemiological studies will be able to more thoroughly explore the use of 

PM10-2.5 as a more appropriate indicator for thoracic coarse particles. 

If a PM10 indicator is retained, the Agency should consider limiting the Federal Reference Method 

to include only low volume PM10 samplers, as high volume PM10 samplers do not produce 

comparable results. 

8. Form (Section 3.3.3): What are CASAC’s views on the approach taken to considering 

the form of the standard and on staff’s conclusion that revising the form to a 98th

percentile form would be appropriate for a 24-hour PM10 standard meant to protect 

against exposures to thoracic coarse particles? 

CASAC felt strongly that it is appropriate to change the statistical form of the PM10 standard to a 

98th percentile form. Published work has shown that the percentile form has greater power to 

identify non-attainment and a smaller probability of misclassification relative to the expected 

exceedance form of the standard. This change in form will lead to changes in levels of stringency 

across the country, a topic needing further exploration. 

9. Level (Section 3.3.4): What are CASAC’s views on the following: 

a. The approach taken by staff to identify potential alternative PM10 standard levels, 

in conjunction with a 98th percentile form, including the weight placed on different 

studies? 

7
b. Staff’s conclusion that the evidence most strongly supports standard levels around 

85 μg/m3? 

c. The alternative approach to considering the evidence that could support standard 

levels as low as 65 μg/m3

? 

CASAC concurs that the approach in identifying potential alternative PM10 standard levels are 

appropriate, with the discussion regarding the weight placed on different studies clearly and 

cogently presented. CASAC also considered that the proposed alternative standard levels of 85 

and 65 ug/m3

 (based on consideration of 98th percentile PM10 concentration) could be justified. 

CASAC, however, does not agree that scientific evidence most strongly supports an upper bound 

standard level of 85 μg/m3

. As stated in the Second Draft Policy Assessment, scientific evidence 

supports the adoption of a standard at least as stringent as the current standard of 150 μg/m3 based 

on one expected exceedance. Table A3 suggests that a 98th percentile level of 85μg/m3

 is less 

stringent as compared to the current standard, protecting a smaller fraction of the population. 

Results instead point to a 98th percentile level between 75 and 80 μg/m3

 as comparable to the 

current standard. CASAC further notes that setting new 24-hour PM10 standard levels should also 

consider the impact of corresponding changes in PM2.5 standards, which will likely result in lower 

24-hour PM2.5 concentrations and lower measured PM10 values. Thus, proportionately more coarse 

particle mass could be airborne at the standard level. Absent corresponding reduction in the PM10

standard, these lower PM2.5 concentrations would lessen the level of protection provided by the 

PM10 standard for exposure to PM10-2.5

The Second Draft Policy Assessment does not adequately convey the possible rationale for 

selecting the lower end of the proposed range of levels. Therefore, the considerations that might 

lead to selecting a PM10 standard level more stringent than afforded by the current standard should 

be more clearly elaborated. These considerations focus on margin of safety, particularly as it 

relates to the impact and weight given to suggestive findings of causality, to findings of positive 

but statistically insignificant results, and to exposure measurement error and other sources of 

uncertainty. 

10. Key Uncertainties and Areas for Future Research and Data Collection (Section 3.5): 

What are CASAC’s views on the areas for future research and data collection outlined in 

this section, on relative priorities for research in these areas, and on any other areas that 

ought to be identified?

See comments on Chapter 2. 

8

The key distinction for this chapter is the need to seriously focus on PM10-2.5 for both mass and 

composition. CASAC looks forward to the planned implementation of monitors that measure 

PM10-2.5, rather than PM10. There is a critical need for national monitoring data on PM10-2.5 in order 

to provide a basis for epidemiological studies that focus on this size fraction. Furthermore, there is 

a need for speciated data to support health effects research. Spatial and temporal variability in 

coarse particle mass and composition need to be characterized. In addition, the national 

monitoring data will support a baseline for ambient air quality in order to compare with health 

effects data in order to assess whether there is a need for a more stringent standard. 
The research areas described in the draft Section 3.5 are reasonable, but there needs to be strong 

emphasis on the critical need for coarse PM data, in order that the NAAQS can move beyond PM10

as an indicator for thoracic coarse PM in a future NAAQS revision. 

Another question to be considered is regarding what size cut-points are most appropriate, and also 

regarding what specific components are of most interest or concern with respect to health effects. 

There is a need for continuous monitoring of coarse PM (and of PM2.5) in order to support health 

effects studies and to be able to assess alternative forms of possible future standards. 

Other challenges for future research: (a) it may be difficult to get useful data from rodent 

inhalation studies since they can breathe particles only up to about 2 to 3 microns into their lung 

airways; (b) getting good chemical characterization of the particles will be a problem, since there 

are primary biological materials of potential interest in the thoracic coarse size range. 

Prioritization of the research topics is needed, such as via a separate meeting or workshop. 

Secondary Standard for PM-related Visibility

11. Current Approach (Section 4.1.3): 

a. What are CASAC’s views regarding our approach for translating technical 

evidence and assessment results into the basis for assessing current fine particle 

standards and considering alternative standards to provide protection against 

PM-related visibility impairment? 

The translation of technical evidence and assessment results as a basis for reviewing and revising 

the current secondary fine particle standard is logically conceived, clearly presented, and 

responsive to previous CASAC recommendations. The combined evidence-based and impactbased assessments effectively contrast and integrate the various combinations of metrics for 

protecting urban visibility. While this approach is inherently complex, it is clearly explained in the 

text and concisely summarized in Figure 4-1. The various tables and graphics in Chapter 4 and its 

associated appendices are helpful in communicating the inherent complexity that results from the 

evaluation of so many possible combinations of indicators, averaging times, levels and forms. 

b. Has staff appropriately applied this approach in reviewing the adequacy of the 

current standard (Section 4.2) and potential alternative standards (Section 4.3)? 

The detailed estimates of hourly PM light extinction under current conditions and for “what if” 

scenarios of just meeting current standards clearly indicate that the current PM2.5 standards do not 

protect against levels of visual air quality which have been judged to be unacceptable in all of the 

available urban visibility preference studies. The levels are too high, the averaging times are too 

long, and the PM2.5 mass indicator could be improved to correspond more closely to the light 

scattering and absorption properties of suspended particles in the ambient air. 

9

While not discussed in detail in the Second Draft Policy Assessment, direct measurements of light 

extinction are the preferable indicator for an alternate standard to make an accurate assessment of 

the PM effect on urban visibility. These measurements would provide timely and easy-to-
understand results to address the protection of the public welfare from PM impacts, but without a 

Federal Reference Method (FRM) adopted or in the development process – these data are not 

currently available for most urban areas. Additional discussion of the timeline and process 

anticipated by EPA to advance direct measurement of light extinction monitoring methods to FRM 

status would be helpful. 

Given this limitation, the detailed estimates of PM light extinction employed for 15 urban areas in 

the UFVA, and used to evaluate alternative new indicators including hourly PM2.5 mass and 

“speciated PM2.5 mass-calculated light extinction” in the Second Draft Policy Assessment are 

appropriate for the initial promulgation and first generation of regulatory air quality analysis and 

planning; similar to the process for the Regional Haze Rule. The speciated PM2.5 mass-calculated 

light extinction indicator produces hourly extinction values quite similar to those resulting from 

more complex calculations, and it could be an appropriate indicator for a revised secondary 

standard, if employed on an interim basis until methods for direct light extinction measurements 

can be developed and deployed. 

While the stated intent of the Second Draft Policy Assessment is “to provide as broad an array of 

options as is supportable by the available information”, the CASAC recommends providing 

additional and more focused discussion of the policy implications that may be associated with 

selecting and implementing specific combinations of indicators, levels and forms from within this 

broad array of options. Some discussion should also be provided to indicate that reductions in light 

scattering aerosols could decrease light extinction but increase radiative forcing, while reductions 

in light absorbing aerosols would decrease both light extinction and radiative forcing. The 

contributions of anthropogenic controllable “Short-Lived-Climate-Forcers” that contribute 

significantly to urban visibility impairment would also be worthy of some attention in the analysis 

of policy implications. 

12. Nature of the Indicator (Section 4.3. 1): What are CASAC’s views on the following: 

a. Staff’s consideration of the three indicators identified in this section and our 

conclusions on the appropriateness of these indicators for consideration in this 

review? 

b. The development and evaluation of a new approach that is based on using 

speciated PM2.5 mass and relative humidity to calculate PM2.5 light extinction by 

means of the IMPROVE algorithm? 

c. The assessment approach and results comparing the PM components that 

contribute to the hours selected in the top percentiles for PM2.5 mass and PM10

light extinction? 

As noted in past comments, CASAC strongly prefers directly measuring light extinction to using 

estimates based on mass measurements (e.g., the other options provided in the Second Draft Policy 

Assessment). In their recent review, the Ambient Air Monitoring and Methods Subcommittee 

(AAMMS) noted that there are commercial instruments available that provide light extinction 

measurements directly, and promising additional technologies may soon become available. The 

AAMMS also encouraged the EPA to begin the process of developing performance standards for 

PM light extinction measurements. However, a FRM for light extinction measurement does not yet 

10
exist, and as EPA does not view it as practical to develop an FRM in time for this rule making, 

CASAC recognizes that alternative approaches need to be considered. 

A current weakness of the Second Draft Policy Assessment is that it does not explicitly state the 

reasons that EPA does not currently recommend using a direct measurement of light extinction. It 

also does not provide any indication that the proposed mass-based indicators are intended for use 

on an interim basis, to be replaced with direct light extinction-based measurements as those 

methods are developed, tested and deployed. If staff consider it impractical to develop performance 

standards for an FRM in time for this round of rule making, this should be clearly stated and a 

schedule for developing such performance standards and evaluating candidate instruments should 

be specified well in advance of the next PM NAAQS review. 

Assuming it is currently impractical to develop a FRM for direct measurements of PM light 

extinction in a sufficiently timely manner, CASAC agrees that for this rule making, a method to 

estimate extinction based on measurements from continuous PM2.5 monitors, preferably adjusted 

by PM2.5 speciation and relative humidity (RH) data, is appropriate. The “speciated PM2.5 masscalculated light extinction” method described in the Second Draft Policy Assessment appears to be 

a reasonable approach for estimating hourly light extinction. For purposes of “near real time” 

visibility tracking, CASAC recommends considering a simpler calculation in which historical, 

rather than concurrent, monthly or seasonal speciation averages would be used to estimate 

speciation for combining with real-time continuous PM2.5 and RH data, even though the most 

recent speciation data would be used for developing plans for improving visibility. CASAC also 

recommends that the Agency consider developing the monthly or seasonal speciation estimates on 

a regional basis as well as on a site-specific basis, as this would allow light extinction estimates at 

all (>700) sites with continuous PM2.5 data, rather than just the relatively few sites with collocated 

continuous PM2.5 and speciation monitors. 

13. Alternative Levels and Forms (Section 4.3.3): What are Panel views on the following: 

a. The performance assessment which focused on the Candidate Protection Levels of 

64, 112, 191 Mm-1 for PM2.5 light extinction and speciated PM2.5 mass-calculated 

light extinction, and alternative levels of 10, 20, and 30 μg/m3

 for PM2.5 mass 

concentration? 

These are appropriate CPL and PM2.5 levels. The CPL values were based on all visibility 

preference data that are available and bound the study results as represented by the 50% 

acceptability criteria. However, the presentation could be improved by expanding some of the 

tables to include 10 and 40 dv values, in that at 10 dv, no viewer found the scene to be 

unacceptable, and at 40 dv, virtually all viewers found all scenes to be unacceptable. What would 

these dv levels correspond to in the context of PM2.5 and the various percentile levels? 

b. Use of three-year averaged 90th and 95th percentiles in conjunction with a 1-hour 

daily maximum form and use of three-year averaged 98th percentile in 

conjunction with the all daylight hours form? 

11

While these levels may be appropriate, they are not well justified. A cursory argument was made 

that the 90–95th percentiles in conjunction with the 1-hour daily maximum identified similar days 
and hours of non-compliance, as did the 98th percentile in conjunction with all daylight hours, and 

this correspondence was a sufficient basis to pick these two approaches. It would be informative to 

compare all, or at that least the same, percentiles for both all days and the daily hourly daily 

maximum. These analyses should be informative as to whether one approach is preferred. 

Whether different sources might be identified, depending on use of daily average or maximum 

values has not been adequately addressed. For example, a significantly extended episode of low 

visibility might be attributed to a single source, such as a large wildfire or prescribed fire, which 

would result in the all hour, all day approach targeting only one large emission episode that 

occurred for only one or a few time periods. For wintertime episodes in many cities of multi-day 

poor urban visibility conditions, the events can cross the end of the calendar year, tracking the 

highest daily hour for each day to form a full 3-year distribution of values (i.e., N = ~1,095) for 

which the compliance value is then compared to the percentile level selected by EPA. 

c. Insights to be drawn by comparing the PM components for hours included among 

the 10% highest for a 1-hour daily maximum form with the hours included among 

the 2% highest for an all daylight hours form, for the various indicators 

considered (Appendix C)? 

See comments above. These two approaches appear to be similar; however, it would be helpful to 

quantify the similarities as opposed to relying only on a qualitative discussion. A scatter plot might 

be useful for the 14 sites that provides the average fractional contribution of a species in relation to 

the time metric used. Additionally, comparisons should be shown for the specific days found in 

non-compliance by metric. 

14. Key Uncertainties and Areas for Future Research and Data Collection (Section 4.5): 

What are CASAC’s views on the areas for future research and data collection outlined in 

this section, on relative priorities for research in these areas, and on any other areas that 

ought to be identified?

The major areas of research and data collection needed to address key uncertainties related to a 

visibility-based secondary standard are nicely captured in Section 4.5 of the Second Draft Policy 

Assessment. The section appropriately identifies two major areas of need, one related to visibility 

preference, and one related to methods of measurement. 

In the first category, preference studies, the details noted by EPA all identify a strong need for 

additional urban visibility preference studies conducted using consistent methodology. The range 

of 50% acceptability values discussed as possible standards are based on just four studies (Figure 

4-2), which, given the large spread in values, provide only limited confidence that the benchmark 

candidate protection levels cover the appropriate range of preference values. Studies using a range 

of urban scenes (including, but not limited to, iconic scenes – “valued scenic elements” such as 

those in the Washington DC study), should also be considered. 

12

In the second category related to methods of measurement, CASAC supports the proposal to 

conduct studies in several cities, pairing direct monitoring of light extinction with enhanced 

monitoring of PM size and composition distributions (i.e., continuous PM speciation monitoring). 

Additional work should also be conducted to understand the contribution of PM10-2.5 in 
13

southwestern areas other than Phoenix, to address the lack of information for scattering associated 

with this fraction of PM10 as is noted on page 4-30. 

Underlying this overall discussion is a clear need for better particle size – composition distribution 

information (i.e., particle composition distributions as a function of particle size). These data gaps 

are addressed in different ways in the discussion of future research needs elsewhere in the Second 

Draft Policy Assessment (Sections 2.5 and 3.5). Moreover, the development of continuous 

monitoring methods for specific PM components addressed in Section 2.5 is equally applicable 

here. Improved understanding of size-dependent PM composition would also help address the 

questions related to the role of scattering and absorbing aerosols in climate forcing that are raised 

in Section 5.2.4. 

Finally, a number of research and data collection topics overlap between the secondary PM 

NAAQS, and the PM2.5 and PM10 primary PM NAAQS. For example, the fraction of combustionrelated primary carbon PM species can be an important indicator of harmful health effects, 

visibility impairment and climate forcing. 

With these characteristics, research to jointly quantify and reduce these primary PM carbon species 

from combustion sources would advance the information available to the Administrator for her 

judgment about the necessary level of protection to be provided by the future PM NAAQS, to be 

assessed in the next review cycle. 

CASAC suggests that EPA look for additional opportunities to align health and welfare 

improvement strategies simultaneously for common indicators, such that the next reviews of the 

PM and other NAAQS have not only the analyses of the effects of PM and other NAAQS 

indicators on health and welfare, but also include metrics useful for measuring progress toward 

attainment. 
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B-1

APPENDIX B 

Ambient PM Monitoring Networks 

The measurement of ambient air pollution in the United States is provided through a 

number of ambient air monitoring networks operated almost exclusively by state, local, and 

tribal air monitoring programs. This section briefly describes the objectives for each of the PM 

monitoring networks as well as the coverage for each network across the country. 

The ambient air monitoring networks are designed to meet three basic monitoring 

objectives. Each objective is important and must be considered individually. The objectives are: 

 to support compliance with ambient air quality standards and emissions strategy 

development, including comparison to the NAAQS, assess ambient exposures, 

development of attainment and maintenance plans, evaluation of regional air quality 

models used in developing emission strategies, and tracking trends in air pollution 

abatement control measures’ impact on improving air quality; 

 to provide air pollution data to the general public in a timely manner such as reporting 

the Air Quality Index (AQI) through AIRNow (www.airnow.gov), monitoring agency 

web sites, conventional media outlets such as newspapers, radio and television news, 

and emerging outlets such as social networking sites; and 

 to support air pollution research studies, including atmospheric, health, and 

epidemiological studies that are used to inform future reviews of the NAAQS. 

The sections below briefly summarize the monitoring networks for PM2.5, including 

PM2.5 mass, PM2.5 speciation, PM10 mass, PM10-2.5 mass, and the forthcoming National Core 

(NCore) multi-pollutant network.

B.1 PM2.5 

The PM2.5 design criteria require State and Local Air Monitoring Stations (SLAMS) in 

Metropolitan Statistical Areas (MSAs) based on a combination of population and design value 

(Table D-5, 40 CFR Part 58) with higher populated locations having more polluted air required 

to have the most stations. Background and transport sites are also required of each state with 

options for utilizing Interagency Monitoring of Protected Visual Environments (IMPROVE) 

stations and other PM2.5 data to provide for flexibility in meeting the requirement. 

In urban areas, required PM2.5 monitoring stations are sited to represent community-wide 

air quality. These monitoring stations will typically be at neighborhood or urban scale; however, 

where a population-oriented micro- or middle-scale PM2.5 monitoring station represents many 

such locations throughout a metropolitan area, these smaller scales can be approved by the 
B-2

applicable EPA Regional Office to also represent community-wide air quality. The EPA’s 

existing network design criteria for PM2.5 states: “(1) at least one monitoring station is to be 

sited in a population-oriented area of expected maximum concentration and (2) for areas with 

more than one required SLAMS, a monitoring station is to be sited in an area of poor air quality” 

(40 CFR, PART 58, Appendix D, section 4.7). Since monitors sited for either of these network 

design criteria must represent community-wide air quality, they are also representative of 

population exposure. Most PM2.5 monitoring in urban areas should be representative of a 

neighborhood scale. 

In rural areas, the design of the network relies on IMPROVE, rural NCore stations, a 

limited number of smaller cities, and partner monitoring agencies to provide for regional 

characterization of PM2.5. Stations in these areas are typically sited to represent regional scale air 

quality and are therefore located away from any local sources, should they exist. 

B.1.1 PM2.5 Federal Reference Method (FRM) Network 

The network of PM2.5 FRMs has been operational since 1999. This network includes 

over 900 monitoring stations throughout the country. The FRMs are primarily used to determine 

compliance with the NAAQS, but also serve other objectives. Since FRMs are filter-based 

methods requiring post-sampling laboratory analysis, which is labor intensive, monitoring 

programs have some flexibility in how often they must be operated. Approximately 150 FRMs 

operate every day, 600 every third day, and another 150 every sixth day. Sample frequencies of 

every third and sixth day are based on a national sample calendar provided by EPA. The number 

of PM2.5 FRMs may decrease over the coming years as PM2.5 continuous FEMs1

 are now 

available and can replace FRMs for purposes of comparison to the NAAQS. Figure B-1 

illustrates the locations of PM2.5 FRMs reporting to the Air Quality System (AQS). 

B.1.2 PM2.5 Continuous Monitor Network 

Continuous PM2.5 monitors are required in MSAs at one half (rounded up) the number of 

monitoring stations that are required to have an FRM/FEM monitor. Since most deployed PM2.5

continuous monitors are not approved as FEMs, many of these monitors are collocated at 

monitoring locations with an FRM so that the availability of data from both instruments supports 

each of the major monitoring objectives described earlier in this section. Collocation with PM2.5

FRMs and continuous monitors also ensures that reference data are available to validate the 

performance of the continuous monitor. While PM2.5 continuous monitors primarily support 

forecasting and reporting the AQI, they are also used in interpreting the diurnal characterization 

 1

 A Federal Equivalent Method (FEM) means a method for measuring the concentration of an air pollutant in the 

ambient air that has been designated as an equivalent method in accordance with 40 CFR Part 53. 
B-3

of PM2.5. The network of PM2.5 continuous monitors has grown to over 700 locations throughout 

the country with approximately 160 monitors in operation as continuous FEMs. Continuous 

FEMs are compared to the NAAQS when designated as the primary monitor at a station. A 

recent assessment of continuous PM2.5 FEM data quality, as compared to collocated FRMs, 

indicates that some FEMs are meeting the performance criteria used to approve these methods, 

which are based on daily measurements, while others are not. The assessments and 

recommendations for addressing data quality issues are detailed in Hanley and Reff, 2011.2

 

Additionally, as part of a review of 1-hour data from all continuous FEM PM2.5 instruments 

operating at state/local monitoring sites, we have recently become aware of the occurrence of 

questionable outliers in 1-hour data submitted to AQS from continuous FEM PM2.5 instruments 

at some of these sites (Evangelista, 2011).3 Figure B-2 illustrates the locations of PM2.5

continuous monitors reporting to AQS, including those that are now approved as FEMs. 

B.1.3 PM2.5 Speciation 

As part of the PM2.5 NAAQS review completed in 1997, EPA established a PM2.5

Chemical Speciation Network (CSN) consisting of 54 Speciation Trends Network (STN) sites. 

The STN was established to conduct routine speciation monitoring in primarily urban areas to 

provide nationally consistent data for the assessment of trends and to provide a long-term record 

of the chemical composition of PM2.5 in the United States. The initial STN monitoring began 

with a pilot of 13 sites in February 2000. In addition to the STN, EPA also implemented a 

network of about 200 supplemental speciation sites for multiple monitoring objectives including 

support for: the development of modeling tools and the application of source apportionment 

modeling for control strategy development to implement the NAAQS; health effects and 

exposure research studies; assessment of the effectiveness of emission reductions strategies 

through the characterization of air quality; and development of state implementation plans 

(SIPs). The STN and supplemental speciation monitoring sites together are referred to as the 

CSN. The CSN sampling systems do not include any FRM/FEMs; therefore, data produced from 

this network are not used for comparison to the NAAQS. However, FRMs are almost always 

collocated with the CSN since these are among the most important PM2.5 sites in a network. 

 2

 Hanley, T. and Reff, A (2011). Assessment of PM2.5 FEMs Compared to Collocated FRMs. Memorandum to PM 

NAAQS Review Docket, EPA-HQ-OAR-2007-0492. April 7, 2011. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 3

 Evangelista, M. (2011). Investigation of 1-hour PM2.5 Mass Concentration Data from EPA-Approved Continuous 

Federal Equivalent Method Analyzers. Memorandum to PM NAAQS Review Docket (EPA-HQ-OAR-2007-0492). 

April 5, 2011. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 
B-4

In 2005, EPA conducted an assessment specifically focused on the PM2.5 speciation 

monitoring network. In consultation with State and local monitoring agencies, EPA evaluated 

CSN sites to determine which ones might be shut down to provide resources for future 

monitoring needs. The EPA ranked the sites according to their overall information value. The 

ranking was based on several factors, including whether the site was in a non-attainment area and 
B-5

Figure B-1. PM2.5 FRMs Reporting to AQS 
B-6

Figure B-2. PM2.5 Continuous Monitors Reporting to 

AQS
B-7

whether other sites were nearby. There was general agreement that some of the sites should be 

shut down when FY 2005 funding ran out. Other sites were identified as high value sites, 

particularly with regard to the PM2.5 NAAQS program. The EPA evaluated each of these sites 

when FY 2006 regional funding allocations for continued operation and maintenance were 

developed. In doing so, EPA balanced filter-based PM2.5 speciation against funding for other 

PM2.5 measurements, such as FRM site operations, filter analysis, and startup of additional 

precursor gas sites and continuous speciation sites. 

As of 2010, the PM2.5 CSN includes about 50 STN sites and about 150 SLAMS 

supplemental sites. All STN sites operate on a one-in-three day sample collection schedule. A 

majority of the SLAMS supplemental sites operate on a one-in-six day sample collection 

schedule. These sites collect aerosol samples over 24 hours on filters that are analyzed for PM2.5

mass, a number of trace elements, major ions (e.g., sulfate, nitrate, and ammonium), and organic 

and elemental carbon. 

The IMPROVE program was established in 1985 to aid the creation of federal and state 

implementation plans for the protection of visibility in Class 1 areas (155 national parks and 

wilderness areas) as stipulated in the 1977 amendments to the CAA and further supported in the 

goals set forth in the 1999 Regional Haze Rule.4

 Similar to the CSN, the IMPROVE program 

provides PM2.5 mass and speciation data for organic and elemental carbon, major ions, and trace 

elements; however, unlike CSN IMPROVE also samples for PM10 mass. The IMPROVE 

network is presently comprised of 110 regionally representative monitoring sites; 7 sites operated 

collaboratively with the Clean Air Status and Trends Network (CASTNET); 30 sites operated by 

State and local agencies referred to as “IMPROVE Protocol Stations”; several sites operated by 

Tribal air monitoring programs; and about 9 sites operated by federal land managers. IMPROVE 

samplers are also collocated at 5 CSN stations to assess on-going comparability of the programs. 

Figure B-3 provides a map of all IMPROVE and CSN stations. 

From May 2007 until October 2009, the approximately 200 CSN sites transitioned to a 

new method of sampling and analysis for carbon measurements that is consistent with the 

IMPROVE network methodology. This transition occurred in three phases: 56 CSN stations 

began using the new method in the first phase which started May 1, 2007; 63 stations were in the 

second phase beginning on April 1, 2009; and 78 stations were in the third phase beginning on 

October 1, 2009. 

While the network of approximately 200 CSN sites provide valuable data for 

development and tracking of control strategies, its use for supporting studies of health and 

welfare effects is somewhat limited. The CSN sites provide data on a one-in-three or one-in-six 

 4

 Additional information is available at http://www.epa.gov/visibility/actions.html
B-8

Figure B-3. IMPROVE and Chemical Speciation Network 
B-9

day schedule and do not capture data every day or everywhere. In April 2008, EPA cosponsored a workshop to discuss modifications to the current ambient air quality monitoring 

networks that would advance our understanding of the impacts of PM2.5 exposures on public 

health/welfare in the most meaningful way. This workshop was a major step in a series of 

interactions to foster improved long-term communication between external stakeholders, 

including air quality monitoring experts and health researchers. These researchers expressed a 

strong interest in having access to PM2.5 speciation measurements collected more frequently.5

B.2 PM10

Measurements from PM10 monitoring stations are primarily used for comparison to the 

NAAQS; however, most serve multiple objectives. PM10 monitoring stations generally have an 

urban focus and are required in MSAs according to population and concentrations relative to the 

NAAQS. Local considerations are also a factor in determining the actual required number of 

monitoring sites. More stations are required in larger MSAs and MSAs with more evidence of 

poor air quality, while monitors are also required in clean MSAs of certain size. The number of 

monitors in areas where MSA populations exceed 1,000,000 must be in the range from 2 to 10 

stations, while in low population urban areas no more than two stations are required (see Table 

D–4 of Appendix D to CFR Part 58). Because sources of air pollutants and local control efforts 

can vary from one part of the country to another, some flexibility is allowed in selecting the 

actual number of PM10 stations in any one locale. 

The network of PM10 monitors has been operational since 1987. The network currently 

includes over 800 monitoring stations throughout the country with most metropolitan areas 

operating more PM10 monitors than required by current monitoring requirements. The PM10

monitoring stations operate FRMs using different sampling frequencies including: daily, one-intwo day, one-in-three day, or one-in-six day sampling. The sampling frequency is based on the 

relative concentration level of the site with respect to the 24-hour standard. There are also FEMs 

that are operated continuously. PM10 monitors operating across the country are almost 

exclusively FRMs or FEMs. Figure B-4 illustrates the locations of the PM10 FRMs and FEMs 

reporting to AQS. 

The PM10 monitoring stations are currently required to collect and report monitoring data 

under standard temperature and pressure (STP) conditions. PM2.5 and PM10-2.5 are required to be 

collected and reported at local conditions. Correction of the sampled aerosol volume to 

"standard" conditions is typically small (e.g., less than a few percent) except in locations at 

 5

 A summary of the workshop including recommendations was published in December 2008 and is available at 

www.epa.gov/ORD/npd/pdfs/FINAL-April-2008-AQ-Health-Research-Workshop-Summary-Dec-2008.pdf. 
B-10

Figure B-4. PM10 Monitoring Network Reporting to AQS 
B-11

higher altitudes and those with large diurnal or seasonal temperature changes (US EPA, 1996, 

Volume I, Section 4.2.4). 

B.3 PM10-2.5 

The EPA is requiring approximately 80 PM10-2.5 monitoring stations as part of the 

National Core (NCore) multiple pollutant network. This network is planned to be fully 

operational by January 1, 2011.6

 As State and local agencies measure PM10-2.5 mass as required 

through the NCore program, we anticipate the number of stations will increase. In addition to 

NCore, it is appropriate to calculate PM10-2.5 concentrations from collocated PM2.5 and PM10

methods of the same make and model. However, in many cases the PM10 data will need to be 

adjusted to local conditions since most PM10 data are reported as STP, while PM2.5 is reported at 

local conditions (LC). Note: only low-volume samplers are used in calculating PM10-2.5 as these 

are the only approved reference and equivalent methods for this measurement.7

 

For PM10-2.5 speciation, we do not expect methods to be fully developed in time to meet 

the January 1, 2011, start date for monitoring at NCore. The EPA has been working with the 

CASAC Ambient Air Monitoring and Methods Subcommittee (AAMMS) on this issue and has 

implemented a pilot program evaluating PM10-2.5 speciation methods at two locations in 2010.8 

B.4 NATIONAL CORE (NCORE) 

The NCore network is a multi-pollutant network that includes measurements of particles, 

gases, and meteorology (71 FR 61236, October 17, 2006). The network is intended to support 

integrated air program management needs. While measurements made at NCore stations will 

often be used for comparison to one or more NAAQS as well as for public reporting of air 

quality data, their enhanced value is in providing multi-pollutant data for validation of models, 

trends, and as an input to research studies to determine the relative importance of collocated 

pollutants. The NCore monitoring network began January 1, 2011 at approximately 80 stations. 

NCore stations are intended to be long-term stations useful for a variety of applications. The 

locations of these stations are specified to be placed away from direct emissions sources that 

could substantially impact the ability to detect area-wide concentrations. The NCore network is 

comprised of stations in both urban and rural areas. Urban NCore stations are to be generally 

 6

 Monitoring agencies are bringing their PM10-2.5 methods online now and, as of April 2011, 21 NCore sites are 

already reporting data to the AQS. 

7

 A list of approved FRMs and FEMs is available on EPA’s web site at: http://www.epa.gov/ttn/amtic/criteria.html. 

8

 A February 2009 consultation with the CASAC AAMM subcommittee discussed issues related to coarse particle 

speciation measures. For more information on this consultation, please see: 

http://yosemite.epa.gov/sab/sabproduct.nsf/264cb1227d55e02c85257402007446a4/3494de4d0ccb394485257463006

4d4e4!OpenDocument
B-12

located at an urban or neighborhood scale to provide representative concentrations of exposure 

expected throughout the metropolitan area. Rural NCore stations are to be located, to the 

maximum extent practicable, at a regional or larger scale away from any large local emission 

source, so that they represent ambient concentrations over an extensive area. States and where 

applicable, local monitoring agencies submitted plans for meeting the NCore requirements 

during the summer of 2009. The proposed NCore stations are collocated with several other well 

leveraged networks such as CSN, Photochemical air monitoring stations (PAMS), National Air 

Toxics Trends Stations (NATTS), IMPROVE, and CASTNET. The expected NCore locations 

are identified in Figure B-5. 
B-13

Figure B-5. NCore Monitoring Network 
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 APPENDIX 

C

C-1 

Table C-1. Predicted Percent of Counties with Monitors (and percent of population in counties w

Not Likely to Meet Current and Alternative Annual and 24-hour PM2.5 Standards

Region > All U.S. Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwest So

Ca

Total # of counties > 532 93 149 135 45 17 69 

Total population (x 1,000)> 184,180 44,345 40,271 37,512 7,694 8,962 20,821 2

Current Standards 

annual 

μg/m3 

24-

hour 

μg/m3 

Statistic Numbers of counties, populations, and percentages of total 

15 35 

# counties 35 0 1 5 0 1 19 

population 28,801 0 662 3,683 0 180 6,615 

% # counties 7% 0% 1% 4% 0% 6% 28% 

% population 16% 0% 2% 10% 0% 2% 32% 

Alternative Standards 

annual 

μg/m3 

24-

hour 

μg/m3 

Statistic Numbers of counties, populations, and percentages of total 

12 35 

# counties 149 21 30 66 0 2 20 

population 76,579 14,936 10,318 23,998 0 218 6,634 

% # counties 28% 23% 20% 49% 0% 12% 29% 

% population 42% 34% 26% 64% 0% 2% 32% 

11 35 

# counties 263 39 82 102 5 3 22 

population 107,447 22,952 21,224 31,346 1,491 3,290 6,668 2

% # counties 49% 42% 55% 76% 11% 18% 32% 

% population 58% 52% 53% 84% 19% 37% 32% 

13 30 

# counties 130 21 10 50 2 3 32 

population 78,286 13,688 6,152 23,692 1,627 393 12,210 

% # counties 24% 23% 7% 37% 4% 18% 46% 

% population 43% 31% 15% 63% 21% 4% 59% 
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 APPENDIX 

D

D-1 

Table D-1. Predicted Percent of Counties with Monitors (and percent of population in counties wi

Not Likely to Meet Current and Alternative 24-hour PM10 Standards 

Region> All U.S. Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwest 

Total # of counties > 307 37 57 50 40 25 77 

Total population (x1,000) 

> 120,090 15,397 27,181 21,352 5,917 11,112 15,270 

Current Standard: 3-Year Expected Exceedance Equivalent Design Value 

μg/m3 Statistic Numbers of counties, populations, and percentages of total 

150 

# counties 41 0 3 0 1 11 13 

population 32,835 0 4,626 0 14 5,485 1,878 

% # counties 13% 0% 5% 0% 3% 44% 17% 

% population 27% 0% 17% 0% 0% 49% 12% 

Alternative Standards: 24-hour level (3-year average 98th percentile) 

μg/m3 Statistic Numbers of counties, populations, and percentages of total 

87 

# counties 37 0 2 2 2 11 10 

population 20,515 0 4,063 507 552 5,924 1,789 

% # counties 12% 0% 4% 4% 5% 44% 13% 

% population 17% 0% 15% 2% 9% 53% 12% 

85 

# counties 39 0 2 3 2 12 10 

population 21,887 0 4,063 1,789 552 6,014 1,789 

% # counties 13% 0% 4% 6% 5% 48% 13% 

% population 18% 0% 15% 8% 9% 54% 12% 

80 

# counties 45 0 2 4 3 13 11 

population 24,535 0 4,063 3,183 599 6,131 1,833 

% # counties 15% 0% 4% 8% 8% 52% 14% 

% population 20% 0% 15% 15% 10% 55% 12% 

75 

# counties 55 0 3 6 5 13 15 

population 35,703 0 4,626 3,491 637 6,131 2,570 

% # counties 18% 0% 5% 12% 13% 52% 19% 

% population 30% 0% 17% 16% 11% 55% 17% 

70 

# counties 71 0 4 7 7 13 27 

population 43,823 0 4,644 8,868 881 6,131 5,052 

% # counties 23% 0% 7% 14% 18% 52% 35% 

% population 36% 0% 17% 42% 15% 55% 33% 

65 

# counties 87 2 4 9 10 14 33 

population 49,394 775 4,644 10,421 1,029 7,507 5,989 

% # counties 28% 5% 7% 18% 25% 56% 43% 

% population 41% 5% 17% 49% 17% 68% 39% 
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 E-1

APPENDIX E 

Information Regarding a 1-hour PM2.5 Mass Indicator 

 

This Appendix presents information on 2005-2007 levels of 1-hour PM2.5 mass 

concentrations in 14 urban study areas1

 and on the “what if” PM10 light extinction conditions that 

would exist if the study areas met each of 10 alternative secondary PM NAAQS scenarios based 

on a 1-hour PM2.5 mass indicator. With respect to the latter subject, this Appendix is therefore 

similar to Chapter 4 of the Particulate Matter Urban-Focused Visibility Assessment (UFVA, US 

EPA, 2010b)2

, which presented similar information for 18 secondary PM NAAQS scenarios 

based on PM10 light extinction as the indicator, for the current annual and 24-hour PM2.5

NAAQS, and for a scenario with an annual NAAQS of 12 μg/m3

 and a 24-hour NAAQS of 25 

μg/m3. 

In section B.1.2 of Appendix B, it was noted that recent investigations have identified 

issues with data quality for measurements of both 24-hour concentrations and 1-hour 

concentrations from continuous Federal Equivalent Methods for PM2.5. This appendix on a 1-

hour PM2.5 mass indicator is based only on 2005-2007 data from continuous instrument models 

that pre-date the introduction of continuous Federal Equivalent Methods for PM2.5. While no 

systematic investigation was performed on these 2005-2007 data similar to the investigations 

described in Hanley and Reff, 2011 and Evangelista, 2011,3,4 no similar issues were noticed in 

the course of using these 2005-2007 data. 

E.1 INDICATOR AND MONITORING METHOD 

 As in Chapter 4 of the UFVA, this Appendix excludes from all NAAQS scenarios and 

results all non-daylight hours and all daylight hours with relative humidity greater than 90 

percent. This applies to both the definition of 10 secondary NAAQS scenarios, and to graphics 

and tables that characterize ambient conditions. While ambient humidity should not affect 

 1

 The UFVA assessed light extinction conditions in 15 study areas, one of which was St. Louis. The PM10-2.5 values 

for St. Louis in the UFVA were determined by difference between two nearby monitoring sites. Based on comments 

received on the draft UFVA to the effect that the PM10 monitoring site used for St. Louis was not representative of 

the St. Louis urban area, EPA does not consider the PM10-2.5 concentrations for St. Louis to be credible. The PM10

light extinction results for St. Louis have therefore been excluded from tables and figures in this appendix that 

involve PM10 light extinction results. 2 Particulate Matter Urban-Focused Visibility Assessment Final Document, EPA 452/R-10-004, July 2010. 

Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 3

 Hanley, T and Reff, A (2011). Assessment of PM2.5 FEMs Compared to Collocated FRMs. Memorandum to PM 

NAAQS Review Docket EPA-HQ-OAR-2007-0492. April 7, 2011. Available: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 4

 Evangelista, M. (2011). Investigation of 1-hour PM2.5 Mass Concentration Data from EPA-Approved Continuous 

Federal Equivalent Method Analyzers. Memorandum to PM NAAQS Review Docket (EPA-HQ-OAR-2007-0492). 

April 5, 2011. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 
 E-2

conventional measurement approaches for 1-hour PM2.5 mass, the issue of co-occurrence of high 

humidity levels with light extinction due to natural conditions would still apply. See section 

3.3.5 of the UFVA, and section 4.2.1 of this document at “Current Visibility Levels.” The 

assumed hours of daylight are the same as those used in the UFVA, as shown in Table 3-5 of the 

UFVA. 

 All values for 1-hour PM2.5 mass concentration in this appendix come from the 

continuous instruments at the 14 urban study sites, with no adjustment to make these values 

consistent with the collocated 24-hour FRM measurement of PM2.5 mass. Appendix A of the 

UFVA provides details on the type of continuous instrument at each study site. TEOMs were 

used at all sites except for beta attenuation instruments in Fresno and Philadelphia, nephelometer 

instruments in Tacoma and Phoenix, and an FDMS instrument in Salt Lake City. 

 For conciseness, only the daily maximum daylight 1-hour PM2.5 mass concentration 

indicator is considered in this Appendix. It would also be possible to construct alternative 

NAAQS scenarios of an all-hours type, which could be analyzed in the same manner as 

presented in this Appendix. 

E.2 CURRENT CONDITIONS OF 1-HOUR PM2.5 MASS 

Figure E-1 is a box plot of 2005-2007 daily maximum daylight 1-hour PM2.5 mass 

concentrations for the 14 study areas, excluding hours with relative humidity greater than 90 

percent, to give a sense of the range and central tendency of this parameter. The horizontal 

reference lines are at 10, 20, 30, 40 and 60 μg/m3.

 The relative positions of the 90th percentile 

concentrations (indicated by the horizontal stroke at the top of the whisker) are generally 

consistent with the relative ranking of these sites according to their design values for the 24-hour 

PM2.5 NAAQS (see Table 3-2 of the UFVA); similarly, the relative positions of the median 

concentrations are generally consistent with the annual PM2.5 design values. Table E-1, based on 

the same data as Figure E-1, presents the percentage of days in 2005-2007 on which the daily 

maximum daylight 1-hour PM2.5 concentration exceeded the reference levels represented by the 

horizontal lines in Figure E-1. 
 E-3

Figure E-1. 2005-2007 Daily Maximum Daylight 1-hour PM2.5 Mass Concentrations 

(μg/m3) for the 14 Study Areas (excluding hours with relative humidity > 90 %) 

Table E-1. Percentage of Days with Daily Maximum Daylight 1-hour PM2.5 Mass 

Concentration Exceeding Reference Levels in 2005-2007 (excluding hours with relative 

humidity > 90 %)

1-hour PM2.5 Mass Reference Level (μg/m3) 

Study Area Number of Days 

with Estimates 10 20 30 40 60 

Tacoma 109 50 11 1 0 0 

Fresno 324 88 62 37 20 8 

Los Angeles 300 92 81 67 46 20 

Phoenix 86 60 8 1 1 1 

Salt Lake City 306 64 20 11 7 2 

Dallas 273 75 25 5 0 0 

Houston 148 80 42 14 5 0 

Birmingham 349 92 60 37 23 8 

Atlanta 279 86 56 28 10 1 

Detroit 141 92 72 52 36 13 

Pittsburgh 277 94 57 28 15 3 

Baltimore 181 90 46 22 8 1 

Philadelphia 143 99 84 63 45 20 

New York 225 75 43 25 13 3 
 E-4

E.3 ALTERNATIVE NAAQS SCENARIOS BASED ON 1-HOUR PM2.5 MASS AS 

THE INDICATOR 

 To ensure examination of a wide enough range of alternative standards based on 1-hour 

PM2.5 mass to encompass the range of standards that might be considered as alternatives to the 

PM10 light extinction NAAQS scenarios examined in Chapter 4 of the UFVA, we considered 

levels of 10, 20, 30, 40, and 60 μg/m3. Only the daily maximum daylight hour form was 

considered. Each level was combined with two statistical forms: the three-year average of the 

annual 90th percentile value and the three-year average of the annual 95th percentile value. For 

ease of reference, these scenarios are designated by letters from “aa” to “jj” and listed in Table 

E-2. Looking somewhat ahead to results presented below, the scenarios are arranged in Table E2 in order of least to most stringent in terms of the reductions in ambient PM2.5 needed from 

current levels to meet the current and alternative NAAQS levels and forms. 

Table E-2. Alternative NAAQS Scenarios Based on Daily Maximum Daylight 1-Hour 

PM2.5 Mass, Averaged Over Three Years 

(excluding hours with relative humidity > 90 %) 

NAAQS Scenario Level (μg/m3) Statistical Form 

aa 60 3-year average of 90th percentile 

bb 60 3-year average of 95th percentile 

cc 40 3-year average of 90th percentile 

dd 40 3-year average of 95th percentile 

ee 30 3-year average of 90th percentile 

ff 30 3-year average of 95th percentile 

gg 20 3-year average of 90th percentile 

hh 20 3-year average of 95th percentile 

ii 10 3-year average of 90th percentile 

jj 10 3-year average of 95th percentile 

E.4 APPROACH TO MODELING “WHAT IF” CONDITIONS OF PM10 LIGHT 

EXTINCTION FOR ALTERNATIVE SECONDARY NAAQS BASED ON 1-

HOUR PM2.5 MASS 

 Before modeling “what if” conditions, we augmented the data set described in Table 3-4 

of the UFVA in the same manner as described in section 4.1.4 of the UFVA, to achieve seasonal 

balance despite the lack of monitoring data for one quarter in each of Houston and Phoenix. In 

Tacoma and Phoenix, which had data only for two years in the 2005-2007 period, we averaged 

the percentile values from the only two available years rather than the three years defined for the 

statistical form of the NAAQS scenarios. 

 The modeling of daily maximum daylight 1-hour PM2.5 mass under each of the scenarios 

listed in Table E-2 used a rollback approach that combined relevant concepts and steps from the 

rollback methods described in sections 4.1.4 (for PM10 light extinction scenarios) and 4.2.2 (for 

scenarios based on annual average and 24-hour average PM2.5) of the UFVA. The following are 

the steps in the modeling. 
 E-5

1. Identify the 90th percentile daily maximum daylight 1-hour PM2.5 mass value in each 

of 2005, 2006, and 2007 for a study area. Average these to determine the 3-year 

average design value for that percentile form. Repeat for the 95th percentile form. 

These design values are presented in Table E-3. They range from 22 to 81 μg/m3

, 

indicating that some study areas meet some of the NAAQS scenarios under current 

conditions. In such cases, PM2.5 concentrations were not adjusted, i.e., there was no 

“roll up” for any area in any scenario. 

2. Using the same days and hours as contributed by the three annual 90th percentile 

values for actual 1-hour PM2.5 mass, find the three corresponding values of policy 

relevant background (PRB) 1-hour PM2.5 mass. Average these three annual values of 

PRB 1-hour PM2.5 to obtain the 3 year average PRB portion of the actual 1-hour 

PM2.5 design value for the 90th percentile form. Repeat for the 95th percentile form. 

In the modeling for the NAAQS scenarios examined in the UFVA, PRB for 1-hour 

PM2.5 mass was not explicitly calculated because it was not needed in the rollback 

modeling for the scenarios addressed in the UFVA. Therefore, it was necessary to 

reconstruct this parameter by adding the values for the PRB concentrations of the five 

components of PM2.5: nitrate, sulfate, elemental carbon, organic carbon material, and 

soil. The method for estimating PRB for these five components is described in 

Appendix C of the UFVA. 

3. Subtract the value from step 2 from the value from step 1, to determine the non-PRB 

portion of the 1-hour PM2.5 mass design value. 

4. Calculate the percentage reduction required in non-PRB 1-hour PM2.5 mass in order 

to reduce the design value to the level that defines the NAAQS scenario, using the 

following equation: 

Percent reduction required = 1 – (NAAQS level – PRB portion of the design value)

(non-PRB portion of the design value) 

The percentage reductions determined in step 4 are shown in Table E-4. Note that for 

some combinations of area and scenario no reduction is required because the 2005-

2007 design value already meets the NAAQS scenario. These cases with a required 

percentage reduction of zero are shaded blue in Table E-4. 

5. Turning to the entire set of day/hour-specific actual and PRB daylight 1-hour 

concentrations of the five PM2.5 components for the three (or two) year period, 

determine the non-PRB portion of each of the five components in an hour by 

subtracting the PRB value from actual value, reduce it by the percentage determined 

in step 4, and add back in the PRB 1-hour concentration of the component. 

6. Finally, calculate PM10 light extinction using the reduced values of the five 

components, the original value of 1-hour PM10-2.5, and the 1-hour value of f(RH), 

according to the following equation for PM10 light extinction (see section 3.2.3 of the 

UFVA for an explanation of the variables in this equation). 
 E-6

bextPM = 3 x f(RH) x [Sulfate] 

+ 3 x f (RH) x [Nitrate] 

+ 4 x [Organic Mass] 

+10 x [Elemental Carbon] 

+ 1 x [Fine Soil] 

+ 0.6 x [Coarse Mass] 

 These steps assume that in order to meet a PM NAAQS scenario based on 1-hour PM2.5

as the indicator, each component of PM2.5 is reduced by an equal percentage, across the five 

components and across all hours. In actual implementation of such a NAAQS, each state would 

develop an attainment strategy, which might result in unequal percentage reductions of the 

components. If the strategy emphasized reductions in the fine soil component, for example, PM 

light extinction levels would remain high relative to those estimated by these steps, because fine 

soil is not efficient in terms of reducing visibility compared to the other four components on a 

dry mass-to-mass basis. On the other hand, a strategy that involves relatively large reductions in 

sulfate or nitrate would achieve greater reductions in PM light extinction than estimated by these 

steps. The uncertainty in how the results of this rollback method compare to the results of actual 

attainment strategies should be kept in mind when comparing the results of “what if” scenarios 

for NAAQS based on PM2.5 mass as the indicator versus scenarios based on PM10 light 

extinction. Unlike the effect of humidity variation between areas, this source of uncertainty is 

not reflected in any of the results presented in this Appendix and will not be apparent in 

comparisons of results in this Appendix to results presented in the UFVA for NAAQS scenarios 

based on PM10 light extinction. 

 These steps also assume no change in PM10-2.5 concentrations between current conditions 

and “what if” conditions. While reductions in PM10-2.5 would not be needed to meet a secondary 

NAAQS based on 1-hour PM2.5 mass, it is possible that strategies to control PM2.5 concentrations 

might also achieve reductions in PM10-2.5 concentrations because some sources emit both and 

some control methods achieve some reductions in both. However, in most of the 14 study areas, 

PM10-2.5 makes a small contribution to estimated PM10 light extinction. For those areas for which 

no local data on PM10-2.5 concentrations were available, a low contribution of PM10-2.5 to light 

extinction was inevitable because of the method used to fill the PM10-2.5 data gap. That method 

was to apply a long-term regional ratio of PM10-2.5 to PM2.5 to the local hourly PM2.5

concentration, which by its nature cannot produce estimates of hourly PM10-2.5 that are as high as 

might actually be created by shorter-term PM10.2.5 emissions episodes 
 E-7

Table E-3. 2005-2007 Design Values for 1-Hour PM2.5 Mass (μg/m3) 

Study Area Percentile Form 

90th 95th

Tacoma 22 27 

Fresno 55 66 

Los Angeles 72 81 

Phoenix 20 24 

Salt Lake City 32 45 

Dallas 26 29 

Houston 33 37 

Birmingham 55 74 

Atlanta 40 45 

Detroit 64 79 

Pittsburgh 46 51 

Baltimore 37 43 

Philadelphia 67 77 

New York 44 55 

Table E-4. Percentage Reductions in non-PRB PM2.5 Components Required to Meet 

NAAQS Scenarios based on 1-Hour PM2.5 Mass 

Scenario aa bb cc dd ee ff gg hh ii jj 

Level (μg/m3) 60 60 40 40 30 30 20 20 10 10 

Percentile Form 90 95 90 95 90 95 90 95 90 95 

Study Area Percentage Reduction 

Tacoma 0 0 0 0 0 0 11 27 60 69 

Fresno 0 10 28 40 46 55 65 71 83 86 

Los Angeles 17 26 45 51 59 64 73 76 87 88 

Phoenix 0 0 0 0 0 0 0 15 51 58 

Salt Lake City 0 0 0 12 7 34 39 56 70 78 

Dallas 0 0 0 0 0 0 23 34 64 69 

Houston 0 0 0 0 9 20 40 49 71 78 

Birmingham 0 19 28 46 46 60 65 74 84 87 

Atlanta 0 0 0 12 25 34 51 57 77 80 

Detroit 7 24 38 50 54 63 70 75 85 88 

Pittsburgh 0 0 13 22 35 42 57 62 79 81 

Baltimore 0 0 0 8 19 31 47 55 74 78 

Philadelphia 10 22 40 49 55 62 71 75 86 88 

New York 0 0 8 28 32 46 55 65 78 83 
 E-8

E.5 1-HOUR PM2.5 MASS RESULTS FOR “JUST MEETING” ALTERNATIVE 

SECONDARY NAAQS SCENARIOS BASED ON 1-HOUR PM2.5 MASS 

 As a check on the reasonableness of the rollback method described in section 4.0 and on 

the accuracy of the code used to implement it, it is of interest to examine the distribution of the 

levels of 1-hour PM2.5 that result from the method. Ideally, after rollback any area that had a 

non-zero required reduction should have a post-rollback design value for 1-hour PM2.5 mass that 

is exactly equal to the target design value. Also, there should be a progression of reductions in 1-

hour PM2.5 medians and other percentile points on the distribution as progressively more 

stringent scenarios are modeled. 

 Table E-5 shows the post-rollback 1-hour PM2.5 mass design values for the scenarios, 

with percentile forms matched. Design values for area-scenario combinations for which the 

required reductions were zero have been omitted, because the current conditions design values 

for these combinations would not be expected to reflect the target design value. It can be seen 

that the design values progress as expected and are in the vicinity of the target design values, but 

are not always exactly equal to the targets. EPA staff attributes this to the fact that PRB 

concentrations of 1-hour PM2.5 mass vary from hour to hour. It is possible for the daily 

maximum PM2.5 mass concentration on a certain day in 2005 with a percentile rank of, for 

example, 96th to have a relatively small PRB portion and a large non-PRB portion compared to 

the daily maximum concentration that ranks 95th. When an equal reduction is made to the nonPRB portion of each total concentration, the two values may switch rank positions, and so a new 

day and hour becomes the 2005 contributor to the rolled back three-year design value. Since this 

day and hour was not used to determine the required percentage reduction, the resulting design 

value will not exactly meet the target design value. It would be possible to iterate with higher 

and lower percentage reductions until the rolled back design value exactly matched the target 

design value, but EPA considered this degree of refinement to be unnecessary in order to meet 

the objectives of the Policy Assessment, given other uncertainties in the underlying data and in 

the assumptions used to estimate PM10 light extinction values. 

Staff also generated and examined box plots of daily maximum daylight 1-hour PM2.5

mass concentrations as a check for conceptual or programming errors, and found them to match 

expectations. They are not included here, for conciseness. 
 E-9

Table E-5. Post-rollback Design Values for Daily Maximum 1-Hour PM2.5 Mass 

Design values are shown only for combinations of study area and scenario for which the study area does not meet 

the scenario under current conditions, such that reductions were made during the rollback modeling.

Scenario aa bb cc dd ee ff gg hh ii jj 

Level (μg/m3) 60 60 40 40 30 30 20 20 10 10 

Statistical Form 90th 95th 90th 95th 90th 95th 90th 95th 90th 95th

Study Area Corresponding Design Value (μg/m3) (same percentile form as the scenario) 

Tacoma 20 21 11 12 

Fresno 63 40 42 30 31 20 21 10 10 

Los Angeles 53 53 35 35 26 26 18 18 9 9 

Phoenix 19 10 10 

Salt Lake City 38 29 28 19 19 10 10 

Dallas 23 23 12 11 

Houston 29 27 19 18 10 9 

Birmingham 58 42 39 32 29 21 20 11 10 

Atlanta 36 28 27 19 18 10 10 

Detroit 52 59 34 39 26 29 17 20 9 10 

Pittsburgh 33 33 24 25 16 17 8 9 

Baltimore 38 31 28 21 19 10 10 

Philadelphia 46 44 31 30 23 22 16 15 8 8 

New York 42 40 32 30 21 20 11 10 

E.6 PM10 LIGHT EXTINCTION RESULTS FOR “JUST MEETING” ALTERNATIVE 

SECONDARY NAAQS SCENARIOS BASED ON 1-HOUR PM2.5 MASS 

 The rollback steps described in section 4.0 resulted in estimates of PM10 light extinction 

for each day and hour in each study area, for each of the 10 NAAQS scenarios based on 1-hour 

PM2.5 mass as the indicator. Two summaries of these conditions are presented here. 

 Figure E-2 presents a box plot of daily maximum daylight 1-hour PM10 light extinction 

for each NAAQS scenario based on 1-hour PM2.5 mass. These can be compared to Figure 3-8(a) 

of the UFVA representing pre-rollback daily maximum PM10 light extinction, and to the upper 

panel of the figures in Appendix F of the UFVA representing the daily maximum PM10 light 

extinction levels resulting from the 20 NAAQS scenarios examined in the UFVA (18 scenarios 

based on PM10 light extinction as the indicator, the current annual and 24-hour PM2.5 NAAQS, 

and a scenario with an annual NAAQS of 12 μg/m3

 and a 24-hour NAAQS of 25 μg/m3). It can 

be seen that the distribution of PM2.5 mass in a given study area shifts downward as the NAAQS 

scenarios progress from least to most stringent (as indicated by the required percentage 

reduction) and in most cases become more similar to other areas (once the progression of more 

stringent scenarios begins to require reductions in a given area). 

 

Table E-6 presents the percentage of days in 2005-2007 on which daily maximum 1-hour 

PM10 light extinction exceeded each of the CPLs, under each of the 10 secondary PM NAAQS 

scenarios based on 1-hour PM2.5 mass. These percentages are necessarily based on the days for 
 E-10

which data to estimate PM10 light extinction were available, but are best estimates of the 

percentage of all days in the year given that the days with data were well distributed across the 

year on either a one-in-three or one-in-six sampling schedule. These percentages can be 

compared to the same-basis percentages presented in Table 4-7 of the UFVA.
 E-11

Figure E-2. Distributions of Daily Maximum Daylight 1-Hour PM10 Light Extinction 

under “Just Meet” Conditions for NAAQS Scenarios Based on 1-Hour PM2.5 Mass 

(excluding hours with > 90% RH)

(aa) NAAQS Scenario: 60 μg/m3 and 90th percentile

(bb) NAAQS Scenario: 60 μg/m3 and 95th percentile 
 E-12

Figure E-2 (cont). Distributions of Daily Maximum Daylight 1-Hour PM10 Light 

Extinction under “Just Meet” Conditions for NAAQS Scenarios Based on 1-Hour PM2.5

Mass (excluding hours with > 90% RH) 

(cc) NAAQS Scenario: 40 μg/m3 and 90th percentile 

(dd) NAAQS Scenario: 40 μg/m3 and 95th percentile 
 E-13

Figure E-2 (cont). Distributions of Daily Maximum Daylight 1-Hour PM10 Light 

Extinction under “Just Meet” Conditions for NAAQS Scenarios Based on 1-Hour PM2.5

Mass (excluding hours with > 90% RH) 

(ee) NAAQS Scenario: 30 μg/m3 and 90th percentile

(ff) NAAQS Scenario: 30 μg/m3 and 95th percentile 
 E-14

Figure E-2 (cont). Distributions of Daily Maximum Daylight 1-Hour PM10 Light 

Extinction under “Just Meet” Conditions for NAAQS Scenarios Based on 1-Hour PM2.5

Mass (excluding hours with > 90% RH) 

(gg) NAAQS Scenario: 20 μg/m3 and 90th percentile 

(hh) NAAQS Scenario: 20 μg/m3 and 95th percentile 
 E-15

Figure E-2 (cont). Distributions of Daily Maximum Daylight 1-Hour PM10 Light 

Extinction under “Just Meet” Conditions for NAAQS Scenarios Based on 1-Hour PM2.5

Mass (excluding hours with > 90% RH) 

(ii) NAAQS Scenario: Daily Max: 10 μg/m3 and 90th percentile

(jj) NAAQS Scenario: Daily Max: 10 μg/m3 and 95th percentile 
E-16

Table E-6. Percentage of Days Across Three Years (two years in the case of Phoenix and Houston) with 

Daylight PM10 Light Extinction Above CPLs when “Just Meeting” NAAQS Scenarios Based on 1-Ho

(Blue shading indicates no reduction required from current conditions.)

Days with max hour above 

64 Mm-1

Days with max hour above 

112 Mm-1

Days w

Scenario aa bb cc dd ee ff gg hh ii jj aa bb cc dd ee ff gg hh ii jj aa bb cc d

NAAQS Level 

(μg/m3) 60 60 40 40 30 30 20 20 10 10 60 60 40 40 30 30 20 20 10 10 60 60 40 4

NAAQS 

Percentile 

Form 90 95 90 95 90 95 90 95 90 95 90 95 90 95 90 95 90 95 90 95 90 95 90 9

Area Percentage of days Percentage of days Per

Tacoma 53 53 53 53 53 53 53 53 43 35 23 23 23 23 23 23 23 23 11 6 4 4 4 

Fresno 76 73 65 57 69 60 55 44 28 17 52 48 37 31 44 32 29 18 9 4 30 27 17 

Los Angeles 89 87 84 81 84 79 74 69 41 30 78 76 65 57 65 53 41 31 11 7 52 46 30 

Phoenix 44 44 44 44 44 44 44 44 37 32 6 6 6 6 6 6 6 6 6 6 1 1 1 

Salt Lake 

City 45 45 45 37 45 45 45 26 17 10 17 17 17 15 17 17 17 11 8 5 8 8 8 

Dallas 81 81 81 81 81 81 81 71 41 29 41 41 41 41 41 41 41 32 8 5 10 10 10 

Houston 79 79 79 79 79 79 74 65 32 27 44 44 44 44 44 44 35 28 6 3 11 11 11 

Birmingham 89 85 80 68 87 80 72 62 41 34 65 56 51 36 58 51 40 30 15 12 34 26 21 

Atlanta 91 91 91 89 91 89 82 77 47 34 75 75 75 68 74 66 51 35 3 3 31 31 31 

Detroit 84 80 74 72 76 73 65 60 40 33 67 57 51 43 53 48 34 21 9 6 43 28 13 

Pittsburgh 85 85 81 77 81 77 63 55 27 19 57 57 51 45 52 44 29 22 3 0 26 26 18 

Baltimore 81 81 81 76 81 74 64 56 31 20 51 51 51 45 51 44 31 23 4 3 23 23 23 

Philadelphia 84 78 71 62 72 63 55 43 17 10 60 54 33 29 37 31 16 10 3 3 26 17 8 

New York 83 83 80 71 81 73 63 56 27 19 60 60 56 39 56 40 32 22 6 3 29 29 25 

Average 76 75 72 68 73 69 64 56 34 25 50 48 43 37 45 38 31 23 7 4 23 21 15 1
F-1 

APPENDIX F 

Two Simplified Approaches to Calculate Hourly PM2.5 Light 

Extinction Values from Hourly PM2.5 Mass and Relative Humidity 

Data Plus 24-hour Mean PM2.5 Composition Data 

F.1 OVERVIEW 

The goal of this appendix is to describe two procedures for calculating PM2.5 light 

extinction that are considerably simpler than the complex method used in the original 15-city 

assessment (UFVA, Section 3.3).1

 The possible benefits of moving to a simpler approach 

include (1) more transparency in the required calculations, (2) less intensive data processing, (3) 

an increase in the number of monitoring sites that could meet the data requirements of the 

approach without adding new sampling equipment or additional laboratory analysis, and (4) an 

increase in the number of days per year for which the calculation of PM light extinction could be 

conducted. 

The two simpler procedures addressed in this memo, denoted “T” and “W”, are similar in 

many respects to approaches “D” and “F” for which a draft analysis was presented in appendix 

4B of the second external review draft of the Policy Assessment Document.2

 The original 

UFVA and the draft analysis of approaches D and F were based on 2005-2007 data. The 

analysis of approaches T and W versus the UFVA approach presented here is based on 2007-

2009 data. The UFVA approach has been re-executed on this more recent data set to provide a 

consistent point of comparison to approaches T and W. 

In section B.1.2 of Appendix B, it was noted that recent investigations have identified 

issues with data quality for measurements of both 24-hour concentrations and 1-hour 

concentrations from continuous Federal Equivalent Methods for PM2.5. This appendix on 

calculated PM2.5 light extinction values is based on 2007-2009 data, most of which came from 

continuous instrument models that pre-date the introduction of continuous Federal Equivalent 

Methods for PM2.5. Some of the 2009 data may have come from continuous FEM instruments. 

While no systematic investigation was performed on the 2007-2009 non-FEM data similar to the 

investigations described in Hanley and Reff, 2011 and Evangelista, 2011,3,4 no similar issues 

 

1 Particulate Matter Urban-Focused Visibility Assessment (UFVA) EPA 452/R-10-004, July 2010. The UFVA 

presented a method and results for PM10 light extinction. The assessment in this appendix focuses on PM2.5 light 

extinction. The UFVA is available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_risk.html. 2 The analysis of approaches D and F has been finalized in the form of a technical memo so that it is available for 

reference. Simplified Approaches for Calculation of Hourly PM2.5 Light Extinction Values from Hourly PM2.5 Mass 

and Relative Humidity Data and 24-hour PM2.5 Composition Data, Phil Lorang, EPA, November 17, 2010. 

Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 

3 Hanley, T and Reff, A (2011). Assessment of PM2.5 FEMs compared to collocated FRMs. Memorandum to PM 

NAAQS Review Docket EPA-HQ-OAR-2007-0492. April 7, 2011. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 4 Evangelista, M. (2011). Investigation of 1-hour PM2.5 Mass Concentration Data from EPA-Approved Continuous 

Federal Equivalent Method Analyzers. Memorandum to PM NAAQS Review Docket (EPA-HQ-OAR-2007-0492). 

April 5, 2011. Available at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.htm.l
F-2 

were noticed in the course of using these data. Moreover, all of the 1-hour data used in this 

Appendix was normalized to 24-hour concentrations measured by filter-based Federal Reference 

Method samplers on the same day at the same site. 

F.2 COMPLEXITY OF THE UFVA APPROACH 

The UFVA approach to estimation of hourly PM2.5 light extinction has the following 

complex aspects: 

1. The SANDWICH mass balance model is used to estimate 24-hour average PM2.5 sulfate, 

nitrate, and organic carbonaceous material (OCM) mass loading on the FRM filter for 

each Chemical Speciation Network (CSN) sample day.5 This requires information on 

daily temperature and relative humidity. The SANDWICH-estimated FRM loadings are 

initially used to compare with FRM mass. The sulfate and nitrate components are 

initially derived from the relevant CSN filters, with adjustments to represent FRM mass. 

FRM sulfate includes estimated particle bound water while the FRM nitrate loading may 

under-represent the ambient concentration of nitrate. These are re-adjusted in a 

subsequent step to represent ambient conditions prior to calculation of light extinction. 

2. The estimates of 24-hour average PM2.5 elemental carbon and fine soil component 

concentrations are determined from the analysis of the relevant CSN filter. 

3. Monthly mean diurnal variations of each of the major PM2.5 components from CMAQ air 

quality simulation modeling results for the location of each monitoring site are applied to 

sample day-specific CSN 24-hour samples to create preliminary estimates of hourly 

component concentrations. For the UFVA, available output from a 2004 CMAQ 

modeling platform was used. This step can result, for example, in preliminary estimates 

of concentrations of sulfate that are fairly uniform throughout a day while concentrations 

of nitrate may show much more variation because of temperature effects on the 

gas/particle partitioning of nitrate. 

4. Estimates of hourly PM2.5 mass are developed by normalizing continuous PM2.5

measurements to the 24-hour FRM filter mass.6

5. The preliminary estimates of hourly components from step 3 above are scaled up or down 

in equal proportion to reconcile their sum to the estimate of hourly PM2.5 mass from step 

4.

 

5 Frank, N., Retained Nitrate, Hydrated Sulfates, and Carbonaceous Mass in Federal Reference Method Fine 

Particulate Matter for Six Eastern U.S. Cities, J. Air Waste Manage. Assoc., 56, 500-511, 2006. 6 The UFVA, Appendix 4B of the second external review draft of the Policy Assessment Document, and the analysis 

in the technical memo referenced in footnote 2 were all based on 2005-2007 data. The continuous PM2.5 instruments 

operating in this period were not EPA-approved as Federal Equivalent Methods (FEMs). The EPA-approved FEMs 

were first used in state networks in significant numbers in 2009. The EPA staff envisions that in any future 

monitoring of hourly PM2.5 mass to implement a visibility-based secondary PM NAAQS, EPA-approved continuous 

FEMs will be required. For the UFVA, Appendix 4B of the second draft Policy Assessment, and the analysis in the 

technical memo referenced in footnote 2, therefore, hourly PM2.5 mass values from continuous instruments were 

adjusted day-by-day to match the 24-hour average PM2.5 mass reported by the collocated filter-based sampler. This 

was intended to make the values of hourly PM2.5 mass more like the values that would be obtained by FEMs. The 

analysis in this appendix is based on 2007-2009 data from continuous instruments, but only a fraction of the data are 

from FEMs. Therefore, the approach of adjusting hourly PM2.5 mass values from continuous instruments day-byday to match the 24-hour average filter-based PM2.5 mass was maintained in the analysis presented in this appendix.
F-3 

6. The resulting hourly PM2.5 sulfate and nitrate component concentrations are adjusted to 

reflect actual atmospheric concentration, which is assumed to be represented by the CSN 

filter sulfate and nitrate measurements. (This step in effect un-does the estimated FRM 

sulfate mass enhancement due to particle bound water and the FRM nitrate loss that were 

both introduced by the SANDWICH mass balance model.) 

7. The original IMPROVE algorithm is used to estimate hourly PM2.5 light extinction from 

hourly PM2.5 component and hourly relative humidity values. 

F.3 RESULTS OF THE RE-EXECUTION OF THE UFVA APPROACH FOR 2007-

2009 

Table F-1 shows the number of daily samples of suitable data that were available for the 

re-execution of the UFVA approach for each quarter and study area for the 2007-2009 period. 

Due to the discontinuation of CSN monitoring at AQS site 040137020 in the Phoenix area as of 

the end of 2006, PM2.5 FRM and speciation data from site 040139997 (JLG SUPERSITE) were 

used in the re-execution of the UFVA method for 2007-2009. Continuous PM2.5 data were taken 

from site 040139998. The needed combination of data was available from these two sites only 

for 2009, for only 84 days. Results for Phoenix should be viewed in the light of this limited set 

of data compared to other areas. All other study areas’ monitoring sites were as documented in 

the UFVA. 

Figure F-1 (which is also Figure 4-4 of this document) shows the box-and-whisker plots 

of 2007-2009 daily maximum PM2.5 light extinction. Table F-2 shows the 90th and 95th

percentile design values for daily maximum PM2.5 light extinction.7 These displays can be 

compared to 2005-2007 results presented in UFVA Table 3-4, the bottom panel of Figure 3-8 

(also reproduced as Figure 4-3 of this document), and the top half of Table 4-2, respectively, 

keeping in mind that the tables and figures in the UFVA included the estimated contribution of 

PM10-2.5 to PM10 light extinction. This comparison shows that many of the eastern areas’ median 

and 90th percentile 2007-2009 PM2.5 light extinction levels are notably lower than the 

corresponding 2005-2007 PM10 light extinction levels, even though PM10-2.5 in general did not 

contribute much to the UFVA estimates of PM10 light extinction in eastern areas. This is 

directionally consistent with the known substantial improvement in PM2.5 air quality in the 

eastern U.S. over the 2005-2009 period. 

 

7 As in the UFVA, 2007-2009 design values have been calculated using whatever daily maximum values were 

available in each year. In a future regulatory program there would presumably be a data completeness requirement 

in order to find that an area meets or does not meet the NAAQS.
F-4 

Table F-1. Number of Days per Quarter in Each Study Area

Study Area

Year 2007 2008 2009 

Total 

Number of 

Days 

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 

Tacoma 150 13 13 14 13 14 14 14 12 7 14 11 11 

Fresno 325 26 28 30 28 29 27 30 29 15 27 28 28 

Los Angeles 161 21 26 24 24 29 28 9 0 0 0 0 0 

Phoenix 84 0 0 0 0 0 0 0 0 11 24 21 28 

Salt Lake City 276 23 25 19 29 28 12 27 15 13 29 28 28 

Dallas 257 18 23 24 21 22 25 26 20 13 23 21 21 

Houston 144 15 14 9 0 15 13 12 14 7 15 15 15 

St. Louis 287 29 25 22 10 24 22 27 28 14 30 29 27 

Birmingham 330 30 30 27 26 31 27 30 29 13 28 28 31 

Atlanta 258 25 19 26 21 22 25 24 21 12 22 21 20 

Detroit-Ann Arbor 133 11 11 12 11 7 12 13 13 3 15 10 15 

Pittsburgh 264 22 22 23 23 23 24 24 25 10 25 22 21 

Baltimore 140 12 12 17 16 0 0 0 0 13 26 21 23 

Philadelphia-Wilmington 98 13 14 12 12 9 11 14 13 0 0 0 0 

New York 145 19 15 19 21 20 14 13 24 0 0 0 0 

Figure F-1. Distributions of Estimated Daily Maximum Daylight 1-Hour PM2.5 Light 

Extinction Across the 2007-2009 Period, by Study Area (excluding hours with relative 

humidity > 90%) 
F-5 

Table F-2. 2007-2009 Daily Maximum PM2.5 Light Extinction Design Values for the Study 

Areas 

Study Area Design Value for 90th Percentile 

Form (Mm-1) 

Design Value for 95th Percentile Form 

(Mm-1) 

Tacoma 121 132

Fresno 312 394

Los Angeles 434 503

Phoenix 71 97

Salt Lake City 152 285

Dallas 162 204

Houston 145 177

St. Louis 224 267

Birmingham 269 326

Atlanta 189 212

Detroit-Ann Arbor 275 436

Pittsburgh 222 256

Baltimore 183 212

Philadelphia-Wilmington 280 320

New York 295 365

F.4 SIMPLIFIED APPROACHES T AND W 

This analysis examines the difference between calculated hourly PM2.5 light extinction 

values used in the UFVA and values generated using two simpler approaches. Both of the 

simpler approaches (designated by letters T and W) use the original IMPROVE algorithm 

without the Rayleigh and PM10-2.5 contributions to total light extinction. 

Approach T is similar to the previous approach F. The difference is that while approach 

F involved the calculation and use of monthly-averaged PM2.5 component percentages for each 

of the five components, approach T uses a two-factor rearrangement of the IMPROVE algorithm 

and accordingly involves the calculation and use of only two monthly-averaged parameters. 

These parameters are the hygroscopic fraction (HF) and the dry light extinction efficiency 

(DLEE). A technical memo explains in more detail the definition of these parameters and their 

relationship to the IMPROVE algorithm.8

 Looking to a hypothetical future program based on 

approach T, it would be possible to develop estimates of PM2.5 light extinction for any day for 

which hourly PM2.5 mass concentrations are available from a continuous FEM instrument, 

including days that do not have PM2.5 speciation data, by applying the monthly-averaged HF and 

DLEE values to each hourly PM2.5 measurement. 

Approach W differs from approach T in that the HF and DLEE parameters are applied 

day-by-day rather than averaged across a month. The inclusion of this approach in this analysis 

allows a close examination of the effect of the monthly-averaging step in approach T. Approach 

 

8

 Pitchford, M (2010). Assessment of the Use of Speciated PM2.5 Mass-Calculated Light Extinction as a Secondary 

PM NAAQS Indicator of Visibility. Memorandum to PM NAAQS Review Docket (EPA-HQ-OAR-2007-0492), 

Marc Pitchford, NOAA, November 17, 2010. Available at: 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_2007_td.html. 
F-6 

W can yield estimates of PM2.5 light extinction only for days on which PM2.5 speciation data are 

available. The EPA staff notes that many PM2.5 and PM10 monitoring sites currently operate on 

1-day-in-3 or 1-day-in-6 schedules, and are nevertheless considered to provide sufficiently robust 

data for air quality management purposes. 

Both approaches T and W assume that within a day, the percentage mix of PM2.5

components is the same hour-to-hour. 

Table F-3 explicitly defines the calculation steps for approaches T and W. Table F-4 

compares the UFVA approach, approach T, and approach W in conceptual terms. 
F-7

Table F-3. Calculation Steps for Approaches T and W 

Approach T Approach W Comm

(i) For each CSN sampling day, subtract a samplerdependent estimate of the OC artifact from the OC measurement, and multiply by 1.6 to estimate organic carbonaceous material (OCM). 

 OCM = (OC – artifact) *1.6 

Same as T. The values for the OC artif

ranged from 0.32 to 1.53 μ

sampler model. The artifacURG 3000N sampler is of mprospectively, because it is in use for carbon sampling 3000N was used only at abstudy sites and only in the sthose sites and days, an org0.4 μg/m3 was assumed for 

UFVA and this document, experience with this sampleexploring whether there is a organic carbon artifact baselarger field blank and back-A section in the body of thiselection of 1.6 as the mult

(ii) For each CSN sampling day, calculate fine soil/crustal PM2.5 (FS) from CSN measurements of 

crustal elements AL, Si, Ca, Fe, and Ti, using the formula 

 FS = 2.20 x [Al] + 2.49 × [Si] + 1.63 × 

[Ca] + 2.42 × [Fe] + 1.94 × [Ti] 

Same as T. This is the same equation a

network and the Regional Hfrom the corresponding step 

(iii) For each CSN sampling day, multiply CSN measurement of sulfate ion by 1.375, and multiply CSN measurement of nitrate ion by 1.29, to reflect associated ammonium under an assumption of full neutralization. 

 AS = Sulfate ion * 1.375  AN = Nitrate ion * 1.29 

Same as T. 

(iv) For each CSN sampling day, sum the above estimates of the 5 components of PM2.5: 

 Sum = AS + AN + OCM + EC + FS 

Same as T. 
F-8

Approach T Approach W Comm

(v) For each CSN sampling day, calculate HF and DLEE: 

 HFdaily = (AS+AN) / Sum 

 DLEEdaily = 

[3*(AS+AN)+4*OCM+10*EC+1.0*FS] / Sum 

Same as T. 

(vi) Average the values of HFdaily and DLEEdaily

from step (v) across the CSN sampling days of each calendar month. This results in values for 

 HFmonthly_average

 DLEEmonthly_average

This step is omitted. HF and DLEE are dayspecific: 

 HFdaily

 DLEEdaily

Note: An alternative approaHFmonthly_average would be to 

average of the daily values the monthly average of the similar alterative definition DLEEmonthly_average. 

In the analysis described in applied a minimum requiremonth, which is usually 80scheduled per month at a sisampling. If there were fewapproach T estimates were approach W estimates were
F-9

Approach T Approach W Comm

(vii) For each daylight hour of each day of a month (including days without CSN sampling), calculate PM2.5 light extinction using the 1-hour PM2.5 mass 

concentration from a continuous instrument, HF, DLEE, and 1-hour relative humidity (RH): 

 Hourly PM2.5 light extinction = PM2.5hourly

* { 3*[f(RHhourly)-1] * [HFmonthly_average] + 

DLEEmonthly_average } 

Estimates of PM2.5 light extinction in hours with 

RH greater than 90% are not valid. 

(vii) For each daylight hour of each day of a month for which CSN speciation data is available, calculate PM2.5 light extinction using the 1-hour 

PM2.5 mass concentration from a continuous 

instrument, HF, DLEE, and 1-hour relative humidity (RH): 

 Hourly PM2.5 light extinction = PM2.5hourly

* { 3*[f(RHhourly)-1] * [HFdaily] + 

DLEEdaily } 

Estimates of PM2.5 light extinction in hours with 

RH greater than 90% are not valid. 

Prospectively, this appendicontinuous instruments appequivalent methods (FEM) purposes of estimating hou

Accordingly, the equations the instrument-reported hou

concentration. As explaineavailable hourly PM2.5 mas

continuous instruments for from non-FEM instruments

concentrations were therefoday basis to match the 24-hreported by a collocated FRsampler, prior the step descwas intended to better simuof interest. 
F-10

Table F-4. Detailed Comparative Description of the UFVA Approach, Approach T, and Approach W for 

PM2.5 Light Extinction 

UFVA Step*** Aspect of Approach UFVA Approach Approach T 

1 Estimation of 24-hour organic 

carbonaceous mass 

The SANDWICH method†††

is used to subdivide the 24-hour PM2.5 mass reported by 

the FRM for each day and site into hydrated ammonium sulfate, ammonium nitrate, elemental carbon, organic carbonaceous material (OCM), fine soil, and water. This is done using information from the CSN measurements, physical models, and day-specific temperatures and relative humidity. OCM is estimated as the residual needed to reach mass closure after estimation of the other components. 

Organic carbonaceous mass is assumed to equal the organic carbon value reported from CSN sampling, minus a blank filter artifact correction value that depends on PM sampler model, times 1.6. 

Same 

1, continued Estimation of 24-hour 

elemental carbon mass 

CSN elemental carbon concentration 

Same as UFVA Same 

1, continued Estimation of 24-hour 

hydrated ammonium sulfate mass 

CSN sulfate ion concentration, with dayspecific SANDWICH estimates of associated ammonium and water. 

Sulfate ion measurement from the CSN filter is multiplied by 1.375 to represent dry ammonium sulfate. 

Same 

***

 The numbering of steps follows that used to describe the UFVA approach in section 3.3.1 of the UFVA. ††† Frank NH. (2006). Retained nitrate, hydrated sulfates, and carbonaceous mass in federal reference method fine particulate matter fo

Air Waste Manag Assoc, 56: 500-11
F-11

UFVA Step*** Aspect of Approach UFVA Approach Approach T 

1, continued Estimation of 24-hour 

ammonium nitrate mass 

Nitrate ion on the FRM Teflon filter is estimated by SANDWICH, with dayspecific estimates of associated ammonium and water. 

Nitrate ion measurement from the CSN filter is multiplied by 1.29 to represent dry ammonium nitrate. 

Same 

1, continued Estimation of 24-hour fine 

soil/crustal mass 

Calculated from 4 CSN elements, not including Al (a difference from the IMPROVE approach) 

IMPROVE approach, using 5 elements 

Same 

2 Diurnal pattern of PM2.5

components 

The CMAQ-derived monthly normalized diurnal profiles for the sulfate, nitrate, elemental carbon, organic carbonaceous material and fine soil/crustal components (each of which averages to 1.0 across 24 hours) were multiplied by the day-specific SANDWICH-based estimates of the 24-hour average concentrations of the five PM2.5 components, to get 

intermediate day-specific hourly estimates of the five components (including ammonium and water associated with sulfate and nitrate ion). 

No diurnal profiles are used. Same 
F-12

UFVA Step*** Aspect of Approach UFVA Approach Approach T 

3 Sum the 5 components The hourly concentrations of 

these five components (including day-specific ammonium and water associated with sulfate and nitrate ion when the FRM Teflon filter is weighed) were added together, to get a sumof-components estimate of hourly PM2.5 mass for the day 

of FRM/CSN sampling. 

The following approach was used instead of steps 3, 5, 7, and 8. 

 Calculate HF and 

DLEE for each day. 

 Average the daily 

values of HF and DLEE across the available CSN sampling days in the month. 

 Use the monthlyaveraged HF and 

DLEE values in a two-factor version of the original IMPROVE algorithm. 

The fused and 8

4 Hourly PM2.5 concentration, 

consistent with 24-hour FRM concentration. 

The hourly data from the continuous PM2.5 instrument 

on the day of the FRM sampling were normalized by their 24-hour average, to get a normalized diurnal profile. This profile was applied to the 24-hour PM2.5 mass 

reported by the FRM sampler. This keeps the average of the valid 1-hour PM2.5 values 

equal to the 24-hour value from the FRM sampler. 

See the comment on this topic in Table F-3. 

See tin Ta
F-13

UFVA Step*** Aspect of Approach UFVA Approach Approach T 

5, 6 Adjust preliminary estimates 

of hourly PM2.5 component 

concentrations (reflecting CMAQ diurnal profiles and 24-hour measurements) to be consistent with the estimate of hourly PM2.5 mass. 

The two estimates of hourly PM2.5 mass from steps 3 and 

4 were compared, hour-byhour. Within each hour, the estimates of all five components from step 3 were increased or decreased by a common percentage so that the sum of the five components after this adjustment was equal to the estimate of the hourly PM2.5

mass from step 4. The adjustment percentage varied from hour-to-hour. 

Not applicable. Not a

7 Adjust the FRM-consistent 

estimate of sulfate to the CSN/IMPROVE-consistent basis expected by the IMPROVE algorithm. 

Each hourly estimate of sulfate concentration on the FRM filter from step 6 (which includes estimates of associated ammonium and particle bound water) was adjusted so that it excludes water and reflects full neutralization and therefore is consistent with the reporting practices of the IMPROVE program and the IMPROVE algorithm. 

Not applicable. Not a
F-14

UFVA Step*** Aspect of Approach UFVA Approach Approach T 

8 Adjust the FRM-consistent 

estimate of nitrate to the CSN/IMPROVE-consistent basis expected by the IMPROVE algorithm. 

A similar adjustment as in step 7 (for sulfate) was made to each hour’s nitrate concentration from step 6, so that the estimate of hourly nitrate would reflect actual atmospheric conditions and be consistent with the IMPROVE algorithm. This can result in the estimate of nitrate used in the IMPROVE algorithm being higher than the FRMconsistent estimate, for days on which the SANDWICH method predicts a loss of nitrate from the FRM filter. 

Not applicable. Implication: On days when the FRM filter has lost nitrate mass, the estimates of hourly PM2.5 will be lower than 

actual atmospheric mass. All hourly PM2.5 components will 

be biased low relative to the values that should be used in the IMPROVE algorithm, by the fraction that the lost nitrate is of total PM2.5 mass. 

In the opposite direction, any water mass included in the hourly PM2.5 mass reported 

by the continuous PM2.5

instrument will in effect be allocated among the five components. 

Same 

Not numbered in UFVA Estimation of PM2.5 light 

extinction from estimates of hourly concentrations of PM2.5 components. 

Original IMPROVE algorithm, including f (RH) determined from hourly RH. Hours with RH >90% were excluded from design values and from most graphical displays of results. 

Use a two-factor rearrangement of the original IMPROVE algorithm, per table F-3. Hours with RH >90% were excluded from design values and from all graphical displays of results. 

Same 
F-15 

F.5 COMPARATIVE PERFORMANCE OF SIMPLIFIED PM2.5 LIGHT 

EXTINCTION APPROACHES T AND W 

The performance assessment of simplified approaches T and W for calculated PM2.5 light 

extinction was accomplished by comparing hourly values of PM2.5 light extinction generated by 

each approach to their corresponding paired values generated using the original UFVA method. 

Data from 2007-2009 were used for the comparison. Annual box plots of the percentage 

differences between the paired values, as well as annual and monthly scatter plots and regression 

analysis of these paired data, were generated. As the results below show, approaches T and W 

produce hourly PM2.5 light extinction values that in most cases are quite well correlated to the 

hourly PM2.5 light extinction values generated by the original UFVA method, with a few notable 

exceptions discussed below. Selected graphs and summary tables of regression statistics are 

included and discussed below to show the degree of comparability. Note that approaches T and 

W can be compared to the UFVA approach only for days with CSN data. Similarly, approaches 

T and W can be compared to each other only for those days, as approach W does not produce 

estimates of PM2.5 light extinction for other days. 

The box plots of the percentage differences between calculated hourly PM2.5 light 

extinction by approaches T and W versus the UFVA approach are shown in Figure F-2. In this 

box plot, the percentage difference is calculated as follows: 

Percentage difference = 100% * [(T or W estimate) – (UFVA estimate)]/(UFVAestimate) 

The patterns of percentage bias for the 15 urban areas are notably similar in both the T 

and W plots.11 Fresno and Los Angeles are noticeably biased low by approaches T and W, while 

Houston and Philadelphia are biased high. However, these percent difference plots do not reveal 

the absolute level of PM2.5 light extinction for the points corresponding to smaller or larger 

percent differences. 

 Scatter plots of calculated hourly PM2.5 light extinction for all daylight hours comparing 

approaches T and W to the UFVA approach for all 15 areas are shown in Figure F-3. The same 

comparisons are made for daily maximum PM2.5 light extinction in Figure F-4. Note that the 

vertical scale is not the same in all plots. The dashed red line is the 1:1 line, and regression 

statistics appear at the top of each plot. These figures give a better visual sense of how the 

deviations between the simpler approaches and the UFVA approach vary by area and, by 

implication, with the nature of PM2.5 in each study area. For example, both simpler approaches 

have an obvious negative bias in Fresno, Los Angeles, and New York across a wide range of 

PM2.5 light extinction values, consistent with the percentage difference box plot. 

The degree of comparability for paired hourly PM2.5 light extinction values between 

approach T and the UFVA approach and between approach W and the UFVA approach, by 

month and urban area, can also be displayed via regression statistics, as in Tables F-5 (approach 

T) and F-6 (approach W). Note that the independent and dependent variables in Tables F-5 and 

F-6 are reversed from the order used to provide regression statistics in the scatter plots of Figures 

F-3 and F-4. Color shading is used to highlight cells (representing a given month across 2007-

2009) in which the slope of the regression falls outside the range of 0.9 to 1.1, with deeper 

 

11 In the box plots, some extreme values of the percentage change are due to rounding effects when the values 

involved were very small.
F-16 

shading indicating a larger deviation from a slope of 1.0. In both tables, for most eastern urban 

areas and months the regression lines have slopes in the range of 0.9 to 1.1, R2 values near one, 

and small intercepts implying that the values are highly comparable. The western urban areas 

(Fresno, Houston, Los Angeles, Phoenix, Salt Lake City, and Tacoma) have intercepts, slopes, 

and R2 values for some months that imply a bias and/or noisier relationship than the eastern 

areas between values calculated by approaches T and W versus the UFVA approach. Los 

Angeles and Fresno, and to a lesser degree other western areas, stand out as having regression 

slopes that indicate that approaches T and W are biased low relative to the UFVA estimates for 

most of the year. 

A comprehensive assessment of the reasons for the differences between approaches T and 

W versus the UFVA approach has not been conducted. However, some explanations are 

suggested by the results themselves and by the previous draft analysis of similar approaches D 

and E. The fact that the CMAQ-based hour-to-hour variations and the monitoring-based day-today variations in the dry PM2.5 composition in the UFVA approach can in most areas be replaced 

by either daily-averaged or monthly-averaged values without much loss of precision in 

calculated hourly PM2.5 light extinction suggests that these shorter-term variations within a single 

month at a single monitoring site are usually not very influential. This may imply that the 

differences between approaches T and W and the UFVA approach are probably not mostly due 

to the relative composition changes caused by use of the mass-closure SANDWICH model 

versus the simpler multiplier model used in approaches T and W. This suggests that the 

difference may be due to differences in the value of PM2.5 mass to which the HF and DLEE 

factors are applied. Approaches T and W lack any step that would effectively reconcile the 

implicit nitrate portion of the average of the 24 hourly values of PM2.5 mass to the CSNmeasured 24-hour average nitrate concentrations, to compensate for any loss of nitrate from the 

continuous PM2.5 instrument.12 This explanation is consistent with the direction of the biases 

seen for sites that are known to have high ammonium nitrate (e.g., Los Angeles and Fresno). For 

Los Angeles and Fresno, a logical explanation is that the FRM-adjusted estimates of 1-hour 

PM2.5 mass do not include the entire ambient nitrate that is present in particulate form. The 

UFVA approach includes an adjustment to restore this mass in the estimation of light extinction 

while approaches T and W do not. There are variations of approaches T and W that may 

improve the correlation with actual ambient light extinction in certain areas of the country, such 

as one that would retain some version of a mass-closure model to account for the negative 

artifact for ammonium nitrate, but that otherwise has the remaining simplifications of approaches 

T or W.

 

12 FRM sampling is well known to have a negative artifact for nitrate compared to ambient concentrations under 

some conditions. This negative artifact presumably carries over to any continuous PM2.5 FEM instrument that is 

well correlated with the FRM. A negative artifact would logically lead to an underestimate of PM2.5 light extinction.
F-17 

Figure F-2. Box and Whisker Plot of the Percent Difference in Calculated Hourly PM2.5

Light Extinction between Approach T and the UFVA Approach (top plot) and between 

Approach W and the UFVA Approach (bottom plot) by Urban Study Area 

Approach T 

Approach W 
F-18 

Figure F-3. Scatter Plots of Hourly PM2.5 Light Extinction (all daylight hours) Calculated 

by Approach T (y, left plot) and W (y, right plot) versus the UFVA Approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in this figure. 
F-19 

Figure F-3. Scatter Plots of Hourly PM2.5 Light Extinction (all daylight hours) Calculated 

by Approach T (y, left plot) and W (y, right plot) versus the UFVA Approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in this figure. 
F-20 

Figure F-3. Scatter Plots of Hourly PM2.5 Light Extinction (all daylight hours) Calculated 

by Approach T (y, left plot) and W (y, right plot) versus the UFVA Approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in this figure. 
F-21 

Figure F-3. Scatter Plots of Hourly PM2.5 Light Extinction (all daylight hours) Calculated 

by Approach T (y, left plot) and W (y, right plot) versus the UFVA Approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in this figure. 
F-22 

Figure F-3. Scatter Plots of Hourly PM2.5 Light Extinction (all daylight hours) Calculated 

by Approach T (y, left plot) and W (y, right plot) versus the UFVA Approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in this figure. 
F-23 

Figure F-3. Scatter Plots of Hourly PM2.5 Light Extinction (all daylight hours) Calculated 

by Approach T (y, left plot) and W (y, right plot) versus the UFVA Approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in this figure. 
F-24 

Figure F-3. Scatter Plots of Hourly PM2.5 Light Extinction (all daylight hours) Calculated 

by Approach T (y, left plot) and W (y, right plot) versus the UFVA Approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in this figure. 
F-25 

Figure F-3. Scatter Plots of Hourly PM2.5 Light Extinction (all daylight hours) Calculated 

by Approach T (y, left plot) and W (y, right plot) versus the UFVA Approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in this figure. 
F-26 

Figure F-4. Scatter Plots of Daily Maximum Daylight Hourly PM2.5 Light Extinction 

Calculated by Approach T (y, left plot) and W (y, right plot) versus the UFVA approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in the scatter plots in this figure.
F-27 

Figure F-4. Scatter Plots of Daily Maximum Daylight Hourly PM2.5 Light Extinction 

Calculated by Approach T (y, left plot) and W (y, right plot) versus the UFVA approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in the scatter plots in this figure.
F-28 

Figure F-4. Scatter Plots of Daily Maximum Daylight Hourly PM2.5 Light Extinction 

Calculated by Approach T (y, left plot) and W (y, right plot) versus the UFVA approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in the scatter plots in this figure.
F-29 

Figure F-4. Scatter Plots of Daily Maximum Daylight Hourly PM2.5 Light Extinction 

Calculated by Approach T (y, left plot) and W (y, right plot) versus the UFVA approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in the scatter plots in this figure.
F-30 

Figure F-4. Scatter Plots of Daily Maximum Daylight Hourly PM2.5 Light Extinction 

Calculated by Approach T (y, left plot) and W (y, right plot) versus the UFVA approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in the scatter plots in this figure.
F-31 

Figure F-4. Scatter Plots of Daily Maximum Daylight Hourly PM2.5 Light Extinction 

Calculated by Approach T (y, left plot) and W (y, right plot) versus the UFVA approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in the scatter plots in this figure.
F-32 

Figure F-4. Scatter Plots of Daily Maximum Daylight Hourly PM2.5 Light Extinction 

Calculated by Approach T (y, left plot) and W (y, right plot) versus the UFVA approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in the scatter plots in this figure.
F-33 

Figure F-4. Scatter Plots of Daily Maximum Daylight Hourly PM2.5 Light Extinction 

Calculated by Approach T (y, left plot) and W (y, right plot) versus the UFVA approach (x) 

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide 

regression statistics in the scatter plots in this figure.
F-34

Table F-5. Linear regression equation and R2 values (Rsq) for relating hourly PM2.5 light extinction value

calculated using approach T (x in the equation) to those using the UFVA approach (y in the equation) by 

areas. A slope >1 indicates that approach T is biased low relative to the UFVA approac

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide regression statisticFigures F-3 and F-4. 

Slope = 1.25 + Approach T is biased low. Slope = 1.10 – 1.24 0.90 < slope < 1.10 Slope = 0.76 – 0.9 Approach T is biased high. Slope <= 0.75 

Month 1 2 3 4 5 6 7 8 9 10 Tacoma, WA 

y=1.01*x+1.92;Rsq=0.99

y=0.9*x+7.15;Rsq=0.96

y=0.96*x+4.53;Rsq=0.96

y=1.28*x+‐4.87;Rsq=0.97

y=1.16*x+‐1.53;Rsq=0.92

y=0.99*x+1.42;Rsq=0.96

y=0.97*x+2.99;Rsq=0.97

y=1.04*x+0.19;Rsq=0.96

y=1.11*x+‐1.16;Rsq=0.98

y=0.93*+7.08;Rsq=0.9

Fresno, CA y=1.06*x

+6.65;Rsq=0.95

y=1.15*x+‐0.69;Rsq=0.97

y=1.42*x+4.5;Rsq=0.89

y=1.72*x+‐10.63;Rsq=0.77

y=1.08*x+4.36;Rsq=0.79

y=1.14*x+0;Rsq=0.95

y=1.02*x+2.87;Rsq=1

y=1.3*x+‐5.87;Rsq=0.94

y=1.1*x+6.77;Rsq=0.93

y=1.52*+‐6.34;Rsq=0.9

Los Angeles, CA 

y=1.34*x+‐6.07;Rsq=0.98

y=1.28*x+‐5.96;Rsq=0.99

y=1.35*x+‐6.53;Rsq=0.98

y=1.41*x+‐10.4;Rsq=0.98

y=1.35*x+‐7.94;Rsq=0.97

y=1.56*x+‐12.42;Rsq=0.94

y=1.74*x+‐30.53;Rsq=0.89

y=2.21*x+‐74.46;Rsq=0.92

y=1.83*x+‐27.22;Rsq=0.93

y=1.39*+‐3.97;Rsq=0.8

Phoenix, AZ 

NA y=1.2*x+0

.58;Rsq=0.98

y=1.36*x+‐6.26;Rsq=0.78

y=1.09*x+‐1.26;Rsq=0.95

y=0.76*x+4.92;Rsq=0.72

y=1.3*x+‐4.25;Rsq=0.84

y=0.52*x+11.94;Rsq=0.79

y=1.12*x+‐3.46;Rsq=0.94

y=1.02*x+‐0.69;Rsq=0.94

y=1.38*+‐6.57;Rsq=0.9

Salt Lake City, UT 

y=0.93*x+6.67;Rsq=0.99

y=1.02*x+4.81;Rsq=0.98

y=1.15*x+2.05;Rsq=0.96

y=1.21*x+‐0.35;Rsq=0.87

y=1.05*x+1.72;Rsq=0.83

y=1.02*x+1.41;Rsq=0.9

y=1.07*x+‐0.69;

Rsq=0.99

y=1*x+0.81;Rsq=0.96

y=1.07*x+‐0.45;Rsq=0.93

y=1.06*+2.39;Rsq=0.9

Dallas, TX y=1.14*x

+1.25;Rsq=0.93

y=1.1*x+1.86;Rsq=0.96

y=1.27*x+‐6.95;Rsq=0.96

y=1.13*x+‐3.03;Rsq=0.94

y=1.05*x+‐1.42;Rsq=0.95

y=0.97*x+1.62;Rsq=0.9

y=0.91*x+4.45;Rsq=0.93

y=0.98*x+0.58;Rsq=0.95

y=1*x+‐2.67;Rsq=0.98

y=1.04*+‐0.44;Rsq=0.9

Houston, TX 

y=0.9*x+0.54;Rsq=0.95

y=0.94*x+1.15;Rsq=0.95

y=0.8*x+5.75;Rsq=0.97

y=0.96*x+1.77;Rsq=0.93

y=0.88*x+2.6;Rsq=0.96

y=0.71*x+13.31;Rsq=0.89

y=0.74*x+11.79;Rsq=0.91

y=0.9*x+1.86;Rsq=0.95

y=0.75*x+7.22;Rsq=0.97

y=0.97*+‐1.47;Rsq=0.9
F-35

Month 1 2 3 4 5 6 7 8 9 10 St Louis, IL 

y=0.99*x+1.76;Rsq=0.98

y=0.83*x+13.19;Rsq=0.98

y=1.12*x+‐3.82;

Rsq=0.98

y=1.08*x+0.2;Rsq=0.97

y=1.24*x+‐10.62;Rsq=0.92

y=1.25*x+‐16.39;Rsq=0.9

y=1.19*x+‐17.28;Rsq=0.95

y=0.99*x+‐4.12;Rsq=0.98

y=1.02*x+‐3.36;Rsq=0.97

y=1.17*+‐4.2;Rsq=0.9

Birmingham AL 

y=0.92*x+7.51;Rsq=0.97

y=0.98*x+3.84;Rsq=0.96

y=0.95*x+4.16;Rsq=0.97

y=1.06*x+‐2.25;Rsq=0.97

y=0.92*x+4.45;Rsq=0.98

y=0.98*x+‐2.15;Rsq=0.99

y=0.95*x+‐0.84;

Rsq=0.97

y=0.96*x+‐0.66;Rsq=0.96

y=0.91*x+1.63;Rsq=0.95

y=0.96*+2.56;Rsq=0.9

Atlanta, GA 

y=0.97*x+5.24;Rsq=0.94

y=1*x+1.59;Rsq=0.98

y=0.81*x+13.95;Rsq=0.94

y=1.01*x+‐2.51;Rsq=0.92

y=0.9*x+4.55;Rsq=0.94

y=0.92*x+4.58;Rsq=0.96

y=0.94*x+0.95;Rsq=0.95

y=0.98*x+‐0.29;Rsq=0.99

y=1.02*x+‐4.52;Rsq=0.96

y=1.02*+‐0.64;Rsq=0.9

Detroit, MI y=1.08*x

+‐3.49;Rsq=0.97

NA‡‡‡‡ y=1.05*x+

0.45;Rsq=0.98

y=1.17*x+‐3.64;Rsq=0.97

y=1.23*x+‐5.53;Rsq=0.95

y=1.07*x+‐1.23;Rsq=0.97

y=1.12*x+‐7.1;Rsq=0.97

y=1.01*x+0.72;Rsq=0.92

y=1.13*x+‐3.04;Rsq=0.93

y=1.29*+‐7.99;Rsq=0.9

Pittsburgh, PA 

y=0.94*x+7.2;Rsq=0.9

y=0.97*x+3.64;Rsq=0.98

y=0.95*x+3.97;Rsq=0.98

y=1.02*x+1.71;Rsq=0.94

y=1.06*x+‐3.12;Rsq=0.91

y=1.06*x+‐2.33;Rsq=0.91

y=1.09*x+‐9.79;Rsq=0.97

y=1.08*x+‐11.34;

Rsq=0.98

y=1.06*x+‐5.84;

Rsq=0.97

y=1.04*+‐0.78;

Rsq=0.9

Baltimore, MD 

y=0.98*x+2.1;Rsq=0.98

y=0.84*x+8.13;Rsq=0.98

y=0.99*x+2.51;Rsq=0.99

y=1.12*x+‐1.54;Rsq=0.94

y=1.06*x+‐0.39;Rsq=0.93

y=1.07*x+‐2.87;Rsq=0.83

y=1.19*x+‐13.9;Rsq=0.92

y=1*x+‐7.75;Rsq=0.94

y=1.09*x+‐6.14;Rsq=0.97

y=1.14*+‐6.88;Rsq=0.9

Philadelphia PA 

y=0.95*x+3.19;Rsq=0.99

NA‡‡‡ y=1.05*x+

‐0.06;

Rsq=0.98

y=1*x+1.67;Rsq=0.93

y=1.03*x+‐2.46;Rsq=0.97

y=1.06*x+‐6.29;Rsq=0.91

y=0.89*x+‐0.11;Rsq=0.98

y=0.86*x+3.68;Rsq=0.97

y=0.89*x+2.27;Rsq=0.98

y=1.07*+‐0.74;Rsq=0.9

New York, NY 

y=1.08*x+1.14;Rsq=0.96

y=0.99*x+‐1.41;Rsq=0.99

y=0.92*x+10.01;Rsq=0.98

y=1.15*x+‐5.37;Rsq=0.98

y=1.2*x+‐5.67;Rsq=0.96

y=1.02*x+‐0.08;Rsq=0.95

y=1.17*x+‐19.48;Rsq=0.93

y=1.05*x+‐4.46;Rsq=0.97

y=1.28*x+‐2.87;Rsq=0.89

y=1.3*x+‐8.98;Rsq=0.9

‡‡‡‡ Because approach T requires a minimum of four CSN samples in a calendar month to calculate valid values of HF and DLEE, estDetroit and Philadelphia are not available from approach T so no regression results are shown. These months do have regression resultF-6.
F-36

Table F-6. Linear regression equation and R2 values (Rsq) for relating hourly PM2.5 light extinction value

calculated using approach W (x in the equation) to those using the UFVA approach (y in the equation) by 

areas. A positive intercept indicates that approach W is biased low relative to the UFVA app

Note that the independent and dependent variables in Tables F-5 and F-6 are reversed from the order used to provide regression statisticFigures F-3 and F-4. 

Slope = 1.25 + Approach W is biased low. Slope = 1.10 – 1.24 0.90 < slope < 1.10 Slope = 0.76 – 0.9 Approach W is biased high. Slope <= 0.75 

Month 1 2 3 4 5 6 7 8 9 10 Tacoma, WA y=1.07*x

+0.02;Rsq=0.99

y=1.06*x+1.07;Rsq=0.99

y=1.15*x+‐1.13;Rsq=0.99

y=1.23*x+‐4.3;Rsq=0.96

y=1.26*x+‐4.68;Rsq=0.95

y=1.01*x+1.04;Rsq=0.96

y=0.96*x+3.1;Rsq=0.96

y=1.1*x+‐1.77;Rsq=0.97

y=1.18*x+‐3.19;Rsq=0.98

y=1.03*x+1.15;Rsq=0.98

Fresno, CA 

y=1.03*x+6.82;Rsq=0.97

y=1.06*x+7.93;Rsq=0.98

y=1.32*x+8.06;Rsq=0.89

y=1.75*x+‐12.08;Rsq=0.8

y=0.94*x+9.16;Rsq=0.78

y=1.07*x+1.79;Rsq=0.95

y=0.98*x+4.51;

Rsq=1

y=1.2*x+‐3.17;Rsq=0.94

y=1.17*x+2.83;Rsq=0.96

y=1.25*x+4.14;Rsq=0.94

Los Angeles, CA 

y=1.04*x+8.5;

Rsq=0.99

y=1.02*x+7.16;Rsq=0.99

y=1.05*x+15.49;Rsq=0.98

y=1.17*x+4.22;Rsq=0.98

y=1.13*x+8.45;Rsq=0.97

y=1.36*x+5.73;Rsq=0.92

y=1.56*x+‐15.11;Rsq=0.91

y=1.94*x+‐56.56;Rsq=0.93

y=1.56*x+‐12.84;Rsq=0.95

y=1.25*x+1.07;Rsq=0.94

Phoenix, AZ 

NA

y=1.25*x+0.12;Rsq=0.98

y=1.31*x+‐4.86;Rsq=0.95

y=1.07*x+‐0.71;Rsq=0.98

y=0.93*x+1.96;Rsq=0.89

y=1.2*x+‐2.49;Rsq=0.83

y=1.09*x+‐1.41;Rsq=0.98

y=1.11*x+‐2.87;Rsq=0.97

y=1.04*x+‐1.29;Rsq=0.97

y=1.1*x+‐1.01;Rsq=1

Salt Lake City, UT y=0.91*x

+6.4;

Rsq=1

y=0.94*x+8.75;Rsq=0.98

y=1.07*x+3.8;Rsq=0.96

y=1.17*x+‐0.09;Rsq=0.92

y=1.17*x+‐0.77;Rsq=0.87

y=1.02*x+1.15;Rsq=0.94

y=1.06*x+‐0.55;Rsq=1

y=1.01*x+0.56;Rsq=0.98

y=1.04*x+0.16;Rsq=0.98

y=1.11*x+1.19;Rsq=0.96
F-37

Month 1 2 3 4 5 6 7 8 9 10 Dallas, TX 

y=1.12*x+0.63;Rsq=0.95

y=1.09*x+2.57;Rsq=0.97

y=1.18*x+‐4.36;Rsq=0.96

y=1.01*x+1.32;Rsq=0.94

y=0.93*x+3.16;Rsq=0.96

y=1*x+0.88;Rsq=0.95

y=0.96*x+3.17;Rsq=0.93

y=0.96*x+2.08;Rsq=0.97

y=0.93*x+0.44;Rsq=0.98

y=0.98*x+0.71;Rsq=0.97

Houston, TX y=0.88*x

+1.47;Rsq=0.97

y=0.86*x+2.75;Rsq=0.99

y=0.86*x+2.15;Rsq=0.99

y=0.94*x+0.32;Rsq=0.99

y=0.85*x+2.96;Rsq=0.99

y=0.92*x+2.05;Rsq=0.96

y=0.92*x+3.75;Rsq=0.95

y=0.89*x+3.22;Rsq=0.98

y=0.89*x+0.03;Rsq=0.99

y=0.91*x+0.18;Rsq=1

St Louis, IL y=0.95*x

+3.53;Rsq=0.98

y=0.85*x+10.08;Rsq=0.97

y=1.08*x+‐1.58;Rsq=0.99

y=1.04*x+1.61;Rsq=0.98

y=1.09*x+‐2.77;Rsq=0.95

y=1.16*x+‐12.62;Rsq=0.93

y=1.05*x+‐7.34;Rsq=0.98

y=0.95*x+‐1.47;Rsq=0.99

y=1*x+‐2.58;Rsq=0.98

y=1.16*x+‐4.78;Rsq=0.96

Birmingham AL y=1*x+1.

7;Rsq=0.99

y=1.03*x+0.47;Rsq=0.99

y=1.01*x+‐1.58;Rsq=0.99

y=1.03*x+‐1.5;Rsq=0.99

y=0.96*x+‐0.53;Rsq=0.99

y=0.98*x+‐1.93;Rsq=0.99

y=0.92*x+2.04;Rsq=0.99

y=0.91*x+3.61;Rsq=0.98

y=0.94*x+‐0.87;

Rsq=0.99

y=1.01*x+‐0.64;Rsq=0.98

Atlanta, GA y=0.97*x

+4.46;Rsq=0.96

y=0.97*x+1.55;Rsq=0.96

y=0.9*x+7.22;Rsq=0.97

y=1*x+‐2.29;Rsq=0.95

y=1*x+‐3.22;Rsq=0.98

y=0.94*x+2.64;Rsq=0.98

y=0.88*x+7.28;Rsq=0.96

y=0.96*x+1.44;Rsq=0.99

y=0.98*x+‐1.52;Rsq=0.97

y=0.95*x+4.24;Rsq=0.98

Detroit, MI 

y=1.03*x+1.64;Rsq=0.97

y=0.97*x+1.05;Rsq=1

y=0.98*x+5.45;Rsq=0.98

y=1.17*x+‐3.56;Rsq=0.99

y=1.02*x+3.5;Rsq=0.95

y=1.02*x+0.66;Rsq=0.98

y=1.09*x+‐5.24;Rsq=0.98

y=1.04*x+‐0.25;Rsq=0.93

y=1.09*x+‐2.11;Rsq=0.96

y=1.15*x+‐2.29;Rsq=0.99

Pittsburgh, PA y=0.92*x

+8.37;Rsq=0.92

y=1.03*x+0.13;Rsq=0.97

y=1.06*x+‐3.39;Rsq=0.98

y=1.07*x+‐1.33;Rsq=0.93

y=1.05*x+‐3.5;Rsq=0.93

y=1.01*x+‐1.36;

Rsq=0.95

y=1.03*x+‐6.36;

Rsq=0.98

y=0.97*x+‐1.25;Rsq=0.99

y=1.04*x+‐4.35;Rsq=0.98

y=1.04*x+‐1.42;Rsq=0.97

Baltimore, MD y=1.06*x

+‐2.44;

Rsq=0.99

y=0.93*x+3.3;Rsq=0.99

y=0.96*x+3.44;Rsq=0.99

y=1.1*x+‐1.37;Rsq=0.95

y=0.98*x+4.15;Rsq=0.95

y=1.08*x+‐4.61;

Rsq=0.85

y=1.14*x+‐11.71;Rsq=0.94

y=0.97*x+‐5.3;Rsq=0.97

y=1.07*x+‐5.52;Rsq=0.96

y=1.1*x+‐4.68;Rsq=0.96

Philadelphia, PA y=1.01*x

+‐0.42;Rsq=1

y=1.03*x+‐3.16;Rsq=0.99

y=0.99*x+2.14;Rsq=0.99

y=0.98*x+1.53;Rsq=0.97

y=1.02*x+‐2.14;Rsq=0.97

y=1.05*x+‐6.57;Rsq=0.92

y=0.94*x+‐3.14;Rsq=0.98

y=0.89*x+2.37;Rsq=0.98

y=0.89*x+2.41;Rsq=0.99

y=1.05*x+‐0.49;Rsq=0.97
F-38

Month 1 2 3 4 5 6 7 8 9 10 New York, NY y=1.07*x

+1.42;Rsq=0.99

y=1.06*x+‐3.48;Rsq=0.99

y=1.01*x+5.6;Rsq=0.99

y=0.99*x+0.63;Rsq=0.98

y=1.01*x+2.12;Rsq=0.98

y=1.04*x+‐2.02;Rsq=0.98

y=1.09*x+‐10.86;Rsq=0.97

y=1*x+0.3;Rsq=0.99

y=1.25*x+‐1.39;Rsq=0.89

y=1.17*x+‐2.29;Rsq=0.98
F-39 

Figure F-5 compares the three-year, daily maximum design values for PM2.5 light 

extinction developed using approaches T and W versus the UFVA approach, for both the 90th and 

95th percentile forms. In many cases, all three methods yield similar design values, suggesting 

that the stringency of emission controls required to meet a given level standard may be about the 

same regardless of which approach is used to estimate PM2.5 light extinction. The exceptions are 

for the highest levels of light extinction, which occur in Los Angeles, Fresno, and Detroit. Table 

F-7 provides the same information as Figure F-5 in tabular form. 

Figure F-6 compares the 1-hour PM2.5 light extinction estimates generated by approaches 

T and W to each other in scatter plot format, for both all daylight hours and daily maximum 

daylight hours. The dashed red line is the 1:1 line, and regression statistics are shown with each 

scatter plot. The agreement between the two approaches is very close in most cases. In Fresno, 

Los Angeles and to some degree also Dallas, there is a tendency for approach T to be biased low 

relative to approach W for the points at the high end of the range of PM2.5 light extinction. This 

is consistent with the expected tendency of the monthly averaging of HF and DLEE that is part 

of approach T to mute the extreme daily values of HF and DLEE, extreme values that are 

directly used for their respective days under approach W. 

Figure F-7 compares the 2007-2009 90th and 95th percentile PM2.5 light extinction 

design values calculated based on approach T versus approach W. Despite some scatter between 

the approaches in Figure F-6, the design values from the two approaches are nearly identical. 

This is not surprising, since using monthly average values of HF and DLEE instead of dayspecific values will sometimes result in an overestimate and sometimes in an underestimate of 

PM2.5 light extinction. Also, the bias in approach T for Los Angeles, Fresno, and Dallas noted in 

the previous paragraph mostly affects value above the 90th and 95th percentile points. This 

suggests that approach T’s ability to produce estimates of light extinction on every day, thereby 

allowing the full history of hourly PM2.5 concentrations to be taken into account, may make it the 

preferable approach because using all days’ PM2.5 concentration provides more stability to the 

90th or 95th percentile value than using only one-day-in-three or one-day-in six data. 

It is interesting to note that in the “all hours” panels of Figure F-6, there are instances of 

several points falling on a straight line somewhat offset from the main data cloud, which itself 

seems less organized. These data points can be interpreted as days on which the day-specific 

values of HF and/or DLEE were markedly different from their monthly-averaged values, while 

relative humidity was stable for several hours. When RH is stable within part of a day, approach 

T (and W) results in estimates of hourly light extinction that are directly proportional to hourly 

PM2.5 concentration, with the proportionality constant dependent on HF and DLEE for the day. 

The HF and DLEE for the day are always different between approaches T and W. The scatter 

plot for Phoenix appears to have three such groups of points, for example. Houston appears to 

have two groups.
F-40 

Figure F-5. Comparison of 2007-2009 PM2.5 light extinction design values based on 

approaches T and W vs. the UFVA approach 

Table F-7. Comparison of 2007-2009 PM2.5 light extinction design values based on 

approaches T and W vs. the UFVA approach 

90th Percentile Daily Maximum 

PM2.5 Light Extinction (Mm-1)

95th Percentile Daily Maximum 

PM2.5 Light Extinction (Mm-1)

UFVA T W UFVA T W

Tacoma, WA 121 121 111 132 145 127 

Fresno, CA 312 260 276 394 370 403 

Los Angeles, CA 434 332 358 503 402 461 

Phoenix, AZ 71 68 66 97 74 75 

Salt Lake City, UT 152 144 150 285 301 310 

Dallas, TX 162 144 151 204 170 194 

Houston, TX 145 164 162 177 192 197 

St Louis, IL 224 209 217 267 254 254 

Birmingham, AL 269 257 271 326 336 337 

Atlanta, GA 189 190 186 212 212 213 

Detroit, MI 275 252 261 436 446 433 

Pittsburgh, PA 222 219 214 256 261 273 

Baltimore, MD 183 188 176 212 213 218 

Philadelphia, PA 280 287 294 320 306 315 

New York, NY 295 272 275 365 315 335 
F-41 

Figure F-6. Scatter plots of daily maximum daylight hourly PM2.5 light extinction 

calculated by approach T (x) versus approach W (y).

All Daylight Hours Daily Maximum Daylight Hour 
F-42 

Figure F-6. Scatter plots of daily maximum daylight hourly PM2.5 light extinction 

calculated by approach T (x) versus approach W (y).

All Daylight Hours Daily Maximum Daylight Hour 
F-43 

Figure F-6. Scatter plots of daily maximum daylight hourly PM2.5 light extinction 

calculated by approach T (x) versus approach W (y).

All Daylight Hours Daily Maximum Daylight Hour 
F-44 

Figure F-6. Scatter plots of daily maximum daylight hourly PM2.5 light extinction 

calculated by approach T (x) versus approach W (y).

All Daylight Hours Daily Maximum Daylight Hour 
F-45 

Figure F-6. Scatter plots of daily maximum daylight hourly PM2.5 light extinction 

calculated by approach T (x) versus approach W (y).

All Daylight Hours Daily Maximum Daylight Hour 
F-46 

Figure F-6. Scatter plots of daily maximum daylight hourly PM2.5 light extinction 

calculated by approach T (x) versus approach W (y).

All Daylight Hours Daily Maximum Daylight Hour 
F-47 

Figure F-6. Scatter plots of daily maximum daylight hourly PM2.5 light extinction 

calculated by approach T (x) versus approach W (y).

All Daylight Hours Daily Maximum Daylight Hour 
F-48 

Figure F-6. Scatter plots of daily maximum daylight hourly PM2.5 light extinction 

calculated by approach T (x) versus approach W (y).

All Daylight Hours Daily Maximum Daylight Hour 
F-49 

Figure F-7. Comparison of 2007-2009 PM2.5 light extinction design values based on 

approach T vs. approach W. 

F.6 EXPLANATION OF THE SELECTION OF 1.6 AS THE MULTIPLIER FOR 

OCM 

The original IMPROVE algorithm uses a multiplier of 1.4 to estimate organic 

carbonaceous material (OCM) from the measurement of organic carbon (OC), after subtraction 

of an estimate of the organic carbon blank filter artifact. This value has been extensively 

questioned and investigated since its initial selection, and it is clear from the literature that the 

actual ratio of OCM to OC varies by site, season, PM sampler characteristics, the approach used 

the artifact correction, and the OC quantification method. Various researchers and organizations 

have suggested that other, generally higher, values be used instead of 1.4. The revised 

IMPROVE algorithm, for example, is based on a factor of 1.8. For simplified approaches T and 

W, the issue of an appropriate single value for this multiplier was re-investigated. This 

investigation focused on data only from samples taken in 2007-2009 from sites and months 

where the URG 3000N sampler was in use. The investigation considered all such CSN sites, of 

which there were 171 sites with an average of 77 sample days each. Because of the phased 

introduction of the URG 3000N sampler, there was a wide range in the number of sample days 

across sites, and not all sites were seasonally balanced. These samples were all analyzed for OC 

by Desert Research Institute using the current IMPROVE TOR method. 

At each site, the SANDWICH estimate of OCM was used as the dependent (y) variable in 

a linear regression with the OC measurement (minus an assumed artifact of 0.4 μg/m3

) as the 

independent (x) variable. The slope of the regression line is an estimate of the OC-to-OCM 
F-50 

multiplier for that site, across the seasons represented in the monitoring data. In addition, the 

data from all sites were pooled and a single regression was performed for the pooled data. The 

following results were observed. 

 The average value for R-squared from the 171 regressions was 0.68, with a wide 

range from site-to-site.

 The slopes of the 171 regression lines ranged from 0.57 to 3.05 (not including one 

negative value from a site with only nine samples), with a median of 1.73 and an 

average of 1.72. 

 The sample size-based median slope was 1.68 and the sample size-weighted 

average slope was 1.65.

 The slope of the pooled regression was 1.6.

The value of 1.6 from the pooled regression was selected for use as the carbon multiplier 

in approaches T and W. Note that this is midway between the factor of 1.4 used in the original 

IMPROVE algorithm and the factor of 1.8 used in the revised IMPROVE algorithm. 

F.7 VARIABILITY IN ESTIMATES OF MONTHLY-AVERAGED HF AND DLEE 

AND SELECTION OF A MINIMUM SAMPLE SIZE 

As part of the analysis of simplified methods for this appendix, EPA staff investigated the 

issue of the variability of monthly-averaged values of HF and DLEE, in light of the one-day-insix sampling schedule at many CSN sites. A one-day-in-six CSN site will typically attempt to 

collect five samples in a month, versus 10 or 11 at a one-day-in-three site. 

The margin of error (MOE) for the 90 percent confidence range for monthly-average HF 

and DLEE was calculated for each site-month of data from CSN sites for 2007-2009 (including 

many site-months prior to conversion to the URG 3000N sampler). The MOE is effectively the 

half-width of the confidence range. The MOE reflects both the similarity of the parameter of 

interest from day-to-day and the sample size.14 Site-months were then binned according to the 

number of samples in the month. Figure F-7 shows the box plots of the MOEs for HF and DLEE 

(referred to as ADLEE in the plot), versus the number of samples available. The “N” along the 

top of each box plot indicates the number of CSN site-months that had the indicated number of 

sample days. Based on these box plots, EPA staff decided to incorporate a minimum 

requirement of four sample days per month for the calculation of valid monthly-average values 

of HF and DLEE via approach T, for the purposes of this appendix. It should be noted that based 

on the “N” values in the box plots, 85 percent of monthly averages for HF and DLEE were 

actually based on five or more samples and have a smaller margin of error than shown for the 

four-sample case.

 

14 The formula used was MOE = (STD ERR)*t(n-1, 0.95), where STD ERR = (sample standard deviation)/sqrt(n) 

and t(n-1, 0.95) is the 95th percentile t-distribution with n-1 degrees of freedom (effectively removing 5% from each 

tail to get 90%).
F-51 

Figure F-7. Margin of errors in monthly-averaged values of hygroscopic fraction (HF) and 

dry light extinction efficiency (ADLEE in this figure) versus the number of CSN samples in 

a site-month. 
This page left intentionally blank. 
G-1 

APPENDIX G 

Calculated 24-hour Average PM2.5 Light Extinction and Adjusted 

Candidate Protection Levels 

G.1 OVERVIEW 

The concept of considering and adopting a relatively long averaging period for a NAAQS 

indicator in order to control shorter-term ambient conditions is not at all unfamiliar in the history 

of NAAQS reviews. This appendix investigates the possibility of using calculated 24-hour 

average PM2.5 light extinction as the indicator and averaging period for a secondary PM2.5

NAAQS that would be aimed at controlling daily maximum daylight 4-hour average light 

extinction conditions. This appendix assumes that the form for both standards is based on a 

three-year average of annual 90th percentile values. A calculated 24-hour average approach 

would avoid data quality uncertainties that have recently been associated with currently available 

instruments for direct measurement of hourly PM2.5 light extinction or for measurement of 

hourly PM2.5 mass. 

The calculation procedure for the 24-hour average indicator addressed in this appendix is 

based on the general method used by the IMPROVE monitoring program and the Regional Haze 

Program to characterize 24-hour light extinction levels based on 24-hour filter-based sampling 

for PM2.5 composition.1 There are several variations of this IMPROVE method, distinguished by 

the use of the original versus the revised IMPROVE algorithm for light extinction and by the use 

of same-day relative humidity (RH) at the monitoring site versus long-term (climatological) 

relative humidity conditions. This appendix reports the results of using the original IMPROVE 

algorithm, for consistency with previous results reported in the Urban Focused Visibility 

Assessment (UFVA)2 and in Chapter 4 and Appendices E and F of this Policy Assessment (PA). 

For relative humidity, this appendix applies long-term conditions, which is the approach used in 

the Regional Haze Program for assessing progress towards the Program’s goal of achieving 

natural conditions of visibility in Class I areas by 2064. By using site-specific daily data on both 

PM2.5 mass and composition and site-specific long-term relative humidity conditions, it can be 

expected that the 24-hour average PM2.5 light extinction indicator explored in this appendix 

would provide more protection of visibility than would the current secondary PM2.5 NAAQS 

based only on 24-hour average and annual average PM2.5 mass. In particular, the systematic 

difference in humidity conditions between the eastern states and the western states would be 

accounted for in large degree.3

 

1 The IMPROVE monitoring program and the Regional Haze Program also use filter-based data on PM2.5 and PM10

mass, in order to estimate light extinction due to PM10-2.5. PM2.5 and PM10 mass values are not used in the indicator 

investigated in this appendix, because this indicator excludes light extinction due to PM10-2.5. 2 Particulate Matter Urban-Focused Visibility Assessment Final Document, EPA 452/R-10-004, July 2010. The 

UFVA presented a method and results for PM10 light extinction. The assessment in this memo focuses on PM2.5

light extinction. 3 The use of long-term relative humidity conditions in defining the NAAQS indicator would avoid the need for onsite relative humidity data and hence would avoid the need to establish a reference method for relative humidity, 

establish siting and operational requirements for the relative humidity measurements, and invest in new relative 

humidity instruments. On the other hand, the use of long-term relative humidity conditions can be expected to 

weaken to some degree the correlation between the calculated values of 24-hour average PM2.5 light extinction and 
G-2 

This appendix investigates approaches for calculating adjusted Candidate Protection 

Levels (CPLs) for a 24-hour average PM2.5 light extinction indicator that are generally equivalent 

to CPLs of 20, 25, and 30 deciviews (dv) for a daily maximum daylight 4-hour average PM2.5

light extinction indicator, on an aggregate or “central tendency” basis. It was initially expected 

that the values of 24-hour average PM2.5 light extinction and daily maximum daylight 4-hour 

average PM2.5 light extinction would differ on any given day, with the shorter term peak value 

generally being larger. This would mean that, in concept, the level of a NAAQS based on a 24-

hour average indicator should include a downward adjustment compared to the level that would 

be applied to a NAAQS based on a daily maximum daylight 4-hour average indicator. As 

discussed in section G.5, this initial expectation was verified for the 30 dv level but not for the 

other two levels (25, 20 dv). 

In developing adjusted CPLs, comparisons are made between values of 24-hour average 

PM2.5 light extinction calculated as described in section G.2 and values of daily maximum 

daylight 4-hour average PM2.5 light extinction calculated from the hourly results of the UFVA 

approach for modeling hourly PM2.5 light extinction, for the original 15 study areas using 2007-

2009 data. The UFVA approach and the results of re-executing it for 2007-2009 data are 

described in Appendix F. Table G-1 shows the size and temporal distribution of the available 

2007-2009 data base for the 15 study areas. Because 24-hour average PM2.5 light extinction can 

be calculated with fewer required measurements as input, it is possible to calculate it for more 

days than shown in Table G-1. However, comparisons of 24-hour and 4-hour values for the 

purpose of developing adjusted CPLs can only be made for these days. 

Table G-1. Number of Days per Quarter in Each Study Area 

Study Area Total Number 

 of Days 

2007 2008 2009 

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 

Tacoma 150 13 13 14 13 14 14 14 12 7 14 11 11 

Fresno 325 26 28 30 28 29 27 30 29 15 27 28 28 

Los Angeles 161 21 26 24 24 29 28 9 0 0 0 0 0 

Phoenix 84 0 0 0 0 0 0 0 0 11 24 21 28 

Salt Lake City 276 23 25 19 29 28 12 27 15 13 29 28 28 

Dallas 257 18 23 24 21 22 25 26 20 13 23 21 21 

Houston 144 15 14 9 0 15 13 12 14 7 15 15 15 

St. Louis 287 29 25 22 10 24 22 27 28 14 30 29 27 

Birmingham 330 30 30 27 26 31 27 30 29 13 28 28 31 

Atlanta 258 25 19 26 21 22 25 24 21 12 22 21 20 

Detroit 133 11 11 12 11 7 12 13 13 3 15 10 15 

Pittsburgh 264 22 22 23 23 23 24 24 25 10 25 22 21 

Baltimore 140 12 12 17 16 0 0 0 0 13 26 21 23 

Philadelphia 98 13 14 12 12 9 11 14 13 0 0 0 0 

New York 145 19 15 19 21 20 14 13 24 0 0 0 0 

 

actual daily maximum daylight 4-hour PM2.5 light extinction, since the latter depends on actual relative humidity 

conditions along the sight path in the specific 4-hour period. It can be expected that the variability illustrated in this 

appendix would be somewhat less if the 24-hour indicator were based on same-day measurements of relative 

humidity either at the PM2.5 site or another site in the same area. 
G-3 

G.2 CALCULATED 24-HOUR PM2.5 LIGHT EXTINCTION 

This section describes and implements an approach for calculating 24-hour average PM2.5

light extinction using on-site 24-hour average PM2.5 chemical speciation data but no other sameday data. The PM2.5 light extinction values calculated by the method described here are based on 

the original IMPROVE algorithm and on long-term relative humidity conditions. Possible 

variations, not further analyzed here, include the use of the revised IMPROVE algorithm and the 

use of same-day relative humidity data. When converting PM2.5 light extinction values in Mm-1

to deciviews, the Rayleigh term must be included to avoid the possibility of negative values. 

Table G-2 explicitly defines the calculation steps for the 24-hour average indicator 

approach. The 24-hour average PM2.5 component concentrations needed for the calculation are 

derived from 24-hour filter measurements, including a blank correction for organic carbon 

artifacts, a multiplier to estimate organic carbonaceous material from the blank-corrected organic 

carbon measurement, and the IMPROVE formula for estimating fine soil from concentrations of 

five crustal elements. Instead of using same-day on-site relative humidity data, climatological 

data are used for the humidity adjustment factor, f(RH). A spatial interpolation analysis of 1988-

1997 relative humidity data completed in 2001 for the Regional Haze program provided 

monthly-average f(RH) values across the United States. 4 For this analysis, the monthly values 

from the grid point nearest to a given Chemical Speciation Network (CSN) monitoring site were 

used in the calculation of daily 24-hour PM2.5 light extinction values for 2007-2009. 

Figure G-1 presents monthly-average relative humidity values graphically. Table G-3 

contains the month-specific 24-hour average values of f(RH)hourly for each of the 15 cities. The 

values in Figure G-1 and Table G-3, as expected, show that the more eastern of the 15 cities have 

higher values of f(RH) with the notable and expected exception of Tacoma which has the most 

humid conditions of all the areas. For those areas with strong seasonal patterns of relative 

humidity, the seasonal patterns of the values in Figure G-1 and Table G-3 are also as expected. 

Note that while some individual hours in 1988-1997 had relative humidity above 90%, no 

monthly average relative humidity exceeds 90%. The highest value of monthly average f(RH) is 

5.05 for December in Tacoma, when the average relative humidity value is 89%. The average 

f(RH) value of 5.05 for this month is higher than the value of f(89%) = 3.93 because of the nonlinear nature of the f(RH) function. The single hour value of relative humidity corresponding to 

an f(RH) value of 5.05 would be between 92% and 93%. 

Figure G-2 presents the results of applying the 24-hour average approach in the form of a 

box-and-whisker plot of the daily 24-hour PM2.5 light extinction values for 2007-2009. For ease 

of comparison to similar figures in this document that present estimates of other metrics for 

PM2.5 light extinction, for example Figure F-1, horizontal lines are drawn to represent 65, 100, 

and 190 Mm-1, although we emphasize that these are unadjusted values meant to be compared to 

short-term averages of light extinction rather than to 24-hour averages. As expected, the 

estimated values for 24-hour PM2.5 light extinction generally are lower than for daily maximum 

daylight 1-hour PM2.5 light extinction (Figure F-1), but the comparisons across cities are 

generally the same. 

 

4

 Interpolating Relative Humidity Weighting Factors to Calculate Visibility Impairment and the Effects of 

IMPROVE Monitor Outliers, Report by Science Applications International Corporation to EPA, August 30, 2001. 

http://vista.cira.colostate.edu/improve/publications/guidancedocs/DraftReportSept20.pdf. Note that this reference 

describes a procedure in which hourly f(RH) values were capped at f(98%). Subsequently, the IMPROVE Steering 

Committee decided to cap f(RH) values at f(95%). The f(RH) values used in here are based on the 95% cap.
G-4

Table G-2. Calculation Steps for the 24-hour Average PM2.5 Light Extinction 

24-hour Average Approach Comments

(i) Using historical data, determine a representative long-term monthly average of hourly f(RH) at the monitoring site, for each month of the year. There will be 12 such values for any monitoring site. 

A spatial interpolation analysis of 1988-1997 relative humidity completed in 2001 for the Regional Ha

average f(RH) values for 15,000 1⁄4-degree grid points of latitude/longitude across the United States. F

values from the grid point nearest to a given PM2.5 (CSN) monitoring site were used in the calculation 

extinction values for 2007-2009. If a 24-hour average indicator were to be adopted as part of a second

gridded data could be specified, or EPA could develop updated tables of gridded monthly average f(RH

determining the values to be applied to any visibility monitoring site. Alternatively, the NAAQS could 

site-specific relative humidity data, or data from another monitoring site in the same area, for example Service site. 

(ii) For each CSN sampling day, subtract a sampler-dependent estimate of the OC artifact from the OC measurement, and multiply by 1.4 to estimate organic carbonaceous material (OCM). OCM = (OC – artifact) *1.4 

The IMPROVE blank filter correction values are not representative of conditions in urban areas, and c

The values for the OC artifact used for this analysis ranged from 0.32 to 1.53 μg/m3, depending on CSN 

adjustment for the URG 3000N sampler is of most interest prospectively, because it is the single samplsampling in CSN. For those sites and days, an organic carbon artifact of 0.4 μg/m3 was assumed for th

document, based on early experience with this sampler. EPA staff is currently exploring whether there 

organic carbon artifact based on a more recent, larger field blank and back-up filter data set. 1.4 is used for the multiplier to maintain consistency with the original IMPROVE algorithm. 

(iii) For each CSN sampling day, calculate fine soil/crustal PM2.5 (FS) from CSN measurements of crustal elements AL, 

Si, Ca, Fe, and Ti, using the formula 

 FS = 2.20 x [Al] + 2.49 × [Si] + 1.63 × [Ca] + 

2.42 × [Fe] + 1.94 × [Ti] 

This is the same equation as used in the IMPROVE network and the Regional Haze program. 

(iv) For each CSN sampling day, multiply CSN measurement of sulfate ion by 1.375, and multiply CSN measurement of nitrate ion by 1.29, to reflect associated ammonium under an assumption of full neutralization. 

 AS = Sulfate ion * 1.375  AN = Nitrate ion * 1.29 

(v) Apply the original IMPROVE algorithm to calculate 24-hour average PM2.5 light extinction. 

PM2.5 bext (Mm-1) = 

3[AS]f(RH) + 3[AN]f(RH) + 4[OCM] + 1[FS] + 10[EC] Where f(RH) is the monthly-averaged value from step (i) for the particular month.
G-5 

Figure G-1. Month-specific 24-Hour Average Values of Relative Humidity from 1988-1997 

Table G-3. Month-specific 24-Hour Average Values of f(RH) from 1988-1997 

Month Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 

Tacoma, WA 4.85 4.35 3.98 3.81 3.49 3.26 3.02 3.17 3.68 4.56 5.04 5.05 

Fresno, CA 3.47 2.97 2.65 2.09 1.88 1.67 1.62 1.66 1.77 1.97 2.53 3.26 

Los Angeles, CA 2.48 2.40 2.39 2.16 2.16 2.10 2.11 2.17 2.20 2.18 2.08 2.23 

Phoenix, AZ 1.86 1.67 1.50 1.21 1.13 1.06 1.25 1.41 1.33 1.30 1.49 1.85 

Salt Lake City, UT 3.07 2.77 2.09 1.96 1.89 1.55 1.29 1.32 1.53 1.85 2.56 2.88 

Dallas, TX 2.81 2.59 2.42 2.42 2.70 2.52 2.25 2.25 2.42 2.50 2.60 2.82 

Houston, TX 3.56 3.38 3.31 3.28 3.45 3.43 3.27 3.30 3.32 3.33 3.41 3.60 

St Louis, IL 3.26 2.93 2.66 2.52 2.73 2.73 2.87 3.05 2.98 2.73 2.93 3.20 

Birmingham, AL 3.30 3.03 2.80 2.76 3.09 3.33 3.47 3.38 3.43 3.28 3.13 3.29 

Atlanta, GA 3.26 2.95 2.68 2.54 2.81 3.13 3.31 3.40 3.31 3.16 3.08 3.26 

Detroit, MI 3.06 2.81 2.71 2.47 2.44 2.62 2.71 3.04 3.07 2.88 2.93 3.13 

Pittsburgh, PA 3.07 2.86 2.83 2.55 2.92 3.02 3.16 3.36 3.50 3.11 2.94 3.16 

Baltimore, MD 2.85 2.60 2.68 2.47 2.84 2.89 3.04 3.20 3.27 3.11 2.77 2.88 

Philadelphia, PA 2.87 2.59 2.70 2.49 2.84 2.83 3.03 3.16 3.24 3.16 2.78 2.84 

New York, NY 2.71 2.49 2.58 2.51 2.76 2.72 2.85 2.96 2.99 2.93 2.65 2.68 
G-6

Figure G-2. Distributions of Estimated 24-Hour Average PM2.5 Light Extinction Across the 2007-2009 Pe

using the 24-Hour Average Approach 
G-7 

G.3 CALCULATED DAILY MAXIMUM DAYLIGHT 4-HOUR PM2.5 LIGHT 

EXTINCTION 

Values for the daily maximum daylight 4-hour average PM2.5 light extinction in the 15 

study cities were calculated using the results of the re-execution of the UFVA approach for 

2007-2009, described in Appendix F. The UFVA-approach modeling results (rather than the 

modeling results using simplified approach T reported in Appendix F) are used because these 

results are based on modeling assumptions believed to give the best available representation of 

actual hourly light extinction, in that they incorporate the SANDWICH method for estimating 

organic carbonaceous material (rather than a simple multiplier as in approach T) and on 

information on site-specific diurnal concentration patterns from a run of a 2004 CMAQ 

modeling platform. Note that these 4-hour estimates are based on same-day hourly relative 

humidity data from the nearest site able to provide such data, not on gridded long-term data as 

are the 24-hour PM2.5 light extinction values to which they are compared. 

The daily maximum daylight 4-hour average PM2.5 light extinction values were 

calculated from hourly UFVA-approach results by applying a moving 4-hour window to average 

the hourly light extinction estimates. Only 4-hour periods that were entirely within the daylight 

window were considered. At least three of four hours were required to have a valid 1-hour light 

extinction estimate, meaning that necessary hourly PM2.5 mass and relative humidity data were 

available to calculate light extinction for the hour and the relative humidity was not greater than 

90%. As such, the resulting estimates represent indicator values that could be considered 

comparable to a NAAQS with a 4-hour averaging period with the same restrictions for indicator 

value validity. The requirement for at least 3 out of 4 hourly PM2.5 light extinction values 

resulted in the elimination of 39 of the 3052 site-days shown in Table G-1, because those days 

had no 4-hour daylight periods with at least 3 hours that had hourly PM2.5 data and had relative 

humidity of 90% or less. Figure G-3 presents the results of this screening and calculation 

procedure in the form of a box and whisker plot of the daily maximum daylight 4-hour average 

PM2.5 light extinction values for 2007-2009. As expected, the comparisons across cities are 

generally the same as for daily maximum daylight 1-hr PM2.5 light extinction (Figure F-1). 
G-8

Figure G-3. Distributions of Estimated Daily Maximum Daylight 4-Hour Average PM2.5 Light Extinctio

2009 Period, by Study Area, Based on the UFVA Modeling Approach 
G-9 

G.4 COMPARISON OF CALCULATED DAILY MAXIMUM DAYLIGHT 4-HOUR 

PM2.5 LIGHT EXTINCTION AND CALCULATED 24-HOUR PM2.5 LIGHT 

EXTINCTION 

The daily values of the 24-hour average PM2.5 light extinction from section G.2 and the 

values of the daily maximum daylight 4-hour average PM2.5 light extinction from section G.3 are 

compared here across days within each city, to gain a sense of how they compare and as a quality 

control check for reasonableness. Before doing so, both sets of daily PM2.5 light extinction 

values were converted to deciview units, using the following equation for the deciview metric. 

Note that the Rayleigh term of 10 Mm-1 is included in order avoid the possibility of negative 

deciview values, but no term is included for light extinction due to PM10-2.5. 

 Deciview (dv) = 10 ln ((PM2.5 bext in Mm-1 + 10)/10) 

Figures G-4 and G-5 compare the magnitudes of the daily values of the 24-hour and daily 

maximum daylight 4-hour average PM2.5 light extinction estimates expressed in deciviews. 

Figure G-4 shows a scatter plot for each of the 15 cities including the best-fit linear regression 

line for each city, with the 4-hour light extinction value treated as the independent (x-axis) 

variable in the regression. It can be seen that there is scatter around the regression line for each 

city, because the estimate of 4-hour light extinction includes day-specific and hour-specific 

influences that are not captured by the simpler 24-hour approach. Figure G-5 shows a scatter 

plot for the pooled data points from all 15 cities. In both figures, the linear regression line is 

solid and the 1:1 line is dashed. In both figures, there are some data points – each representing a 

site-day – for which the value of 24-hour average PM2.5 light extinction is higher than the value 

of daily maximum daylight 4-hour PM2.5 light extinction. Directionally, the focus on the daily 

maximum 4-hour average should tend to make the 4-hour average number higher than the 24-

hour average number because PM2.5 concentrations in urban areas generally peak during the day 

rather than at night. The directionally opposite comparison seen for some data points can result 

due to the use of 1988-1997 humidity values that in some cases may be higher than the actual 

same-day conditions used to calculate the 4-hour light extinction values. Approximately onehalf of all site days can be expected to have same-day conditions that are more humid than the 

long-term average conditions, if there is no average bias between the gridded 1988-1997 relative 

humidity data and 2007-2009 site conditions. The use of same-day hourly relative humidity data 

to calculate daily 24-hour average f(RH) would avoid this, and in theory result in data points that 

lie closer to the regression line. It is also quite possible for 24-hour average values of f(RH) to 

be higher than for hours during the daylight. A higher value for 24-hour PM2.5 light extinction 

can also in theory result from the invalidation of one or more 4-hour PM2.5 light extinction values 

on a day due to the screen for hourly relative humidity above 90% (resulting in a time period 

with lower light extinction providing the valid daily daylight 4-hour maximum), or from a 

diurnal pattern in which 4-hour average PM2.5 mass peaks during the night rather than during 

daylight. 

Table G-3 presents the city-specific regression coefficients for the scatter plots in Figure 

G-4, the unweighted average of these coefficients across cities, and the regression coefficients 

for the pooled 15-city data shown in Figure G-5. While in broad terms most of the individual 

scatter plots seem to have similar variability about the regression line, some values of R-squared 

are notably lower. Closer examination shows that the lower values of R-squared are for the 

cities with the smaller range of light extinction conditions. 
G-10 

Figure G-6 and Table G-4 describe the distributions of the daily ratio of the 24-hour and 

daily maximum daylight 4-hour values of PM2.5 light extinction. For each day, the 4-hour value 

was divided by the 24-hour value. The distribution of these ratios across 2007-2009 is shown in 

Figure G-6 in box-and-whisker plot format. Table G-4 presents the minimum, 10th percentile, 

25th percentile, mean, median (50th percentile), 75th percentile, 90th percentile, and maximum 

values from the distribution of ratios in each city. 

It is also of interest to compare the annual 90th percentile values and the 3-year design 

values for 24-hour average and daily maximum daylight 4-hour average PM2.5 light extinction 

across areas. Table G-5 presents the annual 90th percentile values and the 3-year 2007-2009 

design values.5

 There are only a few cases in which the 24-hour average annual percentile or 3-

year design value is notably larger than the corresponding daily maximum daylight 4-hour 

average value, despite the occurrence of many such points in the scatter plots of daily values. 

Figure G-7 is a scatter plot of the 4-hour versus the 24-hour 90th percentile values for individual 

years of data. Figure G-8 is a scatter plot of the 4-hour versus the 24-hour design values. The 

equation of the linear regression line for each scatter plot is shown below the graphic. 

 

5 For the purpose of this analysis, the “3-year” design values have been calculated with only 2 years of data for Los 

Angeles, Baltimore, Philadelphia, and New York City and one year of data for Phoenix because the re-execution of 

the UFVA approach for 2007-2009 created estimates only for those years. See Table G-1. Also, no data 

completeness minimums have been applied. The smallest sample used to determine an annual 90th percentile was 37 

days, for Houston in 2007. 
G-11

Figure G-4. Scatter Plots of 24-Hour Light Extinction (y-axis) and Daily Maximum Daylight 4-Hour Ligh

for 15 Individual Cities, in deciviews

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 40 45

Tacoma

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 40 45

Fresno

dv_24

05

1015202530354045

dv

_

4x

0 5 10 15 20 25 30 35 40 45

Los Angeles

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 40 45

Phoenix

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 40 45

Salt Lake City

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 40 45

Dallas

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 

Houston
G-12

Figure G-4. Scatter Plots of 24-Hour Light Extinction (y-axis) and Daily Maximum Daylight 4-Hour Ligh

for 15 Individual Cities, in deciviews (cont.) 

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 40 45

Birmingham

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 40 45

Atlanta

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 40 45

Detroit

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 40 45

St. Louis

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 40 45

Pittsburgh

dv_24

05

1015202530354045

dv

_

4x

0 5 10 15 20 25 30 35 40 45

Baltimore

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 

Philadelphia

dv_24

05

1015202530354045

dv_4x

0 5 10 15 20 25 30 35 

New York City
G-13 

Figure G-5. Scatter Plot of 24-Hour Light Extinction (y-axis) versus Daily Maximum 

Daylight 4-Hour Light Extinction (x-axis) for All Cities Pooled Together, in deciviews 

dv_24

0

5

10

15

20

25

30

35

40

45

dv 4x

0 5 10 15 20 25 30 35 40 45

All 15 cities

Blue line = regression line

Black dashed line = 1:1 line
G-14 

Table G-3. Regression Results for 24-Hour Light Extinction versus Daily Maximum 

Daylight 4-Hour Light Extinction for Individual Cities and for All Cities Pooled Together, 

in deciviews (4-hour light extinction is the independent variable) 

Study Area Intercept Slope R-squared 

Tacoma, WA 1.520 0.948 0.586 

Fresno, CA -0.726 0.961 0.823 

Los Angeles, CA 2.749 0.760 0.814 

Phoenix, AZ 3.360 0.804 0.616 

Salt Lake City, UT -1.255 0.926 0.795 

Dallas, TX 5.418 0.625 0.634 

Houston, TX 10.224 0.500 0.423 

St Louis, IL 6.563 0.655 0.608 

Birmingham, AL 7.210 0.684 0.641 

Atlanta, GA 4.912 0.740 0.641 

Detroit, MI 4.163 0.780 0.816 

Pittsburgh, PA 4.101 0.828 0.797 

Baltimore, MD 5.925 0.736 0.701 

Philadelphia, PA 2.797 0.761 0.564 

New York, NY 8.109 0.626 0.645 

Average Across Cities 

(equal weighting) 

4.338 0.756 0.674 

Regression Using 

Pooled Data 

3.507 0.790 0.715 
G-15

Figure G-6. Distributions of the Ratio of Daily Maximum Daylight 4-Hour Light Extinction, in deciview

24-Hour Light Extinction, in deciviews (denominator)* 

* Whiskers are at 10th and 90th percentiles.
G-16 

Table G-4. Points on the Distributions of the Ratio of Daily Maximum Daylight 4-Hour 

Light Extinction (numerator) and 24-Hour Light Extinction (denominator), in deciviews 

Study Area Min 10th

%-tile 

25th

%-tile Mean Median 75th%-tile 

90th

%-tile Max 

Tacoma, WA 0.169 0.689 0.882 1.009 1.015 1.135 1.254 2.521 

Fresno, CA 0.718 0.892 0.986 1.106 1.097 1.192 1.314 2.087 

Los Angeles, CA 0.648 0.960 1.089 1.163 1.172 1.246 1.340 1.638 

Phoenix, AZ 0.535 0.788 0.868 0.970 0.962 1.039 1.149 1.840 

Salt Lake City, UT 0.562 0.946 1.041 1.223 1.182 1.359 1.566 2.319 

Dallas, TX 0.574 0.908 1.009 1.126 1.114 1.224 1.351 1.902 

Houston, TX 0.458 0.751 0.862 0.970 0.972 1.084 1.205 1.501 

St Louis, IL 0.282 0.831 0.953 1.056 1.057 1.163 1.268 1.600 

Birmingham AL 0.498 0.775 0.879 0.987 0.986 1.095 1.191 1.523 

Atlanta, GA 0.121 0.882 0.965 1.047 1.044 1.137 1.240 1.541 

Detroit, MI 0.631 0.828 0.938 1.037 1.032 1.139 1.220 1.692 

Pittsburgh, PA 0.614 0.839 0.913 0.986 0.987 1.052 1.132 1.348 

Baltimore, MD 0.569 0.794 0.857 0.979 0.996 1.077 1.154 1.393 

Philadelphia PA 0.796 0.956 1.064 1.170 1.150 1.262 1.420 2.008 

New York, NY 0.584 0.829 0.922 1.028 1.018 1.133 1.241 1.542 

Average Across Cities 

(equal weighting) 

1.057 1.052 

Table G-5. Annual 90th Percentile Values and 3-year 2007-2009 90th Percentile Design 

Values for Daily Maximum Daylight 4-Hour PM2.5 Light Extinction and 24-Hour PM2.5

Light Extinction, in deciviews 

Study Area 4-hour light extinction 24-hour light extinction* 

Design Value 2007 2008 2009 Design Value 2007 2008 2009 

Tacoma, WA 22.35 22.93 21.40 22.72 24.29 26.74 23.61 22.51 

Fresno, CA 31.71 33.95 32.96 28.21 31.45 34.78 31.66 27.91 

Los Angeles, CA 34.64 35.50 33.78 29.95 31.22 28.68 

Phoenix, AZ 18.08 18.08 20.54 20.54 

Salt Lake City, UT 24.80 26.10 26.10 22.19 24.54 26.46 25.88 21.28 

Dallas, TX 26.61 27.91 26.10 25.80 22.90 23.22 23.51 21.97 

Houston, TX 24.59 24.85 24.68 24.25 24.20 24.90 24.20 23.60 

St Louis, IL 29.03 28.90 29.55 28.62 27.10 28.27 27.60 25.42 

Birmingham AL 28.23 29.60 29.12 25.95 28.07 30.45 27.97 25.80 

Atlanta, GA 27.35 29.65 27.15 25.26 25.89 28.85 25.80 23.03 

Detroit, MI 30.38 31.18 29.23 30.73 29.96 31.57 29.34 28.96 

Pittsburgh, PA 28.87 30.54 29.55 26.53 28.39 30.87 27.85 26.46 

Baltimore, MD 27.94 28.74 27.15 27.15 28.51 25.80 

Philadelphia PA 31.30 30.77 31.82 28.27 29.07 27.47 

New York, NY 32.51 33.50 31.53 29.31 30.11 28.51 

* Entries in italics indicate cases in which the 24-hour average value is larger than the corresponding 4-hour average 

value.

 
G-17 

Figure G-7. Scatter Plot of 2007-2009 Annual 90th Percentile Values for Daily Maximum 

Daylight 4-Hour PM2.5 Light Extinction (x-axis) and 24-Hour Average PM2.5 Light 

Extinction (y-axis), in deciviews 

Annual24_pct90

0

5

10

15

20

25

30

35

40

45

Annual4x pct90

0 5 10 15 20 25 30 35 40 45

All 15 cities

Model: Annual24_pct90 = 6.636 + 0.725 * Annual4x_pct90 (rsquare=0.734)
G-18 

Figure G-8. Scatter Plot of 90th Percentile 2007-2009 3-year Design Values for Daily 

Maximum Daylight 4-Hour PM2.5 Light Extinction (x-axis) and 24-Hour Average PM2.5

Light Extinction (y-axis), in deciviews 

DV24_pct90

0

5

10

15

20

25

30

35

40

45

DV4x_pct90

0 5 10 15 20 25 30 35 40 45

All 15 cities

Model: DV24_pct90 = 8.786 + 0.646 * DV4x_pct90 (rsquare=0.820)
G-19 

G.5 DEVELOPMENT OF ADJUSTED INDICATOR LEVELS APPROPRIATE FOR 

COMPARISON TO CALCULATED 24-HOUR PM2.5 LIGHT EXTINCTION 

This section explores several different approaches for calculating adjusted CPLs for a 24-

hour average PM2.5 light extinction indicator that may be considered to be generally equivalent 

on an aggregate or “central tendency” basis to CPLs of 20, 25, and 30 deciviews (dv) for a daily 

maximum daylight 4-hour average PM2.5 light extinction indicator. 

Several possible approaches are investigated in order to examine how robust the adjusted 

levels are to the choice of a specific approach. In some of the approaches explored in this 

section, the “equivalent” levels for individual cities are determined and then averaged across 

cities to get a single adjusted level. For these approaches, the range of city-specific adjusted 

levels is provided to give a sense of the possible variability in protection resulting from the use 

of the longer averaging period and long-term relative humidity data. 

The following approaches were applied. The results are shown in Table G-6, rounded to 

the nearest whole-number value in deciviews. While some deciview results in Table G-5 were 

expressed with two decimal digits, EPA staff believes it would be most appropriate to set a 

NAAQS level in whole deciviews, given the uncertainties in the chain of analyses that may be 

used to determine that level. 

 Approach A – The regression line based on city-specific 3-year design values 

displayed in Figure G-8 was used to find the 24-hour equivalents of 4-hour levels 

of 20, 25, and 30 dv. 

 Approach B – The regression line based on city-specific annual 90th percentile 

values displayed in Figure G-7 was used to find the 24-hour equivalents of 4-hour 

levels of 20, 25, and 30 dv. 

 Approach C – The regression line using all days for each city (Figure G-4 and 

Table G-3) was used to find 24-hour equivalents of 4-hour levels of 20, 25, and 

30 dv. These results were then averaged across the 15 cities. Table G-7 shows 

the intermediate adjusted levels for each of the cities. 

 Approach D – The pooled regression line using all site-days (Figure G-5 and the 

last row of Table G-3) was used to find the 24-hour equivalents of 4-hour levels 

of 20, 25, and 30 dv. 

 Approach E – In each city, the median ratio of the 24-hour PM2.5 light extinction 

indicator to the 4-hour PM2.5 light extinction indicator was applied to 20 dv (and 

also applied to 25 and 30 dv in turn). The median ratio was used to minimize the 

influence of days with extreme ratios, although Table G-4 indicates that in most 

cases the difference between the mean and median ratios is small. These cityspecific results were averaged across the 15 cities. Table G-8 shows the 

intermediate adjusted levels for each of the cities. 

 Of these approaches, EPA staff considers Approaches A and B, which give nearly 

identical results, to be the most appropriate for further consideration, given the high R-squared of 

the regressions and the fact that the regression lines are determined by data from days with PM2.5

light extinction conditions in the range of 20 to 40 dv. In contrast, the city-specific regression 

lines and the pooled data regression lines are influenced by PM2.5 light extinction conditions well 
G-20 

below 20 dv because of the large amount of data in that range. Approaches A and B yield 

identical results except for the case of a 4-hour level of 20 dv, for which the A and B results are 

only 0.65 dv different before rounding but after rounding differ by 1 dv. Both A and B result in 

a 24-hour level that is higher than 20 dv for this case. EPA staff recommends consideration of 

the results from Approach B, which are adjusted CPLs of 21, 25, and 28 dv, as the 24-hour PM2.5

light extinction indicator levels. These levels can be considered to be generally equivalent in an 

aggregate or central tendency sense to CPLs of 20, 25, and 30 dv applied to a daily maximum 

daylight 4-hour PM2.5 light extinction indicator. 

As seen above, the adjusted 24-hour CPL can be greater than an "equivalent" 4-hour 

level. The regression-based Approach B is sensitive to specific cases where use of the monthly 

average historical f(RH) values yields higher estimates of light extinction than seen when using 

the actual same-day RH conditions. This appears to occur more frequently at the upper end of 

the 24-hour distribution (i.e., design values or 90th percentiles). Figure G-7 shows three data 

points, out of 39, in which the annual 90th percentile, 24-hour average PM2.5 light extinction 

value are significantly greater than their daily maximum 4-hour daylight counterpart. These data 

points represent Tacoma (2007), Tacoma (2008), and Phoenix (2009). The most extreme case is 

Tacoma (2007). Because there are 51 observations for this site-year, the sixth high value is the 

90th percentile value. For the daily maximum 4-hour case the sixth high occurs on 10/9/2007 

(22.93 dv). The average of the four hourly RH values that went into that day's calculation of the 

4-hour indicator was 78%. For the 24-hour average case, the sixth high occurs on 11/02/2007 

(26.74 dv). The 24-hour average of the RH values that went into that calculation was 89% 

(based on the monthly average RH estimates). On this day, the 4-hour value was only 17.23 dv 

(24th ranked) and the average of the four hourly RH values that went into that day's calculation 

of the 4-hour indicator was 59%. On the five higher 24-hour light extinction days in Tacoma in 

2007, the RH used for the 24-hour calculation was, on average, 15% higher than the actual used 

for the sub-daily maximum. As a quick sensitivity analysis, we determined that if one were to 

remove these three data points (i.e., Tacoma (2007 and 2008) and Phoenix (2009)) from the 

Approach B regression analysis, the resulting CPLs would be: 20, 24, and 28 dv. While some of 

these RH differences are surely due to diurnal variations and are therefore inherent and 

appropriate in the CPL adjustment process, some may also be due to trends in RH over the years 

and/or site-related differences (the climatological values were based on spatial interpolation of 

only NWS monitoring sites, while many of the RH values used to calculate the 4-hour indicator 

were obtained from non-NWS instruments at the CSN site itself). Consideration should be given 

to re-executing the analysis in this Appendix using same-day, same-site relative humidity data, to 

eliminate the possibility that differences in humidity conditions between 1988-1997 and 2007-

2009 and/or differences in RH sites have substantially affected the final equivalent levels. 

 In considering the results shown in Table G-6, it can be kept in mind that 1 deciview is 

about the amount that a person can distinguish when viewing scenic vistas, and that a difference 

of 1 deciview is equivalent to about a 10% difference in light extinction expressed in Mm-1. 

Examining Table G-5, there are three cases in which comparing the original CPLs of 20, 25, and 

30 dv to calculated 4-hour PM2.5 light extinction produces a different outcome than comparing 

the adjusted CPLs of 21, 25, and 28 dv to calculated 24-hour PM2.5 light extinction. Dallas is 

estimated to fail a 4-hour CPL of 25 dv, but to pass a 24-hour CPL of 25 dv, using an appropriate 

rounding convention. Philadelphia is estimated to fail a 4-hour CPL of 30 dv, but to pass a 24-

hour CPL of 28 dv. Detroit is estimated to pass a 4-hr CPL of 30 dv, but to fail a 24-hr CPL of 

28 dv. In each case, only a small difference in design value is responsible. 
G-21 

Table G-6. “Equivalent” Levels for Calculated 24-Hour PM2.5 Light Extinction Using Five 

Approaches

Approach Description 

24-hour level 

equivalent to 

20 dv 

for 4-hour 

(range among 

15 cities) 

24-hour level 

equivalent to 

25 dv 

for 4-hour 

(range among 

15 cities) 

24-hour level 

equivalent to 

30 dv 

for 4-hour 

(range among 

15 cities) 

A 3-year 90th percentile design 

values regression 22 dv 25 dv 28 dv 

B 

(Preferred) 

Annual 90th percentile values 

regression 21 dv 25 dv 28 dv 

C All-days city-specific 

regressions, then averaged 

19 dv 

(17-20) 

23 dv 

(20-24) 

27 dv 

(24-29) 

D All-days pooled regression 19 dv 23 dv 27 dv 

E Median ratios, then averaged 19 dv 

(16-20) 

24 dv 

(20-25) 

29 dv 

(26-30) 

Table G-7. Intermediate Results for Approach C 

Study Area Intercept 

(Table G-3) 

Coefficient 

(Table G-3) 20 dv equiv. 25 dv equiv. 30 dv equiv. 

Tacoma, WA 1.520 0.948 20.47 25.21 29.95

Fresno, CA -0.726 0.961 18.49 23.29 28.09

Los Angeles, CA 2.749 0.760 17.96 21.76 25.56

Phoenix, AZ 3.360 0.804 19.44 23.46 27.47

Salt Lake City, UT -1.255 0.926 17.27 21.91 26.54

Dallas, TX 5.418 0.625 17.92 21.04 24.17

Houston, TX 10.224 0.500 20.22 22.72 25.22

St Louis, IL 6.563 0.655 19.67 22.95 26.22

Birmingham AL 7.210 0.684 20.89 24.31 27.73

Atlanta, GA 4.912 0.740 19.72 23.42 27.13

Detroit, MI 4.163 0.780 19.77 23.67 27.57

Pittsburgh, PA 4.101 0.828 20.66 24.80 28.94

Baltimore, MD 5.925 0.736 20.65 24.33 28.02

Philadelphia PA 2.797 0.761 18.02 21.82 25.63

New York, NY 8.109 0.626 20.62 23.75 26.88

Average 19.45 23.23 27.01

Minimum 17.27 21.04 24.17

Maximum 20.89 25.21 29.95
G-22 

Table G-8. Intermediate Results for Approach E 

Study Area 

Median Ratio 

4-hr : 24-hour

(Table G-4) 

20 dv equiv. 25 dv equiv. 30 dv equiv. 

Tacoma, WA 1.015 19.70 24.63 29.55

Fresno, CA 1.097 18.23 22.79 27.35

Los Angeles, CA 1.172 17.06 21.32 25.59

Phoenix, AZ 0.962 20.80 25.99 31.19

Salt Lake City, UT 1.182 16.92 21.15 25.38

Dallas, TX 1.114 17.95 22.44 26.92

Houston, TX 0.972 20.58 25.73 30.87

St Louis, IL 1.057 18.92 23.64 28.37

Birmingham AL 0.986 20.29 25.37 30.44

Atlanta, GA 1.044 19.16 23.95 28.74

Detroit, MI 1.032 19.37 24.21 29.06

Pittsburgh, PA 0.987 20.27 25.33 30.40

Baltimore, MD 0.996 20.08 25.10 30.12

Philadelphia PA 1.150 17.39 21.73 26.08

New York, NY 1.018 19.65 24.56 29.47

Average 19.09 23.86 28.64

Minimum 16.92 21.15 25.38

Maximum 20.80 25.99 31.19

G.6 SUMMARY 

EPA staff has investigated the possibility of using a particular calculated 24-hour average 

PM2.5 light extinction as the indicator and averaging period for a secondary PM2.5 NAAQS that 

would be aimed at controlling daily maximum daylight 4-hour average light extinction 

conditions, focusing on a form for both standards that is based on a three-year average of annual 

90th percentile values. A calculated 24-hour average approach would avoid data quality 

uncertainties that have recently been associated with currently available instruments for direct 

measurement of hourly PM2.5 light extinction or for measurement of hourly PM2.5 mass. The 

particular 24-hour indicator considered by EPA staff uses the original IMPROVE algorithm and 

long-term (1988-1997) relative humidity conditions to calculate light extinction due to PM2.5. 

By using site-specific daily data on PM2.5 composition and long-term relative humidity 

conditions interpolated to each specific site, this 24-hour average indicator would provide more 

consistent protection of visibility than would a secondary PM2.5 NAAQS based only on 24-hour 

or annual average PM2.5 mass. In particular, this approach would account for the systematic 

difference in humidity conditions between most eastern states and most western states. Possible 

variations include the use of the revised IMPROVE algorithm and the use of same-day 

measurements of relative humidity either at the visibility monitoring site or another site in the 

same area. These variations in the method used to calculate PM2.5 light extinction on a 24-hour 
G-23 

basis would be expected to provide somewhat more consistent protection in areas across the 

country. 

In concept, in order to provide generally equivalent protection, the level of a NAAQS 

based on a 24-hour average indicator should include an adjustment compared to the level that 

would be applied to a NAAQS based on a daily maximum daylight 4-hour average indicator. 

Using 15 study sites, EPA staff investigated five approaches to making this adjustment, for 4-

hour indicator NAAQS levels of 20, 25, and 30 dv. An approach (B) thought by EPA staff to be 

more appropriate for further consideration yielded adjusted NAAQS levels of 21, 25, and 28 dv 

as the 24-hour PM2.5 light extinction indicator levels that are generally equivalent in an aggregate 

or central tendency sense to levels of 20, 25, and 30 dv applied to a daily maximum daylight 4-

hour PM2.5 light extinction indicator. 
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H-1

APPENDIX H 

Table H-1. Predicted Number of Counties Not Likely to Meet Current Secondary PM2.5 Standa

Alternative Secondary Standards Based on a 4-Hour Calculated PM2.5 Light Extinction 

Region > All U.S. Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwest 

Total # of counties with suitable data to 

calculate the indicator > 114 24 28 31 8 2 15 

Standard Statistic Number of counties and percentage of counties with suitable dat

Current Standards - PM2.5 mass indicator 

Annual 24-Hour 

15 μg/m3 35 μg/m3 # counties 15 0 1 3 0 0 6 

% counties 13% 0% 4% 10% 0% 0% 40% 

Alternative Standards – Calculated PM2.5 Light Extinction Indicator 

Form2 Level (Mm-1) 

90th 25 # counties 96 23 22 31 6 1 7 

% counties 84% 96% 79% 100% 75% 50% 47% 

90th 26 # counties 79 20 14 29 5 1 4 

% counties 69% 83% 50% 94% 63% 50% 27% 

90th 27 # counties 58 17 6 27 1 0 1 

% counties 51% 71% 21% 87% 13% 0% 7% 

90th 28 # counties 47 12 3 26 1 0 1 

% counties 41% 50% 11% 84% 13% 0% 7% 

90th 29 # counties 32 10 0 16 1 0 1 

% counties 28% 42% 0% 52% 13% 0% 7% 

90th 30 # counties 18 6 0 7 0 0 1 

% counties 16% 25% 0% 23% 0% 0% 7% 

1 3-year average of annual 90th percentile daily maximum 4-hour daylight PM2.5 light extinction, excluding hours with relative humidity 2 Design values for comparison with the level of the standard were calculated based on approach T, using 2007-2009 monitoring data, i

2005-2007 data were available. (See Appendix F for the description of approach T). Actual future outcomes may differ from these estinstrumentation, siting, and/or the specific procedure for calculating the indicator. 
H-2

Table H-2. Predicted Number of Counties Not Likely to Meet Current Secondary PM2.5 Standards and P

Secondary Standards Based on a 24-Hour Average Calculated PM2.5 Light Extinction Indic

Region > All U.S. Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwest 

Total # of counties with suitable data 

to calculate the indicator > 187 33 47 53 10 9 26 

Standard Statistic Number of counties and percentage of counties with suitable data2

Current Standards - PM2.5 mass indicator 

Annual 24-Hour 

15 μg/m3 35 μg/m3 # counties 21 0 1 3 0 0 11 

% counties 11% 0% 2% 6% 0% 0% 42% 

Alternative Standards – Calculated PM2.5 Light Extinction Indicator 

Form4 Level (Mm-1) 

90th 25 # counties 122 26 28 49 4 0 8 

% counties 65% 79% 60% 92% 40% 0% 31% 

90th 26 # counties 95 22 14 48 1 0 4 

% counties 51% 67% 30% 91% 10% 0% 15% 

90th 27 # counties 72 16 5 42 0 0 3 

% counties 39% 48% 11% 79% 0% 0% 12% 

90th 28 # counties 44 6 2 27 0 0 3 

% counties 24% 18% 4% 51% 0% 0% 12% 

3 3-year average of annual 90th percentile 24-hour average PM2.5 light extinction.4 Design values for comparison with the level of the standard were calculated based on the approach specified in Table G-2 using 2007

Actual future outcomes may differ from these estimates due to changes in instrumentation, siting, and/or the specific procedure for calc
This page left intentionally blank. 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC 

Publication N