Review of the National Ambient Air Quality

Standards for Particulate Matter:

Policy Assessment of Scientific

and Technical Information

OAQPS Staff Paper

EPA-452/R-05-005

June 2005

Review of the National Ambient Air Quality

Standards for Particulate Matter:

Policy Assessment of Scientific

and Technical Information

OAQPS Staff Paper

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

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 Staff Paper contains the conclusions and recommendations of the staff of the OAQPS

and does not necessarily represent those of the EPA. Mention of trade names or commercial

products is not intended to constitute endorsement or recommendation for use.
ACKNOWLEDGMENTS

This Staff Paper is the product of the Office of Air Quality Planning and Standards

(OAQPS). For the section on PM-related health effects and primary standards, the principal

authors include Dr. Mary Ross, who also led the PM NAAQS review team, Mr. Harvey

Richmond, and Dr. Karen Martin, who also managed the project. For the section on PM-related

welfare effects and secondary standards, the principal authors include Mr. Rich Damberg, Ms.

Amy Vasu, Mr. Mark Schmidt, Ms. Vicki Sandiford, and Dr. Karen Martin. The principal

authors of the chapter on the characterization of ambient PM include Mr. Mark Schmidt, Mr.

John Langstaff, and Mr. Scott Mathias. The authors would also like to acknowledge Mr.

Venkatesh Rao, Mr. David Mintz, Mr. Lance McCluney, Mr. Neil Frank, Mr. Tim Hanley, and

Mr. Lewis Weinstock for providing input and analyses on specific topics. Staff from other EPA

offices, including the Office of Research and Development, the Office of General Counsel, and

the Office of Transportation and Air Quality, also provided valuable comments. Special

acknowledgment is given to Mr. John Bachmann for his leadership in past PM NAAQS reviews

and for his expert advice and valuable input in this review.

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

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

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

comments submitted by a number of independent scientists, officials from State and local air

pollution organizations, environmental groups, and industrial groups and companies.

i

Table of Contents

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Abbreviations and Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi

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

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

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

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

1.2.2 History of PM NAAQS Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4

1.2.3 Litigation Related to 1997 PM Standards . . . . . . . . . . . . . . . . . . . . . . . 1-5

1.2.4 Current PM NAAQS Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-7

1.3 GENERAL APPROACH AND ORGANIZATION OF DOCUMENT . . . . . . 1-8

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11

2. CHARACTERIZATION OF AMBIENT PM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.2 PROPERTIES OF AMBIENT PM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.2.1 Particle Size Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1

2.2.1.1 Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-2

2.2.1.2 Sampler Cut Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-4

2.2.2 Sources and Formation Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-6

2.2.3 Chemical Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-8

2.2.4 Fate and Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-9

2.2.5 Optical Properties of Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-10

2.2.6 Other Radiative Properties of Particles . . . . . . . . . . . . . . . . . . . . . . . . 2-12

2.3 AMBIENT PM MEASUREMENT METHODS . . . . . . . . . . . . . . . . . . . . . . . 2-13

2.3.1 Particle Mass Measurement Methods . . . . . . . . . . . . . . . . . . . . . . . . . 2-13

2.3.2 Indirect Optical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-15

2.3.3 Size-Differentiated Particle Number Concentration Measurement

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-16

2.3.4 Chemical Composition Measurement Methods . . . . . . . . . . . . . . . . . . 2-16

2.3.5 Measurement Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-17

2.4 PM CONCENTRATIONS, TRENDS, AND SPATIAL PATTERNS . . . . . . 2-18

2.4.1 PM2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-20

2.4.2 PM10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-30

2.4.3 PM10-2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-33

2.4.4 Ultrafine Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-40

2.4.5 Components of PM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-40

2.4.6 Relationships Among PM2.5, PM10, and PM10-2.5 . . . . . . . . . . . . . . . . . 2-46
ii

2.5 PM TEMPORAL PATTERNS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-49

2.5.1 PM2.5 and PM10-2.5 Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-49

2.5.2 Ultrafine Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-63

2.6 PM BACKGROUND LEVELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-63

2.7 RELATIONSHIP BETWEEN AMBIENT PM MEASUREMENTS AND

HUMAN EXPOSURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-67

2.7.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-68

2.7.2 Centrally Monitored PM Concentration as a Surrogate for Particle

Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-69

2.8 RELATIONSHIP BETWEEN AMBIENT PM AND VISIBILITY . . . . . . . . 2-73

2.8.1 Particle Mass and Light Extinction . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-74

2.8.2 Other Measures of Visibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-76

2.8.3 Visibility at PM Background Conditions . . . . . . . . . . . . . . . . . . . . . . . 2-76

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-79

PM-RELATED HEALTH EFFECTS

AND PRIMARY PM NAAQS

3. POLICY-RELEVANT ASSESSMENT OF HEALTH EFFECTS EVIDENCE . . 3-1

3.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1

3.2 MECHANISMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-4

3.3 NATURE OF EFFECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-10

3.3.1 Premature Mortality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-11

3.3.1.1 Mortality and Short-term PM Exposure . . . . . . . . . . . . . . . . . 3-12

3.3.1.2 Mortality and Long-term PM Exposure . . . . . . . . . . . . . . . . . . 3-17

3.3.2 Morbidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-19

3.3.2.1 Hospitalization and Medical Visits . . . . . . . . . . . . . . . . . . . . . 3-19

3.3.2.2 Effects on the Respiratory System from Short-term Exposures

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-22

3.3.2.3 Effects on the Respiratory System from Long-term Exposures

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-23

3.3.2.4 Effects on the Cardiovascular System . . . . . . . . . . . . . . . . . . . 3-23

3.3.3 Developmental effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-24

3.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-24

3.4 INTEGRATIVE ASSESSMENT OF HEALTH EVIDENCE . . . . . . . . . . . . 3-25

3.4.1 Strength of Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-25

3.4.2 Robustness of Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-27

3.4.3 Consistency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-28

3.4.4 Temporality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-30

3.4.5 Nature of concentration-response relationships . . . . . . . . . . . . . . . . . . 3-30

3.4.6 Natural Experiment Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-31

3.4.7 Coherence and Plausibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-31

3.4.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-34
iii

3.5 PM-RELATED IMPACTS ON PUBLIC HEALTH . . . . . . . . . . . . . . . . . . . . 3-35

3.5.1 Potentially Susceptible and Vulnerable Subpopulations . . . . . . . . . . . 3-35

3.5.2 Potential Public Health Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-36

3.6 ISSUES RELATED TO QUANTITATIVE ASSESSMENT OF

EPIDEMIOLOGIC EVIDENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-37

3.6.1 Air Quality Data in Epidemiologic Studies . . . . . . . . . . . . . . . . . . . . . 3-38

3.6.2 Measurement Error and Exposure Error . . . . . . . . . . . . . . . . . . . . . . . 3-39

3.6.3 Alternative Model Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-42

3.6.3.1 Time-series epidemiologic studies . . . . . . . . . . . . . . . . . . . . . . 3-42

3.6.3.2 Prospective cohort epidemiologic studies . . . . . . . . . . . . . . . . 3-43

3.6.4 Co-pollutant Confounding and Effect Modification . . . . . . . . . . . . . . 3-44

3.6.4.1 Co-pollutant Confounding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-44

3.6.4.2 Effect Modification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-46

3.6.5 Issues Related to Alternative Exposure Periods in Epidemiologic

Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-47

3.6.5.1 Lag Structure in Short-term Exposure Studies . . . . . . . . . . . . 3-47

3.6.5.2 Seasonal Differences in Time-Series Epidemiologic Results . 3-51

3.6.5.3 Health Effects Related to Different Short-term Exposure

Time Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-52

3.6.5.4 Exposure Periods Used in Prospective Cohort Studies . . . . . . 3-53

3.6.6 Concentration-Response Relationships and Potential Thresholds . . . 3-55

3.7 SUMMARY AND CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-57

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-60

4. CHARACTERIZATION OF HEALTH RISKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1

4.1.1 Overview of Risk Assessment From Last Review . . . . . . . . . . . . . . . . 4-1

4.1.2 Development of Approach for Current Risk Assessment . . . . . . . . . . . 4-2

4.2 SCOPE OF PM RISK ASSESSMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-3

4.2.1 Selection of Health Endpoint Categories . . . . . . . . . . . . . . . . . . . . . . . . 4-4

4.2.2 Selection of Study Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-5

4.3 COMPONENTS OF THE RISK MODEL . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-7

4.3.1 Air Quality Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-15

4.3.1.1 Estimating PM Background Levels . . . . . . . . . . . . . . . . . . . . . 4-15

4.3.1.2 Simulating PM Levels That Just Meet Specified Standards . . 4-17

4.3.2 Concentration-Response Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-19

4.3.2.1 Linear and Nonlinear Models . . . . . . . . . . . . . . . . . . . . . . . . . 4-20

4.3.2.2 Single and Multi-City Models . . . . . . . . . . . . . . . . . . . . . . . . . 4-23

4.3.2.3 Single and Multi-Pollutant Models . . . . . . . . . . . . . . . . . . . . . 4-23

4.3.2.4 Single, Multiple, and Distributed Lag Functions . . . . . . . . . . . 4-24

4.3.2.5 Alternative Short-Term Exposure Model Specifications . . . . . 4-25

4.3.2.6 Long-term Exposure Models . . . . . . . . . . . . . . . . . . . . . . . . . . 4-25

4.3.3 Baseline Health Effects Incidence Rates and Population Estimates . . 4-26

4.3.4 Characterizing Uncertainty and Variability . . . . . . . . . . . . . . . . . . . . . 4-26
iv

4.4 PM2.5 RISK ESTIMATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-35

4.4.1 Recent Air Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-35

4.4.1.1 Base Case Risk Estimates Above Initial Cutpoint . . . . . . . . . . 4-35

4.4.1.2 Base Case Risk Estimates Above Various Cutpoints . . . . . . . 4-42

4.4.1.3 Risk Estimates from Sensitivity Analyses . . . . . . . . . . . . . . . . 4-43

4.4.2 Just Meeting Current PM2.5 Standards . . . . . . . . . . . . . . . . . . . . . . . . . 4-49

4.4.2.1 Base Case Risk Estimates Above Initial Cutpoint . . . . . . . . . . 4-49

4.4.2.2 Base Case Risk Estimates Above Various Cutpoints . . . . . . . 4-52

4.4.2.3 Risk Estimates from Sensitivity Analyses . . . . . . . . . . . . . . . . 4-52

4.4.3 Just Meeting Alternative PM2.5 Standards . . . . . . . . . . . . . . . . . . . . . . 4-56

4.4.3.1 Base Case Risk Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-56

4.4.3.2 Risk Estimates from Sensitivity Analyses . . . . . . . . . . . . . . . . 4-62

4.4.4 Key Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-66

4.5 PM10-2.5 RISK ESTIMATES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-70

4.5.1. Recent Air Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-70

4.5.1.1 Base Case Risk Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-70

4.5.1.2 Risk Estimates from Sensitivity Analyses . . . . . . . . . . . . . . . . 4-71

4.5.2 Just Meeting Alternative PM10-2.5 Standards . . . . . . . . . . . . . . . . . . . . 4-71

4.5.3 Key Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-76

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-78

5. STAFF CONCLUSIONS AND RECOMMENDATIONS ON PRIMARY PM

NAAQS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.2 APPROACH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1

5.3 FINE PARTICLE STANDARDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4

5.3.1 Adequacy of Current PM2.5 Standards . . . . . . . . . . . . . . . . . . . . . . . . . . 5-4

5.3.1.1 Evidence-based Considerations . . . . . . . . . . . . . . . . . . . . . . . . . 5-7

5.3.1.2 Risk-based Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10

5.3.1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14

5.3.2 Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-15

5.3.3 Averaging Times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-19

5.3.4 Alternative PM2.5 Standards to Address Health Effects Related to

Long-term Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-21

5.3.4.1 Evidence-based Considerations . . . . . . . . . . . . . . . . . . . . . . . . 5-22

5.3.4.2 Risk-based Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-23

5.3.4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-28

5.3.5 Alternative PM2.5 Standards to Address Health Effects Related to

Short-term Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-29

5.3.5.1 Evidence-based Considerations . . . . . . . . . . . . . . . . . . . . . . . . 5-29

5.3.5.2 Risk-based Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-33

5.3.5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-39

5.3.6 Alternative Forms for Annual and 24-Hour PM2.5 Standards . . . . . . . 5-40

5.3.6.1 Form of Annual Standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-40

5.3.6.2 Form of 24-Hour Standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-42

5.3.7 Summary of Staff Recommendations on Primary PM2.5 NAAQS . . . . 5-45
v

5.4 THORACIC COARSE PARTICLE STANDARDS . . . . . . . . . . . . . . . . . . . . 5-47

5.4.1 Adequacy of Current PM10 Standards . . . . . . . . . . . . . . . . . . . . . . . . . 5-47

5.4.2 Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-53

5.4.2.1 Evidence Related to Urban and Rural Thoracic Coarse

Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-54

5.4.2.2 Related Requirements for PM10-2.5 Monitors and Monitoring

Network Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-58

5.4.3 Averaging Times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-61

5.4.4 Alternative Standards to Address Health Effects Related to

Short-term Exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-61

5.4.4.1 Evidence-based Considerations . . . . . . . . . . . . . . . . . . . . . . . . 5-62

5.4.4.2 Risk-based Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-69

5.4.4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-69

5.4.5 Summary of Staff Recommendations on Primary PM10-2.5 NAAQS . . 5-70

5.5 SUMMARY OF KEY UNCERTAINTIES AND RESEARCH

RECOMMENDATIONS RELATED TO SETTING PRIMARY PM

STANDARDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-71

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-75

PM-RELATED WELFARE EFFECTS

AND SECONDARY PM NAAQS

6. POLICY-RELEVANT ASSESSMENT OF PM-RELATED WELFARE

EFFECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.2 EFFECTS ON VISIBILITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1

6.2.1 Overview of Visibility Impairment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-2

6.2.2 Visibility Trends and Current Conditions in Class I and Non-Urban

Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3

6.2.3 Visibility Conditions in Urban Areas . . . . . . . . . . . . . . . . . . . . . . . . . . 6-5

6.2.3.1 ASOS Airport Visibility Monitoring Network . . . . . . . . . . . . . 6-5

6.2.3.2 Correlation between Urban Visibility and PM2.5 Mass . . . . . . . 6-6

6.2.4 Economic and Societal Value of Improving Visual Air Quality . . . . . 6-13

6.2.5 Programs and Goals for Improving Visual Air Quality . . . . . . . . . . . . 6-18

6.2.5.1 Regional Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-18

6.2.5.2 Local, State, and International Goals and Programs . . . . . . . . 6-19

6.2.6 Approaches to Evaluating Public Perceptions and Attitudes . . . . . . . 6-20

6.2.6.1 Photographic Representations of Visual Air Quality . . . . . . . 6-20

6.2.6.2 Survey Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-22

6.2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-24

6.3 EFFECTS ON VEGETATION AND ECOSYSTEMS . . . . . . . . . . . . . . . . . . 6-25

6.3.1 Major Ecosystem Stressors in PM . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-26

6.3.2 Direct Vegetation Effects of Particulate Nitrate and Sulfate

Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-28
vi

6.3.3 Ecosystem Effects of Chronic Inputs of Reactive Nitrogen and

Acidifying Compounds From PM Deposition and Other Sources . . . 6-29

6.3.3.1 Environmental Effects of Reactive Nitrogen (Nr) Deposition 6-29

6.3.3.2 Environmental Effects of PM-Related Acidic and Acidifying

Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-38

6.3.4 Characteristics and Location of Sensitive Ecosystems in the U.S. . . . 6-44

6.3.5 Ecosystem Exposures to PM Deposition . . . . . . . . . . . . . . . . . . . . . . . 6-46

6.3.6 Critical Loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-48

6.3.7 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-51

6.4 EFFECTS ON MATERIALS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-53

6.4.1 Materials Damage Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-54

6.4.2 Soiling Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-55

6.4.3 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-56

6.5 EFFECTS ON CLIMATE CHANGE AND SOLAR RADIATION . . . . . . . . 6-56

6.5.1 Climate Change and Potential Human Health and Environmental

Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-57

6.5.2 Alterations in Solar UV-B Radiation and Potential Human Health

and Environmental Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-58

6.5.3 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-60

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-62

7. STAFF CONCLUSIONS AND RECOMMENDATIONS ON

SECONDARY PM NAAQS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

7.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

7.2 APPROACH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-1

7.3 STANDARDS TO ADDRESS VISIBILITY IMPAIRMENT . . . . . . . . . . . . . 7-3

7.3.1 Adequacy of Current PM2.5 Standards . . . . . . . . . . . . . . . . . . . . . . . . . . 7-4

7.3.2 Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

7.3.3 Averaging Times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-6

7.3.4 Alternative PM2.5 Standards to Address Visibility Impairment . . . . . . . 7-7

7.3.5 Alternative Forms of a Short-term PM2.5 Standard . . . . . . . . . . . . . . . 7-11

7.3.6 Summary of Staff Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 7-12

7.4 STANDARDS TO ADDRESS OTHER PM-RELATED WELFARE

EFFECTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-13

7.4.1 Vegetation and Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-14

7.4.2 Materials Damage and Soiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-16

7.4.3 Climate Change and Solar Radiation . . . . . . . . . . . . . . . . . . . . . . . . . . 7-17

7.4.4 Summary of Staff Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 7-17

7.5 SUMMARY OF KEY UNCERTAINTIES AND RESEARCH

RECOMMENDATIONS RELATED TO SETTING SECONDARY

PM STANDARDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-18

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-20
vii

CHAPTER APPENDICES:

APPENDIX 3A: Mortality and Morbidity Effect Estimates and PM

Concentrations from U.S. and Canadian Studies for Short-term Exposures to

PM10, PM2.5, and PM10-2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3A-1

APPENDIX 3B: Mortality and Morbidity Effect Estimates and PM

Concentrations from U.S. and Canadian Studies for Long-Term Exposures to

PM10, PM2.5, and PM10-2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3B-1

APPENDIX 4A: Study-specific information for PM2.5 and PM10-2.5 studies . . . . . . . 4A-1

APPENDIX 4B: Estimated annual mortality associated with short- and long-term

exposure to PM2.5 when alternative standards are met for Los Angeles, Philadelphia,

Pittsburgh, and St. Louis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4B-1

APPENDIX 5A: Reductions in PM2.5-related mortality risk for alternative

standards (99th percentile form) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5A-1

APPENDIX 5B: Predicted percent of counties not likely to meet alternative

standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5B-1

APPENDIX 6A: Images of Visual Air Quality in Selected Urban Areas in the U.S.

available at: . . . . . . . . . . . http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html

APPENDIX 7A:

Figure 7A-1 Estimated exceedances (%) of various PM2.5 levels for 12 pm – 4 pm

based on daily county maximum, 2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7A-1

Table 7A-1. Predicted percent of counties with monitors (and percent of population

in counties with monitors) not likely to meet alternative 4-hour (12 pm – 4 pm)

PM2.5 secondary standards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7A-2

ATTACHMENT A: Clean Air Scientific Advisory Committee Letter
viii

List of Tables

Number Page

2-1 Particle Size Fraction Terminology Used in Staff Paper . . . . . . . . . . . . . . . . . . . . . . . . 2-7

2-2 Comparison of Ambient Fine Particles (Ultrafine plus Accumulation-Mode) and

Coarse Particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-11

2-3 Summary of PM2.5 FRM Data Analyses in 49 Metropolitan Areas, 2001-2003 . . . . . 2-29

2-4 Summary of Estimated PM10-2.5 Analyses in 21 Metropolitan Areas, 2001-2003 . . . . 2-39

2-5 PM2.5 Composition on High Mass Days in Select Urban Areas, 2003 . . . . . . . . . . . . 2-57

2-6 IMPROVE Sites Selected for Estimates of Regional Background . . . . . . . . . . . . . . . 2-66

2-7 Estimates of Long-Term Means, Daily Standard Deviations and 99th Percentiles

of PM2.5 Background Concentrations (μg/m3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-67

4-1 Mortality Health Endpoints, Urban Locations, and Studies Selected for Use in the

PM2.5 Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-8

4-2 Morbidity Health Endpoints, Urban Locations, and Studies Selected for Use in the

PM2.5 Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9

4-3 Morbidity Health Endpoints, Urban Locations, and Studies Selected for Use in the

PM10-2.5 Risk Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9

4-4 Sensitivity Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-12

4-5 Summary of PM Ambient Air Quality Data for Risk Assessment Study Areas . . . . . 4-16

4-6 Relevant Population Sizes for PM Risk Assessment Locations . . . . . . . . . . . . . . . . . 4-27

4-7 Baseline Mortality Rates for 2001 for PM2.5 Risk Assessment Locations . . . . . . . . . . 4-28

4-8 Baseline Hospitalization Rates for PM Risk Assessment Locations . . . . . . . . . . . . . . 4-30

4-9 Estimated Annual Health Risks of Short-Term Exposure Mortality Associated with

Recent PM2.5 Concentrations Assuming Various Cutpoint Levels . . . . . . . . . . . . . . . 4-44

4-10 Estimated Annual Health Risks of Long-Term Exposure Mortality Associated with

Recent PM2.5 Concentrations Assuming Various Cutpoint Levels . . . . . . . . . . . . . . . 4-46
ix

4-11 Air Quality Adjustments Required to Just Meet the Current Annual PM2.5 Standard

of 15 μg/m3 Using the Maximum vs. the Average of Monitor-Specific Averages . . . 4-50

4-12 Estimated Annual Mortality Associated with Short-Term Exposure to PM2.5 When

the Current Annual Standard of 15 μg/m3 and the Current Daily Standard of 65 μg/m3

Are Just Met, Assuming Various Cutpoint Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-54

4-13 Estimated Annual Mortality Associated with Long-Term Exposure to PM2.5 When the

Current Annual Standard of 15 μg/m3 and the Current Daily Standard of 65 μg/m3 Are

Just Met, Assuming Various Cutpoint Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-55

4-14 Sensitivity Analysis Comparing the Use of Multi-City vs. Single-City ConcentrationResponse Relationships on Estimates of Short-Term Exposure Mortality Associated with

Just Meeting the Current PM2.5 Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-57

4-15 Alternative Sets of PM2.5 Standards Considered in the PM2.5 Risk Assessment . . . . . 4-59

4-16 Estimated Design Values for Annual and 98th and 99th Percentile Daily PM2.5 Standards

Based on 2001-2003 Air Quality Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-59

4-17 Estimated Annual Mortality Associated with Short-Term Exposure to PM2.5 When

Alternative Standards Are Just Met, Assuming Various Cutpoint Levels for

Detroit, MI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-60

4-18 Estimated Annual Mortality Associated with Long-Term Exposure to PM2.5 When

Alternative Standards Are Just Met, Assuming Various Cutpoint Levels for

Detroit, MI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-63

4-19 Alternative PM10-2.5 Standards Considered in the PM10-2.5 Risk Assessment . . . . . . . . 4-74

4-20 Estimated Design Values for 98th and 99th Percentile Daily PM10-2.5 Standards Based

on 2001-2003 Air Quality Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-74

4-21 Estimated Annual Hospital Admissions for Ischemic Heart Disease Associated with

Short-Term PM10-2.5 Exposures When Alternative Standards Are Just Met, Assuming

Various Cutpoint Levels for Detroit, MI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-75

5-1(a) Estimated PM2.5-related annual total mortality associated with long-term exposure

when current PM2.5 standards are met . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-12

5-1(b) Estimated PM2.5-related annual mortality associated with short-term exposure

when current PM2.5 standards are met . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-13
x

5-2 Estimated PM10-2.5-related annual incidence of hospital admissions and cough in children

associated with short-term exposure with 2003 air quality . . . . . . . . . . . . . . . . . . . . . 5-51
xi

List of Figures

Number Page

2-1 Distribution of coarse, accumulation, and nucleation-mode particles by three

characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3

2-2 An idealized distribution of ambient PM showing fine and coarse particles and

the fractions collected by size-selective samplers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-5

2-3 Regions used in PM Staff Paper in data analysis summaries . . . . . . . . . . . . . . . . . . . 2-19

2-4 Distribution of annual mean PM2.5 and estimated annual mean PM10-2.5

concentrations by region, 2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-21

2-5 Distribution of 98th percentile 24-hour average PM2.5 and estimated PM10-2.5

concentrations by region, 2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-22

2-6 County-level maximum annual mean PM2.5 concentrations, 2001-2003 . . . . . . . . . . . 2-23

2-7 County-level maximum 98th percentile 24-hour average PM2.5 concentrations, 

2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-24

2-8 Regional trends in annual average PM2.5 concentrations in the EPA FRM network,

1999-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-26

2-9 Average annual mean trend in PM2.5 mass, ammonium sulfate, ammonium nitrate,

total carbonaceous mass, and crustal material at IMPROVE sites, 1993-2003 . . . . . 2-28

2-10 County-level maximum PM10 annual mean concentrations, 2001-2003 . . . . . . . . . . . 2-31

2-11 County-level maximum 24-hour PM10 “design value” concentrations, 2001-2003 . . 2-32

2-12 Estimated county-level maximum annual mean PM10-2.5 concentrations, 2001-

2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-34

2-13 Estimated county-level maximum 98th percentile 24-hour average PM10-2.5

concentrations, 2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-35

2-14 Average measured annual average PM10-2.5 concentration trend at IMPROVE

sites, 1993-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-37

2-15 Urban versus rural estimated PM10-2.5 concentrations in select areas . . . . . . . . . . . . . 2-38

2-16 Annual average composition of PM2.5 by region, 2003 . . . . . . . . . . . . . . . . . . . . . . . . 2-42
xii

2-17 Average PM10-2.5 PM2.5, and PM0.1 (ultrafine) chemical composition at the USC

EPA ‘supersite’ monitor in Los Angeles, CA, 10/2001 to 9/2002 . . . . . . . . . . . . . . . 2-43

2-18 Average PM10-2.5 composition for Los Angeles and two eastern urban-rural pairs . . . 2-45

2-19 Distribution of ratios of PM2.5 to PM10 by region, 2001-2003 . . . . . . . . . . . . . . . . . . . 2-47

2-20 Regional average correlations of 24-hour average PM by size fraction . . . . . . . . . . . 2-48

2-21 Urban 24-hour average PM2.5 concentration distributions by region and month,

2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-50

2-22 Urban 24-hour average estimated PM10-2.5 concentration distributions by region

and month, 2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-51

2-23 Seasonal average composition of urban PM2.5 by region, 2003 . . . . . . . . . . . . . . . . . . 2-52

2-24 Seasonal average composition of rural PM2.5 by region, 2003 . . . . . . . . . . . . . . . . . . 2-53

2-25 Distribution of annual mean vs. 98th percentile 24-hour average PM2.5

concentrations, 2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-55

2-26 Distribution of estimated annual mean vs. 98th percentile 24-hour average PM10-2.5

concentrations, 2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-56

2-27 Hourly average PM2.5 and PM10-2.5 concentrations at a Greensboro, NC monitoring

site, 2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-59

2-28 Seasonal hourly average PM2.5 and PM10-2.5 concentrations at a Greensboro, NC

monitoring site, 2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-60

2-29 Hourly average PM-2.5 and PM10-2.5 concentrations at a Denver, CO monitoring

site, 2001-2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-61

2-30 Hourly PM2.5 and PM10-2.5 concentrations at an El Paso, TX monitoring site, April

26, 2002-April 27, 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-62

2-31 Relationship between light extinction, deciviews, and visual range . . . . . . . . . . . . . 2-77

3-1 Excess risk estimates for total nonaccidental, cardiovascular, and respiratory

mortality with short-term exposure to PM in single-pollutant models for U.S. and

Canadian studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-13
xiii

3-2 Excess risk estimates for hospital admissions and emergency department visits for

cardiovascular and respiratory diseases in single-pollutant models from U.S. and

Canadian studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-20

3-3 Associations between PM2.5 and total mortality from U.S. studies, plotted against

gaseous pollutant concentrations from the same locations . . . . . . . . . . . . . . . . . . . . . 3-48

3-4 Natural logarithm of relative risk for total and cause-specific mortality with longterm exposure to PM2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-57

4-1 Major components of particulate matter health risk assessment.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-11

4-2 Relationship between estimated log-linear concentration-response relationship and

hockeystick model with cutpoint C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-22

4-3 Estimated annual percent (top panel) and cases per 100,000 general population (bottom

panel) of total (non-accidental) mortality associated with short-term exposure to PM2.5

above background (and 95 percent confidence intervals): single-pollutant, single-city

models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-37

4-4 Estimated annual percent (top panel) and cases per 100,000 general population (bottom

panel) of total (non-accidental) mortality associated with short-term exposure to PM2.5

above background (and 95 percent confidence intervals): single-city versus multi-city

models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-38

4-5 Estimated annual percent (top panel) and cases per 100,000 general population (bottom

panel) of health effects associated with short-term exposure to PM2.5 above background

(and 95 percent confidence intervals): single-pollutant versus multi-pollutant models.4-39

4-6 Estimated annual percent (top panel) and cases per 100,000 general population (bottom

panel) of total (non-accidental) mortality associated with long-term exposure to PM2.5

above 7.5 μg/m3 (and 95 percent confidence intervals): single-pollutant models . . . . 4-40

4-7 Estimated annual percent (top panel) and cases per 100,000 general population (bottom

panel) of total (non-accidental) mortality associated with long-term exposure to PM2.5

above 7.5 μg/m3 (and 95 percent confidence intervals): multi-pollutant models . . . . 4-41

4-8 Estimated annual percent (top panel) and cases per 100,000 general population (bottom

panel) of total (non-accidental) mortality associated with short-term exposure to PM2.5

above background (and 95 percent confidence intervals) for air quality just meeting the

current PM2.5 standards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-51
xiv

4-9 Estimated annual percent (top panel) and cases per 100,000 general population (bottom

panel) of total mortality associated with long-term exposure to PM2.5 above 7.5 μg/m3

(and 95 percent confidence intervals) for air quality just meeting the current PM2.5 standards.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-53

4-10 Distribution of average daily PM2.5 concentrations in Detroit (2003 air quality data) (top

panel), estimated non-accidental mortality per day in Detroit associated with exposure to

daily PM2.5 concentrations (middle panel), and estimated non-accidental mortality in

Detroit associated with exposure to daily PM2.5 concentrations over the course of a year

(bottom panel). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-68

4-11 Estimated annual percent (top panel) and cases per 100,000 general population (bottom

panel) of hospital admissions associated with short-term exposure to PM10-2.5 (and 95

percent confidence intervals) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-72

4-12 Estimated annual percent (top panel) and cases per 100,000 general population (bottom

panel) of respiratory symptoms associated with short-term exposure to PM10-2.5 (and 95

percent confidence intervals) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-73

5-1(a) Estimated percent reduction in PM2.5-related long-term mortality risk for alternative

standards (98th percentile form) relative to risk associated with meeting current

standards (based on assumed cutpoint of 7.5 :g/m3) . . . . . . . . . . . . . . . . . . . . . . . . . . 5-25

5-1(b) Estimated percent reduction in PM2.5-related long-term mortality risk for alternative

standards (98th percentile form) relative to risk associated with meeting current

standards (based on assumed cutpoint of 10 :g/m3

) . . . . . . . . . . . . . . . . . . . . . . . . . . 5-26

5-1(c) Estimated percent reduction in PM2.5-related long-term mortality risk for alternative

standards (98th percentile form) relative to risk associated with meeting current

standards (based on assumed cutpoint of 12 :g/m3

) . . . . . . . . . . . . . . . . . . . . . . . . . . 5-27

5-2(a) Estimated percent reduction in PM2.5-related short-term mortality risk for alternative

standards (98th percentile form) relative to risk associated with meeting current

standards (based on assumed cutpoint equal to policy-relevant background) . . . . . . . 5-34

5-2(b) Estimated percent reduction in PM2.5-related short-term mortality risk for alternative

standards (98th percentile form) relative to risk associated with meeting current

standards (based on assumed cutpoint of 10 :g/m3) . . . . . . . . . . . . . . . . . . . . . . . . . . 5-35

5-2(c) Estimated percent reduction in PM2.5-related short-term mortality risk for alternative

standards (98th percentile form) relative to risk associated with meeting current

standards (based on assumed cutpoint of 15 :g/m3

) . . . . . . . . . . . . . . . . . . . . . . . . . . 5-36
xv

5-2(d) Estimated percent reduction in PM2.5-related short-term mortality risk for alternative

standards (98th percentile form) relative to risk associated with meeting current

standards (based on assumed cutpoint of 20 :g/m3) . . . . . . . . . . . . . . . . . . . . . . . . . . 5-37

6-1 PM2.5 concentration differences between eastern and western areas and between

rural and urban areas for 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7

6-2 Distribution of hourly and 24-hour average relative humidity at eastern and

western U.S. National Weather Service sites, 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-8

6-3 Relationship between reconstructed light extinction (RE) and 24-hour average

PM2.5, 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-10

6-4 Model slope for relationship between reconstructed light extinction (RE) and

hourly PM2.5 (increase in RE due to incremental increase in PM2.5), 2003 . . . . . . . . . 6-11

6-5 Relationship between reconstructed light extinction (RE) and 12 p.m. - 4 p.m.

average PM2.5, 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-13

6-6 Illustration of the nitrogen cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-29

7-1 Distributions of PM2.5 concentrations for 12 p.m. - 4 p.m. corresponding to visual

ranges of 25 km (panel a), 30 km (panel b), and 35 km (panel c) – by region . . . . . . . 7-9
xvi

Abbreviations and Acronyms

AC Automated colorimetry

ACS American Cancer Society

AHSMOG Adventist Health and Smoke Study

AIRS Aerometric Information Retrieval System

ANC Acid neutralizing capacity

APHEA Air Pollution and Health, a European Approach

AQCD Air Quality Criteria Document

AQS Air Quality System

ARS Air Resource Specialists, Inc.

ASOS Automated Surface Observing System

BC Black carbon

BS British or black smoke

CAA Clean Air Act

CAMM Continuous Ambient Mass Monitor

CAP Concentrated ambient particles

CASAC Clean Air Scientific Advisory Committee

CASTNet Clean Air Status and Trends Network

CB Base cation

CBSA Core Based Statistical Area

CD Criteria Document

CDC Centers for Disease Control

CDPHE Colorado Department of Public Health and Environment

CFR Code of Federal Regulations

CL Critical loads

C:N Carbon-to-nitrogen ratio

CO Carbon monoxide

COH Coefficient of haze

COPD Chronic obstructive pulmonary disease

CPSC Consumer Product Safety Commission

C-R Concentration-response

CSA Combined Statistical Area

CSS Coastal sage scrub community

CV Contingent valuation

EC Elemental carbon

ECG Electrocardiogram

ED Emergency department

EEA Essential Ecological Attribute

EMAP Environmental Monitoring and Assessment Program

EPA Environmental Protection Agency

EPEC Ecological Processes and Effects Committee

ERP Episodic Response Project

FDMS Filter Dynamics Measurement System

FLM Federal Land Manager
xvii

FRM Federal reference method

GAM Generalized additive models

GCVTC Grand Canyon Visibility Transport Commission

GLM Generalized linear models

HAPs Hazardous air pollutants

HEI Health Effects Institute

HF Heart failure

hosp. adm. Hospital admissions

IC Ion chromatography

IFS Integrated Forest Study

IHD Ischemic heart disease

IMPROVE Interagency Monitoring of Protected Visual Environments

LML Lowest measured level

LPC Laser particle counter

LRS Lower respiratory symptoms

mort. Mortality

NAAQS National ambient air quality standards

NADP National Atmospheric Deposition Program

NAPAP National Acid Precipitation Assessment Program

NCEA National Center for Environmental Assessment

NDDN National Dry Deposition Network

NEG/ECP New England Governors/Eastern Canadian Premiers

NMMAPS National Mortality and Morbidity Air Pollution Study

N2 Nonreactive, molecular nitrogen

NO2 Nitrogen dioxide

non-accid

 mort Non-accidental mortality

Nr Reactive nitrogen

NSMPS Nano-scanning mobility particle sizer

NuCM Nutrient cycling model

NWS National Weather Service

O3 Ozone

OAQPS Office of Air Quality Planning and Standards

OAR Office of Air and Radiation

OC Organic carbon 

ORD Office of Research and Development

OSHA Occupational Safety and Health Administration

PAHs Polynuclear aromatic hydrocarbons

pneum. Pneumonia

PTEAM EPA’s Particle Total Exposure Assessment Methodology

PCBs Polychlorinated biphenyls

PCDD/F Polychlorinated dibenzo-p-dioxins/dibenzofurans

PM Particulate matter

PM10-2.5 Particles less than or equal to 10 μm in diameter and greater than 2.5 μm in

diameter
xviii

PM2.5 Particles less than or equal to 2.5 μm in diameter

PM10 Particles less than or equal to 10 μm in diameter

PnET-BGC A forest net productivity model (PnET) linked to a soil model (BGC)

POPs Persistent organic pollutants

PRB Policy relevant background

REVEAL Regional Visibility Experimental Assessment in the Lower Fraser Valley

RR Relative risk

SAB Science Advisory Board

SMPS Standard scanning mobility particle sizer

SO2 Sulfur dioxide

SO4 Sulfate

SOCs Semivolatile organic compounds

STN PM2.5 Chemical Speciation Trends Network

SP Staff Paper

TEOM Tapered Element Oscillating Microbalance sensor

TIME/LTM Temporally Integrated Monitoring of Ecosystems/Long-Term Monitoring Project

TL Target load

TMO Thermal manganese oxidation method

TOR Thermal/optical reflectance method 

TOT Thermal/optical transmission method 

TSD Technical support document

TSP Total suspended particulates

:g micrograms

:g/m3 micrograms per cubic meter

UNEP United Nations Environmental Program

UPM10-2.5 Thoracic coarse urban particulate matter

URS Upper respiratory symptoms

U.S. United States

UV Ultraviolet

UV-B Ultraviolet-B

Vd Deposition velocity

VOCs Volatile organic compounds

WMO World Meteorological Organization

XRF X-ray fluorescence
1

 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.

1-1

1. INTRODUCTION

1.1 PURPOSE

This Staff Paper, prepared by staff in the U.S. Environmental Protection Agency’s (EPA)

Office of Air Quality Planning and Standards (OAQPS), evaluates the policy implications of the

key studies and scientific information contained in the document, Air Quality Criteria for

Particulate Matter (EPA, 2004; henceforth referred to as the Criteria Document (CD) and cited

as CD), prepared by EPA’s National Center for Environmental Assessment (NCEA). This Staff

Paper also presents and interprets results from staff analyses (e.g., air quality analyses, human

health risk assessments, and visibility analyses) that staff believes should be considered in EPA's

current review of the national ambient air quality standards (NAAQS) for particulate matter

(PM). Finally, this Staff Paper presents staff conclusions and recommendations as to potential

revisions of the primary (health-based) and secondary (welfare-based) PM NAAQS, based on

consideration of the available scientific information and analyses and related limitations and

uncertainties.

The policy assessment presented in this document is intended to help “bridge the gap”

between the scientific review contained in the CD and the judgments required of the EPA

Administrator in determining whether, and if so, how, it is appropriate to revise the NAAQS for

PM. This assessment focuses on the basic elements of PM air quality standards: indicators,

averaging times, forms1

, and levels. These elements, which serve to define each standard within

the suite of PM NAAQS, must be considered collectively in evaluating the health and welfare

protection afforded by the standards.

While this Staff Paper should be of use to all parties interested in the PM NAAQS

review, it is written for those decision makers, scientists, and staff who have some familiarity

with the technical discussions contained in the CD.

1.2 BACKGROUND

1.2.1 Legislative Requirements

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

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

pollutants” that “in his judgment, may reasonably be anticipated to endanger public health and

welfare” and whose “presence . . . in the ambient air results from numerous or diverse mobile or

stationary sources” and to issue air quality criteria for those that are listed. Air quality criteria

are intended to “accurately reflect the latest scientific knowledge useful in indicating the kind
2

 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)].

3

 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.”

1-2

and extent of identifiable effects on public health or welfare which may be expected from the

presence of [a] pollutant in ambient air . . . .”

Section 109 (42 U.S.C. 7409) directs the Administrator to propose and promulgate

“primary” and “secondary” NAAQS for pollutants listed under section 108. 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.”2 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.”3

In setting standards that are “requisite” to protect public health and welfare, 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).

The requirement that primary standards include 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. 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 U.S. 1034 (1982). 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 include 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.

In selecting a margin of safety, the EPA considers such factors as the nature and severity

of the health effects involved, the size of the sensitive population(s) at risk, and the kind and

degree of the uncertainties that must be addressed. The selection of any particular approach to
4

 The more precise term is 50 percent cut point or 50 percent diameter (D50). This is the aerodynamic

particle diameter for which the efficiency of particle collection is 50 percent. Larger particles are not excluded

altogether, but are collected with substantially decreasing efficiency and smaller particles are collected with

increasing (up to 100 percent) efficiency.

1-3

providing an adequate margin of safety is a policy choice left specifically to the Administrator’s

judgment. Lead Industries Association v. EPA, supra, 647 F.2d at 1161-62.

Section 109(d)(1) of the Act 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) of EPA’s Science Advisory Board.

1.2.2 History of PM NAAQS Reviews

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. Particles originate from a variety of anthropogenic stationary and mobile sources as well

as natural sources. Particles may be emitted directly or formed in the atmosphere by

transformations of gaseous emissions such as sulfur oxides, nitrogen oxides, and volatile organic

compounds. The chemical and physical properties of PM vary greatly with time, region,

meteorology, and source category, thus complicating the assessment of health and welfare

effects.

EPA first established national ambient air quality standards for PM in 1971, based on the

original criteria document (DHEW, 1969). 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 (44 FR 56731), 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 particles from

TSP to PM10, the latter including particles with a mean aerodynamic diameter4

 less than or equal
1-4

to 10 μm, which delineates that subset of inhalable particles small enough to penetrate to the

thoracic region (including the tracheobronchial and alveolar regions) of the respiratory tract

(referred to as thoracic particles). EPA also revised the level and form of 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. Administrator, 902

F. 2d 962 (D.C. Cir. 1990), cert. denied, 498 U.S. 1082 (1991).

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). In that decision, EPA revised the PM NAAQS in several respects. While it was

determined that the PM NAAQS should continue to focus on particles less than or equal to 10

μm in diameter, it was also determined that the fine and coarse fractions of PM10 should be

considered separately. New standards were added, using PM2.5, referring to particles with a

nominal mean aerodynamic diameter less than or equal to 2.5 μm, as the indicator for fine

particles, with PM10 standards retained for the purpose of regulating the coarse fraction of PM10

(referred to as thoracic coarse particles or coarse-fraction particles; generally including particles

with a nominal mean aerodynamic diameter greater than 2.5 μm and less than or equal to 10 μm,

or PM10-2.5). 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. A new reference method for the measurement of PM2.5 in the ambient air was also

established, as were rules for determining attainment of the new standards. To continue to

address thoracic coarse particles, the annual PM10 standard was retained, while 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. EPA revised the secondary standards by making them identical in all

respects to the primary standards.

1.2.3 Litigation Related to the 1997 PM Standards

Following promulgation of the revised PM NAAQS, petitions for review were filed by a

large number of parties, addressing a broad range of issues. In May 1998, a three-judge 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
1-5

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 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 (65 FR 80776, December 22,

2000). The pre-existing 1987 PM10 standards remained in place. Id. at 80777. In the current

review, EPA is addressing thoracic coarse particles in part by considering standards based on a

more narrowly defined indicator.

 More generally, the Panel held (with one dissenting opinion) that EPA’s approach to

establishing the level of the standards in 1997, both for PM and for ozone 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, the Panel also

reaffirmed prior rulings holding that in setting NAAQS EPA is “not permitted to consider the

cost of implementing those standards.” Id. at 1040-41.

 Both sides filed cross appeals on these issues to the United States Supreme Court, and

the Court 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 guided 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).
5

 HEI is an independent research institute, jointly sponsored by EPA and a group of U.S.

manufacturers/marketers of motor vehicle and engines, that conducts health effects research on major air pollutants

related to motor vehicle emissions.

1-6

1.2.4 Current PM NAAQS Review

In October 1997, EPA published its plans for the current periodic review of the PM

criteria and NAAQS (62 FR 55201, October 23, 1997), including the 1997 PM2.5 standards and

the 1987 PM10 standards. As part of the process of preparing the PM CD, NCEA hosted a peer

review workshop in April 1999 on drafts of key CD chapters. The first external review draft CD

was reviewed by CASAC and the public at a meeting held in December 1999. Based on CASAC

and public comment, NCEA revised the draft CD and released a second draft in March 2001 for

review by CASAC and the public at a meeting held in July 2001. A preliminary draft Staff

Paper (EPA, 2001) was released in June 2001 for public comment and for consultation with

CASAC at the same public meeting. Taking into account CASAC and public comments, a third

draft CD was released in May 2002 for review at a meeting held in July 2002.

Shortly after EPA released the third draft CD, the Health Effects Institute (HEI)5

announced that researchers at Johns Hopkins University had discovered problems with

applications of statistical software used in a number of important epidemiological studies that

had been discussed in that draft CD. In response to this significant issue, EPA took steps in

consultation with CASAC to encourage researchers to reanalyze affected studies and to submit

them expeditiously for peer review by a special expert panel convened at EPA’s request by HEI. 

EPA subsequently incorporated the results of this reanalysis and peer-review process into a

fourth draft CD, which was released in June 2003 and reviewed by CASAC and the public at a

meeting held in August 2003.

The first draft Staff Paper, based on the fourth draft CD, was released at the end of

August 2003, and was reviewed by CASAC and the public at a meeting held in November 2003. 

During that meeting, EPA also consulted with CASAC on a new framework for the final chapter

(integrative synthesis) of the CD and on ongoing revisions to other CD chapters to address

previous CASAC comments. EPA held additional consultations with CASAC at public meetings

held in February, July, and September 2004, leading to publication of the final CD in October

2004. The second draft Staff Paper, based on the final CD, was released at the end of January

2005, and was reviewed by CASAC and the public at a meeting held in April 2005. The

CASAC’s advice and recommendations to the Administrator, based on their review of the

second draft Staff Paper, were further discussed during a public teleconference held in May 2005

and are provided in a letter to the Administrator (Henderson, 2005), which is reproduced in

Attachment 2. This final Staff Paper takes into account the advice and recommendations from

CASAC and public comments received on the earlier drafts of this document. Any subsequent
1-7

advice and recommendations received from CASAC related to this final Staff Paper will also be

considered by the Administrator.

The schedule for completion of this review is now governed by a consent decree

resolving a lawsuit filed in March 2003 by a group of plaintiffs representing national

environmental organizations. The lawsuit alleged that EPA had failed to perform its mandatory

duty, under section 109(d)(1), of completing the current review within the period provided by

statute. American Lung Association v. Whitman (No. 1:03CV00778, D.D.C. 2003). An initial

consent decree was entered by the court in July 2003 after an opportunity for public comment. 

The consent decree, as modified by the court, provides that EPA will sign for publication notices

of proposed and final rulemaking concerning its review of the PM NAAQS no later than

December 20, 2005 and September 27, 2006, respectively.

1.3 GENERAL APPROACH AND ORGANIZATION OF DOCUMENT

This policy assessment is based on staff evaluation of the policy implications of the

scientific evidence contained in the CD and the results of quantitative analyses based on that

evidence, which taken together help inform staff conclusions and recommendations on the

elements of the PM standards under review. While the CD focuses on new scientific information

available since the last criteria review, it appropriately integrates that information with scientific

criteria from previous reviews. The quantitative analyses presented herein (and described in

more detail in a number of technical support documents) are based on the most recently available

air quality information, so as to provide current characterizations of PM air quality patterns,

estimated human health risks related to exposure to ambient PM, and PM-related visibility

impairment.

Partly as a consequence of EPA's decision in the last review to consider fine particles and

thoracic coarse particles separately, 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. 

This information adds to the body of evidence on PM10 that has continued to grow since the

introduction of that indicator in the first PM NAAQS review. Since the purpose of this review is

to evaluate the adequacy of the current standards that separately address fine and thoracic coarse

particles, staff has focused this policy assessment and associated quantitative analyses primarily

on the evidence related directly to PM2.5 and PM10-2.5. In so doing, staff has considered PM10-

related evidence primarily to help inform our understanding of key issues and to help interpret

and provide context for the more limited PM2.5 and PM10-2.5 evidence.

Following this introductory chapter, this Staff Paper is organized into three main parts: 

the characterization of ambient PM; PM-related health effects and primary PM NAAQS; and

PM-related welfare effects and secondary PM NAAQS. The characterization of ambient PM is

presented in Chapter 2, which focuses on properties of ambient PM, measurement methods,

spatial and temporal patterns in ambient PM concentrations, PM background levels, and ambient
1-8

PM relationships with human exposure and with visibility impairment. Thus, Chapter 2 provides

information relevant to both the health and welfare assessments in the other two main parts of

this document.

Chapters 3 through 5 comprise the second main part of this Staff Paper dealing with

human health and primary standards. Chapter 3 presents a policy-relevant assessment of PM

health effects evidence, including an overview of the evidence, key human health-related

conclusions from the CD, and an examination of issues related to the quantitative assessment of

the epidemiologic health evidence. Chapter 4 presents a quantitative assessment of PM-related

health risks, including risk estimates for current air quality levels as well as those associated with

just meeting the current NAAQS and various alternative standards that might be considered in

this review. Chapter 5 presents the staff review of the current primary standards for fine and

thoracic coarse particles. This chapter begins with a discussion of the broader approach used by

staff in this review of the primary PM NAAQS than in the last review, generally reflecting both

evidence-based and quantitative risk-based considerations. This review includes consideration

of the adequacy of the current standards, conclusions as to alternative indicators, averaging

times, levels and forms, and staff recommendations on ranges of alternative primary standards

for consideration by the Administrator.

Chapters 6 and 7 comprise the third main part of this Staff Paper dealing with welfare

effects and secondary standards. Chapter 6 presents a policy-relevant assessment of PM welfare

effects evidence, including evidence related to visibility impairment as well as to effects on

vegetation and ecosystems, climate change processes, and man-made materials. This chapter’s

emphasis is on visibility impairment, reflecting the availability of a significant amount of policyrelevant information and staff analyses which serve as the basis for staff consideration of a

secondary standard specifically for protection of visual air quality. Chapter 7 presents the staff

review of the current secondary standards, beginning with a discussion of the approach used by

staff in this review of the secondary PM NAAQS. This review includes consideration of the

adequacy of the current standards, conclusions as to alternative indicators, averaging times,

levels and forms, and staff recommendations on ranges of alternative secondary standards for

consideration by the Administrator.
1-9

REFERENCES

Environmental Protection Agency. (2001) Review of the National Ambient Air Quality Standards for Particulate

Matter: Policy Assessment of Scientific and Technical Information – Preliminary Draft OAQPS Staff

Paper. June.

Environmental Protection Agency. (2003) Review of the National Ambient Air Quality Standards for Particulate

Matter: Policy Assessment of Scientific and Technical Information – First Draft OAQPS Staff Paper.

August.

Environmental Protection Agency. (2004) Air Quality Criteria for Particulate Matter. Research Triangle Park, NC:

Office of Research and Development; report no. EPA/600/P-99/002a,bF. October.

Henderson, R. (2005) Letter from Dr. Rogene Henderson, Chair, Clean Air Scientific Advisory Committee, to

Administrator Stephen L. Johnson. Re: Clean Air Scientific Advisory Committee (CASAC) Particulate

Matter (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); and Particulate Matter Health Risk Assessment for Selected Urban Areas: 

Second Draft Report (Second Draft PM Risk Assessment, January 2005). June 6, 2005.

U.S. Department of Health, Education and Welfare (DEHW). (1969) Air Quality Criteria for Particulate Matter. 

U.S. Government Printing Office, Washington DC, AP-49.

2-1

2. CHARACTERIZATION OF AMBIENT PM

2.1 INTRODUCTION

This chapter generally characterizes various classes of ambient PM in terms of physical

and chemical properties, measurement methods, recent concentrations and trends, and

relationships with human exposure and visibility impairment. This information is useful for

interpreting the available health and welfare effects information, and for making

recommendations on appropriate indicators for primary and secondary PM standards. The

information presented in this chapter was drawn from the CD and additional analyses of data

from various PM monitoring networks.

Section 2.2 presents information on the basic physical and chemical properties of classes

of PM. Section 2.3 presents information on the methods used to measure ambient PM and some

important considerations in the design of these methods. Section 2.4 presents data on PM

concentrations, trends, and spatial patterns in the U.S. Section 2.5 provides information on the

temporal variability of PM. Much of the information in Sections 2.4 and 2.5 is derived from

analyses of data collected by the nationwide networks of PM2.5 and PM10 monitors through 2003. 

Section 2.6 defines and discusses background levels of ambient PM. Section 2.7 addresses the

relationships between ambient PM levels and human exposure to PM. Section 2.8 addresses the

relationship between ambient PM2.5 levels and visibility impairment.

2.2 PROPERTIES OF AMBIENT PM

PM represents a broad class of chemically and physically diverse substances that exist as

discrete particles in the condensed (liquid or solid) phase. Particles can be characterized by size,

formation mechanism, origin, chemical composition, and atmospheric behavior. This section

generally focuses on size since classes of particles have historically been characterized largely in

that manner. Fine particles and coarse particles, which are defined in Section 2.2.1.1, are

relatively distinct entities with fundamentally different sources and formation processes,

chemical composition, atmospheric residence times and behaviors, transport distances, and

optical and radiative properties. The CD concludes that these differences justify consideration of

fine and coarse particles as separate subclasses of PM pollution (CD, pp. 2-111 and 9-21). 

2.2.1 Particle Size Distributions

Particle properties and their associated health and welfare effects differ by particle size. 

The diameters of atmospheric particles span 5 orders of magnitude, ranging from
1

 In this Staff Paper, particle size or diameter refers to a normalized measure called aerodynamic diameter

unless otherwise noted. Most ambient particles are irregularly shaped rather than spherical. The aerodynamic

diameter of any irregular shaped particle is defined as the diameter of a spherical particle with a material density of 1

g/cm3

 and the same settling velocity as the irregular shaped particle. Particles with the same physical size and shape

but different densities will have different aerodynamic diameters (CD, p. 2-4).

2

 Particle size distributions, such as those in Figure 2-1, are often expressed in terms of the logarithm of the

particle diameter (Dp) on the X-axis and the measured concentration difference per logarithmic increment in particle

diameter on the Y-axis. When the Y-axis concentration difference is plotted on a linear scale, the number of

particles, the particle surface area, and the particle volume (per cm3

 air) having diameters in the size range from log

Dp to log(Dp + )Dp) are proportional to the area under that part of the size distribution curve.

3

 Mass is proportional to volume times density.

2-2

0.001 micrometers to 100 micrometers (μm).1 The size and associated composition of particles

determine their behavior in the respiratory system, including how far the particles are able to

penetrate, where they deposit, and how effective the body's clearance mechanisms are in

removing them. Furthermore, particle size is one of the most important parameters in

determining the residence time and spatial distribution of particles in ambient air, key

considerations in assessing exposure. Particle size is also a major determinant of visibility

impairment, a welfare effect linked to ambient particles. Particle surface area, number, chemical

composition, and water solubility all vary with particle size, and are also influenced by the

formation processes and emissions sources.

Common conventions for classifying particles by size include: (1) modes, based on

observed particle size distributions and formation mechanisms; and (2) “cut points,” based on the

inlet characteristics of specific PM sampling devices. The terminology used in this Staff Paper

for describing these classifications is summarized in Table 2-1 and discussed in the following

subsections.

2.2.1.1 Modes

Based on extensive examinations of particle size distributions in several U.S. locations in

the 1970's, Whitby (1978) found that particles display a consistent multi-modal distribution over

several physical metrics, such as mass or volume (CD, p. 2-7). These modes are apparent in

Figure 2-1, which shows average ambient distributions of particle number, surface area, and

volume by particle size.2

 Panel (a) illustrates that by far, the largest number of ambient particles

in a typical distribution are very small, below 0.1 μm in diameter, while panel (c) indicates most

of the particle volume, and therefore most of the mass, is found in particles with diameters larger

than 0.1 μm.3

 Most of the surface area (panel b) is between 0.1 and 1.0 μm. The surface area

distribution in panel (b) peaks around 0.2 μm. Distributions may vary across locations,

conditions, and time due to differences in sources, atmospheric conditions, topography, and the

age of the aerosol.
Figure 2-1. Distribution of coarse (c), accumulation (a), and nuclei (n) mode particles by 

three characteristics: (a) number, N; (b) surface area, S; and (c) volume, V 

for the grand average continental size distribution. DGV = geometric mean 

diameter by volume; DGS = geometric mean diameter by surface area; 

DGN = geometric mean diameter by number; Dp = particle diameter. 

Source: Whitby (1978); CD, p. 2-8.

2-3
4

 Whitby (1978) did not identify multiple ultrafine particle modes between 0.01 and 0.1 μm, and therefore

separate nucleation and Aitken modes are not illustrated in Figure 2-1. See CD Figure 2-6 for a depiction of all

particle modes.

5

 40 CFR Part 50, Appendix B, Reference Method for the Determination of Suspended Particulate Matter

in the Atmosphere (High-Volume Method).

2-4

As illustrated in panel (c) of Figure 2-1, volume distributions typically measured in

ambient air in the U.S. are found to be bimodal, with overlapping tails, and an intermodal

minimum between 1 and 3 μm (CD, p. 2-25). The distribution of particles that are mostly larger

than this minimum make up the coarse mode and are called “coarse particles,” and the

distribution of particles that are mostly smaller than the minimum are called “fine particles.” 

Fine particles can be subcategorized into smaller modes: “nucleation mode,” “Aitken mode,”

and “accumulation mode.” Together, nucleation-mode and Aitken-mode particles make up

“ultrafine particles.”4

 Ultrafine particles are apparent as the largest peak in the number

distribution in panel (a), and are also visible in the surface area distribution in panel (b). 

Nucleation-mode and Aitken-mode particles have relatively low mass and grow rapidly into

accumulation-mode particles, so they are not commonly observed as a separate mode in volume

or mass distributions. The accumulation mode is apparent as the leftmost peak in the volume

distribution in panel (c) and the largest peak in the surface area distribution in panel (b). 

2.2.1.2 Sampler Cut Points

Another set of particle size classifications is derived from the characteristics of ambient

particle samplers. Particle samplers typically use size-selective inlets that are defined by their 50

percent cut point, which is the particle aerodynamic diameter at which 50 percent of particles of

a specified diameter are captured by the inlet, and their penetration efficiency as a function of

particle size. The usual notation for these classifications is “PMx”, where x refers to

measurements with a 50 percent cut point of x μm aerodynamic diameter. Because of the

overlap in the size distributions of fine and coarse-mode ambient particles, and the fact that inlets

do not have perfectly sharp cut points, no single sampler can completely separate them. Given a

specific size cut, the smaller the particles the greater the percentage of particles that are captured. 

The objective of size-selective sampling is usually to measure particle size fractions that provide

a relationship to human health impacts, visibility impairment, or emissions sources.

Since 1987, the EPA has defined indicators of PM for NAAQS using cut points of

interest. Figure 2-2 presents an idealized distribution of ambient PM showing the fractions

collected by size-selective samplers. Prior to 1987, the indicator for the PM NAAQS was total

suspended particulate matter (TSP), and was defined by the design of the High Volume Sampler

(Hi Vol).5

 As illustrated in Figure 2-2, TSP typically includes particles with diameters less than

about 40 μm, but the upper size cut varies substantially with placement and wind velocity . 

When EPA established new PM standards in 1987, the selection of PM10 as an indicator was
Figure 2-2. An idealized distribution of ambient PM showing fine and coarse particles a

the fractions collected by size-selective samplers. (WRAC is the Wide Range 

Classifier which collects the entire coarse mode).

Source: Adapted from Wilson and Suh (1997) and Whitby (1978); CD page 2-18 

2-5
6

 40 CFR Part 50, Appendix J, Reference Method for the Determination of Particulate Matter as PM10 in

the Atmosphere.

7

 40 CFR Part 50, Appendix L, Reference Method for the Determination of Fine Particulate Matter as PM2.5

in the Atmosphere.

2-6

intended to focus regulatory attention on particles small enough to be inhaled and to penetrate

into the thoracic region of the human respiratory tract. In 1997, EPA established standards for

fine particles measured as PM2.5 (i.e., the fine fraction of PM10). The dashed lines in Figure 2-2

illustrate the distribution of particles captured by the PM10 Federal Reference Method (FRM)

sampler6, including all fine and some coarse particles, and the distribution captured by the PM2.5

FRM sampler7, including generally all fine particles and potentially capturing a small subset of

coarse particles.

The EPA is now considering establishing standards for another PM indicator identified in

Table 2-1 as PM10-2.5, which represents the subset of coarse particles small enough to be inhaled

and to penetrate into the thoracic region of the respiratory tract (i.e., the coarse fraction of PM10,

or thoracic coarse particles). Section 2.3 discusses measurement methods for this indicator. 

2.2.2 Sources and Formation Processes

In most locations, a variety of activities contribute to ambient PM concentrations. Fine

and coarse particles generally have distinct sources and formation mechanisms, although there is

some overlap (CD, p. 3-60). Coarse particles are generally primary particles, meaning they are

emitted from their source directly as particles. Most coarse particles result from mechanical

disruption of large particles by crushing or grinding, from evaporation of sprays, or from dust

resuspension. Specific sources include industrial process emissions, fugitive emissions from

storage piles, traffic related emissions including tire and paving materials and grinding and

resuspension of crustal, biological, industrial, and combustion materials that have settled on or

near roadways, construction and demolition activities, agriculture, mining and mineral

processing, sea spray, and wind-blown dust and biological materials. The amount of energy

required to break down primary particles into smaller particles normally limits coarse particle

sizes to greater than 1.0 μm diameter (EPA 1996a, p. 13-7). Some combustion-generated

particles, such as fly ash, are also found as coarse particles.

By contrast, a significant amount of fine particles are produced through combustion

processes and atmospheric transformation processes of precursor gases. Common directly

emitted fine particles include unburned carbon particles from combustion, and nucleation-mode

particles emitted as combustion-related vapors that condense within seconds of being exhausted

to ambient air. Fossil-fuel combustion sources include motor vehicles and off-highway

equipment, power generation facilities, industrial facilities, residential wood burning,

agricultural burning, and forest fires.
Table 2-1. Particle Size Fraction Terminology Used in Staff Paper

Term Description

Size Distribution Modes

Coarse Particles The distribution of particles that are mostly larger than the intermodal

minimum in volume or mass distributions; also referred to as coarse-mode

particles. This intermodal minimum generally occurs between 1 and 3 μm. 

Thoracic Coarse Particles A subset of coarse particles that includes particles that can be inhaled and

penetrate to the thoracic region (i.e., the tracheobronchial and the

gas-exchange regions) of the lung. This subset includes the smaller coarse

particles, ranging in size up to those with a nominal aerodynamic diameter less

than or equal to 10 microns.

Fine Particles The distribution of particles that are mostly smaller than the intermodal

minimum in volume or mass distributions; this minimum generally occurs

between 1 and 3 μm. This includes particles in the nucleation, Aitken, and

accumulation modes.

Accumulation-Mode Particles A subset of fine particles with diameters above about 0.1 μm. Ultrafine

particles grow by coagulation or condensation and “accumulate” in this size

range. 

Ultrafine Particles A subset of fine particles with diameters below about 0.1 μm, encompassing

the Aitken and nucleation modes. 

Aitken-Mode Particles A subset of ultrafine particles with diameters between about 0.01 and 0.1 μm.

Nucleation-Mode Particles Freshly formed particles with diameters below about 0.01 μm.

Sampling Measurements

Total Suspended Particles (TSP) Particles measured by a high volume sampler as described in 40 CFR Part 50,

Appendix B. This sampler has a cut point of aerodynamic diameters that

varies between 25 and 40 μm depending on wind speed and direction.

PM10 Particles measured by a sampler that contains a size fractionator (classifier)

designed with an effective cut point (50% collection efficiency) of 10 μm

aerodynamic diameter. This measurement includes the fine particles and a

subset of coarse particles, and is an indicator for particles that can be inhaled

and penetrate to the thoracic region of the lung; also referrred to as thoracic

particles.

PM2.5 Particles measured by a sampler that contains a size fractionator (classifier)

designed with an effective cut point (50% collection efficiency) of 2.5 μm

aerodynamic diameter. This measurement, which generally includes all fine

particles, is an indiator for fine particles; also referred to as fine-fraction

particles. A small portion of coarse particles may be included depending on

the sharpness of the sampler efficiency curve.

PM10-2.5 Particles measured directly using a dichotomous sampler or by subtraction of

particles measured by a PM2.5 sampler from those measured by a PM10

sampler. This measurement is an indicator for the coarse fraction of thoracic

particles; also referred to as thoracic coarse particles or coarse-fraction

particles.

2-7
8

 Also called light absorbing carbon and black carbon. The terms elemental carbon and black carbon are

often used interchangeably, but may be defined differently by different users. Black carbon is most often used in

discussions of optical properties and elemental carbon is most often used when referring to chemical composition. 

In many cases, there is little difference between the two, but care must be taken when comparing data from studies

with different purposes. In addition, the term soot is also used in many instances to refer to either EC or BC. The

differences between soot and either EC or BC can be significant, as soot refers to elemental carbon formed from gas

phase hydrocarbons in the combustion process, and tends to be in the submicron fraction and often in the fraction of

particles that are smaller than 0.10 microns in aerodynamic diameter. EC and BC both include carbonaceous

particles formed from incomplete burnout of solid carbonaceous fuels; these particles have distinctly different

physical characteristics compared to char. As an additive to automotive tires, commercially produced ‘carbon black’

and associated contaminants can also be found in resuspended urban road dust.

2-8

The formation and growth of fine particles are influenced by several processes including: 

(1) nucleation (i.e., gas molecules coming together to form a new particle); (2) condensation of

gases onto existing particles; (3) coagulation of particles, the weak bonding of two or more

particles into one larger particle; (4) uptake of water by hygroscopic components; and (5) gas

phase reactions which form secondary PM. Gas phase material condenses preferentially on

smaller particles since they have the greatest surface area, and the efficiency of coagulation for

two particles decreases as the particle size increases. Thus, ultrafine particles grow into the

accumulation mode, but accumulation-mode particles do not normally grow into coarse particles

(CD, p. 2-29).

Secondary formation processes can result in either new particles or the addition of PM to

pre-existing particles. Examples of secondary particle formation include: (1) the conversion of

sulfur dioxide (SO2) to sulfuric acid (H2SO4) droplets that further react with gaseous ammonia

(NH3) to form various sulfate particles (e.g., ammonium sulfate (NH4)2SO4 or ammonium

bisulfate NH4HSO4); (2) the conversion of nitrogen dioxide (NO2) to nitric acid (HNO3) vapor

that reacts further with ammonia to form ammonium nitrate (NH4NO3) particles; and (3)

reactions involving gaseous volatile organic compounds (VOC) yielding organic compounds

with low ambient temperature (saturation) vapor pressures that nucleate or condense on existing

particles to form secondary organic aerosol particles (CD, p. 3-65 to 3-71). In most of the

ambient monitoring data displays shown later in this chapter, the first two types of secondary PM

are generally labeled plurally as ‘sulfates’ and ‘nitrates’ (respectively), which implies that the

ammonium content is encompassed. The third type of secondary PM may be lumped with the

directly emitted elemental or organic carbon particles and labeled ‘total carbonaceous mass,’ or

the two types of carbonaceous PM may be reported separately as elemental carbon (EC) and

organic carbon (OC).

2.2.3 Chemical Composition

Based on studies conducted in most parts of the U.S., the CD reports that a number of

chemical components of ambient PM are found predominately in fine particles including: 

sulfate, ammonium, and hydrogen ions; elemental carbon8

, secondary organic compounds, and
2-9

primary organic species from cooking and combustion; and certain metals, primarily from

combustion processes. Chemical components found predominately in coarse particles include: 

crustal-related materials such as calcium, aluminum, silicon, magnesium, and iron; and primary

organic materials such as pollen, spores, and plant and animal debris (CD, p. 2-38).

Some components, such as nitrate and potassium, may be found in both fine and coarse

particles. Nitrate in fine particles comes mainly from the reaction of gas-phase nitric acid with

gas-phase ammonia to form ammonium nitrate particles. Nitrate in coarse particles comes

primarily from the reaction of gas-phase nitric acid with pre-existing coarse particles (CD, p. 2-

38). Potassium in coarse particles comes primarily from soil, with additional contributions from

sea salt in coastal areas. Potassium in fine particles, generally not a significant contributor to

overall mass, comes mainly from emissions of burning wood, with infrequent but large

contributions from fireworks, as well as significant proportions from the tail of the distribution

of coarse soil particles (i.e., < 2.5 μm in diameter ) in areas with high soil concentrations. 

Many ambient particles also contain water (i.e., particle-bound water) as a result of an

equilibrium between water vapor and hygroscopic PM (CD, p. 2-40). Particle-bound water

influences the size of particles and in turn their aerodynamic and light scattering properties

(discussed in section 2.2.5). Particle-bound water can also act as a carrier to convey dissolved

gases or reactive species into the lungs which, in turn, may cause heath consequences. (CD, p.

2-112). The amount of particle-bound water in ambient particulate matter will vary with the

particle composition and the ambient relative humidity. Sulfates, nitrates, and some secondary

organic compounds are much more hygroscopic than elemental carbon (EC), primary organic

carbon (OC), and crustal material.

2.2.4 Fate and Transport

Fine and coarse particles typically exhibit different behaviors in the atmosphere. These

differences may affect several exposure-related considerations, including the representativeness

of central-site monitored values and the penetration of particles formed outdoors into indoor

spaces. The ambient residence time of atmospheric particles varies with size. Ultrafine particles

have a very short life, on the order of minutes to hours, since they are more likely to reach the

accumulation mode. However, their chemical content persists in the accumulation mode. 

Ultrafine particles are also small enough to be removed through diffusion to falling rain drops. 

Accumulation-mode particles remain suspended longer (i.e. accumulate ) in the atmosphere

because they are too large to diffuse rapidly to surfaces or to other particles and too small to

settle out or impact on stationary objects. They can be transported thousands of kilometers and

remain in the atmosphere for days to weeks. Accumulation-mode particles serve as

condensation nuclei for cloud droplet formation and are eventually removed from the

atmosphere in falling rain drops. Accumulation-mode particles that are not involved in cloud
2-10

processes are eventually removed from the atmosphere by gravitational settling and impaction on

surfaces.

By contrast, coarse particles can settle rapidly from the atmosphere with lifetimes

ranging from minutes to days depending on their size, atmospheric conditions, and altitude. 

Larger coarse particles are not readily transported across urban or broader areas, because they

are generally too large to follow air streams, and they tend to be easily removed by gravitational

settling and by impaction on surfaces. Smaller coarse particles extending into the tail of the

distribution can have longer lifetimes and travel longer distances, especially in extreme

circumstances. For example, dust storms in desert areas of Africa and Asia lift coarse particles

to high elevations and these ‘dust clouds’ are readily observed to undergo intercontinental

transport to North America (CD, p. 2-49). Coarse particles also are readily removed by falling

rain drops (CD, p. 2-50).

The characteristics of ultrafine, accumulation-mode, and coarse-mode particles that were

discussed in the preceding sections are summarized in Table 2-2.

2.2.5 Optical Properties of Particles

Particles and gases in the atmosphere scatter and absorb light and, thus, affect visibility. 

As discussed in section 4.3 of the CD, the efficiency of particles in causing visibility impairment

depends on particle size, shape, and composition. Accumulation-mode particles are more

efficient per unit mass than coarse particles in causing visibility impairment. The accumulationmode particle components principally responsible for visibility impairment are sulfates, nitrates,

organic matter, and elemental carbon. Soil dust, particularly in the fine tail of the coarse particle

distribution, can also impair visibility. All of these particles scatter light to some degree, but, of

these, elemental carbon plays the most significant role in light absorption. Since elemental

carbon, which is a product of incomplete combustion from activities such as the burning of wood

or diesel fuel, is a relatively small component of PM in most areas, visibility impairment is

generally dominated by light scattering rather than by light absorption.

Because humidity causes hygroscopic particles to grow in size, humidity plays a

significant role in particle-related visibility impairment. The amount of increase in particle size

with increasing relative humidity depends on particle composition. Humidity-related particle

growth is a more important factor in the eastern U.S., where annual average relative humidity

levels are 70 to 80 percent compared to 50 to 60 percent in the western U.S. Due to relative

humidity differences, aerosols of a given mass, dry particle size distribution, and composition

would likely cause greater visibility impairment in an eastern versus a western location. The

relationship between ambient PM and visibility impairment is discussed below in Section 2.8.
Table 2-2. Comparison of Ambient Fine Particles

(Ultrafine plus Accumulation-Mode) and Coarse Particles

Fine 

Ultrafine Accumulation Coarse 

Formation 

Processes: 

Combustion, high-temperature 

processes, and atmospheric reactions 

Break-up of large solids/droplets 

Formed by: Nucleation 

Condensation 

Coagulation 

Condensation 

Coagulation 

Reactions of gases in or

 on particles 

Evaporation of fog and cloud 

droplets in which gases have 

dissolved and reacted 

Mechanical disruption (crushing,

 grinding, abrasion of surfaces) 

Evaporation of sprays 

Suspension of dusts 

Reactions of gases in or on particles 

Composed 

of: 

Sulfate 

Elemental carbon 

Metal compounds 

Organic compounds

 with very low

 saturation vapor

 pressure at ambient

 temperature 

Sulfate, nitrate, ammonium,

 and hydrogen ions 

Elemental carbon 

Large variety of organic

 compounds 

Metals: compounds of Pb, Cd,

 V, Ni, Cu, Zn, Mn, Fe, etc. 

Particle-bound water 

Suspended soil or street dust 

Fly ash from uncontrolled combustion

 of coal, oil, and wood 

Nitrates/chlorides/sulfates from

 HNO3/HCl/SO2 reactions with

 coarse particles 

Oxides of crustal elements

 (Si, Al, Ti, Fe) 

CaCO3, CaSO4, NaCl, sea salt 

Pollen, mold, fungal spores 

Plant and animal fragments 

Tire, brake pad, and road wear debris 

Solubility: Probably less soluble

 than accumulation

 mode 

Largely soluble, hygroscopic,

 and deliquescent 

Largely insoluble and nonhygroscopic 

Sources: Combustion 

Atmospheric

 transformation of

 SO2 and some

 organic compounds 

High temperature

 processes 

Combustion of coal, oil,

 gasoline, diesel fuel, wood 

Atmospheric transformation

 products of NOx, SO2, and

 organic compounds,

 including biogenic organic

 species (e.g., terpenes) 

High-temperature processes,

 smelters, steel mills, etc. 

Resuspension of industrial dust and

 soil tracked onto roads and streets 

Suspension from disturbed soil (e.g.,

 farming, mining, unpaved roads) 

Construction and demolition 

Uncontrolled coal and oil combustion 

Ocean spray 

Biological sources 

Atmospheric 

half-life: 

Minutes to hours Days to weeks Minutes to hours 

Removal 

Processes: 

Grows into

 accumulation mode 

Diffuses to raindrops 

Forms cloud droplets and

 rains out 

Dry deposition 

Dry deposition by fallout 

Scavenging by falling rain drops 

Travel 

distance: 

< 1 to 10s of km 100s to 1000s of km < 1 to 10s of km (small size tail, 

100s to 1000s in dust storms) 

Source: Adapted from Wilson and Suh (1997); CD, p. 2-52.

2-11
2-12

2.2.6 Other Radiative Properties of Particles

In addition to the optical properties related to visibility summarized above, ambient

particles scatter and absorb radiation across the full electromagnetic spectrum, including

ultraviolet, visible, and infrared wavelengths, affecting climate processes and the amount of

ultraviolet radiation that reaches the earth. As discussed in section 4.5 of the CD, the effects of

ambient particles on the transmission of these segments of the electromagnetic spectrum depend

on the radiative properties of the particles, which in turn are dependent on the size and shape of

the particles, their composition, the distribution of components within individual particles, and

their vertical and horizontal distribution in the lower atmosphere.

The effects of PM on the transfer of radiation in the visible and infrared spectral regions

play a role in global and regional climate. Direct effects of particles on climatic processes are

the result of the same processes responsible for visibility degradation, namely radiative

scattering and absorption. However, while visibility impairment is caused by particle scattering

in all directions, climate effects result mainly from scattering light away from the earth and into

space. This reflection of solar radiation back to space decreases the transmission of visible

radiation to the surface 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 elemental carbon, results in an increase in the heating

rate of the lower atmosphere.

The relative proportions of scattering and absorption by ambient particles are highly

dependent on their composition and optical properties and on the wavelength of the radiation. 

For example, sulfate and nitrate particles effectively scatter solar radiation, and they weakly

absorb infrared, but not visible, radiation. The effects of mineral dust particles are complex;

depending on particle size and refractive index, mineral aerosol can reflect or absorb radiation. 

Dark minerals absorb across the solar and infrared spectral regions leading to warming of the

atmosphere. Light-colored mineral particles in the appropriate size range can scatter visible

radiation, reducing radiation received at the earth’s surface. Organic carbon particles mainly

reflect radiation, whereas elemental carbon particles strongly absorb radiation; however, the

optical properties of carbonaceous particles are modified if they become coated with water or

sulfuric acid. Upon being deposited onto surfaces, particles can also either absorb or reflect

radiation depending in part on the relative reflectivity of the particles and the surfaces on which

they are deposited.

The transmission of solar radiation in the ultraviolet (UV) range through the earth’s

atmosphere is affected by ozone and clouds as well as by particles. The effect of particles on

radiation in the ultraviolet-B (UV-B) range, which has been associated with various biological

effects, is of particular interest. Relative to ozone, the effects of ambient particles on the

transmission of UV-B radiation are more complex. The CD notes that even the sign of the effect

can reverse as the composition of the particle mix in an air mass changes from scattering to
9

 Refer to CD Chapter 2 for more comprehensive assessments of particle measurement methods. A recent

summary of PM measurement methods is also given in Fehsenfeld et al. (2003). Significant improvements and

understanding of routine and advanced measurement methods is occurring through EPA’s PM Supersites Program

(see www.epa.gov/ttn/amtic/supersites.html).

2-13

absorbing types (e.g., from sulfate to elemental carbon), and that there is an interaction in the

radiative effects of scattering particles and absorbing molecules, such as ozone, in the lower

atmosphere.

2.3 AMBIENT PM MEASUREMENT METHODS

The methods used to measure ambient PM are important to understanding population

exposure to PM, evaluating health and welfare risks, and developing and evaluating the

effectiveness of risk management strategies. Because PM is a complex mixture of substances

with differing physical and chemical properties , measuring and characterizing particles

suspended in the atmosphere is a significant challenge.9 Ambient measurements include particle

mass, composition, and particle number. Most instruments collect PM by drawing a controlled

volume of ambient air through a size-selective inlet, usually defined by the inlet’s 50 percent cut

point. Measurable indicators of fine particles include PM2.5, PM1.0, British or black smoke (BS),

coefficient of haze (COH), and PM10 (in areas dominated by fine particles). Measurable

indicators of coarse-mode particles include PM10-2.5, PM15-2.5, and PM10 (in areas dominated by

coarse-mode particles).

2.3.1 Particle Mass Measurement Methods

Ambient PM mass can be measured directly, by gravimetric methods, or indirectly, using

methods that rely on the physical properties of particles. Methods can also be segregated as

either discrete or continuous according to whether samples require laboratory analysis or the data

are available in real-time. Discrete methods provide time integrated data points (typically over a

24- hour period) that allow for post-sampling gravimetric analyses in the laboratory. These

methods are typically directly linked to the historical data sets that have been used in health

studies that provide the underlying basis for having a NAAQS. Continuous methods can provide

time resolution on the order of minutes and automated operation up to several weeks, facilitating

the cost-effective collection of greater amounts of data compared with discrete methods.

The most common direct measurement methods include filter-based methods where

ambient aerosols are collected for a specified period of time (e.g., 24 hours) on filters that are

weighed before and after collection to determine mass by difference. Examples include the FRM

monitors for PM2.5 and PM10. Dichotomous samplers contain a separator that splits the air stream

from a PM10 inlet into two streams so that both fine- and coarse-fraction particles can be

collected on separate filters. These gravimetric methods require weighing the filters after they
10 The Performance Evaluation Program (PEP) is designed to determine total bias for the PM2.5 sample

collection and laboratory analysis processes. Federally referenced audit samplers are collocated adjacent to a

monitoring site's routine sampler and run for a 24-hour period. The concentrations are then determined

independently by EPA laboratories and compared in order to assess bias. The performance evaluations are

conducted four times per year (once per quarter) at one-fourth (25 percent) of the sampling sites in a reporting

organization.

2-14

are subjected to specific equilibrium conditions (i.e., 20 - 23° C and 30 - 40 percent RH in most

cases).

Discrete, gravimetric methodologies have been refined over the past 20 years as PM

monitoring networks have evolved from sampling based on the high volume TSP and PM10

method to the PM2.5 FRM. The inclusion of such measures as size-selective inlets and

separators, highly specific filter media performance criteria, active flow control to account for

ambient changes in temperature and pressure, and highly prescriptive filter weighing criteria

have reduced levels of measurement uncertainty, compared with earlier methods.

National quality assurance data analyzed by EPA between 1999-2001 indicate that the

PM2.5 FRM has been a robust indicator of ambient levels by meeting the data quality objectives

(DQO) established at the beginning of the monitoring program. Three-year average estimates

from reporting organizations aggregated on a national basis for collocated sampler precision (7.2

percent), flow rate accuracy (0.18 percent), and method bias (-2.06 percent, from the

Performance Evaluation Program)10 are well within their respective goals of +10 percent, +4

percent, and +10 percent. 

There are a number of continuous PM measurement techniques. A commonly used

method is the Tapered Element Oscillating Microbalance (TEOM®) sensor, consisting of a

replaceable filter mounted on the narrow end of a hollow tapered quartz tube. The air flow

passes through the filter, and the aerosol mass collected on the filter causes the characteristic

oscillation frequency of the tapered tube to change in direct relation to particle mass. This

approach allows mass measurements to be recorded on a near-continuous basis (i.e., every few

minutes).

The next generation of the TEOM® is the Filter Dynamics Measurement System

(FDMS®) monitor. This method is based upon the differential TEOM that is described in the

CD (CD, p. 2-78). The FDMS method employs an equilibration system integrated with a

TEOM® having alternating measurements of ambient air and filtered air. This self-referencing

approach allows the method to determine the amount of volatile PM that is evaporating from the

TEOM sensor for 6 of every 12 minutes of operation. An hourly measurement of the total

aerosol mass concentration, including non-volatile and volatile PM, is calculated and reported

every 6 minutes. 

Other methods that produce near-continuous PM mass measurements include the beta

attenuation sampler and the Continuous Ambient Mass Monitor (CAMM). A beta attenuation
11 When data are sent to the AIRNOW website, they are assumed to be "FRM like" which means that their

values are highly correlated (R2

 > 0.8) with actual FRM concentrations so that values can be compared not only to

the FRM measurements but also across State boundaries. Statistical adjustments to the raw continuous data are

necessary because some of the sampling methodologies, such as the TEOM monitors, have inlets heated from 30oC

to 50oC which causes semi-volatile fine particulate matter including nitrates to be vaporized and never measured. 

The result of this vaporization is a lower measured TEOM concentration when compared to the FRM. Adjustments

have been accomplished on a seasonal basis as well as using meteorologic variables (e.g., ambient temperature) with

linear and non-linear regression techniques. The need to adjust the continuous data can depend on several factors

including the type of method, the location of the site in the country and the composition of the ambient particulate

matter being measured.

2-15

(or beta gauge) sampler determines the mass of particles deposited on a filter by measuring the

absorption of electrons generated by a radioactive isotope, where the absorption is closely

related to the mass of the particles. The CAMM measures the pressure drop increase that occurs

in relation to particle loading on a membrane filter. Both methods (beta-attenuation and

CAMM) require calibration against standard mass measurements as neither measures PM mass

directly by gravimetric analysis.

The number of continuous PM2.5 monitors across the U.S. has increased from 300 to over

500 between 2003 and 2005. Although a subset of these monitors were required by regulation to

be placed in metropolitan areas of greater than 1 million population, a higher percentage were

installed to provide improved temporal resolution for daily air quality index reporting and PM2.5

forecast verifications through EPA's AIRNOW program. Some of the continuous PM2.5 data

reported through the AIRNOW program are adjusted to better match FRM results.11 The

continuous data used in the analyses in this chapter were obtained from EPA’s Air Quality

System (AQS); some of these AQS data are adjusted and some are not. There is currently an

effort underway to better characterize this facet of the continuous data in AQS. Still, the AQS

continuous data utilized in analyses here do show excellent correlation with collocated FRM

measurements; over 95 percent of the continuous/FRM site pairs had a correlation coefficient of

over 0.72, and almost 75 percent had a correlation of 0.9 or higher (Schmidt et al., 2005).

Work also continues on the development of national approval criteria for determining

regional and national equivalency for continuous PM2.5 monitors. Once promulgated, these

criteria would provide the regulatory basis for approving appropriate continuous methods as

equivalent to FRMs, and permit the assessment of NAAQS attainment status with continuous

PM2.5 data, reducing the number of manually-operated FRM monitors that need to continue

operating.

2.3.2 Indirect Optical Methods

PM has also been characterized in the U.S. and elsewhere by indirect optical methods

that rely on the light scattering or absorbing properties of either suspended PM or PM collected
12 See Section 2.2.5 of this chapter for a discussion of the optical properties of PM.

13 The reader is referred to Chapter 2, section 2.2, of the CD for a more thorough discussion of sampling

and analytical techniques for measuring PM. Methods used in EPA’s National PM2.5 Speciation Trends Network

and other special monitoring programs are summarized in Solomon et al. (2001).

2-16

on a filter.12 These include BS, COH, and estimates derived from visibility measurements. In

locations where they are calibrated to standard mass units, these indirect measurements can be

useful surrogates for particle mass. The BS method typically involves collecting samples from a

4.5 μm inlet onto white filter paper where blackness of the stain is measured by light absorption. 

Smoke particles composed primarily of elemental carbon (EC), including black carbon (BC),

typically make the largest contribution to stain darkness. COH is determined using a light

transmittance method. This involves collecting samples from a 5.0 μm inlet onto filter tape

where the opacity of the resulting stain is determined. This technique is somewhat more

responsive to non-carbon particles than the BS method. Nephelometers measure the light

scattered by ambient aerosols in order to calculate light extinction. This method results in

measurements that can correlate well with the mass of fine particles below 2 μm diameter. Since

the mix of ambient particles varies widely by location and time of year, the correlation between

BS, COH, and nephelometer measurements and PM mass is highly site- and time-specific. The

optical methods described here, as well as the particle counters described below, are based on the

measurement of properties such as light scattering and electric mobility, which are inherently

different than previous methods described based on aerodynamic diameter. 

2.3.3 Size-Differentiated Particle Number Concentration Measurement Methods

Recently there has been increasing interest in examining the relationship between the

particle number concentration by size and health effects. Several instruments are needed to

provide size distribution measurements (number and size) over the 5 orders of magnitude of

particle diameters of interest. A nano-scanning mobility particle sizer (NSMPS) counts particles

in the 0.003 to 0.15 μm range. A standard scanning mobility particle sizer (SMPS) counts

particles in the 0.01 to 1 μm range, and a laser particle counter (LPC) counts particles in the 0.1

to 2 μm range. An aerodynamic particle sizer measures particles in the 0.7 to 10 μm range. 

These techniques, while widely used in aerosol research, have not yet been widely used in health

effects studies.

2.3.4 Chemical Composition Measurement Methods

There are a variety of methods used to identify and describe the characteristic

components of ambient PM.13 X-ray fluorescence (XRF) is a commonly used laboratory

technique for analyzing the elemental composition of primary particles deposited on filters. Wet

chemical analysis methods, such as ion chromatography (IC) and automated colorimetry (AC)
2-17

are used to measure ions such as nitrate (NO3

-

), sulfate (SO4

=

), chloride (Cl-), ammonium (NH4

+

),

sodium (Na+), organic cations (such as acetate), and phosphate (PO4

3-).

There are several methods for separating organic carbon (OC) and elemental carbon (EC)

or black carbon (BC) in ambient filter samples. Thermal optical reflectance (TOR), thermal

manganese oxidation (TMO), and thermal optical transmittance (TOT) have been commonly

applied in aerosol studies in the United States. The thermal optical transmission (TOT) method, 

used in the EPA speciation program, uses a different temperature profile than TOR, which is

used in the Interagency Monitoring of Protected Visual Environments (IMPROVE) visibility

monitoring program. The two methods yield comparable estimates of total carbon, but give a

different split between OC and EC. 

Commercial instruments are now available to measure carbon (OC, EC, TC), nitrate, and

sulfate on a near-continuous basis. These instruments provide time-resolved measurements from

a few minutes to a few hours. The semi-continuous methods involved a variety of techniques

that include thermal reduction; wet impaction and flash vaporization; and thermal oxidation with

non-dispersive infrared (NDIR) detection. They have been field tested and compared through

the EPA’s Environmental Technology Verification (ETV) program and the Supersites program

and proven to be good candidates for additional testing (EPA, 2004a). Data are now becoming

available from regional planning and multi-state organizations and the EPA to understand the

comparison with filter-based methods and the potential limitations of these technologies. 

The U.S. EPA is coordinating a pilot study of semi-continuous speciation monitors at five

Speciation Trends Network (STN) sites. The pilot study began in 2002. The goals of the pilot

study are to assess the operational characteristics and performance of continuous carbon, nitrate,

and sulfate monitors for routine application at STN sites; work with the pilot participants and the

vendors to improve the measurement technologies used; and evaluate the use of an automated

data collection and processing system for real time display and reporting. After the pilot

monitoring and data evaluation phase, proven semi-continuous monitors will become the

framework for a long-term network of up to 12 STN sites equipped with semi-continuous sulfate,

nitrate, and carbon monitors. 

2.3.5 Measurement Issues

There is no perfect PM sampler under all conditions, so there are uncertainties between

the mass and composition collected and measured by a sampler and the mass and composition of

material that exists as suspended PM in ambient air (Fehsenfeld et al., 2003). To date, few

standard reference materials exist to estimate the accuracy of measured PM mass and chemical

composition relative to what is found in air. At best, uncertainty is estimated based on

collocated precision and comparability or equivalency to other similar methods, which

themselves have unknown uncertainty, or to the FRM, which is defined for regulatory purposes

but is not a standard in the classical sense. There are a number of measurement-related issues
2-18

that can result in positive or negative measurement artifacts which could affect the associations

that epidemiologic researchers find between ambient particles and health effects.

The semi-volatile components of PM can create both positive and negative measurement

artifacts. Negative artifacts arise from evaporation of the semi-volatile components of PM

during or after collection, which is caused by changes in temperature, relative humidity, or

aerosol composition, or due to the pressure drop as collected air moves across the filter. Nitrate

losses due to evaporation may represent as much as 10-20 percent of total PM2.5 mass, as shown

in southern California studies (CD, p. 2-68). Positive artifacts arise when gas-phase compounds

absorb onto or react with filter media or already collected PM, or when particle-bound water is

not removed. The chemical interaction of gases being collected with particles already on the

filter and conversion of PM components to gas-phase chemicals can also result in negative

artifacts. These interactions depend on the compounds contained in collected particles and in the

gas phase, and also depend on both location and time.

Particle-bound water can represent a significant fraction of ambient PM mass under

conditions where relative humidity is more than 60 percent (CD; p. 2-63, p. 2-109). It can also

represent a substantial fraction of gravimetric mass at normal equilibrium conditions (i.e., 22° C,

35 percent RH) when the aerosol has high sulfate content. The amount of particle-bound water

will vary with the composition of particles, as discussed in section 2.2.3. The use of heated

inlets to remove particle-bound water (e.g. TEOM at 50° C) can result in loss of semi-volatile

compounds unless corrective techniques are applied, although the newer generation TEOM’s use

diffusion dryers rather than heating to reduce the relative humidity (CD, p. 2-100, Table 2-7).

In areas with significant amounts of dust, high wind conditions resulting in blowing dust

can interfere with accurate separation of fine- and coarse-fraction particles. In these unique

conditions a significant amount of coarse-fraction material can be found in the inter-modal

region between 1 and 3 μm, thus overstating the mass of fine-fraction particles. The addition of

a PM1.0 measurement in these circumstances can provide greater insights into the magnitude of

this problem (CD, p. 9-12).

2.4 PM CONCENTRATIONS, TRENDS, AND SPATIAL PATTERNS

This section provides analysis of the latest available PM air quality data, including PM

levels, composition, and spatial patterns. The EPA and the States have been using a national

network to measure and collect PM10 concentrations since 1987, and PM2.5 concentrations since

1999. Summaries through the end of 2003, based on data publicly available from EPA’s Air

Quality System (AQS) as of August 2004, are presented here. PM2.5 data from the IMPROVE

network are also presented. Many data summaries are presented by region, as shown in

Figure 2-3. These regions are the same as those defined in the CD and have proven useful for

understanding potential differences in the characteristics of PM in different parts of the U.S..
Figure 2-3. Regions used in PM Staff Paper in data analysis summaries.

2-19
14 See 40 CFR Parts 50 and 58 for monitoring program requirements.

15 810 of the 827 monitors are located in the contiguous continental U.S. covered by the regions shown in

Figure 2-3. The remainder are located in Alaska, Hawaii, and U.S. territories.

16 No conclusions should be drawn from these data summaries regarding the potential attainment status of

any area. EPA regulations, in 40 CFR Part 50, Appendix N, require 3 consecutive years of monitoring data and

specify minimum data completeness requirements for data used to make decisions regarding attainment status. 

Although 11 samples per quarter, as required in these analyses, is sufficient to show nonattainment, additional data

capture (at least 75 percent per quarter) is required to show attainment of the standards. Not all of the PM federal

reference method (FRM) sites that contributed data to the summaries presented here recorded 75 percent data

capture for all four calendar quarters for each of the 3 years. 

2-20

As is the case with all surface-based ambient monitoring data, these data can be considered

representative of exposures in typical breathing zones in the lowest 15 meters of the atmosphere.

2.4.1 PM2.5

Following the establishment of new standards for PM2.5 in 1997, the EPA led a national

effort to deploy and operate over 1000 PM2.5 monitors. Over 90 percent of the monitors are

located in urban areas. These monitors use the PM2.5 FRM which, when its procedures are

followed, assures that PM data are collected using standard equipment, operating procedures,

and filter handling techniques.14 Most of these FRM monitors began operation in 1999. The

EPA has analyzed the available data collected by this network from 2001-2003. Data from the

monitors were screened for completeness with the purpose of avoiding seasonal bias. To be

included in these analyses, a monitoring site needed all 12 quarters (2001-2003), each with 11 or

more observations. A total of 827 FRM sites in the U.S. met these criteria.15

The 3-year average annual PM2.5 mean concentrations range from about 4 to 28 μg/m3,

with a median of about 13 μg/m3. The 3-year average annual 98th percentiles of the 24-hour

average concentrations range from about 9 to 76 μg/m3

, with a median of about 32 μg/m3. 

Figures 2-4 and 2-5 depict the regional distribution of site-specific 3-year average annual mean

and 3-year average 98th percentile 24-hour average PM2.5 (and PM10-2.5, discussed in section

2.4.3) concentrations, respectively, by geographic region (excluding Alaska, Hawaii, Puerto

Rico, and the Virgin Islands). In general, with the exception of southern California, PM2.5 annual

average mass is greater in the eastern regions than in the western regions, whereas PM10-2.5

annual average mass is greater in the western regions. Figures 2-6 and 2-7 are national maps that

depict county-level 3-year average annual mean and 3-year average annual 98th percentile 24-

hour average PM2.5 concentrations, respectively, from the FRM network.16 The site with the

highest concentration in each monitored county is used to represent the value in that county. The

map and box plots show that many locations in the eastern U.S. and in California had annual

mean PM2.5 concentrations above 15 μg/m3

. Mean PM2.5 concentrations were above 18 μg/m3 in

several urban areas throughout the eastern U.S., including Chicago, Cleveland, Detroit,
Northeast

Southeast

Industrial Midwest

Southwest

Upper Midwest

Northwest

Annual Mean (μg/m

3

)

Figure 2-4. Distribution of annual mean PM2.5 and estimated annual mean PM10-2.5 concentr

region, 2001-2003. Box depicts interquartile range and median; whiskers depict 5th 

asterisks depict minima and maxima. N = number of sites.

PM2.5 PM10-2.5 PM2.5 PM10-2.5 PM2.5 PM10-2.5 PM2.5 PM10-2.5 PM2.5 PM10-2.5 PM2.5 PM10-2.

N= 121 63 216 97 217 97 71 41 33 32 110 108 

Source: Schmidt et al. (2005)

2-21
PM2.5 PM10-2.5 PM2.5 PM10-2.5 PM2.5 PM10-2.5 PM2.5 PM10-2.5 PM2.5 PM10-2.5 PM2.5 PM10-2.5 PM2

N= 121 63 216 97 217 97 71 41 33 32 110 108 42 

Figure 2-5. Distribution of 98th percentile 24-hour average PM2.5 and estimated PM10-2.5 con

region, 2001-2003. Box depicts interquartile range and median; whiskers depict 5th 

asterisks depict minima and maxima. N = number of sites.

Northeast

Southeast

Industrial Midwest

Southwest

Upper Midwest

Northwest

Southern California

98th Percentile (

μg/m

3

)

Source: Schmidt et al. (2005)

2-22
x <= 12 12 < x <= 

15 < x <= 18 x > 18

Figure 2-6. County-level maximum annual mean PM2.5 concentrations, 2001-2003.

PM2.5 Concentration (μg/m3) 

562 counties

Source: Schmidt et al. (2005)

2-23
x <= 30 30 < x <= 

40 < x <= 65 x > 65

PM2.5 Concentration (μg/m3) 

562 counties

Figure 2-7. County-level maximum 98th percentile 24-hour average PM2.5 concentrations, 

Source: Schmidt et al. (2005)

2-24
2-25

Indianapolis, Pittsburgh, and St. Louis. Los Angeles and the central valley of California also

were above 18 μg/m3. Sites in the upper midwest, southwest, and northwest regions had

generally lower 3-year average annual mean PM2.5 concentrations, most below 12 μg/m3

. Threeyear average annual 98th percentile 24-hour average PM2.5 concentrations above 65 μg/m3

 appear

only in California. Values in the 40 to 65 μg/m3

 range were more common in the eastern U.S.

and on the west coast, mostly in or near urban areas, but relatively rare in the upper midwest and

southwest regions. In these regions, the 3-year average 98th percentile PM2.5 concentrations were

more typically below 40 μg/m3

, with many below 25 μg/m3.

The PM maps shown in this chapter encompass all valid data, including days that were

flagged for episodic events, either natural or anthropogenic. Examples of such events include

biomass burning, construction/demolition activities, dust storms, and volcanic and seismic

activity. PM concentrations can increase dramatically with these ‘natural’ or ’exceptional’

events. Although these events are rare (e.g., affecting less than 1 percent of reported PM2.5

concentrations between 2001 and 2003), they can affect people’s short-term PM exposure,

briefly pushing daily PM levels into the unhealthy ranges of the Air Quality Index (AQI). 

Analyses of 2001-2003 PM2.5 data found that over 9 percent of the days above (site-based)

98th percentile 24-hour concentrations were flagged for events. The events, in fact, were found

to cause the 98th percentiles to inflate by up to 18 μg/m3, with an average increase of 0.8 μg/m3. 

Natural and exceptional events, however, rarely have a significant effect on annual or longer

averages of PM. In the afore-mentioned analyses of 2001-2003 PM2.5 data, the average effect of

natural and exceptional events on 3-year annual means was less than 0.1 μg/m3 (Schmidt, et al.,

2005). Episodic event-flagged data are often excluded from trends-type analyses and are

addressed for the purpose of determining compliance with the NAAQS by EPA’s national and

exceptional events policies, as described below in section 2.6.

PM2.5 short-term trends were recently evaluated by EPA in The Particle Pollution Report

(EPA, 2004b, p. 14). In the EPA FRM network, PM2.5 annual average concentrations decreased

10 percent nationally from 1999 to 2003. The northeast, where moderate concentrations are

found, was the only region that did not show a decline between these years; annual

concentrations in that region were somewhat flat or rose slightly (about 1 percent) over the 5-

year period. Except in the northeast, PM2.5 generally decreased the most in the regions with the

highest concentrations - the southeast (20 percent), southern California (16 percent), and the

industrial midwest (9 percent) from 1999 to 2003. The remaining regions with lower

concentrations (the upper midwest, the southwest, and the northwest) posted modest declines in

PM2.5; see Figure 2-8 (EPA, 2004b, p. 15). 

The IMPROVE monitoring network, which consists of sites located primarily in national

parks and wilderness areas throughout the U.S., generally provides data for long-term PM2.5
Figure 2-8. Regional trends in annual average PM2.5 concentrations in the EPA network

Source: EPA (2004b) 

2-26
17 IMPROVE monitoring instruments and protocols (defined at http://vista.cira.colostate.edu/improve/) are

not identical to FRM monitors.

18 Metropolitan areas for use in federal statistical activities, as defined by the Office of Management and

Budget, include core-based statistical areas (CBSA) that are comprised of “metropolitan” and “micropolitan” areas,

and combined statistical areas (CSA) that are comprised of two or more core-based statistical areas. Counties are the

geographic building blocks for defining CBSA's. The analysis described here, and other analyses throughout this and

subsequent chapters, utilize the latest area definitions which are available at:

http://www.whitehouse.gov/omb/bulletins/fy05/b05-02.html.

2-27

trends in rural areas.17 Figure 2-9 shows the composite long-term trend at 8 eastern sites, 17

western sites, and one urban site in Washington, D.C. The 4 westernmost U.S. subregions

(northwest, southern California, upper midwest, and southwest) are considered ‘western sites’

and the 3 eastern subregions (northeast, southeast, and industrial midwest) are considered

‘eastern sites.’ At the eastern rural sites, measured PM2.5 mass decreased about 23 percent from

1993 to 2003. At the western rural sites, PM2.5 mass decreased about 21 percent from 1993 to

2003. At the Washington, D.C. site, the annual average PM2.5 concentration in 2003 was about

31 percent lower than the value in 1993.

The relative spatial homogeneity of the ambient air across a specified area can be

assessed by examining the values at multiple sites using several indicators, including: (1) site

pair correlations, (2) differences in long-term (e.g., annual and multi-year) average

concentrations, and (3) differences in short-term (e.g., daily) average concentrations. An

analysis of these indicators for site pairs in 27 Metropolitan Statistical Areas (MSAs) using PM2.5

FRM monitoring data from 1999-2001 is included in the CD (CD, Appendix 3A). A similar

analysis, for 49 urban areas defined as either Core Based Statistical Areas (CBSAs) or Combined

Statistical Areas (CSAs), was conducted on PM2.5 FRM monitoring data from 2001-2003

(Schmidt et al., 2005).18

An analysis of site pairs from each of the 49 urban areas indicates that multiple sites in

these areas were highly correlated throughout the period. About 83 percent (1901 out of 2290)

of the between-site correlation coefficients in all 49 areas were greater than or equal to 0.80, and

more than 48 percent (1113 out of 2290) of the correlations were greater than or equal to 0.90. 

Further, every area had at least one monitor pair with a correlation coefficient greater than or

equal to 0.82. 

A summary of the analyses of long-term and short-term concentration differences for the 

49 urban areas is shown in Table 2-3. The difference in 3-year average annual mean PM2.5

concentrations between monitor pairs in the 49 cities ranged from less than 1 μg/m3 in four

areas to about 18 μg/m3 in Los Angeles. Large differences in mean concentrations across a

metropolitan area may be due to differences in emissions sources, meteorology, or topography. 

Small differences may be due only to measurement imprecision (CD, p. 3-46). Most sites in the

49 areas had annual means within 15 percent of the area spatial average; the largest percent
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0

1

2

3

4

5

WESTERN UNITED STATES

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0

2

4

6

8

10

12

EASTERN UNITED STATES

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

0

5

10

15

20

25

PM2.5 Mass Amm. Sulfate TCM Crustal Amm. Nitrate

WASHINGTON, DC

Figure 2-9. Average annual mean trend in PM2.5 mass, ammonium

sulfate, ammonium nitrate, total carbonaceous mass, and 

crustal material at IMPROVE sites, 1993-2003.

Source: Schmidt et al. (2005) Annual Mean (

μg/m3) Annual Mean (

μg/m3) Annual Mean (

μg/m3)

2-28
Area 

Avg

Max 

Site

Min 

Site

Largest 

diff., any 

site versus 

Area Avg

Max site 

versus Min 

site

Albuquerque, NM 4 7.0 10.2 5.0 31% 51% 0.42 10.9 2.6 0.42

Atlanta-Sandy Springs-Gainesville, GA 8 15.9 18.0 14.1 12% 22% 0.71 9.4 3.5 0.71

Bakersfield, CA 5 15.3 21.8 6.7 56% 69% 0.00 44.8 6.0 0.16

Baton Rouge-Pierre Part, LA 5 12.3 13.1 10.8 12% 18% 0.85 7.7 2.4 0.62

Birmingham-Hoover-Cullman, AL 8 14.8 18.0 12.6 18% 30% 0.78 12.7 3.5 0.78

Charlotte-Gastonia-Salisbury, NC-SC 5 14.3 14.9 14.0 4% 6% 0.94 4.1 1.7 0.92

Chicago-Naperville-Michigan City, IL-IN-WI 28 14.7 17.7 11.7 20% 34% 0.77 13.6 2.2 0.73

Cincinnati-Middletown-Wilmington, OH-KY-IN 12 16.0 17.8 14.5 10% 19% 0.95 7.0 2.4 0.95

Cleveland-Akron-Elyria, OH 13 15.5 18.3 13.4 15% 27% 0.87 11.4 3.2 0.87

Dallas-Fort Worth, TX 7 12.8 13.9 11.7 9% 16% 0.92 5.2 2.3 0.92

Denver-Aurora-Boulder, CO 6 8.7 10.8 4.5 48% 58% 0.40 11.4 4.0 0.42

Detroit-Warren-Flint, MI 14 15.2 19.5 12.6 22% 35% 0.85 14.1 3.2 0.85

Eugene-Springfield, OR 4 9.4 13.4 6.6 30% 51% 0.57 19.3 4.8 0.57

Grand Rapids-Muskegon-Holland, MI 4 13.0 13.8 12.3 6% 11% 0.91 5.8 3.2 0.90

Greensboro--Winston-Salem--High Point, NC 4 14.6 15.8 14.0 8% 11% 0.94 5.5 2.5 0.93

Houston-Baytown-Huntsville, TX 6 11.7 14.2 9.6 18% 32% 0.78 8.9 6.2 0.64

Indianapolis-Anderson-Columbus, IN 6 15.3 16.7 13.6 11% 19% 0.93 6.8 2.0 0.93

Kansas City-Overland Park-Kansas City, MO-KS 10 12.0 13.9 10.8 14% 22% 0.76 9.1 1.4 0.76

Knoxville-Sevierville-La Follette, TN 5 15.3 16.7 14.2 8% 15% 0.86 6.2 2.7 0.86

Las Vegas-Paradise-Pahrump, NV 5 7.1 11.0 4.0 44% 64% 0.03 17.6 2.5 -0.03

Lexington-Fayette--Frankfort--Richmond, KY 4 14.4 15.7 13.5 8% 14% 0.86 5.9 3.3 0.86

Little Rock-North Little Rock-Pine Bluff, AR 5 13.0 14.1 11.9 8% 16% 0.79 7.6 5.1 0.78

Los Angeles-Long Beach-Riverside, CA 22 19.0 27.8 9.9 48% 64% 0.50 39.6 5.3 0.50

Louisville-Elizabethtown-Scottsburg, KY-IN 6 15.6 16.9 14.1 10% 17% 0.85 8.2 3.9 0.85

Memphis, TN-MS-AR 6 13.1 14.0 11.7 11% 16% 0.86 6.3 2.2 0.82

Miami-Fort Lauderdale-Miami Beach, FL 6 8.2 9.5 7.4 14% 22% 0.73 5.5 1.7 0.73

Milwaukee-Racine-Waukesha, WI 6 13.1 13.2 12.5 5% 5% 0.96 4.1 2.2 0.93

Minneapolis-St. Paul-St. Cloud, MN-WI 12 10.5 12.0 9.7 13% 19% 0.79 8.0 2.6 0.79

New Orleans-Metairie-Bogalusa, LA 4 11.5 12.2 10.4 10% 15% 0.91 4.0 2.8 0.90

New York-Newark-Bridgeport, NY-NJ-CT-PA 29 13.5 16.4 11.2 18% 32% 0.85 12.5 2.0 0.84

Omaha-Council Bluffs-Fremont, NE-IA 7 10.4 10.7 9.8 6% 8% 0.86 5.2 2.1 0.78

Philadelphia-Camden-Vineland, PA-NJ-DE-MD 14 14.9 16.4 13.8 9% 16% 0.94 7.6 3.1 0.94

Phoenix-Mesa-Scottsdale, AZ 5 9.3 11.4 6.3 32% 45% 0.22 14.0 4.2 0.22

Pittsburgh-New Castle, PA 13 15.8 21.2 13.2 25% 38% 0.75 21.8 3.2 0.69

Portland-Vancouver-Beaverton, OR-WA 6 8.2 9.5 6.1 26% 36% 0.84 9.5 3.0 0.76

Provo-Orem, UT 4 9.8 10.9 8.8 10% 19% 0.88 6.5 3.0 0.92

Raleigh-Durham-Cary, NC 5 13.3 13.9 12.2 8% 12% 0.93 5.7 2.4 0.88

Richmond, VA 5 13.4 14.0 12.8 4% 9% 0.88 5.8 3.2 0.88

Sacramento--Arden-Arcade--Truckee, CA-NV 5 9.9 12.5 7.6 23% 39% 0.37 16.0 6.0 0.21

Salt Lake City-Ogden-Clearfield, UT 7 11.4 14.0 9.0 21% 36% 0.92 11.0 3.8 0.92

San Diego-Carlsbad-San Marcos, CA 5 15.0 15.9 12.8 15% 19% 0.89 10.6 4.6 0.69

San Jose-San Francisco-Oakland, CA 9 10.8 11.8 8.4 22% 29% 0.67 13.5 4.7 0.67

San Juan-Caguas-Fajardo, PR 5 7.2 9.3 5.1 29% 45% 0.71 6.8 1.7 0.71

Seattle-Tacoma-Olympia, WA 10 9.4 11.1 5.3 44% 52% 0.30 19.1 2.9 0.30

St. Louis-St. Charles-Farmington, MO-IL 12 15.0 17.5 14.0 14% 20% 0.82 10.3 2.2 0.76

Virginia Beach-Norfolk-Newport News, VA-NC 5 12.5 13.0 11.9 5% 8% 0.93 4.6 2.7 0.90

Washington-Baltimore-Northern Virginia, DC-MD-VA-WV 20 14.5 16.7 12.2 16% 27% 0.82 9.7 2.6 0.82

Weirton-Steubenville, WV-OH 4 17.1 17.8 16.2 5% 9% 0.87 8.3 6.1 0.86

Wichita-Winfield, KS 4 10.9 11.1 10.2 6% 8% 0.96 2.9 1.3 0.91

r 

(Max site 

versus Min 

site)

Percent Difference

Max 

Pair

Min 

Pair

Area *

Table 2-3. Summary of PM2.5 FRM Data Analyses in 49 Metropolitan Areas, 2001-2003

** 'P90' is the 90th percentile of the distribution of differences in 24-hour averages between two sites in the same urban area.

3-year Average Annual Mean 24-Hour P90 (μg/m3

) **

N Sites

* 'Area' is the larger of a Combined Statistical Area (CSA) or a Core Based Statistical Area (CBSA). See http://www.whitehouse.gov/omb/bulletins/fy05/b05-02.html.

Levels (μg/m3)

r 

(Max 

Pair)

2-29
19 These figures do not depict officially designated PM10 nonattainment areas. As of January 1, 2005, there

were a total of 58 areas classified as moderate or serious nonattainment areas, mostly in the western U.S. See

designated nonattainment areas at www.epa.gov/oar/oaqps/greenbk/pnc.html. Further, note that these maps (like the

other PM ones in this Chapter) do not exclude event-flagged data (natural or exceptional). Data flagged for events

are sometimes excluded from regulatory design value calculations. 

20 The form of the 1987 PM10 24-hour standard is based on the number of exceedances; the metric used for

this map, “concentration-based 24-hour ‘design value’ type metric” is almost always calculated to be 150 :g/m3

 or

higher when the monitoring site violates the explicit exceedance-based NAAQS. Utilization of the concentrationbased metric permits delineation of gradients and facilitates comparisons with PM2.5 and PM10-2.5.

2-30

difference between any site in an area and the area’s spatial average ranged from 4 to 56 percent

with a median of 14 percent. In most urban areas (39 of the 49), the site pair with the maximum

and minimum annual mean concentration was highly correlated (r(max,min) $0.70); there are,

however, some notable exceptions (i.e., 8 areas had r(max,min) < 0.50).

The spatial analysis also examined differences in 24-hour average concentrations

between the urban site pairs. Small differences throughout the distribution would indicate

relatively homogeneous concentration levels between the sites. Table 2-3 presents a summary of

the 90th percentile of the distribution (P90) of daily site pair differences in each urban area. The

site pairs with the largest difference (max pair) and the smallest difference (min pair) are shown. 

The P90 values for the 2290 monitor pairs in the 49 urban areas ranged from about 1 to 45 μg/m3. 

Often the site pair with the maximum P90 value in each city was also the pair with the largest

annual mean difference. The site pair with the highest P90 values in each city was generally

highly correlated (rmax$0.70), and in some cases was more highly correlated than the sites with

the largest annual mean differences.

2.4.2 PM10

For the purpose of comparison to PM2.5 and PM10-2.5 concentrations, PM10 data from

2001-2003 are presented in Figures 2-10 and 2-11. Figure 2-10 shows the PM10 annual mean

concentrations and Figure 2-11 shows the concentration-based 24-hour average ‘design value’

type metric.19,20 As in the earlier PM2.5 maps, the monitor with the highest value in each

monitored county is used to represent the value in each county. Most areas of the country had

concentrations below the level of the annual PM10 standard of 50 μg/m3. Exceptions include six

counties in central and southern California. Most areas of the country also had concentrations

below the level of the 24-hour standard of 150 μg/m3, with exceptions concentrated in the

southwestern U.S. and isolated counties scattered across the east.

EPA recently examined national and regional PM10 trends from 1988 to 2003 (EPA,

2004b, p. 13). The EPA found a national average decline in annual average concentrations of

approximately 31 percent over the 16-year period, with regional average declines ranging from

16 to 39 percent.
<= 20 20 < x 

30 < x <= 50 x > 50

Figure 2-10. County-level maximum PM10 annual mean concentrations, 2001-2003.

PM10 Concentration (

μg/m3) 

585 counties

Source: Schmidt et al. (2005)

2-31
Figure 2-11. County-level maximum 24-hour PM10 'design value' concentrations, 20

PM10 Concentration (

μg/m3) 

585 counties

Source: Schmidt et al. (2005)

<= 50 50 < x 75 < x <= 150 x > 150

2-32
21 Note that the urban PM10-2.5 estimates derived in this review, labeled ‘2001-2003', actually represent

either the entire 12-quarter period or the most recent consecutive 4- or 8-quarter period (from that 3-year period)

with 11 or more samples each. This technique was used to maximize the number of usable sites (and not introduce

seasonal bias). Of the 489 total sites, 230 had 12 complete quarters, 122 sites had 8 quarters, and 137 had 4. Similar

to PM2.5 and PM10 processing, ‘annual’ means and ‘annual’ 98th percentiles were first constructed from 4-quarter

periods, albeit for PM10-2.5, not all necessarily from the same calender year. The 4-quarter statistics were then

averaged together for the 8- and 12-quarter sites. Hence there is some temporal variability intrinsic in 2001-2003

estimates. The 1-, 2-, or 3-year averages of the ‘annual’ statistics are subsequently referred to simply as ‘annual

means’ or ‘98th percentiles’.

2-33

2.4.3 PM10-2.5

PM10-2.5 is a measure of the coarse-mode fraction of PM10 being considered in this review. 

It can be directly measured by a dichotomous sampler, or by using a difference method with

collocated PM10 and PM2.5 monitors. For the latter, collocated PM10 and PM2.5 monitors using

identical inlets, sampling flow rates, and analysis protocols produce the most precise results. A

nationwide network of samplers with the specific intent to consistently and accurately measure

PM10-2.5 does not currently exist. The EPA is currently evaluating a variety of monitoring

platforms, including alternative continuous methods, to permit establishment of reference and

equivalent methods for PM10-2.5. These could be used in the future to design a national network

of monitors to measure coarse-fraction particles. Until such a network is established, estimates

of PM10-2.5 can be generated for a limited number of locations using a difference method on

same-day data. For this review, PM measurements collected from collocated PM10 and PM2.5

FRM monitors are utilized. Since the protocol for each monitor is not usually identical, the

consistency of these PM10-2.5 measurements is relatively uncertain, and they are referred to as

“estimates” in this Staff Paper.21

The 98th percentile 24-hour average PM10-2.5 concentrations range from about 5 to 208

μg/m3, with a median of about 28 μg/m3. The box plots in Figures 2-4 and 2-5 (introduced in

section 2.4.1) depict the regional distribution of site-specific estimated annual mean and 98th

percentile 24-hour average PM10-2.5 concentrations, respectively, by geographic region (excluding

Alaska, Hawaii, Puerto Rico, and the Virgin Islands). Figures 2-12 and 2-13 are national maps

that depict estimated county-level annual mean PM10-2.5 concentrations and 98th percentile 24-

hour average concentrations, respectively. To construct the maps, the site with the highest

concentration in each monitored county is used to represent the value in that county. The annual

mean PM10-2.5 concentrations are generally estimated to be below 40 μg/m3, with one maximum

value as high as 64 μg/m3 (see Figure 2-4), and with a median of about 10-11 μg/m3. Compared

to annual mean PM2.5 concentrations, annual mean PM10-2.5 estimates are more variable, with

more distinct regional differences. As shown in Figure 2-4, eastern U.S. estimated annual mean

PM10-2.5 levels tend to be lower than annual mean PM2.5 levels, and in the western U.S. estimated

PM10-2.5 levels tend to be higher than PM2.5 levels. The highest estimated annual mean PM10-2.5

concentrations appear in the southwest region and southern California. The estimated 98th
x <= 10 10 < x <= 

15 < x <= 25 x > 25

Figure 2-12. Estimated county-level maximum annual mean PM10-2.5 concentrations, 200

PM10-2.5 Concentration (μg/m3) 

351 counties

Source: Schmidt et al. (2005)

2-34
<= 25 25 < x <= 

45 < x <= 75 x > 75

Figure 2-13. Estimated county-level maximum 98th percentile 24-hour average PM10-2.5 co

 2001-2003.

PM10-2.5 Concentration (μg/m3) 

351 counties

Source: Schmidt et al. (2005)

2-35
2-36

percentile 24-hour average PM10-2.5 concentrations are generally highest in the southwest,

southern California, and upper midwest, where a few sites have estimated concentrations well

above 100 μg/m3

 (see Figure 2-5). As noted before, these maps include days that were flagged

for natural or exceptional episodic events. Episodic events can affect PM10-2.5 98th percentiles

even more than for PM2.5. An evaluation of 2001-2003 PM10-2.5 data found that such events

caused 98th percentile values to be elevated by an average of 2.5 μg/m3

 (Schmidt et al., 2005).

The IMPROVE monitoring network generally provides long-term PM10-2.5 trends for rural

areas. Figure 2-14 presents the composite long-term trend at 7 eastern sites, 17 western sites,

and one urban site in Washington, D.C. At the eastern rural sites, measured PM10-2.5 in 2003 was

about 33 percent lower then the corresponding value in 1993. At the western rural sites,

measured PM10-2.5 was about 17 percent higher in 2003 than the corresponding value in 1993. At

the Washington, D.C. site, the annual average PM10-2.5 concentration in 2003 was about 25

percent lower than the 10-year peak in 1994, but nearly 2 μg/m3

 (over 40%) higher than the 1998

low point.

The long-term PM10-2.5 levels in the relatively remote non-urban IMPROVE sites shown

in Figure 2-14 are notably lower than those found in most urban areas. While PM10-2.5

concentrations in rural areas affected by sources such as windblown dry lake beds, unpaved

roads, or agricultural activities can be quite high, comparison of paired urban and nearby rural

sites suggest that PM10-2.5 levels are generally higher in urban areas. Figure 2-15 shows urban

and corresponding rural PM10-2.5 concentrations for several large metropolitan areas in the eastern

and western U.S.. The urban data represent inter-city or suburban monitoring sites located in

densely populated regions of the metro areas, and the rural data typically represent one or more

sites situated on the outskirts of the areas where population density is low. In all the metro areas

shown, the urban PM10-2.5 concentrations exceed those in the nearby rural locations.

The CD contains an analysis of 1999-2001 PM10-2.5 estimates in 17 MSAs that is useful

for assessing the spatial homogeneity of PM10-2.5 across the urban areas (CD, Appendix 3A). A

similar analysis, for 21 urban areas defined as either Core Based Statistical Areas (CBSAs) or

Combined Statistical Areas (CSAs), was conducted on PM10-2.5 estimates from 2001-2003

(Schmidt et al., 2005). These analyses are similar to the 49-city analysis for PM2.5 discussed in

section 2.4.1 and summarized earlier in Table 2-3. However, since there were fewer site

pairings, fewer urban areas covered, and because of higher uncertainty in daily concentration

estimates, the PM10-2.5 results are not as robust as the PM2.5 results. The PM10-2.5 analysis is

summarized in Table 2-4. The analysis reveals generally lower correlations for PM10-2.5

compared to the PM2.5 correlations in the same city. Of the 200 monitor pairs analyzed, only 17

(9%) had correlation coefficients greater than or equal to 0.80, in contrast to around 83 percent

(1901 of 2290) of the pairs for PM 2.5.

The difference in estimated annual mean PM10-2.5 between site pairs in the 21 areas also

covered a greater range than was seen for PM2 .5, with differences up to almost 31 μg/m3

 in Los
Figure 2-14. Average measured annual average PM10-2.5 concentration trend at IM

sites, 1993-2003. 

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 

3456789

μg/m

3

7 Eastern Sites 17 Western Sites Washington, D.C. Site

Source: Schmidt et al. (2005) 

2-37
Figure 2-15. Urban versus rural estimated PM10-2.5 concentrations in select areas. Estimated 98th 

 average PM10-2.5 concentrations shown in top panel and estimated annual mean PM10-2.5 c

 bottom panel. Urban bar (left) is average of urban sites in area, rural bar (right) is aver

 Urban / rural designation from AQS. N= number of sites (urban / rural). 

Los AngelesLas VegasSacramento Birmingham BostonIndianapolis MobileNew Y

0

1020304050607080

Urban Rural 

Los AngelesLas VegasSacramento Birmingham BostonIndianapolis MobileNew Y

0

5

10

15

20

25

30

35

Urban Rural 

Estimated 98th percentile 24-hour average PM10-2.5 concentrations 

Estimated annual mean PM10-2.5 concentrations 

N= 10 / 2 3 / 2 7 / 2 5 / 2 3 / 1 2 / 1 1 / 1 1

West EastWest East

Concentration (μg/m

3

) Concentration (μg/m3)

Schmidt et al. (2005)

2-38
Area 

Avg

Max 

Site

Min 

Site

Largest 

diff., any 

site versus 

Area Avg

Max site 

versus Min 

site

Anchorage, AK 3 14.8 23.7 9.6 38% 59% 0.13 52.3 22.5 0.13

Birmingham-Hoover-Cullman, AL 5 7.0 9.0 5.6 22% 38% 0.76 10.0 3.0 0.55

Cleveland-Akron-Elyria, OH 8 11.6 16.3 5.6 52% 66% 0.55 26.0 8.0 0.64

Denver-Aurora-Boulder, CO 3 15.5 22.1 7.7 50% 65% 0.54 29.3 14.5 0.54

Detroit-Warren-Flint, MI 3 15.3 18.7 8.8 42% 53% 0.60 30.5 25.0 0.32

El Paso, TX 4 23.2 28.3 13.9 40% 51% 0.89 31.0 15.0 0.92

Las Vegas-Paradise-Pahrump, NV 5 23.2 33.3 9.0 61% 73% 0.65 40.0 17.0 0.65

Los Angeles-Long Beach-Riverside, CA 11 21.6 44.5 13.7 51% 69% 0.38 57.5 8.5 0.03

Miami-Fort Lauderdale-Miami Beach, FL 4 10.2 15.3 8.4 33% 45% 0.63 14.0 3.0 0.63

Minneapolis-St. Paul-St. Cloud, MN-WI 3 19.1 23.6 15.5 19% 34% 0.62 23.0 19.5 0.38

New York-Newark-Bridgeport, NY-NJ-CT-PA 5 8.7 22.3 2.9 67% 87% 0.21 35.3 6.5 0.21

Orlando-The Villages, FL 3 9.5 10.2 8.5 11% 17% 0.71 6.0 4.0 0.71

Philadelphia-Camden-Vineland, PA-NJ-DE-MD 3 5.5 6.4 4.3 22% 33% 0.48 10.0 6.0 0.48

Pittsburgh-New Castle, PA 6 6.4 8.5 3.5 45% 59% 0.67 13.0 5.0 0.46

Sacramento--Arden-Arcade--Truckee, CA-NV 3 10.4 12.0 8.2 21% 32% 0.38 17.5 6.5 0.25

Salt Lake City-Ogden-Clearfield, UT 3 17.9 24.1 14.4 26% 40% 0.72 24.0 9.0 0.72

San Jose-San Francisco-Oakland, CA 7 10.8 13.4 7.8 28% 42% 0.69 13.5 4.5 0.53

San Juan-Caguas-Fajardo, PR 3 24.4 30.2 18.0 26% 40% 0.64 22.0 17.0 0.64

Virginia Beach-Norfolk-Newport News, VA-NC 3 4.2 4.5 4.0 7% 11% 0.54 5.0 3.0 0.54

Weirton-Steubenville, WV-OH 4 12.4 13.8 10.7 14% 22% 0.53 15.0 11.5 0.43

Wichita-Winfield, KS 3 11.9 13.7 10.3 13% 25% 0.81 11.0 5.0 0.69

Area *

Table 2-4. Summary of Estimated PM10-2.5 Analyses in 21 Metropolitan Areas, 2001-2003

** 'P90' is the 90th percentile of the distribution of differences in 24-hour averages between two sites in the same urban area.

3-year Average Annual Mean 24-Hour P90 (μg/m3

) **

N Sites

* 'Area' is the larger of a Combined Statistical Area (CSA) or a Core Based Statistical Area (CBSA). See http://www.whitehouse.gov/omb/bulletins/fy05/b05-02.html.

Levels (μg/m3) Percent Difference r 

(Max site 

versus Min 

site)

Max 

Pair

Min 

Pair

r 

(Max 

Pair)

2-39
2-40

Angeles, CA. The largest percent difference between any site’s annual mean and it’s

corresponding area spatial average ranged from 7 to 67 percent with a median of 28 percent,

which is about double the median from the corresponding PM2.5 analyses. Of the 18 common

metropolitan areas analyzed for both PM2.5 and PM10-2.5, only 2 areas (Sacramento, CA and San

Juan, PR) had higher values for this indicator (largest percent difference for any site in area

verus area spatial average) for PM2.5 compared to PM10-2.5. The P90 values (described in section

2.4.1) for the 200 PM10-2.5 site pairs ranged from about 3 μg/m3 to about 58 μg/m3, which is wider

than the range of about 1 to 45 μg/m3

 observed for PM2.5.

These analyses indicate that spatial distribution of PM10-2.5 is more heterogeneous than

PM2.5 in many locations but may be similar in other areas. Any conclusions should be tempered

by the inherent uncertainty in the PM10-2.5 estimation method (discussed at the beginning of this

section), and the relatively small sample size for PM10-2.5 relative to PM2.5.

2.4.4 Ultrafine Particles

There are no nationwide monitoring networks for ultrafine particles (i.e., those with

diameters < 0.1 μm), and only a few recently published studies of ultrafine particle counts in the

U.S. At an urban site in Atlanta, GA, particles in three size classes were measured on a

continuous basis between August 1998 and August 1999 (CD, p. 2B-21). The classes included

ultrafine particles in two size ranges, 0.003 to 0.01 μm and 0.01 to 0.1 μm, and a subset of

accumulation-mode particles in the range of 0.1 to 2 μm. In Atlanta, the vast majority (89

percent) of the number of particles were in the ultrafine mode (smaller than 0.1 μm), but 83

percent of the particle volume was in the subset of accumulation-mode particles. The

researchers found that for particles with diameters up to 2 μm, there was little evidence of any

correlation between number concentration and either volume or surface area. Similarly poor

correlations between PM2.5 mass and number of ultrafine particles were confirmed for sites in

Los Angeles and nearby Riverside, CA (Kim et al., 2002). This suggests that PM2.5 cannot be

used as a surrogate for ultrafine mass or number, so ultrafine particles need to be measured

independently.

Studies of near-roadway particle number and size distributions have shown sharp

gradients in ultrafine concentrations around Los Angeles roadways (CD, p. 2-35 to 2-36). 

Ultrafine PM concentrations were found to decrease exponentially with distance from the

roadway source, and were equal to the upwind “background” location at 300 m downwind. 

2.4.5 Components of PM

Atmospheric PM is comprised of many different chemical components that vary by

location, time of day, and time of year. Further, as discussed in section 2.2, fine and coarse

particles have fundamentally different sources and composition. Recent data from the rural

IMPROVE network and from the EPA urban speciation network provide indications of regional
22 More information can be found at http://www.epa.gov/ttn/amtic/supersites.html. 

2-41

composition differences for fine particles. Although both programs provide detailed estimates of

specific PM chemical components (individual metals, ions, etc.), only gross-level speciation

breakouts are shown here. Figure 2-16 shows urban and rural 2003 annual average PM2.5 mass

apportionment among chemical components averaged over several sites within each of the U.S.

regions. In general:

• While PM2.5 mass and all component concentrations are higher in urban areas than

in IMPROVE sites, in general, nitrates and carbonaceous components appear to

have a greater urban/rural enhancement as compared to sulfates.

• PM2.5 in the eastern U.S. regions is dominated by sulfates and carbonaceous mass.

• PM2.5 in the western U.S. urban sites has a greater proportion of carbonaceous

mass. 

Trends concentrations of fine particle components from the IMPROVE network from

1993 to 2003 are shown in Figure 2-9 for rural areas and for urban Washington, D.C. (section

2.4.1 above). The top two panels of this figure aggregate rural IMPROVE sites in the eastern

and western U.S. The bottom panel shows a comparable period for the Washington, D.C. urban

IMPROVE site. Consistent with more recent data in Figure 2-15, levels of rural annual average

PM2.5 mass are significantly higher in the east than in the west, but are trending downward in

both regions. Annual levels of sulfates have decreased the most (and contributed the most to the

reductions in PM2.5 mass) both in eastern and western rural areas. At the Washington, D.C.,

IMPROVE site, mass has decreased 31 percent from 1993-2003. Total carbonaceous mass (34

percent reduction) and sulfates (down 29 percent) are the biggest contributors to the mass

reduction over the past 10 years. Both total carbonaceous mass and sulfates dropped

significantly at this site in 1995, but have not shown significant improvements since then. All

other components in all areas have shown small changes over the 10-year period.

Though most of the speciation data currently available are for PM2.5, there are a limited

amount of recent data available on speciation profiles for the coarse fraction, and still less for

ultrafine particles. The EPA “Supersite” program addresses a number of scientific issues

associated with PM.22 A Supersite location in the Los Angeles metropolitan area (USC site)

provides a unique comparison of the composition of ultrafine, fine, and coarse particles (Sardar

et al., 2005). Based on the reported measurement data, ultrafine, fine, and coarse PM have

distinctly different compositions at this site (Figure 2-17). Increasing in size from ultrafine to

fine to coarse, the relative fraction of organic carbon (dominant for ultrafine) drops, and the

crustal element portion goes from a minor component (ultrafine, fine) to the dominant fraction
0

2

4

6

8

10

0

5

10

15

20

Southern California

Southwest

Northwest

Upper Midwest

Southeast

Industrial Midwest

Northeast

Figure 2-16. Annual average composition of PM2.5 by region, 2003. Rural data

(top panel) from IMPROVE network, urban data (bottom panel) from

EPA Speciation Network. Components (from top to bottom) are

crustal material, total carbonaceous mass (TCM), ammonium

nitrate, and ammonium sulfate.

PM2.5 concentration (

μg/m3)

Crustal

TCM

Amm. 

Nitrate

Amm.

Sulfate

Rural

Urban

Source: Schmidt et al. (2005) 

2-42
Figure 2-17. Average PM10-2.5 , PM2.5, and PM0.1 (ultrafine) chemical composition at the

USC EPA ‘supersite’ monitor in Los Angeles, CA, 10/2001 to 9/2002.

components represent measured ions, carbon and crustal elements including

trace metals and are shown in clockwise order (starting with nitrate) as listed

in legend from top to bottom. 

21.0%

5.8%

59.5%

12.9%

0.7%

PM10-2.5 (‘coarse’)

Nitrate

30.1%

13.0% 22.5%

30.0%

4.4%

PM2.5 (‘fine’)

Nitrate

Source: Sardar et al. (2005)

Nitrate

Sulfate

Crustal Elements

OC

EC

Nitrate

Sulfate

Crustal Elements

OC

EC

PM0.1 (‘ultrafine’)

1.2%

5.7%

8.9%

71.2%

13.0%

Nitrate

Nitrate

Sulfate

Crustal Elements

OC

EC

2-43
23 See http://www.atmospheric-research.com/ for information on SEARCH.

24 Inorganic nitrate and sulfate concentrations were assumed to be solely associated with their ammonium

salts, the crustal component reflect the measured elements plus their common oxides and organic carbon mass was

estimated by multiplying measured organic carbon by a factor of 2.5 to account for the mass of H, O, and other

elements in the coarse particle organic compounds. For the SEARCH sites, the total carbonaceous mass is estimated

as the difference between measured coarse particle mass and its inorganic constituents. The OC-EC split is derived

from a special carbon measurement study during 2000, 2001, 2003 and 2004.

2-44

(coarse). The ultrafine results are consistent with other work in Southern California (CD, p.

3-39). The large crustal fraction in the coarse mode is typical of earlier work on western sites

reported in the 1996 Criteria Document (EPA, 1996a, p. 6-165 to 6-167, Figure 6-85a-c), as well

as for more recent work in Phoenix (CD, p. 3-36).

Other recent work on ambient PM10-2.5 particle composition comes from the SouthEastern

Aerosol Research and Characterization (SEARCH) Study.

23 This study examined two urban

sites (Birmingham, AL and Atlanta, GA) and nearby rural sites in the southeast. Figure 2-18

presents the results of this work together with the Los Angeles results. In this graphic, the

measured chemical components are presented in terms of their estimated coarse particle mass, as

derived from the reported measurement data.

24

 Although the scope of these results are limited, staff notes the following:

• Consistent with the mass-based comparisons in Section 2.4.3, the western site has

more coarse mass than any of the 4 eastern locations, and the urban

concentrations are clearly higher than nearby non-urban sites.

• The larger absolute and relative crustal and nitrate contributions in LA appear to

be the main source of the higher mass. In contrast, the carbonaceous fraction is

more significant at the eastern sites. While this may be due in part to a greater

contribution of biological materials in the southeast, such materials would not

explain the larger elemental carbon contribution, particularly in Birmingham.

• The higher urban concentrations of PM10-2.5 in the southeast appear to be due to

higher crustal and carbonaceous components than are found in nearby rural areas,

suggesting urban sources make a substantial contribution to both components.

These recent studies have focused more on the indicators of the major categories of

coarse particles - crustal, carbonaceous, and inorganic anions, and less so on trace elements and

specific organic constituents. The CD notes that the concentrations of a number of trace

elements in the coarse fraction can be comparable or higher than that for fine particles (e.g. Cr,

Ni Zn, Pb, Cu), while the crustal elements (Al, Si, K, Ca, Fe) are, of course, much higher (CD,

p3-37-38). While urban sources apparently increase total crustal materials, the relative

proportions of some crustal elements may be enriched by urban sources relative to the proportion
Figure 2-18. Average PM10-2.5 composition for Los Angeles and two eastern urbanrural pairs. Based on USC Supersite data (10/2002 to 9/2003), and

Birmingham, AL (BHM, urban), Centerville, AL (CTR, rural), Atlanta, GA

(ATL, urban) and Yorkville, GA (YRK, rural) monitoring sites in the

Southeastern Aerosol Research and Characterization (SEARCH) Study, 4/2003-

12/2003. The top panel shows mass concentration in μg/m3 and the bottom panel

shows composition as percent of measured mass.

Source: USC site data (Sardar et al., 2005); eastern data (SEARCH website) adjusted as described in

Schmidt et al. (2005)

0

5

10

15

20

25

USC BHM CTR ATL YRK

Concentration, mg/m3

unknown

EC

OCM

Crustal

Sulfates

Nitrates

0%

20%

40%

60%

80%

100%

USC BHM CTR ATL YRK

Relative Composition

unknown

EC

OCM

Crustal

Sulfates

Nitrates

2-45
25 In this section’s analyses, information was gleaned from the 489 site (4-, 8-, 12-quarter) PM10-2.5 database

for all 3 sizes in order to get seasonally unbiased estimates of their statistical relationships (i.e., to ensure a minimum

number of data pairs each quarter for 4-, 8-, or 12 quarters).

2-46

found in soils. For example, urban industrial (e.g. steel) and automobiles (rust) can increase the

relative amount of iron in urban coarse particles.

The CD review (Appendix 3C) lists no recent studies that speciated any substantial

portion of organic components of PM10-2.5, but some inferences can be drawn from analyses of

the composition of road dusts. The CD reports on two California studies that found organic

substances consistent with particles of biologic origin, tire and brake wear, asphalt, and

combustion in fine fraction samples of resuspended road dust particles (CD, p. 3D-3 to 5). One

of these (Rogge et al., 1993) suggests that the action of automobile traffic on leaves and other

vegetative debris on roads may serve to elevate their atmospheric concentrations and decrease

particle size as compared what might be found in more natural settings. The findings regarding

road dust as well as trace elements are buttressed by a very recent report of similar work

comparing urban and rural road dust in and near Pittsburgh (Robinson et al., 2005). These

authors found that most of the over 100 organic species examined were “significantly enriched”

in urban as compared to rural resuspended road dust samples. Marker substances suggested both

vegetative debris and non-biological sources. Comparing trace elements, the authors found that

urban road dust in Pittsburgh was enriched in metals associated with anthropogenic sources,

notably Fe, Zn, Cu, Pb, Cr, Ni, Mo, and Sb. Ca and Mn were more prevalent in the rural road

dust sample.

2.4.6 Relationships Among PM2.5, PM10, and PM10-2.5

In this section, information on the relationships among PM indicators in different regions

is presented based on data from the nationwide PM FRM monitoring networks.25 Figure 2-19

shows the distribution of ratios of annual mean PM2.5 to PM10 at sites in different geographic

regions for 2001-2003. The ratios are highest in the eastern U.S. regions with median ratios of

about 0.6 to 0.65, and lowest in the Southwest region, with a median ratio near 0.3. These data

are generally consistent with earlier findings reported in the 1996 CD from a more limited set of

sites. Ratios greater than one are an artifact of the uncertainty in the independent PM10 and PM2.5

measurement methods.

Correlations among pollutant indicators can provide insights into how well one indicator

can represent the variability in another indicator. Figure 2-20 shows the results of a nationwide

analysis of correlations among PM size fractions using 24-hour average data from the FRM

monitoring networks for 2001-2003. PM2.5 and PM10 measured on the same days at collocated

monitors are fairly well correlated, on average, in the eastern regions, and not as well correlated

in the western regions, particularly in the upper midwest. PM10 is fairly well correlated with
N = 63 97 97 41 32 108 

Northeast Southeast Industrial

Midwest

Upper

Midwest

Northwest 

Southwest

Figure 2-19. Distribution of ratios of PM2.5 to PM10 by region, 2001-2003. Box depicts interq

median; whiskers depict 5th and 95th percentiles; asterisks depict minima and maxi

of sites.

Source: Schmidt et al. (2005) 

Ratio of PM2.5 to PM10

2-47
Figure 2-20. Regional average correlations of 24-hour average PM by size fraction.

PM2.5 vs. estimated PM10-2.5 PM2.5 vs. PM10 PM10 vs. estimated PM10-2.5

Northeast Southeast Industrial

Midwest

Upper

Midwest

Northwest Sout

Calif

Southwest

Correlation Coefficient (r)

Source: Schmidt et al. (2005) 

2-48
2-49

estimated PM10-2.5 in most regions, with the highest average correlations in the southwest, upper

midwest, and southern California regions. These data suggest that PM10 might be a suitable

indicator for either fine or coarse particles, depending upon location-specific factors. However,

in all locations estimated PM10-2.5 and PM2.5 are very poorly correlated, which should be expected

due to their differences in origin, composition, and atmospheric behavior.

2.5 PM TEMPORAL PATTERNS

2.5.1 PM2.5 and PM10-2.5 Patterns

Data from the PM FRM networks from 2001-2003 generally show distinct seasonal

variations in PM2.5 and estimated PM10-2.5 concentrations. Although distinct, the seasonal

fluctuations are generally not as sharp as those seen for ozone concentrations. Figure 2-21

shows the monthly distribution of 24-hour average urban PM2.5 concentrations in different

geographic regions. The months with peak urban PM2.5 concentrations vary by region. The

urban areas in the northeast, industrial midwest, and upper midwest regions all exhibit peaks in

both the winter and summer months. In the northeast and industrial midwest regions, the

summer peak is slightly more pronounced than the winter peak, and in the upper midwest region

the winter peak is slightly more pronounced than the summer peak. In the southeast, a single

peak period in the summer is evident. In western regions, peaks occur in the late fall and winter

months.

Figure 2-22 shows the distributions of estimated 24-hour average urban PM10-2.5

concentrations by U.S. geographic region. The lowest concentrations generally occur in the

winter months. Elevated levels are apparent in the easternmost regions in April. In the upper

midwest, northwest, and southern California regions, the highest levels occur in the mid- to latesummer to mid-fall. The southwest region exhibits the greatest range of variability throughout

the year. Elevated levels are apparent in the spring, consistent with winds that contribute to

windblown dust. In the southwest and southern California, highly elevated levels in the fall,

especially October, were caused by forest fires in the vicinity of the monitoring sites.

The chemical components of fine particles also exhibit seasonal patterns. Figures 2-23

and 2-24 show seasonal 2003 urban and rural patterns for each of the U.S. regions. Seasonal

patterns are shown by calendar quarter. In general:

• PM2.5 values in the east are typically higher in the third calendar quarter (JulySeptember) when sulfates are more readily formed from SO2 emissions from

power plants predominantly located there and sulfate formation is supported by

increased photochemical activity.

• Urban PM2.5 values tend to be higher in the first (January-March) and fourth

(October-December) calendar quarters in many areas of the western U.S., in part

because more carbon is produced when woodstoves and fireplaces are used and
Figure 2-21. Urban 24-hour average 

PM2.5 concentration distributions by 

region and month, 2001-2003. Box 

depicts interquartile range and median; line 

connects monthly means. Counts above 

boxes indicate number of 24-hour 

observations

Source: Schmidt et al. (2005)

1916 1721

1944

1890

2006

1904

1987 2010

1962

2033

1937 1981

Upper Midwest

0

5

10

15

20

25

30

35

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

3655

3296

3721

3313 3452 3300

3349 3408 3287

3667

3494

3593

Northwest

0

5

10

15

20

25

30

35

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1055

937

1063 998 1053 1002 1031 1041 1035

1033 1031

1053

Southwest

0

5

10

15

20

25

30

35

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

4186

3803 4101

4025

4265

4122

4284 4274

4119 4339

4214 4244

Northeast

0

5

10

15

20

25

30

35

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

7399

6676

7484

7305 7530

7238 7536 7499

7401

7506

7333 7495

Industrial Midwest

0

5

10

15

20

25

30

35

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

7531 6970 7682 7505

7730 7438

7871

7770

7485

7809 7452

7684

Southeast

0

5

10

15

20

25

30

35

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1510

1435

1585

1472

1489 1460 1496 1496 1471

1544

1473

1534

Southern California

0

5

10

15

20

25

30

35

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Monthly Average (

μg/m3) Monthly Average (

μg/m3) Monthly Average (

μg/m3)

Monthly Average (

μg/m3) Monthly Average (

μg/m3) Monthly Average (

μg/m3) Monthly Average (

μg/m3)

2-50
584

524

585

547

606

559

590 588

581

588

581

589

Upper Midwest

0

5

10

15

20

25

30

35

40

45

50

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2007 1813 1963

1704 1768 1641

1731 1791 1670 1839

1786

1899

Northwest

0

5

10

15

20

25

30

35

40

45

50

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

497

469

516 483 519 481

520 536

508

521

505

526

Southwest

0

5

10

15

20

25

30

35

40

45

50

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1074 1009 1082

1029

1082

1018 1080 1054 1057 1077

1015 1047

Northeast

0

5

10

15

20

25

30

35

40

45

50

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1714 1564 1723

1700

1751 1663 1765 1750 1694 1728

1684 1705

Industrial Midwest

0

5

10

15

20

25

30

35

40

45

50

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1185 1094 1141

1135 1165 1129

1231

1235 1183 1225 1132

1142

Southeast

0

5

10

15

20

25

30

35

40

45

50

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

417 387 399 395

406

386 410

422

394

415

395

407

Southern California

0

5

10

15

20

25

30

35

40

45

50

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 2-22. Urban 24-hour average 

PM10-2.5 concentration distributions by 

region and month, 2001-2003. Box 

depicts interquartile range and median; 

line connects monthly means. Counts 

above boxes indicate number of 24-hour 

observations.

Source: Schmidt et al. (2005) Monthly Average (

μg/m3) Monthly Average (

μg/m3) Monthly Average (

μg/m3)

Monthly Average (

μg/m3) Monthly Average (

μg/m3) Monthly Average (

μg/m3) Monthly Average (

μg/m3)

2-51
0

5

10

15

20

25

1st Quarter

0

5

10

15

25

2nd Quarter

20

0

5

10

15

25

3rd Quarter

20

Northeast

Southeast

Industrial Midwest

Upper Midwest

Southwest

Northwest

Southern California

0

5

10

15

25

4th Quarter

20

Figure 2-23. Seasonal (calendar quarter) average composition of urban PM2.5 by

region, 2003. Data from EPA Speciation Network. Components (from

top to bottom) are crustal material, total carbonaceous mass (TCM),

ammonium nitrate, and ammonium sulfate.

Crustal

TCM

Amm.

Nitrate

Amm.

Sulfate

Source: Schmidt et al. (2005) Quarterly Average (

μg/m

3

)

2-52
0

2

4

6

8

10

12

4th Quarter

TCM

0

2

4

6

8

10

12

3rd Quarter

0

2

4

6

8

10

12

1stQuarter

0

2

4

6

8

10

12

2nd Quarter

Figure 2-24. Seasonal (calendar quarter) average composition of rural PM2.5 by

region, 2003. Data from IMPROVE Network. Components (from top to

bottom) are crustal material, total carbonaceous mass (TCM), ammonium

nitrate, and ammonium sulfate.

Northeast

Southeast

Industrial Midwest

Upper Midwest

Southwest

Northwest

Southern California

Crustal

TCM

Amm. 

Nitrate

Amm.

Sulfate

Source: Schmidt et al. (2005) Quarterly Average (

μg/m

3

)

2-53
2-54

particulate nitrates are more readily formed in cooler weather. In addition, the

effective mixing depth is restricted due to enhanced thermal stability in the

planetary boundary layer during the cooler seasons.

• Urban concentrations of PM2.5 are seen to be generally higher than rural

concentrations in all four quarters, though in the west the difference seems to be

greatest in the cooler months.

The relationship between the annual mean at a site and the shorter-term 24-hour average

peaks is useful for examining the relationships between short- and long-term air quality

standards. The box plots in Figures 2-25 and 2-26 show the relationships for PM2.5 and

estimated PM10-2.5, respectively, between annual mean PM concentrations and peak daily

concentrations as represented by the 98th percentile of the distribution of daily average

concentrations at FRM sites across the U.S. Although there is a clear monotonic relationship

between 98th percentiles and annual means, there is considerable variability in peak daily values

for sites with similar annual means. For annual mean PM2.5 values between 10 and 15 μg/m3

, the

interquartile range of 98th percentiles spans about 5 to 6 μg/m3 for each 1 μg/m3 interval. The

range between the 5th and 95th percentile values for each interval varies substantially. For all

sites with an annual mean less than or equal 15 μg/m3, the corresponding 98th percentile value is

less than 65 :g/m3. Estimated PM10-2.5 generally exhibits greater variability in 98th percentile

values for sites with similar annual means than seen for PM2.5. The maximum estimated PM10-2.5

values are quite high relative to the rest of the distribution for annual mean intervals above

20 μg/m3

.

Staff evaluated speciated PM2.5 data for 2003 from the urban EPA network in order to

compare the component profiles on high PM2.5 mass days to annual average profiles (Schmidt et

al., 2005). Table 2-5 shows the analysis results for 8 different sites in large metropolitan areas

(in the east: Birmingham, AL; Atlanta, GA; New York City, NY; Cleveland, OH; Chicago, IL; 

and St. Louis, MO; in the west: Salt Lake City, UT; and Fresno, CA). Mass is proportioned 

into four categories: sulfates, nitrates, crustal, and total carbonaceous mass (TCM, the sum of EC

and OCM). For each site, the table shows the 2003 annual average speciation pattern, the profile

for the five highest PM2.5 mass days in that year -- both individually and averaged together --

and corresponding FRM mass values (annual average, five highest days, and average of five

highest). The table shows some notable differences in the percentage contribution of each of the

species to total mass when looking at the high end of the distribution versus the annual average. 

In all of the eastern city sites, the percentage of sulfates is somewhat higher on the five high days

as compared to the annual averages. In the two western cities, the percentage of nitrates is

higher on the five high days as compared to the annual averages. TCM appears somewhat lower

percentage on the five high days compared to the annual averages in most cities. It is of note
<6 6-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 1

N= 24 64 38 57 79 88 101 120 105 65 4

Annual mean (μg/m3)

98th Percentile (μg/m

3

)

Figure 2-25. Distribution of annual mean vs. 98th percentile 24-hour average PM2.5 concentra

Box depicts interquartile range and median; whiskers depict 5th and 95th percentiles; 

minima and maxima. N= number of sites.

Source: Schmidt et al. (2005) 

2-55
98th Percentile (

μg/m

3

)

Figure 2-26. Distribution of estimated annual mean vs. 98th percentile 24-hour average PM10

concentrations, 2001-2003. Box depicts interquartile range and median; whiskers d

95th percentiles; asterisks depict minima and maxima. N= number of sites.

<6 6-8 8-10 10-12 12-14 14-16 16-20 20-30 

N= 84 66 83 66 58 37 49 32 

Annual mean (μg/m3)

max=208 

Source: Schmidt et al. (2005) 

2-56
Amm. 

Nitrate

Amm. 

Sulfate Crustal TCM

• Αnnual average 8.5 35.6 7.6 48.3 17.9

• Αverage of 5 highest PM2.5 mass days 3.8 40.0 7.8 48.3 40.7

• Highest PM2.5 mass day 1.9 55.1 5.5 37.4 46.6

• 2nd highest PM2.5 mass day 4.2 26.9 11.0 57.9 40.4

• 3rd highest PM2.5 mass day 15.3 15.7 10.7 58.4 39.2

• 4th Highest PM2.5 mass day 2.7 51.1 7.4 38.7 39.1

• 5th Highest PM2.5 mass day 2.6 34.6 6.4 56.3 38.3

• Αnnual average 8.1 42.8 4.0 45.0 15.2

• Αverage of 5 highest PM2.5 mass days 2.6 60.1 2.3 34.3 35.2

• Highest PM2.5 mass day 2.0 70.5 1.9 25.6 37.8

• 2nd highest PM2.5 mass day 2.0 47.8 2.5 47.8 37.1

• 3rd highest PM2.5 mass day 2.4 67.6 2.1 27.9 36.8

• 4th Highest PM2.5 mass day 3.2 50.8 2.9 43.1 35.0

• 5th Highest PM2.5 mass day 3.6 67.5 1.9 27.0 29.3

• Αnnual average 20.2 38.3 5.1 36.4 13.1

• Αverage of 5 highest PM2.5 mass days 11.6 57.9 3.0 27.4 40.5

• Highest PM2.5 mass day 3.6 58.3 5.5 32.6 45.9

• 2nd highest PM2.5 mass day 5.0 69.0 1.4 24.6 45.8

• 3rd highest PM2.5 mass day 27.8 42.1 3.1 27.0 38.2

• 4th Highest PM2.5 mass day 5.1 59.4 4.6 30.9 36.4

• 5th Highest PM2.5 mass day 9.7 62.2 2.0 26.1 36.0

• Αnnual average 22.3 38.3 7.4 32.1 17.6

• Αverage of 5 highest PM2.5 mass days 21.4 42.5 6.3 30.0 44.1

• Highest PM2.5 mass day 32.7 43.2 2.3 21.7 57.9

• 2nd highest PM2.5 mass day 25.1 41.5 4.0 29.3 46.4

• 3rd highest PM2.5 mass day 4.8 64.4 8.7 22.1 45.5

• 4th Highest PM2.5 mass day 8.8 37.5 14.7 39.0 35.7

• 5th Highest PM2.5 mass day 31.4 20.5 4.0 44.0 35.0

• Αnnual average 28.0 31.8 4.6 35.6 15.2

• Αverage of 5 highest PM2.5 mass days 41.2 34.0 2.3 22.4 34.4

• Highest PM2.5 mass day 46.0 30.7 1.2 22.1 38.3

• 2nd highest PM2.5 mass day 49.2 36.4 0.8 13.6 35.3

• 3rd highest PM2.5 mass day 51.8 27.7 1.2 19.3 35.1

• 4th Highest PM2.5 mass day 5.6 61.7 3.8 28.9 32.5

• 5th Highest PM2.5 mass day 47.8 16.1 5.3 30.8 30.7

• Αnnual average 20.0 36.0 5.6 38.4 14.5

• Αverage of 5 highest PM2.5 mass days 12.2 61.9 3.9 22.0 35.9

• Highest PM2.5 mass day 6.2 69.1 3.6 21.0 50.6

• 2nd highest PM2.5 mass day 5.0 67.0 2.0 26.0 36.0

• 3rd highest PM2.5 mass day 6.4 69.2 3.2 21.3 33.1

• 4th Highest PM2.5 mass day 5.0 58.9 8.2 28.1 30.8

• 5th Highest PM2.5 mass day 40.2 42.3 2.7 14.7 28.9

• Αnnual average 28.3 12.2 8.5 51.1 10.0

• Αverage of 5 highest PM2.5 mass days 46.3 10.8 2.9 40.0 40.6

• Highest PM2.5 mass day 50.6 6.3 2.5 40.5 59.5

• 2nd highest PM2.5 mass day 43.5 11.9 2.6 42.0 52.1

• 3rd highest PM2.5 mass day 42.4 13.5 3.7 40.4 34.2

• 4th Highest PM2.5 mass day 48.2 5.9 4.7 41.3 28.7

• 5th Highest PM2.5 mass day 45.4 20.2 1.5 32.8 28.4

• Αnnual average 35.5 10.2 3.6 50.7 18.0

• Αverage of 5 highest PM2.5 mass days 42.4 4.7 1.3 51.6 54.2

• Highest PM2.5 mass day 55.2 4.6 2.1 38.2 59.0

• 2nd highest PM2.5 mass day 58.4 8.5 0.9 32.2 56.3

• 3rd highest PM2.5 mass day 17.5 1.5 1.3 79.7 54.4

• 4th Highest PM2.5 mass day 35.1 5.3 1.0 58.6 52.6

• 5th Highest PM2.5 mass day 44.6 3.7 1.3 50.3 50.0

Atlanta, GA

New York City, 

NY

St. Louis, MO

Salt Lake City, 

UT

Fresno, CA

Composition Percents (%)

Urban Area Statistic*

Cleveland, OH

Chicago, IL

Birmingham, AL

PM2.5 

mass** 

(μg/m3)

8%

43%

4%

45%

3%

61%

2%

35%

20%

5% 38%

36%

12%

58%

3%

27%

22%

38%

7%

32%

21%

42%

6%

30%

36%

10%

4%

51%

42%

1% 5%

52%

28%

12%

8%

51%

46%

11%

3%

40%

20%

36%

6%

38%

12%

62%

4%

22%

28%

32%

5%

36%

41%

34%

2%

22%

Amm. Nitrate 9%

Amm. Sulfate 36%

Crustal 8%

TCM

48%

Key:

9%

36%

8%

48%

4%

40%

8%

48%

Annual 

average 

Average of 5 

highest days

Table 2-5. PM2.5 composition on high mass days in select urban areas, 2003 

The 5 highest days shown (and aggregated) for each site actually represent the 5 highest days (based on 

collocated FRM mass; see next bullet) that the speciation monitor sampled. FRM monitors at different 

locations in the metropolitan area and/or collocated FRM measurements on days that the speciation sampler did 

not record valid data may have had higher values than some or all of the 5 high values shown. Event-flagged 

data were omitted from this analyses.

‘PM2.5 mass’ concentration represents the collocated (w/ speciation monitor) same-day FRM measurement 

unless not available, in which case the speciation monitor gravimetric mass was substituted.

*

**

2-57
2-58

that event-flagged data were excluded from this analyses; the carbonaceous fraction of mass

would be significantly higher on sites where peak days are affected by smoke from wildfires.

Monitors that provide near-continuous measurements can provide insights into short-term

(e.g., hourly average) patterns in PM, which could be important to understanding associations

between elevated PM levels and adverse health and welfare effects. Examples of average hourly

profiles for PM2.5 and PM10-2.5 from 2001-2003 are shown in Figures 2-27 and 2-28 for a

monitoring site in the Greensboro, NC, metropolitan area. As with most eastern urban sites, the

PM2.5 concentrations are significantly higher than those for PM10-2.5. Profiles, for both PM2.5 and

PM10-2.5, in Figure 2-27 indicate that elevated hourly average levels occurred most often between

the hours of 6:00 am and 9:00 am, corresponding to the typical morning rush of automobile

traffic. An evening peak starting about 5:00 pm is also evident for both size indicators. The

95th percentile concentrations during peak hours can be as high as three to four times the median

level for the same hour. As indicated in Figure 2-28 the lowest seasonal levels for both size

fractions occur in the winter. For PM2.5, the summer concentrations are considerably higher than

the other season. These profiles of hourly average PM2.5 and PM10-2.5 levels are typical of many,

but not all, eastern U.S. urban areas. 

Figure 2-29 shows hourly average PM2.5 and PM10-2.5 concentrations for a monitoring site

in the Denver metropolitan area from 2001-2003. Like many western U.S. sites, the PM10-2.5

concentrations are higher than the PM2.5 levels for all hours of the day. Similar to the eastern

example site, this western one also shows a morning and afternoon rush hour traffic signal. 

Some western monitoring sites, located in areas subject to routine episodes of windblown dust,

can have unusual diurnal concentration distributions (e.g., 95th percentile concentrations for

some hours more than ten times the median levels; and hourly means significantly higher than

the medians and even 75th percentiles) (Schmidt et al., 2005). Figure 2-30 highlights how

continuous data can be used to pinpoint an unusual or episodic source, in this case a short but

significant dust storm in El Paso, Texas. On April 26, 2002, this dust storm caused large

increases in both PM2.5 and PM10-2.5 concentrations. As might be expected, the dust had a greater

impact on the PM10-2.5 concentrations than the PM2.5. (Note that the PM10-2.5 scale is about 6 times

as large as the PM2.5 scale.) Hourly PM10-2.5 levels approaching 3000 μg/m3 were recorded this

day.

The hourly ranges shown in Figures 2-27 and 2-29 suggest that hour-to-hour changes in

PM2.5 concentrations encompass several μg/m3; however, extreme values for hour-to-hour

variations can be much larger. An analysis of the distribution of increases in hour-to-hour

concentrations at multiple sites across the U.S. for 2001-2003 found site-level median hourly

increases ranging up to 6 μg/m3 (maximum), with an average median increase of about

1.8 μg/m3

.
, 

0

10

20

30

40

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

10

20

30

40

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Concentration (

μg/m3)

Hour

Figure 2-27. Hourly average PM2.5 and PM10-2.5 concentrations at a Greensboro,

NC monitoring site, 2001-2003. Upper panel shows the distribution of

PM2.5 concentrations and the lower panel shows the distribution of PM10-2.5

concentrations. (Box plots of interquartile ranges, means, medians, 5th and

95th percentiles.)

PM2.5

PM10-2.5

Source: Schmidt et al. (2005) 

2-59
Concentration (

μg/m3)

Hour

Figure 2-28. Seasonal hourly average PM2.5 and PM10-2.5 concentrations at a

Greensboro, NC monitoring site, 2001-2003. Upper panel shows the

PM2.5 concentrations and the lower panel shows the PM10-2.5

concentrations. 

Spring (Mar-May)

Summer (Jun-Aug)

Fall (Sep-Nov)

Winter (Dec-Feb)

0

10

20

30

40

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

10

20

30

40

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

PM2.5

PM10-2.5

Source: Schmidt et al. (2005) 

2-60
Figure 2-29. Hourly average PM2.5 and PM10-2.5 concentrations at a Denver, CO 

monitoring site, 2001-2003. Upper panel shows the distribution of PM2.5

concentrations and the lower panel shows the distribution of PM10-2.5

concentrations. (Box plots of interquartile ranges, means, medians, 5th

and 95th percentiles.)

Concentration (

μg/m3)

Hour

PM2.5

PM10-2.5

Source: Schmidt et al. (2005) 

0

10

20

30

40

50

60

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

0

10

20

30

40

50

60

2-61
0

1000

2000

3000

012345678910 1

1 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

April 26, 2002 April 27, 2002

0

50

100

150

200

250

300

350

400

450

500

012345678910 1

1 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 3

9 40 41 42 43 44 45 46 47

April 26, 2002 April 27, 2002

Concentration (

μg/m3)

Figure 2-30. Hourly PM2.5 and PM10-2.5 concentrations at a El Paso, TX monitoring

site, April 26, 2002-April 27, 2002. Upper panel shows the hourly PM2.5

concentrations and the lower panel shows the hourly PM10-2.5

concentrations. Note the different scales.

PM2.5

PM10-2.5

Hour

Source: Schmidt et al. (2005) 

2-62
2-63

2.5.2 Ultrafine Patterns

Diurnal or seasonal patterns for ultrafine particles have been studied in relatively few

areas of the U.S. A study done at the most extensively studied urban location in the U.S.,

Atlanta, GA, is discussed in the CD (p.3-32). In this study, (CD, p. 3-32 to 3-33) ultrafine

particle number concentrations were found to be higher in the winter than in the summer. 

Concentrations of particles in the range of 0.01 to 0.1 μm were higher at night than during the

daytime, and tended to reach their highest values during the morning period when motor vehicle

traffic is heaviest. Smaller particles in the range of 0.004 to 0.01 μm were elevated during the

peak traffic period, most notably in cooler temperatures, below 50°F. . 

2.6 PM BACKGROUND LEVELS

For the purposes of this document, background PM is defined as the distribution of PM

concentrations that would be observed in the U.S. in the absence of anthropogenic (man-made)

emissions of primary PM and precursor emissions (e.g., VOC, NOx, SO2, and NH3) in the U.S.,

Canada, and Mexico. Background levels so defined are referred to policy-relevant background,

since this definition of background facilitates separating pollution levels that can be controlled

by U.S. regulations (or through international agreements with neighboring countries) from levels

that are generally uncontrollable by the U.S.. As defined here, background includes PM from

natural sources in the U.S. and transport of PM from both natural and man-made sources outside

of the U.S. and its neighboring countries.

Section 3.3.3 of the CD discusses annual average background PM levels, and states that

"[e]stimates of annually averaged PRB concentrations or their range have not changed from the

1996 PM AQCD" (CD, p. 3-105). Annual average background estimates for PM10 range from 4

to 8 :g/m3

 in the western U.S. and 5 to 11 :g/m3 in the eastern U.S.; for PM2.5, estimates range

from 1 to 4 :g/m3 in the west and 2 to 5 :g/m3 in the east. The lower bounds of these ranges are

based on estimates of "natural" background midrange concentrations. The upper bounds are

derived from the multi-year annual averages of the remote monitoring sites in the IMPROVE

network (EPA, 1996a, p. 6-44). Ranges presented in the CD for background PM10-2.5 levels were

derived from the PM10 and PM2.5 ranges by subtraction, resulting in relatively wide ranges with

mid-point estimates of 3.5 :g/m3 in the west and 4.5 :g/m3 in the east (CD, p. 3-83). Since the

IMPROVE data unavoidably reflect some contributions from the effects of anthropogenic

emissions from within the U.S., Canada, and Mexico, as well as background, they likely

overestimate the U.S. background concentrations as defined here.

There is a distinct geographic difference in background levels, with lower levels in the

western U.S. and higher levels in the eastern U.S. The eastern U.S. is estimated to have more

natural organic fine particles and more water associated with hygroscopic fine particles than the

western U.S. due to generally higher humidity levels.
2-64

Background levels of PM vary by geographic location and season, and have a natural

component and an anthropogenic component. The natural background arises from: (1) physical

processes of the atmosphere that entrain coarse particles (e.g., windblown crustal material, sea

salt spray); (2) volcanic eruptions (e.g., sulfates); (3) natural combustion such as wildfires (e.g.,

elemental and organic carbon, and inorganic and organic PM precursors); and (4) biogenic

sources such as vegetation, microorganisms, and wildlife (e.g., organic PM, inorganic and

organic PM precursors). The exact magnitude of the natural portion of background PM for a

given geographic location cannot be precisely determined because it is difficult to distinguish

local sources of PM from the long-range transport of anthropogenic particles and precursors.

PM can be transported long distances from natural or quasi-natural events occurring

outside the continental U.S. (CD, p. 3-82). The occurrence and location of these long-range

transport events are highly variable and their impacts on the U.S. are equally variable. The

contributions to background from sources outside of the U.S., Canada, and Mexico can be

significant on an episodic, but probably not on an annual basis (CD, p. 3-91). Several studies

have focused on identifying the origin, sources, and impacts of recent trans-national transport

events from Canada, Mexico, and extra-continental sources. 

• The transport of PM from biomass burning in Central America and southern

Mexico in 1998 has been shown to contribute to elevated PM levels in southern

Texas and throughout the entire central and southeastern United States (CD, p.

3-86).

• Wildfires in the boreal forests of northwestern Canada may impact large portions

of the eastern United States. The CD estimates that a July 1995 Canadian wildfire

episode resulted in excess PM2.5 concentrations ranging from 5 μg/m3

 in the

southeast, to nearly 100 μg/m3 in the northern plains states (CD, p. 3-87).

• Windblown dust from dust storms in the North African Sahara desert has been

observed in satellite images as plumes crossing the Atlantic Ocean and reaching

the southeast coast of the U.S., primarily Florida; North African dust has also

been tracked as far as Illinois and Maine. These events have been estimated to

contribute 6 to 11 μg/m3

 to 24-hour average PM2.5 levels in affected areas during

the events (CD, p. 3-84).

• Dust transport from the deserts of Asia (e.g., Gobi, Taklimakan) across the Pacific

Ocean to the northwestern U.S. also occurs. Husar et al. (2001) report that the

average PM10 level at over 150 reporting stations throughout the northwestern

U.S. was 65 μg/m3

 during an episode in the last week in April 1998, compared to

an average of about 20 μg/m3 during the rest of April and May (CD, p. 3-84).

Background concentrations of PM2.5, PM10-2.5, and PM10 may be conceptually viewed as

comprised of baseline and episodic components. The baseline component is the contribution
26 There are two policies which allow PM data to be flagged for special consideration due to natural events:

the Exceptional Events Guideline (EPA, 1986) and the PM10 Natural Events Policy (Nichols, 1996). Under these

policies, EPA will exercise its discretion not to designate areas as nonattainment and/or to discount data in

circumstances where an area would attain but for exceedances that result from uncontrollable natural events. Three

categories of natural PM10 events are specified in the natural events policy: volcanic or seismic activity, wildland

fires, and high wind dust events. The exceptional events policy covers natural and other events not expected to recur

at a given location and applies to all criteria pollutants. Categories of events covered in the exceptional events

guidance include, but are not limited to, high winds, volcanic eruptions, forest fires, and high pollen counts. EPA is

drafting further guidance concerning how to handle data affected by natural events related to the PM standards.

2-65

from natural sources within the U.S., Canada, and Mexico and from transport of natural and

anthropogenic sources outside of the U.S., Canada, and Mexico that is reasonably well

characterized by a consistent pattern of daily values each year, although they may vary by region

and season.

In addition to this baseline contribution to background concentrations, a second

component consists of more rare episodic high-concentration events over shorter periods of time

(e.g., days or weeks) both within the U.S., Canada, and Mexico (e.g., volcanic eruptions, large

forest fires) and from outside of the U.S., Canada, and Mexico (e.g., transport related to dust

storms from deserts in North Africa and Asia). Over shorter periods of time (e.g., days or

weeks), the range of background concentrations is much broader than the annual averages. 

Specific natural events such as wildfires, volcanic eruptions, and dust storms, both of U.S. and

international origin, can lead to very high levels of PM comparable to, or greater than, those

driven by man-made emissions in polluted urban atmospheres. Because such excursions can be

essentially uncontrollable, EPA has in place policies that can remove consideration of them,

where appropriate, from attainment decisions.26

Disregarding such large and unique events, an estimate of the range of "typical"

background on a daily basis can be obtained from reviewing multi-year data at remote locations.

Estimates of background concentrations for time scales shorter than daily averages are not

feasible at this time, since almost all of the rural measurements of speciated PM are 24-hour

averages. EPA staff have conducted an analysis of daily PM2.5 measurements from 1990 to 2002

at IMPROVE sites across the U.S., focused on the non-sulfate components of PM2.5 (Langstaff,

2005). Ambient sulfate concentrations are almost entirely due to anthropogenic sources (with

the exception of sulfates from volcanic eruptions), so while non-sulfate PM2.5 is partly of

anthropogenic origin, it captures almost all of the background.

Based on regional differences in geography and land use, the U.S. is divided into a

number of regions for estimating regional background levels. The "eastern U.S." region extends

west to include Minnesota, Iowa, Missouri, Arkansas, and Louisiana. The "central west" region

is comprised of states west of the eastern U.S. region and east of Washington, Oregon, and

California. Washington, Oregon, and northern California make up the "north west coast," and
27 The ‘eastern’ region roughly equates to the combined southeast, northeast, industrial midwest, and

eastern portion (MN, IA, & MO) of the upper midwest regions as defined previously in this chapter (Figure 2-4). 

The ‘central west’ region roughly corresponds to the western portion of the upper midwest region and the eastern

two thirds (ID, MT, CO, UT, NV) of the northwest region. The ‘north west coast’ approximates the remaining one

third (northern CA, OR, and WA) of the northwest region. The ‘south west coast’ area is similar to the southern

California region.

28 Non-sulfate PM2.5 is defined as measured PM2.5 minus reported ammonuim sulfate.

2-66

southern California (south of about 40 degrees latitude) makes up the "south west coast"

regions.27

To arrive at estimates of background we use the averaged non-sulfate PM2.5 values28 at

IMPROVE sites in these regions. The Eastern U.S. region is heavily impacted by anthropogenic

emissions and we selected sites in northern states, which we judge to be affected to a lesser

extent by anthropogenic pollution, to derive estimates of background concentrations, using all

IMPROVE sites in the selected states. In all of the other regions we include all of the

IMPROVE sites. Table 2-6 describes the IMPROVE sites selected to represent these different

regions of the U.S. We recognize that these estimates will likely be biased high, as they include

an anthropogenic component, some sites more than others.

The 99th percentile concentrations at each of these sites were calculated to assess high

values measured at these sites, while avoiding excursions that potentially reflect exceptional

natural events. Standard deviations were also calculated for characterization of the daily

variation of background concentrations. Table 2-7 presents the results of this analysis as means

and ranges of individual site statistics within each of the background regions.

Table 2-6. IMPROVE sites selected for estimates of regional background

Region IMPROVE Sites

Eastern All sites in Maine, New Hampshire, Vermont, Minnesota, and Michigan

Central West All sites in this region (sites in ID, MT, WY, ND, SD, CO, UT, NV, AZ)

North West Coast All sites in this region (all Washington and Oregon sites, and the northern

California sites REDW and LAVO)

South West Coast All sites in this region (all California sites except the northern sites

REDW and LAVO)

Alaska All sites in Alaska

Hawaii All sites in Hawaii
2-67

Table 2-7. Estimates of long-term means, daily standard deviations and 99th percentiles of

 PM2.5 background concentrations (:g/m3)

Region # Sites Means St Devs 99th %iles

Eastern U.S. 7 3.0 (2.5-3.6) 2.5 (2.1-2.8) 13 (11-15)

Central West 37 2.5 (1.6-4.6) 1.9 (1.3-3.7) 10 (6-17)

North West Coast 8 3.4 (2.2-6.6) 2.8 (2.1-4.2) 14 (10-21)

South West Coast 8 5.2 (2.6-8.6) 3.7 (1.8-6.8) 20 (9-33)

Alaska 1 1.2 1.5 9

Hawaii 3 1.1 (0.7-1.8) 0.9 (0.8-1.0) 4 (4-5)

Notes:

1) Some of these estimates likely contain a significant North American anthropogenic component.

2) The “Means” column has the mean of the long-term averages of the sites representing the region followed by the

minimum and maximum of the long-term averages of these sites in parentheses. Similarly for the “St Devs” column,

which presents standard deviations of the daily concentrations about the annual means, and the “99th %iles” column,

which presents the 99th percentiles of the daily concentrations over the 23-year period.

Considering these factors, the distributions of daily PM2.5 concentrations at these sites

provide an indication of the ranges for the daily variability of PM2.5 background concentrations,

and the 99th percentiles of these distributions are an estimate of the highest daily background

concentrations. Staff notes that these recent findings are generally consistent with those from the

last review, which suggested a range of about 15 to 20 μg/m3 as the upper end of the distribution

of daily PM2.5 background concentrations in the U.S. (EPA, 1996b).

2.7 RELATIONSHIP BETWEEN AMBIENT PM MEASUREMENTS AND HUMAN

EXPOSURE

The statutory focus of the primary NAAQS for PM is protection of public health from

the adverse effects associated with the exposure to ambient PM – that is, the focus is on particles

in the outdoor atmosphere that are either emitted directly by sources or formed in the atmosphere

from precursor emissions. We refer to PM in the ambient air as ambient PM. An understanding

of human exposure to ambient PM helps inform the evaluation of underlying assumptions and

interpretation of results of epidemiologic studies that characterize relationships between

monitored ambient PM concentrations and observed health effects (discussed in Chapter 3).

An important exposure-related issue for this review is the characterization of the

relationships between ambient PM concentrations measured at one or more centrally located

monitors and personal exposure to ambient PM, as characterized by particle size, composition,

source origin, and other factors. Information on the type and strength of these relationships,

discussed below, is relevant to the evaluation and interpretation of associations found in

epidemiologic studies that use measurements of PM concentrations at centrally located monitors
29 Consideration of exposure measurement error and the effects of exposure misclassification on the

interpretation of the epidemiologic studies are addressed in Chapter 3.

2-68

as a surrogate for exposure to ambient PM.29 The focus here is on particle size distinctions; the

CD (CD, Section 5.4) also discusses exposure relationships related to compositional differences.

2.7.1 Definitions

Exposure to a contaminant is defined as contact at a boundary between a human and the

environment (e.g., the breathing zone) at a specific contaminant concentration for a specific

interval of time; it is measured in units of concentration(s) multiplied by time (or time interval)

(National Research Council, 1991). An individual’s total personal exposure to PM results from

breathing air containing PM in different types of environments (e.g., outdoors near home,

outdoors away from home, indoors at home, indoors at office or school, commuting, restaurants,

malls, other public places). These environments may have different concentrations of PM with

particles originating from a wide variety of sources.

Ambient PM is comprised of particles emitted by anthropogenic and natural sources and

particles formed in the atmosphere from emissions of gaseous precursors. This includes

emissions not only from outdoor sources such as smokestacks, industrial sources, and

automobiles, but also from sources located indoors with emissions vented outdoors, such as

fireplaces, wood stoves, and some cooking appliances. Exposure to ambient PM can occur both

outdoors and indoors to the extent that ambient PM penetrates into indoor environments – we

use the term PM of ambient origin to refer to both outdoor and indoor concentrations of ambient

PM. We use the term nonambient PM to refer to concentrations of PM that are only due to

indoor sources of particles that are not vented outdoors such as smoking, cooking, other nonvented sources of combustion, cleaning, mechanical processes, and chemical interactions

producing particles. In characterizing human exposure to PM concentrations relevant to setting

standards for ambient air quality, the CD conceptually separates an individual’s total personal

exposure to PM into exposure to PM of ambient origin and exposure to all other sources of PM

(i.e., nonambient PM exposure).

Outdoor concentrations of PM are affected by emissions, meteorology, topography,

atmospheric chemistry, and removal processes. Indoor concentrations of PM are affected by

several factors, including outdoor concentrations, processes that result in infiltration of ambient

PM into buildings, indoor sources of PM, aerosol dynamics and indoor chemistry, resuspension

of particles, and removal mechanisms such as particle deposition, ventilation, and airconditioning and air cleaning devices (CD, p. 5-122). Concentrations of PM inside vehicles are

subject to essentially the same factors as concentrations of PM inside buildings. Personal

exposure to PM also includes a component which results specifically from the activities of an
2-69

individual that typically generate particles affecting only the individual or a small localized area

surrounding the person, such as walking on a carpet, referred to as the personal cloud.

Epidemiologic studies generally use measurements from central monitors to represent the

ambient concentrations in an urban or rural area. We use the term central site to mean the site of

a PM monitor centrally located with respect to the area being studied. In many cases,

epidemiologic studies combine the measurements from more than one monitor to obtain a

broader representation of area-wide PM concentrations than a single monitor provides.

2.7.2 Centrally Monitored PM Concentration as a Surrogate for Particle Exposure

The 1996 Criteria Document (EPA, 1996a) presented a thorough review of PM exposurerelated studies up to that time. The 1996 Staff Paper (EPA, 1996b) drew upon the studies,

analyses, and conclusions presented in the 1996 Criteria Document and discussed two

interconnected PM exposure issues: (1) the ability of central fixed-site PM monitors to represent

population exposure to ambient PM and (2) how differences between fine and coarse particles

affect population exposures. Distinctions between PM size classes and components were found

to be important considerations in addressing the representativeness of central monitors. For

example, fine particles have a longer residence time and generally exhibit less variability in the

atmosphere than coarse fraction particles. As discussed in the 1996 Staff Paper, the 1996

Criteria Document concluded that measurements of daily variations of PM have a plausible

linkage to daily variations of human exposures to PM of ambient origin for the populations

represented by the nearby ambient monitoring stations, and that this linkage is stronger for fine

particles than for PM10 or the coarse fraction of PM10. The 1996 Criteria Document further

concluded that central monitoring can be a useful, if imprecise, index for representing the

average exposure of people in a community to PM of ambient origin (EPA, 1996b, p. IV-15, 16).

Exposure studies published since 1996 and reanalyses of studies that appeared in the

1996 Criteria Document are reviewed in the current CD, and provide additional support for these

findings. The CD discusses two classes of fine particles: ultrafine and accumulation-mode

particles (see Chapter 2). Ultrafine, accumulation-mode, and coarse particles have different

chemical and physical properties which affect personal exposures in different ways (CD, Table

9-2, p. 9-17).

An individual’s total personal exposure to PM may differ from the ambient concentration

measured at the central site monitor because: (1) spatial differences in ambient PM

concentrations exist across a city or region; (2) generally only a fraction of the ambient PM is

present in indoor or in-vehicle environments, whereas individuals generally spend a large

percentage of time indoors; and (3) a variety of indoor sources of PM contribute to total personal

exposure. Thus, the amount of time spent outdoors, indoors, and in vehicles and the types of

activities engaged in (e.g., smoking, cooking, vacuuming) also will heavily influence personal
2-70

exposure to PM. The first two factors are important for determining the strength of the

relationship between ambient PM and ambient personal exposure.

With regard to the first factor that influences the relationship between total personal

exposure and concentrations measured at central sites, the spatial variability of PM plays a large

role. As discussed in Section 2.4, for many areas PM2.5 concentrations are fairly uniform

spatially, with higher concentrations near roadways and other direct sources of PM2.5. Analyses

of PM2.5 data for 27 urban areas indicate that differences in annual mean concentrations between

monitoring sites in an urban area range from less than 1 μg/m3

 to as much as 8 μg/m3. However,

the correlations of daily PM2.5 between sites are typically greater than 0.80. Daily mean PM2.5

concentrations exhibit much higher spatial variability than annual means, even when the daily

concentrations at sites are highly correlated. Although the spatial variability of PM2.5 varies for

different urban areas, overall, some degree of uniformity results from the widespread formation

and long lifetime of the high regional background of secondary PM2.5. In summarizing the key

findings related to spatial variability in PM2.5 concentrations, the CD states (p. 3-101):

Differences in annual mean PM2.5 concentrations between monitoring sites in

urban areas examined are typically less than 6 or 7 μg/m3

. However, on

individual days, differences in 24-h average PM2.5 concentrations can be much

larger. Some sites in metropolitan areas are highly correlated with each other but

not with others, due to the presence of local sources, topographic barriers, etc. 

Although PM2.5 concentrations at sites within a MSA can be highly correlated,

significant differences in their concentrations can occur on any given day. 

Consequently, additional measures should be used to characterize the spatial

variability of PM2.5 concentrations. The degree of spatial uniformity in PM2.5

concentrations in urban areas varies across the country. These factors should be

considered in using data obtained by the PM2.5 FRM network to estimate

community-scale human exposure, and caution should be exercised in

extrapolating conclusions obtained in one urban area to another. PM2.5 to PM10

ratios were generally higher in the east than in the west, and values for this ratio

are consistent with those found in numerous earlier studies presented in the 1996

PM AQCD.

Relative to fine particles, coarse and ultrafine particles are likely to be more variable

across urban scales. Daily mean PM10-2.5 concentrations tend to be more variable and have lower

inter-site correlations than PM2.5, possibly due to their shorter atmospheric lifetime (travel

distances < 1 to 10s of km) and the more sporadic nature of PM10-2.5 sources (CD, Section 3.2.5). 

Ultrafine particles also have shorter atmospheric lifetimes (travel distances < 1 to 10s of km,

compared with 100s to 1000s of km for PM2.5) and spatially variable sources. High

concentrations of ultrafine particles have been measured near roadways, but with concentrations

falling off rapidly with increasing distance from the roadway. Both coarse and ultrafine particles

also have reduced concentrations indoors compared to PM2.5, due to lower infiltration rates,
2-71

greater deposition rates, and coagulation of ultrafine particles into larger particles. These

differences make it more difficult to find a relationship between ambient concentrations and

personal exposures to these size fractions than for PM2.5.

The second factor influencing the relationship between ambient PM concentrations

measured at central sites and total personal exposure to PM is the extent to which ambient PM

penetrates indoors and remains suspended in the air. If the flow of ambient PM into the home

from the outdoors is very restricted, the relationship between ambient PM concentrations

measured at a central site and total exposure to PM will tend to be weaker than in a situation

where ambient PM flows more readily into the home and is a greater part of the overall indoor

PM concentrations. This is heavily dependent on the building air exchange rate, and also on

penetration efficiency and deposition or removal rate, both of which vary with particle

aerodynamic size. Air exchange rates (the rates at which the indoor air in a building is replaced

by outdoor air) are influenced by building structure, the use of air conditioning and heating,

opening and closing of doors and windows, and meteorological factors (e.g., difference in

temperature between indoors and outdoors). Based on physical mass-balance considerations,

usually the higher the air exchange rate the greater the fraction of PM of ambient origin found in

the indoor and in-vehicle environments. Higher air exchange rates also dilute the concentration

of indoor- generated PM. Rates of infiltration of outdoor PM into homes through cracks and

crevices are higher for PM2.5 than for PM10, PM10-2.5, or ultrafine particles (CD, p. 5-123). Since

PM10-2.5 and ultrafine particles penetrate indoors less readily than PM2.5 and deposit to surfaces

more rapidly than PM2.5, a greater proportion of PM2.5 of ambient origin is found indoors than

PM10-2.5 and ultrafine particles, relative to their outdoor concentrations. Thus, the particle size

distribution influences the amounts of PM of ambient origin found indoors.

Since people typically spend a large part of their time indoors at home, the air exchange

rate of the home has a large impact on exposures to ambient pollution. Homes with low air

exchange rates are more protected from outdoor sources, and vice-versa. Homes in regions with

moderate climate tend to be better ventilated and have higher air exchange rates than areas which

have very cold or very hot climates. Thus, climate plays an important role in regional population

exposure to ambient pollution.

The third factor influencing the relationship between ambient concentrations measured at

central sites and total personal exposure is the contribution of indoor sources to total personal

exposure. On average, individuals spend nearly 90 percent of their time indoors. The

contribution of indoor sources to indoor concentrations of PM is significant, and can be quite

variable on different days and between individuals. Indoor sources such as combustion devices

(e.g., stoves and kerosene heaters) generate predominantly fine particles; cooking produces both

fine and coarse particles; and resuspension (e.g., dusting, vacuuming, and walking on rugs)

generates predominantly coarse particles (CD, p. 5-82). This factor, however, does not influence

exposure to PM of ambient origin.
30 EPA’s Particle Total Exposure Assessment Methodology (PTEAM) field study (Clayton et al., 1993;

Özkaynak et al., 1996a;b) is a large-scale probability sample based field study. The study measured indoor, outdoor,

and personal PM10, the air exchange rate for each home, and time spent in various indoor residential and outdoor

environments for 147 subjects/households, 12-hr time periods in Riverside, California.

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These three factors related to total personal exposure can give rise to measurement error

in estimating exposures to fine and coarse PM (CD, Section 5.5.3), thus making the

quantification of relationships between concentrations measured at central site monitors and

health effects more difficult due to reduction in statistical power. Moreover, exposure

measurement errors can also affect the magnitude and the precision of the health effects

estimates. However, as discussed in the CD and below in Chapter 3, exposure measurement

errors under most ordinary circumstances are not expected to influence the overall interpretation

of findings from either the long-term exposure or time-series epidemiologic studies that have

used ambient concentration data (CD, p. 5-121). They will more likely affect the magnitude of

the effects found from these studies and result in higher effects estimates, since exposure

measurement errors tend to bias towards the null hypothesis.

The CD discusses the finding by some researchers that some epidemiologic studies yield

statistically significant associations between ambient concentrations measured at a central site

and health effects even though there is a very small correlation between ambient concentrations

measured at a central site and total personal exposures. The explanation of this finding is that

total personal exposure includes both ambient and nonambient generated components, and while

the nonambient portion of personal exposure is not generally correlated with ambient

concentrations, the exposure to concentrations of ambient origin is correlated with ambient

concentrations. Thus, it is not surprising that health effects might correlate with central site PM

concentrations, because exposure to PM of ambient origin correlates with these concentrations,

and the lack of correlation of total exposure with central site PM concentrations does not

statistically alter that relationship. By their statistical design, time-series epidemiologic studies

of this type only address the ambient component of exposure, since the impact of day-to-day

fluctuations in ambient PM on acute health effects is examined.

In looking more specifically at the relationship between personal exposure to PM of

ambient origin and concentrations measured at central site monitors, an analysis of data from the

PTEAM study30 provides important findings, as discussed in the CD (p. 5-63 to 5-66 and 5-125

to 5-126). The PTEAM study demonstrated that central site ambient PM10 concentrations are

well correlated with personal exposure to PM10 of ambient origin, while such concentrations are

only weakly correlated with total personal exposure. This study also found that estimated

exposure to nonambient PM10 is effectively independent of PM10 concentrations at central site

monitors, and that nonambient exposures are highly variable due to differences in indoor sources

across the study homes.
2-73

When indoor sources only have minor contributions to personal exposures, total exposure

is mostly from PM of ambient origin. In these cases high correlations are generally found

between total personal exposure and ambient PM measured at a central site (CD, p. 5-54). For

example, measurements of ambient sulfate, which is mostly in the fine fraction, have been found

to be highly correlated with total personal exposure to sulfate (CD, p. 5-124). Since in these

studies there were minimal indoor sources of sulfate, the relationship between ambient

concentrations and total personal exposure to sulfate was not weakened by possible presence of

small indoor-generated sulfates in some environments.

It is recognized that existing PM exposure measurement errors or uncertainties most

likely will reduce the statistical power of PM health effects analyses, thus making it more

difficult to detect a true underlying association between the exposure metric and the health

outcome of interest. However, the use of ambient PM concentrations as a surrogate for personal

ambient exposures is not expected to change the principal conclusions from PM epidemiological

studies that use community average health and pollution data (CD, p. 5-121). Based on these

considerations and on the review of the available exposure-related studies, the CD concludes that

for epidemiologic studies, ambient PM2.5 concentration as measured at central site monitors is a

useful surrogate for exposure to PM2.5 of ambient origin. However, for coarse and ultrafine PM,

such ambient concentrations are not likely to be as good a surrogate for personal ambient

exposure. While nonambient PM may also be responsible for health effects, since the ambient

and nonambient components of personal exposure are independent, the health effects due to

nonambient PM exposures generally will not bias the risk estimated for ambient PM exposures 

(CD, p. 9-17).

2.8 RELATIONSHIP BETWEEN AMBIENT PM AND VISIBILITY

The effect of ambient particles on visibility is dependent upon particle size and

composition, atmospheric illumination, the optical properties of the atmosphere, and the optical

properties of the target being viewed. The optical properties of particles, discussed in section

2.2.5, can be well characterized in terms of a light extinction coefficient. For a given distribution

of particle sizes and compositions, the light extinction coefficient is strictly proportional to the

particle mass concentration. Light extinction is a measure of visibility impairment, and, as such,

provides a linkage between ambient PM and visibility, as discussed below in section 2.8.1. 

Other measures directly related to the light extinction coefficient are also used to characterize

visibility impairment, including visual range and deciviews, as discussed below in section 2.8.2. 

Light extinction associated with background levels of PM is also discussed below in section

2.8.3.
2-74

2.8.1 Particle Mass and Light Extinction 

Fine particle mass concentrations can be used as a general surrogate for visibility

impairment. However, as described in many reviews of the science of visibility, the different

constituents of PM2.5 have variable effects on visibility impairment. For example, sulfates and

nitrates contribute substantially more to light scattering per unit mass than other constituents,

especially as relative humidity levels exceed 70 percent. Thus, while higher PM2.5 mass

concentrations generally indicate higher levels of visibility impairment, it is not as precise a

metric as the light extinction coefficient. By using historic averages, regional estimates, or

actual day-specific measurements of the component-specific percentage of total mass, however,

one can develop reasonable estimates of light extinction from PM mass concentrations (see

section 6.2.2 for further discussion).

The light extinction coefficient has been widely used in the U.S. for many years as a

metric to describe the effect of concentrations of particles and gases on visibility. It can be

defined as the fraction of light lost or redirected per unit distance through interactions with gases

and suspended particles in the atmosphere. The light extinction coefficient represents the

summation of light scattering and light absorption due to particles and gases in the atmosphere. 

Both anthropogenic and non-anthropogenic sources contribute to light extinction. The light

extinction coefficient (bext) is represented by the following equation (CD, 4-155):

bext = bap + bag + bsg + bsp (5-1)

where bap = light absorption by particles

bag = light absorption by gases

bsg = light scattering by gases (also known as Rayleigh scattering)

bsp = light scattering by particles.

Light extinction is commonly expressed in terms of inverse kilometers (km-1) or inverse

megameters (Mm-1), where increasing values indicate increasing impairment.

Total light extinction can be measured directly by a transmissometer or it can be

estimated from ambient pollutant concentrations. Transmissometers measure the light

transmitted through the atmosphere over a distance of 1 to 15 kilometers. The light transmitted

between the light source (transmitter) and the light-monitoring component (receiver) is

converted to the path-averaged light extinction coefficient. Transmissometers operate

continuously, and data are often reported in terms of hourly averages.

Direct relationships exist between measured ambient pollutant concentrations and their

contributions to the extinction coefficient. The contribution of each aerosol constituent to total

light extinction is derived by multiplying the aerosol concentration by the extinction efficiency

for that aerosol constituent. Extinction efficiencies vary by type of aerosol constituent and have
2-75

been obtained for typical atmospheric aerosols by a combination of empirical approaches and

theoretical calculations. For certain aerosol constituents, extinction efficiencies increase

significantly with increases in relative humidity.

EPA guidance for tracking progress under the regional haze rule specifies an algorithm

for calculating total light extinction as the sum of aerosol light extinction for each of the five

major fine particle components and for the coarse fraction mass, plus 10 Mm-1 for light

extinction due to Rayleigh scattering, discussed below. This algorithm is represented by the

following equation (CD, 4-169):

bext = (3)f(RH) [SULFATE]

 + (3)f(RH) [NITRATE]

 + (4) [ORGANIC CARBON]

 + (10) [LIGHT ABSORBING CARBON] (5-2)

 + (1) [SOIL]

 + (0.6) [COARSE PM]

 + 10 (for Rayleigh scattering by gases)

The estimated mass for each component is multiplied by its dry extinction efficiency and,

in the case of sulfate and nitrate, by a relative humidity adjustment factor, f(RH), to account for

their hygroscopic behavior (CD, p. 4-169). The relative humidity adjustment factor increases

significantly with higher humidity, ranging from about 2 at 70 percent, to 4 at 90 percent, and

over 7 at 95 percent relative humidity (CD, p. 4-170, Figure 4-38).

Rayleigh scattering represents the degree of natural light scattering found in a particlefree atmosphere, caused by the gas molecules that make up "blue sky" (e.g., N2, O2). The

magnitude of Rayleigh scattering depends on the wavelength or color of the light being

scattered, as well as on the density of gas in the atmosphere, and varies by site elevation,

generally from 9 to 11 Mm-1 for green light at about 550 nm (CD, p. 4-156 to 4-157). A standard

value of 10 Mm-1

 is often used to simplify comparisons of light extinction values across a

number of sites with varying elevations (Malm, 2000; CD, p. 4-157). The concept of Rayleigh

scattering can be used to establish a theoretical maximum horizontal visual range in the earth's

atmosphere. At sea level, this maximum visual range is approximately 330 kilometers ignoring

the Earth’s curvature. Since certain meteorological conditions can lead to visibility conditions

that are close to "Rayleigh," it is analogous to a baseline or boundary condition against which

other extinction components can be compared. 

The light extinction coefficient integrates the effects of aerosols on visibility, yet is not

dependent on scene-specific characteristics. It measures the changes in visibility linked to

emissions of gases and particles. By apportioning the light extinction coefficient to different
2-76

aerosol constituents, one can estimate changes in visibility due to changes in constituent

concentrations (Pitchford and Malm, 1994). 

2.8.2 Other Measures of Visibility 

Visual range is a measure of visibility that is inversely related to the extinction

coefficient. Visual range can be defined as the maximum distance at which one can identify a

large black object against the horizon sky. The colors and fine detail of many objects will be lost

at a distance much less than the visual range, however. Visual range has been widely used in air

transportation and military operations in addition to its use in characterizing air quality. 

Conversion from the extinction coefficient to visual range can be made with the following

equation (NAPAP, 1991):

Visual Range (km) = 3912/bext(Mm-1) (5-3)

Another important visibility metric is the deciview, a unitless metric which describes

changes in uniform atmospheric extinction that can be perceived by a human observer. It is

designed to be linear with respect to perceived visual changes over its entire range in a way that

is analogous to the decibel scale for sound (Pitchford and Malm, 1994). Neither visual range nor

the extinction coefficient has this property. For example, a 5 km change in visual range or 0.01

km-1 change in extinction coefficient can result in a change that is either imperceptible or very

apparent depending on baseline visibility conditions. Deciview allows one to more effectively

express perceptible changes in visibility, regardless of baseline conditions. A one deciview

change is a small but perceptible scenic change under many conditions, approximately equal to a

10 percent change in the extinction coefficient (Pitchford and Malm, 1994). Deciview can be

calculated from the light extinction coefficient (bext) by the equation:

Haziness (dv) = 10 ln(bext/10 Mm-1) (5-4)

Figure 2-31 graphically illustrates the relationships among light extinction, visual range, and

deciview.

2.8.3 Visibility at PM Background Conditions

Light extinction caused by PM from natural sources can vary significantly from day to

day and location to location due to natural events such as wildfire, dust storms, and volcanic

eruptions. It is useful to consider estimates of natural background concentrations of PM on an

annual average basis, however, when evaluating the relative contributions of anthropogenic

(man-made) and non-anthropogenic sources to total light extinction. Background PM is defined

and discussed in detail in section 2.6, and Table 2-65 provides the annual average regional

background PM2.5 mass ranges for the eastern and western U.S.. 
2-77

Figure 2-31. Relationship between light extinction, deciviews, and visual

range.

Source: Malm (1999)

The National Acid Precipitation Assessment Program report (NAPAP, 1991) provides

estimates of extinction contributions from background levels of fine and coarse particles, plus

Rayleigh scattering. In the absence of anthropogenic emissions of visibility-impairing particles,

these estimates are 26 + 7 Mm-1 in the east, and 17 + 2.5 Mm-1 in the west. These equate to a

naturally-occurring visual range in the east of 150 + 45 km, and 230 + 35 km in the west. 

Excluding light extinction due to Rayleigh scattering, annual average background levels of fine

and coarse particles are estimated to account for approximately 14 Mm-1 in the east and about 6

Mm-1 in the west. The primary non-anthropogenic substances responsible for natural levels of

visibility impairment are naturally-occurring organics, suspended dust (including coarse

particles), and water associated with hygroscopic particles. At the ranges of fine particle

concentrations associated with background conditions, discussed above in section 2.6, small

changes in fine particle mass have a large effect on total light extinction. Thus, higher levels of

background fine particles and associated average humidity levels in the east result in a fairly

significant difference between naturally occurring visual range in the rural east as compared to

the rural west. This issue is discussed further in Chapter 6, section 6.2.

Fine particles originate from both natural and anthropogenic, or man-made, sources. 

Background concentrations of fine particles are those originating from natural sources. On an

annual average basis, concentrations of background fine particles are generally small when

compared with concentrations of fine particles from anthropogenic sources (NRC, 1993). The

same relationship holds true when one compares annual average light extinction due to

background fine particles with light extinction due to background plus anthropogenic sources. 

Table VIII-4 in the 1996 Staff Paper makes this comparison for several locations across the

country by using background estimates from Table VIII-2 and light extinction values derived

from monitored data from the IMPROVE network. These data indicate that anthropogenic

emissions make a significant contribution to average light extinction in most parts of the country,

as compared to the contribution from background fine particle levels. Anthropogenic

contributions account for about one-third of the average extinction coefficient in the rural west

and more than 80 percent in the rural east (NAPAP, 1991).
2-78

It is important to note that, even in areas with relatively low concentrations of

anthropogenic fine particles, such as the Colorado plateau, small increases in anthropogenic fine

particle concentrations can lead to significant decreases in visual range. As discussed in the CD,

visibility in an area with lower concentrations of air pollutants (such as many western Class I

areas) will be more sensitive to a given increase in fine particle concentration than visibility in a

more polluted atmosphere. Conversely, to achieve a given amount of visibility improvement, a

larger reduction in fine particle concentration is required in areas with higher existing

concentrations, such as the east, than would be required in areas with lower concentrations. This

relationship between changes in fine particle concentrations and changes in visibility (in

deciviews) also illustrates the relative importance of the overall extinction efficiency of the

pollutant mix at particular locations. At a given ambient concentration, areas having higher

average extinction efficiencies, due to the mix of pollutants, would have higher levels of

impairment. In the east, the combination of higher humidity levels and a greater percentage of

sulfate as compared to the west causes the average extinction efficiency for fine particles to be

almost twice that for sites on the Colorado Plateau. 
2-79

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Spengler, J. D. (1993). Particle total exposure assessment methodology (PTEAM) study: distributions of

aerosol and elemental concentrations in personal, indoor, and outdoor air samples in a southern California

community. J. Exposure Anal. Environ. Epidemiol. 3: 227-250.

Environmental Protection Agency (1986). Guideline on the Identification and Use of Air Quality Data Affected by

Exceptional Events. EPA-450/4-86-007.

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Feldman, G. C.; McClain, C.; Frouin, R. J.; Merrill, J.; DuBois, D.; Vignola, F.; Murayama, T.;

Nickovic, S.; Wilson, W. E.; Sassen, K.; Sugimoto, N.; Malm, W. C. (2001) Asian dust events of

April 1998. J. Geophys. Res. (Atmos.) 106: 18,317-18,330.

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source and receptor sites of the Los Angeles basin. J. Air. Waste Manag. Assoc.., Mar; 52(3):297-307.

Langstaff, J. E. (2005). OAQPS Staff Memorandum to PM NAAQS Review Docket (OAR-2001-0017). Subject:

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States. Report III. Colorado State University, Cooperative Institute for Research in the Atmosphere. Fort

Collins, CO. Available: http://vista.cira.colostate.edu/improve/Publications/Reports/2000/2000.htm [22

March, 2002].

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National Research Council. 1993. Protecting Visibility in National Parks and Wilderness Areas. National

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particles and metals: results from the particle TEAM study in Riverside, California. J. Exp. Anal. Environ.

Epidemiol. 6: 57-78.

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3-1

3. POLICY-RELEVANT ASSESSMENT OF HEALTH EFFECTS EVIDENCE

3.1 INTRODUCTION

This chapter assesses key policy-relevant information on the known and potential health

effects associated with exposure to ambient PM, alone and in combination with other pollutants

that are routinely present in ambient air. More specifically, this assessment focuses on health

effects associated with exposures to ambient fine particles and to thoracic coarse particles,

consistent with EPA's decision in the last review to establish new standards for fine particles

separate from those intended to address effects related to thoracic coarse particles. The

presentation here first summarizes the qualitative assessment of health evidence contained in the

CD, as a basis for the evidence-based assessment of primary standards for PM presented in

Chapter 5. Secondly, this assessment addresses key issues relevant to quantitative assessment of

the epidemiologic health evidence available in this review so as to provide a foundation for the

quantitative health risk assessment discussed in Chapter 4 and used in the risk-based assessment

of primary standards for PM presented in Chapter 5.

In the last review of the PM NAAQS, a variety of health effects had been associated with

ambient PM at concentrations extending from those elevated levels found in the historic London

episodes down to levels below the 1987 PM10 standards. The epidemiologic evidence for PMrelated effects was found to be strong, suggesting a “likely causal role” of ambient PM in

contributing to a range of health effects (62 FR 38657). Of special importance in the last review

were the conclusions that (1) ambient particles smaller than 10 μm that penetrate into the

thoracic region of the respiratory tract remained of greatest concern to health, (2) the fine and

coarse fractions of PM10 should be considered separately for the purposes of setting ambient air

quality standards, and (3) the consistency and coherence of the health effects evidence greatly

added to the strength and plausibility of the observed PM associations. Important uncertainties

remained, however, such as issues related to interpreting the role of gaseous co-pollutants in PM

associations with health effects, and the lack of demonstrated biologic mechanisms that could

explain observed effects.

EPA’s conclusion in the last review that fine and thoracic coarse particles should be

considered as separate pollutants was based on differences in physical and chemical properties,

sources, atmospheric formation and transport, relationships with human exposure, and evidence

of health effects (62 FR 38667). In this review, the CD has evaluated the newly available

evidence related to the physics and chemistry of particulate matter, exposure relationships, and

particle dosimetry. The CD notes that the chemical and physical distinctions between fine and

coarse particles recognized in the last review remain generally unchanged; recent studies

continue to show that fine and coarse particles generally have different sources and composition

and different formation processes (see Table 2-2 herein). Recent exposure research finds that

accumulation-mode fine particles can infiltrate into buildings more readily than can thoracic
3-2

coarse particles, and that ambient concentrations of PM10-2.5 are less well correlated and less

uniform across a community than ambient concentrations of PM2.5 (CD, p. 9-21). The CD also

concludes that the new evidence from dosimetry studies continues to reinforce distinctions

between fine and coarse particles, and submodes within fine particles, with regard to deposition

patterns in the respiratory tract, though there is significant overlap between particle size classes

(CD, p. 9-21 to 9-22). Based on these considerations, the CD concludes that it remains

appropriate to consider fine and thoracic coarse particles as separate subclasses of PM (CD, p.

9-22).

The assessment of health evidence in this chapter therefore focuses on health effects

associated with fine and thoracic coarse particles, drawing from the CD’s evaluation and

conclusions on the full body of evidence from health studies, summarized in Chapters 6 through

9 of the CD, with particular emphasis on the integrative synthesis presented in Chapter 9. That

integrative synthesis focuses on integrating newly available scientific information with that

available from the last review and integrated from various disciplines, so as to address a set of

issues central to EPA’s assessment of scientific information upon which this review of the PM

NAAQS is to be based. It is intended to provide a coherent framework for assessment of human

health effects elicited by ambient PM in the U.S., and to facilitate consideration of the key

policy-related issues to be addressed in this Staff Paper, including recommendations as to

appropriate indicators, averaging times, levels, and forms for PM NAAQS.

As summarized in Chapters 6 through 9 of the CD, a large number of new studies

containing further evidence of serious health effects have been published since the last review,

with important new information coming from epidemiologic, toxicologic, controlled human

exposure, and dosimetric studies. As was true in the last review, evidence from epidemiologic

studies plays a key role in the CD’s evaluation of the scientific evidence. As discussed further in

section 3.3, some highlights of the new evidence include:

• New multi-city studies that use uniform methodologies to investigate the effects

of various indicators of PM on health with data from multiple locations with

varying climate and air pollution mixes, contributing to increased understanding

of the role of various potential confounders, including gaseous co-pollutants, on

observed associations. These studies provide more precise estimates of the

magnitude of an effect of exposure to PM than most smaller-scale individual city

studies.

• More studies of various health endpoints evaluating independent associations

between effects and fine and thoracic coarse particles, as well as ultrafine

particles or specific components (e.g., sulfates, metals). 

• Numerous new studies of cardiovascular endpoints, with particular emphasis on

assessment of cardiovascular risk factors or physiological changes.
1

 “Crustal” is used here to describe particles of geologic origin, which can be found in both fine- and

coarse-fraction PM. 

3-3

• Studies relating population exposure to PM and other pollutants measured at

centrally located monitors to estimates of exposure to ambient pollutants at the

individual level have lead to a better understanding of the relationship between

ambient PM levels and personal exposures to ambient PM.

• New analyses and approaches to addressing issues related to potential

confounding by gaseous co-pollutants, possible thresholds for effects, and

measurement error and exposure misclassification.

• Preliminary attempts to evaluate the effects of air pollutant combinations or

mixtures including PM components using factor analysis or source apportionment

methods to link effects with different PM source types (e.g., combustion, crustal1

sources).

• Several “intervention studies” have reported improvements in health in areas

where policy, economic or regulatory changes resulted in reduced air pollutant

concentrations (section 8.2.3.4 in the CD). 

In addition, the body of evidence on PM-related effects has greatly expanded with

findings from studies that help inform mechanisms of action, including important new

dosimetric, toxicologic and controlled human exposure studies.

• Animal and controlled human exposure studies using concentrated ambient

particles (CAPs), new indicators of response (e.g., C-reactive protein and

cytokine levels, heart rate variability), and animal models simulating sensitive

subpopulations, that are relevant to demonstrating plausibility of the

epidemiologic evidence and provide insights into potential mechanisms for PMrelated effects.

• Dosimetric studies using new modeling methods that provide increased

understanding of the dosimetry of different particle size classes and in members

of potentially sensitive subpopulations, such as people with chronic respiratory

disease.

In presenting that evidence and conclusions based on it, this chapter first summarizes

information from the CD’s evaluation of health evidence from the different disciplines. Sections

3.2 and 3.3 provide overviews of the CD’s findings on the evidence of potential mechanisms for

PM-related effects and on the nature of effects associated with PM exposures, respectively. 

Drawing from the integration of evidence in Chapter 9 of the CD, the chapter summarizes the
3-4

CD’s integrative findings and conclusions regarding causality in section 3.4, with a particular

focus on results for fine and thoracic coarse particles. Section 3.5 also draws from the CD’s

integrative synthesis to characterize potential at-risk subpopulations and potential public health

impacts of exposure to ambient PM. Finally, section 3.6 addresses several key issues relevant to

the staff’s interpretation and quantitative assessment of the health evidence, including:

(1) considerations related to air quality measurements and data used in the health studies;

(2) measurement error and exposure error in fine and thoracic coarse particle studies;

(3) specification of models used in epidemiologic studies; (4) approaches to evaluating the role

of co-pollutants and potential confounding in PM-effects associations; (5) questions related to

exposure time periods used in associations between air quality and health effects, including lag

periods used in short-term exposure studies and the selection of time periods used to represent

exposures in long-term exposures studies; and (6) questions related to the form of concentrationresponse relationships and potential threshold levels. In this final section, staff builds upon the

CD’s detailed evaluation and integration of the scientific evidence on these issues to reach

conclusions regarding the use of the health study results in quantitative evaluation and the PM

risk assessment discussed in Chapter 4.

3.2 MECHANISMS 

This section provides an overview of evidence presented in the CD on potential

mechanisms by which exposure to PM may result in effects, drawing from Chapters 6 and 7 of

the CD. Evidence from dosimetric studies has played a key role in previous PM NAAQS

reviews, especially in the decision to revise the indicator from total suspended particulates (TSP)

to PM10 to focus on thoracic particles (52 FR 24634, July 1, 1987). In contrast, in previous

reviews of the PM NAAQS there has been little available evidence on potential biologic

mechanisms by which deposited particles could affect the lungs or heart. 

An evaluation of the ways by which inhaled particles might ultimately affect human

health must take account of patterns of deposition and clearance in the respiratory tract (CD,

p. 6-1). Briefly, the human respiratory tract can be divided into three main regions: (1)

extrathoracic, (2) tracheobronchial, and (3) alveolar (CD, Figure 6-1). The regions differ

markedly in structure, function, size, mechanisms of deposition and removal, and sensitivity or

reactivity to deposited particles. Overall, the health concerns related to ambient particles are

greater for the two lower regions.

Fine particles, including accumulation mode and ultrafine particles, and thoracic coarse

particles can all penetrate into and be deposited in the tracheobronchial and alveolar regions of

the respiratory tract, though (as noted above) there are differences among these size fractions. 

Penetration into the tracheobronchial and alveolar regions is greater for accumulation mode

particles than coarse or ultrafine particles, since coarse and ultrafine particles are more
3-5

efficiently removed from the air in the extrathoracic region than are accumulation-mode fine

particles (CD, 6-105). 

Once past the extrathoracic region, deposition fraction in the tracheobronchial and

alveolar regions varies with different exertion levels or breathing patterns, and whether breathing

is through the mouth or nose. As shown in Figures 6-16 and 6-17 of the CD, deposition fractions

in these regions are largest for particles in the coarse fraction and ultrafine modes. More

specifically, the CD concludes that fractional deposition in the alveolar region of the respiratory

system for healthy individuals is greatest for particles in the size ranges of approximately 2.5 to

5 μm and 0.02 to 0.03 μm, and fractional deposition to the tracheobronchial region is greatest for

particles in the size range of approximately 4 to 6 μm (CD, p. 6-109). 

Respiratory disease status can also affect regional particle deposition patterns. Studies

have indicated that, in general, enhanced deposition of particles occurs at airway bifurcations

(CD, p. 6-20). New evidence confirms that people with chronic obstructive lung diseases can

have increased total lung deposition and can also show increases in local deposition (“hot spots”)

due to uneven airflow in diseased lungs (CD, p. 6-34). In such cases, the respiratory condition

can enhance sensitivity to inhaled particles by increasing the delivered dose overall as well as

increased doses to localized regions. Such dosimetry studies are of obvious relevance to

identifying sensitive populations (see section 3.5).

The potential effects of deposited particles are influenced by the rate and nature of

removal. The predominant clearance and translocation mechanisms vary across the three regions

of the respiratory system. For example, dissolution or absorption of particles or particle

constituents and endocytosis by cells such as macrophages are two primary mechanisms

operating in the alveolar region. These mechanisms also occur in the tracheobronchial region,

but the primary mechanisms for particle clearance or translocation from the tracheobronchial

region are mucociliary transport and coughing (CD, 6-44, Table 6-2). Soluble components of

particles may also move into the circulatory system and thus throughout the body. Recent

studies also suggest that ultrafine particles or their soluble constituents may move directly from

the lungs into the systemic circulation, providing a pathway by which ambient PM exposure

could affect extrapulmonary organs (CD, p. 6-55). 

In summary, new evidence from dosimetry studies has advanced our understanding of the

complex and different patterns of particle deposition and clearance in the respiratory tract

exhibited by fine particles in the accumulation mode, ultrafine particles, and thoracic coarse

particles. The evidence shows that all three size fractions can enter the tracheobronchial or

alveolar regions of the respiratory system and potentially cause effects. 

A major research need identified in the last review was the need to understand the

potential biologic mechanisms by which deposited particles could result in the varying effects

observed in epidemiologic studies with PM exposure. New evidence from toxicologic and

controlled human exposure studies has helped to identify and provide support for a number of
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potential pathways by which particles could have biologic effects, as discussed in Chapter 7 of

the CD. Fully defining the mechanisms of action for PM would involve description of the

pathogenesis or origin and development of any related diseases or processes resulting in

premature mortality. While the evidence summarized in the CD has provided important insights

that contribute to the plausibility of effects observed in community health studies, this more

ambitious goal of fully understanding fundamental mechanisms has not yet been attained. Some

of the more important findings presented in chapter 7 of the CD, including those related to the

cardiovascular system, may be more accurately described as intermediate responses potentially

caused by PM exposure rather than complete mechanisms. It appears unlikely that the complex

mixes of particles that are present in ambient air would act alone through any single pathway of

response. Accordingly, it is plausible that several physiological responses might occur in

concert to produce reported health endpoints.

By way of illustration, Mauderly et al. (1998) discussed particle components or

characteristics hypothesized to contribute to PM health, producing an illustrative list of 11

components or characteristics of interest for which some evidence existed. The list included:

1) PM mass concentration, 2) PM particle size/surface area, 3) ultrafine PM, 4) metals, 5) acids,

6) organic compounds, 7) biogenic particles, 8) sulfate and nitrate salts, 9) peroxides, 10) soot,

and 11) co-factors, including effects modification or confounding by co-occurring gases and

meteorology. The authors stress that this list is neither definitive nor exhaustive, and note that

“it is generally accepted as most likely that multiple toxic species act by several mechanistic

pathways to cause the range of health effects that have been observed” (Mauderly et al., 1998).

In assessing the more recent animal, controlled human, and epidemiologic information,

the CD developed a summary of current thinking on pathophysiological mechanisms for the

effects related to PM exposure. Section 7.10.1 of the CD discusses a series of potential

mechanisms or potential general pathways for effects on the heart and lung. The CD’s

conclusions on the evidence supporting different types of effects is briefly summarized below. 

The relative support for these potential mechanisms/intermediate effects and their relevance to

real world inhalation of ambient particles varies significantly. Moreover, the CD highlights the

variability of results that exist among different approaches, investigators, animal models, and

even day-to-day within studies. Nonetheless, the CD states that “[f]indings since 1996 have

provided evidence supporting many hypotheses regarding induction of PM effects; and this body

of evidence has grown substantially.” (CD, p. 7-205). For the most part, the evidence from

toxicologic and controlled human exposure studies discussed below reflects the effects of fine

particles or fine particle constituents.

Direct Pulmonary Effects. Potential pathways for direct pulmonary effects include:

lung injury and inflammation, increased airway reactivity and asthma exacerbation, and

increased susceptibility to respiratory infections. The CD finds “particularly compelling”

evidence that PM exposure causes lung injury and inflammation. Evidence that supports
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hypotheses on direct pulmonary effects includes toxicologic and controlled human exposure

studies using both sources of ambient particles and combustion-related particles. Toxicologic

studies using inhalation or intratracheal instillation of ambient particles from various locations

have shown that ambient particles can cause lung inflammation and injury (CD, Tables 7-4 and

7-5). Several studies using filter extracts from Utah Valley ambient samples collected before,

during and after the shut-down of a major particle-emitting facility have reported effects such as

increases in oxidant generation, release of cytokines such as IL-8, and evidence of pulmonary

injury such as increased levels of lactate dehydrogenase (CD, p 7-46, 7-47). Administration of

residual oil fly ash has been shown to produce acute lung injury and severe inflammation, with

effects including recruitment of neutrophils, eosinophils and monocytes into the airway (CD, p.

7-60). New toxicologic or controlled human exposure studies using exposure to CAPs have

reported some evidence of inflammatory responses in animals, as well as increased susceptibility

to infections, though the results of this group of studies are more equivocal (CD, p. 7-85). In

vitro studies, summarized in section 7.4.2 of the CD, also report evidence of lung injury,

inflammation, or altered host defenses with exposure to ambient particles or particle constituents. 

Some toxicologic evidence also indicates that PM can aggravate asthmatic symptoms or increase

airway reactivity, especially in studies of the effects of diesel exhaust particles (CD, section

7.3.5). Finally, some new evidence suggests that particles can initiate neurogenic responses in

the respiratory system. For example, several studies have indicated that some particles can

activate sensory nerve receptors in the airways, leading to inflammatory responses such as

cytokine release (CD, section 7.4.4.4)

Systemic Effects Secondary to Lung Injury. Adding to the list of direct pulmonary

effects, these pathways include: impairment of lung function leading to cardiac effects,

pulmonary inflammation and cytokine production leading to systemic hemodynamic effects,

lung inflammation leading to increased blood coagulability, and lung inflammation leading to

hematopoiesis effects. While more limited than for direct pulmonary effects, some new evidence

from toxicologic studies suggests that injury or inflammation in the respiratory system can lead

to changes in heart rhythm, reduced oxygenation of the blood, changes in blood cell counts, or

changes in the blood that can increase the risk of blood clot formation, a risk factor for heart

attacks or strokes (CD, pp. 7-209 to 7-212). 

Effects on the Heart. In addition, potential pathways for effects on the heart include:

effects related to uptake of particles or particle constituents in the blood, and effects on the

autonomic control of the heart and circulatory system. In the last review, little or no evidence

was available on potential cardiovascular effects from toxicologic studies. More recent studies

have provided some initial evidence that particles can have direct cardiovascular effects. As

shown in Figure 7-1 of the CD, there are several pathways by which particle deposition in the

respiratory system could lead to cardiovascular effects, such as PM-induced pulmonary reflexes

resulting in changes in the autonomic nervous system that then could affect heart rhythm (CD, p.
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7-8). Also, inhaled PM could affect the heart or other organs if particles or particle constituents

are released into the circulatory system from the lungs; some new evidence indicates that the

smaller ultrafine particles or their soluble constituents can move directly from the lungs into the

systemic circulation (CD, p. 6-55). The CD concludes that the data remain limited but provide

some new insights into mechanisms by which particles, primarily fine particles, could affect the

cardiovascular system (CD, 7-35, 7-212).

The above list of potential mechanisms and/or general pathways for effects was

developed mainly in reference to effects from short-term rather than long-term exposure to PM. 

Repeated occurrences of some short-term insults, such as inflammation, might contribute to

long-term effects, but wholly different mechanisms might also be important in the development

of chronic responses. Some mechanistic evidence is available, however, for potential

carcinogenic or genotoxic effects of particles. Section 7.10.1 of the CD also includes a

discussion of the evidence for mutagenic or genotoxic effects of particles or particle constituents,

concluding that “both ambient PM and combustion products of coal, wood, diesel, and gasoline

are mutagenic/genotoxic.” (CD, p.7-215). 

While some new studies have exposed animals or humans to ambient fine particles, many

toxicologic and controlled human exposure studies have used exposures to fine particle

constituents or emission-related particles, such as fly ash or diesel exhaust particles. The

evidence related to fine particle types or components is summarized in section 7.10.2 of the CD. 

Overall, the findings indicate that different health responses are linked with different particle

characteristics and that both individual components and complex particle mixtures appear to be

responsible for many biologic responses relevant to fine particle exposures (CD, p. 7-206).

In addition to the evidence discussed above that related primarily to fine particles, there

is some limited evidence from toxicologic studies on PM10-2.5, for either acute or chronic

exposures (CD, p. 9-55). The CD includes results from several in vitro toxicologic studies that

provide some insight into potential effects of thoracic coarse particles, particularly related to

inflammatory or allergic effects. Two recent studies report inflammatory responses in cells

exposed to extracts of water-soluble and water-insoluble materials from thoracic coarse particles

and fine particles collected in Chapel Hill, NC (CD, p. 7-83, Monn and Becker, 1999; and CD, p.

7-101 and 7-102, Soukup and Becker, 2001). One study focused on water-soluble materials, and

reported significant cytotoxicity and cytokine production with water-soluble extracts of ambient

PM10-2.5, in contrast to the lack of effects observed with extracts from ambient PM2.5 as well as

indoor-collected PM10-2.5 and PM2.5. The authors report that endotoxin appeared to have a role in

inflammatory effects, while metals appeared to have a role in the cytoxocity of thoracic coarse

particle materials (CD, p. 7-83, Monn and Becker, 1999). Soukup and Becker (2001) used both

soluble and insoluble components of thoracic coarse particles and fine particles, and report that

the insoluble materials from thoracic coarse particles resulted in cytokine production, decreased

phagocytic ability and oxidant generation (CD, p. 7-101 and 7-102). In this extract of thoracic
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coarse particles, endotoxin appeared to be the most pro-inflammatory component, but “other

moieties” (not endotoxin or metals) appeared to contribute to oxidant generation (CD, p. 7-102). 

Using particles collected in two urban areas in the Netherlands, Becker et al. (2003) reported that

thoracic coarse particles, but not fine or ultrafine particles, resulted in effects related to

inflammation and decreased pulmonary defenses (CD, p. 7-106). This small group of studies

thus suggests that exposure to thoracic coarse particles may cause pro-inflammatory effects, as

well as cytotoxicity and oxidant generation.

In addition, Diociaiuti et al. (2001) reported greater hemolytic effects with fine particles

than with thoracic coarse particles when exposing blood cell cultures to extracts of particles

collected in an urban area of Rome; increased hemolysis was seen with only the highest PM10-2.5

dose (CD, p. 7-102). Hornberg et al. (1998) reported evidence of genotoxic activity in human

bronchoepithelial cells exposed to both PM2.5 and PM10-2.5, with stronger evidence for

genotoxicity in fine particles (CD, p. 7-171). These two studies suggest only limited hemolytic

or carcinogenic effects of thoracic coarse particle exposures.

Road dust is a common source of thoracic coarse particles and can be considered as a PM

sample that is more representative of thoracic coarse particles than fine particles. In the 1996

Staff Paper, results from one key toxicologic study were highlighted in which immunological

and cellular toxicity was observed in rats with exposure to road dust. Higher concentrations of

road dust were needed to cause effects, compared with exposures to fine particle components

(e.g., sulfates, nitrates), but it was observed that some of the apparent differential toxicity was

due to differential penetration efficiencies of particles in the rat (EPA, 1996b, p. V-70). A recent

study reported that road tunnel dust particles had greater adjuvant activity in two animal models

of allergy than several other particle samples, including residual oil fly ash and diesel exhaust

particles (Steerenberg et al., 2003; CD, p. 7-136 to 7-137). In contrast, a number of studies have

reported that Mt. St. Helens volcanic ash, which is generally in the size range of thoracic coarse

particles, has very little toxicity in animal or in vitro toxicologic studies (CD, p. 7-216).

Many of the newer studies use relatively high doses (in mg or hundreds of μg), though

some have used doses that are close to ambient concentrations. A key consideration for

evaluating the results of animal toxicologic studies is the relation between effects reported in

animals with high dose exposures to effects that would be expected in human populations with

ambient exposures. The CD presents an illustrative set of analyses evaluating the doses and

responses reported in human and animal studies in Appendix 7A of the CD. In the analyses,

dosimetric models were used to predict doses of deposited and retained particles in various

regions of the respiratory system for humans and rats. In this series of analyses, the dose ratios

for humans to rats were quite variable across dose metrics and respiratory system regions. For

example, even when humans and rats are similarly exposed (i.e., exposed at rest to the same

aerosol for 6 hours) the equivalent exposure ratios can range from 0.09 to 33 (CD, p. 7A-30,

Table 7A-7a). The CD also evaluated relative dose levels using data from two sets of studies in
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which toxicologic and controlled human exposure studies used the same type of ambient

particles (Utah Valley dust and concentrated ambient particles). Based on these data, deposited

and retained doses in the alveolar and tracheobronchial regions were estimated for three studies

using concentrated ambient particles, and doses were 40- to 67-fold higher in rats than in humans

from these inhalation exposure studies (CD, pp. 7A-61). However, the CD observed that similar

and/or lesser inflammatory responses were reported for rats than for humans, suggesting that rats

may be less susceptible to effects of concentrated ambient particles than healthy humans (CD, p.

7A-61). Recognizing the limitations of this small set of illustrative analyses, the CD concludes

that larger doses in rats may be dosimetrically equivalent to lower doses in humans, given the

faster particle clearance rates in rats (CD, p. 7A-62). However, the CD also observed that the

prediction of dose levels depends on a number of factors, and estimated equivalent exposure

ratios for rats and humans vary substantially (CD, 7-163).

The CD also observes that particles may help carry other airborne substances into the

respiratory tract (CD, section 7.9). For example, hygroscopic particles can take up moisture and

grow in the humid atmosphere of the respiratory tract, thus potentially altering the deposition

and clearance patterns of the particles. Water-soluble gases can be carried into the lung on

particles, and delivery of reactive gases such as SO2 and formaldehyde to the lower respiratory

regions can be increased when carried on particles since these gases would otherwise be more

likely trapped in the upper airways. Particles can also carry reactive oxygen species, such as

hydrogen peroxide, and other toxic compounds such as polynuclear aromatic hydrocarbons or

allergens, into the lower respiratory regions (CD, pp, 7-203, 7-204). 

In summary, while investigation of potential mechanisms for the effects of particles

remains an important research question, new mechanistic studies provide evidence to support a

number of hypothesized mechanisms of action for ambient PM, primarily for fine PM. In

evaluating this new body of evidence, the CD states: “Thus, there appear to be multiple biologic

mechanisms that may be responsible for observed morbidity/mortality due to exposure to

ambient PM. It also appears that many biologic responses are produced by PM whether it is

composed of a single component or a complex mixture” (CD, p. 7-206). 

3.3 NATURE OF EFFECTS

An extensive body of new epidemiologic studies has been published since completion of

the 1996 PM CD. In the last review, epidemiologic evidence indicated that exposure to PM

(using various indicators) was associated with increased risk for various cardiopulmonary

effects, including mortality and a range of indices of morbidity associated with respiratory and

cardiovascular disease such as hospital admissions and emergency room visits, school absences,

work loss days, restricted activity days, effects on lung function and symptoms, morphological

changes, and altered host defense mechanisms. The CD finds that recent epidemiologic studies

have continued to report associations between various indicators of ambient PM and effects such
2

 Findings of U.S. and Canadian studies are more directly applicable for quantitative considerations in this

review, since studies conducted in other countries may well reflect quite different population and air pollution

characteristics. 

3

 For consistency across studies, the effect estimates summarized in Appendices 3A and 3B, and the results

presented in figures in this section, are from single-pollutant models. Results of multi-pollutant models are

discussed in the text. As presented in the CD, effect estimates are presented using standardized PM increments to

allow for comparison across studies. For short-term exposures studies, increments of 50 μg/m3

 for PM10 and 25

μg/m3

 for PM2.5 and PM10-2.5 were used; for long-term exposures studies, increments of 20 μg/m3 for PM10 and 10

μg/m3

 for PM2.5 and PM10-2.5 were used (CD, p. 8-4).

4

 For studies that include results for GLM analyses using several methods to adjust for temporal or weather

variables, if no judgment is offered by the authors on model selection, staff has presented results from the models

using adjustment methods most closely matching those of the initial study.

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as premature mortality, hospital admissions or emergency department visits for respiratory and

cardiovascular disease, and effects on lung function and symptoms (CD, p. 9-23). In addition,

recent studies now identify several new types of health outcomes reported to be associated with

exposure to various indicators of PM, including physicians’ office or clinic visits, cardiovascular

health indicators such as heart rate variability or increased C-reactive protein levels, and

developmental effects such as low birth weight, and infant mortality (CD, p. 9-23, 9-24).

The discussions that follow draw primarily from epidemiologic evidence evaluated in

Chapter 8 of the CD as well as the CD’s integration of evidence across disciplines (section 9.2). 

The CD evaluates evidence from the full body of epidemiologic studies conducted world-wide

and summarizes results of all such mortality and morbidity studies in Appendices 8A and 8B,

respectively, in the CD. For the purposes of this Staff Paper, staff draws from the CD’s

qualitative evaluation of all studies, but focuses on those conducted in the U.S. and Canada for

quantitative assessments.2

 Effect estimates for mortality and morbidity effects associated with

increments of PM10, PM2.5, and PM10-2.5 from multi-city and single-city U.S. and Canadian studies

are summarized in Appendices 3A and 3B to this chapter for short-term and long-term exposure

studies, respectively, as a consolidated reference for the following discussions.3 

A number of the new time-series epidemiologic studies have used generalized additive

models (GAM) in their analyses, and issues have been found with the convergence criteria and

the method for determining standard errors when using GAM, as discussed in section 3.6.3 more

fully and in section 8.4.2 of the CD. In Appendix 3A, results are presented from those shortterm exposure studies that have been reanalyzed to address issues related to GAM, or that did

not use GAM in their analyses. In presenting study results in figures in this section, for studies

in which multiple reanalysis results were presented, staff has selected effect estimates based on

the authors’ stated judgments, where offered, or selected results from models using generalized

linear models (GLM).4
5

 The effect estimates in Figure 3-1 (for mortality effects) and in Figure 3-2 (for morbidity effects;

discussed below in section 3.3.2) have been plotted in order of decreasing study power, using as an indicator the

natural log of the product of the number of study days and number of health events per day. 

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3.3.1 Premature Mortality

This section includes an overview of the CD’s findings on (1) mortality associations with

short-term PM exposure, with emphasis on results from newly available multi-city analyses; and

(2) mortality associations with long-term PM exposure.

3.3.1.1 Mortality and Short-term PM Exposure

Historical reports of dramatic pollution episodes have provided clear evidence of

mortality associated with high levels of PM and other pollutants, as summarized in the 1996 CD

(EPA, 1996a, pp. 12-28 to 12-31). More recently, associations between increased daily mortality

and various indicators of PM have been reported at much lower concentrations in a large number

of areas with differing climates, PM composition, and levels of gaseous co-pollutants. Since the

last review, a large number of new time-series studies of the relationship between short-term

exposure to various indicators of PM and mortality have been published, including several multicity studies that are responsive to the recommendations from the last review (CD, p. 8-24).

Included in the PM CD are results from numerous studies that have been conducted in single

cities or locations in the U.S. or Canada, as well as locations in Europe, Mexico City, South

America, Asia and Australia (Table 8A in the CD). As was observed based on the more limited

studies available in the last review, the associations reported in the recent studies on short-term

exposure to PM10 and mortality are largely positive, and frequently statistically significant. Staff

have focused on the results of studies conducted in the U.S. and Canada in this assessment;

effect estimates from U.S. and Canadian multi-city and single-city studies are presented in

Figure 3-1 for associations between PM10, PM2.5 and PM10-2.5 and mortality.5

In this review, the CD has emphasized the results of the multi-city studies as being of

particular relevance. The multi-city studies combine data from a number of cities that may vary

in climate, air pollutant sources or concentrations, and other potential risk factors. The

advantages of multi-city analyses include: (1) evaluation of associations in larger data sets can

provide more precise effect estimates than pooling results from separate studies; (2) consistency

in data handling and model specification can eliminate variation due to study design; (3) effect

modification or confounding by co-pollutants can be evaluated by combining data from areas

with differing air pollutant combinations; (4) regional or geographical variation in effects can be

evaluated; and (5) “publication bias” or exclusion of reporting of negative or nonsignificant

findings can be avoided (CD, p. 8-30). 

The National Morbidity, Mortality and Air Pollution Study (NMMAPS) is the largest

available multi-city analysis, and included analyses of PM10 effects on mortality in 90 U.S. cities

(Samet et al., 2000a,b; Dominici et al., 2003a). Additional, more detailed, analyses were 

conducted in a subset of the 20 largest U.S. cities (Samet et al., 2000b). The NMMAPS study
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Figure 3-1. Excess risk estimates for total nonaccidental, cardiovascular, and respiratory mortality in m

bold font below) and single-pollutant models for U.S. and Canadian studies. PM incrementsand 25 μg/m3 for PM2.5 and PM10-2.5. Results presented from time-series studies that did not reanalyzed using GLM. 

 1. Dominici et al. (2003a), 90 U.S. cities 2. Schwartz (2003b), 10 U.S. cities 3. Klemm and Mason (2003), 6 U.S. cities 4. Burnett and Goldberg (2003), 8 Canadian cities 5. Moolgavkar (2003), Cook County 6. Kinney et al. (1995), Los Angeles 7. Schwartz (2003b), Chicago 8. Ito and Thurston (1996), Cook County 9. Schwartz (2003b), Pittsburgh10. Styer et al. (1995), Cook County11. Schwartz (2003b), Detroit12. Moolgavkar (2003), Los Angeles13. Schwartz (2003b), Seattle

14. Schwartz (2003b), Minneapolis15. Klemm and Mason (2003), St. Louis16. Klemm and Mason (2003), Boston17. Schwartz (2003b), Birmingham18. Schwartz (2003b), New Haven19. Chock et al. (2000), Pittsburgh (< 75 y.o.)20. Chock et al. (2000), Pittsburgh (75+ y.o.)21. Klemm and Mason (2003), Kingston-Harriman22. Klemm and Mason (2003), Portage23. Schwartz (2003b), Canton24. Schwartz (2003b), Spokane25. Ito (2003), Detroit26. Fairley (2003), Santa Clara County

27. Schwartz (2003b), Co28. Klemm and Mason (229. Tsai et al. (2000), Ne30. Klemm and Mason (231. Pope et al. (1992), Ut32. Tsai et al. (2000), Eli33. Tsai et al (2000), Cam34. Lipfert et al. (2000), P35. Mar et al. (2003), Pho36. Ostro et al. (2003), C37. Klemm and Mason (238. Ostro et al. (1995), So
3-14

was designed to use a multi-city approach such as that recommended following an earlier report

of time-series study reanalyses that recommended investigating the role of co-pollutants in PMhealth outcome relationships by conducting multi-city studies, using consistent analytical

approaches across cities (HEI, 1997, p. 38; Samet et al., 2000c, p. 1). The NMMAPS used a

uniform methodology to evaluate the relationship between mortality and PM10 for the different

cities, and the results were synthesized to provide a combined estimate of effects across the

cities. The authors reported associations between total and cardiorespiratory mortality and PM10

that were robust to different modeling approaches and to adjustment for gaseous co-pollutants. 

For total mortality, the overall risk estimate for all cities is a statistically significant increase of

1.4% (using more stringent GAM) or 1.1% (using GLM) per 50 μg/m3

 PM10 (Dominici et al.,

2003a; CD, p. 8-33). Key components of the NMMAPS analyses include assessment of the

potential heterogeneity in effects and effects of co-pollutants, as discussed below in sections

3.4.3 and 3.6.4, respectively.

Another major multi-city study used data from 10 U.S. cities that were selected from

NMMAPS cities where daily PM10 monitoring data were available (in many areas, monitoring is

done on a 1-in-3 or 1-in-6 day basis) (Schwartz, 2003b). The authors reported a statistically

significant association between PM10 and total mortality, with an effect estimate of an increase of

3.4% per 50 μg/m3

 PM10 (in reanalyzed GAM results) or 2.8% per 50 μg/m3 PM10 (using GLM)

(Schwartz, 2003b; CD, p. 8-38). The CD observes that the effect estimates from this study are

larger than those reported in NMMAPS, and suggests that the availability of more frequent

monitoring data may partly account for the differences (CD, p. 8-39).

In the previous review, results for one key multi-city study were available, in which

associations were assessed between daily mortality and PM10, PM2.5, and PM10-2.5 measurements

from six U.S. cities (the “Six Cities” study) (Schwartz, et al., 1996). The authors reported

significant associations for total mortality with PM2.5 and PM10, but not with PM10-2.5. Reanalyses

of Six Cities data have reported results consistent with the findings of the original study, with

statistically significant increases in total mortality ranging from 2% to over 3% reported for

results from more stringent GAM or GLM analyses using either PM2.5 (per 25 μg/m3

 increment)

or PM10 (per 50 μg/m3

 increment), whereas PM10-2.5 was only significantly associated with

mortality in one of the six cities (Steubenville) (Schwartz, 2003a; Klemm and Mason, 2003; CD,

p. 8-40 to 8-41).

Using data for the eight largest Canadian cities, mortality was associated with PM2.5,

PM10, and PM10-2.5 and the effect estimates were of similar magnitude for each PM indicator

(Burnett et al., 2000; Burnett and Goldberg, 2003). Using either more stringent GAM or GLM,

the authors reported increases ranging from 2% to 3% in total mortality for each PM indicator. 

The association between mortality and PM2.5 generally remained statistically significant in a

number of analyses when gaseous co-pollutants and 0- and 1-day lags were included in the

models, although in a few instances the effect estimates were reduced and lost statistical
3-15

significance. Associations with PM10, and PM10-2.5 did not reach statistical significance, though

the effect estimates were similar in magnitude to those for PM2.5. While the associations

reported with PM10-2.5 were somewhat increased in magnitude in reanalyses, they did not reach

statistical significance. The CD concludes that it is difficult to compare the relative significance

of associations with PM2.5 and PM10-2.5, but for this study, “overall, they do not appear to be

markedly different” (Burnett and Goldberg, 2003; CD, p. 8-42).

The CD also highlights results of analyses from a major European multi-city study, the

Air Pollution and Health: A European Approach (APHEA) study, that evaluated associations

between mortality and various PM measures (CD, section 8.2.2.3.3). In the analyses that

included data from 29 European cities, overall effect estimates of 2 to 3% increased risk of

mortality per 50 μg/m3

 PM10 were reported; reanalysis resulted in reduced effect estimate size,

though the authors conclude that their findings are robust to the application of alternative

modeling strategies (Katsouyanni et al., 2003; CD, p. 8-47). Taken together, the CD concludes

that multi-city studies in the U.S., Canada, and Europe reported statistically significant

associations with effect estimates ranging from ~1.0 to 3.5% increased risk of total mortality per

50 μg/m3

 PM10 (CD, p. 8-50).

In considering the results from single-city analyses, Figure 3-1 shows that almost all

effect estimates for PM2.5 are positive and a number are statistically significant, particularly

when focusing on the results of studies with greater precision. As summarized in the CD, effect

estimates for total mortality from the multi-city studies range from ~1 to 3.5% per 25 μg/m3

PM2.5. For the relatively more precise single-city studies, effect estimates range from

approximately 2 to 6% per 25 μg/m3 PM2.5 (CD, p. 9-28). Figure 3-1 also shows effect estimates

for PM10-2.5 that are generally positive and similar in magnitude to those for PM2.5 and PM10, but

for total mortality, none reach statistical significance. Staff notes that on a unit mass basis, the

effect estimates for both PM2.5 and PM10-2.5 are generally larger than those for PM10, which is

consistent with PM2.5 and PM10-2.5 having independent effects (CD, p. 9-25).

In general, effect estimates are somewhat larger for respiratory and cardiovascular

mortality than for total mortality. In the NMMAPS analyses using data from the 20 largest U.S.

cities, the effect estimates for deaths from cardiorespiratory causes were somewhat larger than

those for deaths from all causes (1.6% versus 1.1% increased risk per 50 μg/m3 PM10, using

GLM) (Dominici, et al., 2003a; CD, p. 8-78). In Figure 3-1, for all three PM indicators, it can be

seen that not only is the effect estimate size generally larger for cardiovascular mortality, but the

effect estimates are also more likely to reach statistical significance. This is particularly true for

PM10-2.5, where two of the five effect estimates for cardiovascular mortality shown are positive

and statistically significant (Mar et al., 2003; Ostro et al., 2003). For respiratory mortality,

effect estimates are often larger than those for either total or cardiovascular mortality, but they

are often less precise, which would be expected since respiratory deaths comprise a small

proportion of total deaths. The CD concludes that effect estimates fall in the range of 3 to 7%
3-16

per 25 μg/m3

 PM2.5 for cardiovascular or cardiorespiratory mortality, and 2 to 7% per 25 μg/m3

PM2.5 for respiratory mortality in U.S. and Canadian cities. The magnitude of the effect

estimates for PM10-2.5 are similar to those for PM2.5, generally falling in the range of 3 to 8% for

cardiovascular mortality and 3 to 16% for respiratory mortality per 25 μg/m3 PM10-2.5 (CD, p.

8-306).

 While some of the studies conducted in Europe, Mexico or South America use

gravimetric PM measurements (e.g., PM10, PM2.5, PM10-2.5), many of the non-North American

studies use PM indicators such as TSP, black smoke (BS) or coefficient of haze (COH), and the

Australian studies used nephelometric measures of PM. While effect estimates for different PM

indicators may not be quantitatively comparable, the CD observes that “many of the newly

reported analyses continue to show statistically significant associations between short-term

(24-hr) PM exposures indexed by a variety of ambient PM measurements and increases in daily

mortality in numerous U.S. and Canadian cities, as well as elsewhere around the world” (CD,

p. 8-24). These effect estimates are generally within (but toward the lower end of) the range of

PM10 estimates previously reported in the 1996 PM AQCD. 

As discussed in section 8.2.2.5 of the CD, associations have been reported between

mortality and short-term exposure to a number of PM components, especially fine particle

components. Three recent studies have used PM2.5 speciation data to evaluate the effects of air

pollutant combinations or mixtures using factor analysis or source apportionment methods to

link effects with different PM2.5 source types. These studies reported that fine particles from

combustion sources, including motor vehicle emissions, coal combustion, oil burning and

vegetative burning, were associated with increased mortality. No significant increase in

mortality was reported with a source factor representing crustal material in fine particles (CD, p.

8-85). These studies indicate that exposure fine particles from combustion sources, but not

crustal material, is associated with mortality. 

The findings of these studies, while providing some insight into what sources of fine

particles might be associated with mortality, are not directly relevant to evaluating effects of

thoracic coarse particles from different sources. Combustion sources are a major contributor to

PM2.5 emissions, but not PM10-2.5, while crustal material is an important component of PM10-2.5 but

only a small portion of PM2.5. Staff observes that no epidemiologic evidence is available to

evaluate effects of different components or sources of thoracic coarse particles. One study that

does have some relevance to considering the effects of PM10-2.5 from different sources assessed

the contribution of dust storms to PM10-related mortality. The authors focused on days when

dust storms or high wind events occurred, during which thoracic coarse particles are the

dominant fraction of PM10, in Spokane. No evidence was reported of increased mortality on

days with high PM10 levels related to dust storms (average PM10 level was 221 μg/m3

 higher on
6

In addition, studies conducted in several areas in the western U.S. have reported that associations between

PM10 and mortality or morbidity remained unchanged or became larger and more precise when days indicative of

wind-blown dust or high PM10 concentration days were excluded from the analyses (Pope et al., 1999; Schwartz,

1997; Chen et al., 2000; Hefflin et al., 1994). This group of studies does not provide conclusive evidence, however,

of any effects or lack of effects associated with wind-blown dust or high concentration days, but does indicate that

associations between PM10 and health outcomes in these western areas are not overly influenced or “driven by” such

days. 

3-17

dust storm days than on other study days) (Schwartz, et al., 1999), suggesting that PM10-2.5 from

wind-blown dust is also not likely associated with mortality.6

3.3.1.2 Mortality and Long-term PM Exposure

In the 1996 PM CD, results were presented for three prospective cohort studies of adult

populations (i.e., the Six Cities, American Cancer Society (ACS), and California Seventh Day

Adventist (AHSMOG) studies). The 1996 CD concluded that the chronic exposure studies,

taken together, suggested associations between increases in mortality and long-term exposure to

PM (EPA, 1996a, p. 13-34). New studies discussed in the CD (section 8.2.3) include a

comprehensive reanalysis of data from the Six Cities and ACS studies, new analyses using

updated data from the AHSMOG and ACS studies, and a new analysis using data from a cohort

of veterans. Effect estimates from all four of these studies are provided in Appendix 3B.

The reanalysis of the Six Cities and ACS studies included two major components, a

replication and validation study, and a sensitivity analysis, where alternative risk models and

analytic approaches were used to test the robustness of the original analyses. The reanalysis

investigators replicated the original results, confirming the original investigators’ findings of

associations with both total and cardiorespiratory mortality (Krewski et al., 2000; CD, p. 8-95). 

In single-pollutant models, none of the gaseous co-pollutants was significantly associated with

mortality except SO2. Further reanalyses of the ACS study included multi-pollutant models with

the gaseous pollutants, and the associations between mortality and both fine particles and

sulfates were unchanged in these models, except for those including SO2. While recognizing that

increased mortality may be attributable to more than one component of ambient air pollution, the

authors report that the reanalysis confirmed the association between mortality and fine particle

and sulfate exposures (Krewski et al., 2000; CD, p. 8-95).

The extended analyses for the ACS cohort study included follow-up health data and air

quality data from the new fine particle monitoring network for 1999-2000, and reported

significant associations between long-term exposure to fine particles (using various averaging

periods for air quality concentrations) and premature mortality from all causes, cardiopulmonary

diseases, and lung cancer (Pope et al., 2002; CD p. 8-102). This extended analysis included the

use of data on gaseous pollutant concentrations, more recent data on fine particle concentrations,

and evaluated further the influence of other covariates (e.g., dietary intake data, occupational

exposure) and model specification for the PM-mortality relationship (e.g., new methods for
3-18

spatial smoothing and random effects models in the Cox proportional hazards model) (CD, p. 8-

97). The investigators reported that the associations found with sulfate and fine particle

concentrations were robust to the inclusion of many covariates for socioeconomic factors or

personal health variables (e.g., dietary factors, alcohol consumption, body mass index); however,

as was found in the reanalysis of the original ACS study, education level was found to be an

effect modifier, in that associations were stronger for those with lower education levels (Pope et

al., 2002; CD, p. 8-104). In both the reanalyses and extended analyses of the ACS cohort study,

long-term exposure to PM10-2.5 was not significantly associated with mortality (CD, p. 8-105;

Krewski et al., 2000; Pope et al., 2002).

There are also new analyses using updated data from the AHSMOG cohort. These 

include more recent air quality data for PM10 and estimated PM2.5 concentrations from visibility

data, along with new health information from continued follow-up of the Seventh Day Adventist

cohort (CD, pp. 8-105, 8-110; Abbey et al., 1999; McDonnell et al., 2000). In contrast to the

original study in which no statistically significant results were reported with TSP, a significant

association was reported between total mortality and PM10 for males, but not for females (CD, p.

8-106). Additional analyses were conducted using data from males only and estimated PM2.5 and

PM10-2.5 concentrations; larger effect estimates were reported for mortality with PM2.5 than with

PM10-2.5, but the estimates were generally not statistically significant (McDonnell et al., 2000;

CD, pp. 8-110 and 8-117). 

In the VA cohort study, analyses were done using subsets of PM exposure and mortality

time periods, and the investigators report inconsistent and largely nonsignificant associations

between PM exposure (including, depending on availability, TSP, PM10, PM2.5, PM15 and

PM15-2.5) and mortality (CD, pp. 8-110 to 8-111; Lipfert et al., 2000b). 

Based on an evaluation of all the available long-term exposure studies, the CD places

greatest weight on the results of the Six Cities and ACS studies. In so doing, the CD notes that

the Six Cities and ACS studies (including reanalyses and extended analyses) included measured

PM data (in contrast with AHSMOG PM estimates based on TSP or visibility measurements),

have study populations more similar to the general population than the VA study cohort, and

have been validated through an exhaustive reanalysis (CD, pp. 8-116 and 8-118; 9-33). 

One new effect reported in the extended analysis of the ACS study was a statistically

significant association between fine particle and sulfate concentrations and lung cancer

mortality, with a 13% increased risk of lung cancer mortality per 10 μg/m3 PM2.5, using air

quality data averaged across all available years (CD, p. 8-99). This effect estimate is little

changed and remains significant with adjustment for covariates, random effects modeling and

spatial smoothing methods (CD, Figure 8-8). Also, in new analyses using updated data from the

AHSMOG cohort, positive associations were reported between long-term PM10 exposure lung

cancer mortality that were statistically significant for males, but not females (CD, p. 8-108 and

8-109).
3-19

Thus, emphasizing the results from the Six Cities and ACS cohorts, the CD finds that

there are significant associations for mortality with long-term exposure to PM2.5. Based on these

studies, effect estimates for deaths from all causes fall in a range of 6 to 13% increased risk per

10 μg/m3

 PM2.5, while effect estimates for deaths from cardiopulmonary causes fall in a range of

6 to 19% per 10 μg/m3 PM2.5. For lung cancer mortality, the effect estimate was a 13% increase

per 10 μg/m3 PM2.5 in the results of the extended analysis from the ACS cohort (Pope et al.,

2002; CD, Table 8-12). In addition, based on evidence from reanalyses and extended analyses

using ACS cohort data, the CD concludes that the long-term exposure studies find no

associations between long-term exposure to thoracic coarse particles and mortality (CD, p.

8-307).

3.3.2 Morbidity

The epidemiologic evidence also includes associations between various indicators of PM

and a wide range of endpoints reflecting both respiratory- and cardiovascular-related morbidity

effects. The following sections summarize the CD’s findings on PM-related morbidity effects,

beginning with hospital admissions and medical visits for respiratory and cardiovascular

diseases. Subsequent sections provide overviews of the CD’s evaluation of evidence for effects

on the respiratory and cardiovascular systems. Effect estimates for associations between shortterm exposure to PM10, PM2.5 and PM10-2.5 with hospitalization and medical visits from U.S. and

Canadian studies are presented below in Figure 3-2. Appendix 3A includes effect estimates for

associations with hospitalization and medical visits, as well as those for respiratory symptoms

and lung function and physiological cardiovascular effects, with short-term exposures to PM10,

PM2.5 or PM10-2.5 from U.S. and Canadian studies. The results for all new cardiovascular and

respiratory admissions/visits studies, including those using nongravimetric PM measurements

and studies from non-North American locations, are summarized in the CD in section 8.3, and a

more complete discussion of all studies is available in Appendix 8B of the CD.

3.3.2.1 Hospitalization and Medical Visits

Numerous recent studies have continued to report significant associations between shortterm exposures to PM and hospital admissions or emergency department visits for respiratory or

cardiovascular diseases. The new studies have included multi-city analyses, numerous

assessments using cardiovascular admissions/visits, and evaluation of the effects of fine and

thoracic coarse particles. 

The NMMAPS multi-city analysis included analyses of associations with hospital

admissions among the elderly, and reported statistically significant associations between PM10

and hospital admissions in the elderly for cardiovascular diseases, pneumonia and chronic

obstructive pulmonary disease (COPD) in 14 cities (Samet et al., 2000; Schwartz et al., 2003). 

Increases of 5% in hospital admissions for cardiovascular disease and 8% and 6% in hospital

admissions for COPD or pneumonia, respectively, per 50 μg/m3

 PM10 were reported in the 
3-20

Figure 3-2. Excess risk estimates for hospital admissions and emergency department visits for cardiova

and respiratory diseases in single-pollutant models from U.S. and Canadian studies, includinfrom one multicity study (as denoted in bold below). PM increments: 50 μg/m3 for PM10 anand PM10-2.5. Results presented from time-series studies that did not use GAM or were rean

PM effect size estimate (± 95% confidence intervals) are depicted for the studies listed belowFigure 9-5)

1. Zanobetti and Schwartz (2003)

U.S. 14 cities

2. Linn et al. (2000), Los Angeles3. Moolgavkar (2003), Cook County4. Moolgavkar (2003), Los Angeles5. Schwartz and Morris (1995), Detroit6. Morris and Naumova (1998), Chicago

 7. Burnett et al. (1997), Toronto 8. Ito (2003), Detroit 9. Stieb et al. (2000), St. John10. Schwartz (1994), Detroit11. Sheppard (2003), Seattle12. Nauenberg and Basu (1999), Los Angeles

13. Thurston et al. (1994), To14. Tolbert et al. (2000), Atla15. Lipsett et al. (1997), Sant16. Choudhury et al. (1997), M17. Delfino et al. (1997), Mon18. Delfino et al. (1998), Mon
3-21

NMMAPS. Effect estimates with PM10 were not correlated with city-specific correlations

between PM10 and co-pollutant levels, which the authors conclude indicates a lack of

confounding by co-pollutants, although the CD recognizes that further evaluation is needed on

this method to assess potential confounding (CD, p. 8-146, 8-175).

Numerous single-city studies have been published that report associations between shortterm PM10 exposure and hospitalization or medical visits for cardiovascular or respiratory

diseases. Overall, the CD reports that the more precise effect estimates for hospitalization range

from 2 to 6% per 50 μg/m3

 PM10 for cardiovascular diseases and 2 to 12% per 50 μg/m3 PM10 for

respiratory diseases. The CD also observes that new studies reporting associations between

PM10 and medical (e.g., physicians’ office) visits for respiratory diseases offer a link between the

more severe endpoints, such as increased mortality and hospital admissions or emergency room

visits for respiratory diseases, and less serious effects such as respiratory symptoms and

decreased lung function. These new studies also indicate the potentially more widespread public

health impact of exposure to PM (CD, p. 8-194).

As shown in Figure 3-2, associations between PM2.5 and hospitalization or emergency

room visits for the general category of respiratory diseases that are all positive and statistically

significant, while the results for individual disease categories (COPD, pneumonia, and asthma)

are less consistent, perhaps due to smaller sample sizes for the specific categories. Associations

with the general category of cardiovascular diseases are also all positive and statistically

significant or nearly so, but again the results for specific diseases (ischemic heart disease,

dysrhythmia, congestive heart disease or heart failure, and stroke) are positive but often not

statistically significant. Similarly, associations between hospital admissions for respiratory and

cardiovascular diseases and PM10-2.5 are generally positive and the more precise estimates are

statistically significant. Overall, the CD finds that excess risks for cardiovascular admissions

range from about 1 to 10% per 25 μg/m3 PM2.5 or PM10-2.5 (CD, p. 8-310). For total respiratory or

COPD admissions, risk estimates tend to fall in the range of 5 to 15% per 25 μg/m3

 PM2.5 or

PM10-2.5 (CD, p. 8-193). 

Many studies using PM10 or other PM indicators have been conducted in areas where fine

particles are the dominant fraction of PM10; results of these studies would likely be reflective of

associations with fine particles. In the last review, staff recognized that information about the

effects of thoracic coarse particles can also come from studies linking health effects with PM10 in

areas where thoracic coarse particles are the dominant fraction of PM10. Evidence available at

that time suggested that aggravation of asthma and respiratory infections and symptoms were

associated with PM10 in areas where thoracic coarse particles were dominant, such as Anchorage,

AK, and southeast Washington (62 FR 38679). Staff observes that several recent studies have

also been conducted in urban areas where thoracic coarse particles are the dominant fraction of

PM10, such as Reno, NV; Tucson, AZ; and Anchorage, AK, and these findings support the

evidence from the limited group of studies that have found associations between measured
3-22

PM10-2.5 and morbidity. In these areas, most of which have levels exceeding the PM10 standards,

statistically significant associations have been reported between PM10 and increased

hospitalization for cardiovascular diseases (Schwartz, 1997), hospitalization for respiratory

diseases (Chen et al., 2000) and medical visits for asthma (Choudhury et al., 1997).

3.3.2.2 Effects on the Respiratory System from Short-term Exposures

As was found in the last review, some significant associations have been reported

between increased respiratory symptoms and decreased lung function and short-term exposures

to PM (section 8.3.3 in the CD). For asthmatic subjects, associations were reported between

PM10 and PM2.5 and decreases in lung function measures (e.g., decreased peak expiratory flow

rate); some but not all of the associations reached statistical significance. In addition, positive

associations were reported between PM10 and PM2.5 and one or more of a range of respiratory

symptoms (e.g., cough, wheeze, shortness of breath), but the findings were less consistent than

those for lung function (CD, p. 8-199). In studies of nonasthmatic subjects, while inconsistent

results were reported for changes in lung function, there were generally positive associations for

respiratory symptoms that often were not statistically significant. Generally similar results were

found for both PM10 and PM2.5 (CD, p. 8-206).

Few studies of respiratory symptoms and lung function have included both PM2.5 and

PM10-2.5 data. The CD summarizes findings from a Six Cities study analysis (Schwartz and Neas,

2000), a study in Philadelphia (Neas et al., 1999) and a study in Kupio, Finland (Tiittanen et al.,

1999). The findings of these studies suggest roles for both fine and thoracic coarse PM in

reduced lung function and increased respiratory symptoms (CD, p. 8-313). For example, in the

Six Cities study, lower respiratory symptoms were found to be significantly increased for

children with PM2.5 but not with PM10-2.5, while the reverse was true for cough. When both PM2.5

and PM10-2.5 were included in models, the effect estimates were reduced for each, but PM2.5

retained significance in the association with lower respiratory symptoms and PM10-2.5 retained

significance in the association with cough (Schwartz and Neas, 2000). The new epidemiologic

studies continue to show effects of short-term exposure to PM10 and PM2.5 and offer additional

evidence for associations between PM10-2.5 and respiratory morbidity (CD, p. 8-312).

The CD finds that the recent epidemiologic findings are consistent with those of the

previous review in showing associations with both respiratory symptom incidence and decreased

lung function (CD, p. 9-70). PM10 and PM2.5 were associated with small decreases in lung

function and increases in respiratory symptoms, though the associations were not always

statistically significant, and a few new studies reported associations between PM10-2.5 and

respiratory morbidity. The findings from studies of physicians’ office visits for respiratory

diseases offer new evidence of acute respiratory effects with exposure to ambient PM that is

coherent with evidence of increased respiratory symptoms and admissions/visits to the hospital

or emergency room for respiratory disease. 
3-23

3.3.2.3 Effects on the Respiratory System from Long-term Exposures

In the last review, several studies had reported that long-term PM exposure was linked

with increased respiratory disease and decreased lung function. One study, using data from 24

U.S. and Canadian cities (“24 Cities” study), reported associations with these effects and longterm exposure to fine particles or acidic particles, but not with PM10 exposure (Dockery et al.,

1996; Raizenne et al., 1996). The 1996 Staff Paper included further staff evaluation of the data

from this study that suggested that lung function decrements were not associated with long-term

exposure to thoracic coarse particles (EPA, 1996b, p. V-67a). 

Several new epidemiologic analyses have been conducted on long-term pollutant

exposure effects on respiratory symptoms or lung function in the U.S.; numerous new European,

Asian, and Australian studies have also been published. In the U.S., studies have been based on

data from two cohort studies, cohorts of schoolchildren in 12 Southern California Communities

and an adult cohort of Seventh Day Adventists (AHSMOG). Results for the new studies,

together with the findings available in the last review, are presented in Appendix 3B. 

In general, these studies have indicated that long-term exposure to PM2.5 is associated

with reduced lung function growth and increased risk of developing chronic respiratory illness

(CD, p. 8-313). In section 8.3.3.2.2, the CD describes results from Southern California cohorts,

where significant decreases in lung function growth were associated with increasing exposure to

PM10, PM2.5 and PM10-2.5 in one analysis (Gauderman et al., 2000), while in a second cohort of

children recruited in this study there were decreases in lung function growth with long-term

exposure to PM10 and PM2.5 (PM10-2.5 data were not included in this study) but the results were

generally not statistically significant (Gauderman et al., 2002). In an analysis of cohort

participants who moved during the course of the study, those who moved to areas with lower PM

concentrations (using PM10 as the indicator) showed increased lung function growth, whereas

lung function growth decreased in the group of children who moved to areas with high pollution

levels (Avol et al., 2001; CD, p. 8-213). A number of long-term studies of respiratory effects

also have been conducted in non-North American countries, and many report significant

associations between indicators of long-term PM exposure and either decreases in lung function

or increased respiratory disease prevalence (Table 8-B8 of the CD). 

Considered together, the CD finds that the long-term exposure studies on respiratory

morbidity reported positive and statistically associations between fine particles or fine particle

components and lung function decrements or chronic respiratory diseases, such as chronic

bronchitis (CD pp. 8-313, 8-314). The CD observes that little evidence is available on potential

effects of long-term to exposure to PM10-2.5 (CD pp. 8-313, 8-314). 

3.3.2.4 Effects on the Cardiovascular System

In contrast with the limited information available in the previous review, the CD observes

that new epidemiologic studies provide much more evidence of effects on the cardiovascular

system with short-term exposures to PM, particularly PM10 and PM2.5 (CD, p. 9-67). 
3-24

Epidemiologic studies have reported associations between short-term exposures to ambient PM

(often using PM10) and measures of changes in cardiac function such as arrhythmia, alterations in

electrocardiogram (ECG) patterns, heart rate or heart rate variability changes, though the CD

urges caution in drawing conclusions regarding the effects of PM on heart rhythm (CD, p.

8-166). Recent studies have also reported increases in blood components or biomarkers such as

increased levels of C-reactive protein and fibrinogen (CD, p. 8-169). In addition, one new study

reported an association between PM2.5 and onset of myocardial infarction, though another study

reported no significant associations between PM10 and sudden cardiac death (CD, pp. 8-165 to

8-166). Several of these studies report significant associations between various cardiovascular

endpoints and short-term PM2.5 exposures; only one of the new set of studies included PM10-2.5, in

which significant associations were reported between onset of myocardial infarction and shortterm PM2.5 exposures but not with PM10-2.5 exposures (CD, p. 8-165; Peters et al., 2001). These

new epidemiologic findings can provide some insight into potential biologic mechanisms that

underlie associations between short-term PM exposure and cardiovascular mortality and

hospitalization that have been reported previously (see Section 3.2). 

3.3.3 Developmental effects

Some new evidence is available that is suggestive of adverse effects of exposure to PM

and gaseous co-pollutants on prenatal development, including both mortality and morbidity

effects. Several recent studies have shown significant associations between PM10 concentration

averaged over a month or a trimester of gestation and risk of intrauterine growth reduction

(AEGIR) and low birth weight. In addition, several new studies have suggested that infant

mortality may be associated with exposure to PM and gaseous co-pollutants during gestation.

The CD concludes that these effects are emerging as potentially more important than was

appreciated in the 1996 CD, but the evidence is still preliminary regarding these effects (CD, pp.

8-347).

3.3.4 Summary

In summary, exposure to various PM indicators is associated with a broad range of

cardiovascular and respiratory health endpoints. Newer studies report associations between

short-term exposure to various indicators of PM and cardiopulmonary mortality, hospitalization

or emergency department visits, and respiratory symptoms. In addition, there is now evidence

for associations with cardiovascular health outcomes, such as myocardial infarction or

physiological changes such as C-reactive protein increases. There are also a broader range of

respiratory health effects associated with exposure to various indicators of PM than those

previously documented. These effects include visits to physicians or clinics for treatment of

respiratory illnesses (CD, p. 9-23). 
3-25

More specifically, the epidemiologic evidence includes associations between short-term

exposure to PM2.5 and cardiorespiratory mortality, hospitalization and emergency department

visits for respiratory diseases, respiratory symptoms and decreased lung function, as well as

effects on the cardiovascular system, including changes in physiological indicators or biomarkers

for cardiovascular health (CD, pp. 8-338, 8-342). New studies also build upon previous

evidence for associations between long-term exposure to fine particles and cardiopulmonary

mortality or respiratory morbidity, with new evidence suggesting that long-term exposure to fine

particles is associated with lung cancer mortality (CD, p. 8-345). 

Epidemiologic studies have linked short-term exposure to PM10-2.5 with respiratory

morbidity, such as hospitalization or respiratory symptoms, with suggested associations with

mortality in some areas. Available studies have not supported a link between long-term

exposure to PM10-2.5 and mortality or morbidity.

3.4 INTEGRATIVE ASSESSMENT OF HEALTH EVIDENCE

In Chapter 9, the CD assesses the new health evidence, integrating findings from

epidemiologic studies with experimental (e.g., dosimetric and toxicologic) studies, to make

judgments about the extent to which causal inferences can be made about observed associations

between health endpoints and various indicators or constituents of ambient PM, acting alone

and/or in combination with other pollutants. In evaluating the evidence from epidemiologic

studies in section 9.2.2, the CD focuses on well-recognized criteria, including (1) the strength of

reported associations; (2) the robustness of reported associations to the use of alternative model

specifications, potential confounding by co-pollutants, and exposure misclassification related to

measurement error; (3) the consistency of findings in multiple studies of adequate power, and in

different persons, places, circumstances and times; (4) temporality between exposure and

observed effects; (5) the nature of concentration-response relationships; and (6) information

from so-called natural experiments or intervention studies (CD, p. 9-23). Integrating more

broadly across epidemiologic and experimental evidence in section 9.2.3, the CD also focuses on

the coherence and plausibility of observed PM-related health effects to reach judgments about

causality. The following discussion summarizes the conclusions and judgments from the CD’s

integrative assessment.

3.4.1 Strength of Associations

The strength of associations most directly refers to the magnitude of the reported relative

risk estimates. Taking a broader view, the CD draws upon the criteria summarized in a recent

report from the U.S. Surgeon General, which define strength of an association as “the magnitude

of the association and its statistical strength” which includes assessment of both effect estimate

size and precision, which is related to the statistical power of the study (CD, p. 9-6; CDC, 2004). 

In general, when associations are strong in terms of yielding large relative risk estimates, it is
7

 For example, in the week of the well-documented episode that occurred in London in 1952, when PM

concentrations exceeded 500 μg/m3

, the relative risk of all-cause mortality was 2.6, and the relative risk for

bronchitis mortality was 9.3 (62 FR 38659).

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less likely that the association could be completely accounted for by a potential confounder or

some other source of bias (CDC, 2004). With associations that yield small relative risk estimates

it is especially important to consider potential confounding and other factors in assessing

causality.

As observed in the previous PM NAAQS review, in historical air pollution episodes with

very high concentrations, reported relative risks were quite large.7

 In more recent studies with

much lower ambient concentrations, the CD observes that the associations reported between

health effects and PM yield much smaller relative risk estimates (CD, p. 9-24). Focusing on the

results from more precise mortality studies done in the U.S. and Canada, the CD reports that

associations with short-term exposure, expressed as relative risks, are in the range of about 1.02

to 1.06 per 25 μg/m3

 PM2.5 or PM10-2.5 (CD, p. 9-28), while relative risks associated with longterm exposure range upward to about 1.2 per 25 μg/m3

 PM2.5 for cardiovascular mortality (CD, p.

8-117). Regarding the size of relative risk estimates, the CD states: “In contrast with the marked

increase in health effects observed during historic episodes of very high air pollution levels,

relatively small effect estimates would generally be expected with current ambient PM

concentrations in the United States. The etiology of most air pollution-related health outcomes

is multifactorial, and the impact of ambient air pollution exposure on these outcomes may be

small in comparison to that of other risk factors.” (CD, p. 9-24). Thus, while the associations

reported in the more recent body of epidemiologic studies are appropriately characterized as

being weak in terms of the magnitude of the relative risk estimates, such weak associations are

generally coherent with outcomes that may reasonably be expected.

In considering both the magnitude and statistical strength of the associations, the CD

observes a pattern of positive and often statistically significant associations for cardiovascular

and respiratory health outcomes with short-term exposure to PM10 and PM2.5 with estimates of

similar magnitude but less precision with PM10-2.5 (CD, p. 9-32). Of particular note are several

multi-city studies that have yielded relative risk estimates for associations between short-term

exposure to various indices of PM and mortality or morbidity that, while small in size, have great

precision due to the statistical power of the studies. Such associations are strong relative to the

precision of the studies; that is, the associations were strong enough to have been reliably

measured by the studies such that many of the associations can be distinguished from the null

hypothesis with statistical confidence. 

In considering the strength of the associations between long-term exposure to fine

particles and mortality or morbidity, the CD concludes that the magnitude and precision of

associations with long-term exposure to PM2.5 constitute “strong evidence” for associations with
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mortality and “fairly strong evidence” for associations with respiratory morbidity. However, the

little evidence available for PM10-2.5 provide no evidence for associations with mortality and

allow no conclusions to be drawn regarding associations with morbidity (CD, p. 9-34).

3.4.2 Robustness of Associations

In section 9.2.2.2, the CD evaluates the robustness of epidemiologic associations in part

by considering the effect of differences in statistical model specification, potential confounding

by co-pollutants and exposure error on PM-health associations. The 1996 CD included an

assessment of evidence then available on these issues, and concluded that the effects observed in

epidemiologic studies “cannot be wholly attributed to” issues such as confounding by copollutants, differing model specifications, or measurement error (EPA, 1996a, p. 13-92). These

issues have been further evaluated in many new studies available in this review.

As discussed below in section 3.6.3, the CD assesses the findings of studies that

evaluated alternative modeling strategies, with a particular focus on the recent set of analyses to

address issues related to the use of GAM in time-series epidemiologic studies. The reanalyses

included the use of alternative statistical models and methods of control for time-varying effects,

such as weather or season. In the results of these reanalyses, some studies showed little change

in effect estimates, while others reported reduced effect estimate size, though the CD observes

that the reductions were often not substantial (CD, p. 9-35). Overall, the CD concludes that

associations between short-term exposure to PM and various health outcomes are generally

robust to the use of alternative modeling strategies, though further evaluation of alternative

modeling strategies is warranted (id.). The CD also notes that the results of reanalyses indicated

that effect estimates were more sensitive to the modeling approach used to account for temporal

effects and weather variables than to the GAM specifications, and recommended further

exploration of alternative modeling approaches for time-series analyses (CD, pp. 8-236 to

8-237).

In addition, the reanalysis and extended analyses of data from prospective cohort studies

have shown that reported associations between mortality and long-term exposure to fine particles

are robust to alternative modeling strategies. As stated in the reanalysis report, “The risk

estimates reported by the Original Investigators were remarkably robust to alternative

specifications of the underlying risk models, thereby strengthening confidence in the original

findings” (Krewski et al., 2000, p. 232).

The CD also included extensive evaluation of the sensitivity of PM-health responses to

confounding by gaseous co-pollutants, as discussed in detail in section 8.4.3 of the CD, and more

briefly below in section 3.6.4. In the new multi-city studies, as well as in many of the single-city

studies, health outcome associations with short-term exposures to PM10, PM2.5 and PM10-2.5 are

little changed in multi-pollutant models including one or more of the gaseous co-pollutants (CD,

p. 8-253). However, in some single-city analyses, PM-health outcome associations were
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attenuated in multi-pollutant models; the CD observes that collinearity between co-pollutants can

make interpretation of multi-pollutant models difficult (id.). Similarly, in the prospective cohort

studies, associations between long-term exposure to PM2.5 and mortality were generally not

sensitive to inclusion of co-pollutants, except for SO2, which was also associated with mortality

(CD, p. 8-136). Overall, the CD concludes that these studies indicate that effect estimates for

associations between mortality and morbidity and various PM indices are robust to confounding

by co-pollutants, while recognizing that disentangling the effects attributable to various

pollutants within an air pollution mixture is challenging (CD, p. 9-37). 

Finally, as discussed in section 3.6.2, a number of recent studies have evaluated the

influence of exposure error on PM-health associations. This includes both consideration of error

in measurements of PM, and the degree to which measurements from an individual monitor

reflect exposures to the surrounding community. As further discussed in section 3.6.2, several

studies have shown that fairly extreme conditions (e.g., very high correlation between pollutants

and no measurement error in the “false” pollutant) are needed for complete “transfer of

causality” of effects from one pollutant to another (CD, p. 9-38). In comparing fine and thoracic

coarse particles, the CD observes that exposure error is likely to be more important for

associations with PM10-2.5 than with PM2.5, since there is generally greater error in PM10-2.5

measurements, PM10-2.5 concentrations are less evenly distributed across a community, and less

likely to penetrate into buildings (CD, p. 9-38). Therefore, while the CD concludes that

associations reported with PM10, PM2.5 and PM10-2.5 are generally robust, the CD recognizes that

factors related to exposure error may result in reduced precision for epidemiologic associations

with PM10-2.5 (CD, p. 9-46).

3.4.3 Consistency

Consistency refers to the persistent finding of an association between exposure and

outcome in multiple studies of adequate power in different persons, places, circumstances and

times (CDC, 2004). The 1996 CD reported associations between short-term PM exposure and

mortality or morbidity from studies conducted in locations across the U.S. as well as in other

countries, and concluded that the epidemiologic data base had “general internal consistency”

(EPA, 1996a, p. 13-30). This epidemiologic data base has been greatly expanded with numerous

studies conducted in single locations, as well as several key multi-city studies. As described

above, the CD finds that the epidemiologic studies generally report positive and often

statistically significant associations with various cardiorespiratory health outcomes. The larger

body of evidence also has shown more variability in effect estimate size for a given health

outcome than was apparent in the last review. 

New multi-city studies have allowed evaluation of consistency in effect estimates across

geographic locations, using uniform statistical modeling approaches. In the NMMAPS results,

effect estimates for many individual cities exhibited wide confidence ranges, with varied effect
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estimate sizes, that suggested potentially more heterogeneity in effect estimates across cities than

had been seen with single-city studies in the last review. However, the authors observed that

there was no statistically significant heterogeneity across the effect estimates in the NMMAPS

analyses (Samet et al., 2000; Dominici et al., 2003a). The Canadian multi-city study also

reported some limited evidence suggesting heterogeneity in responses for PM2.5 and PM10-2.5 in

the reanalysis to address GAM questions, whereas there had been no evidence of heterogeneity

in initial study findings (Burnett and Goldberg, 2003; CD, p. 9-39). Finally, in the European

multi-city study, there were differences seen between effect estimates from eastern and western

European cities in initial analyses, but these differences were less clear with reanalysis to address

GAM issues (CD, pp. 8-46 to 8-47; Katsouyanni et al., 2003). Overall, the new multi-city study

results suggest that effect estimates differ from one location to another, but the extent of

heterogeneity is not clear.

The CD discusses a number of factors that would be likely to cause variation in PMhealth outcomes in different populations and geographic areas in section 9.2.2.3. The CD

recognizes that differences might well be expected in effects across locations, and discusses

investigation of a number of factors that appeared to be associated with variation in effect

estimates, including indicators of exposure to traffic-related pollution and climate-related

increases in exposure to ambient pollution (CD, p. 9-39). Other factors might also be expected

to cause variation in observed effects between locations, including population characteristics that

affect susceptibility or exposure differences, distribution of PM sources, or geographic features

that would affect the spatial distribution of PM (CD, p. 9-41). 

In addition, the CD observes that NMMAPS, while advantageous in including data from

many different locations with different climates and pollutant mixes, included many locations for

which the sample size (i.e., population size and PM10 data) was inherently smaller for a given

study period than that used in most single-city studies (CD, p. 9-40). The Canadian 8-city study,

as well, used PM data from a monitoring network that operated primarily on a 1-in-6 day

collection schedule, although the data were available for a long time period. In general, the CD

observes that the use of data collected on every sixth day results in reduced statistical power,

resulting in less precision for estimated effect estimates for the individual cities and increased

potential variability in results (CD, p. 9-40).

Overall, the CD finds that “[f]ocusing on the studies with the most precision, it can be

concluded that there is much consistency in epidemiologic evidence regarding associations

between short-term and long-term exposures to fine particles and cardiopulmonary mortality and

morbidity.” (CD, p. 9-47). For short-term exposure to thoracic coarse particles, the CD

concludes that there is some consistency in effect estimates for hospitalization for cardiovascular

and respiratory causes, though fewer studies are available on which to make such an assessment

(CD, p. 9-47).
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3.4.4 Temporality

Temporality refers to the occurrence of a cause before its purported effect, and is most

relevant to studies of diseases that develop over time. This factor is difficult to investigate in

situations where the pollutant concentrations are correlated over time as is the case to some

degree in PM time series studies and to a greater degree in the cohort studies. The short-term

exposure studies evaluate associations between acute health outcomes and PM measured on a

daily or hourly basis. In many studies, associations have been reported between health events

and pollutants measured contemporaneously. For example, in studies of total and cardiovascular

mortality, the CD observes that effects have been most clearly linked with PM measured on the

same day or the preceding day (CD, p. 8-273). This would be expected for acute health effects,

however, it is difficult to characterize these associations in terms of temporality. Issues related

to the evaluation and selection of lag periods among studies are further discussed in section

3.6.5.

The studies of effects related to long-term PM exposures have generally used air quality

levels averaged over months or years as exposure indicators. It is important to recognize that

these studies do not test specifically for latency in an exposure-effect relationship. Instead, the

average PM levels are used to represent long-term exposure to ambient PM, and the exposure

comparisons are basically cross-sectional in nature (CD, p. 9-42). Thus, the long-term exposure

studies do not allow an assessment of the temporal relationship between exposure and health

outcome. Taken together, it is difficult to assess temporality in the available studies of both

short-term and especially long-term exposures to PM, given that PM concentrations are

generally correlated over time in any given area.

3.4.5 Nature of concentration-response relationships

This is an assessment of whether increases in the potential causal factor result in

increased effects, also referred to as a biologic gradient. In epidemiologic time-series analyses,

the results have consistently shown positive associations, indicating that increases in various PM

indicators are associated with increases in health outcomes (CD, pages 9-28 to 9-29). The

prospective cohort studies have also generally reported positive associations between long-term

exposure to PM, primarily PM2.5, and increases in mortality or morbidity (CD, pp. 8-344 to

8-345). The available toxicologic studies have generally not been designed to quantify doseresponse relationships (CD, p. 7-2). Among the studies reviewed in chapter 7 are some that

report no evidence of a dose-response gradient (CD, p. 7-152), while some do (CD, p. 7-155),

and the CD draws no overall conclusions regarding dose-response relationships from toxicologic

studies. Therefore, while epidemiologic studies provide clear indication of increasing response

with increasing concentration, no conclusions can be drawn from toxicologic evidence.
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3.4.6 Natural Experiment Studies

Few studies are available that assess the extent to which reductions in ambient PM

actually lead to reductions in health effects attributable to PM. As discussed in section 9.2.2.6 of

the CD, and in somewhat more detail in section 3.4.7 below, one set of studies in the Utah

Valley were conducted over a time period when a major source of PM was closed, resulting in

markedly decreased PM10 concentrations. An epidemiologic study reported that respiratory

hospital admissions decreased during the plant closure time period (CD, p. 8-131; Pope et al.,

1989). Newly available controlled human exposure and animal toxicology studies, using

particles extracted from stored PM10 sampling filters from the Utah Valley, have shown

inflammatory responses that are greater with extracts of particles collected during the time

period of source operation than when the source was closed (CD, p. 9-73). Epidemiologic

studies in Dublin, Ireland and Hong Kong also provides evidence for reduced relative risks for

mortality when PM (measured as BS or PM10) and/or SO2 were reduced as the result of

interventions aimed at reducing air pollution (CD, pp. 8-131 to 8-135). From this small group of

new studies, the CD concludes:

By providing evidence for improvement in community health following reduction in air

pollutant emissions, these studies add further support to the results of the hundreds of

other epidemiologic studies linking ambient PM exposure to an array of health effects. 

Such studies showing improvements in health with reductions in emissions of ambient

PM and/or gaseous co-pollutants provide strong evidence that reducing emissions of PM

and gaseous pollutants has beneficial public health impacts. (CD, p. 9-45 to 9-46). 

3.4.7 Coherence and Plausibility

Section 9.2.3 of the CD integrates and evaluates evidence from the different health

disciplines to draw conclusions regarding the coherence of effects observed in the cardiovascular

and respiratory systems, as well as evidence for biologic plausibility of these effects. The CD

finds that progress has been made in substantiating and expanding epidemiologic findings on

cardiovascular- and respiratory-related effects of PM, and in obtaining evidence bearing on the

biologic plausibility of observed effects and potential mechanisms of action for particles (CD, p.

9-49).

As was concluded in the previous review, in considering evidence from epidemiologic

studies using PM10 and other PM indicators, the CD finds coherence for effects on the

cardiovascular and respiratory systems. Figures 8-24 through 8-28 of the CD show effect

estimates for associations between short-term exposures to PM10 and a range of cardiovascular

and respiratory health endpoints from within the same geographic location. In addition, the CD

finds that epidemiologic studies report associations for PM2.5 with a broad range of effects on the

cardiovascular and respiratory systems, primarily from short-term exposure studies, but also
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supported by associations reported for long-term fine particle exposure with cardiovascular

mortality (CD, pp. 9-67). 

As described briefly in section 3.2 above, and in more depth in Chapter 7 of the CD, the

findings of new toxicologic and controlled human exposure studies, while still limited, support a

number of potential biologic mechanisms or pathways for PM-related effects, and this evidence

is largely from studies of fine particles or fine particle components. 

Focusing first on effects related to the cardiovascular system, in section 9.2.3.2.1, the CD

summarizes evidence from both epidemiologic and toxicologic studies on subtle changes in

cardiovascular health. These changes include increased blood fibrinogen and fibrin formation,

certain ECG parameters (e.g., heart rate variability or HRV), and vascular inflammation. The

CD notes that vascular inflammation induces release of C-reactive proteins and cytokines that

may cause further inflammatory responses which, on a chronic basis, could lead to

atherosclerosis. 

Where a series of studies have been conducted in the same location, these studies can

provide evidence for coherence of effects, linking results from different study types for exposure

to PM in the same airshed. As discussed in the CD, in Boston, epidemiologic associations were

reported between PM2.5 and incidence of myocardial infarction, increases in recorded discharges

from implanted cardiovertex defibrillators, and decreases in HRV measures. Toxicologic studies

in Boston, using PM2.5 CAPs exposures in dogs, also suggested changes in cardiac rhythm with

PM2.5 mass and changes in blood parameters with certain PM2.5 components (CD, p. 9-68, 9-69). 

The CD observes: “While many research questions remain, the convergence of evidence related

to cardiac health from epidemiologic and toxicologic studies indicates both coherence and

plausibility in this body of evidence.” (CD, p. 9-78). 

In the last review, evidence was available suggesting coherence of effects on the

respiratory system, and the CD finds that new epidemiologic and toxicologic studies expand

upon that knowledge (CD, p. 9-74). In locations where epidemiologic studies have been

conducted, toxicologic or controlled human exposure studies using exposures to concentrated

ambient particles have shown effects related to lung inflammation, though minimal effects on

lung function have been reported (CD, p. 9-72). As discussed in section 3.2, toxicologic and

controlled human exposure studies have provided substantial evidence that particles can cause

lung injury and inflammatory responses. 

Interesting new evidence that links toxicologic and epidemiologic findings is available

from some “intervention studies” in the Utah Valley area. Epidemiologic studies in the Utah

Valley area observed that respiratory hospital admissions decreased during a period when a

major source of PM10 (a steel mill) was closed. More recent toxicologic and controlled human

exposure studies have used particles collected from this locale during the same time period, and

reported increased inflammatory responses with particles collected while the PM source was

operating than when it was closed. Several in vitro studies have also reported evidence of
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increased oxidative stress in lung cell cultures exposed to particles collected in Utah Valley. In

some toxicologic studies, the transition metal content of the particles appeared to be more

closely linked to reported effects than the quantity of particles (CD pp. 7-46 to 7-48). While

urging caution in interpreting the findings of the toxicologic studies where higher doses were

used, the CD concludes that “[t]he fact that instillation of ambient PM collected from different

geographical areas has been shown to cause pulmonary inflammation and injury tends to support

epidemiologic studies that report increased PM-associated respiratory effects living in some of

the same geographical areas” (CD, p. 7-48). Staff observes that, in contrast with most evidence

discussed here, this group of studies may well implicate thoracic coarse particles, since such

particles generally dominate PM10 concentrations in the Utah Valley area.

As was true in the last review, there is some coherence in the epidemiologic evidence

linking long-term exposure to fine particles with mortality and effects on the respiratory system. 

Available toxicologic studies have generally not studied cardiopulmonary effects of long-term or

chronic exposures to ambient air pollution mixtures, so for the most part, no conclusions can be

drawn regarding biologic plausibility of observed effects with long-term PM2.5 exposures and

mortality from heart and lung diseases (CD, p. 9-69). However, for lung cancer, the CD

summarizes evidence that supports coherence and plausibility in the epidemiologic associations

reported between long-term exposures to fine particles and lung cancer mortality. The CD

discusses toxicologic evidence on mutagenic or genotoxic potential of ambient PM, particles

from wood and coal combustion, and particles from diesel and gasoline engine emissions (CD

Section 7.8). These toxicologic studies have provided evidence of mutagenicity or genotoxicity

with exposure to combustion-related particles or to ambient particles collected in Los Angeles,

Germany and the Netherlands (CD, p. 9-76). In addition, the Health Assessment Document for

diesel engine exhaust concludes that diesel engine exhaust, one source of PM emissions, is a

likely human carcinogen (EPA, 2002). On the results of the new epidemiologic studies, the CD

concluded “[o]verall, these new cohort studies confirm and strengthen the published older

ecological and case-control evidence indicating that living in an area that has experienced higher

PM exposures can cause a significant increase in RR of lung cancer incidence and associated

mortality” (CD, p. 8-318). A number of toxicologic studies, summarized in section 7.10.1 of the

CD, report evidence of genotoxicity or mutagenicity with particles. The CD also finds that the

evidence indicates that fine particles may be more mutagenic than thoracic coarse particles (CD,

p. 7-214), which is consistent with the evidence from epidemiologic studies. Considered with

the results of toxicologic studies, the CD finds that this new evidence supports the plausibility of

a relationship between fine particles and lung cancer mortality (CD, p. 9-78).

Less information is available to allow conclusions to be drawn about coherence or

plausibility for associations with PM10-2.5 Based on the epidemiologic evidence discussed

previously, the CD concludes that the results are suggestive of associations between short-term

exposure to PM10-2.5 and morbidity effects, especially effects on the respiratory system (CD, p.
3-34

9-80). From the limited number of toxicologic studies using PM10-2.5, as noted in section 3.2,

there is some evidence supporting effects such as inflammation or oxidative stress. In addition,

allergic adjuvant effects were linked with road dust exposures, but coarse particle sample of

geologic origin, Mt. St. Helens ash, has not been linked with effects in toxicologic studies. As

discussed above, fractional deposition to the tracheobronchial region is greatest for thoracic

coarse particles in the size range of 4 to 6 μm (CD, p. 6-109). This would be consistent with

epidemiologic evidence linking PM10-2.5 with respiratory morbidity, such as increased respiratory

symptoms or risk of hospitalization for asthma. 

3.4.8 Summary

The new evidence from epidemiologic studies builds upon the conclusions of the last

review regarding the strength, robustness and consistency of the evidence. While uncertainties

remain and the new studies raise some new questions, the CD concludes:

In conclusion, the epidemiological evidence continues to support likely causal

associations between PM2.5 and PM10 and both mortality and morbidity from

cardiovascular and respiratory diseases, based on an assessment of strength, robustness,

and consistency in results. For PM10-2.5, less evidence is available, but the studies using

short-term exposures have reported results that are of the same magnitude as those for

PM10 and PM2.5, though less often statistically significant and thus having less strength,

and the associations are generally robust to alternative modeling strategies or

consideration of potential confounding by co-pollutants. (CD, p. 9-48).

Much more evidence is now available related to the coherence and plausibility of effects

than in the last review. For short-term exposures, the CD finds that the integration of evidence

from epidemiologic and toxicologic studies indicates both coherence and plausibility of effects

on the cardiovascular and respiratory systems, particularly for fine particles (CD, p. 9-78). Also,

there is evidence supporting coherence and plausibility for the observed associations between

long-term exposures to fine particles and lung cancer mortality (CD, p. 9-79). The smaller body

of evidence on thoracic coarse particles, especially the limited evidence from toxicologic studies,

provides only limited evidence of coherence for effects of thoracic coarse particles. 

Epidemiologic and dosimetric evidence, along with limited support from toxicologic studies,

support associations between PM10-2.5 and the respiratory system, with less evidence available on

cardiovascular effects.

Finally, the evaluation of these criteria leads the CD to draw conclusions regarding

causality of effects seen with fine or with thoracic coarse particles. Overall, the CD concludes

that the available evidence supports the general conclusion that PM2.5 or fine particle components

are “likely causally related to cardiovascular and respiratory mortality and morbidity” (CD, p.

9-79). For PM10-2.5, the “much more limited body of evidence is suggestive of associations
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between short-term (but not long-term) exposures . . . and various mortality and morbidity

effects observed at times in some locations.” (CD, p. 9-79).

3.5 PM-RELATED IMPACTS ON PUBLIC HEALTH

The following discussion draws from sections 9.2.4 and 9.2.5 of the CD to characterize

subpopulations potentially at risk for PM-related effects and potential public health impacts

associated with exposure to ambient PM. In particular, the potential magnitude of at-risk

population groups is discussed, along with other key considerations related to impacts on public

health, such as the concept of “mortality displacement” or “harvesting.”

3.5.1 Potentially Susceptible and Vulnerable Subpopulations

The CD summarizes information on potentially susceptible or vulnerable groups in

section 9.2.4. As described there, the term susceptibility refers to innate (e.g., genetic or

developmental) or acquired (e.g., personal risk factors, age) factors that make individuals more

likely to experience effects with exposure to pollutants. A number of population subgroups

have been identified as potentially susceptible to health effects as a result of PM exposure,

including people with existing heart and lung diseases, including diabetes, and older adults and

children. In addition, new attention has been paid to the concept of some population groups

having increased vulnerability to pollution-related effects due to factors including socioeconomic

status (e.g., reduced access to health care or low socioeconomic status) or particularly elevated

exposure levels, such as residence near sources such as roadways (CD, p. 9-81). Most available

studies have used PM10 or other measures of thoracic particles, with little specific evidence on

potential susceptibility to effects of PM2.5 or PM10-2.5.

A good deal of evidence indicates that people with existing heart or lung diseases are

more susceptible to PM-related effects. In addition, new studies have suggested that people with

diabetes, who are at risk for cardiovascular disease, may have increased susceptibility to PM

exposures. This body of evidence includes findings from epidemiologic studies that associations

with mortality or morbidity are greater in those with preexisting conditions, as well as evidence

from toxicologic studies using animal models of cardiopulmonary disease (CD, section 9.2.4.1). 

In addition, as described previously in section 3.2, dosimetric evidence indicates that deposition

of particles is increased, and can be focused in “hot spots” in the respiratory tract, in people with

chronic respiratory diseases.

Two age groups, older adults and the very young, are also potentially at greater risk for

PM-related effects. Epidemiologic studies have generally not shown striking differences

between adult age groups. However, some epidemiologic studies have suggested that serious

health effects, such as premature mortality, are greater among older populations (CD, p. 8-328). 

In addition, preexisting respiratory or cardiovascular conditions are more prevalent in older
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adults than younger age groups; thus there is some overlap between potentially susceptible

groups of older adults and people with heart or lung diseases.

Epidemiologic evidence has reported associations with emergency hospital admissions

for respiratory illness and asthma-related symptoms in children (CD, p. 8-328). The CD also

observes that several factors may make children susceptible to PM-related effects, including the

greater ventilation per kilogram body weight in children and the fact that children are more

likely to be active outdoors and thus have greater exposures (CD, p. 9-84). In addition, the CD

describes a limited body of new evidence from epidemiologic studies for potential PM-related

health effects in infants, using various PM indicators. Results from this body of evidence,

though mixed, are suggestive of possible effects; more research is needed to further elucidate the

potential risks of PM exposure for these health outcomes (CD, p. 8-222).

The CD also discusses other potentially susceptible groups for which less evidence is

available. Gender is a potential factor, and there are suggested differences in epidemiologic

study results, but the findings are not always consistent (CD, section 9.2.4.4). There is some new

suggestive evidence on genetic susceptibility to air pollution, but no conclusions can be drawn at

this time (CD section 9.2.4.3). 

In considering populations groups that might be more vulnerable to PM-related effects,

there is some new evidence from epidemiologic studies that people from lower socioeconomic

strata, or who have greater exposure to sources such as roadways, may be more vulnerable to PM

exposure. Such population groups would be considered to be more vulnerable to potential

effects on the basis of socioeonomic status or exposure conditions, as distinguished from

susceptibility due to biologic or individual health characteristics (CD, section 9.2.4.5).

In summary, there are several population groups that may be susceptible or vulnerable to

PM-related effects. These groups include those with preexisting heart and lung diseases, older

adults and children. Emerging evidence indicates that people from lower socioeconomic strata

or who have particularly elevated exposures may be more vulnerable to PM-related effects. The

available evidence does not generally allow distinctions to be drawn between the PM indicators,

in terms of which groups might have greater susceptibility or vulnerability to PM2.5 and/or

PM10-2.5

3.5.2 Potential Public Health Impact

As summarized above, there are several populations groups that may be susceptible or

vulnerable to effects from exposure to PM. The CD provides estimates of the size of population

subgroups, such as young children or older adults, and people with pre-existing heart or lung

diseases (CD, section 9.2.5.1) that are the subpopulations considered to be likely susceptible to

the effects of PM exposure. As shown in Table 9-4 of the CD, approximately 22 million people,

or 11% of the U.S. population, have received a diagnosis of heart disease, about 20% of the

population have hypertension and about 9% of adults and 11% of children in the U.S. have been
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diagnosed with asthma. In addition, about 26% of the U.S. population are under 18 years of age,

and about 12% are 65 years of age or older (CD, p. 9-89). The CD concludes that combining

fairly small risk estimates and small changes in PM concentration with large groups of the U.S.

population would result in large public health impacts (CD, p. 9-93). 

These health statistics also generally illustrate increasing frequency of less serious health

outcomes that would be expected in a “pyramid of effects.” Along the spectrum of severity, it is

expected that incidence or frequency of health endpoints would be larger for the less severe

effects, such as respiratory symptoms or the more subtle measures of cardiovascular health such

as levels of C-reactive protein. In contrast, with more severe health outcomes, such as

hospitalization or mortality, lower incidence would be expected. 

One issue that is important for interpreting the public health implications of the

associations reported between mortality and short-term exposure to PM is whether mortality is

occurring only in very frail individuals (sometimes referred to as “harvesting”), resulting in loss

of just a few days of life expectancy. A number of new analyses are discussed in the CD

(section 8.4.10.1) that assess the likelihood of such “harvesting” occurring in the short-term

exposure studies. Overall, the CD concludes from the time-series studies that there appears to be

no strong evidence to suggest that short-term exposure to PM is only shortening life by a few

days (CD, p. 8-334). 

In addition to the evidence from short-term exposure studies discussed above, one new

report used the mortality risk estimates from the ACS prospective cohort study to estimate

potential loss of life expectancy from PM-related mortality in a population. The authors

estimated that the loss of population life expectancy associated with long-term exposure to PM2.5

was substantial, on the order of a year or so (CD, p. 9-94). Taken together, these results suggest

that exposure to ambient PM, especially PM2.5, can have substantial public health impacts (CD,

p. 9-93). Furthermore, in the ACS cohort, the strongest associations between PM2.5 and

mortality were among the less educated participants who form a relatively small portion of the

total study cohort. If the education distribution were adjusted to reflect the education distribution

in the general U.S. population, the summary effect estimate would increase.

3.6 ISSUES RELATED TO QUANTITATIVE ASSESSMENT OF EPIDEMIOLOGIC

EVIDENCE 

The 1996 CD included extensive discussions of methodological issues for epidemiologic

studies, including questions about model specification or selection, co-pollutant confounding,

measurement error in pollutant measurements, and exposure misclassification. Based on

information available in the last review, the 1996 PM CD concluded that PM-health effects

associations reported in epidemiologic studies were not likely an artifact of model specification,

since analyses or reanalyses of data using different modeling strategies reported similar results
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(EPA 1996a, p. 13-92). Little information was available at that time to allow for evaluation of

these and other related methodological issues.

A large number of studies now available in this review have provided new insights on

these and other issues as evaluated in Chapters 8 and 9 of the CD. The following discussion

builds upon the CD’s evaluation of key methodological issues related to epidemiologic studies as

a basis for staff judgments specifically regarding the use of epidemiologic evidence in

quantitative assessments, as discussed in Chapters 4 and 5. 

This section addresses a number of key methodological issues. Section 3.6.1 discusses

issues related to air quality data used in epidemiologic studies, and section 3.6.2 discusses the

potential impact of measurement error and exposure error, related to the use of ambient air

concentrations as indicators of population exposures, on epidemiologic studies. Section 3.6.3

addresses statistical modeling and model specifications used in epidemiologic studies. Section

3.6.4 addresses the issue of potential confounding by co-pollutants, as it relates to staff

conclusions about the use of specific study results in quantitative assessments. Section 3.6.5

includes discussion of several topics related to the exposure periods used in epidemiologic

studies, with an emphasis on the question of lag periods. In section 3.6.6, the form of

concentration-response relationships in both short-term and long-term exposure studies is

discussed, as is evidence related to the potential existence of population threshold levels for

effects.

3.6.1 Air Quality Data in Epidemiologic Studies

In general, epidemiologic studies use ambient measurements to represent population

exposures to PM of ambient origin. This section discusses some considerations with regard to

the ambient PM measurements. First, staff observes that PM measurements from several

different monitoring methods were used in epidemiologic studies. Many studies have used PM2.5

and PM10-2.5 measurements from dichotomous samplers or Harvard impactors, as well as PM2.5

and PM10 measurements from co-located TEOMs or BAMs, and other methods (see Chapter 2

for more detailed descriptions of monitors). In reviewing results from studies using various

monitoring methods for PM2.5 and PM10-2.5, staff finds that there appear to be no systematic

differences in the effect estimates related to the use of differing monitoring methods. 

In considering the frequency of PM data collection, staff observes that it can have a

systematic effect on the results reported from epidemiologic analyses. The CD discusses the use

of less-than-everyday monitoring data as a source of uncertainty for time-series analyses (CD, p.

8-296). Many such studies were conducted in areas where PM was monitored on a daily basis;

in fact, the availability of every-day monitoring is cited as a basis for study location in a number

of reports. This is particularly true for panel studies on respiratory or cardiovascular symptoms,

all of which use daily PM monitoring data, though generally for shorter time periods. However,

staff observes that a small number of the recent studies have been based on less frequently
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collected data. Data collection frequency is one component of statistical power for time-series

studies, and missing data would result in increased uncertainty and reduced precision in study

results. In addition, for either PM2.5 or PM10-2.5, one would expect that a substantial proportion of

missing data may complicate time-series analyses (CD, p. 9-41). As illustrated in the CD, effect

estimates for PM10 and mortality varied in size and statistical significance in a series of analyses

of data collected on a 1-in-6 day schedule (CD, p. 8-297). The CD presents results from a study

in Chicago, IL, where a significant association was reported between PM10 and mortality using

data collected on a daily basis (Ito et al., 1996). However, when the data set was divided into 6

subsets representing 1-in-6 day monitoring frequency, the effect estimates for the PM10-mortality

association were quite variable in size and more uncertain. Consistent with the CD’s observation

that uncertainty is increased in studies using infrequently collected PM data, staff judges that

greater weight should be placed on those studies with daily or near-daily PM data collection in

drawing quantitative conclusions (CD, p. 9-41).

3.6.2 Measurement Error and Exposure Error

Measurement error, or uncertainty in the air quality measurements can be an important

source of uncertainty in epidemiologic associations with PM10-2.5 or PM2.5. The CD summarizes

the findings of several new analyses that show the potential influence of differential

measurement error on epidemiologic analysis results, for either PM with gaseous pollutants, or

PM10-2.5 and PM2.5 as separate pollutants (section 8.4.5). Several studies used simulation analyses

of a “causal” pollutant and a “confounder” with differing degrees of measurement error and

collinearity between the pollutants. These studies found that, in some circumstances, a causal

variable measured with error may be overlooked and its significance transferred to a surrogate. 

However, for “transfer of apparent causality” from the causal pollutant to the confounder to

occur, there must be high levels of both measurement error in the causal variable and collinearity

between the two variables (CD, p. 8-282, 8-283). The conditions required for the error to

substantially influence the epidemiologic findings are severe and unlikely to exist in current

studies. Thus, while the potential remains for differential error in pollutant measurements to

influence the results of epidemiologic studies, it is unlikely that the levels of measurement error

and correlation between pollutants reported in existing studies would result in transfer of

apparent causality from one pollutant to another (CD, p. 9-38).

One analysis applied measurement error models to data from the Harvard Six Cities

study, specifically testing relationships between mortality and either fine or thoracic coarse

particles (Carrothers and Evans, 2000). The authors identified several variables that could result

in biased effect estimates for fine- or coarse-fraction particles: the true correlation of fine- and

coarse-fraction particles, measurement errors for both, and the underlying true ratio of the

toxicity of fine- and coarse-fraction particles. The existence of measurement error and

collinearity between pollutants could result in underestimation of the effects of the less well-
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measured pollutant. However, the authors conclude “it is inadequate to state that differences in

measurement error among fine and coarse particles will lead to false negative findings for coarse

particles. If the underlying true ratio of the fine and coarse particle toxicities is large (i.e.,

greater than 3:1), fine particle exposure must be measured significantly more precisely in order

not to underestimate the ratio of fine particle toxicity versus coarse particle toxicity” (Carrothers

and Evans, 2000, p. 72; CD, p. 8-286). These analyses, using data from a study in which

significant associations were reported for mortality with PM2.5, but not with PM10-2.5, indicate that

it is unlikely that measurement error in one PM measurement will result in “false negative”

results for coarse particles or “false positive” results for fine particles (CD, p. 8-286). Thus, for

either PM2.5 or PM10-2.5 measurement error is not likely to be falsely attributing effects from one

pollutant to another pollutant in the existing epidemiologic studies. 

However, it must be recognized that measurement error is a larger issue for PM10-2.5 than

for fine particles, especially when PM10-2.5 concentrations are calculated as the difference

between PM10 and PM2.5 measurements (see section 2.4.3). It is likely that measurement error

would increase the uncertainty of an epidemiologic association. With increased error in PM10-2.5

monitoring methods, any reported epidemiologic associations would be less likely to reach

statistical significance (CD, p. 5-126). Thus, a set of positive but generally not statistically

significant associations between PM10-2.5 and a health outcome could be reflecting a true

association that is measured with error. Decreases in study precision would also occur even if

gravimetric PM10-2.5 were perfectly measured, but the sources and relative composition of the

coarse particles were highly variable. In evaluating the implications of the epidemiologic studies

showing effects of PM10-2.5, therefore, staff places more emphasis on the pattern of results in a

series of studies than on the statistical significance of any single effect estimate.

Exposure error is an issue that is closely linked with the preceding discussion of PM air

quality monitoring. Concentrations measured at ambient monitoring stations are generally used

to represent a community’s exposure to ambient PM. For time-series studies, the emphasis is on

the temporal (usually daily) changes in ambient PM. In cohort or cross-sectional studies, air

quality data averaged over a period of months to years are used as indicators of a community’s

long-term exposure to ambient PM and other pollutants. 

As discussed in section 2.7, one component of exposure error is how evenly distributed

PM is across a community, as indicated by levels at different monitoring sites; another

component is how well particles penetrate from ambient air into indoor environments. Several

factors affect how readily particles can move into buildings and remain suspended in indoor air. 

In general, fine particles move indoors and remain suspended more easily than do thoracic

coarse particles. In time-series analyses, measurements of PM2.5 made at a central site are found

to be better correlated with indoor measurements than are measurements of PM10-2.5 (see section

2.7.2). A number of recent studies have evaluated the effect of this type of exposure error on

epidemiologic study results. The results of these studies, primarily focused on fine particles,
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indicate that exposure error related to the use of PM data from central monitoring sites is likely

to result in underestimation of the effect of PM exposure on health (CD, p. 8-288).

Analyses of site-to-site variability for PM2.5 measurements, including time-series

correlations of measurements across monitors and differences in mean concentrations between

monitors, are presented in Table 2-3. The temporal correlation coefficients between monitors

are high, often exceeding 0.80, indicating good correlation between time-series PM2.5

measurements. However, a few areas, such as Los Angeles and Seattle, had lower temporal

correlation coefficients, in the range of 0.60. As observed in the CD, western areas are less

influenced by regional sources of fine particles (CD, p. 8-293), and geographic or topographic

features may make PM2.5 levels less homogeneous. Even where there is good temporal

correlation between monitors, there may be a spatial gradient in PM2.5 across the area. As

discussed in the CD (Table 8-40), some areas had strong correlation coefficients (on the order of

0.90) but substantial differences in annual means were found between some monitor pairs. For

example, correlation coefficients averaged about 0.90 between PM2.5 monitor pairs in Detroit,

but annual mean differences of up to 6 μg/m3

 were found between monitor pairs. 

This same type of analysis was done using available data for PM10-2.5, as discussed in

section 2.4.3. Table 2-4 shows that there are greater differences in concentrations between

paired PM10-2.5 monitors than were seen in data from paired PM2.5 monitors. Differences in

annual mean values of over 20 μg/m3

 are shown between some paired PM10-2.5 monitors,

representing differences of 60-70% in some cases. Correlations between the monitoring sites

were also somewhat lower than those for PM2.5, ranging from about 0.3 to 0.8. In some cities,

for example Cleveland, OH and Detroit, MI, the PM10-2.5 measurements at paired monitors show

both a large difference in magnitude as well as poor correlation in day-to-day changes; for both

cities, the values are 60-70% different between the monitor pairs, and the correlation coefficient

is about 0.4. However, for a number of the cities shown in Table 2-4, the correlation coefficients

between data from paired monitors are in the range of 0.7 to 0.8, indicating that the data are

fairly well correlated temporally, but there remain substantial differences in annual mean

concentrations between the monitors. In interpreting the results of epidemiologic associations

with PM10-2.5, the data from the central monitoring sites may be characterizing day-to-day

changes in PM10-2.5 concentrations adequately, but staff observes that it is difficult to determine

how well such concentrations characterize the magnitude of population exposures to PM10-2.5.

In summary, there are some key exposure-related distinctions between PM2.5 and PM10-2.5. 

In section 9.2.1, the CD concludes that PM2.5 concentrations are frequently evenly distributed

across cities, and frequently have high site-to-site correlations; as summarized above, there can

be differences in some locations. In contrast, the CD concludes that PM10-2.5 is “seldom” evenly

distributed across cities and that there are “frequently low” site-to-site correlations. In such

situations, while the epidemiologic associations may be illustrating true time-series relationships

between PM and a health outcome, it is more difficult to draw inferences about the population
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exposure levels at which those effects are seen. From studies in which significant associations

are reported with PM10-2.5, the distribution of ambient monitoring data available for the study may

reflect levels that are higher or lower than those experienced by neighborhoods in other parts of

the community.

3.6.3 Alternative Model Specifications

As observed earlier, statistical modeling issues for epidemiologic studies were discussed

in great detail in the 1996 PM CD (EPA, 1996a, sections 12.6.2 and 12.6.3). This evaluation led

to the conclusion that PM-related effects observed in epidemiologic studies were unlikely to be

seriously biased by inadequate statistical modeling or confounded by weather (CD, p. 8-22). 

Statistical modeling issues have re-emerged in this review, however, and much attention has

been given to further investigations of approaches to model specification for epidemiologic

analyses. The following discussions draw from the CD’s evaluation of model specification

issues for both short-term and long-term exposure studies.

3.6.3.1 Time-series epidemiologic studies

In 2002, questions were raised about the default convergence criteria and standard error

calculations made using GAM, which have been commonly used in recent time-series

epidemiologic studies. As discussed more completely in the CD (section 8.4.2), a number of

time-series studies were reanalyzed using alternative methods, typically GAM with more

stringent convergence criteria and alternative models such as GLM with natural smoothing

splines. The results of the reanalyses have been compiled and reviewed in an HEI publication

(HEI, 2003a). Reanalyzed PM10 mortality study results are presented in Figure 8-15 in the CD,

where it can be seen that the reanalyses generally did not substantially change the findings of the

original analyses, and the changes in effect estimates with alternative analysis methods were

much smaller than the variation in effects across studies. In the HEI reanalyses, the CD finds

that mortality effect estimates were often, but not always, reduced with the use of GAM with

more stringent convergence criteria; however, the extent of these changes was not substantial in

most cases (CD, p. 8-232). Further, for morbidity studies, the CD finds that the impact of the

reanalyses was relatively small and the basic conclusions regarding the significance of PMrelated hospital admissions remained unchanged when more stringent GAM criteria were used

(CD, p. 8-235).

 These reanalyses also investigated alternative model specifications to control for

potential weather effects and temporal trends. As shown in Figures 8-20 and 8-21 in the CD, the

magnitude of the effect estimate for PM can decrease with increasing control for weather and

temporal trend, though it generally stabilizes at some point. The CD observes that there is no

clear consensus at this time as to what constitutes appropriate control for such variables, while

recognizing that no single approach is likely to be most appropriate in all cases (CD, p. 8-340). 

If the model does not adequately address daily changes in weather variables, then some effects of
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temperature on health would be falsely ascribed to the pollution variable. Conversely, if the

model overcontrols for weather, such that the temperature-health relationship is more “wiggly”

than the true dose-response function, then the result will be a much less efficient estimate of the

pollutant effect (CD, p. 8-236). This would result in incorrectly ascribing some of the true

pollution effect to the temperature variable, which would make it difficult to detect a real but

small pollution effect. The CD concludes that the available studies appear to demonstrate that

there are PM-related effects independent of weather influences, but that further evaluation is

needed on how to best characterize possible combined effects of air pollution and weather (CD,

p. 8-340).

In summary, the reanalyses generally support the findings of the original studies, while

raising questions for further research. For quantitative assessment, staff considers it appropriate

to use results from short-term exposure studies that did not use GAM initially, or that used either

more stringent GAM or GLM analyses. As recognized in the CD, there is no one correct

approach for model specification or covariate adjustment (CD, p. 9-35). An advantage to the use

of GAM is that the model is “data-driven” and selects the degree of smoothing or adjustment for

covariates that best fits the data. The GLM approach is advantageous in allowing more accurate

calculation of standard errors. 

3.6.3.2 Prospective cohort epidemiologic studies

Data from the ACS and Six Cities prospective cohort studies were used in a major

reanalysis study that evaluated a number of issues that had been raised for the long-term

exposure studies. These issues included whether the results were sensitive to alternative

modeling strategies. The reanalysis included two major components, a replication and validation

study, and a sensitivity analysis, where alternative risk models and analytic approaches were

used to test the robustness of the original analyses. In the first phase, the data from the two

studies were found to be of generally high quality, and the original results were replicated,

confirming the original investigators’ findings of associations with both total and

cardiorespiratory mortality (Krewski et al., 2000; CD, p. 8-91). In the second phase, the

sensitivity analyses generally reported that the use of alternative models, including variables that

had not been used in the original analyses (e.g., physical activity, lung function, marital status),

did not alter the original findings. Data were also obtained for additional city-level variables that

were not available in the original data sets (e.g., population change, measures of income,

maximum temperature, number of hospital beds, water hardness) and reanalysis investigators

included these data in the models. The associations between fine particles and mortality were

generally unchanged in these new analyses, with the exception of population change, which did

somewhat reduce the size of the associations with fine particles or sulfates (CD, p. 8-92). 
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3.6.4 Co-pollutant Confounding and Effect Modification

Confounding occurs when a health effect that is caused by one risk factor is attributed to

another variable that is correlated with the causal risk factor; epidemiologic analyses attempt to

adjust or control for potential confounders. A gaseous copollutant (e.g., O3, CO, SO2 and NO2)

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 (CD, p.

8-10). Effect modifiers include variables that may influence the health response to the pollutant

exposure (e.g., co-pollutants, individual susceptibility, smoking or age). Both are important

considerations for evaluating effects in a mixture of pollutants, but for confounding, the

emphasis is on controlling or adjusting for potential confounders in estimating the effects of one

pollutant, while the emphasis for effect modification is on identifying and assessing the level of

effect modification (CD, p. 8-12). 

In addition to acting as confounders or effect modifiers, the CD recognizes that pollutants

may act together in an ambient pollution mixture, potentially having additive or synergistic

effects. For example, recent animal toxicologic studies have tested effects of exposure to PM

(e.g., urban PM, carbon particles, acid aerosols) in combination with O3 and suggested that coexposure to O3 and urban particles resulted in greater effects than those reported with exposure

to O3 alone, while mixed results were reported from studies using combinations of acid aerosols

and O3 (CD, Table 7-13). 

3.6.4.1 Co-pollutant Confounding

 Potential confounding by gaseous copollutants has been most commonly assessed by

using multi-pollutant models. As discussed in the CD (section 8.4.3.2), there are statistical

issues to be considered with multi-pollutant models, such as possibly creating mis-fitting models

by forcing all pollutants to fit the same lag structure, by adding correlated but non-causal

variables, or by omitting important variables. There are issues relating to potential copollutant

confounding that multi-pollutant models may not be able to address. Inclusion of pollutants in a

multi-pollutant model that are highly correlated with one another can lead to misleading

conclusions in identifying a specific causal pollutant. Collinearity between pollutants may occur

if the gaseous pollutants and PM come from the same sources, if PM constituents are derived

from gaseous pollutants (e.g., sulfates from SO2), or if meteorological conditions contribute to

the formation of both PM and gaseous pollutants (CD, p. 8-12). These situations certainly occur. 

For example, sources of fine particle constituents include combustion of various fuels, gasoline

or diesel engine exhaust, and some industrial processes (CD, Table 9-1); these sources also emit

gaseous pollutants. In addition, SO2 and PM2.5 are often derived from the same sources in an

area (e.g., coal-fired power plants) and thus simultaneous inclusion in models may result in

diminished effects for one or both pollutants, which can be misleading (CD, p. 8-14).
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In the NMMAPS multi-city analyses, one key objective was to characterize the effects of

PM10 and the gaseous co-pollutants, alone and in combination. Multi-pollutant modeling was

used in the NMMAPS mortality analyses for 20 and 90 U.S. cities, in which the authors added

first O3, then O3 and another co-pollutant (e.g., CO, NO2 or SO2) to the models (CD, p. 8-35). 

The relationship between PM10 and mortality was little changed in models including control for

O3 and other gaseous pollutants (CD, Figure 8-4, p. 8-35). The authors concluded that the

PM10-mortality relationship was not confounded by co-pollutant concentrations across 90 U.S.

cities (Samet et al., 2000a,b; Dominici, 2003). Single- and multi-pollutant model results for a

range of health outcomes with PM10, PM2.5 and PM10-2.5 from multi- and single-city studies are

presented in Figures 8-16 through 8-19 of the CD. For the most part, the addition of gaseous copollutants had little influence on PM associations, although substantial reduction in associations

with PM could be seen in some cases when gaseous pollutants are added to the model. 

Using an alternative approach, investigators in the NMMAPS morbidity analyses for 14

U.S. cities tested for relationships between the coefficients for the PM10-admissions with PM10-

co-pollutant correlations for each city. No such relationships were found between the PM10

effect estimates for cardiovascular or respiratory hospitalization and PM10-co-pollutant

correlations (CD, pp. 8-146, 8-175). The authors concluded that associations with PM10 were not

dependent on the correlation between PM10 and the gaseous copollutants, though as mentioned

previously, the CD highlights the need for additional evaluation of this type of analysis (CD, pp.

8-146).

In the long-term exposure studies, multi-pollutant models have been tested in some

analyses. The reanalysis of data from the ACS cohort indicated that associations between

mortality and PM2.5 or sulfates were reduced in size in co-pollutant models including SO2 but not

with the other gaseous pollutants. Since SO2 is a precursor for fine particle sulfates, it is

inherently difficult to distinguish effects from the precursor SO2 and fine particles (CD, p. 9-37). 

Some recent exposure studies have collected personal and ambient monitoring data,

collected at a single central site, for PM2.5 and gaseous pollutants (e.g., O3, SO2 and NO2), and

assessed the degree of day-to-day correlation between the different measures of personal and

ambient concentrations. The investigators reported that the personal and ambient PM2.5

measurements were correlated, as were personal exposure to PM2.5 and ambient concentrations

of the gaseous pollutants. However, the personal and ambient concentrations of each of the

gaseous pollutants were not well correlated. These findings suggest that associations reported

with ambient PM2.5 are truly reflecting associations with fine particles and that fine particles are

unlikely to be simply acting as surrogates for other gaseous pollutants (Sarnat et al., 2000, 2001;

CD, p. 5-90).

In summary, where various indicators of PM and the other pollutants are correlated, it

can be difficult to distinguish effects of the various pollutants in multi-pollutant models. 

However, a number of research groups have found the effects of various indicators of PM and
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gases to be independent of one another, as illustrated in Figures 8-16 through 8-19 of the CD. In

addition, new evidence on exposure considerations suggests that it is unlikely that a relationship

found between a health endpoint and ambient PM2.5 concentrations is actually representing

relationships with other pollutants. 

Taking into consideration the findings of single- and multi-city studies and other

evaluations of potential confounding by gaseous co-pollutants described in preceding sections,

the CD concludes that while research questions remain, in general, “associations for various PM

indices with mortality or morbidity are robust to confounding by co-pollutants.” (CD, p. 9-37). 

As shown in figures 8-16 through 8-19 of the CD, effect estimates for PM10, PM2.5 and PM10-2.5

were little changed in multi-pollutant models, as compared with single-pollutant models. This

indicates that effect estimates from single-pollutant models can be used to represent the

magnitude of a concentration-response relationship, though there will remain uncertainty with

regard to potential contributions from other pollutants. For quantitative assessment, staff

concludes that single-pollutant model results provide reasonable indicators of the magnitude of

PM-related effects, supported by analyses including multi-pollutant model results as available.

3.6.4.2 Effect Modification

One approach to evaluate the effect of co-pollutants on associations reported with PM2.5

is illustrated in Figure 3-3. As discussed in the 1996 Staff Paper, if PM is acting independently,

then a consistent association should be observed in a variety of locations of differing levels of

co-pollutants. Effect estimates for PM10-mortality associations were plotted against

concentrations of gaseous pollutants in the study area, and there was no evidence that

associations reported between PM10 and mortality were correlated with copollutant

concentrations. (EPA, 1996b, Figure V-3a,b). Similarly, Figure 3-3 shows the reported effect

estimates for PM2.5 and mortality (from single-pollutant models) from U.S. and Canadian studies

relative to the levels of O3, NO2, SO2, and CO present in the study locations. As was seen in the

last review for PM10, the magnitude and statistical significance of the associations reported

between PM2.5 and mortality in these studies show no trends with the levels of any of the four

gaseous co-pollutants. While not definitive, these consistent patterns indicate that it is more

likely that the effect of PM2.5 is not appreciably modified by differing levels of the gaseous

pollutants. 

An alternative approach would be assessment within a single study of whether the effect

of time-varying PM is modified by time-varying concentrations of the gases in the time-series

models, through addition of interaction terms, for example. However, such studies have not

been conducted. While potential effect modification between various indicators of PM and the

gaseous pollutants has been little studied, the limited available evidence indicates that the gases

do not have a major role as effect modifiers for PM-related health outcomes.
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3.6.5 Issues Related to Alternative Exposure Periods in Epidemiologic Studies

3.6.5.1 Lag Structure in Short-term Exposure Studies

In the short-term exposure epidemiologic studies, many investigators have tested

associations for a range of lag periods between the health outcome and PM concentration (see

CD, sections 8.4.4 and 9.2.2.4). As discussed in the CD, it is important to consider the pattern of

results that is seen across the series of lag periods. If there is an apparent pattern of results

across the different lags, such as that shown in Figure 8-22 of the CD, then selecting the singleday lag with the largest effect from a series of positive associations is likely to underestimate the

overall effect size, since single-day lag effect estimates do not fully capture the risk that may be

distributed over adjacent or other days (CD, p. 8-270). Where effects are found for a series of

lag periods, a distributed lag model will more accurately characterize the effect estimate size. 

However, if there is no apparent pattern or reported effects vary across lag days, any result for a

single day may well be biased (CD, p. 9-42). Staff also observes that the high degree of

autocorrelation in PM measurements complicates the assessment of various lag periods.

 For selecting effect estimates from studies for use in quantitative risk assessment, or for

evaluation of potential revisions to the standards, staff considered patterns of results for PM2.5 or

PM10-2.5 across lag periods from U.S. and Canadian studies. As discussed below, most of the

studies included in Appendix 3A evaluated results for a range of lag periods, with many authors

reporting effect estimates for one lag period based on this evaluation. However, a few

researchers selected lag periods a priori. Examples of studies that used a priori selection of lag

periods includes Liao et al. (1999), in which the 24-hour PM2.5 average preceding measurement

of cardiac function was used, and Schwartz et al. (1996), in which an average of 0-day and 1-day

lagged PM10, PM2.5 and PM10-2.5 measurements was used in analyses of associations with

mortality.

Most authors report testing associations across a range of lag periods, and in many cases

the authors reported a pattern of positive associations across several lag periods. Figure 8-22 in

the CD presents associations for PM2.5 levels over both a series of days and a series of hours

preceding myocardial infarction incidence, and positive associations can be seen over several

adjacent lag periods (CD, p. 8-270; Peters et al., 2001). In an analysis using hospitalization for

asthma, researchers report testing associations for lags to 3-days and beyond, and reported

consistent patterns across lags for associations between asthma hospitalization and PM10, PM2.5

or PM10-2.5 (CD, p. 8-270; Sheppard et al.,1999; 2003). Results for the strongest associations are

presented in this study, with the authors observing:

When considering single (vs. distributed) lag estimates, it is important to put the estimate

in the context of the pattern of lags nearby and to recognize that effect estimates contain

information from adjacent days owing to serial correlation of the pollutant series. The

pollutant effects given for asthma are larger than and consistent with estimates obtained

for adjacent lags. In contrast, adjacent lags to the same-day PM and SO2 effects on
3-48

0.08 0.10 0.12 0.14 0.16

0 10 20 30

Ozone (ppm), 4th highest 8-hour daily maximum

Effect Estimate and 95% CI

A

1

2

3

4

5

6

78

9

10 11 13 14

16

17

12 18

0.05 0.10 0.

0 10 20 30

NO2 (ppm), 2nd highest 1-hour daily maximum

1

2

3

4

5

6

8

9

13 14

16

12 

0.02 0.04 0.06 0.08 0.10

0 10 20 30

SO2 (ppm), 2nd highest 24-hour maximum

Effect Estimate and 95% CI

C

3

4

5

67

8

9

10 11 13 14

16

17

12 18

4 6 8 

0 10 20 30

CO (ppm), 2nd highest 8-hour daily maximum

1

2

3

4

5

6

7

8

9

14

16

17

12 18

Figure 3-3. Associations between PM2.5 and total mortality from U.S. studies, plotted against gaseous po

concentrations from the same locations. Air quality data obtained from the Air Quality Sysstudy time period: (A) mean of 4th highest 8-hour ozone concentration; (B) mean of 2nd highe

concentration; (C) mean of 2nd highest 24-hour SO2 concentration; (D) mean of 2nd highest 8concentration. Study locations are identified below:

1. Chock et al., 2000, Pittsburgh, PA2. Fairley, 2003, Santa Clara County, CA3. Ito, 2003, Detroit, MI4. Klemm and Mason, 2000, Atlanta, GA5. Lipfert et al., 2000a, Philadelphia, PA6. Mar et al., 2003, Phoenix, AZ

7. Moolgavkar, 2003, Los Angeles, CA8. Ostro et al., 2003, Coachella Valley, CA9. Ostro et al., 1995, Southern California10. Schwartz, 2003a, Boston, MA11. Schwartz, 2003a, Knoxville, TN12. Schwartz, 2003a, Portage, WI

13. Schwartz, 2003a, St. Louis14. Schwartz, 2003a, Steubenv15. Schwartz, 2003a, Topeka, K16. Tsai et al., 2000, Camden N17. Tsai et al., 2000, Elizabeth 18. Tsai et al., 2000, Newark N
3-49

0.005 0.010 0.015 0.020 0.025

0 10 20 30

SO2 (ppm), annual mean

Effect Estimate and 95% CI

E

1

3

4

67

8

9

10 11 13 14

16

17

18 12

0.01 0.02 0.

0 10 20 30

NO2 (ppm), annual mean

1

2

3

4

5

8

13 14

1

12

Figure 3-3 (continued). Associations between PM2.5 and total mortality from U.S. studies, plotted agai

concentrations from the same locations. Air quality data obtained from the A(AQS) for each study time period: (E) annual mean SO2 concentration; (F) an

concentration. Study locations are identified below:

1. Chock et al., 2000, Pittsburgh, PA2. Fairley, 2003, Santa Clara County, CA3. Ito, 2003, Detroit, MI4. Klemm and Mason, 2000, Atlanta, GA5. Lipfert et al., 2000a, Philadelphia, PA6. Mar et al., 2003, Phoenix, AZ

7. Moolgavkar, 2003, Los Angeles, CA8. Ostro et al., 2003, Coachella Valley, CA9. Ostro et al., 1995, Southern California10. Schwartz, 2003a, Boston, MA11. Schwartz, 2003a, Knoxville, TN12. Schwartz, 2003a, Portage, WI

13. Schwartz, 2003a, St. Louis14. Schwartz, 2003a, Steubenv15. Schwartz, 2003a, Topeka, K16. Tsai et al., 2000, Camden N17. Tsai et al., 2000, Elizabeth 18. Tsai et al., 2000, Newark N
8

Staff observes that the results for a series of lags show fairly consistent patterns for associations between

PM10 and PM10-2.5 and cardiovascular mortality in this analysis.

9

The air quality measurements available for PM2.5 and PM10-2.5 may also contribute to the more uncertain

findings for PM2.5 in this study. For PM10-2.5, a 10-year series of concentrations was modeled from a 2 1⁄2 year series

of ambient measurements at co-located beta attenuation monitors, while predictive models for PM2.5 concentrations

were not reported to be adequate, so only the 2 1⁄2 year series of measurements were used in PM2.5 analyses.

10 That only 1-in-6 day PM measurements were available in Los Angeles County is likely to be an important

factor contributing to less consistent findings there. 

3-50

appendicitis change much more abruptly, and the overall pattern is unstable. (Sheppard

et al., 1999, p. 27) 

In a study of mortality in Phoenix, positive associations are reported with PM10, PM2.5 and

PM10-2.5 out to a lag period of 4 days, but effect estimates were larger and more often statistically

significant for the 0- or 1-day lag periods (Mar et al., 1999; 2003). In an analysis of mortality

and hospitalization in Detroit, results for models including individual lag days and moving

average lags periods for PM10, PM2.5 and PM10-2.5 are presented in appendices and the results of

the most significant lag results are presented in the body of the report, with the observation that

significant associations often occurred at multiple lags (Lippmann et al., 2000, p. 24). Among

U.S. and Canadian studies, the CD observes that many authors report finding a pattern of PMrelated effects across adjacent lags (CD, p. 8-279).

An example of results that do not follow a consistent pattern across lags can be found in

results for the association between PM2.5 and mortality in Coachella Valley (Ostro et al., 2000;

2003). In this study, the pattern of results across a series of lag periods was not consistent in

associations between PM2.5 and total or respiratory mortality8. Based on the greater uncertainty

on the effect estimate size for the PM2.5-mortality association from this study, staff concludes

that it would not be appropriate to use these PM2.5 results for quantitative assessments.9

 In

addition, a series of studies in Cook County, IL and Los Angeles County, CA, include effect

estimates for 0- to 5-day lag periods and, for most health endpoints, the results follow a pattern. 

However, the pattern of results specifically for COPD mortality with PM2.5 was quite

inconsistent (Moolgavkar, 2000a,b,c; Moolgavkar, 2003, p. 191).10 Based on the considerations

described above, staff concludes that it would not be appropriate to use the results for COPD

mortality from this study for quantitative assessment.

 The CD concludes that it is likely that the most appropriate lag period for a study will

vary, depending on the health outcome and the specific pollutant under study. Some general

observations can be made about lag periods for different health outcomes. For total and

cardiovascular mortality, it appears that the greatest effect size is generally reported for the 0-day

lag and 1-day lag, generally tapering off for longer lag periods (CD, p. 8-279). This is true also

for hospitalization for cardiovascular diseases. For cardiovascular effects such as myocardial
3-51

infarction or HRV change, there appears to be a pattern of larger effects with shorter lag periods,

such as 1- to 4-hours. For respiratory symptoms, many studies report effects over a series of

lags, with larger effect estimates for moving average or distributed lag models. Similarly, for

asthma hospitalization, there appear to be larger effects over longer average time periods, out to

5- to 7-day average lags (CD, p. 8-279).

A number of recent studies that have investigated associations with distributed lags

provide effect estimates for health responses that persist over a period of time (days to weeks)

after the exposure period. The available studies have generally used PM10 or other PM

indicators, but not PM2.5 or PM10-2.5. Effect estimates from distributed lag models are often, but

not always, larger in size that those for single-day lag periods (CD, p. 8-281). For example, in

multi-city analyses of data from 10 U.S. cities, the effect estimates for total mortality from

distributed lag models are about twice those from 0-1 day average lag models (Schwartz, 2003b). 

In the 14-city NMMAPS analysis of hospitalization in the elderly, the combined city effect

estimate for COPD hospitalization is larger (about doubled) in results of distributed lag models

than in 0-1 day average lag models, while the CVD hospitalization effect estimate is only

increased by a small amount, and the effect estimate for pneumonia hospitalization is somewhat

smaller in distributed lag models, compared with a 0-1 day average lag (Schwartz, et al., 2003).

In summary, the CD concludes that distributed lag results would likely provide more

accurate effect estimates for quantitative assessment than an effect estimate for a single lag

period (CD, p. 9-42). However, at this time, studies using PM2.5 and PM10-2.5 have not included

distributed lag models.

 Most U.S. and Canadian studies have reported consistent patterns in

results for different lags; for these studies, an effect estimate for a single-day lag period is likely

to underestimate the effect. In quantitative assessments for PM2.5 and PM10-2.5, since results are

generally not available for distributed lag models, staff concludes that it is appropriate to use

single-day lag period results, recognizing that this is likely to underestimate the effect. For

quantitative assessment, staff concludes that it is appropriate to use results from lag period

analyses consistent with those reported in the CD, focusing on shorter lag periods for

cardiovascular effects and lag periods of several days for respiratory effects, depending on

availability of results. For the few studies that show inconsistent patterns, the use of single-day

lag results are not appropriate for quantitative assessment.

3.6.5.2 Seasonal Differences in Time-Series Epidemiologic Results

As discussed in section 3.5.3, time-series epidemiologic studies generally use some

temporal or seasonal terms in the models to control for seasonal changes in health outcomes. In

addition, a few epidemiologic studies have also evaluated PM-health associations across seasons

by doing analyses on data subdivided into different seasons, thus evaluating differences in

effects across the season rather than trying to control for seasonal influences. The CD observes

that there can be seasonal differences in correlations between PM and other pollutants, or in PM

levels across seasons (CD, p. 8-57).
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The CD presents results for seasonal analyses for individual studies in Chapter 8 and the

Appendices to Chapter 8. In 10 U.S. cities, the relationship between PM10 and mortality was the

same in analyses for data divided into summer and winter seasons (Schwartz, et al., 2000). In

Pittsburgh, relationships between PM10-2.5 and PM2.5 and mortality were “unstable” when

stratified by season, and there was evidence of differing multi-collinearity between seasons

(Chock et al., 2000). In Coachella Valley, associations between mortality and several PM

indicators were stronger in the winter season (October-May) than in the summer season (Ostro et

al., 2000). However, an earlier analysis in two Southern California counties reported significant

associations between estimated PM2.5 and mortality in the summer (April-September) quarter

only (Ostro et al., 1995). Seasonal analyses were done for the mortality-PM2.5 relationship in

San Jose, and there were no significant differences between the four seasons (Fairley, 2003). In

Phoenix, the association between PM10-2.5 and mortality was reported to be highest in spring and

summer, when PM10-2.5 concentrations were lowest (Mar et al., 2003). Associations between

PM10 and hospitalization for cardiovascular diseases in Los Angeles yielded larger effect

estimates in the winter and fall seasons than in spring or summer (Linn et al., 2000). Asthma

hospitalization was significantly associated with PM10 for both “wet” and “dry” seasons in Los

Angeles, but the effect estimates were larger during the wet season (January-March) (Nauenberg

and Basu, 1999). In Seattle, associations between PM10, PM2.5 and PM10-2.5 and asthma

hospitalization were positive in all seasons, but effect estimates were larger in spring and fall

(Sheppard et al., 2003).

Staff observes that these few studies show no general pattern in results across seasons.

The largest of these studies showed no seasonal differences in the results combining data from

10 U.S. cities (Schwartz et al., 2000). Most of the studies listed above show generally positive

results across all seasons tested, with some reporting larger effect estimates in one or more

season(s), but the differences were not statistically significant. Staff concludes that the available

evidence does not support quantitative assessment of seasonal differences in relationships

between PM and health outcomes at this time.

3.6.5.3 Health effects related to different short-term exposure time periods

While most time-series epidemiologic studies use 24-hour average PM measurements,

several new studies have used ambient PM concentrations averaged over shorter time intervals,

such as 1- or 4-hour averages. Many such studies have evaluated associations with

cardiovascular health biomarkers or physiological changes. Section 8.3.1.3.4 of the CD

describes several epidemiologic studies that report statistically significant associations between

2- to 4-hour PM10 or PM2.5 concentrations and cardiovascular health endpoints, including

myocardial infarction incidence and heart rate variability (CD, pp. 8-162 to 8-165). One study

reported effect estimates for myocardial infarction incidence with PM2.5 averaged over 2- and 24

hours that are quite similar in magnitude, and both are statistically significant (Peters et al.,

2001; CD, p. 8-165).
3-53

For respiratory health outcomes, two panel studies of symptoms in asthmatic subjects are

summarized in the CD (section 8.3.3.1.1). One study in a small Southern California community,

reported larger effect estimates for 1- or 8-hour concentrations than for 24-hour PM10

concentrations (Delfino et al., 1998), while the other, in Los Angeles, reported larger effect

estimates for 24-hour PM10 concentrations (Ostro et al., 2001; CD, p. 8-206). However, several

studies of hospital admissions or medical visits for respiratory diseases reported the strongest

associations with several-day average PM concentrations (CD, p. 8-279).

Evidence of health effects associations with different exposure time periods can inform

staff conclusions and recommendations regarding potential NAAQS averaging times. Staff

observes that the very limited information available in the CD suggests that cardiovascular

effects may be associated with acute exposure time periods on the order of an hour or so.

3.6.5.4 Exposure periods used in prospective cohort studies

The prospective cohort studies have used air quality measurements averaged over long

periods of time, such as several years, to characterize the long-term ambient levels in the

community. The exposure comparisons are basically cross-sectional in nature, and do not

provide evidence concerning any temporal relationship between exposure and effect (CD, p.

9-42). As discussed in the CD, it is not easy to differentiate the role of historic exposures from

more recent exposures, leading to potential exposure measurement error (CD, p. 5-118). This

potential misclassification of exposure is increased if average PM concentrations change over

time differentially between areas.

Several new studies have used different air quality periods for estimating long-term

exposure and tested associations with mortality for the different exposure periods. In the

extended analysis of the ACS study, Pope et al. (2002) reported associations between mortality

and PM2.5 using the original air quality data (1979-1983), data from the new fine particle

monitoring network (1999-2000), and the average PM2.5 concentrations from both time periods. 

The authors reported that the PM2.5 concentrations for the different time periods were well

correlated, indicating that the ordering of the cities from low to high pollution levels had

changed little. When using average PM2.5 levels from all years, the associations for total,

cardiopulmonary and lung cancer were slightly larger in size, though not significantly so, than

for either individual air quality data set. 

A new analysis of the Six Cities data has evaluated mortality risk with different estimates

of long-term PM2.5 exposure. The original study (Dockery et al., 1993) averaged PM2.5 and PM10

concentrations over a period of years (1979 to 1986) to represent long-term PM exposure

estimates, while the new analysis includes PM2.5 data from more recent years and evaluates

associations with PM2.5 averaged over a range of time periods, such as 2 or 3-5 years preceding

the individual’s death (Villeneuve et al., 2002). The authors reported that effect estimates for

mortality were lower with time-dependent PM2.5 exposure indicators (e.g., 2 years before

individual’s death), than with the longer-term average concentrations. They postulate that this is
3-54

likely due to the “influence of city-specific variations in mortality rates and decreasing levels of

air pollution that occurred during follow-up” (CD, p. 8-97). This might be expected, if the most

polluted cities had the greatest decline in pollutant levels as controls were applied (CD, p. 8-93). 

The authors observe that the fixed average concentration window may be more representative of

cumulative exposures, and thus a more important predictor of mortality, than a shorter time

period just preceding death (Villeneuve et al., 2002, p. 574).

Using essentially the same air quality data set as that used in the original ACS analyses,

Lipfert et al. (2000b) investigated associations between mortality and PM (using several PM

indicators) over numerous averaging periods. When using methods similar to those of the other

prospective cohort studies, the authors report finding similar associations between fine particles

and mortality (CD, p. 8-115). However, in analyses using mortality and PM data in different

time segments, the results were varied, with some statistically significant negative associations

reported. The authors report that the strongest positive associations were found with air quality

data from the earliest time periods, as well as the average across all data.

All three analyses indicate that averaging PM concentrations over a longer time period

results in stronger associations; as the Six Cities study authors observe, the longer series of data

is likely a better indicator of cumulative exposure. In these studies, spatial variation in the PM

concentrations is the key exposure indicator, and one key question is the extent to which

concentrations change over time, particularly whether there are differential changes across cities. 

As observed above, the order of cities from high to low pollution levels changed little across

time periods in the cities used in the ACS analyses. Where lower effect estimates are reported

with data collected in more recent years, the CD observes: “This is likely indicative of the

effectiveness of control measures in reducing source emissions importantly contributing to the

toxicity of ambient particles in cities where PM levels were substantially decreased over time”

(CD, p. 9-43). The CD concludes that further study is warranted on the importance of different

time windows for exposure indicators in studies of effects of chronic PM exposure. 

For use in quantitative assessments, staff concludes that it appropriate to use results from

analyses that are based on averaging PM levels over longer time periods, since the recent studies

indicate that this provides a better indicator of long-term PM exposure. Thus, as described in

Chapter 4, the results from the extended ACS analyses using average PM2.5 concentrations from

both the original and more recent time periods are used in the PM risk assessment. Staff notes

that this is consistent with the advice to EPA from the Health Effects Subcommittee (HES) of the

SAB’s Clean Air Act Compliance Council (SAB, 2004), in their review of methods used for

EPA’s health benefits assessments. The HES recommended using the results of ACS cohort

analyses that used air quality data averaged over the full study time period, indicating that this

represented the best period to use in order to reduce measurement error. 
3-55

3.6.6 Concentration-Response Relationships and Potential Thresholds

In assessing or interpreting public health risk associated with exposure to PM, the form

of the concentration-response function is a critical component. The CD recognized that it is

reasonable to expect that there likely are biologic thresholds for different health effects in

individuals or groups of individuals with similar innate characteristics and health status. 

Individual thresholds would presumably vary substantially from person to person due to

individual differences in genetic-level susceptibility and pre-existing disease conditions (and

could even vary from one time to another for a given person). Thus, it would be difficult to

detect a distinct threshold at the population level, below which no individual would experience a

given effect, especially if some members of a population are unusually sensitive even down to

very low concentrations. The person-to-person difference in the relationship between personal

exposure to PM of ambient origin and the concentration observed at a monitor may also add to

the variability in observed concentration-response relationships, further obscuring potential

population thresholds within the range of observed concentrations (CD, p. 9-43, 9-44).

The 1996 CD evaluated evidence from epidemiologic studies regarding both linear and

nonlinear forms of concentration-response relationships and whether any effect thresholds could

be identified. Based on the few available studies, the 1996 CD concluded that linear model

results “appear adequate for assessments of PM10 and PM2.5 effects” (EPA, 1996a, p. 13-91). 

Among the new epidemiologic studies of short-term PM exposure are several that use different

modeling methods to investigate alternative forms of concentration-response functions and

potential threshold levels.

Several time-series studies have evaluated potential threshold levels for associations

between mortality and short-term PM exposures. In plots of concentration-response curves from

multi-city analyses, using the NMMAPS data, it is difficult to discern any evident threshold for

relationships between PM10 and total or cardiorespiratory mortality. The authors also present

posterior probabilities for the existence of thresholds at different levels of PM10 showing that if

there is a threshold in the relationships between PM10 and total or cardiorespiratory mortality, the

likelihood of the threshold being above about 25 :g/m3

 is essentially zero (Dominici et al.,

2003b; CD, pp. 8-320, 8-321). In one single-city analysis, various statistical methods were used

to test for thresholds in simulated data sets that were created with assumed threshold levels

ranging from 12.8 to 34.4 :g/m3

 for the relationship between PM10 and mortality. The authors of

this analysis concluded that, in the data for this city, it was highly likely that standard statistical

methods could detect a threshold level, if one existed (Cakmak et al., 1999; CD, p. 8-319). Thus,

a number of studies have thus been unable to detect threshold levels in the PM-mortality

relationship, and in fact one single-city analysis suggests that statistical methods would allow

detection of a threshold in the epidemiologic data if a clear threshold existed. 

However, a few analyses in individual cities have provided suggestions of some potential

threshold levels. One single-city study used PM2.5 and PM10-2.5 measurements in Phoenix and
3-56

reported that there was no indication of a threshold in the association between PM10-2.5 and

mortality, but that there was suggestive evidence of a threshold for the mortality association with

short-term exposure to PM2.5 up to levels of about 20-25 μg/m3

 (Smith et al., 2000; CD, 8-322). 

In addition, single-city analyses in Birmingham and Chicago suggested that the concentrationresponse functions for PM10 and mortality changed to show increasing effects at levels of 80 to

100 μg/m3 PM10, but “not to an extent that statistically significant distinctions were

demonstrated” (CD, p. 8-322).

For long-term exposure to PM and mortality, the shape of the concentration-response

function was evaluated using data from the ACS cohort. The concentration-response

relationships for associations between PM2.5 and all-cause, cardiopulmonary and lung cancer

mortality are shown in Figure 3-4. The authors reported that the associations for all-cause,

cardiovascular and lung cancer mortality “were not significantly different from linear

associations” (Pope, et al., 2002). It is apparent in this figure that the confidence intervals

around each of the estimated concentration-response functions expand significantly as one looks

below around 12-13 :g/m3

, indicating greater uncertainty in the shape of the concentrationresponse relationship at concentration ranges below this level. In addition, for lung cancer, the

relationship appears to have a steeper linear slope at lower concentrations, with a flatter linear

slope at PM2.5 concentrations that exceed about 13 :g/m3

 (CD, p.8-98).

In summary, while staff recognizes that there likely are individual biologic thresholds for

specific health responses, existing studies do not support or refute the existence of thresholds in

PM-mortality relationships at the population level, for either long-term or short-term PM

exposures within the range of air quality observed in the studies (CD, p. 9-44). While

epidemiologic analyses have not identified thresholds in observed associations in the range of air

quality concentrations in the studies, it is possible that such thresholds exist toward the lower

end of these ranges (or below these ranges) but cannot be detected due to variability in

susceptibility across a population. Even in those few studies with suggestive evidence of such

thresholds, the potential thresholds are at fairly low concentrations (CD, p. 9-45). 

Based on the above considerations, staff concludes in part that it is appropriate to use the

linear or log-linear concentration-response models reported in epidemiologic studies in the

quantitative risk assessment. Staff also recognizes, however, the possibility that thresholds may

exist in reported associations at fairly low levels within the range of air quality observed in the

studies, though no specific threshold levels have been clearly identified. While the biologic

plausibility of the existence of individual thresholds supports the potential that concentrationresponse relationships may be non-linear at the lower end of the range of observed

concentrations, statistical evaluations comparing linear and non-linear concentration-models

have been unable to resolve this question. Therefore, the staff also concludes that the

implications of assuming a non-linear concentration-response relationship also should be
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Figure 3-4. Natural logarithm of relative risk for total and cause-specific mortality per

10 μg/m3 PM2.5 (approximately the excess relative risk as a fraction), with

smoothed concentration-response functions. Based on Pope et al. (2002) mean

curve (solid line) with pointwise 95% confidence intervals (dashed lines). 

(Source: CD, Figure 8-7).

included in the quantitative risk assessment. In the absence of published concentration-response

models reflecting typical sigmoidal or "hockey-stick" shaped relationships, staff has included in

the quantitative risk assessment (described in greater detail in Chapter 4) analyses incorporating

a modified linear slope with an imposed cut point. This approach is used as a surrogate for a

non-linear, sigmoidal-shaped function, in which the cut point is intended to reflect an inflection

point at the lower end of the relationship, below which it is assumed that there is little or no

population response.

3.7 SUMMARY AND CONCLUSIONS

Based on the available evidence and the evaluation of that evidence in the CD,

summarized briefly above, staff concludes that the body of evidence supports an inference of

causality for associations between PM2.5 and a broad range of health effects. Short-term
3-58

exposure to PM2.5 is likely causally associated with mortality from cardiopulmonary diseases,

hospitalization and emergency department visits for cardiopulmonary diseases, increased

respiratory symptoms, decreased lung function, and physiological changes or biomarkers for

cardiac changes. Long-term exposure to PM2.5 is likely causally associated with mortality from

cardiopulmonary diseases and lung cancer, and effects on the respiratory system such as

decreased lung function or the development of chronic respiratory disease. Staff concludes that

there is less strength, but suggestive evidence of causality for short-term exposure to PM10-2.5 and

indicators of morbidity, including hospitalization for cardiopulmonary diseases, increased

respiratory symptoms and decreased lung function. Staff concludes that it is appropriate to

consider including the health outcomes listed above in quantitative assessments for PM2.5 and

PM10-2.5. Further, staff notes that more equivocal evidence is available for other PM-health

responses, such as associations between short-term exposure to PM10-2.5 and mortality, and

between PM and effects on infants. Staff believes that less certain evidence, while not

appropriate for quantitative assessment, can inform more general assessments of the evidence.

Several issues that are relevant to the interpretation of health evidence for quantitative

assessment of PM-related effects are discussed above. Measurement error and exposure error

are issues that are distinctly more important for interpretation of results for PM10-2.5 than PM2.5. 

For PM10-2.5, there is greater uncertainty in the relationship between ambient PM measured at

central monitors and individuals’ exposure to ambient PM, based on both variability in PM10-2.5

concentrations across an area and decreased ability for coarse particles to penetrate into

buildings. This uncertainty is likely to broaden the confidence intervals around effect estimates. 

In interpreting results of associations with PM10-2.5, staff places greater emphasis on evaluating

results from the pattern of findings in multiple studies than on statistical significance of any

individual result.

In the evaluation of different epidemiologic model specifications, as described above,

some effect estimates differ upon reanalysis to address issues associated with the use of the

default GAM procedures, but many are little affected. Recognizing that there is no single

“correct” analytical approach, staff concludes that it is appropriate for quantitative assessment to

use results from short-term exposure studies that were reanalyzed with more stringent GAM

criteria or with other approaches such as GLM, or that did not use GAM in the original analysis.

Regarding potential confounding by co-pollutants, the CD concludes that the evidence

supports the existence of independent effects of PM, while recognizing the difficulties in

distinguishing effects from mixtures of correlated pollutants. For quantitative assessment, staff

concludes that single-pollutant model effect estimates can be used as reasonable indicators of the

magnitudes of effect sizes, especially for comparing results across studies. Additional analyses

using multi-pollutant model results, where available, can allow assessment of risks related to PM

exposure with adjustment for co-pollutants. 
3-59

The CD concludes that distributed lag periods may provide the most representative

quantitative estimates of effect for some health outcomes, such as mortality. Recognizing that

distributed lags have not been used in the available studies of PM2.5 and PM10-2.5, staff concludes

that a reasonable approach to selection of effect estimates for use in quantitative assessment is to

evaluate the pattern of lag results available from studies. If the data show a reasonable pattern of

results, then selecting a single lag period is appropriate, recognizing that this result is likely to

underestimate effects. Conversely, if the pattern of results across lag periods is unstable, staff

concludes that it is inappropriate to use such results for quantitative assessment since the “best”

lag day result may be biased upward.

For the long-term exposure studies, recent studies indicate that long-term PM exposure is

likely to be better estimated from air quality data averaged over longer time periods (e.g.,

multiple years of data). Staff concludes that effect estimates based on PM data averaged over

longer times periods are more representative of population health responses for use in risk

assessment. Specifically, for the results from the extended analysis of the ACS study, staff

concludes that it is most appropriate to use the concentration-response functions from the models

using averaged air quality data over the full study time period for quantitative assessment.

Finally, evaluation of the health effects data summarized in the CD provides no evidence

to support selecting any particular population threshold for PM2.5 or PM10-2.5, recognizing that it

is reasonable to expect that, for individuals, there may be thresholds for specific health

responses. Based on the above considerations, staff concludes in part that it is appropriate to use

the linear or log-linear concentration-response models reported in epidemiologic studies in the

quantitative risk assessment. Staff also recognizes, however, the possibility that thresholds may

exist in reported associations at fairly low levels within the range of air quality observed in the

studies, though no specific threshold levels have been clearly identified. Therefore, the staff also

concludes that the implications of assuming a non-linear concentration-response relationship also

should be included in the quantitative risk assessment. To do so, alternative cutpoints can be

used as surrogate for a non-linear, sigmoidal-shaped function, to reflect an inflection point at the

lower end of the relationship, below which it is assumed that there is little or no population

response.
3-60

REFERENCES

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Schwartz, J.; Neas, L. M. (2000) Fine particles are more strongly associated than coarse particles with acute

respiratory health effects in schoolchildren. Epidemiology 11:6-10.

Schwartz, J. (2001) Air pollution and blood markers of cardiovascular risk. Environ. Health Perspect.

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2004].
1

To provide a broader perspective on health risks associated with ambient PM, risk estimates associated

with current PM10 levels also have been included in an appendix to the TSD for those urban areas where PM2.5 risks

have been estimated.

4-1

4. CHARACTERIZATION OF HEALTH RISKS

4.1 INTRODUCTION

This chapter presents information regarding the results from an updated PM health risk

assessment that builds upon the methodology used in the more limited assessment conducted as

part of the last PM NAAQS review. This updated assessment includes estimates of (1) risks of

mortality, morbidity, and symptoms associated with recent ambient PM2.5 and PM10-2.5 levels; (2)

risk reductions associated with just meeting the current suite of PM2.5 NAAQS; and (3) risk

reductions associated with just meeting various alternative PM2.5 and PM10-2.5 standards. This

risk assessment is more fully described and presented in a technical support document,

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

henceforth referred to as the Technical Support Document and cited as TSD).

The goals of this PM risk assessment are: (1) to provide estimates of the potential

magnitude of mortality and morbidity effects associated with current PM2.5 and PM10-2.5 levels,

and with meeting the current suite of PM2.5 NAAQS and alternative PM2.5 and PM10-2.5 standards,

in specific urban areas;1

 (2) to develop a better understanding of the influence of various inputs

and assumptions on the risk estimates; and (3) to gain insights into the distribution of risks and

patterns of risk reductions associated with meeting alternative suites of PM standards. Staff

recognizes that there are many sources of uncertainty and variability inherent in the inputs to this

assessment and that there is a high degree of uncertainty in the resulting PM risk estimates. 

While some of these uncertainties have been addressed quantitatively in the form of estimated

confidence ranges around central risk estimates, other uncertainties and the variability in key

inputs are not reflected in these confidence ranges, but rather are addressed through separate

sensitivity analyses or characterized qualitatively.

Following this introductory section, this chapter discusses the scope of the risk

assessment, including selection of urban areas and health endpoints; components of the risk

model; characterization of uncertainty and variability associated with the risk estimates; and key

results from the assessment for both PM2.5 and PM10-2.5. The TSD provides a more detailed

discussion of the risk assessment methodology and includes additional risk estimates beyond

those summarized herein. 

4.1.1 Overview of Risk Assessment From Last Review

In the last review, PM-associated risks were estimated for two urban areas: Philadelphia

and Los Angeles counties (Abt Associates, 1996). The PM health risk model used in the last
4-2

assessment combined information about daily PM air quality for these two areas with estimated

concentration-response functions derived from epidemiologic studies and baseline health

incidence data for specific health endpoints to derive estimates of the annual incidence of

specific health effects associated with recent air quality levels (termed “as is” air quality in both

the previous and current TSD). Since site-specific relative risks were not available for all

endpoints in both locations (and in the absence of more information concerning which individual

studies might best characterize the health risk in a given location), a type of meta-analysis

(referred to as a “pooled analysis”) was conducted that combined the results of those studies that

met specified criteria. The assessment also examined the reduction in estimated incidence that

would result from just meeting the existing PM10 standards and various alternative PM2.5

standards. In addition, the assessment included sensitivity analyses and integrated uncertainty

analyses to better understand the influence of various inputs and assumptions on the risk

estimates. The methodological approach followed in conducting the last risk assessment and risk

estimates are described in Chapter 6 of the 1996 Staff Paper (EPA, 1996b) and in several

technical reports (Abt Associates, 1996; Abt Associates, 1997a,b) and publications (Post et al.,

2000; Deck et al., 2001). 

In the 1997 review of the PM NAAQS, EPA placed greater weight on the overall

qualitative conclusions derived from the health effect studies – that ambient PM is likely causing

or contributing to significant adverse effects at levels below those permitted by the existing PM10

standards – than on the specific concentration-response functions and quantitative risk estimates

derived from them. Nevertheless, EPA judged that the assessment provided reasonable

estimates as to the possible extent of risk for those effects given the available information (62 FR

at 38656).

4.1.2 Development of Approach for Current Risk Assessment

The scope and methodology for this updated PM risk assessment have been developed

over the last few years. In June 2001, OAQPS released a draft document, PM NAAQS Risk

Analysis Scoping Plan (EPA, 2001c), for CASAC consultation and public comment, which

described staff’s general plan for this assessment. In January 2002, OAQPS released a more

detailed draft document, Proposed Methodology for Particulate Matter Risk Analyses for

Selected Urban Areas (Abt Associates, 2002), for CASAC review and public comment, which

described staff’s plans to assess (a) PM2.5-related risks for several health endpoints, including

mortality, hospital admissions, and respiratory symptoms and (b) PM10-2.5-related risks for

hospital admissions and respiratory symptoms. During a February 2002 teleconference, CASAC

discussed this draft document and public comments were made; CASAC sent an advisory letter

to the Administrator documenting its advice in May 2002 (Hopke, 2002). In its advisory letter,

CASAC “concluded that the general methodology as described in the report is appropriate . . . 
4-3

Thus, the general framework of the approach is the sensible approach to this risk analysis”

(Hopke, 2002).

In response to a request from CASAC to provide additional details about the planned

scope of the PM10-2.5 and PM10 components of the assessment, in April 2003 OAQPS released a

draft memorandum (Abt, 2003a) for CASAC consultation and public comment, addressing these

topics. In August 2003, OAQPS released a draft PM risk assessment report (Abt Associates,

2003b) in conjunction with the first draft PM Staff Paper. The CASAC provided its comments

on the draft PM risk assessment in a letter to the Administrator (Hopke, 2004). A second draft

PM risk assessment report (Abt Associates, 2005a) was released in January 2005 in conjunction

with the second draft PM Staff Paper, and was reviewed by CASAC and the public at a meeting

held in April 2005. The final PM risk assessment report (Abt Associates, 2005b) takes into

account the advice from CASAC and public comments received on the earlier drafts of that

document.

4.2 SCOPE OF PM RISK ASSESSMENT

This risk assessment estimates risks of various health effects associated with exposure to

ambient PM2.5 and PM10-2.5 in a number of urban areas selected to illustrate the public health

impacts of these pollutants. The health endpoints selected for the PM2.5 assessment, discussed in

section 4.2.1, include those related to short- and long-term exposure for which the CD concludes

that the association with PM2.5 (or one or more PM2.5 components), acting alone and/or in

combination with gaseous co-pollutants, is likely causal (CD, p. 9-79). The health endpoints

selected for the PM10-2.5 assessment, also discussed in section 4.2.1, include those related to

short-term exposure for which the CD concludes that the scientific evidence is suggestive of an

association that the staff judges to be likely causal. This assessment includes risk estimates for

nine urban areas for PM2.5 and three urban areas for PM10-2.5. The basis for selection of these

areas is discussed below (section 4.2.2). This assessment is intended to estimate risks

attributable to anthropogenic sources and activities (i.e., risk associated with concentrations

above policy-relevant background or above various higher cutpoints that reflect possible

population thresholds).

This assessment uses concentration-response functions from epidemiologic studies of

short- and long-term exposures to ambient PM based on PM concentrations measured at fixedsite, community-oriented, ambient monitors. As discussed in Chapter 2 (section 2.7) and

Chapter 3 (section 3.6.2), measurements of daily variations of ambient PM concentrations, as

used in the time-series studies, have a plausible linkage to the daily variations of exposure to

ambient PM2.5 and PM10-2.5 for the populations represented by ambient monitoring stations. The

CD concludes that “at this time, the use of ambient PM concentrations as a surrogate for

exposures is not expected to change the principal conclusions from PM epidemiologic studies

that use community average health and pollution data” (CD, p. 5-121). The possible impact of
2

As discussed in Chapter 5 of the CD, EPA and the exposure analysis community are working to improve

exposure models designed specifically to address PM and to collect new information in PM exposure measurement

field studies that will improve the scientific bases for exposure analyses that may be considered in future reviews.

4-4

exposure misclassification on the estimated concentration-response relationships derived from

the community epidemiologic studies is discussed above in Chapter 3 (section 3.6.2). Since the

currently available epidemiologic health effects evidence relates ambient PM concentrations, not

exposures, to health effects, this assessment does not include a quantitative exposure analysis. 

While quantitative estimates of personal or population exposure do not enter into this risk

assessment, an understanding of the nature of the relationships between ambient PM

concentrations and its various components and human exposure underlies the conceptual basis

for this assessment.2

While the NAAQS are intended to provide protection from health effects associated with

exposure to ambient PM, EPA recognizes that exposures to PM from other sources (i.e., nonambient PM) also have the potential to affect health. The EPA’s Office of Radiation and Indoor

Air and other Federal Agencies, such as the Consumer Product Safety Commission (CPSC) and

the Occupational Safety and Health Administration (OSHA), address potential health effects

related to indoor, occupational, environmental tobacco smoke, and other non-ambient sources of

PM exposure. As with the prior PM risk assessment, contributions to health risk from nonambient sources are beyond the scope of the risk assessment for this NAAQS review.

4.2.1 Selection of Health Endpoint Categories

As discussed in Chapter 3, OAQPS staff carefully reviewed the health effects evidence

evaluated in the CD to identify potential health effect categories for inclusion in this assessment. 

Given the large number of endpoints and studies addressing PM-related effects, staff included

only the more severe and better understood (in terms of health consequences) health endpoint

categories. In addition, the staff included only those health endpoints for which the overall

weight of the evidence from the collective body of studies supports the CD conclusion that there

is likely to be a causal relationship or that the scientific evidence is sufficiently suggestive of a

causal relationship that staff judges the effects to be likely causal between PM and the health

effects category. Finally, for the three PM indicators (PM2.5, PM10, PM10-2.5), staff considered

only those endpoint categories which provided concentration-response relationships based on

U.S. and Canadian studies that used PM concentrations obtained by one of the following

approaches: (1) directly measuring fine particles using PM2.5 or PM2.1, (2) estimating the

concentration of fine particles using nepholometry data, and (3) estimating PM10-2.5

concentrations based on co-located PM10 and PM2.5 monitors or based on measurements using

dichotomous samplers.
4-5

Based on a review of the evidence evaluated in the CD and discussed in Chapter 3, as

well as the criteria discussed above, staff included the following broad categories of health

endpoints in the risk assessment for PM2.5 and PM10-2.5:

Related to short-term PM2.5 exposure:

• total (non-accidental), cardiovascular, and respiratory mortality; 

• hospital admissions for cardiovascular and respiratory causes;

• respiratory symptoms not requiring hospitalization

Related to long-term PM2.5 exposure:

• total, cardiopulmonary, and lung cancer mortality. 

Related to short-term PM10-2.5 exposure:

• hospital admissions for cardiovascular and respiratory causes;

• respiratory symptoms.

As discussed in Chapter 3 (sections 3.4 and 3.7), the available evidence for other health

responses, such as associations between short-term exposure to PM10-2.5 and mortality, is more

equivocal. Staff believe that these health endpoints, which are based on less certain evidence,

are not appropriate for inclusion in the quantitative risk assessment. 

4.2.2 Selection of Study Areas

A primary goal of the current PM risk assessment has been to identify and include urban

areas in the U.S. for which epidemiologic studies are available that estimate concentrationresponse relationships for those locations. This goal is in large part motivated by the evaluation

contained in the CD and staff assessment in Chapter 3 that suggests there may be geographic

variability in concentration-response relationships across different urban areas in the U.S. The

selection of urban areas to include in the PM risk assessment was based on the following criteria:

• An area is the same as or close to the location where at least one concentrationresponse function, for one of the selected health endpoints, has been estimated by

an epidemiologic study that satisfies the study selection criteria (see below).

• An area had relatively recent area-specific baseline incidence data available for

those locations with epidemiologic studies reporting PM-related hospital

admissions.

• An area is one in which epidemiologic studies exist that had relatively greater

precision, as discussed below.
3

For PM2.5, an additional requirement was that a city had to have at least 122 days of data (i.e., equivalent to

1 in 3 day monitoring) for a recent year of air quality to be included.

4

The criterion of at least 11 observations per quarter is based on EPA guidance on measuring attainment of

the daily and annual PM standards and is contained in Appendix N of the July 18, 1997 Federal Register notice.

5

Most of the epidemiologic studies reporting total non-accidental mortality also report on one or more cause

specific mortality categories. In such studies, the natural log of mortality days is often less than 9.0 because there

are fewer deaths from a specific cause. We included cause-specific mortality concentration-response functions from

such studies, as long as the natural log of total mortality-days was greater than or equal to 9.0.

4-6

• An area had sufficient air quality data for a recent year (1999 or later). Sufficient

PM2.5 data are defined as having at least one PM monitor at which there are at

least 11 observations per quarter for a one year period.3 Sufficient air quality data

for PM10-2.5 are defined as a one year period with at least 11 daily values per

quarter based on data from co-located PM2.5 and PM10 monitors.4

For the PM2.5 risk assessment, staff focused on selecting urban areas in which studies

reported total and/or cardiovascular mortality associated with short-term exposure to PM2.5

concentrations, since this was the largest data base in terms of number of studies in different

locations. Staff then supplemented this by consideration of other morbidity endpoints (i.e.,

hospital admissions). Based on a review of studies listed in Tables 8A and 8B of the CD (see

also Appendices 3A and 3B of this Staff Paper), a candidate pool of 17 urban locations was

initially suggested based on short-term exposure mortality studies (16 of the candidate

locations); Seattle was added based on a hospital admissions study.

Staff next considered an indicator of study precision for the urban areas associated with

the short-term exposure mortality studies identified in the first step. As discussed above in

Chapter 3 (section 3.3.1.1) and in Chapter 8 of the CD (pp. 8-324 to 8-325), the natural logarithm

of the mortality-days (a product of each city’s daily mortality rate and the number of days for

which PM data were available) can be used as a rough indicator of the degree of precision of

effect estimates; studies with larger values for this indicator should be accorded relatively greater

study weight. While there was no bright line for selecting any particular cutoff, staff chose to

consider only those urban areas in which studies with relatively greater precision were

conducted, specifically including studies that have a natural log of mortality-days greater than or

equal to 9.0 (i.e., approximately 8,000 deaths) for total non-accidental mortality.5

 As a result of

applying this criterion, six urban areas were excluded as potential study areas (Camden, NJ;

Coachella Valley, CA; Elizabeth, NJ; Newark, NJ; Steubenville, OH; and Topeka, KS). 

Finally, staff considered which of the remaining potential study locations identified from

steps one and two above also had sufficient PM2.5 ambient monitoring data consistent with the

above criterion. This final criterion excluded two of the remaining potential study areas

(Knoxville, TN and Portage, WI), leaving nine urban areas (i.e., Boston, MA; Detroit, MI; Los
4-7

Angeles, CA; Philadelphia, PA; Phoenix, AZ; Pittsburgh, PA; San Jose, CA; Seattle, WA; and

St. Louis, MO) in which epidemiologic studies reported concentration-response relationships for

PM2.5 and mortality or hospital admissions and which had sufficient air quality data in a recent

year. The PM2.5 risk assessment for long-term exposure mortality also was conducted for these

same nine urban areas. 

Most of the short-term morbidity and respiratory symptom studies reporting PM2.5-related

effects were conducted in the same set of locations as the short-term exposure mortality studies. 

In considering these other health endpoints, staff applied the same criteria, focusing on locations

which had studies with relatively greater precision, had adequate PM2.5 ambient air quality data,

and, for the hospital admissions effect category, had the necessary baseline incidence data.

The selection of urban areas to include for the PM10-2.5 risk assessment was based on

examining the pool of epidemiologic studies reporting associations for PM10-2.5 with the

morbidity endpoints (hospital admissions and respiratory symptoms) in any of the urban areas

already selected for the PM2.5 risk assessment. As noted earlier, the PM10-2.5 risk assessment is

more limited because of the more limited air quality data as well as the smaller number of health

endpoints and studies. Based on the available data, EPA has included in the PM10-2.5 risk

assessment the following health endpoints and locations: increased hospital admissions in

Detroit and Seattle, and increased respiratory symptoms in St. Louis. 

The health endpoints and urban locations selected for the PM2.5 risk assessment are

summarized in Tables 4-1 and 4-2, for mortality and morbidity endpoints, respectively;

endpoints and locations for the PM10-2.5 risk assessment are summarized in Table 4-3. These

tables also list the specific studies that provided the estimated concentration-response functions

used in the PM2.5 and PM10-2.5 risk assessment. More detailed information on the studies selected

can be found in Appendices 3A, 3B, and 4A of this Staff Paper and Appendix C of the TSD.

4.3 COMPONENTS OF THE RISK MODEL

In order to estimate the incidence of a particular health effect associated with recent

conditions in a specific county or set of counties attributable to ambient PM2.5 or PM10-2.5

exposures in excess of background or various cutpoints, as well as the change in incidence of the

health effect in that county or set of counties corresponding to a given change in PM2.5 or PM10-2.5

levels resulting from just meeting a specified set of PM2.5 or PM10-2.5 standards, the following

three elements are required:

• air quality information including: (1) recent air quality data for PM2.5 and PM10-2.5

from ambient monitors for the selected location, (2) estimates of background

PM2.5 and PM10-2.5 concentrations appropriate for that location, and (3) a method

for adjusting the recent data to reflect patterns of air quality estimated to occur

when the area just meets a given set of PM2.5 (or PM10-2.5) standards;
4-8

Table 4-1. Mortality Health Endpoints, Urban Locations, and Studies Selected for Use in the PM2.5 Ris

Urban Location Mortality Associated with Short-Term Exposure Mortality 

Te

Total (non-accidental) Cardiovascular Circulatory Respiratory

Boston, MA Schwartz et al. (1996)A * Klemm et al. (2000)B

– ischemic heartdisease *

Klemm et al. (2000)B –

COPD *, pneumonia *

Krewski et Krewski et Pope et al. 

Detroit, MI Lippmann et al. (2000)C Lippmann et al.

(2000)C

Lippmann et al. (2000)C Krewski et 

Pope et al. 

Los Angeles,CA

Moolgavkar (2000a)D Moolgavkar (2000a)D Krewski et 

Pope et al. 

Philadelphia, PA Lipfert et al. (2000) Lipfert et al. (2000) * Krewski et 

Pope et al. 

Phoenix, AZ Mar et al. (2000)E Krewski et 

Pope et al. 

Pittsburgh, PA Chock et al. (2000) Krewski et 

Pope et al. 

San Jose, CA Fairley (1999)F Fairley (1999)F Fairley (1999)F Krewski et 

Pope et al. 

Seattle, WA Krewski et 

Pope et al. 

St. Louis, MO Schwartz et al. (1996)A Klemm et al. (2000)B

– ischemic heartdisease *

Klemm et al. (2000)B –

COPD *, pneumonia *

Krewski et Krewski et Pope et al. 

*Includes a multi-city or multi-county concentration-response function

A Reanalyzed in Schwartz (2003a)B Reanalyzed in Klemm and Mason (2003)C Reanalyzed in Ito (2003)D Reanalyzed in Moolgavkar (2003)E Reanalyzed in Mar et al. (2003)F Reanalyzed in Fairley (2003)

GKrewski et al. (2000)-6 cities 

and cardiopulmonary mortality ACS extended provide total, cacancer mortality coefficients
4-9

Table 4-2. Morbidity Health Endpoints, Urban Locations, and Studies Selected for Use in the PM

Assessment

Urban Location Cardiovascular Hospital Admissions Respiratory Hospital Admissions Respirat

Boston, MA Schwartz and Nea

 resp

Detroit, MI Lippmann et al. (2000)A – ischemic heart 

 disease, congestive heart failure,  dysrhythmias

Lippmann et al. (2000)A – pneumonia,  COPD+

Los Angeles, CA Moolgavkar (2000b)B Moolgavkar (2000c)B – COPD+

Seattle, WA Sheppard et al. (1999)C – asthmaSt. Louis, MO Schwartz and Nea

*Includes multi-city concentration-response functionA Reanalyzed in Ito (2003); COPD+ is indicated here because the authors included asthma in their definition of COPD.B Reanalyzed in Moolgavkar (2003); COPD+ is indicated here because the authors included asthma in their definition of COPD.C Reanalyzed in Sheppard (2003)Table 4-3. Morbidity Health Endpoints, Urban Locations, and Studies Selected for Use in the PM10-2.5 R

Urban Location Cardiovascular Hospital Admissions Respiratory Hospital Admissions Respira

Detroit, MI Lippmann et al. (2000)A – 

 Congestive heart disease, Ischemic heart disease Dysrhythmias

Lippmann et al. (2000)A – Pneumonia, 

 COPD+

Seattle, WA Sheppard et al. (1999)B – asthma

St. Louis, MO Schwartz and Nea

*Includes multi-city concentration-response function

A Reanalyzed in Ito (2003); COPD+ is indicated here because the authors included asthma in their definition of COPD.B Reanalyzed in Sheppard (2003)
6

Two additional sensitivity analyses were carried out in single locations: one addressing the impact of an

exceptional event episode in Boston and one examining the effect of different model specifications on annual health

risks associated with recent air quality levels in Los Angeles.

7

For some studies on respiratory hospital admissions used in the risk assessment, a linear concentrationresponse function was estimated.

4-10

(Equation 4-1)

• relative risk-based concentration-response functions (preferably derived in the

assessment location) which provide an estimate of the relationship between the

health endpoints of interest and ambient PM concentrations; and

• annual or seasonal baseline health effects incidence rates and population data

which are needed to provide an estimate of the annual or seasonal baseline

incidence of health effects in an area before any changes in PM air quality.

Figure 4-1 provides a broad schematic depicting the role of these components in the risk

assessment. EPA evaluated several base case scenarios, using various alternative cutpoints (see

section 4.3.4.2). Those points where EPA has conducted analyses of alternative assumptions,

procedures, or data across the various locations are indicated by a diamond with Sx in it. A

summary description of the sensitivity analyses performed is included in Table 4-4.6 Each of the

key components (i.e., air quality information, estimated concentration-response functions, and

baseline incidence and population data) is discussed below, highlighting those points at which

judgments have been made.

The concentration-response relationships used in the PM risk assessment are empirically

estimated relationships between average ambient PM concentrations and the health endpoints of

interest reported by epidemiologic studies for specific urban areas. Most epidemiologic studies

estimating relationships between PM and health effects used a method referred to as “Poisson 

regression” to estimate exponential (or log-linear) concentration-response functions.7

 In this

model, 

where y is the incidence of the health endpoint of interest associated with ambient PM level x, $

is the coefficient of ambient PM concentration, and B is the incidence of the health endpoint

when there is no ambient PM2.5 or PM10-2.5. The difference in health effects incidence, )y = y0 - y, 

from y0 to the baseline incidence rate, y, that corresponds to a given difference in ambient PM2.5

(or PM10-2.5) levels, Sx = x0 - x, is then
4-11

Ambient Populationoriented Monitoring and Estimated Background Levels for Selected Cities

Air Quality Adjustment ProceduresAlternative Proposed Standards

Changes in 

Distribution 

of PM Air 

Quality Risk Es

• Recen

Qualit

• Altern

Scena

= kth Sensitivity Analysis (See Table 4-4): Analysis of effects of alternative assumptions, procedures or da

occurs at these points.

Health 

Risk 

Model

S3,4

Estimates of City-specific Baseline Health Effects Incidence Rates(various health endpoints) and Population DataHuman Epidemiological Studies (various health endpoints)

Concentration -Response Relationships

Recent Air Quality Analysis

S1,2

S5-7

Concentration-Response

Sk

Air Quality

Figure 4-1. Major components of particulate matter health risk assessment.
4-12

Table 4-4. Sensitivity Analyses

Analysis

Number

(Figure 4-1)

PM

Indicator

Component of

the Risk

assessment

Sensitivity Analyses or Comparisons

1 PM2.5,

PM10-2.5

Air Quality Sensitivity analyses of the effect of assuming different

(constant) background PM levels

2 PM2.5 Air Quality Sensitivity analyses of the effect of assuming a

constant background PM level versus a distribution of

daily background levels 

3 PM2.5 Air Quality Sensitivity analyses of the effect of just meeting the

current and alternative annual PM2.5 standards using

the maximum versus the average of monitor-specific

averages

4 PM2.5 Air Quality Sensitivity analyses of the effect of an alternative air

quality adjustment procedure on the estimated risk

reductions resulting from just meeting the current 24-

hr and annual PM2.5 standards

5 PM2.5 ConcentrationResponse

Sensitivity analyses using an approach to estimate the

possible impact of using a distributed lag

concentration-response function

6 PM2.5 ConcentrationResponse

Sensitivity analyses of the impact on mortality

associated with long-term exposure of different

assumptions about the role of historical air quality

concentrations in contributing to the reported effects

7 PM2.5 ConcentrationResponse

Sensitivity analysis of the impact on mortality

associated with short-term exposure of using a multicity concentration-response function compared to

location-specific concentration-response functions

from single-city studies

Source: Abt Associates (2005b)
8

As indicated previously, staff judges that the most relevant risk estimates are for those PM levels in excess

of an estimated policy-relevant background and various cutpoints well above this background. As discussed more

fully in Section 4.3.2.6, risk estimates for long-term exposure mortality are calculated in excess of the minimum of

the lowest measured levels for the long-term exposure studies included in the risk assessment and in excess of two

alternative cutpoint levels.

9

For those areas already meeting the current PM2.5 standards, )x is the difference between the recent

ambient PM concentrations and ambient PM concentrations associated with just meeting the specified standards.

4-13

(Equation 4-2)

(Equation 4-3)

or, alternatively,

where RR)x is the relative risk associated with the change in ambient PM2.5 (or PM10-2.5) levels, 

)x. Equations 4-2 and 4-3 are simply alternative ways of expressing the relationship between a

given difference in ambient PM2.5 (or PM10-2.5) levels and the corresponding difference in health

effects. These are the key equations that combine air quality information, concentrationresponse information, and baseline health effects incidence and population information to

estimate ambient PM2.5 and PM10-2.5 health risks.

For the first part of the risk assessment that characterizes risks associated with recent

ambient PM concentrations, )x is the difference between the recent ambient PM concentrations

(on each day for the short-term exposure [i.e, daily or 24-hour] endpoints or the annual average

for the long-term exposure [i.e., annual average or longer] endpoints) and either the estimated

policy-relevant background concentration or alternative cutpoints for short-term exposure

endpoints or 7.5 μg/m3 or the alternative cutpoints for long-term exposure mortality.8 For the

second part of the risk assessment, characterizing the reduction in health effects incidence

associated with alternative PM standards, )x is the difference between ambient PM

concentrations when the current PM standards are just met (on each day for the short-term

exposure endpoints or the annual average for the long-term exposure endpoints) and ambient PM

concentrations associated with just meeting the specified alternative standards.9

For short-term exposure health endpoints, the risk assessment first calculated the daily

changes in incidence. Since most areas had at least some days for which no ambient PM

concentration data were available, the estimated annual incidence was summed up for each

quarter of the year and adjusted by using the ratio of the total number of days in each quarter to 
10Adjustment was done on a quarterly basis to reduce possible bias that would be introduced where missing

data are not uniformly distributed throughout the year.

4-14

the number of days in the quarter for which air quality data was available.10 This simple

adjustment assumes that missing air quality data occur randomly with respect to level within a

quarter and that the distribution of PM concentrations on the days with missing data is

essentially the same as the distribution on days for which there are PM data. The quarterly

incidence estimates were then summed to derive an annual estimate.

The daily time-series epidemiologic studies used models estimating concentrationresponse functions in which the PM-related incidence on a given day depends only on some

specified lagged PM concentration measure (e.g., 0-day lag, 1-day lag, 2-day lag, average of 0-

and 1-day lag). As discussed in Chapter 3 (section 3.6.5.1), such models necessarily assume that

the longer pattern of PM levels preceding the PM concentration on a given day does not affect

mortality on that day. To the extent that PM-related mortality on a given day is affected by PM

concentrations over a longer period of time, then these models would be mis-specified; and this

mis-specification would affect the predictions of daily incidence based on the model. The extent

to which longer-term (i.e., weekly, monthly, seasonal, or annual) PM2.5 exposures affect the

relationship observed in the daily time-series studies is unknown. However, there is some

evidence, based on analyses of PM10 data, that mortality on a given day is influenced by prior

PM exposures up to more than a month before the date of death (Schwartz, 2000a, reanalyzed in

Schwartz, 2003b). As indicated in section 3.6.5.2, our use of single day lag models which ignore

longer-term influences may result in the risk being underestimated. Currently, there is

insufficient information to adjust for the impact of longer-term exposure (on the order of weeks

or months) on mortality associated with short-term PM2.5 exposures, and this is an important

uncertainty that should be kept in mind as one considers the results from the short-term exposure

PM2.5 risk assessment. 

The estimated PM2.5-related mortality associated with long-term exposure studies is likely

to include mortality related to short-term exposures as well as mortality related to longer-term

exposures. As just discussed, estimates of daily mortality based on the time-series studies also

are likely to be affected by prior exposures. Therefore, the estimated annual incidences of

mortality calculated based on the short- and long-term exposure studies are not likely to be

completely independent and should not be added together.

The statistical uncertainty surrounding the estimated PM2.5 and PM10-2.5 coefficients in the

reported concentration-response functions is reflected in the confidence intervals provided for

the risk estimates in sections 4.4 and 4.5. As discussed in greater detail in section 4.3.2.1, due to

the significant uncertainty associated with whether or not the concentration-response

relationships are approximately linear down to policy-relevant background, additional base case

risk estimates are presented using alternative cutpoints for both short- and long-term exposure
4-15

mortality associated with PM2.5 concentrations. As summarized in Table 4-4, a number of

sensitivity analyses (S1 through S7 in Figure 4-1) were also conducted. The results of these

sensitivity analyses are discussed in sections 4.4 and 4.5.

4.3.1 Air Quality Considerations

As illustrated in Figure 4-1, and noted earlier, air quality information required to conduct

the PM risk assessment includes: (1) recent air quality data for PM2.5 and PM10-2.5 from suitable

monitors for each selected location, (2) estimates of background PM2.5 and PM10-2.5

concentrations appropriate for each location, and (3) air quality adjustment procedures to

modify the recent data to reflect changes in the distribution of PM air quality estimated to occur

when an area just meets a given set of PM2.5 (or PM10-2.5) standards. OAQPS retrieved ambient

air quality data for PM2.5 and PM10 for the potential study areas for the years 1999 through 2003

from EPA’s Air Quality System (AQS). Staff calculated PM10-2.5 concentrations from co-located

PM2.5 and PM10 monitors that met the minimum observation cutoff criterion. Generally, the most

recent year of PM data were used for each study area and PM indicator subject to meeting this

requirement.

A composite monitor data set was created for each assessment location based on

averaging the 24-hour values from all monitors eligible for comparison with the standards for

each day. The resulting composite monitor data set provides a single series of daily

concentrations for the urban area which serves as the surrogate index of exposure for the urban

area. The use of a composite monitor value to represent ambient PM air quality most closely

matches the approach taken in the epidemiology studies that serve as the source of the

concentration-response relationships used in the risk assessment. Table 4-5 provides a summary

of the PM2.5 and PM10-2.5 ambient air quality data for the urban study areas, including the range of

annual and 24-hr average statistics across monitors in each study area and the composite monitor

values used in the risk assessment. Additional tables providing more detailed information on PM 

ambient concentrations for these locations, including the number of observations available on a

quarterly and annual basis for each monitor, can be found in Appendix A of the TSD. 

4.3.1.1 Estimating PM Background Levels 

Background PM concentrations used in the PM risk assessment are defined above in

Chapter 2 as the PM concentrations that would be observed in the U.S. in the absence of

anthropogenic emissions of PM and its precursors in the U.S., Canada, and Mexico. For the

initial base case risk estimates, the midpoint of the appropriate ranges of annual average

estimates for PM2.5 background presented in section 2.6 were used (i.e., eastern values were used

for eastern study locations and western values were used for western study locations). For 

PM10-2.5 analyses, the approximate mid-points of the annual average estimates for PM10-2.5

background presented in section 2.6 were used for the initial base risk estimates. 
June 2005 4-16 Draft - 

Table 4-5. Summary of PM Ambient Air Quality Data for Risk Assessment Study A

Area Population (millions)

PM2.5** (μg/m3) PM10-2.5**(μ

Annual Average 24-hr Average, 98th%ile Annual Average 

Range

Across

Monitors

Composite

Monitor

Range

Across

Monitors

Composite

Monitor

Range

Across

Monitors

Composite

A

M

Boston, MAa 2.8 11.4-13.6 12.1 30.6-41.3 34.1

Detroit, MIb 2.1 14.1-19.1 15.7 33.6-46.2 41.5 10.9-25.0 21.7 40

Los Angeles County, CAc 9.5 9.4-22.1 19.1 17.0-61.3 55.0

Philadelphia County, PAd 1.5 13.2-16.1 14.3 35.6-42.3 38.4Phoenix, AZe 3.1 9.2-10.9 10.4 22.7-35.3 28.9Pittsburgh, PAf 1.3 12.0-20.2 16.9 30.7-66.6 43.9San Jose, CAg 1.7 10.1-11.7 11.1 36.9-40.1 37.6Seattle, WAh 1.7 7.8-10.8 8.3 10.9-28.4 21.7 10.0-12.6 11.4 25

St. Louisi 2.5 13.0-17.5 14.0 30.5-40.8 30.6 10.1-14.9 12.0 24

*Based on air quality data for the year 2003, unless otherwise noted in footnotes below.**Summary statistics for a “composite monitor” based on average of 24-hour values at the different monitors in urban area that reported aIncludes Middlesex, Norfolk, and Suffolk Counties.bIncludes Wayne County.cIncludes Los Angeles County.cIncludes Hennepin and Ramsey Counties.dIncludes Philadelphia County.eIncludes Maricopa County; PM2.5 air quality data are for 2001.fIncludes Allegheny County gIncludes Santa Clara County kIncludes King CountyiIncludes St. Louis, Franklin, Jefferson, St. Charles Counties in MO, Clinton, Madison, Monroe, and St. Clair Counties in IL and St. Lo
11A design value is a statistic that describes the air quality status of a given area relative to the level of the

NAAQS. Design values are often based on multiple years of data, consistent with the specification of the NAAQS in

Part 50 of the CFR. For example, for the base case analyses for the current PM2.5 NAAQS, the 3-year averages (of

annual means or 98th percentiles) based on the maximum monitor within an urban area are the design values.

4-17

In sensitivity analyses, we examine the impact of assuming 1) a constant background set

at the lower and upper ends of the range of estimated background levels for the eastern and 

western United States, depending on the assessment location (S1 in Figure 4-1), and 2) a variable

daily PM2.5 background, using distributions whose means are equal to the values used in the base

case analysis and whose distributions are based on an analysis of PM2.5 data from relatively

remote sites with the sulfate component removed (S2 in Figure 4-1) (see Langstaff, 2005).

4.3.1.2 Simulating PM Levels That Just Meet Specified Standards

To estimate the health risks associated with just meeting the current PM2.5 standards and

alternative PM2.5 and PM10-2.5 standards, it is necessary to estimate the distribution(s) of PM

concentrations that would occur under each specified standard (or sets of standards). Since

compliance with the standards is based on a 3-year average, air quality data from 2001 to 2003

have been used to determine the amount of reduction in PM2.5 concentrations required to meet

the current or alternative suites of standards. Estimated design values11 (see Table 4-13 later in

this Chapter), based on the highest community-oriented monitor within each study area, are used

to determine the percent adjustment necessary to just meet annual, 98th percentile daily, and 99th

percentile daily standards. The amount of control has then been applied to a single year of data

(2003, unless otherwise specified) to estimate risks for a single year. 

Under the current annual PM2.5 standard, urban areas may (under certain circumstances)

use the average of the annual averages of several monitors within an urban area to determine

compliance, commonly referred to as the “spatial averaging approach.” Therefore, a sensitivity

analysis (S3 in Figure 4-1) has been conducted for three urban areas which satisfy the criteria for

use of spatial averaging to allow comparison of the estimated incidence and percent reduction in

incidence associated with using either the highest monitor or the spatial average for determining

the percent adjustment necessary to just meet the current and alternative annual standards.

The percent adjustment to simulate just meeting alternative standards is applied to the

composite monitor for the urban area. The composite monitor is used because it is the best

surrogate indicator of exposure that matches the type of exposure measure used in the original

epidemiologic studies. When assessing the risks associated with long-term exposures, which use

concentration-response functions from epidemiologic studies that are specified in terms of longterm average concentrations, the annual mean is simply set equal to the standard level. In

contrast, when assessing the risks associated with short-term exposures, which use

concentration-response functions from epidemiologic studies that consider the sequence of daily

average concentrations, the distribution of 24-hour values that would occur upon just meeting a

given 24-hour and/or annual PM standard has to be simulated. 
12 The portion of the distribution below the estimated background concentration is not rolled back, since air

quality strategies adopted to meet the standards will not reduce the background contribution to PM concentrations.

13See www.epa.gov/airtrends/pdfs/ for a discussion of how design values are calculated, noting in

particular that concentrations flagged as natural events (e.g, high winds, wildfires, volcanic eruptions) or exceptional

events (e.g., construction, prescribed burning) are not included in these calculations and that no regulatory decisions

on attainment status have been made at this time based on these data.

4-18

There are many possible ways to create an alternative distribution of daily concentrations

that just meets a specified set of PM standards. Both the 1996 assessment (see Abt Associates,

1996, section 8.2) and a more recent analysis of historical air quality data (see Appendix B in the

TSD) have found that PM2.5 levels in excess of estimated background concentrations in general

have historically decreased in a roughly proportional manner (i.e., concentrations at different

points in the distribution of 24-hour PM2.5 values in excess of an estimated background

concentration have decreased by approximately the same percentage). This suggests that, in the

absence of detailed air quality modeling, a reasonable method to simulate PM2.5 reductions that

would result from just meeting a set of standards is to use a proportional adjustment (i.e., to

decrease non-background PM levels on all days by the same percentage) for all concentrations

exceeding the background level.12 We are using that approach in the base case here. The

assessment also includes a sensitivity analysis (S4 in Figure 4-1) to examine the impact on the

PM2.5 risk estimates of an alternative air quality adjustment procedure (e.g., a method that

reduces the top 10% of daily PM2.5 concentrations more than the lower 90%).

Because the PM10-2.5 historical air quality data are substantially more sparse, there were

insufficient data to carry out the type of evaluation of historical data that was done for PM2.5 to

see whether the shape of the distribution of daily values has changed over time. In the absence

of a clearly preferable alternative, the same proportional rollback approach used for PM2.5 has

been used for the PM10-2.5 assessment. This increases the uncertainty about the PM10-2.5 risk

estimates associated with meeting alternative PM10-2.5 standards. 

Where sets of standards are considered, as is the case for PM2.5 where both an annual and

a daily standard are specified, the percent reduction is determined by the “controlling standard.” 

The “controlling standard” is defined as the standard which would require the greatest reduction

in PM levels to just meet the standard. For example, for the current suite of PM2.5 standards, the

existing annual standard of 15 μg/m3 is the controlling standard for the five urban study areas

(i.e., Detroit, Los Angeles, Philadelphia, Pittsburgh, and St. Louis) that do not meet the current

standards based on design values.13 In four of these five urban areas, suites of annual standards

within the range of 12 to 15 μg/m3

 combined with the current daily standard of 65 μg/m3, using a

98th percentile form, requires the same reduction as when these annual standards are combined

with a daily standard of 40 μg/m3

, using the same daily form. Therefore, the risk assessment

only includes the 14 μg/m3

 annual standard combined with the current daily standard for one
14PM2.5 risk estimates for various combinations of statistical estimation approaches (GAM and GLM with

varying degrees of freedom) have been included for Los Angeles as a sensitivity analysis to illustrate the impact of

alternative model specification choices. 

4-19

location (i.e., Philadelphia) where there was a difference in the reduction required between daily

standards of 40 and 65 μg/m3.

4.3.2 Concentration-Response Functions

As indicated in Figure 4-1, another key component in the risk model is the set of

concentration-response functions which provide estimates of the relationship between each

health endpoint of interest and ambient PM concentrations. As discussed above, the health

endpoints that have been included in the PM2.5 risk assessment for short-term exposure include

mortality, hospital admissions, and respiratory symptoms not requiring hospitalization; long-term

exposure mortality is also estimated. The health endpoints that have been included in the PM10-2.5

risk assessment for short-term exposure include hospital admissions and respiratory symptoms

not requiring hospitalization. Once it had been determined that a health endpoint was to be

included in the assessment, the assessment includes all estimates of response magnitude from

studies judged suitable for inclusion in this assessment, including those which are not

statistically significant. As discussed in section 4.2.2 above, one of the criteria for inclusion of

studies in the risk assessment is that studies have enough sample size to provide a sufficient

degree of precision. Effect estimates that are not statistically significant are used from studies

judged suitable for inclusion in this assessment to avoid introducing bias into the estimate of the

magnitude of the effect. Both single-pollutant and, where available, multi-pollutant,

concentration-response functions are used from the studies listed in Tables 8A and 8B of the CD

(see also Appendices 3A and 3B of this Staff Paper).

As discussed in the CD (section 8.4.2) and Chapter 3 (section 3.6.3), questions were

raised in 2002 about the default convergence criteria (which impact the mean estimate) and

standard error calculations (which result in understated standard errors) used in many of the

short-term PM time-series studies employing generalized additive models (GAMs) in a

commonly used statistical software package. To address these concerns, many of the study

authors performed reanalyses of certain of the studies using alternative statistical estimation

approaches (e.g., GLM with different degrees of freedom and different types of splines), in

addition to using GAMs with a more stringent convergence criterion. To avoid producing a

prohibitively large set of results, the PM risk assessment included concentration-response

functions using only GAM with the more stringent convergence criterion, denoted “GAM

(stringent),” for all urban locations, except Los Angeles.14 It should be noted that the GAM

(stringent) concentration-response functions do not address the issue of understated standard

errors of the coefficient estimates. Thus, the confidence intervals included in the risk assessment

involving use of the GAM (stringent) concentration-response functions are somewhat
4-20

understated. As indicated in the CD, “the extent of downward bias in standard error reported in

these data (a few percent to ~15%) also appears not to be very substantial, especially when

compared to the range of standard errors across studies due to differences in population size and

number of days available” (CD, p. 9-35). 

 More detailed information about the concentration-response relationships used in the PM

risk assessment is provided in Appendix 4A of this Staff Paper. This information includes

population characteristics (e.g., age and disease status), form of the model (e.g., log-linear,

logistic), whether other pollutants were included in the model, lags used, observed minimum and

maximum ambient PM concentrations, and PM coefficients along with lower and upper 5th and

95th confidence intervals.

4.3.2.1 Linear and Nonlinear Models

In assessing or interpreting public health risk associated with exposure to PM, the form of

the concentration-response relationships is a critical component. As discussed in Chapter 3

(section 3.6.6), staff recognizes that while there are likely biological thresholds in individuals for

specific health responses, the available epidemiologic studies do not support or refute the

existence of thresholds at the population level for either long-term or short-term PM exposures

within the range of air quality observed in the studies. Thus, staff has concluded that it is

appropriate to consider health risks estimated not only with the reported linear or log-linear

concentration-response functions, but also with modified functions that incorporate alternative

assumed cutpoints as surrogates for potential population thresholds. 

For short-term exposure mortality and morbidity outcomes associated with PM2.5 and

PM10-2.5, the initial base case uses linear or log-linear concentration-response models reported in

the epidemiology studies. These concentration-response relationships are applied down to the

estimated policy-relevant background concentration level. Generally, the lowest measured

concentrations in the short-term exposure studies were relatively near or below the estimated

policy-relevant background levels such that little or no extrapolation was required beyond the

range of data in the studies. In the case of the long-term exposure mortality studies for PM2.5 that

have been included in the risk assessment, the lowest measured long-term levels were in the

range 7.5 to 11 μg/m3

. Staff concludes that the initial base case scenario for this endpoint should

include the reported linear models applied down to 7.5 μg/m3

, which is the lowest of the lowest

measured levels in these long-term studies. Going down to an estimated policy-relevant

background level for short-term exposure studies and to 7.5 μg/m3

 for long-term studies provides

a consistent framework which facilitates comparison of risk estimates across urban locations

within each group of studies and avoids significant extrapolation beyond the range of

concentrations included in these studies. 

Additional base case scenarios involved the use of alternative concentration-response

functions. The approach used to develop the alternative functions incorporates a modified linear

slope with an imposed cutpoint (i.e., an assumed threshold) that is intended to reflect an
4-21

inflection point in a typical non-linear, "hockeystick" shaped function, below which there is little

or no population response. This approach also is a surrogate for a non-linear, sigmoidal-shaped

function, in which the cut point is intended to reflect the inflection point at the lower end of the

relationship, below which there is assumed to be little or no population response. 

The staff recognizes that the alternative cutpoint analyses assume such a hockeystick

shaped relationship, and it is appropriate to adjust the slope of the upper part of the hockeystick

to be consistent with this assumption. If the data in the original study actually supported a

hockeystick model better than a log-linear model, then the slope of the log-linear fitted

relationship reported by the study would have understated the degree to which PM is associated

with mortality or morbidity above the cutpoint, as shown in Figure 4-2. This rationale applies

equally in the case of long- and short-term exposure mortality and morbidity. Therefore, the

slope of the upward-sloping portion of the hockeystick should not use the slope reported for the

concentration-response relationship but should be adjusted upward. 

For the base case scenarios involving alternative cutpoints, the slope of the

concentration-response relationship has been adjusted assuming that the upward-sloping portion

of the hockeystick would be the slope estimated in the original epidemiologic study adjusted by

the inverse of the proportion of the range of PM levels observed in the study that was above the

cutpoint. Staff believes that this simple slope adjustment approach represents a reasonable

approach to illustrate the potential impact of possible non-linear concentration-response

relationships. A more definitive evaluation of the effect of alternative cutpoints and non-linear

models is a subject that should be explored in much needed further research.

Based on the staff evaluation contained in section 3.6.6, a cutpoint of 20 μg/m3

 was

selected as the highest value for inclusion in base case scenarios for short-term exposure

mortality for PM2.5 and short-term exposure morbidity for PM10-2.5. Two additional alternative

cutpoints, 10 and 15 μg/m3, also were selected to be included in base case scenarios for these 

short-term exposure health outcomes, so as to span the range between the initial cutpoint (i.e.,

estimated policy-relevant background) and the upper cutpoint value at roughly 5 μg/m3 intervals. 

With regard to long-term exposure mortality associated with PM2.5 exposures, staff selected

12 μg/m3 as the highest value for an alternative cutpoint based on the following two

considerations: 1) the confidence intervals in the ACS extended study (Pope et al., 2002) begin

to expand significantly starting around 12 to 13 μg/m3 (see Figure 3-4) indicating greater

uncertainty about the shape of the reported concentration-response relationship at and below this

level and 2) it is unlikely that the relationship is non-linear near the reported mean concentration

levels in the long-term exposure studies (e.g., 14 μg/m3

 in the ACS extended study). An

additional alternative cutpoint of 10 μg/m3

 has been included as a base case scenarios for longterm exposure mortality, representing an approximate midpoint value between the cutpoints

already selected.

 Results of these analyses are discussed below in section 4.4 and 4.5.
4-22

PM

ln(mortality)"True" hockeystick model Estimated C-R function

Highest Measured Level

Low est Measured Level

C

Figure 4-2. Relationship between estimated log-linear concentration-response relationship and hockeyst

cutpoint C.Source: Abt Associates (2005b)
4-23

4.3.2.2 Single and Multi-City Models

As described in section 4.2, staff have selected urban areas based on where epidemiologic

studies have estimated concentration-response relationships. This approach avoids uncertainties

associated with estimating health risks for an area based on a relationship developed for a

different location. Staff has included both single-city and multi-city concentration-response

functions in the current assessment. As discussed in section 3.3.1.1 and in the CD, there are a

number of advantages of using concentration-response relationships obtained from multi-city

studies which combine data from a number of locations that may vary in climate, sources and

concentrations, and other potential risk factors. These advantages include, but are not limited to:

(1) more precise effect estimates due to larger data sets, (2) greater consistency in data handling

and model specification that can eliminate city-to-city variation due to study design, and (3) less

likelihood of publication bias or exclusion of reporting of negative or nonsignificant findings. 

However, at this time very few multi-city studies have been carried out in the U.S. that report

concentration-response relationships for PM2.5 and/or PM10-2.5. In the one instance where both

single- and multi-city concentration-response relationships are available for the locations

included in the risk assessment (e.g., the Six Cities study), risk estimates have been developed

using both the single- and multi-city concentration-response relationships. In addition, a

sensitivity analysis (S7 in Figure 4-1) has been conducted to examine the potential impact on

short-term exposure mortality of using a single multi-city concentration-response function from

the Six Cities study (Schwartz, 2003b) across five of the PM2.5 locations included in the risk

assessment compared to use of location-specific concentration-response functions from singlecity studies. The results of this sensitivity analysis are presented in section 4.4.3.2. 

4.3.2.3 Single and Multi-Pollutant Models

For several of the epidemiologic studies from which concentration-response relationships

for the PM risk assessment were obtained, concentration-response functions are reported both for

the case where only PM levels were entered into the health effects model (i.e., single-pollutant

models) and where PM and one or more other measured gaseous co-pollutants (i.e., ozone,

nitrogen dioxide, sulfur dioxide, carbon monoxide) were entered into the health effects model

(i.e., multi-pollutant models). To the extent that any of the co-pollutants present in the ambient

air may have contributed to the health effects attributed to PM in single-pollutant models, risks

attributed to PM might be overestimated where concentration-response functions are based on

single-pollutant models. However, the CD finds that associations for various PM indices with

mortality or morbidity are robust to confounding by co-pollutants (CD, p.9-37). Given that

single and multi-pollutant models each have both potential advantages and disadvantages, with

neither type clearly preferable over the other in all cases, risk estimates based on both single and

multi-pollutant models have been developed for the assessment.
4-24

4.3.2.4 Single, Multiple, and Distributed Lag Functions

The question of lags and the problem of correctly specifying the lag structure in a model

are discussed extensively in the CD (section 8.4.4) and in section 3.6.5 of this Staff Paper. As

noted in those discussions, it is important to consider the pattern of results that is seen across the

series of lag periods. In staff’s judgment, observation of a consistent pattern of results across

adjacent lags in a study supports use of a study in the risk assessment. In contrast, where an

inconsistent pattern of results has been observed, staff judges that it would be inappropriate to

include results from such studies in the risk assessment. 

As noted in section 3.6.5.1, staff concludes that it is appropriate to use single-day lag

period results for the risk assessment. When a study reports several single lag models, unless the

study authors identify a “best lag,” the following lag models were included in the risk assessment

based on the assessment in the CD and in section 3.6.5.1:

• both 0- and 1-day lag models for mortality (both total and cause specific),

• both 0- and 1-day lag models for cardiovascular and respiratory hospital

admissions, and

• 0-, 1-, and 2-day lag models (if all three were available) for COPD hospital

admissions.

When there is an observed pattern showing effects across different lags, use of any

single-day lag with the largest effect, while reasonable, is likely to underestimate the overall

effect size (since the largest single-lag day results do not fully capture the risk also distributed

over adjacent or other days) (CD, p. 8-270). As discussed in section 3.6.5.1, there is recent

evidence (Schwartz, 2000b, reanalyzed in Schwartz, 2003b), that the relationship between PM

and health effects may best be described by a distributed lag (i.e., the incidence of the health

effect on day n is influenced by PM concentrations on day n, day –1, day –2 and so on). If this is

the case, a model that includes only a single lag (e.g., a 0-day lag or a 1-day lag) is likely to

understate the total impact of PM. Because of this, a distributed lag model may be preferable to

a single lag model. However, distributed lag models have been used in only a few cases and

only for PM10. 

The risk assessment includes a sensitivity analysis (S5 in Figure 4-1) which examines the

potential impact of using a distributed lag approach for short-term exposure mortality associated

with PM2.5 based on the distributed lag analysis of PM10 and mortality (Schwartz, 2000b,

reanalyzed in Schwartz, 2003b). This sensitivity analysis has been included to provide a very

rough sense of the possible underestimation of risk due to use of single-day lags models.
4-25

4.3.2.5 Alternative Short-Term Exposure Model Specifications

As discussed in section 3.6.3, time series studies investigating health effects associated

with PM have used a range of alternative model specifications. For the risk assessment base case

analyses, only the concentration-response functions using the more stringent convergence

criterion, denoted “GAM (stringent), have been included to provide a consistent basis for

comparison across studies and locations. In fact, most studies use a limited number of modeling

approaches, in part to avoid producing an unwieldy and confusing number of different estimates.

One study that used a wide variety of modeling approaches is the Moolgavkar (2003) study

which is the basis for the short-term PM2.5 exposure mortality and morbidity estimates for Los

Angeles. This study included two different versions of the “GAM stringent” approach, one with

30 degrees of freedom and the other with 100 degrees of freedom, as well as models using GLM. 

The risk assessment includes a sensitivity analysis to examine the potential impact of alternative

model specifications on estimates of short-term exposure morbidity and mortality in Los Angeles

based on the results from Moolgavkar (2003). 

4.3.2.6 Long-term Exposure Models

The available long-term exposure mortality concentration-response functions are all

based on cohort studies, in which a cohort of individuals is followed over time. As discussed in

section 3.3.1.2, based on the evaluation contained in the CD and the staff’s assessment of the

complete data base addressing mortality associated with long-term exposure to PM2.5, staff have

concluded that two cohorts that have been studied are particularly relevant for the PM2.5 risk

assessment. These include (a) the Six Cities study cohort, referred to here as Krewski et al.

(2000) - Six Cities, and (b) the American Cancer Society (ACS) cohort, referred to as Krewski et

al. (2000) – ACS, containing a larger sample of individuals from many more cities. In addition,

Pope et al. (2002) extended the follow-up period for the ACS cohort to sixteen years and

published findings on the relation of long-term exposure to PM2.5 and all-cause mortality as well

as cardiopulmonary and lung cancer mortality (referred to here as Pope et al. (2002) - ACS

extended). EPA’s use of these particular cohort studies to estimate health risks associated with

long-term exposure to PM2.5 is consistent with the views expressed in the NAS (2002) report,

“Estimating the Public Health Benefits of Proposed Air Pollution Regulations,” and the SAB

Clean Air Act Compliance Council review of the proposed methodology to estimate the health

benefits associated with the Clean Air Act (SAB, 2004).

As explained in section 3.6.5.4, three different indicators of long-term PM2.5 exposure

were considered in the extended ACS study; and staff have selected the concentration-response

function associated with an average of the 1979-1983 and 1999-2000 PM2.5 ambient

concentrations to use in the current risk assessment. The assessment includes a sensitivity

analysis (S6 in Figure 4-1) which examines the potential impact on mortality associated with

long-term exposure of different assumptions about the role of historical air quality

concentrations in contributing to the reported effects.
15See http://factfinder.census.gov/.

16Since the baseline incidence rates are expressed in terms of cases per 100,000 general population, the 

general population estimates have been used in combination with these rates to generate the baseline incidence in

each location for various effects in calculating the risk estimates. 

17The data were annual hospital discharge data, which were used as a proxy for hospital admissions. 

Hospital discharges are issued to all people who are admitted to the hospital, including those who die in the hospital. 

Use of the annual discharge rate is based on the assumption that admissions at the end of the year that carry over to

the beginning of the next year, and are therefore not included in the discharge data, are offset by the admissions in

the previous year that carry over to the beginning of the current year.

4-26

4.3.3 Baseline Health Effects Incidence Rates and Population Estimates

As illustrated in Figure 4-1, the most common health risk model expresses the reduction

in health risk ()y) associated with a given reduction in PM concentrations (Sx) as a percentage

of the baseline incidence (y). To accurately assess the impact of PM air quality on health risk in

the selected urban study locations, information on the baseline incidence of health effects (i.e.,

the incidence under recent air quality conditions) and population size in each location is

therefore needed. Population sizes, for both total population and various age ranges used in the

PM risk assessment were obtained for the year 2000 from the 2000 U.S. Census data15 and are

summarized in Table 4-6. Where possible, county-specific incidence or incidence rates have

been used. County-specific mortality incidences were available for the year 2001 from CDC

Wonder (CDC, 2001), an interface for public health data dissemination provided by the Centers

for Disease Control (CDC). The baseline mortality rates for each risk assessment location are

provided in Table 4-7 and are expressed as a rate per 100,000 general population.16

 County-specific rates for cardiovascular and respiratory hospital discharges, and various

subcategories (e.g., pneumonia, asthma), have been obtained, where possible, from state, local, 

and regional health departments and hospital planning commissions for each of the risk

assessment locations.17 Baseline hospitalization rates used in each PM2.5 and PM10-2.5 risk 

assessment location are summarized in Table 4-8 and are expressed a rate per 100,000 general

population. For respiratory symptoms in children, the only available estimates of baseline

incidence rates were from the studies that estimated the concentration-response relationships for

those endpoints. However, because the risk assessment locations for these endpoints were

selected partly on the basis of where studies were carried out, baseline incidence rates reported in

these studies should be appropriate for the risk assessment locations to which they were applied.

4.3.4 Characterizing Uncertainty and Variability

An important issue associated with any population health risk assessment is the 

characterization of uncertainty and variability. Uncertainty refers to the lack of knowledge

regarding both the actual values of model input variables (parameter uncertainty) and the

physical systems or relationships (model uncertainty – e.g., the shapes of concentration-response
4-27

Table 4-6. Relevant Population Sizes for PM Risk Assessment Locations

City Populationa

Total Ages 7-14 Ages $25 Ages $30 Ages <65 Ages $ 65 Age

Boston1 2806000 283,000 (10%) 1,903,000 (68%) 1,673,000 (60%) --- -

Detroit2 2061000 --- 1,153,000 (56%) --- 249,000 (12%) -Los Angeles3 9519000 --- 5,092,000 (53%) --- 927,000 (10%) -Philadelphia4 1518000 --- 852,000 (56%) --- --- -Phoenix5 3072000 --- 1,684,000 (55%) --- 359,000 (12%) -Pittsburg6 1282000 --- 814,000 (64%) --- --- 1,16

(9

San Jose7 1683000 --- 965,000 (57%) --- --- -

Seattle8 1737000 --- 1,044,000 (60%) 1,555,000

(90%)

--- -

St. Louis9 2518000 308,000 (12%) 1,637,000 (65%) 1,475,000 (59%) --- --- -

a Total population and age-specific population estimates taken from the CDC Wonder website are based on 2000 U.S. Census data. Se

http://factfinder.census.gov/. Populations are rounded to the nearest thousand. The urban areas given in this table are those considered 

PM2.5 risk assessment. The percentages in parentheses indicate the percentage of the total population in the specific age category.

1 Middlesex, Norfolk, and Suffolk Counties. 2 Wayne County. 3 Los Angeles County. 4 Philadelphia County.5 Maricopa County. 6 Allegheny County. 7 Santa Clara County. 8 King County.9 St. Louis, Franklin, Jefferson, St. Charles, Clinton (IL), Madison (IL), Monroe (IL), and St. Clair (IL) Counties and St. Louis City.
4-28

Table 4-7. Baseline Mortality Rates for 2001 for PM2.5 Risk Assessment Locations

Health Effect Boston1 Detroit2 Los

Angeles3

Philadelphia4 Phoenix5 Pittsburgh6 San

Jose7

St.

Loui

 A. Mortality Rates Used in Risk Analysis for Short-Term Exposure Studiesa,b (deaths per 100,000 general popula

Non-accidental (all ages):ICD-9 codes < 800

776 916 581 1070 --- --- 494 869 

Non-accidental (75+):ICD-9 codes < 800 

--- --- --- --- --- 761 --- --- 

Non-accidental (<75):ICD-9 codes < 800 

--- --- --- --- --- 399 --- --- 

Cardiovascular (all ages):ICD-9 codes: 390-459

--- 416 --- --- --- --- 206 --- 

Cardiovascular (all ages):ICD-9 codes: 390-448

--- --- --- 418 --- --- --- --- 

Cardiovascular (65+):ICD-9 codes: 390-448

--- --- --- --- 211 --- --- --- 

Cardiovascular (all ages):ICD-9 codes: 390-429

--- --- 207 --- --- --- --- --- 

Ischemic Heart Disease (allages): ICD-9 codes: 410-414 

122 --- --- --- --- --- --- 206 

Respiratory (all ages):ICD-9 codes: 11, 35, 472-519, 710.0, 710.2, 710.4

--- --- --- --- --- --- 51 --- 

Respiratory (all ages):ICD-9 codes: 460-519

--- 72 --- --- --- --- --- --- 
Health Effect Boston1 Detroit2 Los

Angeles3

Philadelphia4 Phoenix5 Pittsburgh6 San

Jose7

St.

Loui

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COPD without Asthma (allages): ICD-9 codes: 490-492, 494-496

36 --- --- --- --- --- --- 39 

Pneumonia (all ages):ICD-9 codes: 480-487

26 --- --- --- --- --- --- 27 

 B. Mortality Rates Used in Risk Analysis for Long-term Exposure Studiesa,b (deaths per 100,000 general populati

Total mortality (25+):ICD-9 codes: all

803 --- --- --- --- --- --- 905 

Total mortality (30+):ICD-9 codes: all

797 937 591 1100 676 1189 499 897 

CardiopulmonaryMortality (25+): ICD-9codes: 400-440, 485-495

297 --- --- --- --- --- --- 391 

CardiopulmonaryMortality (30+): ICD-9codes: 401-440, 460-519

347 468 313 489 313 573 247 439 

Lung Cancer Mortality(30+): ICD-9 code: 162

55 64 33 72 42 78 30 61 

*The epidemiologic studies used in the risk assessment reported causes of mortality using the 9th revision of the International Classifica

codes. However, the 10th revision has since come out, and baseline mortality incidence rates for 2001 shown in this table use ICD-10 co

ICD-9 codes used in the epidemiologic studies and the corresponding ICD-10 codes used to calculate year 2001 baseline incidence rate

the TSD (Abt Associates, 2005b). 

a Mortality figures were obtained from CDC Wonder for 2001. See http://wonder.cdc.gov/.b Mortality rates are presented only for the locations in which the concentration-response functions were estimated. All incidence rates 

unit. Mortality rates for St. Louis may be slightly underestimated because some of the mortality counts in the smaller counties were repWonder.

1 Middlesex, Norfolk, and Suffolk Counties. 2 Wayne County. 3 Los Angeles County. 4 Phil. County. 5 Maricopa County. 6 Allegheny 

County. 8 St. Louis, Franklin, Jefferson, St. Charles, Clinton (IL), Madison (IL), Monroe (IL), and St. Clair (IL) Counties and St. Louis 
4-30

Table 4-8. Baseline Hospitalization Rates for PM Risk Assessment

Locations*

Health Effect Detroit1 Los Angeles2 Seattle3

Hospital Admissions (per 100,000 general population/year)

Pneumonia admissions (65 and over): ICD codes 480-486 250 --- ---

COPD and asthma admissions (all ages): ICD codes 490-

496

--- 318 ---

COPD and asthma admissions (65 and over): ICD codes

490-496

192 --- ---

Asthma (<65): ICD code 493 --- --- 92

Cardiovascular admissions (65 and over): ICD codes:

390-429

--- 728 ---

Ischemic heart disease (65 and over): ICD codes 410-414 487 --- ---

Dysrhythmias (65 and over): ICD code 427 161 --- ---

Congestive heart failure (65 and over): ICD code 428 341 --- ---

* Hospitalization rates are presented only for the locations in which the concentration-response functions were

estimated. For each location, the number of discharges was divided by the location’s population from the 2000 U.S.

Census estimates to obtain rates. All incidence rates are rounded to the nearest unit.

1

Wayne County. Year 2000 hospitalization data were obtained from the Michigan Health and Hospital Association. 2

Los Angeles County. Year 1999 hospitalization data were obtained from California’s Office of Statewide Health

Planning and Development – Health Care Information Resource Center.

3

King County. Year 2000 hospitalization data were obtained from the State of Washington Department of Health,

Center for Health Statistics, Office of Hospital and Patient Data Systems.
4-31

functions). In any risk assessment uncertainty is, ideally, reduced to the maximum extent

possible, but significant uncertainty often remains. It can be reduced by improved measurement

and improved model formulation. In addition, the degree of uncertainty can be characterized,

sometimes quantitatively. For example, the statistical uncertainty surrounding the estimated

PM2.5 and PM10-2.5 coefficients in the reported concentration-response functions is reflected in the

confidence intervals provided for the risk estimates in this chapter and in the TSD. Additional

uncertainties are addressed quantitatively through sensitivity analyses and/or qualitatively and

have been discussed throughout section 4.3. 

As noted above, the updated risk assessment presents qualitative and quantitative

considerations of uncertainty, including sensitivity analyses of key individual uncertainties. 

Given the existing data gaps in the scientific evidence and associated uncertainties, a more

comprehensive integrated assessment of uncertainties, would be desirable, but in the staff’s

judgment would require use of techniques involving elicitation of probabilistic judgments from

health scientists. While the Agency is currently developing these approaches, such

comprehensive assessments of uncertainty are not available for the current risk assessment for

this PM NAAQS review.

 Variability refers to the heterogeneity in a population or variable of interest that is

inherent and cannot be reduced through further research. For example, there may be variability

among concentration-response functions describing the relation between PM2.5 and mortality

across urban areas. This variability may be due to differences in population (e.g., age

distribution), population activities that affect exposure to PM (e.g., use of air conditioning),

levels and composition of PM and/or co-pollutants, and/or other factors that vary across urban

areas. 

The current risk assessment incorporates some of the variability in key inputs to the

assessment by using location-specific inputs (e.g., location-specific concentration-response

functions, baseline incidence rates, and air quality data). Although spatial variability in these

key inputs across all U.S. locations has not been fully characterized, variability across the

selected locations is imbedded in the assessment by using, to the extent possible, inputs specific

to each urban area. Temporal variability is more difficult to address, because the risk reduction

portions of the risk assessment (i.e., estimated risk reduction associated with just meeting

specified standards) focus on some unspecified time in the future when specified PM standards

are just met. To minimize the degree to which values of inputs to the assessment may be

different from the values of those inputs at that unspecified time, we have used the most current

inputs available (i.e., year 2003 air quality data for most locations and the most recent available

mortality baseline incidence rates (from 2001)). However, we have not tried to predict future

changes in inputs (e.g., future population levels or possible changes in baseline incidence rates).
18A concentration-response function derived from a multi-cities study may not provide an accurate

representation of the concentration-response relationship in a specific assessment location because of (1) variations

in PM composition across cities, (2) the possible role of associated co-pollutants in influencing PM risk, (3)

variations in the relation of total ambient exposure (both outdoor exposure and ambient contributions to indoor

exposure) to ambient monitoring in different locations (e.g, due to differences in air conditioning use in different

regions of the U.S.), (4) differences in population characteristics (e.g., the proportions of members of sensitive

subpopulations) and population behavior patterns across locations.

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Key Uncertainties and Assumptions

The following briefly summarizes the major sources of uncertainties and variability and

how they are dealt with in the risk assessment:

• Causality. There is uncertainty about whether each of the estimated associations

between the two PM indicators (PM2.5 and PM10-2.5) and the various health

endpoints included in this risk assessment actually reflect a causal relationship. 

There are varying degrees of uncertainty associated with the various PM

indicators and health endpoints related to differences in the weight of evidence

supporting judgments about whether an observed association truly reflects a

causal relationship. For example, there is much greater uncertainty associated

with the morbidity effects associated with PM10-2.5 exposures than for PM2.5 due to

the much smaller health effects data base and greater concerns about exposure

measurement error. Chapter 3 presents a more detailed discussion of the staff’s

qualitative assessment of the varying weight of evidence associated with the

effects included in the risk assessment. 

• Empirically estimated concentration-response relationships. In estimating the

concentration-response relationships, there are uncertainties: (1) surrounding

estimates of PM coefficients in concentration-response functions used in the

assessment, (2) concerning the specification of the concentration-response model

(including the shape of the concentration-response relationship) and whether or

not a population threshold or non-linear relationship exists within the range of

concentrations examined in the studies, and (3) related to the extent to which

concentration-response functions derived from studies in a given location and

time when PM concentrations were higher provide accurate representations of the

concentration-response relationships for the same location with lower annual and

daily PM concentrations. For the few instances where multi-city PM

concentration-response functions are included in the base case analyses (e.g., use

of the Six-Cities study function for respiratory symptoms associated with shortterm exposures to PM2.5 applied in Boston and St. Louis), there also is uncertainty

related to the transferability of PM concentration-response functions from

multiple locations to the specific location selected for the risk assessment.18

Statistical uncertainty, based on the standard errors reported in the epidemiologic

studies, is incorporated in the risk assessment and is discussed below. Base case

risk estimates incorporating various cutpoints have been included in the risk

assessment to reflect the uncertainty about whether or not population thresholds
4-33

or non-linear concentration-response relationships might exist at the lower range

of ambient PM2.5 and PM10-2.5 concentrations. As discussed previously (see

section 4.3) several sensitivity analyses have been presented related to

uncertainties in the concentration-response relationships.

• Adequacy of ambient PM monitors as surrogate for population exposure. The

extent to which there are differences in the relationship between spatial variation

in ambient PM2.5 or PM10-2.5 concentrations and ambient exposures in the original

epidemiology studies compared to more recent ambient PM2.5 or PM10-2.5 data

introduces additional uncertainty in the risk estimates. This is expected to be

more of a concern for PM10-2.5 where greater spatial variability in ambient

monitoring data within urban areas has been observed. 

• Adjustment of air quality distributions to simulate just meeting specified

standards. The shape of the daily distribution of PM2.5 and PM10-2.5 ambient

concentrations that would result upon meeting alternative PM standards is

unknown. Based on an analysis of historical data, staff believes it is a reasonable

assumption that PM2.5 concentrations would be reduced by roughly the same

percentage. However, there is much greater uncertainty associated with the use of

this same approach for meeting PM10-2.5 standards given the lack of sufficient data

to evaluate the reasonableness of this assumption.

• Background concentrations. Since one of the base case scenarios includes

estimating risks in excess of estimated policy-relevant background, uncertainty

about background concentrations contributes to uncertainty about the risk

estimates. As discussed previously, the assessment includes sensitivity analyses

examining the impact of alternative constant and varying daily background levels

on the risk estimates.

• Baseline incidence rates and population data. There are uncertainties related to:

(1) the extent to which baseline incidence rates, age distribution, and other

relevant demographic variables that impact the risk estimates vary for the year(s)

when the actual epidemiology studies were conducted, the recent year of air

quality used in the assessment, and some unspecified future year when air quality

is adjusted to simulate just meeting the current or alternative standards; (2) the

use of annual incidence rate data to develop daily health effects incidence data;

and (3) related to the use of an overall combined incidence rate for six cities for

the respiratory symptoms endpoint which is applied to individual cities (i.e.,

Boston and St. Louis). Spatial variability in baseline incidence and population

data is taken into account by use of city-specific data in most cases. 

The uncertainties from some of these sources -- in particular, the statistical uncertainty

surrounding estimates of the PM coefficients in concentration-response functions -- are

characterized quantitatively in the PM risk assessment. It is possible, for example, to calculate
19However, as discussed earlier in section 4.2.6, for the short-term concentration-response functions based

on reanalyzed GAM (stringent) models the confidence intervals are somewhat understated. 

4-34

confidence intervals around risk estimates based on the uncertainty associated with the estimates

of PM coefficients used in the risk assessment. These confidence intervals express the range

within which the risks are likely to fall if the sampling error uncertainty surrounding PM

coefficient estimates were the only uncertainty in the assessment.19 In situations where the point

estimate for a concentration-response function is positive, but the lower confidence limit

estimate is less than 1.0, the lower confidence limit of the risk estimate is a negative value. 

Based on the overall body of evidence on the relationships between PM and health effects, the

staff believes that these negative estimates should not be interpreted as implying that increasing

PM levels will result in reduced risks, but rather that the negative risk estimates are simply a

result of statistical uncertainty in the reported concentration-response relationships in the

epidemiologic studies. 

Steps also have been taken to minimize some of the uncertainties noted above. For

example, the current PM risk assessment includes only health endpoints for which the CD

evaluation or staff assessment (see Chapter 3) find that the overall weight of the evidence

supports the conclusion that PM2.5 is likely causally related. Also, for most of the health

endpoints and locations included in the risk assessment, this assessment uses the concentrationresponse functions derived from epidemiologic studies carried out in those same locations. This

serves to minimize the uncertainties, such as differences in composition and differences in

factors affecting human exposure associated with applying concentration-response functions

developed in one location to a different location. However, the use of functions from single-city

studies does suffer the disadvantage of introducing possible publication bias and single-city

studies generally have lower precision than larger multi-city studies. 

In summary, the key assumptions on which the current PM risk assessment is based

include the following:

• The relationship between PM2.5 and PM10-25 and the health endpoints included in

the assessment are causal;

• Concentration-response models are appropriately specified (including the

functional form and lag structure);

• Baseline incidence rates and population size and age distributions have not

changed appreciably from those used in the assessment;

• For short-term endpoints, that obtaining average daily incidence rates from annual

baseline incidence rates and using them to estimate daily incidences associated

with exposure to PM does not bias the estimates; 
4-35

• The distribution of PM concentrations on missing days is essentially the same as

the distribution on days for which we have PM data;

• The estimated policy-relevant background concentrations for PM2.5 and PM10-2.5

are appropriate for each urban area included in the analysis;

• A single year of air quality data is appropriate to characterize risks associated

with recent air quality levels and just meeting specified standards;

• Proportional rollback of concentrations over an estimated policy-relevant

background appropriately reflects the air quality distribution when specified

standards would just be met.

 

4.4 PM2.5 RISK ESTIMATES

Several “base case” analyses for PM2.5 are presented in this section and include risk

estimates associated with a recent year of air quality (generally, 2003), air quality adjusted to just

meet the current PM2.5 standards, and air quality adjusted to simulate just meeting alternative

PM2.5 standards. The initial base case analyses for the recent air quality and just meeting the

current PM2.5 standards scenarios include concentration-response models that extend down to

estimated policy-relevant background for short-term exposure health outcomes (i.e., equivalent

to setting a cutpoint at estimated policy-relevant background) and extend down to 7.5 μg/m3 for

long-term exposure mortality (equivalent to setting a cutpoint at 7.5 μg/m3

). For this initial set

of base case analyses, the slope of the concentration-response function is based on that obtained

directly from the published studies. A variety of models (single city with different lags, single

city vs. multi-city, single pollutant vs. multi-pollutant) and health outcomes (mortality, hospital

admissions, respiratory symptoms) are included using this set of initial base case analyses.

Following this initial set of base case analyses, risk estimates, additional base case

estimates are developed only for non-accidental short-term exposure mortality (or if not

available, cardiovascular mortality) and for all-cause mortality with long-term exposure for each

study area employing the same cutpoints indicated above, as well as several additional

alternative cutpoints. For the additional alternative cutpoints, the slope of the concentrationresponse function has been modified based on a simple hockeystick model (see discussion in

section 4.3.2.1). Staff considers the initial set of base case analyses, as well as the analyses

estimating health risks associated with alternative cutpoints, as being part of the complete set of

base case analyses.

 

4.4.1 Recent Air Quality

4.4.1.1 Base Case Risk Estimates Above Initial Cutpoint

The base case risk estimates associated with recent PM2.5 concentrations in excess of

policy-relevant background levels for short-term exposure outcomes and in excess of 7.5 μg/m3
20As noted above, in some cases, where the lower confidence limit of the concentration-response function is

less than 1.0, the resulting lower confidence limit of the risk estimate is a negative value. The staff’s interpretation

of these negative values is that while they indicate statistical uncertainty about the concentration-response

relationships, they do not at all suggest that risk reductions would be associated with an increase in PM levels.

4-36

for long-term exposure mortality are presented in a series of figures in this section. The risk

estimates are expressed both in terms of percent of total incidence (the top panel in each figure)

and cases per hundred thousand general population (the bottom panel in each figure). The

percent of total incidence provides information about what portion of total incidence for a given

health outcome is estimated to be due to exposure to ambient PM2.5 levels. Expressing risk in

terms of cases per hundred thousand general population provides a metric that takes into account

the variation in population size for each of the urban areas. For each series of estimates, a point

estimate is provided along with 95% confidence intervals.20 Additional detailed tables which

present the estimated incidence (both as the number of effects and as a percentage of total

incidence) for each risk assessment location are included in the TSD. Risk estimates in a given

assessment location are presented only for those health endpoints for which there is at least one

acceptable concentration-response function reported for that location. Therefore, the set of

health effects shown in the figures varies for the different locations.

Figures 4-3 through 4-7 present the PM2.5 risk estimates across the various assessment

locations associated with recent concentrations in excess of an initial cutpoint. For short-term

exposure outcomes, this initial cutpoint is the estimated policy-relevant background. For longterm exposure mortality the initial cutpoint is 7.5 μg/m3

, the lowest of the lowest measured levels

used in the long-term exposure studies included in the risk assessment. Figure 4-3 compares risk

estimates for mortality associated with short-term (i.e., 24-hour) exposure to PM2.5 above policyrelevant background using single-pollutant, single-city models. The point estimates are in the

range from about 0.8 to 2.5% of total non-accidental mortality incidence. In terms of cases per

hundred thousand general population, the point estimates range from about 4 to 13. The

differences in estimates across locations is due to several factors including differences in recent 

air quality levels, use of different concentration-response functions from various single-city

studies, and variation in baseline incidence rates. Differences in concentration-response

functions across the various single-city studies may reflect methodological differences between

studies and/or real differences due to differences in the population and extent of population

exposure to ambient PM2.5 concentrations. In addition, there are significant differences in

baseline incidence rates among the cities which also contribute to city-to-city variation. For

example, as shown in Table 4-7, the baseline mortality rate for non-accidental mortality (all

ages) is nearly twice as large in Philadelphia as in Los Angeles. 

Figure 4-4 compares risk estimates for non-accidental and cause-specific mortality

associated with short-term exposure to PM2.5 above policy-relevant background based on single
4-37

Figure 4-3. Estimated annual percent (top panel) and cases per 100,000 general

population (bottom panel) of total (non-accidental) mortality associated with

short-term exposure to PM2.5 above background (and 95 percent confidence

intervals): single-pollutant, single-city models. 

Source: Abt Associates (2005b)
4-38

Figure 4-4. Estimated annual percent (top panel) and cases per 100,000 general

population (bottom panel) of total (non-accidental) mortality associated with

short-term exposure to PM2.5 above background (and 95 percent confidence

intervals): single-city versus multi-city models.

Source: Abt Associates (2005b)
4-39

Figure 4-5. Estimated annual percent (top panel) and cases per 100,000 general

population (bottom panel) of health effects associated with short-term

exposure to PM2.5 above background (and 95 percent confidence intervals):

single-pollutant versus multi-pollutant models.

Source: Abt Associates (2005b) 
4-40

Figure 4-6. Estimated annual percent (top panel) and cases per 100,000 general

population (bottom panel) of total (non-accidental) mortality associated with

long-term exposure to PM2.5 above 7.5 :g/m3

 (and 95 percent confidence

intervals): single-pollutant models. Source: Abt Associates (2005b) 
4-41

Figure 4-7. Estimated annual percent (top panel) and cases per 100,000 population

(bottom panel) of total (non-accidental) mortality associated with long-term

exposure to PM2.5 above 7.5 :g/m3

 (and 95 percent confidence intervals):

single-pollutant and multi-pollutant models (based on Krewski et al. (2000) -

ACS study). Source: Abt Associates (2005b) 
21 The addition of a second pollutant reduced the number of cities available for estimating the

concentration-response function from 50 for PM2.5 alone to 44 with addition of CO, to 33 with addition of NO2, to 45

with addition of O3 and to 38 with addition of SO2. The effect of the reduction in the number of cities available for

each analysis is to increase the size of the confidence intervals. 

4-42

city versus multi-city models. Generally, the estimated incidence for the single- and multi-city

models are roughly comparable, with somewhat lower risk estimates seen in Boston for the

multi-city models compared to the single-city models and the reverse being observed in St.

Louis. 

Figure 4-5 compares risk estimates based on single-pollutant versus multi-pollutant

concentration-response models provided in the epidemiologic studies for PM2.5 short-term

exposure health endpoints above policy relevant background. As noted earlier, the multipollutant models reflect where PM2.5 and one or more other measured gaseous co-pollutants (i.e.,

O3, NO2, SO2, CO) were entered into the health effects model. In two cases there is relatively

little difference in the risk estimates between the single-pollutant and multi-pollutant models

(i.e., Pittsburgh and San Jose), while in the third case (Los Angeles) there are larger differences

when either CO or NO2 are added to the model along with PM2.5. 

Figures 4-6 and 4-7 show risk estimates for mortality related to long-term (i.e., annual

average) exposure to PM2.5 levels above 7.5 μg/m3 based on single- and multi-pollutant models,

respectively. The point estimates for the single-pollutant models, based on the ACS-extended

study (Pope et al., 2002), range from about 0.5% in Seattle to as high as about 6.5% of total

mortality in Los Angeles, with most point estimates falling in the 2 to 5% range. The point

estimates based on the original ACS study (Krewski et al., 2000) are somewhat lower in all of

the study areas (ranging from about 0.2 to 5% in terms of percent of total incidence). For Boston

and St. Louis, the point estimates based on the Six Cities study (Krewski et al., 2000) are more

than twice as large as the estimates based on the ACS extended study. As noted in Chapter 3

(section 3.5.2), the strongest associations between PM2.5 and mortality in the ACS study were

among the less educated participants who form a relatively small portion of the total study

cohort. If the education distribution were adjusted to reflect the education distribution in the

general U.S. population, the summary effect estimate would increase and this would narrow the

difference observed in the risk estimates between the ACS and Six Cities studies. As shown in

Figure 4-7, the risk estimates based on multi-pollutant models, involving addition of different

single co-pollutants in the ACS study, show generally greater risk associated with PM2.5 when

CO, NO2, or O3 were added to the models and lower risk associated with PM2.5 when SO2 was

added.21

4.4.1.2 Base Case Risk Estimates Above Various Cutpoints

As discussed above, the assessment includes additional base case annual short- and longterm mortality risk estimates associated with recent air quality levels assuming not only the
4-43

initial cutpoint included in the previous section but the alternative cutpoint levels as well. For

short-term exposure mortality, a single non-accidental mortality function has been included,

except for Philadelphia and Phoenix where cardiovascular mortality has been used since a

suitable non-accidental mortality concentration-response function is not available. For long-term

exposure mortality, a single all cause mortality concentration-response function has been

included based on the ACS-extended study (Pope et al., 2002). Tables 4-9 and 4-10 present the

annual health risks for short- and long-term exposure mortality, respectively. Both tables present

the risk estimates expressed in terms of incidence (i.e., cases), cases per 100,000 general

population, and percent of total incidence, along with 95 percent confidence intervals for each of

these risk metrics. 

4.4.1.3 Risk Estimates from Sensitivity Analyses

As discussed previously, several sensitivity analyses have been carried out to provide

some perspective on the impact of various assumptions and uncertainties on the health risk

estimates (see Table 4-4 above for a summary of different types of sensitivity analyses). Most

of these sensitivity analyses have been conducted in each of the study areas and use the initial set

of cutpoints (i.e., policy relevant background for short-term exposure outcomes and 7.5 μg/m3

for long-term exposure mortality). The complete results of the sensitivity analyses are included

in the TSD. In some cases, sensitivity analyses have been conducted only in one location due to

data constraints (for example Los Angeles is the only city where the sensitivity analysis uses

alternative concentration-response model specifications since, as explained in section 4.3.2.5

above, it is the only study that presents results for a wide range of alternative model

specifications).

Alternative Background Levels

As explained earlier, for purposes of informing decisions about the PM NAAQS, we are

interested in PM-related risks due to concentrations over policy-relevant background levels,

where background excludes anthropogenic emissions of PM and its precursors in the U.S., 

Canada, and Mexico (discussed in section 2.6). One set of sensitivity analyses has examined the

impact of using the lower and upper end of the range of estimated background concentrations

provided in section 2.6. In the nine locations, using the upper- and lower-end of the range of

estimated background generally has a small to modest impact, on the order of roughly +/- 10-

20% change in short-term exposure health endpoint risk estimates compared to use of the

midpoint of the estimated range of background levels. Alternative estimated PM2.5 background

levels have no impact on long-term exposure mortality in any of the PM2.5 locations, because the

range of alternative policy-relevant background levels is lower than the lowest cutpoint of

7.5 μg/m3 used for these analyses.

A sensitivity analysis also has been conducted that focuses on the impact of using a

varying estimated PM2.5 background concentration instead of the fixed level used in each study

area in the base case assessment. Staff developed a Monte Carlo simulation approach to
Table 4-9. Estimated Annual Health Risks of Short-Term Exposure Mortality

Associated with Recent PM2.5 Concentrations Assuming Various Cutpoint Levels*

Policy Relevant 

Background** Cutpoint*** Cutpoint

=2.5 or 3.5 μg/m3 =10 μg/m3 =15 μg/m

Non-accidental all 390 173 82 

(265 - 514) (118 - 228) (56 - 109

14 6 3 

(9 - 18) (4 - 8) (2 - 4) 

1.8% 0.8% 0.4% 

(1.2% - 2.4%) (0.5% - 1.1%) (0.3% - 0.5

Non-accidental all 3 day 170 99 62 

(-170 - 501) (-99 - 293) (-62 - 184

853 

(-8 - 24) (-5 - 14) (-3 - 9) 

0.9% 0.5% 0.3% 

(-0.9% - 2.7%) (-0.5% - 1.6%) (-0.3% - 1.0

Non-accidental all 0 day 494 308 212 

(-62 - 1038) (-38 - 647) (-26 - 445

532 

(-1 - 11) (0 - 7) (0 - 5) 

0.9% 0.6% 0.4% 

(-0.1% - 1.9%) (-0.1% - 1.2%) (-0.1% - 0.8

Cardiovascular all 1 day 412 231 141 

(197 - 628) (110 - 352) (67 - 215

27 15 9 

(13 - 41) (7 - 23) (4 - 14) 

2.5% 1.4% 0.9% 

(1.2% - 3.9%) (0.7% - 2.2%) (0.4% - 1.3

Cardiovascular 65+ 1 day 323 86 56 

(97 - 536) (26 - 143) (17 - 93

11 3 2 

(3 - 17) (1 - 5) (1 - 3) 

5.0% 1.3% 0.9% 

(1.5% - 8.3%) (0.4% - 2.2%) (0.3% - 1.4

Urban Area Study Type Ages Lag

Incidence Associated with PM2.5 Assuming Var

(95% Confidence Interval)

Incidence per 100,000 General Po

(95% Confidence Interval)

Percent of Total Incidence

(95% Confidence Interval)

mean of lag 0 & 1 

day Boston

Schwartz (2003b) [reanalysis of Schwartz et al. (1996)]

Detroit

Ito (2003) [reanalysis of Lippmann et al. (2000)]

Los Angeles

Lipfert et al. (2000) -- 7 counties

Philadelphia

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]Mar (2003) [reanalysis of Mar (2000)]

Phoenix

 4-44
Policy Relevant 

Background** Cutpoint*** Cutpoint

=2.5 or 3.5 μg/m3 =10 μg/m3 =15 μg/m

Urban Area Study Type Ages Lag

Incidence Associated with PM2.5 Assuming Var

(95% Confidence Interval)

Incidence per 100,000 General Po

(95% Confidence Interval)

Percent of Total Incidence

(95% Confidence Interval)

Non-accidental 75+ 0 day 77 48 31 

(-166 - 311) (-103 - 193) (-67 - 125

642 

(-13 - 24) (-8 - 15) (-5 - 10) 

0.8% 0.5% 0.3% 

(-1.7% - 3.2%) (-1.1% - 2.0%) (-0.7% - 1.3

Non-accidental all 0 day 218 80 44 

(45 - 387) (17 - 141) (9 - 77) 

13 5 3 

(3 - 23) (1 - 8) (1 - 5) 

2.6% 1.0% 0.5% 

(0.5% - 4.7%) (0.2% - 1.7%) (0.1% - 0.9

Non-accidental all 233 114 55 

(86 - 379) (42 - 185) (20 - 89

952 

(3 - 15) (2 - 7) (1 - 4) 

1.1% 0.5% 0.3% 

(0.4% - 1.7%) (0.2% - 0.8%) (0.1% - 0.4

*All results are for single pollutant, non-accidental mortality models, unless otherwise specified.

Chock et al. (2000)

Pittsburgh

**Policy relevant background is 2.5 μg/m3 in the West (Los Angeles, Phoenix, and San Jose) and 3.5 μg/m3 in the East (Boston, Detroit, Philadelphia, Pittsbu

***For these alternative cutpoints the slope of the concentration-response relationship has been modified based on a simple hockeystick model (see discussi

mean of lag 0 & 1 

day

Fairley (2003) [reanalysis of Fairley (1999)]

San Jose

Schwartz (2003b) [reanalysis of Schwartz et 

St. Louis al. (1996)]

 4-45
Table 4-10. Estimated Annual Health Risks of Long-Term Exposure Mortality

Associated with Recent PM2.5 Concentrations Assuming Various Cutpoint Levels*

Cutpoint Cutpoint** Cutpoint**

= 7.5 μg/m3 =10 μg/m3 =12 μg/m3

594 309 20

(204 - 1053) (106 - 551) (7 - 36)

21 11 1

(7 - 38) (4 - 20) (0 - 1)

2.7% 1.4% 0.1%

(0.9% - 4.7%) (0.5% - 2.5%) (0.0% - 0.2%)

906 713 519

(313 - 1592) (245 - 1259) (178 - 920)

44 35 25

(15 - 77) (12 - 61) (9 - 45)

4.7% 3.7% 2.7%

(1.6% - 8.2%) (1.3% - 6.5%) (0.9% - 4.8%)

3684 3267 2846

(1280 - 6426) (1132 - 5715) (984 - 4994)

39 34 30

(13 - 68) (12 - 60) (10 - 52)

6.6% 5.8% 5.1%

(2.3% - 11.4%) (2.0% - 10.2%) (1.8% - 8.9%)

650 466 280

(224 - 1146) (160 - 825) (96 - 497)

43 31 18

(15 - 76) (11 - 54) (6 - 33)

3.9% 2.8% 1.7%

(1.3% - 6.9%) (1.0% - 4.9%) (0.6% - 3.0%)

349 55 0

(119 - 620) (19 - 98) (0 - 0)

11 2 0

(4 - 20) (1 - 3) (0 - 0)

1.7% 0.3% 0.0%

(0.6% - 3.0%) (0.1% - 0.5%) (0.0% - 0.0%)

816 678 539

(282 - 1430) (234 - 1193) (185 - 951)

64 53 42

(22 - 112) (18 - 93) (14 - 74)

5.4% 4.5% 3.5%

(1.9% - 9.4%) (1.5% - 7.8%) (1.2% - 6.2%)

172 58 0

(59 - 306) (20 - 104) (0 - 0)

10 3 0

(4 - 18) (1 - 6) (0 - 0)

2.1% 0.7% 0.0%

(0.7% - 3.6%) (0.2% - 1.2%) (0.0% - 0.0%)

50 0 0

(17 - 89) (0 - 0) (0 - 0)

300

(1 - 5) (0 - 0) (0 - 0)

0.5% 0.0% 0.0%

(0.2% - 0.8%) (0.0% - 0.0%) (0.0% - 0.0%)

842 587 330

(290 - 1486) (201 - 1041) (113 - 587)

33 23 13

(12 - 59) (8 - 41) (4 - 23)

3.7% 2.6% 1.5%

(1.3% - 6.6%) (0.9% - 4.6%) (0.5% - 2.6%)

*Based on Pope et al. (2002) -- ACS extended, all cause mortality among ages 30 and older.

Detroit

Urban Areas

Incidence Associated with PM2.5 Assuming Various Cutpoint 

Levels

(95% Confidence Interval)

Incidence per 100,000 General Population

(95% Confidence Interval)

Percent of Total Incidence

(95% Confidence Interval)

Boston

Los Angeles

Philadelphia

**For these alternative cutpoints the slope of the concentration-response relationship has been modified 

based on a simple hockeystick model (see discussion in section 4.3.2.1).

Seattle

St. Louis

Pittsburgh

San Jose

Phoenix

 4-46
4-47

generate a year long series of daily PM2.5 background concentrations for specific urban areas

based on using available distributional information for the observed and background

concentrations to estimate their joint distribution, which yields the distribution of the background

concentrations conditioned on the level of the observed concentrations (see Langstaff, 2004 for

additional details describing the approach). This approach involves assigning a background

value to an observed concentration by randomly selecting a value from the conditional

distribution corresponding to the observed value. The analysis has been done both without any

correlation assumed and with a 0.4 correlation between background and observed concentrations. 

To implement this approach, the mean of the background distribution is assumed to be the midpoint estimate of PM2.5 background discussed in section 2.6. Estimates of the variation in

background concentrations for different regions of the United States have been obtained by an

analysis of daily data from IMPROVE sites with the sulfate component removed (Langstaff,

2005). It is important to recognize that all IMPROVE sites measure some PM2.5 from

anthropogenic sources, and that removing sulfate from the PM2.5 component considered does not

completely remove all anthropogenic contributions to the observed concentrations.

The sensitivity analysis examining varying daily background has been carried out in

Detroit and St. Louis using recent air quality levels for short-term exposure non-accidental

mortality associated with PM2.5. As shown in Exhibit 7.9 in the TSD, the difference between the

risk estimates based on a constant versus a varying daily background are very small in Detroit

(i.e., 0.8 percent of total incidence with varying daily background vs. 0.9 percent with assumed

constant background). The difference is even smaller in St. Louis in both the no correlation and

0.4 correlation cases, with essentially no difference in risk estimates between the constant and

varying daily background cases (see section 7.2 in the TSD). 

It should be noted that the estimated distributions for background may not fully reflect

peak 24-h average natural background concentrations which can be substantially higher than the

annual or seasonal average background concentrations within areas affected by wildfires and

dust storms and long range transport from outside the United States, Canada, and Mexico (see

section 2.6). While the current PM2.5 base case risk estimates do not capture these unusual

events, it should be noted that there are provisions to exclude such events for purposes of

judging whether an area is meeting the current NAAQS (as noted above in section 2.6). The

PM2.5 risk assessment also includes a sensitivity analysis which used 2002 air quality data for

Boston to examine the impact of an extreme example (i.e., the Quebec fire episode in July 2002)

of this type of natural episodic event on short- and long-term exposure mortality (see Exhibit

7.11 in the TSD). This sensitivity analysis shows that there is hardly any difference (i.e.,

differences ranged from 0 to 0.1% of total incidence) in estimated short-term exposure mortality
22This extreme episode included 2 days with PM2.5 levels above 30 μg /m3

 and 1 day above 50 μg/m3.

4-48

associated with PM2.5 including or excluding this fairly extreme, but short-term episode.22 This

same sensitivity analysis shows a difference of about 0.2% in total long-term exposure mortality

incidence associated with PM2.5 with and without inclusion of the Quebec fire episode days. 

 

Alternative Concentration-Response Models

Another sensitivity analysis illustrates the impact on the risk estimates if the

concentration-response functions used for short-term exposure mortality had used distributed lag

models instead of single lag models. Schwartz (2000b) has shown in a study of short-term

exposure mortality in 10 cities using PM10 as the indicator that a distributed lag model predicted

the same relative risk that a single lag model would have predicted if the coefficient was

approximately two times what it was estimated to be. To simulate the possible impact of using a

distributed lag model, the PM2.5 coefficients were multiplied by two in this sensitivity analysis. 

As would be expected, the risk estimates are almost doubled using the distributed lag

approximation (see Appendix D in TSD).

The influence of using different periods of exposure on the risks estimated in long-term

exposure mortality studies also has been examined in a sensitivity analysis. Two alternatives are

examined in the assessment: assuming the relevant PM2.5 ambient concentrations were

respectively 50% higher than and twice as high as the PM2.5 ambient concentrations used in the

original epidemiologic study. These levels have been picked by staff to give a very rough

indication of the possible impact of previous higher ambient PM2.5 levels. Assuming that the

relevant PM2.5 concentrations were 50% higher than and twice as high as the levels reported in

the original studies reduces long-term exposure mortality risk estimates by about one-third and

one-half, respectively. 

As noted earlier, while few studies have reported PM2.5 concentration-response functions

using a wide variety of alternative model specifications (e.g., GAM vs. GLM, different degrees

of freedom, alternative lags), Moolgavkar (2003) did for his study in Los Angeles. Exhibits

7.12.a and 7.12.b in the TSD show the results as a sensitivity analysis for different models that

employed either the more stringent GAM approach or GLM, with either 30 or 100 degrees of

freedom, and included both single and multi-pollutant models. For this particular study, use of

GLM instead of GAM in single-pollutant models tended to either have no impact or to lower by

a small amount the estimated percent incidence of mortality in single pollutant models (e.g.,

changing the estimate from 0.9 to 0.7% of total incidence for 0-day lag with 30 degrees of

freedom). For multi-pollutant models, use of GLM instead of GAM tended to either increase

by 0.2 to 0.3% total incidence for cause-specific mortality and hospital admission estimates for

0-day lag with 100 degrees of freedom. Generally, but not always, the confidence intervals were
23Short-term exposure non-accidental mortality estimates were not included for Philadelphia because the

concentration-response function did not include confidence limits for this endpoint.

4-49

a little wider when GLM functions were used compared to GAM functions. Also, the use of a

greater number of degrees of freedom tended to reduce the estimated incidence for both mortality

and hospital admissions.

4.4.2 Just Meeting Current PM2.5 Standards

The second part of the PM2.5 risk assessment estimates the risk reductions that would

result if the current suite of PM2.5 standards (15 μg/m3 annual average and 65 μg/m3 daily

average) were just met in the assessment locations. This part of the risk assessment only

considers those locations that do not meet the current standards based on 2001-2003 air quality

data (i.e., Detroit, Philadelphia, Pittsburgh, Los Angeles, and St. Louis). As noted previously,

the 15 μg/m3

 annual average standard is the controlling standard in all five study areas. 

Consequently, just meeting this standard also results in each of these areas meeting the 24-hour

standard (65 μg/m3

).

The percent rollback necessary to just meet the annual standards depends on whether the

maximum or the spatial average of the monitor-specific annual averages is used. For the risk

assessment, the approach used to simulate just meeting the current annual average standard for

the base case risk estimates used the maximum of the monitor-specific annual averages as shown

in Table 4-11. Since an area could potentially use the spatial average of the community-oriented

monitors to determine whether or not it met the annual average standard, Table 4-11 also

presents the percent rollbacks and annual average design values that would have resulted from

using this alternative approach in each urban study area which does not meet the current annual

standard and which meets the minimum criteria for use of spatial averaging. A sensitivity 

analysis examining the impact of using design values based on spatial averaging is discussed in

section 4.4.3.2 for both the current and alternative PM2.5 annual average standards. 

4.4.2.1 Base Case Risk Estimates Above Initial Cutpoint

 Similar to the presentation of risk estimates in section 4.4.1 associated with recent air

quality levels, this section presents risk estimates for PM2.5 exposures after PM2.5 levels are

reduced to levels associated with just meeting the current set of standards, using the initial

cutpoint. For short-term exposure outcomes, the initial cutpoint is the estimated policy-relevant

background level. For long-term exposure mortality the initial cutpoint is 7.5 μg/m3

. Risks are

expressed both in terms of percent of total incidence and cases per hundred thousand general

 population. Figure 4-8 shows the estimates for four of the risk assessment study areas that do

not meet the current PM2.5 standards when their air quality is adjusted to simulate meeting the

current standards.23 The point estimates are in the range of about 0.5 to 1 percent of total

incidence or 5 to 10 cases per hundred thousand general population across the four study areas. 
4-50

Table 4-11. Air Quality Adjustments Required to Just Meet the Current

Annual PM2.5 Standard of 15 μg/m3

 Using the Maximum vs. the

Average of Monitor-Specific Averages

Assessment

Location

Percent Rollback Necessary to

Just Meet the Current Annual

PM2.5 Standard

Design Value Based on 2001-2003

Data

Using

Maximum of

MonitorSpecific

Annual

Averages

Using Average

of MonitorSpecific Annual

Averages

Annual

Based on

Maximum

Monitor

Annual Based on

Average of

Monitor-Specific

Annual Averages

Detroit 28.1% 11.5% 19.5 16.5

Los Angeles* 59.2% -- 23.6 --

Philadelphia 10.9% -0.9% 16.4 14.9

Pittsburgh 35.0% 22.8% 21.2 18.4

St. Louis 17.9% 13.5% 17.5 16.8

*Los Angeles does not meet the minimum requirements for use of spatial averaging.

Source: Abt Associates (2005b)
4-51

Figure 4-8.
4-52

Similarly, Figure 4-9 displays the annual risk estimates in terms of percent of total

incidence and cases per hundred thousand general population for all cause mortality associated

with long-term exposure to PM2.5 concentrations above a cutpoint of 7.5 μg/m3 after air quality is

adjusted to simulate just meeting the current standards in the five areas that do not meet the

current PM2.5 standards. The point estimates generally are in the range of about 2 to 5 percent of

total incidence or 12 to 45 cases per hundred thousand general population across the five study

areas. 

4.4.2.2 Base Case Risk Estimates Above Various Cutpoints

In the same manner as the risk estimates for recent air quality levels, additional base case 

short- and long-term exposure annual mortality risk estimates have been developed associated

with air quality levels just meeting the current standards including both the initial cutpoint used

in the previous section and the same alternative cutpoints discussed previously. For short-term

exposure mortality, a single non-accidental mortality function has been included, except for

Philadelphia and Phoenix where cardiovascular mortality has been used since a suitable nonaccidental mortality concentration-response function is not available. For long-term exposure

mortality, a single all cause mortality concentration-response function has been included based

on the ACS-extended study (Pope et al., 2002). Tables 4-12 and 4-13 present the annual health

risks for short- and long-term exposure mortality, respectively, in terms of incidence (i.e., cases), 

cases per 100,000 general population, and percent of total incidence, along with 95 percent

confidence intervals for each of these risk metrics.

4.4.2.3 Risk Estimates from Sensitivity Analyses

Three sensitivity analyses have been conducted associated with the air quality scenario

of just meeting the current PM2.5 standards. Two of these sensitivity analyses are discussed in

this section. The third one examines the impact of using a spatial average of annual average

monitor values versus the use of the maximum of annual average monitor values to determine

the design value. The design value then determines the amount of adjustment required to just

meet a specified set of standards. This third sensitivity analysis is presented in section 4.4.3.2

for both the current and alternative standards. 

The first sensitivity analysis examines the impact of alternative approaches to simulating

air quality levels that just meet the current standards. The base case risk analyses use a

proportional rollback approach to adjust air quality distributions to simulate the pattern that

would occur in an area improving its air quality so that it just meets the current annual average

PM2.5 standard. The support for this approach is briefly discussed in section 4.3.1.2 and in more

detail in Appendix B of the TSD. While the available data suggest that this is a reasonable

approach, other patterns of change are possible. In a sensitivity analysis an alternative air

quality adjustment approach has been used which reduces the top 10 percent of the distribution

of PM2.5 concentrations by 1.6 times as much as the lower 90 percent of concentrations. The
4-53

Figure 4-9.
 of 15 μg/m3 and the Current Daily Standard of 65 μg/m3 Are Just Met, Assuming Various Cutpoint Levels* 

Policy Relevant Background** Cutpoint*** Cutpoint*** 

=2.5 or 3.5 μg/m3 =10 μg/m3 =15 μg/m3 

Non-accidental all 3 day 122 54 26 

(-123 - 358) (-55 - 159) (-27 - 77) 

6 31

(-6 - 17) (-3 - 8) (-1 - 4) 

0.7% 0.3% 0.1% 

(-0.7% - 1.9%) (-0.3% - 0.8%) (-0.1% - 0.4%) 

Non-accidental all 0 day 292 115 58 

(-37 - 612) (-14 - 240) (-7 - 121) 

3 11

(0 - 6) (0 - 3) (0 - 1) 

0.5% 0.2% 0.1% 

(-0.1% - 1.1%) (0.0% - 0.4%) (0.0% - 0.2%) 

Cardiovascular all 1 day 367 189 106 

(175 - 560) (90 - 288) (51 - 162) 

24 12 7 

(12 - 37) (6 - 19) (3 - 11) 

5.8% 3.0% 1.7% 

(2.8% - 8.8%) (1.4% - 4.5%) (0.8% - 2.6%) 

Non-accidental 75+ 0 day 50 22 10 

(-108 - 200) (-48 - 87) (-23 - 41) 

4 21

(-8 - 16) (-4 - 7) (-2 - 3) 

0.5% 0.2% 0.1% 

(-1.1% - 2.1%) (-0.5% - 0.9%) (-0.2% - 0.4%) 

Non-accidental all 191 75 29 

(70 - 311) (28 - 122) (11 - 46) 

8 31

(3 - 12) (1 - 5) (0 - 2) 

0.9% 0.3% 0.1% 

(0.3% - 1.4%) (0.1% - 0.6%) (0.1% - 0.2%) 

*All results are for single pollutant, non-accidental mortality models, unless otherwise specified.**Policy relevant background is 2.5 μg/m3 in the West (Los Angeles) and 3.5 μg/m3 in the East (Detroit, Philadelphia, Pittsburgh, and St. Louis).

***For these alternative cutpoints the slope of the concentration-response relationship has been modified based on a simple hockeystick model (see discussion in sectio

Ito (2003) [reanalysis of Lippmann et al. (2000)]

Table 4-12. Estimated Annual Mortality Associated with Short-Term Exposure to PM2.5 When the Current Annual Standard 

Chock et al. (2000)

Pittsburgh

mean of 

lag 0 & 1 

Schwartz (2003b) [reanalysis of Schwartz et al. (1996)] St. Louis

Detroit

Los Angeles

Lipfert et al. (2000) -- 7 counties

Philadelphia

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Lag

Incidence Associated with PM2.5 Assuming Various Cutpo

(95% Confidence Interval)

Incidence per 100,000 Population

(95% Confidence Interval)

Percent of Total Incidence

(95% Confidence Interval)

Urban Area Study Type Ages

 4-54
Table 4-13. Estimated Annual Mortality Associated with Long-Term Exposure to 

 PM2.5 When the Current Annual Standard of 15 μg/m3 and the Current Daily 

 Standard of 65 μg/m3 Are Just Met, Assuming Various Cutpoint Levels*

Cutpoint Cutpoint** Cutpoint**

= 7.5 μg/m3 =10 μg/m3 =12 μg/m3

522 282 41

(181 - 910) (98 - 494) (14 - 72)

25 14 2

(9 - 44) (5 - 24) (1 - 3)

2.7% 1.5% 0.2%

(0.9% - 4.7%) (0.5% - 2.6%) (0.1% - 0.4%)

1507 823 138

(531 - 2587) (290 - 1415) (48 - 237)

16 9 1

(6 - 27) (3 - 15) (1 - 2)

2.7% 1.5% 0.2%

(0.9% - 4.6%) (0.5% - 2.5%) (0.1% - 0.4%)

536 338 137

(185 - 943) (116 - 597) (47 - 244)

35 22 9

(12 - 62) (8 - 39) (3 - 16)

3.2% 2.0% 0.8%

(1.1% - 5.7%) (0.7% - 3.6%) (0.3% - 1.5%)

403 215 25

(141 - 699) (75 - 373) (9 - 43)

31 17 2

(11 - 55) (6 - 29) (1 - 3)

2.7% 1.4% 0.2%

(0.9% - 4.6%) (0.5% - 2.5%) (0.1% - 0.3%)

596 311 23

(206 - 1047) (107 - 548) (8 - 40)

24 12 1

(8 - 42) (4 - 22) (0 - 2)

2.6% 1.4% 0.1%

(0.9% - 4.6%) (0.5% - 2.4%) (0.0% - 0.2%)

*Based on Pope et al. (2002) -- ACS extended, all cause mortality among ages 30 and older.

**For these alternative cutpoints the slope of the concentration-response relatonship has been modified based on a 

simple hockeystick model (see discussion in section 4.3.2.1).

Percent of Total Incidence

(95% Confidence Interval)

St. Louis

Pittsburgh

Detroit

Los Angeles

Philadelphia

Urban Areas

Incidence Associated with PM2.5 Assuming Various Cutpoint Levels

(95% Confidence Interval)

Incidence per 100,000 Population

(95% Confidence Interval)

 4-55
4-56

result of this alternative hypothetical adjustment which reduces the highest days more than the

rest of the distribution shows only a small difference (less than 1%) in the percent change in

PM-associated incidence (see Exhibit 8.9 and Appendix E, Exhibits E.33 to E.36, in the TSD).

The second sensitivity analysis explores the potential impact on the short-term exposure

non-accidental mortality risk estimates if the same multi-city concentration-response

relationship is used in five risk assessment locations compared to the single-city concentrationresponse relationships used in the base case analysis. As noted earlier, the multi-city

concentration-response relationship used in this sensitivity analysis is from the Six-Cities study

(Schwartz, 2003b), the only U.S. multi-city study on PM2.5 short-term exposure mortality that is

currently available. Table 4-14 shows the results of this sensitivity analysis, including the

results from the base case analysis which used the single-city concentration-response

relationships. As expected, given the generally larger relative risk reported in the Six Cities

study, the estimated incidence and deaths per 100,000 general population are somewhat larger in

four of the five locations using the Six Cities study function. The range of risk estimates across

the five locations also considerably narrows when the same concentration-response relationship

is used in all five locations. For example, using the risk metric that normalizes across locations

with different population sizes, the range goes from 4 to 24 deaths per 100,000 general

population using the single-city functions to 8 to 14 deaths per 100,000 general population using

the Six Cities study function. As noted previously, there are a number of possible sources for

the differences observed in risk estimates based on single-city studies. These include known

differences in baseline mortality incidence rates, possible differences in study methodology,

increased statistical uncertainty due to smaller sample sizes in some single-city studies,

differences in degree and patterns of exposure to ambient PM2.5, differences in sources or

components that might impact the toxicity, and differences in co-pollutants or other unidentified

factors that may play a role in modifying the concentration-response relationship. 

4.4.3 Just Meeting Alternative PM2.5 Standards

4.4.3.1 Base Case Risk Estimates

The third part of the PM2.5 risk assessment estimates the risk associated with just meeting

alternative suites of annual and daily PM2.5 standards, along with the risk reduction associated

with going to these levels from the current standards. For the five urban areas that exceed the

current PM2.5 suite of standards (i.e., Detroit, Los Angeles, Philadelphia, Pittsburgh, and St.

Louis), the estimated risk reductions are those associated with a further reduction in PM2.5

concentrations from just meeting the current standards to just meeting various suites of

alternative PM2.5 standards. For the four urban areas that meet the current PM2.5 standards based

on our analysis of 2001-2003 levels (i.e., Boston, Phoenix, San Jose, and Seattle), the estimated

risk reductions are those associated with a reduction from recent air quality levels to just

meeting various suites of alternative PM2.5 standards. 
Policy Relevant 

Background** Cutpoint*** Cutpoint*** Cutpoint***

=2.5 or 3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

Non-accidental all 3 day 122 54 26 12

Single City (-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35)

63 1 1

(-6 - 17) (-3 - 8) (-1 - 4) (-1 - 2)

0.7% 0.3% 0.1% 0.1%

(-0.7% - 1.9%) (-0.3% - 0.8%) (-0.1% - 0.4%) (-0.1% - 0.2%)

Non-accidental all 224 87 34 15

Multi-City (160 - 286) (62 - 111) (25 - 44) (11 - 19)

11 4 2 1

(8 - 14) (3 - 5) (1 - 2) (1 - 1)

1.2% 0.5% 0.2% 0.1%

(0.9% - 1.5%) (0.3% - 0.6%) (0.1% - 0.2%) (0.1% - 0.1%)

Non-accidental all 0 day 292 115 58 29

Single City (-37 - 612) (-14 - 240) (-7 - 121) (-4 - 61)

31 1 0

(0 - 6) (0 - 3) (0 - 1) (0 - 1)

0.5% 0.2% 0.1% 0.1%

(-0.1% - 1.1%) (0.0% - 0.4%) (0.0% - 0.2%) (0.0% - 0.1%)

Non-accidental all 731 270 123 55

Multi-City (526 - 935) (194 - 344) (89 - 157) (40 - 70)

83 1 1

(6 - 10) (2 - 4) (1 - 2) (0 - 1)

1.3% 0.5% 0.2% 0.1%

(1.0% - 1.7%) (0.4% - 0.6%) (0.2% - 0.3%) (0.1% - 0.1%)

Cardiovascular all 1 day 367 189 106 57

Single City (175 - 560) (90 - 288) (51 - 162) (27 - 87)

24 12 7 4

(12 - 37) (6 - 19) (3 - 11) (2 - 6)

5.8% 3.0% 1.7% 0.9%

(2.8% - 8.8%) (1.4% - 4.5%) (0.8% - 2.6%) (0.4% - 1.4%)

Non-accidental all 213 103 50 24

Multi-City (153 - 273) (74 - 132) (36 - 65) (17 - 31)

14 7 3 2

(10 - 18) (5 - 9) (2 - 4) (1 - 2)

1.3% 0.6% 0.3% 0.2%

(0.9% - 1.7%) (0.5% - 0.8%) (0.2% - 0.4%) (0.1% - 0.2%)

Non-accidental 75+ 0 day 50 22 10 5

Single City (-108 - 200) (-48 - 87) (-23 - 41) (-11 - 18)

42 1 0

(-8 - 16) (-4 - 7) (-2 - 3) (-1 - 1)

0.5% 0.2% 0.1% 0.1%

(-1.1% - 2.1%) (-0.5% - 0.9%) (-0.2% - 0.4%) (-0.1% - 0.2%)

Non-accidental all 174 68 27 11

Multi-City (125 - 223) (49 - 87) (19 - 34) (8 - 14)

14 5 2 1

(10 - 17) (4 - 7) (1 - 3) (1 - 1)

1.2% 0.5% 0.2% 0.1%

(0.8% - 1.5%) (0.3% - 0.6%) (0.1% - 0.2%) (0.1% - 0.1%)

Non-accidental all 191 75 29 9

Single City (70 - 311) (28 - 122) (11 - 46) (3 - 14)

83 1 0

(3 - 12) (1 - 5) (0 - 2) (0 - 1)

0.9% 0.3% 0.1% 0.0%

(0.3% - 1.4%) (0.1% - 0.6%) (0.1% - 0.2%) (0.0% - 0.1%)

Non-accidental all 256 97 36 10

Multi-City (183 - 328) (69 - 124) (25 - 46) (7 - 13)

10 4 1 0

(7 - 13) (3 - 5) (1 - 2) (0 - 1)

1.2% 0.4% 0.2% 0.1%

(0.8% - 1.5%) (0.3% - 0.6%) (0.1% - 0.2%) (0.0% - 0.1%)

*All results are for single pollutant models.

**Policy relevant background is 2.5 μg/m3

 in the West (Los Angeles) and 3.5 μg/m3 in the East (Detroit, Philadelphia, Pittsburgh, and St. Louis).

***For these alternative cutpoints the slope of the concentration-response function has been modified based on a simple hockeystick model (see discussion in section 

4.3.2.1).

Table 4-14. Sensitivity Analysis Comparing the Use of Multi-City vs. Single-City 

Urban Area Study

Type of 

Mortality and 

Single vs. MultiCity 

ConcentrationResponse 

Relationship*

Ages Lag

Incidence Associated with PM2.5 Assuming Various Cutpoint Levels

(95% Confidence Interval)

Incidence per 100,000 General Population

(95% Confidence Interval)

Percent of Total Incidence

(95% Confidence Interval)

Detroit

Ito (2003) [reanalysis of 

Lippmann et al. (2000)]

Schwartz (2003b) 

[reanalysis of Schwartz et 

al. (1996)]

mean of lag 0 

& 1 day

Los Angeles

Moolgavkar (2003) 

[reanalysis of Moolgavkar 

(2000a)]

Schwartz (2003b) 

[reanalysis of Schwartz et 

al. (1996)]

mean of lag 0 

& 1 day

mean of lag 0 

& 1 day

Philadelphia

Lipfert et al. (2000) -- 7 

counties

Schwartz (2003b) 

[reanalysis of Schwartz et 

al. (1996)]

mean of lag 0 

& 1 day

 Concentration-Response Relationships On Estimates of Short-Term Exposure

 Mortality Associated with Just Meeting the Current PM2.5 Standards

St. Louis

Schwartz (2003b) 

[reanalysis of Schwartz et 

al. (1996)]

mean of lag 0 

& 1 day

Schwartz (2003b) 

[reanalysis of Schwartz et 

al. (1996)]

mean of lag 0 

& 1 day

Pittsburgh

Chock et al. (2000)

Schwartz (2003b) 

[reanalysis of Schwartz et 

al. (1996)]

 4-57
24In four of the five urban areas that do not meet the current suite of PM2.5 standards, annual standards

within the range of 12 to 15 μg/m3

 combined with the current daily standard of 65 μg/m3, using a 98th percentile

form, require the same reduction as when these annual standards are combined with a daily standard of 40 μg/m3

,

using the same daily form. Therefore, the risk assessment only included the 14 μg/m3

 annual standard combined

with the current daily standard for the one location (i.e., Philadelphia) and annual standard scenario where there was

a difference in the reduction required between daily standards of 40 and 65 μg/m3

. 

4-58

The selection of the suites of alternative annual and daily standards included in the risk

assessment has been based, in part, on consideration of CASAC and public comments, and is

consistent with the staff recommendations described in Chapter 5. Annual standards of 15, 14,

13, and 12 μg/m3 are each combined with 98th percentile daily standards of 40, 35, 30, and

25 μg/m3, and 99th percentile daily standards at the same levels.24 In addition, an annual

standard of 15 μg/m3 has been combined with a 99th percentile daily standard of 65 μg/m3. The

combinations of annual and daily alternative standards used in the PM2.5 risk assessment are

summarized in Table 4-15. The same proportional adjustment approach used to simulate air

quality just meeting the current standards, described previously in section 4.3.1.2 and in section

2.3 of the TSD, has been used to simulate air quality just meeting the various alternative suites

of standards. Table 4-16 provides the design values for the annual and 98th and 99th percentile

daily standards for all of the PM2.5 risk assessment study areas based on air quality data from

2001-2003 for the base case risk estimates. 

The base case analyses examining alternative PM2.5 standards include non-accidental

mortality (or cause-specific if there was no suitable function for non-accidental mortality

available) associated with short-term exposure to PM2.5 above policy-relevant background and

several alternative cutpoint levels. In addition, the base case analyses include estimates of risk

for all cause mortality, cardiopulmonary mortality, and lung cancer mortality associated with

long-term exposure to PM2.5 above 7.5 μg/m3 and two alternative cutpoint levels based on Pope

et al. (2002) – ACS extended. Since the patterns observed were identical, only the all cause

long-term exposure mortality results are presented in this Staff Paper (see the TSD for the causespecific mortality estimates). As in the earlier base case analyses, in addition to having a

cutpoint set equal to policy-relevant background, cutpoints of 10, 15, and 20 μg/m3 have been

included for health endpoints associated with short-term exposures. For long-term exposure

mortality, cutpoints set equal to 7.5 μg/m3, the lowest measured level in the ACS-extended

study, and alternative cutpoints of 10 and 12 μg/m3

 have been included in the base case analysis.

The base case analysis results for alternative annual standards combined with 98th and

99th percentile daily standards, respectively, are given in Tables 4-17 for Detroit for mortality

associated with short-term exposure. Short-term exposure mortality risk estimates for the other

four urban locations (Los Angeles, Philadelphia, Pittsburgh, and St. Louis) are provided in

Tables 4B-1 through 4B-4 in Appendix 4B. Similarly, the estimated risk reduction in total all

cause mortality associated with long-term PM2.5 exposures for these same alternative standards
4-59

Table 4-15. Alternative Sets of PM2.5 Standards Considered in the PM2.5 Risk

Assessment*

Annual

Standard

98th Percentile Daily Standard 99th Percentile Daily Standard

65 40 35 30 25 65 40 35 30 25

15 x x x x x x x x x

14 x** x x x x x x x x

13 x x x x x x x x

12 x x x x x x x x

*All standards are in μg/m3.

**Only in Philadelphia.

-------------------------------------------------------------------------------------------------------------------

Table 4-16. Estimated Design Values for Annual and 98th and 99th Percentile

Daily PM2.5 Standards Based on 2001-2003 Air Quality Data*

Location Annual 98th Percentile Daily 99th Percentile Daily

Boston 14.4 44 60

Detroit 19.5 44 48

Los Angeles 23.6 62 96

Philadelphia 16.4 51 89

Phoenix 11.5 35 41

Pittsburgh 21.2 63 70

St. Louis 17.5 42 46

San Jose 14.6 47 53

Seattle 11.1 41 48

*The calculation of design values is explained in Schmidt (2005). All design values are in μg/m3. The design

values summarized here for the alternative standards are based on use of the maximum monitor in each urban area. 
 Levels for Detroit, MI*

Annual (μg/m3) Daily (μg/m3) Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

65, 98th percentile value*** 122 54 26 12

(-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35)

0.0% 0.0% 0.0% 0.0%

40, 98th percentile value 122 54 26 12

(-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35)

0.0% 0.0% 0.0% 0.0%

35, 98th percentile value 122 54 26 12

(-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35)

0.0% 0.0% 0.0% 0.0%

30, 98th percentile value 111 45 20 8

(-112 - 325) (-45 - 131) (-20 - 58) (-9 - 24)

9.0% 16.7% 23.1% 33.3%

25, 98th percentile value 90 28 10 3

(-91 - 263) (-29 - 82) (-10 - 28) (-4 - 10)

26.2% 48.1% 61.5% 75.0%

65, 99th percentile value 122 54 26 12

(-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35)

0.0% 0.0% 0.0% 0.0%

40, 99th percentile value 122 54 26 12

(-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35)

0.0% 0.0% 0.0% 0.0%

35, 99th percentile value 120 53 25 11

(-121 - 352) (-53 - 154) (-26 - 74) (-12 - 33)

1.6% 1.9% 3.8% 8.3%

30, 99th percentile value 101 37 15 6

(-102 - 296) (-37 - 107) (-15 - 42) (-6 - 16)

17.2% 31.5% 42.3% 50.0%

25, 99th percentile value 82 22 7 2

(-83 - 239) (-23 - 65) (-7 - 19) (-2 - 6)

32.8% 59.3% 73.1% 83.3%

40, 98th percentile value 111 45 20 8

(-112 - 326) (-46 - 132) (-20 - 58) (-9 - 24)

9.0% 16.7% 23.1% 33.3%

35, 98th percentile value 111 45 20 8

(-112 - 326) (-46 - 132) (-20 - 58) (-9 - 24)

9.0% 16.7% 23.1% 33.3%

30, 98th percentile value 111 45 20 8

(-112 - 325) (-45 - 131) (-20 - 58) (-9 - 24)

9.0% 16.7% 23.1% 33.3%

25, 98th percentile value 90 28 10 3

(-91 - 263) (-29 - 82) (-10 - 28) (-4 - 10)

26.2% 48.1% 61.5% 75.0%

40, 99th percentile value 111 45 20 8

(-112 - 326) (-46 - 132) (-20 - 58) (-9 - 24)

9.0% 16.7% 23.1% 33.3%

35, 99th percentile value 111 45 20 8

(-112 - 326) (-46 - 132) (-20 - 58) (-9 - 24)

9.0% 16.7% 23.1% 33.3%

30, 99th percentile value 101 37 15 6

(-102 - 296) (-37 - 107) (-15 - 42) (-6 - 16)

17.2% 31.5% 42.3% 50.0%

25, 99th percentile value 82 22 7 2

(-83 - 239) (-23 - 65) (-7 - 19) (-2 - 6)

32.8% 59.3% 73.1% 83.3%

14

Table 4-17. Estimated Annual Mortality Associated with Short-Term Exposure to 

 PM2.5 When Alternative Standards Are Just Met, Assuming Various Cutpoint

Alternative Standards

Incidence Associated with PM2.5 Assuming Various Cutpoint Levels

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

15

 4-60
Annual (μg/m3) Daily (μg/m3) Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

Alternative Standards

Incidence Associated with PM2.5 Assuming Various Cutpoint Levels

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

40, 98th percentile value 101 36 14 6

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16)

17.2% 33.3% 46.2% 50.0%

35, 98th percentile value 101 36 14 6

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16)

17.2% 33.3% 46.2% 50.0%

30, 98th percentile value 101 36 14 6

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16)

17.2% 33.3% 46.2% 50.0%

25, 98th percentile value 90 28 10 3

(-91 - 263) (-29 - 82) (-10 - 28) (-4 - 10)

26.2% 48.1% 61.5% 75.0%

40, 99th percentile value 101 36 14 6

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16)

17.2% 33.3% 46.2% 50.0%

35, 99th percentile value 101 36 14 6

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16)

17.2% 33.3% 46.2% 50.0%

30, 99th percentile value 101 36 14 6

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16)

17.2% 33.3% 46.2% 50.0%

25, 99th percentile value 82 22 7 2

(-83 - 239) (-23 - 65) (-7 - 19) (-2 - 6)

32.8% 59.3% 73.1% 83.3%

40, 98th percentile value 90 28 10 3

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10)

26.2% 48.1% 61.5% 75.0%

35, 98th percentile value 90 28 10 3

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10)

26.2% 48.1% 61.5% 75.0%

30, 98th percentile value 90 28 10 3

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10)

26.2% 48.1% 61.5% 75.0%

25, 98th percentile value 90 28 10 3

(-91 - 263) (-29 - 82) (-10 - 28) (-4 - 10)

26.2% 48.1% 61.5% 75.0%

40, 99th percentile value 90 28 10 3

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10)

26.2% 48.1% 61.5% 75.0%

35, 99th percentile value 90 28 10 3

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10)

26.2% 48.1% 61.5% 75.0%

30, 99th percentile value 90 28 10 3

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10)

26.2% 48.1% 61.5% 75.0%

25, 99th percentile value 82 22 7 2

(-83 - 239) (-23 - 65) (-7 - 19) (-2 - 6)

32.8% 59.3% 73.1% 83.3%

*This analysis was performed using Ito (2003). 

***Current standards.

13

12

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

**For these alternative cutpoints the slope of the concentration-response function has been modified based on a simple hockeystick 

model (see discussion in section 4.3.2.1).

 4-61
4-62

are given in Table 4-18 for Detroit and in Appendix 4B (see Tables 4B-5 through 4B-8) for the

other four urban areas. 

Not surprisingly, estimated PM-related incidences varied substantially with both

alternative cutpoint levels and with alternative standards. In Detroit, for example, the estimated

number of cases of non-accidental mortality associated with short-term exposure to PM2.5 when

the current standards are just met decreases from 115 (when the cutpoint is set equal to policyrelevant background) or to 54, 26, and 12 under alternative cutpoints of 10, 15, and 20 μg/m3

,

respectively. Because meeting increasingly lower level standards removes estimated cases at the

higher concentrations and considering higher alternative cutpoint increasingly removes

estimated cases at concentrations between background and the cutpoint, one would expect to see

an increase in the percent reduction associated with just meeting alternative standards for higher

cutpoints. This is exactly what is found. For example, as seen in Table 4-17, going from just

meeting the current standards (15 μg/m3

 annual and 65 μg/m3 daily 98th percentile value) to just

meeting the lowest set of standards considered (12 μg/m3 annual and 25 μg/m3 daily 99th

percentile value) results in a reduction in short-term exposure mortality incidence of (115 - 

75)/115 = 34.8 percent when the cutpoint equals policy-relevant background; but, with a

cutpoint equal to 10 μg/m3, it results in a reduction of (54 - 22)/54 = 59 percent.

As shown in Table 4-18 for all-cause mortality associated with long-term exposure in

Detroit, the reduction in mortality incidence is even more dramatic when alternative cutpoint

levels are considered. Going from just meeting the current standards to just meeting the lowest

set of standards considered (12 μg/m3 annual and 25 μg/m3 daily 99th percentile value) results in

a reduction in long-term exposure mortality incidence of (522-207)/522= 60% with a cutpoint

equal to 7.5 μg/m3

; but, with the cutpoint set equal to 10 μg/m3, it results in a reduction of (282 - 

0)/282 =100 percent. The same general patterns can be seen in all locations and for all health

endpoints considered.

4.4.3.2 Risk Estimates from Sensitivity Analyses

Spatial Averaging Versus Maximum Community Monitor

As discussed previously in section 4.2.3.2, under the current annual PM2.5 standard urban

areas may, under certain circumstances, use the “spatial averaging approach” to determine

compliance with the annual standard. This involves using the average of the annual averages of

several monitors within the urban area. Four of the five urban areas included in the PM2.5 risk

assessment that do not meet the current annual standard based on the maximum communityoriented monitor meet the minimum requirements to allow use of spatial averaging. The design

values and percent rollback required to meet the current annual standard for these four areas are

shown in Table 4-11. Tables 4B-9 and 4B-10 in Appendix 4B present the PM-related mortality

risk estimates associated with short- and long-term exposure, respectively, in Detroit using the

maximum versus the average of monitor-specific averages to determine the design value for the

annual standards. Risk estimates for alternative suites of standards are expressed in terms of
Annual (μg/m3

) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3

65, 98th percentile value*** 522 282 41

(181 - 910) (98 - 494) (14 - 72)

0.0% 0.0% 0.0%

40, 98th percentile value 522 282 41

(181 - 910) (98 - 494) (14 - 72)

0.0% 0.0% 0.0%

35, 98th percentile value 522 282 41

(181 - 910) (98 - 494) (14 - 72)

0.0% 0.0% 0.0%

30, 98th percentile value 435 185 0

(151 - 757) (64 - 323) (0 - 0)

16.7% 34.4% 100.0%

25, 98th percentile value 270 0 0

(94 - 468) (0 - 0) (0 - 0)

48.3% 100.0% 100.0%

65, 99th percentile value 522 282 41

(181 - 910) (98 - 494) (14 - 72)

0.0% 0.0% 0.0%

40, 99th percentile value 522 282 41

(181 - 910) (98 - 494) (14 - 72)

0.0% 0.0% 0.0%

35, 99th percentile value 507 266 23

(176 - 884) (92 - 465) (8 - 40)

2.9% 5.7% 43.9%

30, 99th percentile value 356 97 0

(124 - 619) (34 - 168) (0 - 0)

31.8% 65.6% 100.0%

25, 99th percentile value 207 0 0

(72 - 358) (0 - 0) (0 - 0)

60.3% 100.0% 100.0%

40, 98th percentile value 438 188 0

(152 - 762) (65 - 328) (0 - 0)

16.1% 33.3% 100.0%

35, 98th percentile value 438 188 0

(152 - 762) (65 - 328) (0 - 0)

16.1% 33.3% 100.0%

30, 98th percentile value 435 185 0

(151 - 757) (64 - 323) (0 - 0)

16.7% 34.4% 100.0%

25, 98th percentile value 270 0 0

(94 - 468) (0 - 0) (0 - 0)

48.3% 100.0% 100.0%

40, 99th percentile value 438 188 0

(152 - 762) (65 - 328) (0 - 0)

16.1% 33.3% 100.0%

35, 99th percentile value 438 188 0

(152 - 762) (65 - 328) (0 - 0)

16.1% 33.3% 100.0%

30, 99th percentile value 356 97 0

(124 - 619) (34 - 168) (0 - 0)

31.8% 65.6% 100.0%

25, 99th percentile value 207 0 0

(72 - 358) (0 - 0) (0 - 0)

60.3% 100.0% 100.0%

15

14

Table 4-18. Estimated Annual Mortality Associated with Long-Term Exposure to PM2.5 When

 Alternative Standards Are Just Met, Assuming Various Cutpoint Levels for Detroit, MI*

 

Alternative Standards

Incidence Associated with PM2.5 Assuming Various Cutpoint Levels

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

 4-63
Annual (μg/m3

) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3

Alternative Standards

Incidence Associated with PM2.5 Assuming Various Cutpoint Levels

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

40, 98th percentile value 354 94 0

(123 - 615) (33 - 164) (0 - 0)

32.2% 66.7% 100.0%

35, 98th percentile value 354 94 0

(123 - 615) (33 - 164) (0 - 0)

32.2% 66.7% 100.0%

30, 98th percentile value 354 94 0

(123 - 615) (33 - 164) (0 - 0)

32.2% 66.7% 100.0%

25, 98th percentile value 270 0 0

(94 - 468) (0 - 0) (0 - 0)

48.3% 100.0% 100.0%

40, 99th percentile value 354 94 0

(123 - 615) (33 - 164) (0 - 0)

32.2% 66.7% 100.0%

35, 99th percentile value 354 94 0

(123 - 615) (33 - 164) (0 - 0)

32.2% 66.7% 100.0%

30, 99th percentile value 354 94 0

(123 - 615) (33 - 164) (0 - 0)

32.2% 66.7% 100.0%

25, 99th percentile value 207 0 0

(72 - 358) (0 - 0) (0 - 0)

60.3% 100.0% 100.0%

40, 98th percentile value 271 0 0

(94 - 469) (0 - 1) (0 - 0)

48.1% 100.0% 100.0%

35, 98th percentile value 271 0 0

(94 - 469) (0 - 1) (0 - 0)

48.1% 100.0% 100.0%

30, 98th percentile value 271 0 0

(94 - 469) (0 - 1) (0 - 0)

48.1% 100.0% 100.0%

25, 98th percentile value 270 0 0

(94 - 468) (0 - 0) (0 - 0)

48.3% 100.0% 100.0%

40, 99th percentile value 271 0 0

(94 - 469) (0 - 1) (0 - 0)

48.1% 100.0% 100.0%

35, 99th percentile value 271 0 0

(94 - 469) (0 - 1) (0 - 0)

48.1% 100.0% 100.0%

30, 99th percentile value 271 0 0

(94 - 469) (0 - 1) (0 - 0)

48.1% 100.0% 100.0%

25, 99th percentile value 207 0 0

(72 - 358) (0 - 0) (0 - 0)

60.3% 100.0% 100.0%

*This analysis was performed using Pope et al. (2002) -- ACS extended. 

13

12

**For these alternative cutpoints the slope of the concentration-response function has been modified based on a simple hockeystick model (see discussion 

in section 4.3.2.1).

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

***Current standards.

 4-64
4-65

estimated mortality incidence and percent reduction in incidence from just meeting the current

standards using the initial cutpoint and assuming alternative cutpoints. Similar tables for

Pittsburgh and St. Louis (the other two locations that do not meet the current standards and for

which both approaches result in positive percent rollbacks) are given in Exhibits E.37 to E.40 in

the TSD. Alternative suites of annual and daily PM2.5 standards, where the daily standard is the

controlling standard under both design value approaches, have not been included in this

sensitivity analysis, since there is no change in the risk estimates. 

For those cases where the annual standard is the controlling standard under both design

value approaches, use of spatial averaging requires less reduction in PM2.5, thus higher mortality

incidence and less reduction in risk are associated with the current and alternative annual

standards compared to use of the maximum monitor based approach. There are also cases where

the annual standard is the controlling standard under the maximum monitor based approach, but 

the daily standard becomes controlling when the same annual standard is considered using the

spatial averaging approach. When this occurs, the estimated incidence reduction associated with

the spatially averaged annual standard combined with the daily standard is determined by the

daily standard. In this case, the incidence reduction will be less than that associated with

meeting the annual standard using the maximum-monitor based approach but greater than the

incidence reduction associated with meeting the annual standard using the spatial averaging

approach.

Based on the risk estimates for the three example urban areas (Detroit, Pittsburgh, and St.

Louis) using the initial cutpoint, the estimated mortality incidence associated with long-term

exposure is about 10 to over 40% higher for the current suite of standards where compliance with

the annual standard is based on spatial averaging than the estimated incidence where compliance

is based on the highest population-oriented monitor. The estimated mortality incidence

associated with short-term exposure using the initial cutpoint ranges from about 5 to 25% higher

when the spatial averaging approach is used for the current standards in these three example

urban areas.

As noted above, the use of spatial averaging for alternative suites of standards only has an

impact on risk estimates compared to the maximum-monitor based approach where the annual 

standard is controlling for at least one of these approaches. For such cases in the three example

urban areas, the estimated mortality incidence associated with long-term exposure using the

initial cutpoint in most cases ranges from about 10 to 60% higher when spatial averaging is used

to determine compliance with the annual average. In these three example urban areas, the

estimated mortality incidence associated with short-term exposure using the intial cutpoint in

most cases ranges from about 5 to 25% higher when spatial averaging is used.

 Changing from a maximum-monitor based approach to the spatial average approach

impacts the estimated risks associated with just meeting both the current and lower alternative

standards. Comparing the estimated percent reductions in mortality incidence associated with
4-66

going from just meeting the current standard to alternative lower standards between the two

design value approaches for the three example urban areas (Detroit, Pittsburgh, and St. Louis), 

there does not seem to be any clear pattern.

4.4.4 Key Observations

Recent PM Air Quality Levels

Sections 4.4.1.1, 4.4.1.2, and 4.4.1.3 have presented the PM2.5 health risk estimates and

sensitivity analyses associated with recent PM air quality levels. Presented below are key

observations resulting from this part of the risk assessment:

• A fairly wide range of risk estimates are observed for PM2.5-related morbidity and

mortality incidence across the urban areas analyzed associated with recent air

quality. 

• Most of the point estimates for PM2.5 for the base case analysis are in the range 0.8

to 3% for short-term exposure total non-accidental mortality when the cutpoint

equals estimated policy-relevant background. Generally, the point estimates for

the single- and multi-city models are roughly similar in most of the urban areas

analyzed. The impact of adding additional co-pollutants to the models was

variable; sometimes there was relatively little difference, while in other cases

there were larger differences.

• The point estimates for long-term exposure mortality associated with PM2.5 range

from about 0.5% to as high as 6.6% with most estimates falling in the 2 to 5%

range for single-pollutant models (based on the ACS-extended study) when the

cutpoint equals 7.5 μg/m3. Addition of a single co-pollutant resulted in higher risk

estimates when CO, NO2, or O3 were added to the models for the ACS study and

lower risk estimates when SO2 was added.

• The single most important factor influencing the risk estimates is the

consideration of which of the alternative concentration-response functions

included in this assessment best represents the unknown “true” concentrationresponse relationships.

The wide variability in risk estimates associated with a recent year of air quality is to be

expected given the wide range of PM2.5 levels across the urban areas analyzed and the variation

observed in the concentration-response relationships obtained from the original epidemiologic

studies. Among other factors, this variability may reflect differences in populations, exposure

considerations (e.g., degree of air conditioning use), differences in co-pollutants and/or other

stressors, differences in study design, and differences related to exposure and monitor

measurement error.
25The Detroit PM2.5 example uses the concentration-response function for non-accidental mortality from

Lippmann et al. (2000), reanalyzed in Ito (2003). 

4-67

Based on the results of the sensitivity analyses, the following key observation is made:

• The following uncertainties have a moderate impact on the risk estimates in some

or all of the cities: choice of an alternative estimated constant background level,

use of a distributed lag model, and alternative assumptions about the relevant air

quality for estimating exposure levels for long-term exposure mortality. Use of a

distribution of daily background concentrations had very little impact on the risk

estimates. 

Staff was interested in obtaining insight into the overall pattern of risk associated with

short-term PM2.5 exposures across the distribution of PM2.5 air quality, as typically observed in

urban areas. Figure 4-10 illustrates the relative contribution of different portions of a typical

urban ambient PM2.5 concentration distribution to mortality risk associated with short-term PM2.5

exposures. The top panel in Figure 4-10 shows the annual distribution of 24-hour PM2.5

concentrations in Detroit. The middle panel shows the estimated incidence expressed in terms of

deaths per day for the upper bound of each 5 μg/m3 increment based on the short-term exposure

epidemiology study included in the current PM2.5 risk assessment.25 The bottom panel shows the

corresponding distribution of estimated mortality incidence (for PM2.5) for each 5 μg/m3

increment taking into account the number of days in each interval and the concentration-response

relationship. Not surprisingly, the middle panel shows that higher 24-hour PM2.5 concentrations 

pose greater risk in terms of deaths per day. However, as shown in the bottom panel, on an

annual basis, the very highest days contribute less to the total annual health risk associated with

short-term exposures than the middle of the distribution (i.e., in the range of about 10 to 35 

μg/m3 in this example), due to the much greater number of days that occur in this part of the air

quality distribution. As shown in the prior review (61 FR at 65652, December 13, 1996), a

similar, if somewhat scaled-back pattern, was observed when concentration-response

relationships were used that assumed a cutpoint (or hypothetical threshold).

Meeting the Current PM2.5 Standards

Sections 4.4.2.1, 4.4.2.2, and 4.4.2.3 have presented the PM health risk estimates and

sensitivity analyses associated with just meeting the current PM2.5 standards. Presented below

are key observations resulting from this part of the risk assessment:

• There is a wide range of PM2.5-related incidence of short-term exposure mortality

and morbidity remaining across the five urban areas analyzed. This is likely due,

in large part, to differences in concentration-response relationships among
Figure 4-10. Distribution of average daily PM2.5 concentrations in Detroit (2003 air 

quality data) (top panel), estimated non-accidental mortality per day 

in Detroit associated with exposure to daily PM2.5 concentrations 

(middle panel), and estimated non-accidental mortality in Detroit 

associated with exposure to daily PM2.5 concentrations over the course 

of a year (bottom panel). Source: Abt Associates (2005b) 

-2

-1

0

1

2

3

4

5

5 10 15 20 25 30 35 40 45 50 55

Upper Bounds of 5 ug/m3 Intervals of Average Daily PM2.5 Concentration (ug/m3)

Estimated Average Non-accidental 

Mortality per Day

Mean Estimate of Deaths per Day

2.5th% Estimate of Deaths per Day

97.5th% Estimate of Deaths per Day

-32

-27

-22

-17

-12

-7

-2

3

8

13

18

23

28

33

38

43

48

53

58

63

68

73

78

83

88

93

98

0 5 10 15 20 25 30 35 40 45 50 55 60

Upper Bounds of 5 ug/m3 Intervals of Average Daily PM2.5 Concentration (ug/m3)

Estimated Non-Accidental

 Mortality

Mean Estimate of # of Deaths

2.5th% Estimate of # of Deaths

97.5th% Estimate of # of

Deaths

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30 35 40 45 50 55 60

Upper Bounds of 5 ug/m3 Intervals of Daily Average PM2.5

 Concentration (ug/m3)

Number of Days

4-68
4-69

single-location short-term exposure studies, differences in baseline incidence

rates, and varying population sizes.

• Results of a sensitivity analysis which applied one multi-city concentrationresponse function to all five urban areas analyzed narrowed considerably the

range of risk estimates when a risk metric was used that normalized for different

population sizes. However, it is still unknown whether the wider range of

estimates observed using single-city concentration-response functions reflect

methodological differences between studies and/or real city-to-city differences

related to exposure, population, composition of the particles, or other factors.

• The single most important factor influencing the risk estimates is the

consideration of which of the alternative concentration-response functions

included in this assessment best represents the unknown “true” concentrationresponse relationships. 

• The risk estimates associated with just meeting the current PM2.5 standards

incorporate several additional sources of uncertainty, including: (1) uncertainty in

the pattern of air quality concentration reductions that would be observed across

the distribution of PM concentrations in areas meeting the standards (“rollback

uncertainty”) and (2) uncertainty concerning the degree to which current PM risk

coefficients may reflect contributions from other pollutants, or the particular

contribution of certain constituents of PM2.5, and whether such constituents would

be reduced in similar proportion to the reduction in PM2.5 as a whole. 

• At least one alternative approach to rolling back the distribution of daily PM2.5

concentrations, in which the upper end of the distribution of concentrations was

reduced by a greater amount than the rest of the distribution, had little impact on

the risk estimates.

Meeting Alternative PM2.5 Standards

Section 4.4.3.1 presented the base case PM2.5-related incidence associated with meeting

alternative PM2.5 standards and the percent reduction in incidence from the current PM2.5

standards. Presented below are key observations resulting from this part of the risk assessment:

• The most important factor influencing the base case risk estimates for both shortand long-term exposure mortality associated with PM2.5 concentrations just

meeting alternative standards is the consideration of which of the alternative

concentration-response functions included in this assessment best represents the

unknown “true” concentration-response relationships. 

• For short-term exposure mortality, there is a significant decrease in the incidence

remaining as one considers alternative higher cutpoints. There also is a
4-70

significant increase observed in the percent reduction in PM-associated incidence

upon just meeting alternative standards with higher alternative cutpoints. The

reduction in incidence and increase in percent reduction in PM-associated

incidence are even more dramatic for long-term exposure mortality as higher

alternative cutpoint levels are considered. 

Section 4.4.3.2 presented the results of a sensitivity analysis considering the impact on

risk estimates associated with just meeting the current and alternative standards when the spatial

averaging approach is used to determine compliance with the annual standard. A key

observation resulting from this part of the risk assessment follows:

• There is an increase in estimated short-term and long-term exposure mortality

incidence associated with PM2.5 when a spatial averaging approach is used to

determine compliance with the current annual standard or alternative suites of

standards where the daily standard is not the controlling standard.

4.5 PM10-2.5 RISK ESTIMATES

A similar approach has been taken for PM10-2.5 risk estimates, with initial base case risk

estimates for recent air quality using estimated policy-relevant background as the initial cutpoint

and, then, additional base case estimates for recent air quality and alternative PM10-2.5 standards

including the initial and alternative cutpoints. For the alternative cutpoints, the slope of the

concentration-response relationship has been modified based on the same simple hockeystick

model approach used for PM2.5. 

4.5.1. Recent Air Quality

4.5.1.1 Base Case Risk Estimates

 Figure 4-11 shows risk estimates for hospital admissions associated with short-term

exposure to PM10-2.5 for Detroit and Seattle, and Figure 4-12 shows risk estimates associated with

respiratory symptoms for St. Louis associated with recent PM10-2.5 air quality levels. For Detroit

risk estimates are provided for several categories of cardiovascular and respiratory-related

hospital admissions and show point estimates ranging from about 2 to 7% of cause-specific

admissions being associated with as is short-term exposures to PM10-2.5. The point estimate for

asthma hospital admissions associated with PM10-2.5 exposures for Seattle, an area with lower

PM10-2.5 ambient concentrations than either Detroit or St. Louis, is about 1%. Point estimates for

lower respiratory symptoms and cough in St. Louis are about 12 and 15%, respectively. These

estimates use estimated policy-relevant background as the cutpoint. Table 4-21, discussed

below, provides risk estimates associated with recent PM10-2.5 air quality levels using policyrelevant background and higher alternative cutpoints.
4-71

4.5.1.2 Risk Estimates from Sensitivity Analyses

For PM10-2.5, the sensitivity analysis examining the effects of using the lower- and upperend of the range of estimated policy-relevant background levels shows about a 16% increase in

the risk estimates for various respiratory and cardiovascular-related short-term exposure hospital

admissions in Detroit between the base case (which used a value of 4.5 μg/m3

 for background)

and the lower end where background was estimated to be 1 μg/m3. At the upper end, where

background was estimated to be 9 μg/m3, the short-term exposure hospital admission risk

estimates are reduced by about 19% (see Exhibit 9.5 in the TSD). The effect of different

background concentrations for the other two PM10-2.5 locations is about + 30% for asthma

hospital admissions in Seattle (see Exhibit F.7 in the TSD) and about + 50% for respiratory

symptoms in St. Louis (see Exhibit F.8 in the TSD). 

4.5.2 Just Meeting Alternative PM10-2.5 Standards

The second part of the PM10-2.5 risk assessment estimates the risk associated with just

meeting alternative daily PM10-2.5 standards for the three locations examined earlier (Detroit, St.

Louis, and Seattle), as well as the risk reductions associated with going to these levels from the

current air quality levels. Staff notes that the locations used in this part of the risk assessment are

not representative of urban locations in the U.S. that experience the most significant elevated 24-

hour PM10-2.5 ambient concentrations. Thus, observations regarding risk reductions associated 

with alternative standards in these three urban areas may not be fully relevant to the areas

expected to have the greatest health risks associated with peak daily ambient PM10-2.5

concentrations.

Estimated reductions in risk were developed for going from recent air quality levels

(based on 2003 air quality) to just meeting alternative PM10-2.5 standards. Staff selected the daily

standards to be included in the risk assessment based on the preliminary staff recommendations

described in Chapter 5 of the draft 2005 Staff Paper (EPA, 2005) and consideration of public and

CASAC comments. Table 4-19 summarizes the sets of 98th and 99th percentile daily standards

that were included in the PM10-2.5 risk assessment. The estimated design values which were used

to determine the air quality adjustment to be used in simulating just meeting alternative PM10-2.5

standards are shown in Table 4-20. 

The estimated number of hospital admissions for ischemic heart disease associated with

short-term PM10-.2.5 exposures for alternative 98th and 99th percentile daily standards, respectively,

are given in Table 4-21 for Detroit. This table includes risk estimates which are based on the

cutpoint being policy-relevant background as well as three higher alternative cutpoints. Daily

PM10-.2.5 standards set at 80 (for 98th percentile form) and 100 or 80 (for 99th percentile form)

result in no reduction in risk in Detroit. The reason why no estimated risk reductions are

observed with these alternative standards is that the percent reduction of PM10-2.5 concentrations

at the composite monitor to just meet a standard is determined by comparing the alternative
4-72

Figure 4-11. Estimated annual percent (top panel) and cases per 100,000 general

population (bottom panel) of hospital admissions associated with short-term

exposure to PM10-2.5 above background for recent air quality (and 95 percent

confidence intervals). Source: Abt Associates (2005b)
4-73

Figure 4-12. Estimated annual percent (top panel) and cases per 100,000 general

population (bottom panel) of respiratory symptoms associated with shortterm exposure to PM10-2.5 above background for recent air quality (and 

95 percent confidence intervals). Source: Abt Associates (2005b)
4-74

Table 4-19. Alternative PM10-2.5 Standards Considered in the PM10-2.5

Risk Assessment*

Daily Standards Based on the 98th Percentile

Value

Daily Standards Based on the 99th Percentile

Value

80 100

65 80

50 60

30 35

25 30

*All standards are in μg/m3.

--------------------------------------------------------------------------------------------------------------------

Table 4-20. Estimated Design Values for 98th and 99th Percentile Daily PM10-2.5

Standards Based on 2001-2003 Air Quality Data*

Location 98th Percentile Daily 99th Percentile Daily

Detroit 70 77

St. Louis 33 47

Seattle 31 39

*The calculation of design values is explained in Schmidt (2005). All design values are in μg/m3

.
Table 4-21. Estimated Annual Hospital Admissions for Ischemic Heart Disease Associated with Short-Term 

 Exposure to PM10-2.5 When Alternative Standards Are Just Met, Assuming Various Cutpoint Levels* 

 Detroit, MI, 2003

 (Recent Air Quality Levels = 21.7 μg/m3 Annual Average; 105.9 μg/m3, 98th Percentile Daily Value)

Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=4.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

Recent PM10-2.5 air quality levels 654 569 489 426

(169 - 1083) (149 - 934) (129 - 794) (115 - 682)

0.0% 0.0% 0.0% 0.0%

80 μg/m3

 daily 98th percentile value 654 569 489 426

(169 - 1083) (149 - 934) (129 - 794) (115 - 682)

0.0% 0.0% 0.0% 0.0%

65 μg/m3

 daily 98th percentile value 600 508 425 360

(156 - 989) (134 - 829) (114 - 683) (99 - 567)

8.3% 10.7% 13.1% 15.5%

50 μg/m3

 daily 98th percentile value 443 334 248 183

(117 - 719) (90 - 532) (69 - 384) (54 - 271)

32.3% 41.3% 49.3% 57.0%

30 μg/m3

 daily 98th percentile value 242 125 65 44

(65 - 386) (36 - 190) (20 - 91) (15 - 57)

63.0% 78.0% 86.7% 89.7%

25 μg/m3

 daily 98th percentile value 193 81 39 25

(52 - 307) (24 - 120) (13 - 52) (9 - 30)

70.5% 85.8% 92.0% 94.1%

100 μg/m3

 daily 99th percentile value 654 569 489 426

(169 - 1083) (149 - 934) (129 - 794) (115 - 682)

0.0% 0.0% 0.0% 0.0%

80 μg/m3

 daily 99th percentile value 654 569 489 426

(169 - 1083) (149 - 934) (129 - 794) (115 - 682)

0.0% 0.0% 0.0% 0.0%

60 μg/m3

 daily 99th percentile value 491 387 301 233

(129 - 801) (104 - 621) (83 - 472) (67 - 353)

24.9% 32.0% 38.4% 45.3%

35 μg/m3

 daily 99th percentile value 262 144 79 53

(70 - 419) (41 - 221) (24 - 113) (18 - 68)

59.9% 74.7% 83.8% 87.6%

30 μg/m3

 daily 99th percentile value 218 103 51 34

(59 - 347) (30 - 154) (16 - 70) (12 - 43)

66.7% 81.9% 89.6% 92.0%

*This analysis was performed using Ito (2003). 

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

Recent PM10-2.5 Air Quality Levels and 

Alternative Daily Standards (μg/m3

)

Incidence Associated with PM10-2.5 Assuming Various Cutpoint Levels

(95% Confidence Interval)

Percent Reduction in Incidence from Recent PM10-2.5 Air Quality Levels 

**For these alternative cutpoints the slope of the concentration-response function has been modified based on a simple hockeystick model (see 

discussion in section 4.3.2.1).

 4-75
4-76

standard level with the design value for that location based on 2001-2003 air quality data. In

Detroit, the design value for the 98th percentile daily PM10-2.5 standards is 70 μg/m3

 whereas the

98th percentile daily value in 2003 is 105.9 μg/m3

. Because the design value is lower than

80 μg/m3, the highest 98th percentile daily PM10-2.5 standard considered in the assessment, zero

risk reductions were estimated to result from this standard, even though the 98th percentile daily

value at the composite monitor in 2003, 105.9 μg/m3, is well above the standard level. Similarly,

the design value for the 99th percentile daily PM10-2.5 standards is 77 μg/m3 for Detroit, whereas

the 99th percentile daily value at the composite monitor in Detroit in 2003 is substantially greater

than 100 μg/m3, the highest 99th percentile daily PM10-2.5 standard considered. Thus, zero risk

reductions were estimated to result from both 100 and 80 μg/m3

 standards. In general, estimated

risk reductions increase and the confidence intervals around the estimates widen as lower daily

standards are considered. 

As expected, the maximum reduction in risk, for the set of alternative standards included

in the analysis, is achieved with the 98th percentile 25 μg/m3 standard and 99th percentile

30 μg/m3 standard. The point estimate is that about a 4% reduction in hospital admissions for

ischemic reductions associated with just meeting daily 98th percentile PM10-2.5 standards of

80 μg/m3

 in Detroit, and 80, 65, and 50 μg/m3 in St. Louis or Seattle. Similarly, there are no risk

reductions associated with just meeting daily 99th percentile PM10-2.5 standards of 100 or 80 μg/m3

in Detroit, and 100, 80, or 60 μg/m3 in St. Louis or Seattle. 

4.5.3 Key Observations

Sections 4.5.1.1 and 4.5.1.2 presented the PM10-2.5 health risk estimates and sensitivity

analyses associated with recent PM10-2.5 air quality levels. Presented below are key observations

resulting from this part of the risk assessment:

• Various respiratory and cardiovascular cause-specific hospital admission point

estimates associated with short-term exposure to PM10-2.5 range from 1 to 7%,

depending on location and type of admission. Point estimates for lower

respiratory symptoms and cough were about 12 and 15% of total incidence for

recent air quality levels in a single urban area (St. Louis).

• Results of a sensitivity analysis examining the impact of assuming different

values for policy relevant background showed moderate changes in short-term

morbidity risk estimates ranging from + 16 to +50% depending on the health

endpoint and location considered.

Section 4.5.2 presented the base case PM10-2.5-related incidence associated with meeting

alternative PM2.5 standards and the percent reduction in incidence from recent air quality levels. 

Presented below are key observations resulting from this part of the risk assessment:
4-77

• For short-term exposure morbidity, there is a significant decrease in the remaining

estimated incidence associated with PM10-2.5 as one considers higher alternative

cutpoints for all of the standards that require reductions in recent PM10-2.5 air

quality levels. There also is a significant increase observed in the percent

reduction in PM-associated incidence upon just meeting these same alternative

standards with higher alternative cutpoints compared to recent PM10-2.5 air quality

levels. 

• Based on the point estimates, there are no risk reductions associated with just

meeting daily 98th percentile PM10-2.5 standards of 80 μg/m3

 in Detroit, and 80, 65,

and 50 μg/m3 in St. Louis or Seattle. Similarly, there are no risk reductions

associated with just meeting daily 99th percentile PM10-2.5 standards of 100 or 

80 μg/m3

 in Detroit, and 100, 80, or 60 μg/m3 in St. Louis or Seattle.
4-78

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Abt Associates Inc. (1997a). Abt Associates Memorandum to U.S. EPA. Subject: Revision of Mortality Incidence

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Environmental Protection Agency (2001). Particulate Matter NAAQS Risk Analysis Scoping Plan, Draft. Research

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Hopke, P. (2002). Letter from Dr. Phil Hopke, Chair, Clean Air Scientific Advisory Committee (CASAC) to

Honorable Christine Todd Whitman, Administrator, U.S. EPA. Final advisory review report by the

CASAC Particulate Matter Review Panel on the proposed particulate matter risk assessment. May 23,

2002. Available: http://www.epa.gov/sab/pdf/casacadv02002.pdf.

Langstaff, J. (2004). OAQPS Staff Memorandum to PM NAAQS Review Docket (OAR-2001-0017). Subject: A

Methodology for Incorporating Short-termVariable Background Concentrations in Risk Assessments. 

December 17, 2004. Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_td.html.

Langstaff, J. (2005). OAQPS Staff Memorandum to PM NAAQS Review Docket (OAR-2001-0017). Subject: 

Estimation of Policy-Relevant Background Concentrations of Particulate Matter. January 27, 2005. 

Available: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_td.html.

National Academy of Sciences (2002). Estimating the Public Health Benefits of Proposed Air Pollution Regulations. 

Washington, D.C.: The National Academy Press. Available: 

http://www.nap.edu/books/0309086094/html/.

Post, E.; Deck, L.; Larntz, K.; Hoaglin. D. (2001). An application of an empirical Bayes estimation technique to the

estimation of mortality related to short-term exposure to particulate matter. Risk Anal. 21(5): 837-842.

Schmidt, M.: Mintz, D.; Rao, V.; McCluney, L. (2005). U.S. EPA Memorandum to File. Subject: Draft Analyses

of 2001-2003 PM Data for the PM NAAQS Review. January 31, 2005. Available: 

http://www.epa.gov/oar/oaqps/pm25/docs.html.

 

Science Advisory Board (2004). Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s

Second Prospective Analysis - Benefits and Costs of the Clean Air Act, 1990-2000. Advisory by the

Health Effects Subcommittee of the Advisory Council for Clean Air Compliance Analysis. EPA SAB

Council - ADV-04-002. March. Available: http://www.epa.gov/science1/pdf/council_adv_04002.pdf.

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5. STAFF CONCLUSIONS AND RECOMMENDATIONS ON PRIMARY PM NAAQS

5.1 INTRODUCTION

This chapter presents staff conclusions and recommendations for the Administrator to

consider in deciding whether the existing primary PM standards should be revised and, if so,

what revised standards are appropriate. The existing suite of primary PM standards includes

annual and 24-hour PM2.5 standards, to protect public health from exposure to fine particles, and

annual and 24-hour PM10 standards, to protect public health from exposure to thoracic coarse

particles. Each of these standards is defined in terms of four basic elements: indicator,

averaging time, level and form. Staff conclusions and recommendations on these standards are

based on the assessment and integrative synthesis of information presented in the CD and on

staff analyses and evaluations presented in Chapters 2 through 4 herein.

In recommending a range of primary standard options for the Administrator to consider,

staff notes that the final decision is largely a public health policy judgment. A final decision

must draw upon scientific information and analyses about health effects and risks, as well as

judgments about how to deal with the range of uncertainties that are inherent in the scientific

evidence and analyses. The staff’s approach to informing these judgments, discussed more fully

below, is based on a recognition that the available health effects evidence generally 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. 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 primary standards that, in the

Administrator's judgment, are requisite to protect public health with an adequate margin of

safety. 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 primary standards be set

at a zero-risk level, but rather at a level that avoids unacceptable risks to public health.

5.2 APPROACH

As an initial matter, PM standards for fine particles and for thoracic coarse particles are

addressed separately, consistent with the decision made by EPA in the last review and with the

conclusion in the CD that fine and thoracic coarse particles should continue to be considered as

separate subclasses of PM pollution. As discussed in Chapter 3, section 3.2.3, this conclusion is

based in part on long-established information on the differences in sources, properties, and

atmospheric behavior between fine and coarse particles; and it is reinforced by new information

that advances our understanding of differences in human exposure relationships and dosimetric

patterns characteristic of these two subclasses of PM pollution, as well as the apparent

independence of health effects that have been associated with them in epidemiologic studies.
5-2

In general, in evaluating whether the current primary standards are adequate or whether

revisions are appropriate, and in developing recommendations on the elements of possible

alternative standards for consideration, staff's approach in this review builds upon and broadens

the general approach used by EPA in the last review. In setting PM2.5 standards in 1997, the

Agency mainly used an evidence-based approach that placed primary emphasis on epidemiologic

evidence from short-term exposure studies of fine particles, judged to be the strongest evidence

at that time, in reaching decisions to set an annual PM2.5 standard that was generally controlling,

and to set a 24-hour PM2.5 standard to provide supplemental protection. The risk assessment

conducted in the last review provided qualitative insights, but was judged to be too limited to

serve as a quantitative basis for decisions on the standards. In this review, the more extensive

and stronger body of evidence now available on health effects related to both short- and longterm exposure to PM2.5, together with the availability of much more extensive PM2.5 air quality

data, have facilitated a more comprehensive risk assessment for PM2.5. As a result, staff has used

a broader approach in this review of the PM2.5 standards that takes into account both evidencebased and quantitative risk-based considerations, placing greater emphasis on evidence from

long-term exposure studies and quantitative risk assessment results for fine particles than was

done in the last review. Staff has applied this approach to a more limited degree in reviewing the

PM10 standards, reflecting the far more limited nature of the health effects evidence and air

quality data available for thoracic coarse particles.

In reviewing the PM2.5 standards, for example, staff has taken into account evidencebased considerations primarily by assessing the epidemiologic evidence of associations with

health endpoints that the CD has judged to be likely causal based on an integrative synthesis of

the entire body of evidence. Less weight is given to evidence of associations that are judged to

be only suggestive of possible causal relationships, taking this information into account as part

of margin of safety considerations. In so doing, staff has placed greater weight on U.S. and

Canadian studies reporting statistically significant associations, providing relatively more precise

effects estimates, using relatively more reliable air quality data, and reporting associations that

are generally robust to alternative model specifications and the inclusion of potentially

confounding co-pollutants. By considering the ambient particle levels present during specific

studies, staff has reached conclusions as to the degree to which alternative standards could be

expected to protect against the observed health effects, while being mindful of the inherent

limitations and uncertainties in such evidence.

Staff has also taken into account quantitative risk-based considerations, drawn from the

results of the risk assessment conducted in several example urban areas (discussed in Chapter 4). 

More specifically, staff has considered estimates of the magnitude of PM-related risks associated

with current air quality levels, as well as the risk reductions likely to be associated with attaining

the current or alternative standards. In so doing, staff recognizes the considerable uncertainties

inherent in such risk estimates, and has taken such uncertainties into account by considering the
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sensitivity of the risk estimates to alternative assumptions likely to have substantial impact on

the estimates.

More specifically, in this review a series of questions frames staff's approach to reaching

conclusions and recommendations, based on the available evidence and information, as to

whether consideration should be given to retaining or revising the current primary PM standards. 

Staff's review of the adequacy of the current standards begins by considering whether the

currently available body of evidence assessed in the CD suggests that revision of any of the basic

elements of the standards would be appropriate. This evaluation of the adequacy of the current

standards involves addressing questions such as the following:

• To what extent does newly available information reinforce or call into question

evidence of associations with effects identified in the last review?

• To what extent does newly available information reinforce or call into question

any of the basic elements of the current standards?

• To what extent have important uncertainties identified in the last review been

reduced and have new uncertainties emerged?

To the extent that the evidence suggests that revision of the current standards would be

appropriate, staff then considers whether the currently available body of evidence supports

consideration of standards that are either more or less protective by addressing the following

questions:

• Is there evidence that associations, especially likely causal associations, extend to

air quality levels that are as low as or lower than had previously been observed,

and what are the important uncertainties associated with that evidence?

• Are health risks estimated to occur in areas that meet the current standards; are

they important from a public health perspective; and what are the important

uncertainties associated with the estimated risks?

To the extent that there is support for consideration of revised standards, staff then identifies

ranges of standards (in terms of indicators, averaging times, levels and forms) that would reflect

a range of alternative public health policy judgments, based on the currently available evidence,

as to the degree of protection that is requisite to protect public health with an adequate margin of

safety. In so doing, staff addresses the following questions:

• Does the evidence provide support for considering different PM indicators?

• Does the evidence provide support for considering different averaging times?

• What ranges of levels and forms of alternative standards are supported by the

evidence, and what are the uncertainties and limitations in that evidence?
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• To what extent do specific levels and forms of alternative standards reduce the

estimated risks attributable to PM, and what are the uncertainties in the estimated

risk reductions?

Based on the evidence, estimated risk reductions, and related uncertainties, staff makes

recommendations as to ranges of alternative standards for the Administrator's consideration in

reaching decisions as to whether to retain or revise the primary PM NAAQS.

Standards for fine particles are addressed in section 5.3 below, beginning with staff's

consideration of the adequacy of the current primary PM2.5 standards. Subsequent subsections

address each of the major elements that define specific PM standards: pollutant indicator,

averaging time, level and form. Staff has evaluated separately the protection that a suite of PM2.5

standards would likely provide against effects associated with long-term exposures (section

5.3.4) and those associated with short-term exposures (section 5.3.5). These separate evaluations

provide the basis for integrated recommendations on alternative suites of standards that would

protect against effects associated with both long- and short-term exposures, based on considering

how a suite of standards would operate together to protect public health. In a similar manner,

standards for thoracic coarse particles are addressed in section 5.4 below. This chapter

concludes with a summary of key uncertainties associated with establishing primary PM

standards and with related staff research recommendations in section 5.5.

5.3 FINE PARTICLE STANDARDS

5.3.1 Adequacy of Current PM2.5 Standards

In considering the adequacy of the current PM2.5 standards, staff has first considered the

extent to which newly available information reinforces or calls into question evidence of

associations with effects identified in the last review, as well as the extent to which important

uncertainties have been reduced or have resurfaced as being more important than previously

understood. In looking across the extensive epidemiologic evidence available in this review, the

CD addresses these questions by concluding that “the available findings demonstrate well that

human health outcomes are associated with ambient PM” (CD, p. 9-24) and, more specifically,

that there is now “strong epidemiological evidence” for PM2.5 linking short-term exposures with

cardiovascular and respiratory mortality and morbidity, and long-term exposures with

cardiovascular and lung cancer mortality and respiratory morbidity (CD, p. 9-46). This latter

conclusion reflects greater strength in the epidemiologic evidence specifically linking PM2.5 and

various health endpoints than was observed in the last review, when the 1996 CD concluded that

the epidemiologic evidence for PM-related effects was “fairly strong,” noting that the studies

“nonetheless provide ample reason to be concerned” about health effects attributable to PM at

levels below the then-current PM NAAQS (EPA, 1996, p. 13-92).
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As discussed in Chapter 3 (section 3.5) and the CD (section 9.2.2), the CD concludes that

the extensive body of epidemiologic evidence now available continues to support likely causal

associations between PM2.5 and the above health outcomes based on an assessment of the

strength of the evidence, including the strength and robustness of reported associations and the

consistency of the results. The CD recognizes that while the relative risk estimates are generally

small in magnitude, a number of new studies provide relatively precise estimates that are

generally positive and often statistically significant. Overall, the CD finds that the new evidence

substantiates that the associations are generally robust to confounding by co-pollutants, noting

that much progress has been made in sorting out contributions to observed health effects of PM

and its components relative to other co-pollutants. On the other hand, the CD notes that effect

estimates are generally more sensitive than previously recognized to different modeling

strategies to adjust for temporal trends and weather variables. While some studies showed little

sensitivity, different modeling strategies altered conclusions in other studies.

Although greater variability in effects estimates across study locations is seen in the

much larger set of studies now available, especially in the new multi-city studies, the CD finds

much consistency in the epidemiologic evidence, particularly in studies with the most precision. 

There also are persuasive reasons why variation in associations in different locations could be

expected. Further, the CD concludes that new source apportionment studies and “found

experiments,” showing improvements in community health resulting from reductions in PM and

other air pollutants, lend additional support to the results of other studies that focused

specifically on PM2.5.

Looking more broadly to integrate epidemiologic evidence with that from exposurerelated, dosimetric and toxicologic studies, the CD (section 9.2.3) considered the coherence of

the evidence and the extent to which the new evidence provides insights into mechanisms by

which PM, especially fine particles, may be affecting human health. Progress made in gaining

insights into mechanisms lends support to the biologic plausibility of results observed in

epidemiologic studies. For cardiovascular effects, the CD finds that the convergence of

important new epidemiologic and toxicologic evidence builds support for the plausibility of

associations especially between fine particles and physiological endpoints indicative of increased

risk of cardiovascular disease and changes in cardiac rhythm. This finding is supported by new

cardiovascular effects research focused on fine particles that has notably advanced our

understanding of potential mechanisms by which PM2.5 exposure, especially in susceptible

individuals, could result in changes in cardiac function or blood parameters that are risk factors

for cardiovascular disease. For respiratory effects, the CD finds that toxicologic studies have

provided evidence that supports plausible biologic pathways for fine particles, including

inflammatory responses, increased airway responsiveness, or altered responses to infectious

agents. Further, the CD finds coherence across a broad range of cardiovascular and respiratory

health outcomes from epidemiologic and toxicologic studies done in the same location,
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particularly noting, for example, the series of studies conducted in or evaluating ambient PM

from Boston and the Utah Valley. The CD also finds that toxicologic evidence examining

combustion-related particles supports the plausibility of the observed relationship between fine

particles and lung cancer mortality. With regard to PM-related infant mortality and

developmental effects, the CD finds this to be an emerging area of concern, but notes that current

information is still very limited in support of the plausibility of potential ambient PM

relationships.

 Based on the above considerations and findings from the CD, staff concludes that the

newly available information generally reinforces the associations between PM2.5 and mortality

and morbidity effects observed in the last review. Staff recognizes that important uncertainties

and research questions remain, notably including questions regarding modeling strategies to

adjust for temporal trends and weather variables in time-series epidemiologic studies. 

Nonetheless, staff notes that progress 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, alone and in combination with other pollutants, is causally

linked with cardiovascular, respiratory, and lung cancer associations observed in epidemiologic

studies. Thus, staff finds clear support in the available evidence, as assessed in the CD, for fine

particle standards that are at least as protective as the current PM2.5 standards.

Having reached this initial conclusion, staff also has addressed the question of whether

the available evidence supports consideration of standards that are more protective than the

current PM2.5 standards. In so doing, staff has considered first whether there is evidence that

health effects associations with short- and long-term exposures to fine particles extend to lower

air quality levels than had previously been observed, or to levels below the current standards. In

addressing this question, staff first recognizes that there are likely biologic threshold levels in

individuals for specific health responses. Staff notes, however, that the available epidemiologic

evidence neither supports nor refutes the existence of thresholds at the population level for the

effects of PM2.5 on mortality across the range of concentrations in the studies, for either longterm or short-term PM2.5 exposures, as discussed in Chapter 3 (section 3.6.6) and the CD (section

9.2.2.5). Further, the CD notes that in the multi-city studies and most single-city studies,

statistical tests comparing linear and various nonlinear or threshold models have not shown

statistically significant distinctions between them (CD, p. 9-44). Even in those few studies with

suggestive evidence for thresholds, the potential thresholds are at fairly low concentrations (CD,

p. 9-45). While acknowledging that for some health endpoints, such as total nonaccidental

mortality, it is likely to be extremely difficult to detect thresholds, the CD concludes that

“epidemiologic studies suggest no evidence for clear thresholds in PM-mortality relationships

within the range of ambient PM concentrations observed in these studies.” (CD, p. 9-48).
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5.3.1.1 Evidence-based Considerations

In considering the available epidemiologic evidence (summarized in Chapter 3, section

3.3 and Appendices 3A and 3B), staff has focused on specific epidemiologic studies that show

statistically significant associations between PM2.5 and health effects for which the CD judges

associations with PM2.5 to be likely causal. Many more U.S. and Canadian studies are now

available that provide evidence of associations between PM2.5 and serious health effects in areas

with air quality at and above the level of the current annual PM2.5 standard (15 μg/m3), which

was set to provide protection against health effects related to both short- and long-term

exposures to fine particles. Notably, a few of the newly available short-term exposure mortality

studies provide evidence of statistically significant associations with PM2.5 in areas with longterm average air quality below the level of the current annual PM2.5 standard (summarized in

Appendix 3A). In considering these studies, staff has focused on those studies that include

adequate gravimetric PM2.5 mass measurements, and where the associations are generally robust

to alternative model specification and to the inclusion of potentially confounding co-pollutants. 

Three such studies conducted in Phoenix (Mar et al., 1999, 2003), Santa Clara County, CA

(Fairley, 1999, 2003) and eight Canadian cities (Burnett et al., 2000; Burnett and Goldberg,

2003) report statistically significant associations between short-term PM2.5 exposure and total

and cardiovascular mortality in areas in which long-term average PM2.5 concentrations ranged

between 13 and 14 μg/m3. These studies were reanalyzed to address questions about the use of

GAM with default convergence criteria, and the study results from Phoenix and Santa Clara

County were little changed in alternative models (Mar et al., 2003; Fairley, 2003), although

Burnett and Goldberg (2003) reported that their results were sensitive to using different temporal

smoothing methods.

Beyond these mortality studies, other studies reported statistically significant associations

between short-term PM2.5 exposure and morbidity in such areas. Three studies of emergency

department visits were conducted in areas where the mean PM2.5 concentrations were

approximately 12 μg/m3

 or below, although these studies either had not been reanalyzed to

address the default convergence criteria problem with GAM, did not assess the potential for

confounding by co-pollutants, were not robust to the inclusion of co-pollutants, or were done

only during a single season. Another new study reported statistically significant associations

with incidence of myocardial infarction where the mean PM2.5 concentration was just above

12 μg/m3

; however, the CD urges caution in interpreting the results of the new body of evidence

related to such cardiovascular effects (CD, p. 8-166). Thus, these studies provide no clear

evidence of statistically significant associations with PM2.5 at such low concentrations.

New evidence is also available from U.S. and Canadian studies of long-term exposure to

fine particles (summarized in Appendix 3B). In evaluating this evidence (CD, section 9.2.3), the

CD notes that new studies have built upon studies available in the last review and that these

studies have confirmed and strengthened the evidence of associations for both mortality and
5-8

respiratory morbidity. For mortality, the CD places greatest weight on the reanalyses and

extensions of the Six Cities and the ACS studies, finding that these studies provide “strong

evidence” for associations with fine particles (CD, p. 9-34), notwithstanding the lack of

consistent results in other long-term exposure studies. For morbidity, the CD finds that new

studies of a cohort of children in Southern California have built upon earlier limited evidence to

provide “fairly strong” evidence that long-term exposure to fine particles is associated with

development of chronic respiratory disease and reduced lung function growth (CD, p. 9-34).

As discussed in the CD and in Chapter 3 above, mortality studies of the Six Cities and

ACS cohorts available in the last review had aggregate long-term mean PM2.5 concentrations of

18 μg/m3

 (ranging from approximately 11 to 30 μg/m3 across cities) and 21 μg/m3 (ranging from

approximately 9 to 34 μg/m3 across cities), respectively. Reanalyses of data from these cohorts

continued to report significant associations with PM2.5, using essentially the same air quality

distributions. The extended analyses using the ACS cohort also continued to report statistically

significant associations with PM2.5 with the inclusion of more recent PM2.5 air quality data, with

an average range across the old and new time periods from about 7.5 to 30 μg/m3

 (from figure 1,

Pope et al., 2002) and a long-term mean of approximately 17.7 μg/m3 (Pope et al., 2002). As

with the earlier cohort studies, no evidence of a threshold was observed in the relationships with

total, cardiovascular, and lung cancer mortality reported in this extended study. In the morbidity

studies of the Southern California children’s cohort, the means of 2-week average PM2.5

concentrations ranged from approximately 7 to 32 μg/m3, with an across-city average of

approximately 15 μg/m3

 (Peters et al., 1999). Staff notes that in figures depicting relationships

between lung function growth and average PM concentration, no apparent threshold is evident in

this study (Gauderman et al., 2000, 2002).

Beyond the epidemiologic studies using PM2.5 as an indicator of fine particles, a large

body of newly available evidence from studies that used PM10, as well as other indicators or

components of fine particles (e.g., sulfates, combustion-related components), provides additional

support for the conclusions reached in the last review as to the likely causal role of ambient PM,

and the likely importance of fine particles in contributing to observed health effects. Such

studies notably include new multi-city studies, intervention studies (that relate reductions in

ambient PM to observed improvements in respiratory or cardiovascular health), and sourceoriented studies (e.g., suggesting associations with combustion- and vehicle-related sources of

fine particles). Further, the CD concludes that new epidemiologic studies of ambient PM

associations with potential PM-related infant mortality and/or developmental effects are very

limited. However, if these findings were further substantiated by future research, estimates of

the extent of life shortening due to PM-related premature mortality would likely significantly

increase (CD, p. 9-94). The CD also notes that new epidemiologic studies of asthma-related

increased physicians visits and symptoms, as well as new studies of cardiac-related risk factors,
5-9

suggest likely much larger public health impacts due to ambient fine particles than just those

indexed by the mortality and morbidity effects considered in the last review (CD, p. 9-94).

Staff recognizes, however, that important limitations and uncertainties associated with

this expanded body of evidence for PM2.5 and other indicators or components of fine particles, as

discussed in Chapter 3 herein and section 9.2.2 of the CD, need to be carefully considered in

determining the weight to be placed on the studies available in this review. For example, the CD

notes that while PM-effects associations continue to be observed across most new studies, the

newer findings do not fully resolve the extent to which the associations are properly attributed to

PM acting alone or in combination with other gaseous co-pollutants, or to the gaseous copollutants themselves. The CD notes that available statistical methods for assessing potential

confounding by gaseous co-pollutants may not yet be fully adequate, although the various

approaches that have now been used to evaluate this issue tend to substantiate that associations

for various PM indicators with mortality and morbidity are robust to confounding by copollutants (CD, p. 9-37).

Another issue of particular importance is the sensitivity of various statistical models to

the approach used to address potential confounding by weather- and time-related variables in

time-series epidemiological studies. As discussed in section 3.5.3 herein and in section 9.2.2 of

the CD, this issue resurfaced in the course of reanalyses of a number of the newer studies that

were being conducted to address a more narrow issue related to problems associated with the use

of commonly used statistical software. These reanalyses suggest that weather continues to be a

potential confounder of concern and highlight that no one model is likely to be most appropriate

in all cases. The HEI Review Panel, in reviewing these reanalyses, concluded that this

awareness introduces a degree of uncertainty in evaluating the findings from time-series

epidemiologic studies that had heretofore not been widely appreciated.

In looking beyond PM mass indicators, a number of newly available studies highlight the

issue of the extent to which observed health effects may be associated with various specific

chemical components within the mix of fine particles. The potential for various fine particle

components to have differing relative toxicities with regard to the various health endpoints being

considered adds complexity to the interpretation of study results. The CD recognizes that more

research is needed to address uncertainties about the extent to which various components may be

relatively more or less toxic than other components, or than undifferentiated PM2.5 mass, across

the range of health endpoints studied.

While the limitations and uncertainties in the available evidence suggest caution in

interpreting the epidemiologic studies at the lower levels of air quality observed in the studies,

staff concludes that the evidence now available provides strong support for considering fine

particle standards that would provide increased protection beyond that afforded by the current

PM2.5 standards. More protective standards would reflect the generally stronger and broader

body of evidence of associations with mortality and morbidity now available in this review, both
1 As discussed in Chapter 4, the reported linear or log-linear functions were applied down to 7.5 μg/m3

 in

estimating risk associated with long-term exposure (i.e., the lowest measured level in the extended ACS study), and

down to the estimated policy-relevant background level in estimating risk associated with short-term exposure (i.e.,

3.5 μg/m3

 for eastern urban areas and 2.5 μg/m3 for western urban areas).

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at lower levels of air quality and at levels below the current standards, and with more

understanding of possible underlying mechanisms.

5.3.1.2 Risk-based Considerations

In addition to this evidence-based evaluation, staff has also considered the extent to

which health risks estimated to occur upon attainment of the current PM2.5 standards may be

judged to be important from a public health perspective, taking into account key uncertainties

associated with the estimated risks. In so doing, staff first notes that the risk assessment

discussed in Chapter 4 addresses a number of key uncertainties through various base case

analyses, as well as through several sensitivity analyses. Most importantly, a series of base case

analyses were conducted to characterize the uncertainty associated with the form of the

concentration-response functions drawn from the studies used in the assessment, which had by

far the greatest impact on estimated risks. Other uncertainties, including the use of singleversus multi-pollutant models, single- versus multi-city models, use of a distributed lag model,

alternative assumptions about the relevant air quality for long-term exposure mortality, and

alternative constant or varying background levels, have a more moderate and often variable

impact on the risk estimates in some or all of the cities.

In considering the health risks estimated to occur upon attainment of the current PM2.5

standards, staff focused in particular on base case risk estimates, while recognizing that the

confidence ranges in the selected base case estimates do not reflect all the identified

uncertainties. These risks were estimated using not only the linear or log-linear functions

reported in the studies,1 but also using a series of alternative modified linear functions as

surrogates for assumed non-linear functions that would reflect the possibility that thresholds may

exist in the reported associations within the range of air quality observed in the studies. The

approach used to develop the alternative functions, discussed more fully in Chapter 4 (section

4.3.2.1), incorporates a modified linear slope with an imposed cutpoint (i.e., an assumed

threshold) that is intended to reflect an inflection point in a typical non-linear, "hockey-stick"

shaped function, below which there is little or no population response. As discussed in Chapter

3 (section 3.6.6), staff recognizes that while there are likely biological thresholds in individuals

for specific health responses, the available epidemiologic studies do not support or refute the

existence of thresholds at the population level for either long-term or short-term PM exposures

within the range of air quality observed in the studies (CD, p. 9-44). Thus, staff has concluded

that it is appropriate to consider health risks estimated not only with the reported linear or log-
2 These tables include risk estimates drawn from Tables 4-9, 4-10, 4-12, and 4-13 in Chapter 4.

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linear concentration-response functions, but also with modified functions that incorporate

alternative assumed cutpoints as surrogates for potential population thresholds.

Tables 5-1(a) and (b) summarize the estimated PM2.5-related annual incidence and

incidence rate (in terms of incidence per 100,000 general population) of total mortality

associated with long-term and short-term exposures, respectively, assuming various cutpoint

levels in the example urban areas included in the risk assessment.2 In first looking at the annual

incidence of PM2.5-related mortality estimated to occur upon attainment of the current PM2.5

standards in the five study areas that do not meet the current standards based on 2001-2003 air

quality data (Detroit, Los Angeles, Philadelphia, Pittsburgh, and St. Louis), staff notes that there

is a fairly wide range of estimated incidence across the areas for both long- and short-term

exposures. Such variation would be expected considering, for example, differences in total

population, demographics, baseline mortality rates, exposure considerations (e.g., degree of air

conditioning use), presence of co-pollutants and other environmental stressors, and exposure

measurement error across urban areas. The somewhat greater variation in the estimated

incidence associated with short-term exposure than with long-term exposure would also be

expected, since the assessment uses the same long-term exposure concentration-response

function in all areas, whereas the assessment used different short-term exposure functions (for

different mortality endpoints in some cases) from studies conducted in each area. Staff also

recognizes that there are uncertainties associated with the procedure used to simulate air quality

that would just attain the current standards and in the degree to which various components of the

fine particle mix would likely be reduced in similar proportion to the simulated reduction in

PM2.5 as a whole (as discussed in Chapter 2, section 2.5.1).

In the five study areas that do not meet the current standards, staff observes for long-term

exposure that point estimates of annual incidence of total PM2.5-related mortality associated with

just meeting the current PM2.5 standards, based on the lowest cutpoint of 7.5 μg/m3

, range from

approximately 400 to 600 in four areas (from roughly 25 to 35 deaths per 100,000 general

population in these areas) to over 1500 annual deaths in Los Angeles (roughly 16 deaths per

100,000 general population) associated with long-term exposure. These estimated incidences

associated with long-term exposure represent 2.6 to 3.2 percent of total mortality incidence due

to all causes. In the same five areas, the annual incidence associated with short-term exposure,

based on a cutpoint equal to policy-relevant background, ranges from less than 20 % to over

50% of the estimated incidence associated with long-term exposure. In some areas, the 95%

confidence ranges associated with the estimates of total annual mortality incidence related to

short-term exposure (but not long-term exposure) extend to below zero, reflecting appreciably

more uncertainty in estimates based on positive but not statistically significant associations. 
Table 5-1(a) Estimated PM2.5-related annual total mortality associated with long-term

exposure when current PM2.5 standards are met*

Annual Incidence of All-Cause Mortality

and 95% CI

(deaths/yr)

Annual Incidence Rate of All-Cause Mortality

and 95% CI

(deaths/yr/100,000 general population)

Cutpoints Cutpoints

7.5 μg/m3 10 μg/m3 12 μg/m3 7.5 μg/m3 10 μg/m3 12 μg/m3

Risks associated with just meeting current PM2.5 standards

Detroit 520

180- 910

280

100 - 490

40

10 - 70

25

9 - 44

14

5 - 24

2

1 - 3

Los Angeles 1,510

530 - 2,590

820

290 - 1420

140

50-o 240

16

6 - 27

9

3 - 15

1

1 - 2

Philadelphia 540

190 - 940

340

120-o 600

140

50 - 240

35

12 - 62

22

8 - 39

9

3 - 16

Pittsburgh 400

140-o 700

220

80-o 370

30

10 - 40

31

11 - 55

17

6 - 29

2

1 - 3

St. Louis 600

210 - 1,050

310

110 - 550

20

10 - 40

24

8 - 42

12

4 - 22

1

0 - 2

Risks associated with "as is" air quality (in areas that meet current PM2.5 standards)

Boston 590

200 - 1050

310

110 - 550

20

10 - 40

21

7 - 38

11

4 - 20

1

0 - 1

Phoenix 350

120 - 620

80

30 - 140

0 11

4 - 20

2

1 - 3

0

San Jose 170

60 - 310

60

20-o 100

0 10

4 - 18

3

1 - 6

0

Seattle 50

20 - 90

00 3

1 - 5

0 0

* These estimates are based on using the maximum monitor in an area to calculate the percent rollback needed to

just attain the current PM2.5 annual standard, and applying that percent rollback to the composite monitor in the area,

as described in Chapter 4, section 4.2.3. Estimates of annual mortality incidence based on using a spatially averaged

concentration to calculate the percent rollback needed to just attain the current standard, where this is allowed,

would be higher than the estimates shown here.

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Table 5-1(b) Estimated PM2.5-related annual mortality associated with short-term

exposure when current PM2.5 standards are met*

Annual Incidence of Non-Accidental Mortality

and 95% CI

(except as noted)

(deaths/yr)

Annual Incidence Rate of Non-Accidental

Mortality

and 95% CI

(except as noted)

(deaths/yr/100,000 general population)

Cutpoints Cutpoints

Policyrelevant

Background**

10 μg/m3 15 μg/m3 20 μg/m3

Policyrelevant

Background**

10 μg/m3 15 μg/m3 20 μg/m3

Risks associated with just meeting current PM2.5 standards

Detroit 120

-120 - 360

50

-60 - 160

30

-30 - 80

10

-10 to 40

6

-6 - 17

3

-3 - 8

1

-1 - 4

1

-1 - 2

Los Angeles 290

-40 - 610

120

-10 - 240

60

-10 - 120

30

-4 to 60

3

0 - 6

1

0 - 3

1

0 - 1

0

0 - 1

Philadelphia

cardiovascular mortality

370

180 - 560

190

90 - 290

110

50 - 160

60

30 to907

24

12 - 37

12

6 - 19

7

3 - 11

4

2 - 6

Pittsburgh

over age 74

50

-110 - 200

20

-50 - 90

10

-20 - 40

5

-10 to 20

4

-8 - 16

2

-4 - 7

1

-2 - 3

0

-1 - 1

St. Louis 190

70 - 310

80

30 - 120

30

10 - 50

9

3 to 14

8

3 - 12

3

1 - 5

1

0 - 2

0

0 - 1

Risks associated with "as is" air quality (in areas that meet current PM2.5 standards)

Boston 390

270 - 510

170

120 - 230

80

60 - 110

40

30 - 50

14

9 - 18

6

4 - 8

3

2 - 4

1

1 - 2

Phoenix

 cardiovascular mortality

over age 64

320

 100 - 540

90

30 - 140

60

20 - 90

40

10 - 60

11

3 - 17

3

1 - 5

2

1 - 3

1

0 - 2

San Jose 220

50 - 390

80

20 - 140

40

10 - 80

30

10 - 50

13

3 - 23

5

1 - 8

3

1 - 5

2

0 - 3

* These estimates are based on using the maximum monitor in an area to calculate the percent rollback needed to

just attain the current PM2.5 annual standard, and applying that percent rollback to the composite monitor in the area,

as described in Chapter 4, section 4.2.3. Estimates of annual mortality incidence based on using a spatially averaged

concentration to calculate the percent rollback needed to just attain the current standard, where this is allowed,

would be higher than the estimates shown here.

** Estimated policy-relevant background levels are 3.5 μg/m3

 for eastern urban areas and 2.5 μg/m3 for western

urban areas.

5-13
5-14

In the other four areas that meet the current standards based on recent air quality data (Boston,

Phoenix, San Jose, and Seattle), point estimates of annual incidence of total PM2.5-related

mortality associated with long-term exposure range from about 50 deaths in Seattle (roughly 3

deaths per 100,000 general population) to almost 600 deaths in Boston (roughly 21 deaths per

100,000 general population). Estimated incidence associated with short-term exposure in these

four areas generally falls within the range of the estimates associated with long-term exposure. 

In considering the estimated incidences associated with long-term exposure based on

assumed cutpoint of 10 μg/m3

, staff observes that these estimates are roughly about half as large

as the estimates based on a cutpoint of 7.5 μg/m3. Under this assumption, point estimates of

annual incidence of total PM2.5-related mortality associated with just meeting the current PM2.5

standards range from about 200 to over 300 in four of the areas that do not meet the current

standards (from roughly 12 to 22 deaths per 100,000 general population in these areas) to over

800 annual deaths in Los Angeles (roughly 9 deaths per 100,000 general population) associated

with long-term exposure. In considering an assumed cutpoint as high as 12 μg/m3

, point

estimates associated with long-term exposure in these five areas are roughly 5 to 20% of the

estimates based on the lowest cutpoint. A similar pattern is seen when considering the impact of

alternative assumed cutpoints in the range of 10 to 20 μg/m3

 on risks associated with short-term

exposure.

5.3.1.3 Summary

In considering these estimates of PM2.5-related mortality upon meeting the current

standards in a number of example urban areas, together with the uncertainties in these estimates,

staff concludes that they are indicative of risks that can reasonably be judged to be important

from a public health perspective and that they provide support for consideration of standards that

would provide increased protection beyond that afforded by the current PM2.5 standards. In the

absence of evidence of clear thresholds, staff believes it is appropriate to consider all the

estimates associated with the range of assumed cutpoints used in the risk assessment. Staff

believes that a relatively more precautionary approach to interpreting this evidence would give

more weight to the estimates based on the lowest cutpoints considered. Staff also takes note of

the view expressed by the CASAC PM Panel which "favored the primary use of an assumed

threshold of 10 μg/m3

." (Henderson, 2005). Regardless of the relative weight placed on the

estimates associated with either an assumed cutpoint of 10 μg/m3 or the lowest cutpoints

considered, the risk assessment indicates the likelihood that thousands of premature deaths per

year would occur in urban areas across the U.S. even upon attainment of the current PM2.5

standards. Beyond the estimated incidences of mortality discussed above, staff also recognizes

that similarly substantial numbers of incidences of hospital admissions, emergency room visits,

aggravation of asthma and other respiratory symptoms, and increased cardiac-related risk are

also likely in many urban areas, based on risk assessment results presented in Chapter 4 and on

the discussion related to the pyramid of effects drawn from section 9.2.5 of the CD. Staff also
5-15

believes it is important to recognize how highly dependent any specific risk estimates are on the

shape of the underlying concentration-response functions. In so doing, staff nonetheless

reiterates that based on even the highest assumed cutpoints used in the risk assessment, estimated

mortality risks are not completely eliminated when current PM2.5 standards are met in a number

of example urban areas, including all such areas that do not meet the standards based on recent

air quality data.

Staff also well recognizes that as the body of available evidence has expanded, it has

added greatly both to our knowledge of PM-related effects, as well as to the complexity inherent

in interpreting the evidence in a policy-relevant context as a basis for setting appropriate

standards. In considering available evidence, risk estimates, and related limitations and

uncertainties, staff concludes 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 PM2.5 standards to provide increased public health

protection. Staff conclusions and recommendations for indicators, averaging times, and levels

and forms of alternative, more protective primary standards for fine particles are discussed in the

following sections.

5.3.2 Indicators

In 1997, EPA established PM2.5 as the indicator for fine particles. In reaching this

decision, the Agency first considered whether the indicator should be based on the mass of a

size-differentiated sample of fine particles or on one or more components within the mix of fine

particles. Secondly, in establishing a size-based indicator, a size cut needed to be selected that

would appropriately distinguish fine particles from particles in the coarse mode.

In addressing the first question in the last review, EPA determined that it was more

appropriate to control fine particles as a group, as opposed to singling out any particular

component or class of fine particles. Community health studies had found significant

associations between various indicators of fine particles (including PM2.5 or PM10 in areas

dominated by fine particles) and health effects in areas with significant mass contributions of

differing components or sources of fine particles, including sulfates, wood smoke, nitrates,

secondary organic compounds and acid sulfate aerosols. In addition, a number of animal

toxicologic and controlled human exposure studies had reported health effects associations with

high concentrations of numerous fine particle components (e.g., sulfates, nitrates, transition

metals, organic compounds), although such associations were not consistently observed. It also

was not possible to rule out any component within the mix of fine particles as not contributing to

the fine particle effects found in epidemiologic studies. For these reasons, EPA concluded that

total mass of fine particles was the most appropriate indicator for fine particle standards rather

than an indicator based on PM composition (62 FR 38667, July 18, 1997).
3 In reaching this decision, EPA indicated that it might be appropriate to address undue intrusion of coarse

mode particles resulting in violations of PM2.5 standards in the context of policies established to implement such

standards (62 FR 38668).

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Having selected a size-based indicator for fine particles, the Agency then based its

selection of a specific size cut on a number of considerations. In focusing on a size cut within

the size range of 1 to 3 :m (i.e., the intermodal range between fine and coarse mode particles),

EPA recognized that the choice of any specific sampling size cut within this range was largely a

policy judgment. In making this judgment, the Agency noted that the available epidemiologic

studies of fine particles were based largely on PM2.5; only very limited use of PM1 monitors had

been made. While it was recognized that using PM1 as an indicator of fine particles would

exclude the tail of the coarse mode in some locations, in other locations it would miss a portion

of the fine PM, especially under high humidity conditions, which would result in falsely low fine

PM measurements on days with some of the highest fine PM concentrations. The selection of a

2.5 μm size cut reflected the regulatory importance that was placed on defining an indicator for

fine particle standards that would more completely capture fine particles under all conditions

likely to be encountered across the U.S., especially when fine particle concentrations are likely

to be high, while recognizing that some small coarse particles would also be captured by PM2.5

monitoring.3 Thus, EPA’s selection of 2.5 μm as the size cut for the fine particle indicator was

based on considerations of consistency with the epidemiologic studies, the regulatory importance

of more completely capturing fine particles under all conditions, and the potential for limited

intrusion of coarse particles in some areas; it also took into account the general availability of

monitoring technology (62 FR 38668).

In this current review, the same considerations continue to apply for selection of an

appropriate indicator for fine particles. As an initial matter, the available epidemiologic studies

linking mortality and morbidity effects with short- and long-term exposures to fine particles

continue to be largely indexed by PM2.5. Some epidemiologic studies also have continued to

implicate various PM components (e.g., sulfates, nitrates, carbon, organic compounds, and

metals) as being associated with adverse effects; effects have been reported with a broad range

of PM components, as summarized in Table 9-3 of the CD (p. 9-31). Animal toxicologic and

controlled human exposure studies, evaluated in Chapter 7 of the CD, have continued to link a

variety of PM components or particle types (e.g., sulfates or acid aerosols, metals, organic

constituents, bioaerosols, diesel particles) with health effects, though often at high

concentrations (CD section 7.10.2). In addition, some recent studies have suggested that the

ultrafine subset of fine particles may also be associated with adverse effects (CD, pp. 8-67 and 8-

68, 8-199).

Staff recognizes that, for a given health response, some PM components are likely to be

more closely linked with that response than others (CD, p. 9-30). That different PM constituents
5-17

may have differing biological responses is an important source of uncertainty in interpreting

epidemiologic evidence. For specific effects there may be stronger correlation with individual

PM components than with particle mass. For example, in some toxicologic studies of

cardiovascular effects (such as changes in heart rate, electrocardiogram measures, or increases in

arrhythmia), PM exposures of equal mass did not produce the same effects, indicating that PM

composition was important (CD, p. 7-30). In addition, section 9.2.3.1.3 of the CD indicates that

particles, or particle-bound water, can act as carriers to deliver other toxic agents into the

respiratory tract, suggesting that exposure to particles may elicit effects that are linked with a

mixture of components more than with any individual PM component.

Thus, epidemiologic and toxicologic studies summarized above and discussed in the CD

have provided evidence for effects associated with various fine particle components or sizedifferentiated subsets of fine particles. The CD concludes: “These studies suggest that many

different chemical components of fine particles and a variety of different types of source

categories are all associated with, and probably contribute to, mortality, either independently or

in combinations” (CD, p. 9-31). Conversely, the CD provides no basis to conclude that any

individual fine particle component cannot be associated with adverse health effects. There is not

sufficient evidence that would lead toward the selection of one or more PM components as being

primarily responsible for effects associated with fine particles, nor is there any component that

can be eliminated from consideration. Staff continues to recognize the importance of an

indicator that not only captures all of the most harmful components of fine PM (i.e., an effective

indicator), but also places greater emphasis for control on those constituents or fractions,

including sulfates, transition metals, and organics that have been associated with health effects in

epidemiologic and/or toxicologic studies, are most likely to result in the largest risk reduction

(i.e., an efficient indicator). Taking into account the above considerations, staff concludes that it

remains appropriate to control fine particles as a group; i.e., that total mass of fine particles is the

most appropriate indicator for fine particle standards.

With regard to an appropriate size cut for a size-based indicator of total fine particle

mass, the CD most generally concludes that advances in our understanding of the characteristics

of fine particles continue to support the use of particle size as an appropriate basis for

distinguishing between these subclasses, and that a nominal size cut of 2.5 :m remains

appropriate (CD, p. 9-22). This conclusion follows from a recognition that within the intermodal

range of 1 to 3 :m there is no unambiguous definition of an appropriate size cut for the

separation of the overlapping fine and coarse particle modes (CD, p. 9-8). Within this range,

staff considered size cuts of both 1 :m and 2.5 :m. Consideration of these two size cuts took

into account that there is generally very little mass in this intermodal range, although in some

circumstances (e.g., windy, dusty areas) the coarse mode can extend down to and below 1 :m,

whereas in other circumstances (e.g., high humidity conditions, usually associated with very high

fine particle concentrations) the fine mode can extend up to and above 2.5 :m. The same
4 Changes to the PM2.5 FRM being considered by staff are discussed in Hanley (2005).

5 This work is being done in consultation with the CASAC Subcommittee on Ambient Air Monitoring and

Methods (AAMM). 

5-18

considerations that led to the selection of a 2.5 :m size cut in the last review – that the

epidemiologic evidence was largely based on PM2.5 and that it was more important from a

regulatory perspective to more completely capture fine particles under all conditions likely to be

encountered across the U.S. (especially when fine particle concentrations are likely to be high)

than to avoid some coarse-mode intrusion into the fine fraction in some areas – lead to the same

conclusion in this review. In addition, section 9.2.1.2.3. of the CD discusses the potential health

significance of particles as carriers of water, oxidative compounds, and other components into

the respiratory system. This consideration adds to the importance of ensuring that larger

accumulation-mode particles are included in the fine particle size cut. Therefore, as observed

previously in section 3.1.2, the scientific evidence leads the CD to conclude that 2.5 μm remains

an appropriate upper size cut for a fine particle mass indicator.

Consistent with that conclusion, staff recommends that PM2.5 be retained as the indicator

for fine particles. Staff further concludes that currently available studies do not provide a

sufficient basis for supplementing mass-based fine particle standards with standards for any

specific fine particle component or subset of fine particles, or for eliminating any individual

component or subset of components from fine particle mass standards. 

Further, staff notes that since the last review an extensive PM2.5 monitoring network has

been deployed and operated in cooperative efforts with State, local and Tribal agencies and with

instrument manufacturers. At the same time, EPA has been working on the development of

strategies and programs to implement the 1997 PM2.5 standards, based on the federal reference

method (FRM) sampler for PM2.5. The new monitoring network has provided substantial new air

quality information, in terms of PM2.5, that has been and is being used in ongoing PM research

and air quality analyses that inform this review. EPA also has conducted studies to evaluate

options for improvements to the FRM. As a result of continuing evaluation of the monitoring

network, staff is considering changes to the PM2.5 FRM to improve performance and minimize

the burden on agencies conducting the monitoring.4

 Staff is also considering the addition of

federal equivalent method (FEM) designation criteria for continuous fine particle monitors.5

Continuous monitoring is advantageous in providing additional data for many purposes,

including compliance monitoring, health studies, and air quality forecasting, and it can also ease

the burden of data collection for regulatory agencies.
5-19

5.3.3 Averaging Times

In the last review, EPA established two PM2.5 standards, based on annual and 24-hour

averaging times (62 FR at 38,668-70). This decision was based in part on evidence of health

effects related to both short-term (from less than 1 day to up to several days) and long-term

(from a year to several years) measures of PM. EPA noted that the large majority of community

epidemiologic studies reported associations based on 24-hour averaging times or on multiple-day

averages. Further, EPA noted that a 24-hour standard could also effectively protect against

episodes lasting several days, as well as providing some degree of protection from potential

effects associated with shorter duration exposures. EPA also recognized that an annual standard

would provide effective protection against both annual and multi-year, cumulative exposures that

had been associated with an array of health effects, and that a much longer averaging time would

complicate and unnecessarily delay control strategies and attainment decisions. The possibility

of seasonal effects also was considered, although the very limited available evidence of such

effects and the seasonal variability of sources of fine particle emissions across the country did

not provide a satisfactory basis for establishing a seasonal averaging time.

In considering whether the information available in this review supports consideration of

different averaging times for PM2.5 standards, staff notes that the available information is

generally consistent with and supportive of the conclusions reached in the last review to set

PM2.5 standards with both annual and 24-hour averaging times. In considering the new

information, staff makes the following observations:

• There is a growing body of studies that provide additional evidence of effects

associated with exposure periods shorter than 24-hours (e.g., one to several

hours), as discussed in Chapter 3 (section 3.5.5.1). While staff concludes that this

information remains too limited to serve as a basis for establishing a shorter-than24-hour fine particle primary standard at this time, staff believes that it gives

added weight to the importance of a standard with a 24-hour averaging time. 

Staff recognizes shorter-than-24-hour exposures as an important area of research

that could provide a basis for the consideration of a shorter-term standard in the

future.

• As discussed in Chapter 3 (section 3.5.5), some recent PM10 studies have used a

distributed lag over several days to weeks preceding the health event, although

this modeling approach has not been extended to studies of fine particles. While

such studies continue to suggest consideration of a multiple day averaging time,

staff notes that limiting 24-hour concentrations of fine particles will also protect

against effects found to be associated with PM averaged over many days in health

studies. Consistent with the conclusion reached in the last review, staff again

concludes that a multiple-day averaging time would add complexity but would

not provide more effective protection than a 24-hour average.
5-20

• While some newer studies have investigated seasonal effects, as noted in Chapter

3 (section 3.5.5.3), staff concludes that currently available evidence of such

effects is still too limited to serve as a basis for considering seasonal standards.

Based on the above considerations, staff concludes that the currently available

information supports keeping, and provides no adequate basis for changing, the averaging times of

the current PM2.5 standards. Staff notes that study of shorter-term averaging times, on the order

of one or more hours, is an important research priority, with a particular focus on associations

between exposure to fine particles and fine-particle constituents and indicators of cardiac-related

risk factors. Thus, a shorter-term averaging time may be an important consideration in the next

review of the PM NAAQS. Staff also notes that at present EPA has in place a significant harm

level program (40 CFR Part 51) and a widely disseminated Air Quality Index that could

potentially be adapted to provide information to the public based on episodic very short-term

peak fine particle levels that may be of public health concern.

In the last review, having decided to set both annual and 24-hour PM2.5 standards, EPA

also made judgments as to the most effective and efficient approach to establishing a suite of

standards that, taken together, would appropriately protect against effects associated with both

long- and short-term exposures. At that time, EPA selected an approach that was based on

treating the annual standard as the generally controlling standard for lowering the entire

distribution of PM2.5 concentrations, with the 24-hour standard providing additional protection

against the occurrence of peak 24-hour concentrations. The 24-hour standard was intended to

address in particular those peaks that result in localized or seasonal exposures of concern in areas

where the highest 24-hour-to-annual mean PM2.5 ratios are appreciably above the national

average. This approach was supported by results of the PM risk assessment from the last review

which indicated that peak 24-hour PM2.5 concentrations contribute a relatively small amount to

total health risk, such that much if not most of the aggregated annual risk associated with shortterm exposures results from the large number of days during which the 24-hour average

concentrations are in the low- to mid-range. Further, no evidence suggested that risks associated

with long-term exposures are likely to be disproportionately driven by peak 24-hour

concentrations. Thus, a generally controlling annual standard was judged to reduce risks

associated with both short- and long-term exposures effectively and with more certainty than a

24-hour standard. Further, an annual standard was seen to be more stable over time, likely

resulting in the development of more consistent risk reduction strategies, since an area’s

attainment status would be less likely to change due solely to year-to-year variations in

meteorological conditions that affect the atmospheric formation of fine particles.

In this review, some key considerations that led to establishing a generally controlling

annual standard in the last review are still valid. In particular,

• EPA's updated risk assessment supports the previous conclusion that peak 24-

hour PM2.5 concentrations contribute a relatively small amount to the total health
5-21

risk associated with short-term exposures on an annual basis, such that much if

not most of the aggregated annual risk results from the large number of days

during which the 24-hour average concentrations are in the low- to mid-range, as

discussed in Chapter 4 (section 4.4.4). Support for this conclusion is also found

in studies in which health effect associations remain when high-concentration

days are removed from the analysis (Schwartz et al., 1996; Ostro et al., 1999,

2000).

• It continues to be the case, as discussed in section 4.3.6.6, that available shortterm exposure studies do not provide evidence of clear population thresholds, but

rather reflect relationships between health effects and ambient PM across a wide

distribution of PM concentrations. Thus, as in the last review, staff recognizes

that these studies do not provide a basis for identifying a lowest-observed-effect

level that would clearly translate into a 24-hour standard that would protect

against all effects related to short-term exposures.

Nonetheless, staff believes that the greatly expanded body of epidemiologic evidence and

air quality data provide the basis for considering alternative approaches to establishing a suite of

PM2.5 standards. Thus, staff has not focused a priori on an annual standard as the generally

controlling standard for protection against effects associated with both long- and short-term

exposures. Rather, staff has broadened its view to consider both evidence-based and risk-based

approaches to evaluating the protection that a suite of PM2.5 standards can provide against effects

associated with long-term exposures and against effects associated with short-term exposures. 

These evaluations, discussed in the next two sections, provide the basis for integrated

recommendations on ranges of alternative suites of standards that, when considered together,

protect against effects associated with both long- and short-term exposures.

5.3.4 Alternative PM2.5 Standards to Address Health Effects Related to Long-term

Exposure

In considering alternative PM2.5 standards that would provide protection against health

effects related to long-term exposures, staff has taken into account both evidence-based and riskbased considerations. As discussed below in this section, staff has first evaluated the available

evidence from long-term exposure studies, as well as the uncertainties and limitations in that

evidence, to assess the degree to which alternative annual PM2.5 standards can be expected to

provide protection against effects related to long-term exposures. Secondly, staff has considered

the quantitative risk estimates for long-term exposure effects, discussed in Chapter 4, to assess

the extent to which alternative annual and/or 24-hour standards can be expected to reduce the

estimated risks attributable to long-term exposure to PM2.5. Staff conclusions as to ranges of

alternative annual and/or 24-hour standards that would provide protection against health effects

related to long-term exposures are summarized at the end of this section. The integrated staff

recommendations presented in section 5.3.7 are based in part on the conclusions from this
5-22

section and in part on staff conclusions from the next section, in which alternative PM2.5

standards to address health effects related to short-term exposures are assessed.

5.3.4.1 Evidence-based Considerations

In taking into account evidence-based considerations, staff has focused on long-term

exposure studies of fine particles in the U.S. As discussed above, staff notes that the reanalyses

and extensions of earlier studies have confirmed and strengthened the evidence of long-term

associations for both mortality and morbidity effects. The assessment in the CD of these

mortality studies, taking into account study design, the strength of the study (in terms of

statistical significance and precision of result), and the consistency and robustness of results,

concluded that it was appropriate to give the greatest weight to the reanalyses of the Six Cities

study and the ACS study, and in particular to the results of the extended ACS study (CD, p.

9-33). The assessment in the CD of the relevant morbidity studies noted in particular the results

of the new studies of the children's cohort in Southern California as providing evidence of

respiratory morbidity with long-term PM exposures (CD, pp. 9-33 to 9-34).

Staff believes it is appropriate to consider a level for an annual PM2.5 standard that is

somewhat below the averages of the long-term concentrations across the cities in each of these

long-term exposure studies, recognizing that the evidence of an association in any such 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, 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. Staff also believes it

is appropriate to consider the long-term average concentration at the point where the confidence

interval becomes notably wider, suggestive of a concentration below which the association

becomes appreciably more uncertain and the possibility that an effects threshold may exist

becomes more likely. Staff further notes that in considering a level for a standard that is to

provide protection with an adequate margin of safety, it is appropriate to take into account

evidence of effects for which the reported associations provide only suggestive evidence of a

potentially causal association.

In looking first at the long-term exposure mortality studies, staff notes that the long-term

mean PM2.5 concentration in the Six Cities study was 18 μg/m3

, within an overall range of 11 to

30 μg/m3. In the studies using the ACS cohort, the long-term mean PM2.5 concentration across

the cities was 21 μg/m3

 in the initial study and in the reanalysis of that study, within an overall

range of 9 to 34 μg/m3

. In the extended ACS study, the mean for the more recent time period

used in the analysis (from 1999 to 2000) was 14 μg/m3; in looking at the association based on the

air quality averaged over both time periods (which was the basis for the concentration-response

functions from this study used in the risk assessment, as explained in Chapter 4), the long-term

mean PM2.5 concentration was 17.7 μg/m3, with a standard deviation of + 4, ranging down to

7.5 μg/m3. The CD notes that the confidence intervals around the relative risk functions in this
5-23

extended study, as in the initial ACS study, start to become appreciably wider below

approximately 12 to 13 μg/m3

. In considering the Southern California children's cohort study

showing evidence of decreased lung function growth, staff notes that the long-term mean PM2.5

concentration was 15 μg/m3, ranging from 7 to 32 μg/m3 across the cities. This is approximately

equal to the long-term mean PM2.1 concentration in the earlier 24 City study, showing effects on

children's lung function, in which the long-term mean concentration was 14.5 μg/m3, ranging

from 9 to 17 μg/m3 across the cities.

In considering this evidence, staff concludes that these studies provide a basis for

considering an annual PM2.5 standard somewhat below 15 μg/m3

, down to about 12 μg/m3. A

standard of 14 μg/m3

 would reflect some consideration of the more recent long-term exposure

studies that show associations over a somewhat lower range of air quality than had been

observed in the studies available in the last review. A standard of 13 μg/m3

 would be consistent

with a judgment that appreciable weight should be accorded these long-term exposure studies,

particularly taking into account the most recent extended ACS mortality study and the Southern

California children's cohort morbidity study. A standard level of 13 μg/m3

 would be well below

the long-term mean in the Six Cities mortality study and approximately one standard deviation

below the extended ACS mortality study mean, while being somewhat closer to the long-term

means in the morbidity studies discussed above. A standard of 12 μg/m3

 would be consistent

with a judgment that a more precautionary standard was warranted, potentially reflecting

consideration of the seriousness of the mortality effects, for which there is strong evidence of

likely causal relationships, and of the limited but suggestive evidence of possible links to effects

on fetal and infant development and mortality. As discussed in Chapter 1, these factors are

relevant to judgments about providing an adequate margin of safety to prevent pollution levels

that may pose an unacceptable risk of harm, even if the risk is not precisely identified as to

nature or degree. In staff's view, a standard set below this range would be highly precautionary,

giving little weight to the remaining uncertainties in the broader body of evidence, which

includes other long-term exposure studies that provide far more inconsistent results.

5.3.4.2 Risk-based Considerations

Beyond looking directly at the relevant epidemiologic evidence, staff also has considered

the extent to which specific levels and forms of alternative PM2.5 standards are likely to reduce

the estimated risks attributable to long-term exposure to PM2.5 and the uncertainties in the

estimated risk reductions. As discussed above (section 5.3.1), staff has based this evaluation on

the risk assessment results presented in Chapter 4, in which long-term exposure mortality risks,

based on the extended ACS study, were estimated using the reported concentration-response

function down to a level of 7.5 μg/m3

, the lowest measured level (LML) in that study, as well as

using modified concentration-response functions that incorporate alternative assumed cutpoints

as surrogates for potential population thresholds.
5-24

Figures 5.1(a), (b), and (c) show the estimated percentage reductions in mortality

attributable to long-term exposure to PM2.5 in going from meeting the current PM2.5 standards to

meeting alternative annual and 24-hour PM2.5 standards (with a 98th percentile form) in the five

example cities that do not meet the current standards (based on 2001-2003 air quality data),

based on assumed cutpoints of 7.5, 10, and 12 μg/m3, respectively. To put the estimated

percentage reductions in perspective, these figures also include the estimated PM2.5-related

annual incidence rate (in terms of deaths/year/100,000 general population) and annual incidence

(in terms of deaths/year) of total mortality associated with long-term exposure associated with

just meeting the current PM2.5 standards. A similar series of figures is shown in Appendix 5A for

meeting alternative 24-hour standards with a 99th percentile form. The alternative annual PM2.5

standards considered in these figures include a range of levels from 15 to 12 μg/m3. Attainment

of the standards is simulated based on a percent rollback calculated using the highest monitor in

an area, as noted in Tables 5-1(a) and (b) and discussed in Chapter 4, section 4.2.2. The

alternative 24-hour PM2.5 standards considered in these figures include a range of levels from 65

to 25 μg/m3

. Further discussion of alternative forms of the annual and 24-hour standards is

presented below in section 5.3.6.

In considering the estimates based on a cutpoint of 7.5 μg/m3 [Figures 5-1(a) and 5A1(a)], staff first examined the estimated reductions associated with lower levels of the annual

PM2.5 standard, without changing the 24-hour standard. Staff observes that alternative annual

standard levels of 14, 13, and 12 μg/m3 result in generally consistent estimated risk reductions

from long-term exposure to PM2.5 of roughly 20, 30, and 50 percent, respectively, across all five

example cities. Thus, for this assumed cutpoint, estimated reductions in mortality associated

with long-term exposure to PM2.5 are no greater than 50 percent in any of the five example cities

with changes in the annual standard down to a level of 12 μg/m3. Staff also examined the effect

on mortality reduction associated with alternative 24-hour standards, without changing the

annual standard. Staff first notes that the estimated reductions in long-term mortality risk

associated with changes to the 24-hour standard are much more variable across cities than with

changes in just the annual standard. Further, no combination of standards within the ranges that

staff has considered result in the elimination of all estimated long-term mortality risk in all

example cities. This assessment indicates that estimated reductions in long-term mortality risk

of approximately 50 percent or greater in the five example cities generally result from 24-hour

standards set at 30 to 25 μg/m3

, based on either the 98th or 99th percentile form of such a

standard, depending on the city.

Staff further considered the effects of various combinations of the annual and 24-hour

standard. Staff notes in particular that the estimates of long-term mortality risk reduction, based

on a cutpoint of 7.5 μg/m3, associated with a 24-hour standard set at 25 μg/m3 provides the same

degree of risk reduction regardless of the level of the annual standard within the range of 15 to

12 μg/m3

; a 24-hour standard set at 30 :g/m3 provides the same degree of risk reduction in most
12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 25 (9-44) deaths/yr/100,000

Incidence: 500 (180-910) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 16 (6-27) deaths/yr/100,000

Incidence: 1500 (530-2600) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 35 (12-62) deaths/yr/100,000

Incidence: 540 (190-940) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 31 (11-55) deaths/yr/100,000

Incidence: 400 (140-700) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 24 (8-42) deaths/yr/100,000

Incidence: 600 (210-1000) deaths/yr

Figure 5-1(a) Estimated percent reduction in PM2.5-related long-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 7.5 μg/m3). Risk 

associated with meeting current PM2.5 standards, based on ACS extended study, is 

shown in figures in terms of estimated annual incidence rate and annual incidence 

(and 95% confidence ranges).

Detroit Los Angeles

Philadelphia Pittsburgh

St. Louis

5-25
12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 14 (5-24) deaths/yr/100,000

Incidence: 280 (100-490) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 9 (3-15) deaths/yr/100,000

Incidence: 820 (290-1400) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 22 (8-39) deaths/yr/100,000

Incidence: 340 (120-600) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 17 (6-29) deaths/yr/100,000

Incidence: 220 (75-370) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 12 (4-22) deaths/yr/100,000

Incidence: 310 ( 110-550) deaths/yr

Figure 5-1(b) Estimated percent reduction in PM2.5-related long-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 10 μg/m3). Risk 

associated with meeting current PM2.5 standards, based on ACS extended study, is

shown in figures in terms of estimated annual incidence rate and annual incidence 

(and 95% confidence ranges).

Detroit Los Angeles

Philadelphia Pittsburgh

St. Louis

5-26
12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 2 (1-3) deaths/yr/100,000

Incidence: 40 (10-70) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 1 (1-2) deaths/yr/100,000

Incidence: 140 (50-240) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 9 (3-16) deaths/yr/100,000

Incidence: 140 (50-240) deaths/yr]

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 2 (1-3) deaths/yr/100,000

Incidence: 30 (10-40) deaths/yr]

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 1 (0-2) deaths/yr/100,000

Incidence: 20 (10-40) deaths/yr]

Figure 5-1(c) Estimated percent reduction in PM2.5-related long-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 12 μg/m3). Risk 

associated with meeting current PM2.5 standards, based on ACS extended study, is 

shown in figures in terms of estimated annual incidence rate and annual incidence 

(and 95% confidence ranges).

Detroit Los Angeles

Philadelphia Pittsburgh

St. Louis

5-27
5-28

but not all cases. That is, in the range of 30 to 25 μg/m3

, the 24-hour standard would be the

generally controlling standard in most cases relative to an annual standard in the range of 15 to

12 μg/m3

; and, in those cases, lowering the annual standard to as low as 12 μg/m3 would result in

no additional estimated reductions in long-term mortality risks.

In considering the estimates of risk reductions based on an assumed cutpoint of 10 μg/m3

[Figures 5-1(b) and 5A-1(b)], staff again notes that the estimates of mortality incidence and

incidence rate associated with meeting the current standards are roughly about half as large as

the estimates based on a cutpoint of 7.5 μg/m3

, as discussed above in section 5.3.1. Staff

observes that lowering the annual standard to alternative levels of 14, 13, and 12 μg/m3 (without

changing the 24-hour standard) results in estimated risk reductions of roughly 30 to 40 percent,

50 to 70 percent, and 80 to 100 percent, respectively, across the five example cities. In

considering changes to the annual and/or 24-hour PM2.5 standards in this case, staff first notes

that mortality risk associated with long-term exposure is estimated to be reduced by 100 percent

in all five cities with a 24-hour standard set at 25 μg/m3 (with either a 98th or 99th percentile

form), in combination with the current annual standard. For a 24-hour standard set at 30 μg/m3

with a 98th percentile form, in combination with the current annual standard, estimated risk

reductions remain at or close to 100 percent in three of the cities, but are appreciably lower in the

other two cities. A 24-hour standard set at 35 μg/m3

 with a 98th percentile form results in

appreciable risk reductions in only two of the cities in conjunction with the current annual

standard, although appreciable risk reductions are observed with this 24-hour standard in

conjunction with a lower annual standard.

Further, in considering an assumed cutpoint of 12 μg/m3

 [Figures 5-1(c) and 5A-1(c)],

staff observes that lowering the annual standard to a level of 14 μg/m3 (without changing the 24-

hour standard) results in estimated risk reductions of 100 percent in all five cities. In considering

changes to the 24-hour PM2.5 standard alone in this case, staff notes that long-term mortality risk

is estimated to be reduced by 100 percent in all five cities with a 24-hour standard set at

30 μg/m3, 98th percentile form.

5.3.4.3 Summary

In considering the epidemiologic evidence, estimates of risk reductions associated with

alternative annual and/or 24-hour standards, and the 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-term

exposures. In looking specifically at the evidence of associations between long-term exposure to

PM2.5 and serious health effects, including total, cardiovascular, and lung cancer mortality, as

well as respiratory-related effects on children, staff concludes that it is appropriate to consider an

annual PM2.5 standard in the range of 15 down to 12 μg/m3

. In considering the results of the

quantitative risk assessment, staff believes that it is appropriate to consider all the estimates

associated with the range of assumed cutpoints used in the risk assessment. As discussed above
5-29

in section 5.3.1.3, staff believes that a relatively more precautionary approach to interpreting this

evidence would give more weight to the estimates based on the lowest cutpoint considered,

while giving more weight to the estimates based on an assumed cutpoint of 10 μg/m3 is

consistent with the view of the CASAC PM Panel. Taking into account the estimated risk

reductions based on the use of either cutpoint, staff finds further support for considering an

annual PM2.5 standard in the range of 14 to 12 μg/m3. Alternatively, staff also finds support for a

revised 24-hour standard, in conjunction with retaining the current annual standard, in the range

of 35 to 25 μg/m3

, in conjunction with a 99th percentile form especially with a standard level in

the middle to upper end of this range or with a 98th percentile form with a standard level in the

middle to lower end of this range. Staff notes that a 24-hour standard at a level of 40 μg/m3

 is

estimated to provide no additional protection against the serious health effects associated with

long-term PM2.5 exposures in two or three of the five example cities (for a 99th or 98th percentile

form, respectively) relative to that afforded by the current annual PM2.5 standard, regardless of

the weight that is given to the alternative assumed cutpoints in the range considered by staff. 

Staff believes that a suite of PM2.5 standards selected from the alternatives identified above could

provide an appropriate degree of protection against the mortality and morbidity effects

associated with long-term exposure to PM2.5 in studies in areas across the U.S..

5.3.5 Alternative PM2.5 Standards to Address Health Effects Related to Short-term

Exposure

In considering alternative PM2.5 standards that would provide protection against health

effects related to short-term exposures, staff has similarly taken into account both evidencebased and risk-based considerations. As discussed below in this section, staff has first evaluated

the available evidence from short-term exposure studies, as well as the uncertainties and

limitations in that evidence, to assess the degree to which alternative 24-hour and/or annual

PM2.5 standards can be expected to provide protection against effects related to short-term

exposures. Secondly, staff has considered the quantitative risk estimates for short-term exposure

effects, discussed in Chapter 4, to assess the extent to which alternative annual and/or 24-hour

standards can be expected to reduce the estimated risks attributable to short-term exposure to

PM2.5. Staff conclusions as to ranges of alternative annual and/or 24-hour standards that would

provide protection against health effects related to short-term exposures are summarized at the

end of this section. As noted above, the integrated staff recommendations presented in section

5.3.7 are based in part on the conclusions from this section and in part on staff conclusions from

the previous section, in which alternative PM2.5 standards to address health effects related to

long-term exposures are assessed.

5.3.5.1 Evidence-based Considerations

In taking into account evidence-based considerations, staff has evaluated the available

evidence from short-term exposure studies, as well as the uncertainties and limitations in that
5-30

evidence. In so doing, staff has focused on U.S. and Canadian short-term exposure studies of

fine particles (Appendix 3A). We took into account reanalyses that addressed GAM-related

statistical issues and considered the extent to which the studies report statistically significant and

relatively precise relative risk estimates; the extent to which the reported associations are robust

to co-pollutant confounding and alternative modeling approaches; and the extent to which the

studies used relatively reliable air quality data. In particular, staff has focused on those specific

studies, identified above in section 5.3.1, that provide evidence of associations in areas that

would have met the current annual and 24-hour PM2.5 standards during the time of the study. 

Staff believes that this body of evidence can serve as a basis for 24-hour and/or annual PM2.5

standards that would provide increased protection against effects related to short-term exposures.

As an initial matter, staff recognizes, as discussed above, that these short-term exposure

studies provide no evidence of clear thresholds, or lowest-observed-effects levels, in terms of 24-

hour average concentrations. Staff notes that of the two PM2.5 studies that explored potential

thresholds, one study in Phoenix provided some suggestive evidence of a threshold possibly as

high as 20 to 25 μg/m3, whereas the other study provided evidence suggesting that if a threshold

existed, it would likely be appreciably below 25 μg/m3. While there is no evidence for clear

thresholds within the range of air quality observed in the epidemiologic studies, for some health

endpoints (such as total nonaccidental mortality) it is likely to be extremely difficult to detect

threshold levels (CD, p. 9-45). As a consequence, this body of evidence is difficult to translate

directly into a specific 24-hour standard that would independently protect against all effects

associated with short-term exposures. Staff notes that the distributions of daily PM2.5

concentrations in these studies often extend down to or below typical background levels, such

that consideration of the likely range of policy-relevant background concentrations across the

U.S., as discussed in Chapter 2, section 2.6, becomes important in identifying a lower bound of a

range of 24-hour standards appropriate for consideration.

Being mindful of the difficulties posed by issues relating to threshold and background

levels, staff has first considered this short-term exposure epidemiologic evidence as a basis for

alternative 24-hour PM2.5 standards. In so doing, staff has focused on the upper end of the

distributions of daily PM2.5 concentrations, particularly in terms of the 98th and 99th percentile

values, reflecting the form of the current 24-hour standard and an alternative form considered in

the risk assessment, respectively. In looking at the specific studies identified in section 5.3.1 that

report statistically significant associations in areas that would have met the current PM2.5

standards, including studies in Phoenix (Mar et al., 1999, 2003), Santa Clara County, CA

(Fairley, 1999, 2003) and eight Canadian cities (Burnett et al., 2000 and Burnett and Goldberg,

2003), staff notes that the 98th percentile values range from approximately 32 to 39 μg/m3 in

Phoenix and the eight Canadian cities, up to 59 μg/m3

 in Santa Clara Country; 99th percentile

values range from 34 to 45 μg/m3 in Phoenix and the eight Canadian cities, up to 69 μg/m3 in

Santa Clara Country. These ranges also encompass the 98th and 99th percentile values from the
5-31

short-term exposure studies that reported positive PM-related effects and have long-term mean

PM2.5 concentrations at and somewhat above the current annual PM2.5 standard [up to 18 μg/m3

,

as summarized in Ross and Langstaff ( 2005)]. Based on this information, staff believes that the

range of alternative 24-hour PM2.5 standards appropriate for consideration should extend below

the ranges of 98th and 99th percentile values reported in the studies identified above, so as to

provide protection from the short-term exposure effects seen in these studies.

Since the available epidemiologic evidence provides no clear basis for identifying the

lower end of the range of consideration for a 24-hour standard level, staff has looked to the

information on background concentrations, recognizing that a standard intended to provide

protection from man-made pollution should be set above background levels. As discussed in

Chapter 2, section 2.6, staff notes that long-term average PM2.5 daily background levels are quite

low (ranging from 1 to 5 μg/m3

 across the U.S.), although the upper end (99th percentile values)

of daily distributions of background levels are estimated to extend from approximately 10 to

20 μg/m3 in regions across the U.S, although such levels may include some undetermined

contribution from anthropogenic emissions (Langstaff, 2004). Even higher daily background

levels result from episodic occurrences of extreme natural events (e.g., wildfires, dust storms),

but levels related to such events are generally excluded from consideration under EPA's natural

events policy, as noted in section 2.6. Based on consideration of these background levels, staff

believes that 25 μg/m3

 is an appropriate lower end to the range of 24-hour PM2.5 standards for

consideration in this review. Thus, based on this evidence, staff concludes it is appropriate to

consider alternative 24-hour PM2.5 standards, with either a 98th or 99th percentile form, that range

down to as low as 25 μg/m3 to provide protection from effects associated with short-term

exposures to PM2.5.

As in the last review, staff believes it is also appropriate to consider the evidence

discussed above as a basis for a alternative annual PM2.5 standards that would address risks

associated with short-term exposures. In the last review, annual standard levels were considered

at or somewhat below the long-term mean concentrations in short-term exposure studies

reporting statistically significant associations, recognizing that the evidence of an association in

such studies is strongest at and around this long-term mean, where the data in the study are most

concentrated. This approach follows from the observation that, when aggregated on an annual

basis, much of the risk related to daily exposures results from the large number of days during

which the 24-hour average concentrations are in the low- to mid-range, as discussed in Chapter 4

(section 4.4.4) and in section 5.3.3 above. Thus, to reduce the aggregate risk, it is necessary to

shift the bulk of the distribution to lower levels, not just to limit the concentrations on days when

the PM2.5 concentrations are relatively high. Shifting the distribution can be accomplished

through control strategies aimed at attaining either an annual or 24-hour standard.

Using this approach, the same short-term exposure studies identified above can be

considered as a basis for alternative levels of an annual standard that would provide additional
5-32

protection from effects associated with short-term exposures. In particular, the multi-city

Canadian study (Burnett et al., 2000 and Burnett and Goldberg, 2003) reports statistically

significant associations between short-term PM2.5 exposure and total and cardiovascular

mortality across areas with an aggregate long-term mean PM2.5 concentration of 13.3 μg/m3. The

other two studies, conducted in Phoenix (Mar et al., 1999, 2003) and Santa Clara County, CA

(Fairley, 1999, 2003), each had long-term mean PM2.5 concentrations of approximately 13 μg/m3. 

In considering this evidence, staff concludes that these studies provide a basis for considering an

annual PM2.5 standard within the range of 13 μg/m3

 to about 12 μg/m3. An annual standard of

13 μg/m3 would be consistent with a judgment that appreciable weight should be accorded these

studies as a basis for an annual standard that would protect against PM2.5-related mortality

associated with short-term exposure. An annual standard of 12 μg/m3, somewhat below the

long-term means in these studies, would be consistent with a judgment that a more precautionary

standard was warranted. Such a standard could potentially reflect consideration of the

seriousness of the mortality effects, for which there is strong evidence of a likely causal

relationship, as well as the much more uncertain evidence of respiratory-related emergency

department visits, discussed above in section 5.3.1, in studies with long-term mean PM2.5

concentrations of approximately 12 μg/m3 and below. As discussed in Chapter 1 and above in

section 5.3.4.1, these considerations are relevant to judgments about providing an adequate

margin of safety to prevent pollution levels that may pose an unacceptable risk of harm, even if

the risk is not precisely identified as to nature or degree. In staff's view, an annual standard set

below this range would be highly precautionary based on the evidence discussed above, giving

little weight to the remaining uncertainties in the broader body of short-term exposure evidence,

including the possibility of a threshold within the range of air quality in the studies and the

recognition that results may be sensitive to selection of statistical models beyond the range of

models examined in these particular studies.

Consistent with the conclusions reached in the last review (62 FR 38674-7), however,

staff continues to believe that an annual standard cannot be expected to offer an adequate margin

of safety against the effects of all short-term exposures, especially in areas with unusually high

peak-to-mean ratios of PM2.5 levels, possibly associated with strong local or seasonal sources, or

for potential PM2.5-related effects that may be associated with shorter-than-daily exposure

periods (noted above in section 5.3.3). As a result, in conjunction with an annual standard that

may be adopted in part to provide protection against effects associated with short-term

exposures, staff believes it is appropriate also to consider alternative 24-hour PM2.5 standards as

well. Such a 24-hour standard could reasonably be based on air quality information (from 2001

to 2003) in Chapter 2, Figure 2-23, that shows the distribution of 98th percentile values as a

function of annual means values in urban areas across the U.S. Based on this information, staff

concludes that a 24-hour standard in the range of approximately 40 to 35 μg/m3

 could limit peak

concentrations in areas with relatively high peak-to-mean ratios (i.e., generally in the upper
5-33

quartile to the upper 5th percentile, respectively) and with annual mean concentrations in the

range of 12 to 13 μg/m3.

5.3.5.2 Risk-based Considerations

Beyond looking directly at the relevant epidemiologic evidence, staff has also considered

the extent to which specific levels and forms of alternative 24-hour and annual PM2.5 standards

are likely to reduce the estimated risks attributable to short-term exposure to PM2.5, and the

uncertainties in the estimated risk reductions. As discussed above (section 5.3.1), staff has based

this evaluation on the risk assessment results presented in Chapter 4, in which short-term

exposure risks were estimated using reported city-specific concentration-response functions

down to policy-relevant background, as well as using modified concentration-response functions

that incorporate alternative assumed cutpoints as surrogates for potential population thresholds.

Figures 5-2(a), (b), (c), and (d) show the estimated percentage reductions in mortality

attributable to short-term exposure to PM2.5 in going from meeting the current PM2.5 standards to

meeting alternative annual and 24-hour PM2.5 standards (with a 98th percentile form) in the five

example cities that do not meet the current standards (based on 2001-2003 air quality data),

based on assumed cutpoints equal to estimated policy-relevant background and 10, 15, and

20 μg/m3

, respectively. To put the estimated percentage reductions in perspective, these figures

also include the estimated PM2.5-related annual incidence rate (in terms of deaths/year/100,000

general population) and annual incidence (in terms of deaths/year) of total mortality associated

with short-term exposure associated with just meeting the current PM2.5 standards. A similar

series of figures is shown in Appendix 5A for meeting alternative 24-hour standards with a 99th

percentile form. As in the figures for long-term exposures discussed in section 5.3.4.2, the

alternative annual PM2.5 standards considered in these figures include a range of levels from 15

to 12 μg/m3

, and attainment of the standards is simulated based on a percent rollback calculated

using the highest monitor in an area, as noted in Tables 5-1(a) and (b) and discussed in

Chapter 4, section 4.2.2. The alternative 24-hour PM2.5 standards considered in these figures

include a range of levels from 65 to 25 μg/m3

. Further discussion of alternative forms of the

annual and 24-hour standards is presented below in section 5.3.6.

In considering the estimates based on a cutpoint level equal to estimated policy-relevant

background [Figures 5-2(a) and 5A-2(a)], staff first examined the estimated reductions

associated with lower levels of the annual PM2.5 standard, without changing the 24-hour

standard. Staff observes that lowering the annual standard to alternative levels of 14, 13, and

12 μg/m3 results in small but generally consistent estimated risk reductions of roughly 10 to 15

percent, 15 to 20 percent, and 25 to 30 percent, respectively, across all five example cities. 

Thus, for this assumed cutpoint, estimated reductions in mortality associated with short-term

exposure to PM2.5 are no greater than 30 percent in any of the five example cities with changes in

the annual PM2.5 down to a level of 12 μg/m3

. In examining the effect of changes to the 24-hour

and/or annual PM2.5 standards in this case, staff first notes that the estimated reductions in
. . . . 28

. . . . 18

. . . . 10

. . . . 0 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 6 (-6 - 17) deaths/yr/100,000

Incidence: 120 (-120 - 360) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 3 (0 -6) deaths/yr/100,000

Incidence: 290 (-40 - 610) deaths/yr

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 24 (12 - 37) deaths/yr/100,000

Incidence: 370 (180 - 560) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 4 (-8 - 16) deaths/yr/100,000

Incidence: 50 (-110 - 200) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 8 (3 - 12) deaths/yr/100,000

Incidence: 190 (70 - 310) deaths/yr

Figure 5-2(a) Estimated percent reduction in PM2.5-related short-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint equal to policyrelevant background). Risk associated with meeting current PM2.5 standards, 

based on ACS extended study, is shown in figures in terms of estimated annual 

incidence rate and annual incidence (and 95% confidence ranges). Estimated 

policy-relevant background is 3.5 μg/m3 in eastern cities and 2.5 μg/m3 in 

western cities. 

St. Louis 

Detroit 

Philadelphia 

Los Angeles 

Pittsburgh 

5-34
12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 3 (-3 - 8) deaths/yr/100,000

Incidence: 50 (-60 - 160) deaths/yr

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 1 (0 - 3) deaths/yr/100,000

Incidence: 120 (-10 - 240) deaths/yr

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 12 (6 - 19) deaths/yr/100,000

Incidence: 190 (90 - 260) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 2 (-4 - 7) deaths/yr/100,000

Incidence: 20 (-50 - 90) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 3 (1 - 5) deaths/yr/100,000

Incidence: 80 (30 - 120) deaths/yr

Figure 5-2(b) Estimated percent reduction in PM2.5-related short-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 10 μg/m3

). Risk 

associated with meeting current PM2.5 standards, based on ACS extended study, 

is shown in figures in terms of estimated annual incidence rate and annual 

incidence (and 95% confidence ranges). 

Philadelphia Pittsburgh 

St. Louis 

Detroit Los Angeles 

5-35
12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 1 (-1 - 4) deaths/yr/100,000

Incidence: 30 (-30 - 80) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 1 (0 - 1) deaths/yr/100,000

Incidence: 60 (-10 - 120) deaths/yr

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 7 (3 - 11) deaths/yr/100,000

Incidence: 110 (50 - 160) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 1 (-2 - 3) deaths/yr/100,000

Incidence: 10 (-20 - 40) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 1 (0 - 2) deaths/yr/100,000

Incidence: 30 (10 - 50) deaths/yr

Figure 5-2(c) Estimated percent reduction in PM2.5-related short-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 15 μg/m3

). Risk 

associated with meeting current PM2.5 standards, based on ACS extended study, 

is shown in figures in terms of estimated annual incidence rate and annual 

incidence (and 95% confidence ranges). 

Philadelphia Pittsburgh 

St. Louis 

Detroit Los Angeles 

5-36
12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 1 (-1 - 2) deaths/yr/100,000

Incidence: 10 (-10 - 40) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 0 (0 - 1) deaths/yr/100,000

Incidence: 30 (-4 - 60) deaths/yr

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 4 (2 - 6) deaths/yr/100,000

Incidence: 60 (30 - 90) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 0 (-1 - 1) deaths/yr/100,000

Incidence: 5 (-10 - 20) deaths/yr

 

12

13

14

15 65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3

) 

24-hour

standard (ug/m3

)

Incidence Rate: 0 (0 - 1) deaths/yr/100,000

Incidence: 9 (3 - 14) deaths/yr

Figure 5-2(d) Estimated percent reduction in PM2.5-related short-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 20 μg/m3

). Risk 

associated with meeting current PM2.5 standards, based on ACS extended study, 

is shown in figures in terms of estimated annual incidence rate and annual 

incidence (and 95% confidence ranges). 

Philadelphia Pittsburgh 

St. Louis 

Detroit Los Angeles 

5-37
5-38

short-term mortality risk associated with changes to the 24-hour standard are generally larger

and much more variable across cities than with changes in just the annual standard. Further, no

combination of standards within the ranges that staff has considered results in the elimination of

all estimated mortality risk associated with short-term exposure in all example cities. More

specifically, a 24-hour standard of 25 μg/m3

 results in estimated reductions in short-term

mortality ranging from approximately 30 to 50 percent (98th percentile form) and 35 to 70

percent (99th percentile form) across the five cities in conjunction with any annual standard in the

range of 15 to 12 μg/m3

. A 24-hour standard of 30 μg/m3 results in estimates of reductions in

short-term mortality ranging from approximately 25 to 35 percent (98th percentile form) and 25

to 65 percent (99th percentile form) across the five cities in conjunction with an annual standard

of 12 μg/m3; the lower end, but not the upper end, of these ranges decreases somewhat in

conjunction with annual standards from 13 to 15 μg/m3

. As in the assessment of risk related to

long-term exposures discussed in section 5.3.4.2, this assessment indicates that 24-hour

standards of 30 to 25 μg/m3 become generally controlling standards in most cases within this

range of annual standards.

In considering the estimates of risk reductions based on an assumed cutpoint of 10 μg/m3

[Figures 5-2(b) and 5A-2(b)], staff observes that lowering the annual standard to alternative

levels of 14, 13, and 12 μg/m3

 (without changing the 24-hour standard) results in estimated risk

reductions of roughly 15 to 25 percent, 30 to 35 percent, and 45 to 55 percent, respectively,

across all five example cities. In considering changes to the 24-hour and/or annual PM2.5

standards in this case, staff notes that a 24-hour standard of 25 μg/m3 results in estimates of

reductions in short-term mortality ranging from approximately 45 to 80 percent (98th percentile

form) and 60 to 95 percent (99th percentile form) across the five cities in conjunction with any

annual standard in the range of 15 to 12 μg/m3. A 24-hour standard of 30 μg/m3 results in

estimates of reductions in short-term mortality ranging from approximately 45 to 60 percent (98th

percentile form) and 50 to 95 percent (99th percentile form) across the five cities in conjunction

with an annual standard of 12 μg/m3

; as with the previous case (based on a cutpoint equal to

policy-relevant background), the lower end, but not the upper end, of these ranges decreases

appreciably in conjunction with annual standards from 13 to 15 μg/m3

. Thus, in this case, as in

the previous case, changes in the 24-hour standard, while retaining the current annual standard,

can result in generally larger but much more variable estimated reductions in risks associated

with short-term exposures across the five cities than with changes in just the annual standard.

Further, in considering assumed cutpoints of 15 or 20 μg/m3

, staff observes that lowering

the annual standard to alternative levels of 14, 13, and 12 μg/m3

 (without changing the 24-hour

standard) results in estimated risk reductions of roughly 20 to 45 percent, 40 to 65 percent, and

60 to 90 percent, respectively, across all five example cities. In considering changes to the 24-

hour and/or annual PM2.5 standards in these cases, staff notes that a 24-hour standard of 25 μg/m3

results in estimates of reductions in short-term mortality ranging from approximately 60 to
5-39

100 percent (98th percentile form) and 70 to 100 percent (99th percentile form) across the five

cities in conjunction with any annual standard in the range of 15 to 12 μg/m3. A 24-hour

standard of 30 μg/m3

 results in estimates of reductions in short-term mortality ranging from

approximately 60 to 90 percent (98th percentile form) and 60 to 100 percent (99th percentile form)

across the five cities in conjunction with an annual standard of 12 μg/m3; similarly, the lower

end, but not the upper end, of these ranges decreases appreciably in conjunction with annual

standards from 13 to 15 μg/m3. Thus, in these cases as well, changes in the 24-hour standard,

while retaining the current annual standard, can result in generally larger but much more variable

estimated reductions in risks associated with short-term exposures across the five cities than with

changes in just the annual standard.

5.3.5.3 Summary

In considering the relevant epidemiologic evidence, estimates of risk reductions

associated with alternative annual and/or 24-hour standards, and the 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

short-term exposures. In looking specifically at the evidence of associations between short-term

exposure to PM2.5 and serious health effects, with a particular focus on mortality associations,

staff concludes that it is appropriate to consider a revised 24-hour standard in the range of 30 to

25 μg/m3

 in conjunction with retaining the current annual standard level of 15 μg/m3. 

Alternatively, staff also believes the evidence supports consideration of a revised annual

standard, in the range of 13 to 12 μg/m3, in conjunction with a revised 24-hour standard in the

range of 40 to 35 μg/m3.

In considering the results of the quantitative risk assessment, staff believes that it is

appropriate to consider all the estimates associated with the range of assumed cutpoints used in

the risk assessment. As discussed above in sections 5.3.1.3 and 5.3.4.3, staff believes that a

relatively more precautionary approach to interpreting this evidence would give more weight to

the estimates based on the lowest cutpoint considered, while giving more weight to the estimates

based on an assumed cutpoint of 10 μg/m3

 is consistent with the view of the CASAC PM Panel. 

Taking into account the estimated risk reductions based on the use of either cutpoint, staff finds

additional support for considering a revised 24-hour standard in the range of 30 to 25 μg/m3

 in

conjunction with retaining an annual standard level of 15 μg/m3. In either case, a 24-hour

standard at a level of 35 μg/m3

 is estimated to provide less than 30 percent reduction in mortality

incidence in two or three of the five example cities (for a 99th or 98th percentile form,

respectively) relative to that afforded by the current annual PM2.5 standard alone. Further, in

conjunction with a lower annual standard down to 12 μg/m3

, staff finds support for considering a

revised 24-hour standard in the range of 35 to 30 μg/m3

. Staff finds little support based on the

risk assessment for addressing short-term exposure effects solely with a revised annual standard

in a range down to 12 μg/m3

. Staff believes that a suite of PM2.5 standards selected from the
5-40

alternatives identified above could provide an appropriate degree of protection against the

mortality and morbidity effects associated with short-term exposure to PM2.5 in studies 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 2001 to 2003 air quality) the

percentage of counties, and the population in those counties, that would not likely attain various

PM2.5 annual standards alone in comparison to the percentage of counties that would not likely

attain alternative combinations of annual and 24-hour PM2.5 standards. This assessment, shown

in Appendix 5B (Tables 5B-1(a) and (b), for 98th and 99th percentile forms of the 24-hour

standards, respectively), was not considered as a basis for the above staff conclusions.

5.3.6 Alternative Forms for Annual and 24-Hour PM2.5 Standards

5.3.6.1 Form of 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 arithmetic mean serves to represent the broad distribution of daily air

quality values, and a 3-year average provides a more stable risk reduction target than a singleyear annual average. The current annual PM2.5 standard level is to be compared to measurements

made at the community-oriented monitoring site recording the highest level, or, if specific

constraints are met, measurements from multiple community-oriented monitoring sites may be

averaged (62 FR at 38,672). The constraints on allowing the use of spatially averaged

measurements were intended to limit averaging across poorly correlated or widely disparate air

quality values. This approach was judged to be consistent with the epidemiologic 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 this review, in conjunction with recommending that consideration be given to

alternative annual standard levels, staff is also reconsidering the appropriateness of continuing to

allow spatial averaging across monitors as part of the form of an annual standard. There now

exist much more PM2.5 air quality data than were available in the last review. Consideration of

the spatial variability across urban areas that is revealed by this new database (see Chapter 2,

section 2.4 above, and the CD Chapter 3, section 3.2.5) raises questions as to whether an annual

standard that allows for spatial averaging, within currently specified or alternative constraints,

would provide appropriate public health protection. In conducting analyses to assess these

questions, as discussed below, staff has taken into account both aggregate population risk across

an entire urban area and the potential for disproportionate impacts on potentially vulnerable

subpopulations within an area.
5-41

The effect of allowing the use of spatial averaging on aggregate population risk was

considered as part of the sensitivity analyses included in the health risk assessment discussed in

Chapter 4. In particular, a sensitivity analysis was done in several example urban areas (Detroit,

Pittsburgh, and St. Louis) that compared estimated mortality risks (associated with both longand short-term exposures) based on calculating compliance using air quality values from the

highest community-oriented monitor in an area, with estimated risks based on using air quality

values averaged across all such monitors within the constraints allowed by the current standard. 

As discussed in Chapter 4, section 4.2.2, the monitored air quality values were used to determine

the design value for the annual standard in each area, as applied to a “composite” monitor to

reflect area-wide exposures. Changing the basis of the annual standard design value from the

concentration at the highest monitor to the average concentration across all monitors reduces the

air quality adjustment needed to just meet the current or alternative annual standards. As

expected, the estimated risks remaining upon attainment of the current annual standard are

greater when spatial averaging is used than when the highest monitor is used (i.e., the estimated

reductions in risk associated with just attaining the current or alternative annual standards are

less when spatial averaging is used). Based on the results of this analysis in the three example

cities, estimated mortality incidence associated with long-term exposure based on the use of

spatial averaging is about 10 to over 40% higher than estimated incidence based on the use of the

highest monitor. For estimated mortality incidence associated with short-term exposure, the use

of spatial averaging results in risk estimates that range from about 5 to 25% higher. In

considering estimated risks remaining upon attainment of alternative suites of annual and 24-

hour PM2.5 standards, spatial averaging only has an impact in those cases where the annual

standard is the “controlling” standard. For such cases in the three example cities, the estimated

mortality incidence associated with long-term exposure in most cases ranges from about 10 to

60% higher when spatial averaging is used, and estimated mortality incidence associated with

short-term exposure in most cases ranges from about 5 to 25% higher.

In considering the potential for disproportionate impacts on potentially vulnerable

subpopulations, staff has assessed whether any such groups are more likely to live in census

tracts in which the monitors recording the highest air quality values in an area are located. Data

were obtained for demographic parameters measured at the census tract level, including

education level, income level, and percent minority. These data from the census tract in which

the highest air quality values were monitored were compared to area-wide average values

(Schmidt et al., 2005). Recognizing the limitations of such cross-sectional analyses, staff

observes that the results suggest 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 percentage minority levels. Staff notes that some

epidemiologic study results, most notably the associations between mortality and long-term

PM2.5 exposure in the ACS cohort, have shown larger effect estimates in the cohort subgroup
5-42

with lower education levels (CD, p. 8-103). As discussed in Chapter 3, section 3.4, people with

lower socioeconomic status (e.g., lower education and income levels), or who have greater

exposure to sources such as roadways, may have increased vulnerability to the effects of PM

exposure. Combining evidence from health studies suggesting that people with lower

socioeconomic status may be considered a population more vulnerable to PM-related effects

with indications from air quality analyses showing that higher PM2.5 concentrations are measured

in local communities with lower socioeconomic status, staff finds that this is additional evidence

which supports a change from spatial averaging across PM2.5 monitors to provide appropriate

protection from public health risks associated with exposure to ambient PM2.5.

The allowance to use spatial averaging under certain constraints established in 1997 was

intended to provide for a relatively stable measure of air quality and to characterize area-wide

PM2.5 concentrations, while also precluding averaging across monitors that would leave a portion

of a metropolitan areas with substantially greater exposures than other areas (62 FR 38672). 

Based on the PM2.5 air quality data now available, staff believes that the existing constraints on

spatial averaging may not be adequate to avoid substantially greater exposures in some areas,

potentially resulting in disproportionate impacts on potentially vulnerable subpopulations. 

Thus, in considering whether alternative constraints on the use of spatial averaging may be

appropriate, staff has analyzed existing data on the correlations and differences between monitor

pairs in metropolitan areas (Schmidt et al., 2005). For all pairs of PM2.5 monitors, the median

correlation coefficient based on annual air quality data is approximately 0.9, which is

substantially higher than the current criterion for correlation of at least 0.6, which was met by

nearly all monitor pairs. Similarly, the current criterion that differences in mean air quality

values between monitors not exceed 20% was met for most monitor pairs, while the annual

median and mean differences for all monitor pairs are 5% and 8%, respectively. This analysis

also showed that in some areas with highly seasonal air quality patterns (e.g., due to seasonal

woodsmoke emissions), substantially lower seasonal correlations and larger seasonal differences

can occur relative to those observed on an annual basis.

In considering the results of the analyses discussed above, staff concludes that it is

appropriate to consider eliminating the provision that allows for spatial averaging from the form

of an annual PM2.5 standard. Further, staff concludes that if consideration is given to retaining an

allowance for spatial averaging, more restrictive criteria should be considered. Staff believes

that it would be appropriate to consider alternative criteria such as a correlation coefficient of at

least 0.9, determined on a seasonal basis, with differences between monitor values not to exceed

about 10%.

5.3.6.2 Form of 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 38671-74). EPA selected such a concentration-based form because of its
6 The form of the 1987 24-hour PM10 standard is based on the expected number of days per year (averaged

over 3 years) on which the level of the standard is exceeded; thus, attainment with the one-expected exceedance

form is determined by comparing the fourth-highest concentration in 3 years with the level of the standard.

5-43

advantages over the previously used expected-exceedance form.6

 A concentration-based form is

more reflective of the health risk posed by elevated PM2.5 concentrations because it 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 standard. Further, a

concentration-based form better compensates for missing data and less-than-every-day

monitoring; and, when averaged over 3 years, it has greater stability and, thus, facilitates the

development of more stable implementation programs. After considering a range of

concentration percentiles from the 95th to the 99th, EPA selected the 98th percentile as an

appropriate balance between adequately limiting the occurrence of peak concentrations and

providing increased stability and robustness. Further, by basing the form of the standard on

concentrations measured at population-oriented monitoring sites (as specified in 40 CFR part

58), EPA intended to provide protection for people residing in or near localized areas of elevated

concentrations.

In this review, in conjunction with recommending that consideration be given to

alternative 24-hour standard levels, staff is also considering the appropriateness of

recommending that the current 98th percentile form, averaged over 3 years, be retained or

revised. As an initial matter, staff believes that it is appropriate to retain a concentration-based

form that is defined in terms of a specific percentile of the distribution of 24-hour PM2.5

concentrations at each population-oriented monitor within an area, averaged over 3 years. Staff

bases this recommendation on the same reasons that were the basis for EPA's selection of this

type of form in the last review. As to the specific percentile value to be considered, staff has

narrowed the focus of this review to the 98th and 99th percentile forms. This focus is based on the

observation that the current 98th percentile form already allows the level of the standard to be

exceeded seven days per year, on average (with every-day monitoring), while potentially

allowing many more exceedance days in the worst year within the 3-year averaging period

(Schmidt et al., 2005). As a result, in areas that just attain the standards, EPA's communication

to the public through the Air Quality Index will on one hand indicate that the general level of air

quality is satisfactory (since the standards are being met), but on the other hand it may identify

many days throughout the year as being unhealthy, particularly for sensitive groups. Thus, staff

does not believe it would be appropriate to consider specifying the form in terms of an even

lower percentile value.

 In considering differences between 98th and 99th percentile forms, staff believes it is

appropriate to take into consideration the relative risk reduction afforded by these alternative

forms at the same standard level. Based on the risk assessment results discussed in Chapter 4,
7 This analysis also looked at the number of days in which the reported air quality values were “flagged” as

being heavily influenced by natural events (including forest fires, dust storms) or exceptional events, for which the

Agency’s natural and exceptional events policies would likely apply. While flagged days generally account for less

than 1% of all reported 24-hour average PM2.5 concentrations, they account for about 40% of the highest 100 days

across the country. In looking at the reported values that are above the 99th or 98th percentiles of the distribution of

values, approximately 3 to 6% of the highest 2% of days (above the 98th percentile) were flagged, and approximately

5 to 10% of the highest 1% of days (above the 99th percentile) were flagged.

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and the risk reductions associated with alternative levels and forms discussed above in sections

5.3.4 and 5.3.5, staff notes that the 99th percentile can, in some instances, result in appreciably

greater risk reductions in particular areas than that associated with a standard at the same level

but with a 98th percentile form. More specifically, staff considered the differences in risk

reductions associated with attaining alternative standards with 98th and 99th percentile forms in

five example urban areas which do not meet the current annual standard (Detroit, Los Angeles,

Philadelphia, Pittsburgh, and St. Louis). In looking at estimated risk reductions associated with

meeting a 24-hour standard of 30 μg/m3

, for example, estimated risk reductions for mortality

associated with long-term exposures were higher with the use of a 99th percentile form in some

areas by approximately 15%, ranging up to over 50% higher in Los Angeles. For estimated risk

reductions for mortality associated with short-term exposures, the use of a 99th percentile form

resulted in estimated reductions that were higher by less than 10% to over 30% across the five

urban areas.

Staff also analyzed the available air quality data from 2001 to 2003 to compare the 98th

and 99th percentile forms in terms of the numbers of days that would be expected to exceed the

level of the standard (on average over 3 years and in the worst year within a 3-year averaging

period) and by how much the standard would typically be exceeded on such days (Schmidt et al.,

2005). In so doing, as noted above, staff observes that the current 98th percentile form allows the

level of the standard to be exceeded seven days per year, on average (with every-day

monitoring), and finds that this form allows up to about 20 days in the worst year within the 3-

year averaging period. A 99th percentile form would allow the level of the standard to be

exceeded three days per year, on average (with every-day monitoring), while allowing up to

about 13 days in the worst year within the 3-year averaging period. Further, staff observes that

for either form, daily peak concentrations in the upper 1 to 2% of the annual air quality

distributions are within 5 :g/m3 of the 98th or 99th percentile value somewhat more than half the

time and are almost always within 10 to 15 :g/m3

 above the 98th or 99th percentile values, with

very few excursions above this range.7

Based on these considerations, staff recommends either retaining the 98th percentile form

or revising it to be based on the 99th percentile air quality value. In selecting between these

alternative forms, staff believes primary consideration should be given to the degree of risk

reduction likely to result from the combination of the form and the level of a standard. Staff also
5-45

believes it is appropriate to take into account whether the 24-hour standard is set so as to add to

the protection afforded by a revised annual standard or is intended to be the primary basis for

providing protection against effects associated with short-term exposures. In choosing between

forms of alternative standards that provide generally equivalent levels of public health

protection, staff believes it is appropriate to consider the relatively stability of a standard with

either form as well as the implications from a public health communication perspective of the

extent to which either form allows different numbers of days in a year to be above the level of

the standard in areas that attain the standard. In particular, staff notes that the use of a 99th

percentile form would result in a more consistent public health message to the general public in

the context of the wide-spread use of the Air Quality Index.

5.3.7 Summary of Staff Recommendations on Primary PM2.5 NAAQS

Staff recommendations for the Administrator's consideration in making decisions on the

primary PM2.5 standards, together with supporting conclusions from sections 5.3.1 through 5.3.6,

are briefly summarized below. Staff recognizes 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 risk assessment. In

recommending these alternative suites of primary standards and ranges of levels for

consideration, staff is mindful that the 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 NAAQS be set at

zero-risk levels, but rather at levels that avoid unacceptable risks to public health.

( 1) Consideration should be given to revising the current PM2.5 primary 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 recommendation is based in general on the

evaluation in the CD of the newly available epidemiologic, toxicologic, dosimetric, and

exposure-related evidence, and more specifically on the evidence of mortality and

morbidity effects in areas where the current standards were met, together with judgments

as to the public health significance of the estimated incidence of effects upon just

attaining the current standards.

( 2) The indicator for fine particle standards should continue to be PM2.5. This

recommendation is based on the conclusion that the available evidence does not provide

a sufficient basis for replacing or supplementing a mass-based fine particle indicator with

an indicator for any specific fine particle component or subset of fine particles, nor does

it provide a basis for excluding any components; on the evaluation in the CD of air

quality within the intermodal particle size range of 1 to 3 μm; and on the policy judgment
5-46

made in the last review to place regulatory importance on defining an indicator that

would more completely capture fine particles under all conditions likely to be

encountered across the U.S., while recognizing that some limited intrusion of small

coarse particles will occur in some circumstances. Consideration should be given to

modifying the FRM for PM2.5 based on instrumentation and operational improvements

that have been made since the PM2.5 monitoring network was deployed in 1999, and to

the adoption of FEMs for appropriate continuous measurement methods.

( 3) Averaging times for PM2.5 standards should continue to include annual and 24-hour

averages to protect against health effects associated with short-term (hours to days) and

long-term (seasons to years) exposure periods. Consideration of other averaging times,

especially on the order of one or more hours, was limited by a lack of adequate

information at this time.

( a) Consideration should be given to revising the form of the annual standard to one

based on the highest community-oriented monitor in an area or, alternatively, to

one with more constrained requirements for the use of spatial averaging across

community-oriented monitors.

( b) Consideration should be given to revising the form of the 24-hour standard to a

99th percentile form or, alternatively, to retaining the 98th percentile form, based in

part on considering the degree of risk reduction likely to result from the

combination of the form and the level of a standard.

( 4) Consideration should be given to alternative suites of PM2.5 standards to provide

protection against effects associated with both long- and short-term exposures, taking

into account both evidence-based and risk-based considerations. Integrated

recommendations on ranges of alternative suites of standards that, when considered

together, protect against effects associated with both long- and short-term exposures

include:

( a) Staff recommends consideration of an annual PM2.5 standard at the current level

of 15 μg/m3

 together with a revised 24-hour PM2.5 standard in the range of 35 to

25 μg/m3

, based a 98th percentile form for a standard set at the middle to lower

end of this range, or a 99th percentile form for a standard set at the middle to upper

end of this range. Staff judges that such a suite of standards could provide an

appropriate degree of protection against serious mortality and morbidity effects

associated with long- and short-term exposures to fine particles.

( b) Alternatively, staff also recommends consideration of a revised annual PM2.5

standard, within the range of 14 to 12 μg/m3, together with a revised 24-hour

PM2.5 standard in the range of 30 to 40 μg/m3

. Staff judges that a suite of
8 As discussed in Chapter 1, however, in subsequent litigation regarding the 1997 PM NAAQS revisions,

the court held in part that PM10 is a “poorly matched indicator” for thoracic coarse particles in the context of a rule

that also includes PM2.5 standards because PM10 includes PM2.5. American Trucking Associations v. EPA, 175 F. 3d.

at 1054. Although the court found “ample support” (id.) for EPA’s decision to regulate thoracic coarse particles, it

vacated the 1997 revised PM10 standards for that reason.

9 As noted in section 2.5.3, coarse particle concentrations in EPA’s monitoring network are currently

estimated, not measured directly, using a difference method in locations with same-day data from co-located PM10

and PM2.5 FRM monitors, resulting in air quality characterizations that are more uncertain than those available for

PM2.5 or PM10.

5-47

standards that includes either the annual or the 24-hour standard, or both, set at

the middle to lower end of these ranges could provide an appropriate degree of

protection against serious mortality and morbidity effects associated with longand short-term exposures to fine particles.

5.4 THORACIC COARSE PARTICLE STANDARDS

5.4.1 Adequacy of Current PM10 Standards

In 1997, in conjunction with establishing new PM2.5 standards, EPA determined that the

new function of PM10 standards was to protect against potential effects associated with thoracic

coarse particles in the size range of 2.5 to 10 μm (62 FR 38,677). Although staff had given some

consideration to a more narrowly defined indicator that did not include fine particles (e.g.,

PM10-2.5), EPA decided to continue to use PM10 as the indicator for standards to control thoracic

coarse particles. 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 component of PM10, namely two

fugitive dust studies in areas that substantially exceeded the PM10 standards (62 FR 38,679). The

decision also reflected the fact that there were only very limited ambient air quality data then

available specifically on thoracic coarse particles, in contrast to the extensive monitoring

network already in place for PM10. In essence, EPA concluded at that time that it was

appropriate to continue to control thoracic coarse particles, but that the only information

available upon which to base such standards was indexed in terms of PM10.

8

In the present review, staff has taken into account the information now available from a

growing, but still limited, body of evidence on health effects associated with thoracic coarse

particles from studies that use PM10-2.5 as a measure of thoracic coarse particles. In addition, staff

notes that there is now much more information available to characterize air quality in terms of

estimated PM10-2.5 than was available in the last review.9

 In considering this information, staff

now finds that the major considerations that formed the basis for EPA's 1997 decision to retain

PM10 as the indicator for thoracic coarse particles, rather than a more narrowly defined indicator

that does not include fine particles, no longer apply. In particular, staff concludes that the
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continued use of PM10 as an indicator for standards intended to protect against health effects

associated with thoracic coarse particles is no longer appropriate since information is now

available that supports the use of a more directly relevant indicator, PM10-2.5. Further, staff

concludes that continuing to rely principally on health effects evidence indexed by PM10 is no

longer appropriate since more directly relevant studies, indexed by PM10-2.5, are also now

available. Thus, quite aside from any legal considerations, staff finds that it is appropriate to

revise the current PM10 standards in part by revising the indicator for thoracic coarse particles,

and by basing any such revised standard principally on the currently available evidence and air

quality information indexed by PM10-2.5, but also considering evidence from studies using PM10

in locations where PM10-2.5 is the predominant fraction.

Staff has also considered whether the currently available evidence and information

support consideration of standards for thoracic coarse particles that afford either a similar or

notably different degree of public health protection compared to that afforded by the current

PM10 standards. In so doing, staff first focused on dosimetric and toxicologic evidence, then on

relevant findings from epidemiologic studies, followed by consideration of risk-based

information, as discussed below. 

Dosimetric evidence formed the primary basis for initial development of the PM10

indicator. While considerable advances have been made, the available evidence continues to

support the basic conclusions reached in the 1987 and 1997 reviews of the standards regarding

penetration and deposition of size specific particles; an aerodynamic size of 10 μm remains a

reasonable separation point for particles that penetrate and potentially deposit in the thoracic

regions of the lungs, particularly for the more sensitive case of mouth breathing. As discussed in

Chapter 3, both fine and thoracic coarse particles penetrate to and deposit in the alveolar and

tracheobronchial regions. For a range of typical ambient size distributions, the total deposition

of thoracic coarse particles to the alveolar region can be comparable to or even larger than that

for fine particles. For areas with appreciable coarse particle concentrations, coarse particles

would tend to dominate particle deposition to the tracheobronchial region for mouth breathers

(CD, p. 6-16).

As noted in past reviews (EPA, 1981b, 1996b), deposition of a variety of particle types in

the tracheobronchial region, including resuspended urban dust and coarse-fraction organic

materials, has the potential to affect lung function and aggravate symptoms, particularly in

asthmatics. Of particular note are limited toxicologic studies that found urban road dust can

produce cellular and immunological effects (e.g., Kleinman, et al., 1995; Steerenberg et al.,

2003). In addition, the CD notes that some very limited in vitro toxicologic studies show some

evidence that coarse particles may elicit pro-inflammatory effects (CD, section 7.4.4), as

discussed further in section 3.4.2. The staff assessment of the physicochemical properties and

occurrence of ambient coarse particles (Chapter 2) suggests that both the chemical makeup and
5-49

spatial distribution of coarse particles are likely to be more heterogeneous than fine particles. In

general, however, urban coarse particles can contain all of the components found in more rural

areas, but be contaminated by a number of additional materials, from motor vehicle-related

emissions to metals and transition elements associated with industrial operations. Taken

together, staff believes that the weight of the dosimetric, limited toxicologic, and atmospheric

science evidence lends support to the plausibility of the effects reported in urban epidemiologic

studies, as discussed in Chapter 3 (section 3.4), and provides support for retaining standards for

thoracic coarse particles so as to continue programs to protect public health from such PM10-2.5-

related effects.

Staff has considered also the available epidemiologic evidence of associations between

thoracic coarse particles, as indexed by PM10-2.5, and health endpoints, as well as evidence from

PM10 studies conducted in areas in which the coarse fraction is dominant. As summarized in

Chapter 3 (section 3.4 and Appendix 3-A), several U.S. and Canadian studies now provide

evidence of associations between short-term exposure to PM10-2.5 and various morbidity

endpoints. Three such studies conducted in Toronto (Burnett et al., 1997), Seattle (Sheppard et

al.,1999, 2003), and Detroit (Lippmann et al., 2000; Ito, 2003) report statistically significant

associations between short-term PM10-2.5 exposure and respiratory- and cardiac-related hospital

admissions, and a fourth study (Schwartz and Neas, 2000) conducted in six U.S. cities including

Boston, St. Louis, Knoxville, Topeka, Portage, and Steubenville reports statistically significant

associations with respiratory symptoms in children. The extent to which the results from these

studies are robust to the inclusion of co-pollutants varies depending on the various models used

and the number of co-pollutants included in the models. Staff observes that the morbidity

studies were done in areas in which PM2.5, rather than PM10-2.5, is the predominant fraction of

ambient PM10, such that they are not representative of areas with relatively high levels of

thoracic coarse particles.

The CD found that evidence from health studies on associations between short-term

exposure to PM10-2.5 and mortality was “not as strong” as evidence for associations with PM2.5 or

PM10 but nonetheless was suggestive of associations with mortality (CD, p. 9-32). As described

in Section 3.4, associations between PM10-2.5 and mortality are similar in magnitude, but less

precise, than those for PM2.5 or PM10. Statistically significant mortality associations were

reported in studies conducted in areas with relatively high PM10-2.5 concentrations, including

Phoenix (Mar et al., 2000, 2003), Coachella Valley, CA (Ostro et al., 2000, 2003), and

Steubenville (as part of the Harvard Six Cities study, Schwartz et al., 1996; Klemm et al., 2003). 

In areas with lower PM10-2.5 concentrations, no statistically significant associations were reported

with mortality, though many were positive but not statistically significant.

In addition, some epidemiologic studies that used PM10 and were conducted in areas

where PM10 is typically dominated by coarse fraction particles can provide information relevant

to the evaluation of coarse fraction particles. Such studies include findings of associations with
10 Uncertainties related to estimated policy-relevant background levels of PM10-2.5 were addressed in a

sensitivity analysis, which showed negligible impact on the risk estimates.

11 This table includes risk estimates for Detroit drawn from Table 4-20 in Chapter 4 and for Seattle and St.

Louis drawn from Exhibits E.33 and E.34, respectively, in the Technical Support Document.

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hospitalization for cardiovascular diseases in Tucson, AZ (Schwartz, 1997), hospitalization for

COPD in Reno/Sparks, NV (Chen et al., 2000), and medical visits for asthma or respiratory

diseases in Anchorage, AK (Gordian et al., 1996; Choudhury et al., 1997). In addition, a number

of epidemiologic studies have reported significant associations with mortality, respiratory

hospital admissions and respiratory symptoms in the Utah Valley area (e.g., Pope et al., 1989;

1991; 1992). This group of studies provides additional supportive evidence for associations

between coarse fraction particles and health effects, particularly morbidity effects, in areas with

concentrations generally not meeting the PM10 standard levels (all areas except Tucson).

Taken together, staff concludes that the health evidence, including dosimetric,

toxicologic and epidemiologic study findings, supports retaining standards to protect against

effects associated with short-term exposure to thoracic coarse particles. Staff believes that the

substantial uncertainties associated with this limited body of epidemiologic evidence on health

effects related to exposure to PM10-2.5, however, suggests a high degree of caution in interpreting

this evidence, especially at lower levels of ambient particle concentrations as observed in the

morbidity studies discussed above.

Beyond this evidence-based evaluation, staff has also considered the extent to which

PM10-2.5-related health risks estimated to occur at current levels of ambient air quality may be

judged to be important from a public health perspective, taking into account key uncertainties

associated with the estimated risks. Consistent with the approach used to address this issue for

PM2.5-related health risks, discussed above in section 5.3.1.2, staff has considered the results of a

series of base case analyses that reflect in part the uncertainty associated with the form of the

concentration-response functions drawn from the studies used in the assessment, as presented in

Chapter 4, section 4.5.10 Health risks were estimated in these analyses by using the reported

linear or log-linear concentration-response functions as well as modified functions that

incorporate alternative assumed cutpoints as surrogates for potential population thresholds. Such

estimates of risks attributable to short-term exposure to PM10-2.5 have been developed for Detroit,

Seattle, and St. Louis.11

Table 5-2 summarizes the estimated PM10-2.5-related annual incidence and incidence rates

(in terms of incidence per 100,000 general population) of hospital admissions and respiratory

symptoms (cough) in children associated with short-term exposure in these three example urban

areas. As an initial matter, staff observes that the range of estimates of cardiac-related hospital

admissions in Detroit is generally more than an order of magnitude greater than the range of
Table 5-2 Estimated PM10-2.5-related annual incidence of hospital admissions and cough in children ass

term exposure with 2003 air quality

Annual Incidence

and 95% CI

(events/yr)

Annual Incidence R

and 95% CI

(events/yr/100,000 general 

Cutpoints Cutpoints

Policy-relevant

Background* 10 μg/m3 15 μg/m3 20 μg/m3 Policy-relevant Background* 10 μg/m3 15 μ

Detroit: hospital admissions for ischemic heart disease

650

170 - 1,100

570

150 - 930

490

130 - 790

430

120 - 680

32

8 - 53

28

7 - 45

6 

Seattle: hospital admissions for

 asthma (age <65)

30

0 - 70

10

0 - 20

5

0 - 10

2

0 - 4

2

0 - 4

1

0 - 2

0 

St. Louis: days of cough in children

27,000

11,000 - 41,000

12,000

4 900 - 18,000

5,800

2,500 - 8,600

2,900

1,300 - 4,000

1,070

440 - 1,600

480

190 - 720

2

100 

* Estimated policy-relevant background levels are 4.5 :g/m3 for eastern urban areas and 3.5 :g/m3 for western urban areas.

5-51
12 See www.epa.gov/airtrends/pdfs/pm10_design_values_2001-2003.pdf for a discussion of how these

design values are calculated, noting in particular that concentrations flagged as natural events (e.g, high winds,

wildfires, volcanic eruptions) or exceptional events (e.g., construction, prescribed burning) are not included in these

calculations and that no regulatory decisions on attainment status have been made at this time based on these data.

5-52

estimated asthma-related admissions in Seattle, which can be attributed in part to differences in

baseline risks related to cardiovascular- and respiratory-related health endpoints as well as to

differences in PM10-2.5 air quality levels in these two areas. To provide some context for

considering these risks estimates, staff notes that Detroit and St. Louis did not meet the current

24-hour PM10 standard of 150 μg/m3 based on 2001 to 2003 air quality data (with 24-hour PM10

design values of 191 and 224 μg/m3, respectively), whereas Seattle, with much lower daily

concentrations (with a 24-hour PM10 design value of 73 μg/m3), meets the current PM10

standards.12 More specifically, in considering the risk estimates based on the lowest cutpoint

considered, the point estimate of annual incidence of PM10-2.5-related hospital admissions for

ischemic heart disease in Detroit is approximately 650 events per year (roughly 32 events per

year per 100,000 general population), and the estimate of days of cough in children is

approximately 27,000 days per year (over 1,000 days per year per 100,000 general population,

which would be roughly an order of magnitude higher in terms of days per year per 100,000

children). In considering the estimated incidences based on an assumed cutpoint of 10 μg/m3

,

staff observes that these estimates are about 15 percent lower in Detroit and over 50 percent

lower in St. Louis, whereas at the highest cutpoint considered, the estimates are about 35 percent

lower in Detroit and close to 90 percent lower in St. Louis.

Beyond the specific health endpoints presented in Table 5-2, staff notes that hundreds of

additional hospital admissions for other cardiac- and respiratory-related diseases are also

estimated in Detroit, based on risk assessment results presented in Chapter 4 (across the range of

cutpoints considered), as are thousands of additional days in which children are likely to

experience other lower respiratory tract symptoms in St. Louis. In considering these limited

ranges of estimates, staff concludes that they are indicative of risks that can reasonably be judged

to be important from a public health perspective, in contrast to the appreciably lower respiratory

morbidity risks estimated in Seattle.

In summary, staff recognizes that the substantial uncertainties associated with the limited

available epidemiologic evidence present inherent difficulties in interpreting the evidence for

purposes of setting appropriate standards for thoracic coarse particles. Nonetheless, in

considering the available evidence, the public health implications of estimated risks associated

with current levels of air quality, and the related limitations and uncertainties, staff concludes

that this information supports consideration of standards to provide public health protection from

morbidity effects and possibly mortality associated with current levels of short-term exposure to

thoracic coarse particles in some urban areas. Staff conclusions and recommendations for
5-53

indicators, averaging times, and levels and forms of alternative primary standards for thoracic

coarse particles are discussed in the following sections.

5.4.2 Indicators

In considering an appropriate indicator for a standard intended to afford protection from

health effects associated with exposure to thoracic coarse particles, staff makes the following

observations:

• The most obvious choice for a thoracic coarse particle standard is the sizedifferentiated, mass-based indicator used in the epidemiologic studies that

provide the most direct evidence of such health effects, PM10-2.5.

• The upper size cut of a PM10-2.5 indicator is consistent with the dosimetric

evidence discussed above that continues to reinforce the finding from past

reviews that an aerodynamic size of 10 μm is a reasonable separation point for

particles that penetrate to and potentially deposit in the thoracic regions of the

respiratory tract.

• The lower size cut of such an indicator is consistent with the choice of 2.5 μm as

a reasonable separation point between fine and coarse fraction particles, based on

consideration of evidence from atmospheric sciences; it is also consistent with the

recommended continued use of PM2.5 as the indicator for fine particles (as

discussed above in section 5.3.2), while recognizing that it would exclude the tail

of the coarse mode in some locations.

• Further, the limited available information is not sufficient to define an indicator

for thoracic coarse particles solely in terms of metrics other than sizedifferentiated mass, such as specific chemical components.

• The available epidemiologic evidence for effects of PM10-2.5 exposure is quite

limited and is inherently characterized by large uncertainties, reflective in part of

the more heterogeneous nature of the spatial distribution and chemical

composition of thoracic coarse particles and the more limited and uncertain

measurement methods that have generally been used to characterize their ambient

concentrations.

 In evaluating relevant information from atmospheric sciences, toxicology, and

epidemiology related to thoracic coarse particles, staff notes that there appears to be a clear

distinction between the character and nature of exposures and evidence concerning associated

health effects of coarse particles as found in urban as compared to those found in nonurban and,

more specifically, rural areas. As discussed more fully below, this evidence leads staff to

consider a more narrowly defined indicator for thoracic coarse particles that focuses on thoracic

coarse particles characteristic of urban areas. This is consistent with CASAC’s recommendation
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to “qualify the PM10-2.5 standard . . . with a focus on urban areas” where thoracic coarse particles

are influenced by industrial or traffic-associated sources (Henderson, 2005, p. 8). The following

discussion briefly summarizes key observations from the available scientific and technical

information that are most relevant in comparing the potential health effects associated with

thoracic coarse particles in urban and rural settings.

5.4.2.1 Evidence Related to Urban and Rural Thoracic Coarse Particles

The atmospheric sciences and monitoring information in Chapter 2 indicates not only that

exposures to coarse particles tend to be higher in urban areas than in nearby rural locations, but

also that urban coarse particles are enriched by a number of contaminants not commonly found

in natural crustal materials that are typical of rural coarse particles. The elevation of urban

PM10-2.5 levels as compared to those at nearby rural sites indicates that sources located within

urban areas are generally the cause of elevated urban concentrations; conversely, PM10-2.5

concentrations in such urban areas are not largely composed of particles blown in from more

distant regions. Important sources of thoracic coarse particles in urban areas include dense

traffic that suspends significant quantities of road dust, as well as industrial and combustion

sources that contribute to ambient coarse particles both directly and through deposition to soils

and roads (Chapter 2, Table 2-2). It follows that thoracic coarse particles in urban areas would

differ in composition from those in rural areas, being enriched in components from urban

mobile, stationary, and area source emissions. 

While detailed composition data are more limited for PM10-2.5 than for PM2.5, available

measurements from some areas as well as studies of road dust components do show a significant

influence of urban sources on urban thoracic coarse particle composition and mass. Although

crustal elements and natural biological materials represent a significant fraction of thoracic

coarse particles in urban areas, both their relative quantity and character may be altered by urban

sources. For example, in industrial cities, primary particle emissions from industrial sources

and resuspended road dust can increase the relative amount of iron, one of the metals that has

been noted as being of some interest in the studies of mechanisms of toxicity for PM, as well as

other industrial process-related and potentially toxic materials such as nickel, cadmium, and

chromium (CD, p. 9-63). Traffic-related activities can grind and resuspend vegetative materials

into forms not as common in more natural areas (Rogge et al., 1993). Studies of urban road

dusts find that levels of a variety of components are increased from traffic as well as from other

anthropogenic urban sources, including products of incomplete combustion (e.g. polycyclic

aromatic hydrocarbons) from motor vehicle emissions and other sources, brake and tire wear,

rust, salt and biological materials (CD, p. 3D-3). As discussed in Chapter 2, limited ambient

coarse fraction composition data from various comparisons find that metals and sometimes

elemental carbon contribute a greater proportion of thoracic coarse particle mass in urban areas

than in nearby rural areas. In addition, while large uncertainties exist in emissions inventory

data, staff observes that major sources of PM10-2.5 emissions in the urban counties in which
5-55

epidemiologic studies have been conducted are paved roads and “other” sources (largely

construction), and that such areas also have larger contributions from industrial emissions,

whereas unpaved roads and agriculture are the main sources of PM10-2.5 emissions nationwide. 

Toxicologic studies, although quite limited, support the view that sources of coarse

particles common in urban areas are of greater concern than uncontaminated materials of

geologic origin. As noted above, one major source of urban coarse particles is paved road dust,

and the CD discusses results from a recent study in which road tunnel dust particles had greater

allergic adjuvant activity than several other particle samples, including residual oil fly ash and

diesel exhaust particles, in two animal models of allergy (Steerenberg et al., 2003; CD, p. 7-136-

137). This supports evidence available in the previous PM NAAQS review regarding potential

effects of road dust particles (EPA, 1996b, p. V-70). In contrast, a number of studies have

reported that Mt. St. Helens volcanic ash, an example of natural crustal material of geologic

origin, has very little toxicity in animal or in vitro toxicologic studies (CD, p. 7-216). 

A few toxicologic studies have used ambient thoracic coarse particles from

urban/suburban locations (PM10-2.5), and the results suggest that effects can be linked with several

components of PM10-2.5. As described in more detail in sections 5.4.1 and 3.2, these in vitro

toxicologic studies linked coarse fraction particles with effects including cytotoxicity, oxidant

formation, and inflammatory effects. These studies suggest that several components (e.g.,

metals, endotoxin, other materials) may have roles in various health responses but do not suggest

a focus on any individual component. 

Although largely focused on undifferentiated PM10, the series of epidemiologic

observations and toxicologic experiments related to the Utah Valley suggest that directly emitted

(fine and coarse) and resuspended (coarse) urban industrial emissions are of concern. Of

particular interest are area studies spanning a 13-month period when a major source of PM10 in

the area, a steel mill, was not operating. Observational studies found that respiratory hospital

admissions for children were lower when the plant was shut down (Pope et al., 1989). More

recently, a set of toxicologic and controlled human exposure studies have used particles

extracted from filters from ambient PM10 monitors from periods when the plant did and did not

operate. In both human volunteers and animals, greater lung inflammatory responses were

reported with particles collected when the source was operating, as compared to the period when

the plant was closed (CD, p. 9-73). In addition, in some studies it was suggested that the metal

content of the particles was most closely related to the effects reported (CD, p. 9-74). Staff

observes that while peak days in the Utah Valley occur in conditions that enhance fine particle

concentrations, over the long run, over half of the PM10 was in the coarse fraction. The

aggregation of particles collected on the filters during the study period reflect this long-term

composition. At a minimum, the filter-derived particles represent the kinds of industrial

components that would be incorporated in road dusts in the area.
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Taken together, the epidemiologic studies that examine exposures to thoracic coarse

particles generally found in urban environments and to natural crustal materials support the view

that urban thoracic coarse particles are of concern to public health, in contrast to uncontaminated

natural crustal dusts. With respect to the urban results, several recent studies have shown

associations between PM10-2.5 and health outcomes in a number of sites across the U.S. 

Associations have been consistently reported with morbidity in urban areas, some of which had

relatively low PM10-2.5 concentrations. For mortality, statistically significant associations have

been reported only for urban areas that have notably higher ambient PM10-2.5 concentrations. 

These associations are with short-term exposures to aggregated PM10-2.5 mass, and no

epidemiologic evidence is available on associations with different components or sources of

PM10-2.5. However, staff observes that the studies have all been conducted in urban areas of the

U.S., and thus reflect effects of thoracic coarse particles generally present in urban environments

from urban sources. 

By contrast, recent evidence from epidemiologic studies has suggested that mortality and

possibly other health effects are not associated with thoracic coarse particles from dust storms or

other such wind-related events that result in suspension of natural crustal materials of geologic

origin. The clearest example is provided by a study in Spokane, WA, which specifically

assessed whether mortality was increased on dust-storm days using case-control analysis

methods. The average PM10 level was more than 200 μg/m3

 higher on dust storm days than on

control days, and the authors report no evidence of increased mortality on these specific days

(Schwartz et al., 1999). One caveat of note is the possibility that people may reduce their

exposure to ambient particles on the most dusty days (e.g., Gordian et al., 1996; Ostro et al.,

2000). Nevertheless, the Spokane study provides no suggestion of significant health effects from

uncontaminated natural crustal materials that would typically form a major fraction of coarse

particles in non-urban or rural areas.

Beyond the urban and rural distinctions discussed above, staff has also considered the

extent to which there is evidence of effects with exposure to ambient thoracic coarse particles in

communities predominantly influenced by agricultural or mining sources. For example, in the

last review, staff considered health evidence related to long-term silica exposures from mining

activities, but found that there was a lack of evidence that such emissions contribute to effects

linked with ambient PM exposures (EPA, 1996b, p. V-28). Similarly in this review, there is an

absence of evidence related to such community exposures. While dust generated from

agricultural activity can include biological material such as fungal or bacterial material, and

some occupational studies discussed in the CD report effects at occupational exposure levels

(Table7B-3, p. 7B-11), such studies do not provide relevant evidence for much lower levels of

community exposures. Further, it is unlikely that such sources contribute to the effects that have

been observed in the recent urban epidemiologic studies.
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The CD concludes its integrated assessment of the effects of natural crustal materials as

follows:

Certain classes of ambient particles appear to be distinctly less toxic than others

and are unlikely to exert human health effects at typical ambient exposure

concentrations (or perhaps only under special circumstances). For example,

particles of crustal origin, which are predominately in the coarse fraction, are

relatively non-toxic under most circumstances, compared to combustion-related

particles (such as from coal and oil combustion, wood burning, etc.) However,

under some conditions, crustal particles may become sufficiently toxic to cause

human health effects. For example, resuspended crustal particles may be

contaminated with toxic trace elements and other components from previously

deposited fine PM, e.g., metals from smelters (Phoenix) or steel mills

(Steubenville, Utah Valley), PAHs from automobile exhaust, or pesticides from

agricultural lands. (CD, p. 8-344)

This is consistent with CASAC’s conclusion that the available evidence from health studies

suggests that the focus of an indicator for thoracic coarse particles should be on such particles

found in urban, not rural environments (Henderson, 2005).

The staff assessment of the available evidence relevant to the appropriate scope of an

indicator for coarse particles can be summarized as follows. Thoracic coarse particle

concentrations generally reflect contributions from local sources, and the limited information

available from speciation of thoracic coarse particles and emissions inventory data indicate that

the sources of urban thoracic coarse particles generally differ from those found in nonurban

areas. As a result, the kinds of thoracic coarse particles people are exposed to in urban areas can

be expected to differ significantly from the kinds found in non-urban or rural areas. Ambient

PM10-2.5 exposure is associated with health effects in studies conducted in urban areas, and the

limited available health evidence more strongly implicates coarse particles from industrial and

traffic-related sources than from uncontaminated soil or geologic sources. The limited evidence

does not support either the existence or the lack of causative associations for community

exposures to agricultural or mining industries. Given the apparent differences in composition

and in the epidemiologic evidence, it is not appropriate to conclude that evidence of associations

with health effects related to urban coarse particles would also apply to nonurban or rural coarse

particles.

Collectively, the evidence suggests that a more narrowly defined indicator for thoracic

coarse particles should be considered that would protect public health against effects linked with

thoracic coarse particles present in urban areas. Such an indicator would be principally based on

particle size, but also reflect a focus on those thoracic coarse particles that are generally present

in urban environments. Staff recommends consideration of thoracic coarse urban particulate

matter (UPM10-2.5) as an indicator for a thoracic coarse particle standard, referring to airborne

particles between 2.5 and 10 μm in diameter that are generally present in urban environments,
13 EPA plans to issue proposed and final revisions to related requirements for federal reference and

equivalent methods and monitoring network design concurrently with proposed and final decisions regarding

revisions to the PM NAAQS.

14 This work is being done in consultation with the CASAC AAMM Subcommittee.

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which, as discussed above, are principally comprised of resuspended road dust typical of high

traffic-density areas and emissions from industrial sources. Staff considers that UPM10-2.5 would

more likely be an effective indicator for standards to protect against effects of thoracic coarse

particles than a more broadly focused PM10-2.5 indicator. Staff notes that this indicator would

also be consistent with an appropriately cautious interpretation of the epidemiologic evidence

that does not potentially over-generalize the results of the limited available studies.

5.4.2.2 Related Requirements for PM10-2.5 Monitors and Monitoring Network Design

Along with staff’s recommendations on the definition for an indicator for urban thoracic

coarse particles, it is important to recognize that requirements for federal reference and

equivalent methods and monitoring network design are essential components in fully defining

and applying a PM indicator. While these efforts are not described in detail in this Staff Paper,

the discussions below highlight key components of these activities for an urban thoracic coarse

particle standard indicator.13

First, in conjunction with considering UPM10-2.5 as an indicator for standards to address

thoracic coarse particles, EPA is evaluating various ambient monitoring methods, including

continuous methods. This evaluation is being performed through field studies of commercially

ready and prototype methods to characterize the measurement of PM10-2.5 in urban areas.14 This

PM10-2.5 methods evaluation has resulted in characterizing the performance of multiple PM10-2.5

measurement technologies under a variety of aerosol and meteorological conditions typical of

urban areas in regions across the country. This characterization has demonstrated that the

majority of commercially available methods for the measurement of PM10-2.5 have good precision

and are well correlated with filter-based gravimetric methods, such as the difference method that

has primarily been used to date (i.e., operation of collocated PM10 and PM2.5 low volume FRMs

and calculating PM10-2.5 by difference). EPA is working with the instrument manufacturers to

address design issues that should reduce biases that have been observed among methods, in

preparation for another field study examining the performance of the methods.

EPA has also begun the process of examining data quality objectives for a potential 24-

hour UPM10-2.5 standard. On the basis of preliminary analyses, it is apparent that greater sampling

frequency will be important due to the high variability of PM10-2.5 in the atmosphere in urban

environments. Due to the resource intensive nature of filter sampling on a daily basis, staff

believes that it will be critical to include continuous monitoring in any network deployment

strategy for a possible UPM10-2.5 standard. In addition to providing high temporal resolution to
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PM10-2.5 data, continuous monitors would also support public reporting of UPM10-2.5 episodes and

inclusion of UPM10-2.5 in an air quality forecasting program.

In addition, EPA has also commenced the examination of monitoring network design

issues for a possible UPM10-2.5 standard. One key focus in monitoring network design is

consistency with information from the health studies and with staff conclusions regarding the

appropriateness of an indicator based on PM10-2.5 mass generally present in urban environments

and reflecting local urban sources. The available epidemiologic studies have reported

associations with PM10-2.5 mass that has been measured at sites that are located in or near heavily

populated areas, but not in close proximity to industrial or other specific sources, to reflect

community exposure levels. In the context of applying such a standard, staff has examined

various measures for designing an appropriate monitoring network for an urban PM10-2.5 standard,

including indicators of traffic density and locations of industrial sources, as well as total

population and population density. Staff observes that these traffic- and population-related

measures are very highly correlated with one another. On an initial basis, a focus on

CBSA/CSAs with populations of at least 100,000 would result in a base monitoring network that

would include approximately 350 areas. Further refinements in network size in terms of the

number of monitors to be placed within these areas can be accomplished through additional

criteria such as a graduated scale that increases monitoring requirements for more populous

CBSA/CSAs, as well as a hybrid strategy that combines population/traffic-related measures with

comparisons to historical PM10 or estimated PM10-2.5 design values to focus additional monitors in

areas with elevated concentrations associated with traffic-related and industrial sources, and

potentially reduce monitoring in CBSA/CSAs with lower concentrations levels of thoracic

coarse particles.

As noted elsewhere in this document, PM10-2.5 is more highly variable in the atmosphere

than PM2.5, such that the specific criteria regarding monitor placement will be a particularly

important consideration in the implementation of a PM10-2.5 monitoring network. Staff has

examined various options for targeting monitors within larger CBSA/CSAs to focus

measurements in locations of expected high concentrations of urban coarse thoracic particles that

also represent the population-oriented objective of monitors underlying key epidemiologic

studies. One useful metric for guiding monitor placement appears to be population density, for

example, the placement of monitors in U.S. Census Block Groups characterized by population

densities greater than 500 people per square mile. This type of criterion effectively guides

monitors toward the urban/center city and suburban location settings that likely represent areas

of high population exposure to elevated concentrations, as compared to the rural parts of

urbanized CBSA/CSAs. Staff notes, in addition, that the population density metric based on

Census Block Group has the potential to identify and de-emphasize monitoring in “population

holes” within the urbanized area, such as areas with only industrial development. For PM10,

some monitors have historically been located in such heavily industrialized areas away from
15 Staff notes that these discussions of measurement scale for monitors are consistent with definitions in 40

CFR Part 58, Appendix D, http://www.access.gpo.gov/nara/cfr/waisidx_04/40cfr58_04.html.

16 Staff observes that PM10-2.5 monitors may also be sited to provide data for other purposes such as research

or trends assessment that would be outside the scope of a monitoring network for a UPM10-2.5 standard.

17 Current policy available at http://www.epa.gov/ttn/oarpg/t1/memoranda/nepol.pdf.). EPA plans to issue

proposed and final revisions to the natural events policy concurrently with any proposed and final decisions

regarding revisions to the PM NAAQS.

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significant population exposure, including some existing source-oriented monitors placed on

facility fence-lines to investigate specific emission-related complaints. Staff believes that it

would be inconsistent with the basis of a UPM10-2.5 indicator and, thus, inappropriate to include

any such monitors as part of a UPM10-2.5 monitoring network.

Consideration of specific measurement scale guidance for placement of PM10-2.5 monitors

is influenced by the rapid fallout of coarse fraction particles with increasing distance from

sources. Analysis of existing PM10-2.5 concentrations in cities such as Birmingham, Cleveland,

and Las Vegas has revealed large differences in three-year mean, 98th percentile design values,

as well as poor daily correlations, between monitors located within several kilometers of each

other. In many cases, these large differences can be explained by the immediate proximity of the

higher reading monitor to areas of industrial and high traffic activity, compared with lower

reading monitors that may be farther from coarse-particle generating or resuspending activities. 

As a result, staff believes that to be consistent with the population-oriented objective of the

monitors underlying key epidemiologic studies, population-oriented monitors that represent

middle scale (i.e., 100 meters to 0.5 km) or neighborhood scale (i.e., 0.5 to 4.0 km) sized areas of

relatively uniform land use would be most appropriate for a UPM10-2.5 network, recognizing that

a significant spatial gradient in coarse thoracic particle concentrations may still exist across

middle scale-sized areas.15 Such preferred locations for PM10-2.5 monitors in a UPM10-2.5 network

would include densely populated communities located several hundred meters from significant

sources, such as industrial sources or heavily traveled roadways.16

Finally, with regard to elevations in thoracic coarse particle levels that may occur in

urban areas as a result of dust storms or other such events, staff observes that EPA has

historically used implementation policies to address such issues in the implementation of PM

standards. Examples of such policies include the “natural events” policy for implementation of

the PM10 standards. This policy includes guidance regarding exclusion of PM10 measurements

from natural events in making determinations on attainment or nonattainment of the standards. 

The natural events discussed in this policy include volcanic and seismic activity, wildland fires

and high wind events. EPA is now in the process of revising policies for natural events and

exceptional events to address issues related to the PM2.5 NAAQS, as well as potential new

UPM10-2.5 standards.17
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5.4.3 Averaging Times

In the last review, EPA retained both annual and 24-hour standards to provide protection

against the known and potential effects of short- and long-term exposures to thoracic coarse

particles (62 FR at 38,677-79). This decision was based in part on qualitative considerations

related to the expectation that deposition of thoracic coarse particles in the respiratory system

could aggravate effects in individuals with asthma. In addition, quantitative support came from

limited epidemiologic evidence suggesting that aggravation of asthma and respiratory infection

and symptoms may be associated with daily or episodic increases in PM10, where dominated by

thoracic coarse particles including fugitive dust. Further, potential build-up of insoluble thoracic

coarse particles in the lung after long-term exposures to high levels was also considered

plausible.

New information available in this review on thoracic coarse particles, as discussed above

in section 5.4.1, includes several epidemiologic studies that report statistically significant

associations between short-term (24-hour) exposure to PM10-2.5 and various morbidity effects and

mortality. With regard to long-term exposure studies, while one recent study reported a link

between reduced lung function growth and long-term exposure to PM10-2.5 and PM2.5, other such

studies reported no associations. The CD concludes that the evidence does not suggest an

association with long-term exposure to PM10-2.5 (CD, p. 9-79). Staff also notes that no evidence

is available to suggest associations between PM10-2.5 and very short exposure periods of one or

more hours.

Based on these considerations, staff concludes that the newly available evidence

continues to support a 24-hour averaging time for a standard intended to control thoracic coarse

particles, based primarily on evidence suggestive of associations between short-term (24-hour)

exposure and morbidity effects and, to a lessor degree, mortality. Noting the absence of

evidence judged to be even suggestive of an association with long-term exposures, staff

concludes that there is no evidence that directly supports an annual standard, while recognizing

that it could be appropriate to consider an annual standard to provide a margin of safety against

possible effects related to long-term exposure to thoracic coarse particles that future research

may reveal. Staff observes, however, that a 24-hour standard that would reduce 24-hour

exposures would also likely reduce long-term average exposures, thus providing some margin of

safety against the possibility of health effects associated with long-term exposures.

5.4.4 Alternative Standards to Address Health Effects Related to Short-term Exposure

As noted earlier, in the last review, EPA’s decision to retain the level of the 24-hour PM10

standard of 150 μg/m3 (with revision of the form of the standard) for protection against effects of

exposure to coarse fraction particles was based on two community studies of exposure to fugitive

dust that showed health effects in areas experiencing large exceedances of that standard (Gordian

et al., 1996; Hefflin et al., 1994), as well as on qualitative information regarding the potential for
18 As noted above, the court vacated the 1997 24-hour PM10 standard that had been revised to incorporate a

99th percentile form.

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health effects related to short-term exposure to thoracic coarse particles. Because of the very

limited nature of this evidence, staff concluded that while it supported retention of a standard to

control thoracic coarse particles, it provided no basis for considering a more protective standard. 

However, because of concerns about the expected-exceedance-based form of the 1987 PM10

standard, primarily related to the stability of the attainment status of an area over time and

complex data handling conventions needed in conjunction with less-than-every-day sampling,

EPA adopted a concentration-based form for the 24-hour standard, as was done for the 24-hour

PM2.5 standard, as discussed in section 5.3.6. In making this change, EPA selected a 99th

percentile form,18 in contrast to the 98th percentile form adopted for the 24-hour PM2.5 standard,

so as not to allow any relaxation in the level of protection that had been afforded by the previous

1-expected-exceedance form.

Since the last review, as discussed above in section 5.4.1, new evidence specific to

thoracic coarse particles has become available that reports associations between short-term

PM10-2.5 concentrations in some urban areas and various morbidity effects and, to a lesser degree,

mortality. In considering alternative standards that would provide protection against such health

effects, as discussed below, staff has taken into account evidence-based considerations and has

examined the extent to which risk-based considerations should also be taken into account.

5.4.4.1 Evidence-based Considerations

In considering the available evidence on associations between short-term PM10-2.5

concentrations and morbidity and mortality effects as a basis for setting a 24-hour standard for

thoracic coarse particles, staff has focused on relevant U.S. and Canadian studies (Appendix 3-

A). As discussed above in section 5.4.1 and in Chapter 3, staff has taken into account reanalyses

that addressed GAM-related statistical issues and has considered the extent to which the studies

report statistically significant and relatively precise relative risk estimates; the extent to which

the reported associations are generally robust to co-pollutant confounding and alternative

modeling approaches; and the extent to which the studies used relatively reliable air quality data.

As an initial matter, staff recognizes, as discussed in Chapter 3 (section 3.6.6), that these

short-term exposure studies provide no evidence of clear population thresholds, or lowestobserved-effects levels, in terms of 24-hour average concentrations. Staff notes that in the one

study that explored a potential PM10-2.5 threshold, conducted in Phoenix, no evidence of a

threshold was observed for PM10-2.5, even though that study provided some suggestion of a

potential threshold for PM2.5. The CD concludes that while there is no evidence of a clear
19 Staff notes that the distributions of daily PM10-2.5 concentrations in these studies often extend down to or

below typical background levels, such that the likely range of background concentrations across the U.S., as

discussed in Chapter 2, section 2.6, could be a relevant consideration in this policy evaluation. Staff recognizes,

however, that there are insufficient data to estimate daily distributions of background PM10-2.5 levels (as was done for

background PM2.5 levels, as discussed in Chapter 2, section 2.6).

20 As shown in air quality data trends reports: for Seattle, 1997 Air Quality Annual Report for Washington

State, p. 17, at http://www.ecy.wa.gov/pubs/97208.pdf; for Detroit, Michigan’s 2003 Annual Air Quality Report, p.

46, at http://www.deq.state.mi.us/documents/deq-aqd-air-reports-03AQReport.pdf.

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threshold within the range of air quality observed in the studies,19 for some health endpoints it is

likely to be extremely difficult to detect threshold levels (CD, p. 9-45). As a consequence, this

body of evidence is difficult to translate directly into a specific 24-hour standard that would

protect against the range of effects that have been associated with short-term exposures.

In considering the evidence, staff is mindful of these uncertainties as well as the limited

nature of the available evidence. In examining the available evidence to identify a basis for a

range of standard levels that would be appropriate for consideration, staff has focused on the

upper end of the distributions of daily PM10-2.5 concentrations in the relevant studies, particularly

in terms of the 98th and 99th percentile values, consistent with the forms considered in section

5.3.6 above for a short-term PM2.5 standard. Staff's examination of the evidence is discussed

below, based on air quality information and analyses presented in Ross and Langstaff (2005) and

Ross (2005).

In looking first at the morbidity studies identified in section 5.4.1 that report statistically

significant associations with respiratory- and cardiac-related hospital admissions in Toronto

(Burnett et al., 1997), Seattle (Sheppard et al.,1999, 2003), and Detroit (Lippmann et al., 2000;

Ito, 2003), the reported 98th percentile values in the three areas range from approximately 30 to

36 μg/m3

, and the 99th percentile values range from 36 to 40 μg/m3. To provide some

perspective on these PM10-2.5 levels, staff notes that the level of the 24-hour PM10 standard was

exceeded only on a few occasions during the time periods of the studies in Detroit and Seattle.20

Staff has also looked at the studies identified in section 5.4.1 that report statistically

significant and generally robust associations with mortality and short-term exposures to PM10-2.5.

Studies conducted in Phoenix (Mar et al., 2000, 2003) and Coachella Valley, CA (Ostro et al.,

2000, 2003) report 98th percentile PM10-2.5 values of approximately 70 and 107 μg/m3

, and 99th

percentile values of 75 and 134 μg/m3, respectively. These studies were conducted in areas with

air quality levels that did not meet the current PM10 standards. In addition, a statistically

significant association was reported between PM10-2.5 and mortality in Steubenville as part of the

Harvard Six Cities analysis (Schwartz et al., 1996; Klemm et al., 2003). PM10-2.5 concentrations

were fairly high in this eastern city, with reported 98th and 99th percentile PM10-2.5 values of 53

and 61 μg/m3

, respectively. Staff notes that, in contrast with most areas, PM10-2.5 and PM2.5

concentrations were highly correlated (r=0.69) in Steubenville during the study period (Schwartz
5-64

et al., 1996; Klemm et al., 2003). In contrast to the statistically significant mortality associations

with PM10-2.5 reported in these studies, staff notes that no such associations were reported in a

number of other studies, including in the five other cities that were part of the Harvard Six Cities

study (Boston, St. Louis, Knoxville, Topeka, and Portage), San Jose, Detroit, Philadelphia, and

Pittsburgh. With the exception of Pittsburgh, these cities had much lower 98th percentile PM10-2.5

values, ranging from 18 to 49 μg/m3.

Based on the air quality information reported in these studies, staff makes the following

observations:

• In the morbidity studies that reported statistically significant associations with

respiratory or cardiovascular hospitalization in Detroit and Seattle, and with

respiratory symptoms in six U.S. cities, the reported 98th percentile PM10-2.5 values

ranged from approximately 30 up to 40 μg/m3.

• In the mortality studies that reported statistically significant associations in

Steubenville, Coachella Valley, and Phoenix, the reported 98th percentile PM10-2.5

values were all above 50 μg/m3, ranging from 53 μg/m3 up to 107 μg/m3 in

Coachella Valley.

• In the mortality studies that reported no statistically significant associations in

five of the cities in the Harvard Six Cities study (Boston, St. Louis, Knoxville,

Topeka, and Portage), and in San Jose, Detroit, Philadelphia, and Pittsburgh, the

reported 98th percentile PM10-2.5 values were below 50 μg/m3 for all cities except

Pittsburgh, ranging from 18 to 49 μg/m3.

In looking more closely at air quality data used in the morbidity and mortality studies

discussed above, however, staff recognizes that the uncertainty related to exposure measurement

error can be potentially quite large. For example, in looking specifically at the Detroit study,

staff notes that the PM10-2.5 air quality values were based on air quality monitors located in

Windsor, Canada. The study authors determined that the air quality values from these monitors

were generally well correlated with air quality values monitored in Detroit, where the hospital

admissions data were gathered, and, thus concluded that these monitors were appropriate for use

in exploring the association between air quality and hospital admissions in Detroit. Staff has

observed, however, that the PM10-2.5 levels reported in this study are significantly lower than the

PM10-2.5 levels measured at some of the Detroit monitors in 2003 -- an annual mean level of 13.3

μg/m3 is reported in the study based on 1992 to 1994 data, as compared to an average annual

mean level of 21.7 μg/m3

 measured at two urban-center monitors in 2003 (which is used as the

basis for the risk assessment presented in Chapter 4). This observation prompted staff to further

explore the comparison between PM10-2.5 levels monitored at Detroit and Windsor sites. This

exploration has shown that in recent years, based on available Windsor and Detroit data from

1999 to 2003, the Windsor monitors used in this study typically have recorded PM10-2.5 levels that
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are generally less than half the levels recorded at urban-center Detroit monitors, though the

concentrations measured in Windsor are more similar to concentrations reported for suburban

areas well outside the city. These observations lead staff to conclude that the statistically

significant, generally robust association with hospital admissions in Detroit likely reflects

population exposures that may be appreciably higher in the central city area, but not necessarily

across the broader study area, than would be estimated using data from the Windsor monitors.

Staff also looked more specifically at the Coachella Valley mortality study (Ostro et al.,

2000; 2003), in which data were used from a single monitoring site in one city, Indio, within the

study area where daily measurements were available. The mean PM10-2.5 concentration during

the study period (1989-1998) was 30.5 μg/m3

 at this monitoring site. Consistently lower

concentrations were measured at another city in the Coachella Valley area, Palm Springs; during

the study period the mean value at this site was 17 μg/m3

. The authors report that the data for the

two sites were correlated, with correlation coefficients of about 0.6 for each of the three PM

indicators. Using 2001-2003 data, mean PM10-2.5 levels were reported to be 44 and 15 μg/m3 at

the Indio and Palm Springs sites, respectively. Thus, in Coachella Valley, mortality was

significantly associated with PM10-2.5 measurements made at the Indio site, but a portion of the

study population would have been expected to experience appreciably lower ambient exposure

levels. In contrast to the Detroit study, air quality data used in the mortality study conducted in

Coachella Valley appear to represent concentrations on the high end of PM10-2.5 levels for

Coachella Valley communities.

A closer examination of the air quality data used in the other studies discussed above

generally shows less disparity between air quality levels at the monitoring sites used in the

studies and the broader pattern of air quality levels across the study areas than that described

above in the Detroit and Coachella Valley studies. More specifically, less variation across

monitoring sites was seen in air quality data in both Seattle and St. Louis. In Steubenville, the

PM10-2.5 concentrations measured at the centrally located monitor used in the study are somewhat

higher than those reported at other area monitors. In Phoenix, data from a larger network of

PM10 monitors across the area shows a large gradient in concentrations across the urban area,

with lower concentrations measured at sites on the north side to appreciably higher

concentrations on the south side; data from the one centrally located monitoring site used in the

study appears to fall in the mid-range of concentrations along the north-south gradient.

This closer examination of air quality information generally reinforces the view that

exposure measurement error is potentially quite large in these PM10-2.5 studies. As a

consequence, the air quality levels reported in these studies, as measured by ambient

concentrations at monitoring sites within the study areas, are not necessarily good surrogates for

population exposures that are likely associated with the observed effects in the study areas or

that would likely be associated in other urban areas across the country. The Detroit example

suggests that population exposures were probably appreciably underestimated in the Detroit
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morbidity study, such that the observed effects are likely associated with higher PM10-2.5 levels

than reported. In contrast, the Coachella Valley mortality study provides an example in which

population levels were probably appreciably overestimated, such that the observed effects may

well be associated with lower PM10-2.5 levels than reported. At relatively low levels of air

quality, population exposures implied by these studies as being associated with the observed

effects likely become more uncertain, suggesting an even higher degree of caution in interpreting

the group of morbidity studies as a basis for identifying a standard level that would protect

against the observed effects.

Taking into account this closer examination of the studies, staff concludes that this

evidence suggests consideration of a standard for urban thoracic coarse particles at a PM10-2.5

level at least down to 50 μg/m3, in conjunction with a 98th percentile form (which would be

roughly equivalent to a level of 60 μg/m3

 in conjunction with a 99th percentile form). While

lower levels may be considered to provide a margin of safety against morbidity effects that may

possibly occur at such lower levels, staff believes that consideration of a standard below these

levels may not be warranted based on this evidence. Staff's view takes into account the

conclusion that this evidence is particularly uncertain as to population exposures, especially from

the morbidity studies reporting effects at relatively low concentrations, as well as the general

lack of evidence of associations from the group of mortality studies with reported concentrations

below these levels. A standard set at or somewhat above these levels could be expected to

provide protection against the potential mortality effects observed in studies that reported

ambient concentrations above this level, as well as morbidity effects that may occur above this

level.

An even more cautious or restrained approach to interpreting the limited body of PM10-2.5

epidemiologic evidence would be to judge that the uncertainties in this whole group of studies as

to population exposures that are associated with the observed effects are too large to use the

reported air quality levels directly as a basis for setting a specific standard level. Staff notes that

such a judgment would not be inconsistent with the conclusion reached above in section 5.4.1

that these studies, together with other dosimetric and toxicologic evidence, provide support for

retaining standards for thoracic coarse particles at some level to protect against the morbidity

and mortality effects observed in the studies, regardless of whether an associated population

exposure level can be clearly discerned from the studies.

Considering this more cautious interpretation, staff believes that it would be reasonable

to interpret the available epidemiologic evidence more generally, by considering whether it

provides a sufficient basis for a standard that would afford protection generally “equivalent” to

that afforded by the current PM10 standards. Considering the available evidence in this way leads

to the following observations:
21 Monitors included in this analysis are those in CBSAs with at least 100,000 population and in census

block groups with a population density of at least 500, and that also had 3 years of complete data in each quarter for

both PM10 and PM10-2.5.

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• The statistically significant mortality associations with short-term exposure to

PM10-2.5 reported in the Phoenix and Coachella Valley studies were observed in

areas that did not meet the current PM10 standards.

• The statistically significant morbidity associations with short-term exposure to

PM10-2.5 reported in the Detroit and Seattle studies were observed in areas that

exceeded the level of the current 24-hour PM10 standard only on few occasions

during the time periods of the studies.

• All but one of the statistically significant morbidity and mortality associations

with short-term exposure to PM10 reported in areas dominated by coarse fraction

particles (including Reno/Sparks, NV, Tucson, AZ, Anchorage, AK, and the Utah

Valley area, as discussed above in section 5.4.1) were observed in areas that did

not meet the current PM10 standards.

Based on these considerations, staff finds little basis for concluding that the degree of

protection afforded by the current PM10 standards is greater than warranted, since potential

mortality effects have been associated with air quality levels not allowed by the current

standards, but have not been associated with air quality levels that would generally meet the

current standards. Further, staff finds little basis for concluding that a greater degree of

protection is warranted in light of the very high degree of uncertainty in the relevant population

exposures implied by the morbidity studies. Staff judges, therefore, that it is reasonable to

interpret the available evidence as supporting consideration of a short-term standard for urban

thoracic coarse particles set so as to provide generally “equivalent” protection to that afforded by

the current PM10 standards, recognizing of course that no one PM10-2.5 level will be strictly

equivalent to a specific PM10 level in all areas. Such a standard would likely provide protection

against morbidity effects especially in urban areas where, unlike the study areas, PM10 is

generally dominated by coarse-fraction rather than fine-fraction particles. Such a standard

would also likely provide protection against the more serious, but more uncertain, PM10-2.5-

related mortality effects generally observed at somewhat higher air quality levels.

To identify a range of levels for consideration for a short-term standard for urban thoracic

coarse particles set so as to afford generally "equivalent" protection as the current PM10

standards, staff has analyzed available data on PM10-2.5 and PM10 24-hour average concentrations

from monitors that would be included in the monitoring network design provisions discussed in

section 5.4.2.2.21 Based on a regression analysis of the 205 monitoring sites so identified

(Schmidt et al., 2005), staff finds that a UPM10-2.5 level of approximately 60 μg/m3

 in terms of a
22 Across the U.S., the 95% confidence intervals around these point estimates are approximately + 3 :g/m3

,

while region-specific intervals are approximately + 10 :g/m3

 in the five regions in which all of the areas that do not

meet the current PM10 standards are located.

23 As shown in Table 5B-2(c), staff notes that there are 585 counties with PM10 monitoring sites used in

determining compliance with the PM10 standards, whereas only 309 of those counties have monitor sites that would

be included in the monitoring network design provisions discussed in section 5.4.2.2. Of these 309 counties, 259

have PM10 and PM10-2.5 air quality data that meet the data completeness criteria defined for this analysis, which are

somewhat less restrictive that the criteria that were applied in the regression analysis described above.

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98th percentile form (or approximately 70 μg/m3 in terms of a 99th percentile form) would be

roughly equivalent on average across the U.S. to a PM10 level of 150 μg/m3 in terms of a oneexpected-exceedance form.22 While noting appreciable variability in the estimated point of

equivalence across individual sites, these levels of approximate average equivalence are quite

consistent across each of the five regions in which all of the areas that do not meet the current

PM10 standards are located (including the southern California, southwest, northwest, upper midwest, and southeast regions). Notably different average equivalence levels were observed in the

other two regions, i.e., approximately 40 μg/m3

 in the northeast and over 70 μg/m3 in the

industrial mid-west (in terms of 98th percentile forms).

Another approach to identifying a UPM10-2.5 standard that is generally "equivalent" to the

current PM10 standards is to compare the number of areas, and the population in those areas, that

would likely not meet a specific UPM10-2.5 standard, set at a given level and form, with the same

measures in areas that do not meet the current PM10 standards. Such an analysis, based on 2001

to 2003 data from monitors that would be included in the monitoring network design provisions

discussed in section 5.4.2.2 provides some rough indication of the breadth of protection

potentially afforded by alternative standards. The results of this analysis (Appendix 5B, Tables

5B-2(a), (b) and (c), for the 98th and 99th percentile forms of a 24-hour UPM10-2.5 standard and the

current PM10 standards, respectively) indicate that a UPM10-2.5 standard of about 70 or 65 :g/m3

,

98th percentile form, (or approximately 85 or 80 :g/m3

, 99th percentile form) would impact

approximately the same number of counties or number of people, respectively, as would the

current PM10 standards.23

Based on these alternative analyses of generally "equivalent" UPM10-2.5 standards, staff

concludes that it is reasonable to consider a 24-hour UPM10-2.5 standard in the range of

approximately 60 to 70 :g/m3 with a 98th percentile form (approximately 70 to 85 :g/m3 with a

99th percentile form). Considering standards within these ranges, somewhat above the levels

identified above based on an examination of the air quality concentrations in the relevant

epidemiologic studies (i.e., 50 :g/m3, 98th percentile form, or 60 :g/m3, 99th percentile form),

would reflect an even higher degree of caution in interpreting the epidemiologic evidence. 

Consideration of a generally "equivalent" UPM10-2.5 standard would reflect a judgment that while

the epidemiologic evidence supports establishing a short-term standard for urban thoracic coarse
5-69

particles at such a generally “equivalent” level, the evidence concerning air quality levels of

thoracic coarse particles in the studies is not strong enough to provide a basis for revising the

level of protection generally afforded by the current PM10 standards.

5.4.4.2 Risk-based Considerations

Beyond looking directly at the relevant epidemiologic evidence and related air quality

information, staff has also considered the extent to which the PM10-2.5 risk assessment results

discussed in Chapter 4 can help inform consideration of alternative 24-hour PM10-2.5 standards. 

While one of the goals of the PM10-2.5 risk assessment was to provide estimates of the risk

reductions associated with just meeting alternative PM10-2.5 standards, staff has concluded that the

nature and magnitude of the uncertainties and concerns associated with this portion of the risk

assessment weigh against use of these risk estimates as a basis for recommending specific

standard levels. These uncertainties and concerns include, but are not limited to the following:

• as discussed above, concerns that the current PM10-2.5 levels measured at ambient

monitoring sites in recent years may be quite different from the levels used to

characterize exposure in the original epidemiologic studies based on monitoring

sites in different location, thus possibly over- or underestimating population risk

levels;

• greater uncertainty about the reasonableness of the use of proportional rollback to

simulate attainment of alternative PM10-2.5 daily standards in any urban area due to

the limited availability of PM10-2.5 air quality data over time;

• concerns that the locations used in the risk assessment are not representative of

urban areas in the U.S. that experience the most significant 24-hour peak PM10-2.5

concentrations, and thus, observations about relative risk reductions associated

with alternative standards may not be relevant to the areas expected to have the

greatest health risks associated with elevated ambient PM10-2.5 levels; and

• concerns about the much smaller health effects database that supplies the

concentration-response relationships used in the risk assessment, compared to that

available for PM2.5, which limits our ability to evaluate the robustness of the risk

estimates for the same health endpoints across different locations.

5.4.4.3 Summary

In considering the relevant dosimetric, toxicologic, and epidemiologic evidence,

associated air quality information, and related limitations and uncertainties, staff concludes that

there is support for considering a 24-hour UPM10-2.5 standard to replace the current PM10

standards to provide protection against health effects associated with short-term exposures to

thoracic coarse particles that are generally present in urban environments. In looking at the

evidence of associations between short-term exposure to PM10-2.5 and morbidity and mortality,
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both on its own and in the context of considering a standard set so as to be generally "equivalent"

to the current PM10 standards, staff concludes that it is appropriate to consider a 24-hour standard

in the range of 50 to 70 μg/m3

, with a 98th percentile form, or in the range of 60 to 85 μg/m3, with

a 99th percentile form. A standard set within either of these ranges could be expected to provide

protection against the morbidity effects of PM10-2.5, especially those likely to occur in areas in

which PM10 is dominated by thoracic coarse particles, as well as to protect against the potential

mortality effects of PM10-2.5.

Staff recognizes, however, that the epidemiologic evidence on morbidity and mortality

effects related to PM10-2.5 exposure is very limited at this time. A key area of uncertainty in this

evidence is the potentially quite large uncertainty related to exposure measurement error for

PM10-2.5, as compared with fine particles. PM10-2.5 concentrations can vary substantially across a

metropolitan area and thoracic coarse particles are less able to penetrate into buildings than fine

particles; thus, the ambient concentrations reported in epidemiologic studies may not well

represent area-wide population exposure levels. Other key uncertainties include the very limited

information available on the composition of thoracic coarse particles and the effects of thoracic

coarse particles from various sources, and the lack of evidence on potential mechanisms for

effects of thoracic coarse particles. Staff believes that placing relatively more weight on these

uncertainties would focus consideration of standard levels toward the upper end of the ranges

identified above, whereas a more precautionary approach would focus consideration on the

lower end of these ranges.

5.4.5 Summary of Staff Recommendations on Primary PM10-2.5 NAAQS

Staff recommendations for the Administrator's consideration in making decisions on

standards for thoracic coarse particles, together with supporting conclusions from sections 5.4.1

through 5.4.4, are briefly summarized below. In making these recommendations, staff is mindful

that the Act requires standards to be set that, in the Administrator's judgment, 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 NAAQS be set at

zero-risk levels, but rather at levels that avoid unacceptable risks to public health.

( 1) The current primary PM10 standards should be revised in part by replacing the PM10

indicator with an indicator of urban thoracic coarse particles that does not generally

include fine particles. Any such revised standards should be based primarily on

available health effects evidence and air quality data generally indexed by PM10-2.5, to

provide public health protection more specifically directed toward effects related to

exposure to thoracic coarse particles in the ambient air, together with consideration of

evidence from studies using PM10 in locations where PM10-2.5 is the dominant fraction.
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( 2) The indicator for a thoracic coarse particle standard should be UPM10-2.5, which is

consistent with the recommendation made in section 5.3.7 to retain PM2.5 as the indicator

for fine particle standards.

( a) As noted above, this recommended indicator is primarily based on particle size,

but with a more narrow focus on those thoracic coarse particles that are generally

present in urban environments. The available evidence from studies on

atmospheric chemistry and sources and health effects of thoracic coarse particles

indicates that a UPM10-2.5 indicator is more likely an effective indicator for

standards to protect against health effects of thoracic coarse particles than a more

broadly-focused PM10-2.5 indicator.

( b) In support of this recommendation, work should continue on the development of

an FRM for a UPM10-2.5 indicator based on the ongoing field program to evaluate

various types of PM10-2.5 monitors, and consideration should be given to the

adoption of FEMs for appropriate continuous measurement methods.

( 3) A 24-hour averaging time should be retained for a UPM10-2.5 standard to protect against

health effects associated with short-term exposure periods, with consideration given to

the use of either a 98th or 99th percentile form. There is little basis for also retaining an

annual averaging time for protection against such health effects.

( 4) Consideration should be given to setting a 24-hour UPM10-2.5 standard with a level in the

range of approximately 50 to 70 μg/m3, 98th percentile form, or approximately 60 to

85 μg/m3, 99th percentile form. Staff believes that a more precautionary approach would

focus consideration on the lower end of these ranges, while consideration of a standard

set toward the upper end of these ranges would place relatively more weight on the

uncertainties inherent in the very limited epidemiologic evidence. Consideration of

UPM10-2.5 standards within the ranges recommended above, and design considerations for

an associated UPM10-2.5 monitoring network, should take into account the especially large

variability seen in currently available information on ambient concentrations and

composition of PM10-2.5 in urban areas.

5.5 SUMMARY OF KEY UNCERTAINTIES AND RESEARCH

RECOMMENDATIONS RELATED TO SETTING PRIMARY PM STANDARDS

Staff believes it is important to continue to highlight the unusually large uncertainties

associated with establishing standards for PM relative to other single component pollutants for

which NAAQS have been set. Key uncertainties and staff research recommendations on healthrelated topics are outlined below. In some cases, research in these areas can go beyond aiding in
5-72

standard setting to aiding in the development of more efficient and effective control strategies. 

Staff notes, however, that a full set of research recommendations to meet standards

implementation and strategy development needs is beyond the scope of this discussion.

The 1996 PM Staff Paper included a discussion of uncertainties and research

recommendations (EPA, 1996b, pp. VII-41-44) that addressed the following issues related to

understanding health effects associated with exposure to PM:

• lack of demonstrated biological mechanisms for PM-related effects,

• potential influence of measurement error and exposure error,

• potential confounding by copollutants,

• evaluation of the effects of components or characteristics of particles,

• the shape of concentration-response relationships,

• methodological uncertainties in epidemiologic analyses,

• the extent of life span shortening,

• characterization of annual and daily background concentrations, and

• understanding of the effects of coarse fraction particles.

As has been discussed in depth in the CD, especially in Chapters 5 through 8, an

extensive body of new studies related to understanding health effects associated with exposure to

PM is now available that provides important information on many of the topics listed above. For

example, regarding the lack of demonstrated biological mechanisms, new evidence from

toxicologic and controlled human exposure studies has provided information on an array of

potential mechanisms for effects on the cardiac and respiratory systems, as discussed in Chapters

7 and 9 of the CD. Still, the CD emphasizes that much remains to be learned to fully understand

the pathways or mechanisms by which PM is linked with different health endpoints. For each of

the issues listed above, new evidence has become available that helps to reduce uncertainties,

although uncertainty has been reduced in some areas more than others. Staff has identified the

following key uncertainties and research questions that have been highlighted in this review of

the health-based primary standards:

(1) The body of evidence on effects of thoracic coarse particles has been expanded, but the

uncertainties regarding thoracic coarse particles are still much greater than those for fine

particles. As discussed in Chapter 2, the spatial variability of thoracic coarse particles is

generally greater than that for fine particles, which will increase uncertainty in the

associations between health effects and thoracic coarse particles measured at central site

monitors. Additional research is needed on such intra-city variability as well as on intercity variability and on temporal (e.g., seasonal) variability. Additional exposure research

is needed to understand the influence of measurement error and exposure error on

thoracic coarse particle epidemiology results. In addition, little is known about coarse
5-73

particle composition, and less about the health effects associated with individual

components or sources of thoracic coarse particles, but it is possible that there are

components of thoracic coarse particles (e.g., crustal material in non-urban areas) that are

less likely to have adverse effects, at least at lower concentrations, than other

components.

(2) Identification of specific components, properties, and sources of fine particles that are

linked with health effects remains an important research need. Available evidence

provides no basis for expecting that any one component would be solely responsible for

PM2.5-related effects, but it is likely that some components are more closely linked with

specific effects than others. Continued source characterization, exposure, epidemiologic,

and toxicologic research is needed to help identify components, characteristics, or

sources of particles that may be more closely linked with various specific effects to aid in

our understanding of causal agents and in the development of efficient and effective

control strategies for reducing health risks. Conducting human exposure research in

parallel with such health studies will help reduce the uncertainty associated with

interpreting health studies and provide a stronger basis for drawing conclusions regarding

observed effects.

(3) An important aspect in characterizing risk and making decisions regarding air quality

standard levels is the shape of concentration-response functions for PM, including

identification of potential threshold levels. Recent studies continue to show no evidence

for a clear threshold level in relationships between various PM indicators and mortality,

within the range of concentrations observed in the studies, though some studies have

suggested potential levels.

(4) The relationship between PM and other air pollutants in causing health effects remains an

important question in reducing public health risk from air pollution. Numerous new

analyses have indicated that associations found between PM and adverse health effects

are not simply reflecting actual associations with some other pollutant. However, effects

have been found with the gaseous co-pollutants, and it is possible that pollutants may

interact or modify effects of one another. Further understanding of the sources,

exposures, and effects of PM and other air pollutants can assist in the design of effective

strategies for public health protection.

(5) Methodological issues in epidemiologic studies were discussed at length in the previous

review, and it appeared at the time that the epidemiologic study results were not greatly

affected by selection of differing statistical approaches or methods of controlling for

other variables, such as weather. However, investigation of recently discovered

questions on the use of generalized additive models in time-series epidemiologic studies

has again raised model specification issues. While reanalyses of studies using different

modeling approaches generally did not result in substantial differences in model results,
5-74

some studies showed marked sensitivity of the PM effect estimate to different methods of

adjusting for weather variables. There remains a need for further study on the selection

of appropriate modeling strategies and appropriate methods to control for time-varying

factors, such as temperature.

(6) Selection of appropriate averaging times for PM air quality standards is important for

public health protection, and available information suggests that some effects, including

cardiac-related risk factors, may be linked to exposures of very short duration (e.g., one

or more hours). Data on effects linked with such peak exposures, such as those related to

wildfires, agricultural burning, or other episodic events, would be an important aid to

public health response and communication programs. Investigation into the PM exposure

time periods that are linked with effects will provide valuable information both for the

standard-setting process and for risk communication and management efforts. 

(7) There remain significant uncertainties in the characterization of annual and daily

background concentrations for fine particles and especially for thoracic coarse particles. 

Further analyses of air quality monitoring and modeling that improved these background

characterizations would help reduce uncertainties in estimating health risks relevant for

standard setting (i.e., those risks associated with exposure to PM in excess of background

levels) and would aid in the development and implementation of associated control

programs.
5-75

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Health A 66: 1421-1439.

1

 In assessing information on PM-related effects on climate change processes, consideration is given to

potential indirect impacts on human health and the environment that may be a consequence of changes in climate

and solar radiation attributable to changes in ambient PM.

6-1

6. POLICY-RELEVANT ASSESSMENT OF PM-RELATED WELFARE EFFECTS

6.1 INTRODUCTION

This chapter assesses key policy-relevant information on the known and potential effects

on public welfare associated with ambient PM, alone and in combination with other pollutants

commonly present in the ambient air. It draws upon the most relevant information contained in

the CD, as well as other significant reports referenced therein. The welfare effects to be

considered in this review of the secondary PM NAAQS include effects on visibility (section 6.2),

vegetation and ecosystems (section 6.3), materials (section 6.4), and climate change processes1

(section 6.5). For each category of effects, this chapter presents a summary of the relevant

scientific information and a staff assessment of whether the available information is sufficient to

be considered as the basis for secondary standards distinct from primary standards for PM. Staff

conclusions and recommendations related to secondary standards for PM are presented in

Chapter 7.

It is important to note that discussion of PM-related effects on visibility, vegetation and

ecosystems, and climate change processes in Chapters 4 and 9 of the CD builds upon and

includes by reference extensive information from several other significant scientific reviews of

these topics. Most notably, these reports include the Recommendations of the Grand Canyon

Visibility Transport Commission (1996), the National Research Council’s Protecting Visibility

in National Parks and Wilderness Areas (1993), reports of the National Acid Precipitation

Assessment Program (1991, 1998), previous EPA Criteria Documents, including Air Quality

Criteria for Particulate Matter and Sulfur Oxides (EPA, 1982) and Air Quality Criteria for

Oxides of Nitrogen (EPA, 1993), recent reports of the National Academy of Sciences (NAS,

2001) and the Intergovernmental Panel on Climate Change (IPCC, 1998, 2001a,b). In addition,

numerous other U.S. and international assessments of stratospheric ozone depletion and global

climate change carried out under U.S. Federal interagency programs (e.g., the U.S. Global

Climate Change Research Program), the World Meteorological Organization (WMO), and the

United Nations Environment Programme (UNEP) have been reviewed.

6.2 EFFECTS ON VISIBILITY

Visibility can be defined as the degree to which the atmosphere is transparent to visible

light (NRC, 1993; CD, 4-153). Visibility impairment is the most noticeable effect of fine

particles present in the atmosphere. Particle pollution degrades the visual appearance and
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perceived color of distant objects to an observer and reduces the range at which they can be

distinguished from the background. 

This section discusses the role of ambient PM in the impairment of visibility, drawing

upon the most relevant information contained in the CD (Chapters 4 and 9), as well as significant

reports on the science of visibility referenced therein, and building upon information presented in

section 2.8 of this document. In particular, this section includes new information on the

following topics:

• Summary findings of analyses of hourly PM2.5 measurements and reconstructed light

extinction coefficients for urban areas, for 2003, that demonstrate a significant

correlation between PM2.5 and light extinction across the U.S. during daylight hours.

• An overview of visibility programs, goals, and methods for the evaluation of visibility

impairment as a basis for standard setting, in the U.S. and abroad, illustrating the

significant value placed on visual air quality, as demonstrated by efforts to improve

visibility in national parks and wilderness areas, as well as in urban areas. 

This section summarizes available information as follows: (1) information on the general

types of visibility impairment; (2) trends and conditions in Class I and non-urban areas; (3)

visibility conditions in urban areas; (4) studies of the economic value of improving visual air

quality; (5) current policy approaches to addressing visibility impairment; and (6) approaches to

evaluating public perceptions of visibility impairment and judgments about the acceptability of

varying degrees of visibility impairment.

6.2.1 Overview of Visibility Impairment

Visibility impairment is manifested in two principal ways: as local visibility impairment

(e.g., localized plumes or “brown clouds”) and as regional haze. In some cases, local-scale

visibility degradation is considered to be “reasonably attributable” to a single source or small

group of sources. Such impairment may take the form of a localized plume, a band or layer of

discoloration appearing well above the terrain that results from complex local meteorological

conditions. Localized plumes are composed of smoke, dust, or colored gas that obscures the sky

or horizon relatively near sources. Sources of locally visible plumes, such as the plume from an

industrial facility or a burning field, are often easy to identify. Historically, sources of visible

plumes were thought to be relatively minor contributors to visibility impairment in Class I areas

(i.e., 156 national parks, wilderness areas, and international parks identified for visibility

protection in section 162(a) of the Act). However, there have been a limited number of cases in
2

 Two of the most notable cases leading to emission controls involved the Navajo Generating Station in

Arizona and the Mohave power plant in Nevada. For both plants, it was found that sulfur dioxide emissions were

contributing to visibility impairment in Grand Canyon National Park.

6-3

which Federal land managers have certified the existence of visibility impairment in a Class I

area as being “reasonably attributable” to a particular source.2

In other cases, localized visibility impairment is manifested as an urban haze, sometimes

referred to as a “brown cloud.” This type of impairment is predominantly caused by emissions

from multiple sources in the urban area and is not typically attributable to a single nearby source

or to long-range transport from more distant sources. Brown cloud conditions have been welldocumented in a number of western cities.

The second type of impairment, regional haze, generally results from pollutant emissions

from a multitude of sources located across a broad geographic region. Regional haze impairs

visibility in every direction over a large area, in some cases over multi-state regions. It also

masks objects on the horizon and reduces the contrast of nearby objects. The formation, extent,

and intensity of regional haze are functions of meteorological and chemical processes, which

sometimes cause fine particle loadings to remain suspended in the atmosphere for several days

and to be transported hundreds of kilometers from their sources (NRC, 1993). It is this second

type of visibility degradation, regional haze, that is principally responsible for impairment in

national parks and wilderness areas across the country (NRC, 1993). 

While visibility impairment in urban areas at times may be dominated by local sources, it 

often may be significantly affected by long-range transport of haze due to the multi-day

residence times of fine particles in the atmosphere. Fine particles transported from urban and

industrialized areas, in turn, may, in some cases, be significant contributors to regional-scale

impairment in Class I and other rural areas.

6.2.2 Visibility Trends and Current Conditions in Class I and Non-Urban Areas

In conjunction with the National Park Service, other Federal land managers, and State

organizations, EPA has supported visibility monitoring in national parks and wilderness areas

since 1988. The monitoring network was originally established at 20 sites, but it has now been

expanded to 110 sites that represent all but one (Bering Sea) of the 156 mandatory Federal Class

I areas across the country. This long-term visibility monitoring network is known as IMPROVE

(Interagency Monitoring of PROtected Visual Environments). 

IMPROVE provides direct measurement of fine particles that contribute to visibility

impairment. The IMPROVE network employs aerosol measurements at all sites, and optical and

scene measurements at some of the sites. Aerosol measurements are taken for PM10 and PM2.5

mass, and for key constituents of PM2.5, such as sulfate, nitrate, organic and elemental carbon,

soil dust, and several other elements. Measurements for specific aerosol constituents are used to
6-4

calculate "reconstructed" aerosol light extinction by multiplying the mass for each constituent by

its empirically-derived scattering and/or absorption efficiency, with adjustment for the relative

humidity. Knowledge of the main constituents of a site's light extinction "budget" is critical for

source apportionment and control strategy development. Optical measurements are used to

directly measure light extinction or its components. Such measurements are taken principally

with either a transmissometer, which measures total light extinction, or a nephelometer, which

measures particle scattering (the largest human-caused component of total extinction). Scene

characteristics are typically recorded 3 times daily with 35 millimeter photography and are used

to determine the quality of visibility conditions (such as effects on color and contrast) associated

with specific levels of light extinction as measured under both direct and aerosol-related

methods. Directly measured light extinction is used under the IMPROVE protocol to crosscheck that the aerosol-derived light extinction levels are reasonable in establishing current

visibility conditions. Aerosol-derived light extinction is used to document spatial and temporal

trends and to determine how proposed changes in atmospheric constituents would affect future

visibility conditions.

Annual average visibility conditions (reflecting light extinction due to both

anthropogenic and non-anthropogenic sources) vary regionally across the U.S. The rural East

generally has higher levels of impairment than remote sites in the West, with the exception of

urban-influenced sites such as San Gorgonio Wilderness (CA) and Point Reyes National

Seashore (CA), which have annual average levels comparable to certain sites in the Northeast. 

Regional differences are illustrated by Figures 4-39a and 4-39b in the CD, which show that, for

Class I areas, visibility levels on the 20% haziest days in the West are about equal to levels on

the 20% best days in the East (CD, p. 4-179).

Higher visibility impairment levels in the East are due to generally higher concentrations

of anthropogenic fine particles, particularly sulfates, and higher average relative humidity levels. 

In fact, sulfates account for 60-86% of the haziness in eastern sites (CD, p. 4-236). Aerosol light

extinction due to sulfate on the 20% haziest days is significantly larger in eastern Class I areas as

compared to western areas (CD, p. 4-182; Figures 4-40a and 4-40b). With the exception of

remote sites in the northwestern U.S., visibility is typically worse in the summer months. This is

particularly true in the Appalachian region, where average light extinction in the summer

exceeds the annual average by 40% (Sisler et al., 1996). 

Regional trends in Class I area visibility are updated and presented in the EPA’s National

Air Quality and Emissions Trends Report (EPA, 2001). Eastern trends for the 20% haziest days

from 1992-1999 showed a 1.5 deciview improvement, or about a 16% improvement. However,

visibility in the East remains significantly impaired, with an average visual range of

approximately 20 km on the 20% haziest days. In western Class I areas, aggregate trends

showed little change during 1990-1999 for the 20% haziest days, and modest improvements on
6-5

the 20% mid-range and clearest days. Average visual range on the 20% haziest days in western

Class I areas is approximately 100 km.

6.2.3 Visibility Conditions in Urban Areas

Urban visibility impairment results from the combined effect of stationary, mobile, and

area source emissions. Complex local meteorological conditions may contribute to such

impairment as well. Localized or layered haze often results from emissions from many sources

located across an urban or metropolitan area. A common manifestation of this type of visibility

impairment is the "brown cloud" situation experienced in some cities particularly in the winter

months, when cooler temperatures limit vertical mixing of the atmosphere. The long-range

transport of emissions from sources outside the urban area may also contribute to urban haze

levels. 

Visibility impairment has been studied in several major cities in the past decades because

of concerns about fine particles and their potentially significant impacts (e.g., health-related and

aesthetic) on the residents of large metropolitan areas (e.g., Middleton, 1993). Urban areas

generally have higher loadings of PM2.5 and, thus, higher visibility impairment than monitored

Class I areas. As discussed in Chapter 2, sections 2.4 and 2.5, annual mean levels of 24-hour

average PM2.5 levels are generally higher in urban areas than those found in the IMPROVE

database for rural Class I areas. Urban areas have higher concentrations of organic carbon,

elemental carbon, and particulate nitrate than rural areas due to a higher density of fuel

combustion and diesel emissions.

6.2.3.1 ASOS Airport Visibility Monitoring Network

For many years, urban visibility has been characterized using data describing airport

visibility conditions. Until the mid-1990's, airport visibility was typically reported on an hourly

basis by human observers. An extensive database of these assessments has been maintained and

analyzed to characterize visibility trends from the late-1940's to mid-1990's (Schichtel et al.,

2001). 

In 1992, the National Weather Service (NWS), Federal Aviation Administration (FAA),

and Department of Defense began deployment of the Automated Surface Observing System

(ASOS). ASOS is now the largest instrument-based visibility monitoring network in the U.S.

(CD, p. 4-174). The ASOS visibility monitoring instrument is a forward scatter meter that has

been found to correlate well with light extinction measurements from the Optec transmissometer

(NWS, 1998). It is designed to provide consistent, real-time visibility and meteorological

measurements to assist with air traffic control operations. A total of 569 FAA-sponsored and

313 NWS-sponsored automated observing systems are installed at airports throughout the

country. ASOS visibility data are typically reported for aviation use in small increments up to a

maximum of 10 miles visibility. While these truncated data are not ideal for characterizing

actual visibility levels, the raw, non-truncated data from the 1-minute light extinction and
3

 A preliminary analysis of the archived data for 63 cities across the U.S. was presented in the first draft

Staff Paper (August 2003), but further analysis has not been conducted. While the preliminary analysis

demonstrated relatively well-characterized correlations between predicted PM2.5 concentrations (based on ASOS

extinction values) and measured PM2.5 concentrations in some urban areas, such correlations were not consistently

observed in urban areas across the country.

6-6

meteorological readings are now archived and available for analysis for a subset of the ASOS

sites.3

6.2.3.2 Correlation between Urban Visibility and PM2.5 Mass

In an effort to better characterize urban visibility, staff has analyzed the extensive new

data now available on PM2.5 primarily in urban areas. This rapidly expanding national database,

including FRM measurements of PM2.5 mass, continuous measurements of hourly PM2.5 mass,

and PM2.5 chemical speciation measurements, now provides the opportunity to conduct such an

analysis. In this analysis, described below and documented in detail in Schmidt et al. (2005),

staff has sought to explore the factors that have historically complicated efforts to address

visibility impairment nationally, including regional differences related to levels of primarily fine

particles and relative humidity. Taking these factors into account, staff has compared

correlations between visibility, in terms of reconstructed light extinction (using the IMPROVE

methodology discussed in Chapter 2, section 2.8), with hourly PM2.5 concentrations in urban

areas across the U.S. and in eastern and western regions.

As an initial matter, staff has explored the factors contributing to the substantial

East/West differences that have been characterized primarily for Class I areas across the country,

as discussed above in section 6.2.2. In considering fine particle levels, staff notes that East/West

differences are substantially smaller in urban areas than in rural areas. As shown in Figure 6-1,

24-hour average PM2.5 concentrations in the East and West are much more similar in urban areas

than they are in rural areas. A significantly lower East/West ratio is observed in urban areas,

based on data from either the FRM or the EPA Speciation Network, than in rural areas, based on

data from the IMPROVE network.

In considering relative humidity levels, staff notes that, while the average daily relative

humidity levels are generally higher in eastern than western areas, in both regions relative

humidity levels are appreciably lower during daylight as compared to night time hours. These

differences can be seen in Figure 6-2, based on data from National Weather Service (NWS) sites. 

As discussed in Chapter 2, section 2.8, the reconstructed light extinction coefficient, for a given

mass and concentration, increases sharply as relative humidity rises. Thus, visibility impacts

related to East/West differences in average relative humidity are minimized during daylight

hours, when relative humidity is generally lower.

Taking these factors into account, staff has considered both 24-hour and shorter-term

daylight hour averaging periods in evaluating correlations between PM2.5 concentrations in urban

areas and visibility, in terms of reconstructed light extinction (RE), in eastern and western areas,
Rural - IMPROVE (24-hr) Urban - FRM (24-hr

02

4

68

101214

East West

Note: Urban IMPROVE sites and rural FRM sites excluded

Figure 6-1. PM2.5 concentration differences between eastern and west

areas and between rural and urban areas for 2003.

Rural east/west ratio = 2.04Urban east/west ratio = 1.29

PM2.5 concentration (μg/m3)

Source: Schmidt et al. (2005) 

6-7
East

West

Relative Humidity

Figure 6-2. Distribution of hourly and 24-hour average relative humidity a

National Weather Service Sites, 2003. Box depicts interquartile 

depict 5th and 95th percentiles.

Source: Schmidt et al. (2005) 

Hour

 0 10 20 30 40 50 60 70 80 90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 

 0 10 20 30 40 50 60 70 80 90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 

6-8
4 f(RH) is the relative humidity adjustment factor; it increases significantly with higher humidity. See

section 2.8.1 and Chapter 4 of the CD (CD, pp. 4-149 to 4-170) for further information.

6-9

as well as nationwide. Figure 6-3 shows clear and similarly strong correlations between RE and

24-hour average PM2.5 in eastern, western, and all urban areas. Figure 6-3 is based on data from

161 urban continuous PM2.5 mass monitoring sites across the country with co-located or nearby

24-hour PM2.5 speciation data. RE values were calculated based on a constructed hourly

speciated PM2.5 data set, hourly relative humidity data (either co-located or from nearby NWS

sites), and a coarse PM data set (estimated either by difference method from the continuous

PM2.5 and co-located continuous PM10 instruments, or based on regional ratios of PM fractions)

(Schmidt et al., 2005). In calculating RE, the relative humidity was capped at 95%, reflecting

the lack of accuracy in higher relative humidity values and their highly disproportionate impact

on RE.

For these analyses, staff has considered both 10 years of relative humidity data,

converted to 10-year average hourly f(RH)4 values (Figure 6-3, panel a), as well as actual hourly

relative humidity data for 2003, converted to f(RH) values (Figure 6-3, panel b). Of the two sets

of data, Staff recognizes that 10-year average hourly f(RH) data are more reflective of long-term

humidity patterns, and may provide a more appropriate basis for relating ambient PM2.5 levels to

visibility impairment in the context of consideration of a potential secondary standard to protect

against PM-related visibility impairment. On the other hand, since there can be significant dayto-day variance in relative humidity that is not reflected in long-term average f(RH) data, actual

hourly f(RH) data were also included in the analyses, to reflect the potential ranges of high and

low relative humidity levels likely to occur over the course of a year.

In considering shorter-term daylight hour averaging periods, staff also evaluated the

slope and strength of the correlations between RE and PM2.5 concentrations on an hourly basis

(Schmidt et al., 2005). Figure 6-4 shows plots of the average slope of the correlation between

hourly RE and corresponding PM2.5 concentrations (i.e., the increase in RE due to the

incremental increase in PM2.5) by region, in eastern and western areas, and nationwide. The

slopes are all lower during daytime hours when the disproportionate effects of relative humidity

on the light extinction coefficients for fine particle sulfates and nitrates are diminished. Thus,

during daylight hours, the slope more closely represents the influence of PM2.5 mass on visibility

than the influence of relative humidity. In addition, Figure 6-4 shows that the slopes (and hence,

the relationships between RE and PM2.5) are more comparable across regions during daylight

hours. In considering the strength of these correlations, staff notes that the correlations between

RE and PM2.5, as indicated by the model R2

 values, are strong for individual daylight hours,

similar to that for the 24-hour average (Schmidt et al., 2005). On a national basis, daytime (9

a.m. to 6 p.m.) hourly model R2 values are all above 0.6 for the RE's calculated with actual f(RH)
East (circles): RE = 7.3 * PM2.5 + 8.5; R2=0.94

West (stars): RE = 6.8 * PM2.5 + 4.5; R2=0.83

All: RE = 7.2 * PM2.5 + 7.8; R2=0.91

East (circles): RE = 7.8 * PM2.5 + 8.5; R2=0.70

West (stars): RE = 6.9 * PM2.5 + 4.1; R2=0.72

All: RE = 7.6 * PM2.5 + 7.9; R2=0.70

Figure 6-3. Relationship between reconstructed light extinction

(RE) and 24-hour average PM2.5, 2003. RE in top panel

(a) computed with 10-year average f(RH); RE in bottom

panel (b) computed using actual f(RH).

PM2.5 (ug/m3)

Source: Schmidt et al. (2005) 

a

b

RE (Mm-1) RE (Mm-1)

6-10
Model (RE= PM2.5) Slope

Source: Schmidt et al. (2005) 

Figure 6-4. Model slope for relationship between reconstructed light extinc

(increase in RE due to incremental increase in PM2.5), 2003. R

average f(RH).

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 

Hour

3

4

56

789

10

11

NortheastSoutheast

Industrial MidwestUpper Midwest

SouthwestNorthwest

SoutherEast

6-11
6-12

values and above 0.8 for the RE's calculated with 10-year average f(RH) values (Schmidt et al., 

2005).

On the basis of lower slopes and more inter-region comparability, staff selected a number

of daylight time periods to consider in evaluating additional correlations between PM2.5

concentrations and RE in eastern and western regions, as well as nationwide. Evaluated time

periods included 7 a.m. to 7 p.m.; 9 a.m. to 5 p.m.; 10 a.m. to 6 p.m.; 10 a.m. to 4 p.m.; 12 p.m.

to 4 p.m.; and 8 a.m. to 4 p.m. With a focus on minimizing slope, minimizing regional and 

East/West slope differences, maximizing R2

 values, and considering other related factors, staff

selected the 12 p.m. to 4 p.m. time period for further analyses (Schmidt et al., 2005).

Using the same data as were used for Figure 6-3, Figure 6-5 shows examples of the

correlations between RE and PM2.5 concentrations averaged over a 4-hour time period, for 10-

year average hourly f(RH) data (panel a) and for actual hourly f(RH) data in 2003 (panel b). As

seen in this figure, the correlations between RE and PM2.5 concentrations during daylight hours 

in urban areas are comparably strong (similar R2 values), yet more reflective of PM2.5 mass rather

than relative humidity effects (i.e., lower slopes), in comparison to the correlations based on a

24-hour averaging time. Further, these correlations in urban areas are generally similar in the

East and West, in sharp contrast to the East/West differences observed in rural areas.

6.2.4 Economic and Societal Value of Improving Visual Air Quality

Visibility is an air quality-related value having direct significance to people's enjoyment

of daily activities in all parts of the country. Survey research on public awareness of visual air

quality using direct questioning typically reveals that 80% or more of the respondents are aware

of poor visual air quality (Cohen et al., 1986). The importance of visual air quality to public

welfare across the country has been demonstrated by a number of studies designed to quantify

the benefits (or willingness to pay) associated with potential improvements in visibility

(Chestnut and Dennis, 1997; Chestnut and Rowe, 1991). 

Individuals value good visibility for the sense of well-being it provides them directly,

both in the places where they live and work, and in the places where they enjoy recreational

opportunities. Millions of Americans appreciate the scenic vistas in national parks and

wilderness areas each year. Visitors consistently rate “clean, clear air” as one of the most

important features desired in visiting these areas (Department of Interior, 1998). A 1998 survey

of 590 representative households by researchers at Colorado State University found that 88% of

the respondents believed that "preserving America's most significant places for future

generations" is very important, and 87% of the respondents supported efforts to clean up air

pollution that impacts national parks (Hass and Wakefield, 1998).

Economists have performed many studies in an attempt to quantify the economic benefits

associated with improvements in current visibility conditions both in national parks and in urban

areas (Chestnut and Dennis, 1997). These economic benefits are often described by economists
a

b

PM2.5 (ug/m3)

Figure 6-5. Relationship between reconstructed light extinction

(RE) and 12 p.m.– 4 p.m. average PM2.5, 2003. RE in

top panel (a) computed with 10-year average f(RH); RE

in bottom panel (b) computed using actual f(RH).

East (circles): RE = 5.4 * PM2.5 + 11.7; R2=096

West (stars): RE = 5.7 * PM2.5 + 8.6; R2=0.82

All: RE = 5.5 * PM2.5 + 10.8; R2=0.92

East (circles): RE = 6.0 * PM2.5 + 10.6; R2=0.66

West (stars): RE = 5.9 * PM2.5 + 8.9; R2=0.74

All: RE = 5.9 * PM2.5 + 10.3; R2=0.68

Source: Schmidt et al. (2005) 

RE (Mm-1) RE (Mm-1)

6-13
6-14

as either use values or non-use values. Use values are those aspects of environmental quality

that directly affect an individual’s welfare. These include improved aesthetics during daily

activities (e.g., driving or walking, looking out windows, daily recreations), for special activities

(e.g., visiting parks and scenic vistas, hiking, hunting), and for viewing scenic photography. Use

benefits of better visibility also include improved road and air safety. 

Non-use values are those for which an individual is willing to pay for reasons that do not

relate to the direct use or enjoyment of any environmental benefit. The component of non-use

value that is related to the use of the resource by others in the future is referred to as the bequest

value. This value is typically thought of as altruistic in nature. Another potential component of

non-use value is the value that is related to preservation of the resource for its own sake, even if

there is no human use of the resource. This component of non-use value is sometimes referred to

as existence value or preservation value. Non-use values are not traded, directly or indirectly, in

markets. For this reason, the estimation of non-use values has proved to be more difficult than

the estimation of use values. Non-use values may be related to the desire that a clean

environment be available for the use of others now and in the future, or they may be related to

the desire to know that the resource is being preserved for its own sake, regardless of human use. 

Non-use values may be a more important component of value for recreational areas, particularly

national parks and monuments, and for wilderness areas.

In addition, staff notes that the concept of option value is a key component of the

measured values. The option value represents the value that is tied to preserving improved

visibility in the event of a visit, even though a visit is not certain. This component is considered

by some as a use value and by others as a non-use value.

Tourism in the U.S. is a significant contributor to the economy. A 1998 Department of

Interior study found that travel-related expenditures by national park visitors alone average $14.5

billion annually (1996 dollars) and support 210,000 jobs (Peacock et al., 1998). A similar

estimate of economic benefits resulting from visitation to national forests and other public lands

could increase this estimate significantly. 

It is well recognized in the U.S. and abroad that there is an important relationship

between good air quality and economic benefits due to tourism. McNeill and Roberge (2000)

studied the impact of poor visibility episodes on tourism revenues in Greater Vancouver and the

Lower Fraser Valley in British Columbia as part of the Georgia Basin Ecosystem Initiative of

Environment Canada. Through this analysis, a model was developed that predicts future tourist

revenue losses that would result from a single extreme visibility episode. They found that such

an episode would result in a $7.45 million loss in the Greater Vancouver area and $1.32 million

loss in the Fraser Valley.

 The results of several valuation studies addressing both urban and rural visibility are

presented in the CD (CD, pp. 4-187 to 4-190), the 1996 Criteria Document (EPA, 1996a, p. 8-83,

Table 8-5; p. 8-85, Table 8-6) and in Chestnut and Rowe (1991) and Chestnut et al. (1994). Past
6-15

studies by Schulze et al. (1983) and Chestnut and Rowe (1990) have estimated the preservation

values associated with improving the visibility in national parks in the Southwest to be in the

range of approximately $2-6 billion annually. An analysis of the residential visibility benefits in

the eastern U.S. due to reduced sulfur dioxide emissions under the acid rain program suggests an

annual value of $2.3 billion (in 1994 dollars) in the year 2010 (Chestnut and Dennis, 1997). The

authors suggest that these results could be as much as $1-2 billion more because the above

estimate does not include any value placed on eastern air quality improvements by households in

the western U.S.

Estimating benefits for improvements in visibility can be difficult because visibility is not

directly or indirectly valued in markets. Many of the studies cited above are based on a

valuation method known as contingent valuation (CV). Concerns have been identified about the

reliability of value estimates from contingent valuation studies because research has shown that

bias can be introduced easily into these studies if they are not carefully conducted. Accurately

estimating willingness-to-pay for avoided health and welfare losses depends on the reliability

and validity of the data collected. However, there is an extensive scientific literature and body of

practice on both the theory and technique of contingent valuation. EPA believes that welldesigned and well-executed CV studies are useful for estimating the benefits of environmental

effects such as improved visibility (EPA, 2000).

Some of the studies cited above used an alternative valuation method known as hedonic

pricing. Hedonic pricing is a technique used to measure components of property value (e.g., 

proximity to schools). It relies on the measurement of differentials in property values under

various environmental quality conditions, including air pollution and environmental amenities,

such as aesthetic views. This method works by analyzing the way that market prices change

with changes in environmental quality or amenity. EPA believes that well-designed and wellexecuted hedonic valuation studies, in combination with public perception surveys, are useful for

estimating the benefits of environmental effects such as improved visibility. 

Society also values visibility because of the significant role it plays in transportation

safety. Serious episodes of visibility impairment can increase the risk of unsafe air

transportation, particularly in urban areas with high air traffic levels (EPA, 1982). In some

cases, extreme haze episodes have led to flight delays or the shutdown of major airports,

resulting in economic impacts on air carriers, related businesses, and air travelers. For example,

on May 15, 1998 in St. Louis, Missouri, it was reported that a haze episode attributed to

wildfires in central America resulted in a reduction in landing rates and significant flight delays

at Lambert International Airport. The 24-hour PM2.5 levels reached 68 μg/m3

 during that

episode. In addition, the National Transportation Safety Board (NTSB) has concluded in

accident reports that high levels of pollution and haze, such as those experienced during the July

1999 air pollution episode in the northeastern U.S., have played a role in air transportation
6-16

accidents and loss of life (NTSB, 2000). During this episode, 24-hour levels of PM2.5 ranged

from 35-52 μg/m3 in the New England states.

6.2.5 Programs and Goals for Improving Visual Air Quality

Specific discussion is provided below on local, State, and international efforts to protect

visual air quality. 

6.2.5.1 Regional Protection

Due to differences in visibility impairment levels (as a result of differences in chemical

composition of haze and in relative humidity levels) between the East and West, EPA, land

managers, and States have taken a regional approach, rather than a national approach, to

protecting visibility in non-urban areas in the U.S.. Protection against visibility impairment in

special areas is provided for in sections 169A, 169B, and 165 of the Act, in addition to that

provided by the secondary NAAQS. Section 169A, added by the 1977 CAA Amendments,

established a national visibility goal to “remedy existing impairment and prevent future

impairment” in 156 national parks and wilderness areas (Class I areas). The Amendments also

called for EPA to issue regulations requiring States to develop long-term strategies to make

"reasonable progress" toward the national goal. EPA issued initial regulations in 1980 focusing

on visibility problems that could be linked to a single source or small group of sources. Action

was deferred on regional haze until monitoring, modeling, and source apportionment methods

could be improved.

The 1990 CAA Amendments placed additional emphasis on regional haze issues through

the addition of section 169B. In accordance with this section, EPA established the Grand

Canyon Visibility Transport Commission (GCVTC) in 1991 to address adverse visibility impacts

on 16 Class I national parks and wilderness areas on the Colorado Plateau. The GCVTC was

comprised of the Governors of nine western states and leaders from a number of Tribal nations. 

The GCVTC issued its recommendations to EPA in 1996, triggering a requirement in section

169B for EPA issuance of regional haze regulations.

EPA accordingly promulgated a final regional haze rule in 1999 (EPA, 1999; 65 FR

35713). Under the regional haze program, States are required to establish goals for improving

visibility on the 20% most impaired days in each Class I area, and for allowing no degradation

on the 20% least impaired days. Each state must also adopt emission reduction strategies which,

in combination with the strategies of contributing States, assure that Class I area visibility

improvement goals are met. The first State implementation plans are to be adopted in the 2003-

2008 time period, with the first implementation period extending until 2018. Five multistate

planning organizations are evaluating the sources of PM2.5 contributing to Class I area visibility

impairment to lay the technical foundation for developing strategies, coordinated among many

States, in order to make reasonable progress in Class I areas across the country.
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6.2.5.2 Local, State, and International Goals and Programs

A number of programs, goals, standards, and planning efforts have been established in

the U.S. and abroad to address visibility concerns in urban and non-urban areas. These

regulatory and planning activities are of particular interest because they are illustrative of the

significant value that the public places on improving visibility, and because they have made use

of developed methods for evaluating public perceptions and judgments about the acceptability of

varying degrees of visibility impairment. 

Several state and local governments have developed programs to improve visual air

quality in specific urban areas, including Denver, CO; Phoenix, AZ; and, Lake Tahoe, CA. At

least two States have established statewide standards to protect visibility. In addition, visibility

protection efforts have been undertaken in other countries, including Australia, New Zealand,

and Canada. Examples of these efforts are highlighted below. 

In 1990, the State of Colorado adopted a visibility standard for the city of Denver. The

Denver standard is a short-term standard that establishes a limit of a four-hour average light

extinction level of 76 Mm-1 (equivalent to a visual range of approximately 50 km) during the

hours between 8 a.m. and 4 p.m. (Ely et al., 1991). In 2003, the Arizona Department of

Environmental Quality created the Phoenix Region Visibility Index, which focuses on an

averaging time of 4 hours during actual daylight hours. This visiblity index establishes visual air

quality categories (i.e., excellent to very poor) and establishes the goals of moving days in the

poor/very poor categories up to the fair category, and moving days in the fair category up to the

good/excellent categories (Arizona Department of Environmental Quality, 2003). This approach

results in a focus on improving visibility to a visual range of approximately 48-36 km. In 1989,

the state of California revised the visibility standard for the Lake Tahoe Air Basin and

established an 8-hour visibility standard equal to a visual range of 30 miles (approximately 48

km) (California Code of Regulations). 

California and Vermont each have standards to protect visibility, though they are based

on different measures. Since 1959, the state of California has had an air quality standard for

particle pollution where the “adverse” level was defined as the “level at which there will be . . .

reduction in visibility or similar effects.” California’s general statewide visibility standard is a

visual range of 10 miles (approximately 16 km) (California Code of Regulations). In 1985,

Vermont established a state visibility standard that is expressed as a summer seasonal sulfate

concentration of 2 μg/m3

, that equates to a visual range of approximately 50 km. This standard

was established to represent “reasonable progress toward attaining the congressional visibility

goal for the Class 1 Lye Brook National Wilderness Area, and applies to this Class 1 area and to

all other areas of the state with elevations greater than 2500 ft. 

Outside of the U.S., efforts have also been made to protect visibility. The Australian

state of Victoria has established a visibility objective (State Government of Victoria, 2000a and

2000b), and a visibility guideline is under consideration in New Zealand (New Zealand National
6-18

Institute of Water & Atmospheric Research, 2000a and 2000b; New Zealand Ministry of

Environment, 2000). A survey was undertaken for the Lower Fraser Valley in British Columbia,

with responses from this pilot study being supportive of a standard in terms of a visual range of

approximately 40 km for the suburban township of Chilliwack and 60 km for the suburban

township of Abbotsford, although no visibility standard has been adopted for the Lower Fraser

Valley at this time.

6.2.6 Approaches to Evaluating Public Perceptions and Attitudes

New methods and tools have been developed to communicate and evaluate public

perceptions of varying visual effects associated with alternative levels of visibility impairment

relative to varying pollution levels and environmental conditions. New survey methods have

been applied and evaluated in various studies, such as those for Denver, Phoenix, and the Lower

Fraser Valley in British Columbia, and these studies are described below in more detail. These

methods are intended to assess public perceptions as to the acceptability of varying levels of

visual air quality, considered in these studies to be an appropriate basis for developing goals and

standards for visibility protection. For the Denver and British Columbia studies, actual slides

taken in the areas of interest, and matched with transmissometer and nephelometer readings,

respectively, were used to assess public perceptions about visual air quality. For the Phoenix

study, WinHaze, a newly available image modeling program, discussed below, was used for

simulating images. Staff finds that, even with variations in each study’s approaches, the survey

methods used for the Denver, Phoenix, and British Columbia studies produced reasonably

consistent results from location to location, each with a majority of participants finding visual

ranges within about 40 to 60 km to be acceptable.

6.2.6.1 Photographic Representations of Visual Air Quality

In the past, the principal method for recording and describing visual air quality, for the

purpose of public perception surveys, has been through 35 millimeter photographs. Under the

IMPROVE program, EPA, federal land management agencies, and Air Resource Specialists, Inc.

(ARS) have developed an extensive archive of visual air quality photos for national parks and

wilderness areas. In comparison, we have only a limited archive of photos of urban areas.

The CD discusses some of the methods that are now available to represent different

levels of visual air quality (CD, p. 4-174). In particular, Molenar et al. (1994) describes a

sophisticated visual air quality simulation technique, incorporated into the WinHaze program

developed by ARS, which combined various modeling systems under development for the past

20 years. The technique relies on first obtaining an original base image slide of the scene of

interest. The slide should be of a cloudless sky under the cleanest air quality conditions possible. 

The light extinction represented by the scene should be derived from aerosol and optical data

associated with the day the image was taken, or it should be estimated from contrast

measurements of features in the image. The image is then digitized to assign an optical density
5

 A small pilot study for Washington, D.C. was conducted by EPA and was briefly discussed in the

preliminary draft staff paper (2001).

6-19

to each pixel. At this point, the radiance level for each pixel is estimated. Using a detailed

topographic map, technicians identify the specific location from which the photo was taken, and

they determine the distances to various landmarks and objects in the scene. With this

information, a specific distance and elevation is assigned to each pixel. 

Using the digital imaging information, the system then computes the physical and optical

properties of an assumed aerosol mix. These properties are input into a radiative transfer model

in order to simulate the optical properties of varying pollutant concentrations on the scene.

WinHaze, an image modeling program for personal computers that employs simplified

algorithms based on the sophisticated modeling technique, is now available (Air Resource

Specialists, 2003).

 The simulation technique has the advantage of being readily applicable to any location

as long as a very clear base photo is available for that location. In addition, the lack of clouds

and the consistent sun angle in all images, in effect, standardizes the perception of the images

and enables researchers to avoid potentially biased responses due to these factors. An alternative

to using simulated images is to obtain actual photographs of the site of interest at different

ambient pollution levels. However, long-term photo archives of this type exist for only a few

cities. In addition, studies have shown that observers will perceive an image with a cloud-filled

sky as having a higher degree of visibility impairment than one without clouds, even though the

PM concentration on both days is the same.

As part of a pilot study5

 in Washington, D.C., both survey and photographic techniques

were applied (Abt Associates, 2001). In conjunction with this pilot project, images that illustrate

visual air quality in Washington, DC under a range of visibility conditions were prepared and are

available at http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html (labeled as Appendix

6A: Images of Visual Air Quality in Selected Urban Areas in the U.S.). Included as part of

Appendix 6A, this website also contains actual photographs of Chicago illustrating visibility

conditions associated with a range of PM2.5 concentrations, as well as simulated images for

Denver and Phoenix, as discussed below.

6.2.6.2 Survey Methods

Denver, Colorado: Visibility Standard

The process by which the Denver visibility standard was developed relied on citizen

judgments of acceptable and unacceptable levels of visual air quality (Ely et al., 1991).

Representatives from Colorado Department of Public Health and Environment (CDPHE)

conducted a series of meetings with 17 civic and community groups in which a total of 214

individuals were asked to rate slides having varying levels of visual air quality for a well-known

vista in Denver. The CDPHE representatives asked the participants to base their judgments on
6-20

three factors: 1) the standard was for an urban area, not a pristine national park area where the

standards might be more strict; 2) standard violations should be at visual air quality levels

considered to be unreasonable, objectionable, and unacceptable visually; and 3) judgments of

standards violations should be based on visual air quality only, not on health effects. 

The participants were shown slides in 3 stages. First, they were shown seven warm-up

slides describing the range of conditions to be presented. Second, they rated 25 randomlyordered slides based on a scale of 1 (poor) to 7 (excellent), with 5 duplicates included. Third,

they were asked to judge whether the slide would violate what they would consider to be an

appropriate urban visibility standard (i.e., whether the level of impairment was “acceptable” or

“unacceptable”). 

The Denver visibility standard setting process produced the following findings:

C Individuals' judgments of a slide's visual air quality and whether the slide violated

a visibility standard are highly correlated (Pearson correlation coefficient greater

than 80%) with the group average.

C When participants judged duplicate slides, group averages of the first and second

ratings were highly correlated.

C Group averages of visual air quality ratings and "standard violations" were highly

correlated. The strong relationship of standard violation judgments with the

visual air quality ratings is cited as the best evidence available from this study for

the validity of standard violation judgments (Ely et al., 1991).

The CDPHE researchers sorted the ratings for each slide by increasing order of light

extinction and calculated the percent of participants that judged each slide to violate the

standard. The Denver visibility standard was then established based on a 50% acceptability

criterion. Under this approach, the standard was identified as the light extinction level that

divides the slides into two groups: those found to be acceptable and those found to be

unacceptable by a majority of study participants. The CDPHE researchers found this level to be

reasonable because, for the slides at this level and above, a majority of the study participants

judged the light extinction levels to be unacceptable. In fact, when researchers evaluated all

citizen judgments made on all slides at this level and above as a single group, more than 85% of

the participants found visibility impairment at and above the level of the selected standard to be

unacceptable.

Though images used in the Denver study were actual photographs, more recently,

WinHaze has been used to generate images that illustrate visual air quality in Denver under a

range of visibility conditions (generally corresponding to 10th, 20th, 30th, 40th, 50th, 60th 80th, and

90th percentile values), and these images are available in Attachment 6-A at 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html.
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Phoenix, Arizona: Visibility Index

In 2002, the Arizona Department of Environmental Quality formed the Visibility Index

Oversight Committee. The Committee’s goal was to coordinate the involvement of Phoenix-area

residents in the development of a visibility index. The Phoenix committee patterned its survey

process after the process used by Denver to develop its visibility standard. 

The survey included 385 participants in 27 separate sessions. Participants were carefully

recruited to form a sample group that was demographically representative of the larger Phoenix

population. Three sessions were held in Spanish.

Participants were shown a series of 25 images of the same vista of downtown Phoenix,

with South Mountain in the background at a distance of about 40 km. Photographic slides of the

images were developed using the WinHaze program. The visibility impairment levels ranged

from 15 to 35 deciviews (87 to 12 km visual ranges). Participants first rated the randomlyshown slides on a scale of 1 (unacceptable) to 7 (excellent). Next, the participants rated slides,

again shown in random order, as acceptable or unacceptable. This phase of the survey produced

the following findings:

• At least 90 percent of all participants found visible air quality acceptable between

15 deciviews (87 km visual range) and 20 deciviews (53 km);

• At 24 deciviews (36 km), nearly half of all participants thought the visible air

quality was unacceptable; and

• By 26 deciviews (29 km), almost three-quarters of participants said it was

unacceptable, with nearly all participants considering levels of 31 deciviews (18

km) and higher to be unacceptable.

The information developed in this survey informed the development of recommendations

by the Visibility Index Oversight Committee for a visibility index for the Phoenix Metropolitan

Area (Arizona Department of Environmental Quality, 2003). A final report of the survey

methods and results is available (BBC Research & Consulting, 2002). The Phoenix survey

demonstrates that the rating methodology developed for gathering citizen input for establishing

the Denver visibility standard can be reliably transferred to another city while relying on updated

imaging technology to simulate a range of visibility impairment levels.

Images used in this study were generated using WinHaze. Similar images, also generated

by WinHaze, which illustrate visual air quality in Phoenix under a range of visibility conditions,

are available in Appendix 6A at http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html.

British Columbia, Canada: Public Perception Survey

In 1993, the REVEAL (Regional Visibility Experimental Assessment in the Lower Fraser

Valley) field study was undertaken to characterize summertime visibility and ambient aerosol

loadings in southwestern British Columbia. In 1994, researchers at the University of British
6-22

Columbia conducted a pilot study on the perception of acceptable visibility conditions in the

area, using photographs and optical measurements taken during the summer of 1993 (Pryor,

1996). The study was based on the methodology used in setting the Denver visibility standard

(Ely et al., 1991). 

Participants in the study were shown slides of two suburban locations in British

Columbia: Chilliwack and Abbotsford. After using the same general protocol, Pryor found that

responses from this pilot study would indicate a standard in terms of visual range of

approximately 40 km for Chilliwack and 60 km for Abbotsford. Pryor (1996) discusses some

possible reasons for the variation in standard visibility judgments between the two locations. 

Factors discussed include the relative complexity of the scenes, different levels of development

at each location, potential local source influence on site-specific nephelometer data, and

potential bias of the sample population since only students participated. The author expressed

the view that the pilot study reinforced the conclusion that the methodology originally developed

for the Denver standard-setting process is a sound and effective one for obtaining public

participation in a standard-setting process, and that it could be adapted for such use in another

geographic location with only minor modifications (Pryor, 1996).

6.2.7 Summary and Conclusions

The CD and other reports referenced in section 6.2 provide a significant body of

information documenting the effects of PM and its components on atmospheric visibility. Data

on visibility conditions indicate that urban areas generally have higher loadings of PM2.5 and,

thus, higher visibility impairment than monitored Class I areas. 

Data analyses using extensive new monitoring data now available on PM2.5 primarily in

urban areas show a consistently high correlation between hourly PM2.5 data and RE coefficients

for urban areas across regions of the U.S. during daylight hours. These correlations in urban

areas are generally similar in the East and West, in sharp contrast to the East/West differences

observed in rural areas. 

The importance of visual air quality to public welfare across the country has been

demonstrated by a number of studies designed to quantify the benefits (or willingness to pay)

associated with potential improvements in visibility. The value placed on protecting visual air

quality is further demonstrated by the existence of a number of programs, goals, standards, and

planning efforts that have been established in the U.S. and abroad to address visibility concerns

in urban and non-urban areas.

In some urban areas, poor visibility has led to more localized efforts to better

characterize, as well as improve, urban visibility conditions. The public perception survey

approach used in the Denver, Phoenix, and British Columbia studies yielded reasonably

consistent results, with each study indicating that a majority of citizens find value in protecting

local visibility to within a visual range of about 40 to 60 km. In the cases of Denver and
6-23

Phoenix, these studies provided the basis for the establishment of their visibility standards and

goals.

Staff believes that the findings of the new data analyses, in combination with recognized

benefits to public welfare of improved visual air quality and an established approach for

determining acceptable visual range, provide a basis for considering revisions to the secondary 

PM2.5 standards to protect against PM-related visibility effects in urban areas.

6.3 EFFECTS ON VEGETATION AND ECOSYSTEMS

Information and conclusions regarding what is currently known about the effects of

ambient PM on ecosystems and individual components of ecosystems such as vegetation, soils,

water, and wildlife are discussed in Chapters 4 and 9 of the CD. This section seeks to build upon

and focus this body of science using EPA’s ecological risk paradigm in a manner that highlights

the usefulness and policy relevance of the scientific information. In doing so, staff has drawn

from EPA’s Guidelines for Ecological Risk Assessment (Guidelines) (EPA, 1998), which

expanded upon the earlier document, Framework for Ecological Risk Assessment (EPA, 1992),

with the goal of improving the quality of ecological risk assessments and increasing the

consistency of assessments across the Agency.

According to the Guidelines document, the three main phases of ecological risk

assessment are problem formulation, analysis, and risk characterization. Problem formulation

includes the integration of the available information on ecosystem stressors (which can include

physical, chemical, and/or biological stressors), their sources, and the effects associated with

exposure of sensitive ecosystem components to each stressor. 

During analysis, data are evaluated to determine how exposure to stressors is likely to

occur (exposure profile) and how stressor levels and ecological effects (stressor-response profile)

are related. These products provide the basis for risk characterization. 

During risk characterization, the exposure and stressor-response profiles are integrated

through risk estimation. Risk characterization includes a summary of assumptions, scientific

uncertainties, and strengths and limitations of the analyses. The final product is a risk

description in which the results of the integration are presented, including an interpretation of

ecological adversity and description of uncertainty and lines of evidence.

Keeping these goals and guidelines in mind, the information is organized into the

following seven subsections: major ecosystem stressors in PM (6.3.1); direct vegetation effects

of particulate nitrate and sulfate deposition (6.3.2); ecosystem effects associated with chronic

inputs of reactive nitrogen and acidifying compounds from PM and other sources (6.3.3);

characteristics and location of nutrient and acid sensitive ecosystems within the U.S. (6.3.4);

ecosystem exposures to PM deposition (6.3.5); consideration of critical loads as an approach for

effects characterization and/or as a management tool (6.3.6); and summary and conclusions

(6.3.7).
6-24

This review will also consider and reference where applicable the extent to which PM

affects the essential ecological attributes (EEAs) outlined in the Framework for Assessing and

Reporting on Ecological Condition, recommended by the Ecological Processes and Effects

Committee (EPEC) of EPA’s Science Advisory Board (hereafter EPEC Framework; SAB,

2002), as described in subsections 4.2.1 and 4.2.3 of the CD.

6.3.1 Major Ecosystem Stressors in PM

As previously discussed, PM is not a single pollutant, but a heterogeneous mixture of

particles differing in size, origin, and chemical composition. The heterogeneity of PM exists not

only within individual particles or samples from individual sites, but to an even 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. Though the chemical constitution of individual particles

can be strongly correlated with size, the relationship between particle size and particle

composition can also be quite complex, making it difficult in most cases to use particle size as a

surrogate for chemistry. Because PM size classes do not necessarily have specific differential

relevance for vegetation or ecosystem effects (Whitby, 1978; EPA, 1996a), it is the opinion of

the staff that an ecologically relevant indicator for PM should be based on one or more chemical

species found in ambient PM. At this time it remains to be determined as to what extent

NAAQS standards focused on a given size fraction would result in reductions of the ecologically

relevant constituents of PM for any given area. 

A number of different chemical species found within ambient PM and their effects on

vegetation and ecosystems were discussed in chapter 4 of the PM CD. In particular, the CD

focused on nitrates and sulfates, concluding that these PM constituents are of greatest and most

widespread environmental significance (CD, p. 9-114). Other components of PM, such as dust,

trace metals, and organics, which can at high levels affect plants and other organisms, were also

discussed. However, some of these compounds, such as organics and some metals, are regulated

under separate statutory authorities, e.g., section 112 of the Clean Air Act. Further, 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

recommending a separate secondary NAAQS. Therefore, the remainder of this section will

focus on the effects of particulate nitrates and sulfates, either individually, in combination,

and/or as contributors to total reactive nitrogen deposition and total deposition of acidifying

compounds, on sensitive ecosystem components and essential ecological attributes, which in

turn, affect overall ecosystem structure and function.

At the outset, it must be recognized that of particulate nitrogen and sulfur as ecosystem

stressors with the recognition that nitrogen and sulfur in varying amounts are necessary and
6-25

beneficial nutrients for most organisms that make up ecosystems. Optimal amounts of these

nutrients varies among organisms, populations, communities and ecosystems and across seasons

and time scales. An individual of a given species found in different ecosystems might have

different optimum nutrient requirements, and different species within a given ecosystem may be

stimulated, inhibited or unaffected by a given amount of inputs of these nutrients. Therefore, it

is impossible to generalize to all species in all circumstances as to the amount at which inputs of

these nutrients or acidifying compounds become stressors. On the other hand, while some

species are adapted to benefit from additions of nutrients in the short run, they may not continue

to benefit into the future (or may not benefit at the same rate), due to supplies of other nutrients

becoming short and limiting further growth. Further, species that benefit from nutrient additions

often do so to the detriment of their competitors, shifting the delicate balance that has evolved

under more nutrient-limited conditions. 

The staff recognizes that the public welfare has benefitted from the use of nitrogen and

sulfur nutrients in fertilizers in managed agricultural and commercial forest settings. The focus

of this review is on identifying risks to sensitive species and ecosystems where unintentional

additions of these atmospherically derived nutrient and acidifying compounds may be forcing

unintended change on the nation’s ecosystems and resulting in adverse impacts on essential

ecological attributes including, species shifts, loss of species richness and diversity, impacts on

threatened and endangered species, and alteration of native fire cycles. In these cases, deposited

particulate nitrate and sulfate are appropriately termed ecosystem “stressors”.

6.3.2 Direct Vegetation Effects of Particulate Nitrate and Sulfate Deposition

Nitrogen is a critical limiting nutrient for plant growth. The process of photosynthesis

uses approximately 75% of the nitrogen in a plant leaf, and, thus, to a large extent, governs the

utilization of other nutrients such as phosphorus, potassium (CD, p. 4-95). Plants usually absorb

nitrogen (as NH4

+

 or NO3

-

) through their roots. However, particle deposition of nitrate, together

with other nitrogen-containing gaseous and precipitation-derived sources, can represent a

substantial fraction of total nitrogen reaching vegetation. In nitrogen-limited ecosystems, this

influx of N can act as a fertilizer. Though it is known that foliar uptake of nitrate can occur, the

mechanism of foliar uptake is not well established, and it is not currently possible to distinguish

sources of chemicals deposited as gases or particles using foliar extraction. Since it has proven

difficult to quantify the percentage of nitrogen uptake by leaves that is contributed by ambient

particles, direct foliar effects of nitrogen-containing particles have not been documented. (CD,

pp. 4-69, 4-70).

Sulfur, similar to nitrogen, is an essential plant nutrient that can be deposited on

vegetation in the form of sulfate particles, or be taken up by plants in gaseous form. Greater than

90% of anthropogenic sulfur emissions are as sulfur dioxide (SO2), with most of the remaining

emissions in the form of sulfate. However, sulfur dioxide is rapidly transformed in the
6-26

atmosphere to sulfate, which is approximately 30-fold less phytotoxic than SO2. Low dosages

of sulfur can also serve as a fertilizer, particularly for plants growing in sulfur-deficient soils. 

There are only a few field demonstrations of foliar sulfate uptake, however, and the relative

importance of foliar leachate and prior dry-deposited sulfate particles remains difficult to

quantify. Though current levels of sulfate deposition reportedly exceed the capacity of most

vegetative canopies to immobilize the sulfur, sulfate additions in excess of needs do not typically

lead to plant injury (CD, pp. 4-71, 4-72).

Staff therefore concludes that at current ambient levels, risks to vegetation from short

term exposures to dry deposited particulate nitrate or sulfate are low. Additional studies are

needed, however, to determine possible effects of sulfate particles on physiological

characteristics of plants following chronic exposures (CD, p. 4-72).

Though dry deposition of nitrate and sulfate particles does not appear to induce foliar

injury at current ambient exposures, when found in acidifying deposition, such particles do have

the potential to cause direct foliar injury. This is especially true when the acidifying deposition

is in the form of fog and clouds, which may contain solute concentrations many times those

found in rain. In experiments on seedling and sapling trees, both coniferous and deciduous

species showed significant effects on leaf surface structures after exposure to simulated acid rain

or acid mist at pH 3.5, while some species have shown subtle effects at pH 4 and above. 

Epicuticular waxes, which function to prevent water loss from plant leaves, can be destroyed by

acid rain in a few weeks, which suggests links between acid precipitation and aging. Due to

their longevity and evergreen foliage, the function of epicuticular wax is more crucial in

conifers. For example, red spruce seedlings, which have been extensively studied, appear to be

more sensitive to acid precipitation (mist and fog) when compared with other species (CD, pp. 4-

72, 4-73). In addition to accelerated weathering of leaf cuticular surfaces, other direct responses

of forest trees to acid precipitation include increased permeability of leaf surfaces to toxic

materials, water, and disease agents; increased leaching of nutrients from foliage; and altered

reproductive processes (CD, p. 4-86). All of these effects serve to weaken trees so that they are

more susceptible to other stresses (e.g., extreme weather, pests, pathogens).

Acid precipitation with levels of acidity associated with the foliar effects described above

are currently found in some locations in the U.S.. For example, in the eastern U.S., the mean

precipitation pH ranges from 4.3 (Pennsylvania and New York) to 4.8 (Maine)(EPA, 2003). It

can be assumed that occult (mist or fog) deposition impacting high elevations more frequently,

would contain even higher concentrations of acidity. Thus, staff concludes that the risks of foliar

injury occurring from acid precipitation is high. The contribution of particulate sulfates and

nitrates to the total acidity found in the acid precipitation impacting eastern vegetation is not

clear.
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6.3.3 Ecosystem Effects of Chronic Inputs of Reactive Nitrogen and Acidifying

Compounds From PM Deposition and Other Sources 

Ecosystem-level responses related to PM occur when the effects of PM deposition on the

biological and physical components of ecosystems become sufficiently widespread as to impact

essential ecological attributes such as nutrient cycling and/or shifts in biodiversity. The most

significant PM-related ecosystem-level effects result from long-term cumulative deposition of a

given chemical species (e.g., nitrate) or mix (e.g., acidic and acidifying deposition) that exceeds

the natural buffering or storage capacity of the ecosystem and/or affects the nutrient status of the

ecosystem, usually by indirectly changing soil chemistry, populations of bacteria involved in

nutrient cycling, and/or populations of fungi involved in plant nutrient uptake (CD, pp. 4-90, 4-

91). To understand these effects, long-term, detailed ecosystem or site-specific data usually are

required. The availability of this type of long-term data is limited. The following discussion is

organized according to the speciated effects of PM on ecosystems.

6.3.3.1 Environmental Effects of Reactive Nitrogen (Nr) Deposition

In the environment, nitrogen may be divided into two types: nonreactive, molecular

nitrogen (N2) and reactive nitrogen (Nr). Molecular nitrogen is the most abundant element in the

atmosphere. However, it only becomes available to support the growth of plants and

microorganisms after it is converted into a reactive form. In nature, Nr creation is accomplished

by certain organisms that have developed the capability of converting N2 to biologically active

reduced forms (Galloway and Cowling, 2002; Hornung and Langan, 1999; EPA, 1993). By the

mid-1960's, however, Nr creation through natural terrestrial processes had been overtaken by Nr

creation as a result of human processes (CD, p. 4-95). The deposition of nitrogen in the U.S.

from human activity doubled between 1961 and 1997, with the largest increase occurring in the

1960s and 1970s (CD, p. 4-98). Reactive nitrogen is now accumulating in the environment on

all spatial scales – local, regional and global. The three main sources of anthropogenic Nr are:

(1) the Haber-Bosch process, which converts N2 to Nr to sustain food production and some

industrial activities; (2) widespread cultivation of legumes, rice and other crops that promote the

conversion of N2 to organic nitrogen through biological nitrogen fixation; and (3) combustion of

fossil fuels, which converts both atmospheric N2 and fossil nitrogen to reactive NOx (CD, pp. 4-

95, 4-96; Galloway and Cowling, 2002; Galloway et al., 2003). Though not currently regulated

under the NAAQS program, reduced forms of Nr from food production are estimated to be

approximately 2 - 4 times larger than emissions of oxidized forms of nitrogen produced during

combustion of fossil fuels. Specifically, Galloway and Cowling (2002) estimate that per capita

Nr creation for the world in the mid-1990s was 20 kg/N/person/year for food production and

only 3.9 kg/N/person/year for energy production (see Table 3).

Currently available forms of reactive nitrogen include inorganic reduced forms (e.g.,

ammonia [NH3] and ammonium [NH4

+

]), inorganic oxidized forms (e.g., nitrogen oxides [NOx],
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nitric acid [HNO3], nitrous oxide [N2O], and nitrate [NO3

-

]), and organic compounds (e.g., urea,

amine, proteins, and nucleic acids (CD, p. 4-95).

Emissions of nitrogen oxides from fuel burning increased exponentially from1940 until

the 1970s, leveled off after the passage of the 1970 amendments to the Clean Air Act, and

stabilized at approximately 7 Tg NOx /yr in the late 1990s. However, despite decreases in

emissions from fossil fuel burning industries, emissions from automobiles have increased

approximately 10% since 1970 due to greater total miles driven (Howarth et al., 2002). Some

NOx emissions are transformed into a portion of ambient air PM (particulate nitrate) and

deposited onto sensitive ecosystems.

The term “nitrogen cascade” refers to the sequential transfers and transformations of Nr

molecules as they move from one environmental system or reservoir (atmosphere, biosphere,

hydrosphere) to another, and the multiple linkages that develop among the different ecological

components, as shown in Figure 6-6. Because of these linkages, adding anthropogenic Nr alters

a wide range of biogeochemical processes and exchanges as the Nr moves among the different

environmental reservoirs, with the consequences accumulating through time (Galloway and 

Cowling, 2002; Galloway et al., 2003). These changes in the nitrogen cycle are contributing to

both beneficial and detrimental effects to the health and welfare of humans and ecosystems

(Rabalais, 2002; van Egmond et al., 2002; Galloway, 1998).

Large uncertainties, still exist, however, concerning the rates of Nr accumulation in the

various environmental reservoirs. These uncertainties limit our ability to determine the temporal

and spatial distribution of environmental effects for a given input of Nr. These uncertainties are

of particular significance because of the sequential nature of Nr effects on environmental

processes. Reactive nitrogen does not cascade at the same rate through all environmental

systems. The only way to eliminate Nr accumulation and stop the cascade is to convert Nr back

to nonreactive N2 (Galloway et al., 2003).

Some of the more significant detrimental effects resulting from chronic increased inputs

of atmospheric Nr (e.g., ammonium and nitrate compounds) include: (1) decreased productivity,

increased mortality, and/or shifts in terrestrial plant community composition, often leading to

decreased biodiversity in many natural habitats wherever atmospheric Nr deposition increases

significantly and critical thresholds are exceeded (Aber et al., 1995); (2) leaching of excess

nitrate and associated base cations from terrestrial soils into streams, lakes and rivers and

mobilization of soil aluminum; (3) eutrophication, hypoxia, loss of biodiversity, and habitat

degradation in coastal ecosystems, now considered a major pollution problem in coastal waters

(Rabalais, 2002); (4) acidification and loss of aquatic flora and fauna biodiversity in lakes and

streams in many regions of the world when associated with sulfur deposition (Vitousek et al.,

1997); and (5) alteration of ecosystem processes such as nutrient and energy cycles through
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Figure 6-6 Illustration of the nitrogen cascade showing the movement of humanproduced reactive nitrogen (Nr) as it cycles through the various

environmental reservoirs in the atmosphere and in terrestrial and aquatic

ecosystems (Galloway et al., 2003; Figure 4-15, CD p. 4-97).

changes in the functioning and species composition of beneficial soil organisms (Galloway and

Cowling 2002). 

Additional, indirect detrimental effects of excess Nr on societal values include: (1)

increases in fine PM resulting in regional hazes that decrease visibility at scenic rural and urban

vistas and airports (discussed above in section 6.2); (2) depletion of stratospheric ozone by N2O

emissions which can in turn affect ecosystems and human health; (3) global climate change

induced by emissions of N2O (Galloway et al., 2003); (4) formation of O3 and ozone-induced

injury to crops, forests, and natural ecosystems and the resulting predisposition to attack by

pathogens and insects, as well as human health related impacts (EPA, 1996); (5) decrease in
6-30

quantity or quality of available critical habitat for threatened and endangered species (Fenn et al.,

2003); and (6) alteration of fire cycles in a variety of ecosystem types (Fenn et al., 2003).

A number of the more significant effects of chronic, long-term deposition of Nr on

terrestrial and aquatic ecosystems will be discussed below, specifically those effects which seem

to pose the greatest long-term risks to species or ecosystem health and sustainability or that

threaten ecosystem flows of goods and services important to human welfare.

Nitrogen Saturation of Terrestrial Ecosystems

Long-term, chronic additions of Nr (including ammonium and nitrate deposition from

ambient PM) to terrestrial ecosystems is resulting in numerous ecosystems shifting to a

detrimental ecological condition known as “nitrogen saturation.” Nitrogen saturation does not

occur at a specific point in time, but is a set of gradually developing critical changes in

ecosystem processes which represent the integrated response of a system to increased Nr

availability over time (Aber, 1992). It occurs when Nr inputs exceed the capacity of plants and

soil microorganisms to utilize and retain the nitrogen (Aber et al., 1989, 1998; Garner, 1994;

EPA, 1993). Under conditions of nitrogen saturation, some other resource generally replaces

nitrogen in limiting biotic functions. The appearance of nitrate in soil solution (leaching) is an

early symptom of excess Nr accumulation.

Not all vegetation, organisms, or ecosystems react in the same manner to increased Nr 

availability from atmospheric deposition. This is due in part to the variation both within and

across species in their inherent capacity to utilize additional Nr and the suite of other factors that

influence the range of community or ecosystem types possible at any given location. Such

factors can include the mineral composition of the underlying bedrock, the existing soil nutrient

pools, the local climatic conditions including weather extremes such as drought, high/low

temperatures, topography, elevations, natural/land use history, and fire regimes. 

In U.S. ecosystems, the nutrient whose supply most often sets the limit of possible

primary productivity at a given site is biologically available nitrogen. However, in any given

ecosystem, not all plants are equally capable of utilizing extra nitrogen. Those plants that are

predisposed to capitalize on any increases in Nr availability gain an advantage over those that are

not as responsive to added nutrients. Over time, this shift in the competitive advantage may lead

to shifts in overall plant community composition. Whether or not this shift is considered adverse

would depend on the management context within which that ecosystem falls and the ripple

effects of this shift on other ecosystem components, essential ecological attributes (EEAs), and

ecosystems.

The effect of additions of Nr on plant community succession patterns and biodiversity

has been studied in several long-term nitrogen fertilization studies in both the U.S. and Europe. 

These studies suggest that some forests receiving chronic inputs of Nr may decline in

productivity and experience greater mortality (Fenn et al. 1998). For example, fertilization and

nitrogen gradient experiments at Mount Ascutney, VT suggest that nitrogen saturation may lead
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to the replacement of slow-growing, slow nitrogen-cycling spruce-fir forest stands by fastgrowing deciduous forests that cycle nitrogen rapidly (Fenn et al. 1998). Similarly, experimental

studies of the effects of Nr deposition over a 12-year period on Minnesota grasslands dominated

by native warm-season grasses observed the shift to low-diversity mixtures dominated by coolseason grasses at all but the lowest rates of Nr addition (Wedin and Tilman, 1996). The shift to

low-diversity mixtures was associated with the decrease in biomass carbon to N (C:N) ratios,

increased Nr mineralization, increased soil nitrate, high nitrogen losses, and low carbon storage. 

Grasslands with high nitrogen retention and carbon storage rates were the most vulnerable to loss

of species and major shifts in nitrogen cycling. (Wedin and Tilman, 1996). 

The carbon-to-nitrogen (C:N) ratio of the forest floor can be changed by nitrogen

deposition over time. In Europe, low C:N ratios coincide with high deposition regions. A strong

decrease in forest floor root biomass has also been observed with increased nitrogen availability,

and appears to occur when the ecosystem becomes nitrogen saturated. If root growth and

mycorrhizal formation are impaired by excessive nitrogen deposition, the stability of the forest

floor vegetation may be affected. The forest floor C:N ratio has been used as a rough indicator

of ecosystem nitrogen status in mature coniferous forests and the risk of nitrate leaching. Nitrate

leaching has been significantly correlated with forest floor nitrate status, but not with nitrate

deposition. Therefore, to predict the rate of changes in nitrate leaching, it is necessary to be able

to predict the rate of changes in the forest floor C:N ratio. Understanding the variability in forest

ecosystem response to nitrogen input is essential in assessing pollution risks (Gundersen et al.,

1998; CD, pp. 4-106, 4-107).

In the U.S., forests that are now showing severe symptoms of nitrogen saturation include:

the northern hardwoods and mixed conifer forests in the Adirondack and Catskill Mountains of

New York; the red spruce forests at Whitetop Mountain, Virginia, and Great Smoky Mountains

National Park, North Carolina; mixed hardwood watersheds at Fernow Experimental Forest in

West Virginia; American beech forests in Great Smoky Mountains National Park, Tennessee;

mixed conifer forests and chaparral watersheds in southern California and the southwestern

Sierra Nevada in Central California; the alpine tundra/subalpine conifer forests of the Colorado

Front Range; and red alder forests in the Cascade Mountains in Washington. All these systems

have been exposed to elevated nitrogen deposition, and nitrogen saturated watersheds have been

reported in the above-mentioned areas. Annual nitrogen additions through deposition in the

southwestern Sierra Nevada are similar in magnitude to nitrogen storage in vegetation growth

increments of western forests, suggesting that current nitrogen deposition rates may be near the

assimilation capacity of the overstory vegetation. Ongoing urban expansion will increase the

potential for nitrogen saturation of forests from urban sources (e.g., Salt Lake City, Seattle,

Tucson, Denver, central and southern California) unless there are improved emission controls

(Fenn et al., 1998).
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The composition and structure of the plant community within an ecosystem in large part

determine the food supply and habitat types available for use by other organisms. In terrestrial

systems, plants serve as the integrators between above-ground and below-ground environments

and are influenced by and influence conditions in each. It is because of these linkages that

chronic excess Nr additions can lead to complex, dramatic, and severe ecosystem level/wide

changes/responses. Changes in soil Nr influence below ground communities as well. A

mutualistic relationship exists in the rhizosphere (plant root zone) between plant roots, fungi, and

microbes. Because the rhizosphere is an important region of nutrient dynamics, its function is

critical for the growth of the organisms involved. The plant roots provide shelter and carbon for

the symbionts, whereas the symbionts provide access to limiting nutrients such as nitrogen and

phosphorus for the plant. Bacteria make N, S, Ca, P, Mg, and K available for plant use while

fungi in association with plant roots form mycorrhizae that are essential in the uptake by plants

of mineral nutrients, such as N and P (Section 4.3.3; Wall and Moore, 1999; Rovira and Davy,

1974). Mycorrhizal fungal diversity is associated with above-ground plant biodiversity,

ecosystem variability, and productivity (Wall and Moore, 1999). Studies suggest that during

nitrogen saturation, soil microbial communities change from being predominately fungal, and

dominated by mycorrhizae, to being dominated by bacteria (Aber et al., 1998; CD, pp. 4-107, 4-

108), dramatically affecting both above- and below-ground ecosystems. These types of effects

have been observed in the field. For example, the coastal sage scrub (CSS) community in

California has been declining in land area and in drought deciduous shrub density over the past

60 years, and is being replaced in many areas by Mediterranean annual grasses. At the same

time, larger-spored below-ground fungal species (Scutellospora and Gigaspora), due to a failure

to sporulate, decreased in number with a concomitant proliferation of small-spored species of

Glomus aggregatum, G. leptotichum, and G. geosporum, indicating a strong selective pressure

for the smaller spored species of fungi (Edgerton-Warburton and Allen, 2000). These results

demonstrate that nitrogen enrichment of the soil significantly alters the arbuscular mycorrhizal

species composition and richness, and markedly decreases the overall diversity of the arbuscular

mycorrhizal community. The decline in the coastal sage scrub species can be directly linked to

the decline of the arbuscular mycorrhizal community (Edgerton-Warburton and Allen, 2000;

Allen et al., 1998; Padgett et al., 1999)(CD, pp. 4-108, 4-109).

Impacts on threatened and endangered species. In some rare and unique U.S.

ecosystems, chronic additions of atmospherically-derived nitrogen have already had some dire

and perhaps irreversible consequences. For example, California has many species that occur in

shrub, forb, and grasslands affected by N deposition, with up to 200 sensitive plant species in

southern California CSS alone (Skinner and Pavlik, 1994). Some 25 plant species are already

extinct in California, most of them annual and perennial forbs that occurred in sites now

experiencing conversion to annual grassland. As CSS converts more extensively to annual

grassland dominated by invasive species, loss of additional rare species may be inevitable. 
6-33

Though invasive species are often identified as the main threat to rare species, it is more likely

that invasive species combine with other factors, such as excess N deposition, to promote

increased productivity of invasive species and resulting species shifts. 

Not surprisingly, as sensitive vegetation is lost, wildlife species that depend on these

plants are adversely affected. Included among these species are several threatened or

endangered species listed by the U.S. Fish and Wildlife Service, such as the desert tortoise and

checkerspot butterfly. A native to San Francisco Bay area, the bay checkerspot butterfly

(Euphydryas editha bayensis), has been declining steadily over the past decade, with local

extirpations in some reserves. This decline has been associated with the invasion of exotic

grasses replacing the native forbs on which the butterfly depends. In particular, the larval stage

is dependent on primarily one host plant, Plantago erecta, which is increasingly being outcompeted by exotic grasses. 

Similarly, the desert tortoise has declined due to a number of co-occurring stresses,

including grazing, habitat destruction, drought, disease, and a declining food base. In the desert

shrub inter-spaces, sites where native forbs once flourished, invasive grasses now dominate,

reducing the nutritional quality of foods available to the tortoise (Fenn et al., 2003; Nagy et al.,

1998). Nitrogen deposition contributes to the productivity and density of N-fertilized grasses at

the expense of native forbs (Brooks, 2003). “Thus, protection of endangered species will

require increased exotic grass control, but local land management strategies to protect these

endangered species may not succeed unless they are accompanied by policy changes at the

regional or national level that reduce air pollution” (Fenn et al., 2003).

Community composition of epiphytic lichens is readily altered by small increases in 

nitrogen deposition, an effect that seems to be widespread in the West (Fenn et al., 2003). Most

epiphytic lichens meet their nutritional requirements from atmospheric deposition and can store

N in excess of their nutritional needs (van Herk, 1999). In the San Bernardino Mountains, up to

50% of the lichen species that occurred in the region in the early 1900s have disappeared, with a

disproportionate number of the locally extinct species being (epiphytic) cyanolichens (Fenn et

al., 2003; Nash and Sigal, 1999). The Pacific Northwest, in contrast, still has widespread

populations of pollution-sensitive lichens (Fenn et al., 2003). However, in urban areas, intensive

agricultural zones and downwind of major urban and industrial centers, there is a sparsity of

sensitive lichen species and high levels of N concentrations have been measured in lichen tissue

(Fenn et al., 2003). Replacement of sensitive lichens by nitrophilous species has undesirable

ecological consequences. In late-successional, naturally N-limited forests of the Coast Range

and western Cascades, epiphytic cyanolichens make important contributions to mineral cycling

and soil fertility (Pike 1978; Sollins et al., 1980; Antoine, 2001), and together with other large,

pollution-sensitive macrolichens, are an integral part of the food web for large and small

mammals, insects and birds (McCune and Geiser, 1997).
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Alteration of native fire cycles. Several lines of evidence suggest that Nr deposition may

be contributing to greater fuel loads and thus altering the fire cycle in a variety of ecosystem

types, although further study is needed (Fenn et al., 2003). Invasive grasses promote a rapid fire

cycle in many locations (D’Antonio and Vitousek, 1992). The increased productivity of

flammable understory grasses increases the spread of fire and has been hypothesized as one

mechanism for the recent conversion of CSS to grassland (Minnich and Dezzani, 1998).

Thus, through its effect on habitat suitability, genetic diversity, community dynamics and

composition, nutrient status, energy and nutrient cycling, and frequency and intensity of natural

disturbance regimes (fire), excess Nr deposition is having profound and adverse impact on the

essential ecological attributes associated with terrestrial ecosystems. Strong correlation between

the stressor and adverse environmental response exists in many locations, and N-addition studies

have confirmed this relationship between stressor and response. Research efforts should be

made to elucidate what role particulate deposition is playing in contributing to these effects so as

to facilitate the mitigation of such effects.

Effects of Nitrogen Addition on Aquatic Habitats

Aquatic ecosystems (streams, rivers, lakes, estuaries or oceans) receive increased Nr

inputs either from direct atmospheric deposition (including nitrogen-containing particles),

surface runoff, or leaching from nitrogen saturated soils into ground or surface waters. The

primary pathways of Nr loss from forest ecosystems are hydrological transport beyond the

rooting zone into groundwater or stream water, or surface flows of organic nitrogen as nitrate

and Nr loss associated with soil erosion (Fenn et al., 1998). In the east, high nitrate

concentrations have been observed in streams draining nitrogen saturated watersheds in the

southern Appalachian Mountains (Fenn et al., 1998). The Great Smoky Mountains National

Park in Tennessee and North Carolina receives elevated amounts of total atmospheric deposition

of sulfur and nitrogen. A major portion of the atmospheric loading is from dry and occult

deposition. Nitrogen saturation of the watershed resulted in extremely high exports of nitrate

and promoted both chronic and episodic stream acidification in streams draining undisturbed

watersheds. Significant export of base cations was also observed (CD, pp. 4-110, 4-111; see also

section 6.3.3.2 on acidification from PM deposition).

In the west, the Los Angeles Air Basin exhibited the highest stream water NO3

-

concentrations in wilderness areas of North America (Bytnerowicz and Fenn, 1996; Fenn et al.,

1998). Chronic N deposition in southern California, in the southwestern Sierra Nevada, and in

the Colorado Front Range leads to increased net N mineralization and nitrification rates in soil

and to elevated NO3

-

 concentrations in lakes and streams. These symptoms occur in low- and

mid-elevation, high-deposition areas (>15 kg N/ha/yr) and in high elevation sites with relatively

low N deposition (4 to 8 kg N/ha/yr) but little capacity to assimilate and retain added N.

Estuaries are among the most intensely fertilized systems on Earth (Fenn et al., 1998). 

They receive far greater nutrient inputs than other systems. In the Northeast, for example,
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nitrogen is the element most responsible for eutrophication in coastal waters of the region. Since

the early 1900s, there has been a 3- to 8-fold increase in nitrogen flux from10 watersheds in the

northeast. These increases are associated with nitrogen oxide emissions from combustion which

have increased 5-fold. Riverine nitrogen fluxes have been correlated with atmospheric

deposition onto their landscapes and also with nitrogen oxides emissions into their airsheds. 

Data from 10 benchmark watersheds with good historical records indicate that about 36-80% of

the riverine total nitrogen export, averaging approximately 64%, was derived directly or

indirectly from nitrogen oxide emissions (CD, pp. 4-109, 4-110).

The Pamlico Sound, NC estuarine complex, which serves as a key fisheries nursery

supporting an estimated 80% of commercial and recreational finfish and shellfish catches in the

southeastern U.S. Atlantic coastal region, has also been the subject of recent research (Paerl et

al., 2001) to characterize the effects of Nr deposition on the estuary. Direct atmospheric nitrogen

deposition onto waterways feeding into the Pamlico Sound or onto the Sound itself and indirect

nitrogen inputs via runoff from upstream watersheds contribute to conditions of severe water

oxygen depletion; formation of algae blooms in portions of the Pamlico Sound estuarine

complex; altered fish distributions, catches, and physiological states; and increases in the

incidence of disease. Especially under extreme rainfall events (e.g., hurricanes), massive

influxes of Nr (in combination with excess loadings of metals or other nutrients) into watersheds

and sounds can lead to dramatic decreases of oxygen in water and the creation of widespread

“dead zones” and/or increases in algae blooms that can cause extensive fish kills and damage to

commercial fish and sea food harvesting (Paerl et al., 2001; CD, pp. 4-109, 4-110).

6.3.3.2 Environmental Effects of PM-Related Acidic and Acidifying Deposition

Acid deposition has emerged over the past quarter century as a critical environmental

stress that affects diverse terrestrial and aquatic ecosystems in North America, Europe, and Asia

(Driscoll et al., 2001). In the eastern U.S. for example, the current acidity in precipitation is at

least twice as high as in pre-industrial times, with mean precipitation pH ranges from 4.3

(Pennsylvania and New York) to 4.8 (Maine) (EPA, 2003). Acid deposition is highly variable

across space and time, can originate from transboundary air pollution, can travel hundreds of

miles before being deposited, thereby affecting large geographic areas. It is composed of ions,

gases, and particles derived from the precursor gaseous emissions of SO2, NOx, NH3 and

particulate emissions of other acidifying compounds. Acid deposition disturbs forest and aquatic

ecosystems by giving rise to harmful chemical conditions (Driscoll et al., 2001). 

Terrestrial Effects

Acid deposition has changed the chemical composition of soils by depleting the content

of available plant nutrient cations (e.g., Ca2+, Mg2+, K+) by increasing the mobility of Al, and by

increasing the S and N content (Driscoll et al., 2001). Soil leaching is often of major

importance in cation cycles, and many forest ecosystems show a net loss of base cations (CD, pp.

4-118). In acid sensitive soils, mineral weathering (the primary source of base cations in most
6-36

watersheds) is insufficient to keep pace with leaching rates accelerated by acid deposition

(Driscoll et al., 2001).

In the absence of acid deposition, cation leaching in northeastern forest soils is driven

largely by naturally occurring organic acids derived from the decomposition of organic matter. 

Organic acids tend to mobilize Al through formation of organic-Al complexes, most of which are

deposited lower in the soil profile through adsorption to mineral surfaces. This process, termed

podzolization, results in surface waters with low concentrations of Al. Such concentrations are

primarily in a nontoxic, organic form (Driscoll et al., 1998). Acid deposition, however, has

altered podzolization by solubilizing Al with mobile inorganic anions, facilitating the transport

of inorganic Al into surface waters. In forest soils with base saturation values less than 20%,

acid deposition leads to increased Al mobilization and a shift in chemical speciation of Al from

organic to inorganic forms that are toxic to terrestrial and aquatic biota. 

The toxic effect of Al on forest vegetation is attributed to its interference with plant

uptake of essential nutrients, such as Ca and Mg. Because Ca plays a major role in cell

membrane integrity and cell wall structure, reductions in Ca uptake suppress cambial growth,

reduce the rate of wood formation, decrease the amount of functional sapwood and live crown,

and predispose trees to disease and injury from stress agents when the functional sapwood

becomes less than 25% of cross sectional stem area (Smith, 1990). There are large variations in

Al sensitivity among ecotypes, between and within species, due to differences in nutritional

demands and physiological status, that are related to age and climate, which change over time

(CD, pp. 4-126).

Acid deposition has been firmly implicated as a causal factor in the northeastern highelevation decline of red spruce (DeHayes et al., 1999). Red spruce is common in Maine, where

it is an important commercial species. It is also common at high elevations in mountainous

regions throughout the Northeast, where it is valued for recreation and aesthetics, as well as for

providing a habitat for unique and endangered species. Dieback has been most severe at high

elevations in the Adirondack and Green Mountains, where more than 50% of the canopy trees

died during the 1970s and 1980s. In the White Mountains, about 25% of the canopy spruce died

during that same period (Craig and Friedland 1991). Dieback of red spruce trees has also been

observed in mixed hardwood-conifer stands at relatively low elevations in the western

Adirondack Mountains, areas that receive high inputs of acid deposition (Shortle et al., 1997). 

Results of controlled exposure studies show that acidic mist or cloud water reduces the cold

tolerance of current-year red spruce needles by 3-10 degrees C (DeHayes et al., 1999). This 

increased susceptibility to freezing occurs due to the loss of membrane-associated Ca2+ from

needles through leaching caused by the hydrogen ion. The increased frequency of winter injury

in the Adirondack and Green Mountains since 1955 coincides with increased exposure of red

spruce canopies to highly acidic and acidifying cloud water (Johnson et al., 1984). Recent

episodes of winter injury have been observed throughout much of the range of red spruce in the
6-37

Northeast. (DeHayes et al., 1999). DeHayes et al. (1999) indicate that there is a significant

positive association between cold tolerance and foliar calcium in trees exhibiting deficiency in

foliar calcium, and further state that their studies raise the strong possibility that acid deposition

altering of foliar calcium is not unique to red spruce but has been demonstrated in many other

northern temperate forest tree species including yellow birch (Betula alleghaniensis), white

spruce (Picea glaucus), red maple (Acer rubrum) eastern white pine (Pinus strobus), and sugar

maple (Acer saccharum) (CD, p. 4-120).

Although less well established, there is also a strong possibility that low Ca to Al ratios

in soils may also be impacting northeastern red spruce. Cronan and Grigal (1995) concluded that

a Ca:Al ratio of less than 1.0 in soil water indicated a greater than 50% probability of impaired

growth in red spruce. They cite examples of studies from the northeast where soil solutions in

the field were found to exhibit Ca:Al ionic ratios less than 1.0.

Acid deposition may also be contributing to episodic dieback of sugar maple in the

Northeast through depletion of nutrient cations from marginal soils. Horsley et al. (1999) found

that dieback at 19 sites in northwestern and north-central Pennsylvania and south-western New

York was correlated with combined stress from defoliation and deficiencies of Mg and Ca. 

Dieback occurred predominately on ridgetops and on upper slopes, where soil base availability

was much lower than at mid and low slopes of the landscape (Bailey et al., 1999). Because

multiple factors such as soil mineralogy and landscape position affect soil base status, the extent

to which sugar maple dieback can be attributed to acid deposition is not clear.

Less sensitive forests throughout the U.S. are experiencing gradual losses of base cation

nutrients, which in many cases will reduce the quality of forest nutrition over the long term

(National Science and Technology Council, 1998). In some cases, such effects may not even

take decades to occur because these forests have already been receiving S and N deposition for

many years.

In contrast to contributing to the adverse impacts of acid deposition, particles can also

provide a beneficial supply of base cations to sites with very low rates of supply from mineral

sources. In these areas, atmospheric inputs of bass cations can help ameliorate the acidifying

effects of acid particles. The Integrated Forest Study (IFS) (Johnson and Lindberg, 1992) has

characterized the complexity and variability of ecosystem responses to atmospheric inputs and

provided the most extensive data set available on the effects of atmospheric deposition, including

particle deposition, on the cycling of elements in forest ecosystems. This study showed that in

the IFS ecosystems, inputs of base cations have considerable significance, not only for base

cation status, but also for the potential of incoming precipitation to acidify or alkalize the soils. 

The actual rates, directions, and magnitudes of changes that may occur in soils (if any), however,

will depend on rates of inputs from weathering and vegetation outputs, as well as deposition and

leaching. In other words, these net losses or gains of base cations must be placed in the context

of the existing soil pool size of exchangeable base cations (CD, p. 4-132). Given the wide
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ranges of particulate deposition for each base cation across the IFS sites, however, the unique

characteristics of various sites need to be better understood before assumptions are made about

the role particulate pollution plays in ecosystem impacts (CD, pp. 4-127, 4-128).

In a follow up study, Johnson et al. (1999) used the nutrient cycling model, NuCM, to

simulate the effects of reduced S, N, and base cation (CB) deposition on nutrient pools, fluxes,

soil, and soil solution chemistry in two contrasting southern Appalachian forest ecosystems. The

authors found that in an extremely acidic system, CB deposition can have a major effect on CB

leaching through time and S and N deposition had a major effect on Al leaching. At the less

acidic Coweeta site, CB deposition had only a minor effect on soils and soil solutions; whereas S

and N deposition had delayed but major effects on CB leaching (CD, pp. 4-136, 4-137).

Aquatic Effects

Inputs of acid deposition to regions with base-poor soils have resulted in the acidification

of soil waters, shallow ground waters, streams, and lakes in a number of locations within the

U.S. In addition, perched seepage lakes, which derive water largely from direct precipitation

inputs, are highly sensitive to acid deposition (Charles, 1991). These processes usually result in

lower pH and, for drainage lakes, higher concentrations of inorganic monomeric Al. Such

changes in chemical conditions are toxic to fish and other aquatic animals (Driscoll et al., 2001).

A recent report, Response of Surface Water Chemistry to the Clean Air Act of 1990

(EPA, 2003), analyzes data from 1990 through 2000 obtained from EPA’s Long Term

Monitoring (LTM) and Temporally Integrated Monitoring of Ecosystems (TIME) projects, part

of EMAP (Environmental Monitoring and Assessment Program). The report assesses recent

changes in surface water chemistry in response to changes in deposition, in the northern and

eastern U.S., specifically in the acid sensitive regions defined as New England (Maine, New

Hampshire, Vermont and Massachusetts), the Adirondack Mountains of New York, the Northern

Appalachian Plateau (New York, Pennsylvania and West Virginia), the Ridge and Blue Ridge

Provinces of Virginia, and the Upper Midwest (Wisconsin and Michigan). Acidic waters are

defined as having acid neutralizing capacity (ANC) less than zero (i.e., no acid buffering

capacity in the water), corresponding to a pH of about 5.2. Increases in surface water ANC

values and/or pH would indicate improved buffering capacity and signal the beginning of

recovery (EPA, 2003).

Using National Atmospheric Deposition Program (NADP) data, trends in sulfate and N

(nitrate + ammonium) deposition were analyzed, along with CB deposition, sulfate and nitrate

concentrations in surface waters, ANC and pH levels. Over this timeframe, sulfate deposition

declined significantly across all regions, while N declined slightly in the Northeast and increased

slightly in the Upper Midwest. Base cation deposition showed no significant changes in the East

and increased slightly in the Upper Midwest. Concurrently, all regions except the Ridge/Blue

Ridge province in the mid-Atlantic showed significant declines in sulfate concentrations in
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surface waters, while nitrate concentrations decreased in two regions with the highest ambient

nitrate concentrations (Adirondacks, Northern Appalachian Plateau) but were relatively

unchanged in regions with low concentrations. 

Given the declines in S and N deposition measured for these areas, one would expect to

find increasing values of ANC, pH or both in response. ANC values did increase in the

Adirondacks, Northern Appalachian Plateau and Upper Midwest, despite a decline in base

cations (Ca and Mg) in each region. The loss of base cations limited the extent of ANC and pH

increase. Toxic Al concentrations also declined slightly in the Adirondacks. In New England

and Ridge/Blue Ridge, however, regional surface water ANC did not change significantly (EPA,

2003).

Modest increases in ANC have reduced the number of acidic lakes and stream segments

in some regions. There are an estimated 150 Adirondack lakes with ANC less than 0, or 8.1% of

the population, compared to 13% (240 lakes) in the early 1990s. In the Upper Midwest, an

estimated 80 of 250 lakes that were acidic in mid-1980s are no longer acidic. TIME surveys of

streams in the Northern Appalachian Plateau region estimated that 8.5% (3,600 kilometers) of

streams remain acidic at the present time, compared to 12% (5,014 kilometers) of streams that

were acidic in 1993-94. In these three regions taken together, approximately one-fourth to onethird of formerly acidic surface waters are no longer acidic, although still with very low ANC. 

The report finds little evidence of regional change in the acidity status of New England or the

Ridge/Blue Ridge regions and infers that the numbers of acidic waters remain relatively

unchanged. Despite a general decline in base cations and a possible increase in natural organic

acidity, there is no evidence that the number of acidic waters have increased in any region (EPA,

2003).

Acidification has marked effects on the trophic structure of surface waters. Decreases in

pH and increases in Al concentrations contribute to declines in species richness and in the

abundance of zooplankton, macroinvertebrates, and fish (Schindler et al.,1985; Keller and Gunn

1995). Numerous studies have shown that fish species richness (the number of fish species in a

water body) is positively correlated with pH and ANC values (Rago and Wiener, 1986; Kretser et

al., 1989). Decreases in pH result in decreases in species richness by eliminating acid-sensitive

species (Schindler et al., 1985). Of the 53 fish species recorded by the Adirondack Lakes Survey

Corporation, about half (26 species) are absent from lakes with pH below 6.0. Those 26 species

include important recreational fishes, such as Atlantic salmon, tiger trout, redbreast sunfish,

bluegill, tiger musky, walleye, alewife, and kokanee (Kretser et al., 1989), plus ecologically

important minnows that serve as forage for sport fishes. 

A clear link exists between acidic water, which results from atmospheric deposition of

strong acids, and fish mortality. The Episodic Response Project (ERP) study showed that

streams with moderate to severe acid episodes had significantly higher fish mortality during

bioassays than nonacidic streams (Van Sickle et al., 1996). The concentration of inorganic
6-40

monomeric Al was the chemical variable most strongly related to mortality in the four test

species (brook trout, mottled sculpin, slimy sculpin, and blacknose dace). The latter three

species are acid sensitive. In general, trout abundance was lower in ERP streams with median

episode pH less than 5.0 and inorganic monomeric Al concentrations greater than 3.7 - 7.4 mmol

L-1. Acid sensitive species were absent from streams with median episode pH less than 5.2 and

with a concentration of inorganic monomeric Al greater than 3.7 mmol L-1.

Given the significant decreases in sulfur emissions that have occurred in the U. S. and

Europe in recent decades, the findings of Driscoll et al. (1989, 2001) and Hedin et al. (1994) are

especially relevant. Driscoll et al. (1989, 2001) noted a decline in both SO4

-2 and base cations in

both atmospheric deposition and stream water over the past two decades at Hubbard Brook

Watershed, NH. However, the decreases in SO2 emissions in Europe and North America in

recent years have not been accompanied by equivalent declines in net acidity related to sulfate in

precipitation, and may have, to varying degrees, been offset by steep declines in atmospheric

base cation concentrations over the past 10 to 20 years (Hedin et al., 1994).

Driscoll et al. (2001) envision a recovery process that will involve two phases. Initially,

a decrease in acid deposition following emissions controls will facilitate a phase of chemical

recovery in forest and aquatic ecosystems. Recovery time for this phase will vary widely across

ecosystems and will be a function of the following:

• the magnitude of decreases in atmospheric deposition

• the local depletion of exchangeable soil pools of base cations

• the local rate of mineral weathering and atmospheric inputs of base cations

• the extent to which soil pools of S and N are released as SO4

2- or as NO3-

 to

drainage waters and the rate of such releases (Galloway et al., 1983).

In most cases, it seems likely that chemical recovery will require decades, even with additional

controls on emissions. The addition of base cations, e.g., through liming, could enhance

chemical recovery at some sites.

 The second phase in ecosystem recovery is biological recovery, which can occur only if

chemical recovery is sufficient to allow survival and reproduction of plants and animals. The

time required for biological recovery is uncertain. For terrestrial ecosystems, it is likely to be at

least decades after soil chemistry is restored because of the long life of tree species and the

complex interactions of soil, roots, microbes, and soil biota. For aquatic systems, research

suggests that stream macroinvertebrate populations may recover relatively rapidly 

(approximately 3 years), whereas lake populations of zooplankton are likely to recover more

slowly (approximately 10 years) (Gunn and Mills, 1998). Some fish populations may recover in

5 to 10 years after the recovery of zooplankton populations. Stocking could accelerate fish

population recovery (Driscoll et al., 2001)
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Projections made using an acidification model (PnET-BGC) indicate that full

implementation of the 1990 CAAA will not afford substantial chemical recovery at Hubbard

Brook EF and at many similar acid-sensitive locations (Driscoll et al., 2001) . Model

calculations indicate that the magnitude and rate of recovery from acid deposition in the

northeastern U.S. are directly proportional to the magnitude of emissions reductions. Model

evaluations of policy proposals calling for additional reductions in utility SO2 and NOX

emissions, year round emissions controls, and early implementation indicate greater success in

facilitating the recovery of sensitive ecosystems (Driscoll et al., 2001).

Indirect Radiation and Climate Condition Effects from Atmospheric PM

In addition to the direct and indirect effects of deposited PM, ambient atmospheric PM

can affect radiation and climate conditions that influence overall plant/ecosystem productivity. 

The degree to which these effects occur in any given location will depend on the chemical and

physical composition and concentration of the ambient PM. Because plants are adapted to the

overall light and temperature environments in which they grow, any PM-related changes to these

conditions (see section 6.5 below) potentially alter the overall competitive success these plants

will have in that ecosystem. 

With respect to radiation, the characteristics and net receipts of solar and terrestrial

radiation determine rates of both photosynthesis and the heat-driven process of water cycling. 

Atmospheric turbidity (the degree of scattering occurring in the atmosphere due to particulate

loading) influences the light environment of vegetative canopy in two ways: through conversion

of direct to diffuse radiation and by scattering or reflecting incoming radiation back out into

space. Diffuse radiation increases canopy photosynthetic productivity by distributing radiation

more uniformly throughout the canopy so that it also reaches the lower leaves and improves the

canopy radiation use efficiency (RUE). Acting in the opposite direction, non-absorbing,

scattering aerosols present in PM reduce the overall amount of radiation reaching vegetative

surfaces, by scattering or reflecting it back into space. It appears that global albedo has been

increasing due to an increasing abundance of atmospheric particles. Using World

Meteorological Organization (WMO) data, Stanhill and Cohen (2001) have estimated that

average solar radiation receipts have declined globally by an average of 20 W m-2 since 1958. 

The net effect of atmospheric particles on plant productivity is not clear, however, as the

enrichment in photosynthetically active radiation (PAR) present in diffuse radiation may offset a

portion of the effect of decreased solar radiation receipts in some instances (CD, pp. 4-92, 4-93).

Plant processes also are sensitive to temperature. Some atmospheric particles (most

notably black carbon) absorb short-wavelength solar radiation, leading to atmospheric heating

and reducing total radiation received at the surface. Canopy temperature and transpirational

water use by vegetation are particularly sensitive to long-wave, infrared radiation. Atmospheric

heating by particles can potentially reduce photosynthetic water uptake efficiency and vertical

temperature gradients, potentially reducing the intensity of atmospheric turbulent mixing. 
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Stanhill and Cohen (2001) suggested that plant productivity is more affected by changes in

evapotranspiration induced by changes in the amount of solar radiation plants receive than by

changes in the amount of PAR plants receive (CD, p. 4-93).

6.3.4 Characteristics and Location of Sensitive Ecosystems in the U.S.

Ecosystems sensitive to anthropogenically derived nitrogen and/or acid deposition tend to

have similar characteristics. Some of these ecosystems and characteristics have already been

mentioned in earlier sections but are repeated here to provide a more comprehensive list that can

help ecological risk assessors/managers identify areas of known or potential concern. For

example, lower nitrogen and/or resource environments, such as those with infertile soils, shaded

understories, deserts, or tundras, are populated with organisms specifically adapted to survive

under those conditions. Plants adapted to these conditions have been observed to have similar

characteristics, including inherently slower growth rates, lower photosynthetic rates, and lower

capacity for nutrient uptake, and grow in soils with lower soil microbial activity. When N

becomes more readily available, such plants will be replaced by nitrophilic plants which are

better able to use increased amounts of Nr (Fenn et al., 1998).

Additionally, in some instances, there seem to be important regional distinctions in

exposure patterns, environmental stressors, and ecosystem characteristics between the eastern

and western U.S.. A seminal report describing these distinctive characteristics for the western

U.S. (11 contiguous states located entirely west of the 100th meridian) is Fenn et al., 2003.

In the western U.S., vast areas receive low amounts of atmospheric deposition, 

interspersed with hotspots of elevated N deposition downwind of large, expanding metropolitan

centers or large agricultural operations. In other words, spatial patterns of urbanization largely

define the areas where air pollution impacts are most severe. The range of air pollution levels for

western wildlands is extreme, spanning from near-background to the highest exposures in all of

North America, with the possible exception of forests downwind of Mexico City. Over the same

geographic expanse, climatic conditions and ecosystem types vary widely. Some regions receive

more than 1000 millimeters of precipitation, namely the Pacific coastal areas, the Sierra Nevada,

the Colorado Rockies, and northern Idaho, while other regions are arid or semiarid, with more

than 300 clear days per year (Riebsame et al., 1997). In these latter regions, the contribution of

atmospheric dry deposition is likely to be most important. These characteristics which are

unique to the West require special consideration, and often make application of models and

ecological effects thresholds developed for other regions inappropriate.

In summary, sensitive or potentially sensitive ecosystems in the West include those that:

• are located downwind of large urban source areas; regions with a mix of

emissions sources that may include urban, mobile, agricultural, and industrial

sources; and/or sites near large point sources of N.
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• contain inherently N sensitive ecosystem components, such as lichens, diatoms, or

poorly buffered watersheds which produce high stream water NO3- levels. These

sensitive components can be affected by N deposition rates as low as 3-8

kg/ha/yr.

• occur on top of siliclastic/crystalline bedrock with little potential for buffering

acidity.

• are naturally nitrogen limited. For example, the approximately 16,000 high

elevation western mountain lakes are generally oligotrophic and especially

sensitive to the effects of atmospheric deposition.

A seminal report describing key characteristics of sensitive ecosystems for the eastern

and in particular the northeastern U.S. is Driscoll et al. (2001). In the northeastern United States,

atmospheric deposition is largely a regional problem. Because S and N most often occur

together in the eastern atmosphere and deposit to the environment as acid deposition, acid

deposition is seen as a critical environmental stress. 

Several critical chemical thresholds appear to coincide with the onset of deleterious

effects to biotic resources resulting from acid deposition. Thus, ecosystems sensitive to

additional acid inputs include those with the following characteristics:

• a molar Ca:Al ratio of soil water that is less than 1.0;

• soil percentage base cation saturation less than 20%;

• surface water pH less than 6.0;

• ANC less than 50 meq L-1; and

• concentrations of inorganic monomeric Al greater than 2 mmol L-1.

Knowledge of such indicators is necessary for restoring ecosystem structure and function.

6.3.5 Ecosystem Exposures to PM Deposition

In order for any specific chemical stressor present in ambient PM to impact ecosystems,

it must first be removed from the atmosphere through deposition. Deposition can occur in three

modes: wet (rain/frozen precipitation), dry, or occult (fog, mist or cloud). At the national scale,

all modes of deposition must be considered in determining potential impacts to vegetation and

ecosystems because each mode may dominate over specific intervals of time or space. (CD, p.

4-8 to 4-10). For example, in large parts of the western U.S. which are arid or semiarid, dry

deposition may be the source of most deposited PM (Fenn, et al., 2003). However, in coastal

areas or high elevation forests, where wet or occult deposition may predominate, deposition

amounts may greatly exceed PM amounts measured in the ambient air. Occult deposition is

particularly effective for delivery of dissolved and suspended materials to vegetation because:

(1) concentrations of ions are often many-fold higher in clouds or fog than in precipitation or
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ambient air (e.g., acidic cloud water, which is typically 5-20 times more acid than rainwater, can

increase pollutant deposition and exposure to vegetation and soils at high elevation sites by more

than 50% of wet and dry deposition levels); (2) PM is delivered in a hydrated and bioavailable

form to foliar surfaces and remains hydrated due to conditions of high relative humidity and low

radiation; and (3) the mechanisms of sedimentation and impaction for submicron particles that

would normally be low in ambient air are increased. High-elevation forests can be especially at

risk from depositional impacts because they receive larger particulate deposition loadings than

equivalent low-elevation sites, due to a number of orographic (mountain related) effects. These

orographic effects include higher wind speeds that enhance the rate of aerosol impaction,

enhanced rainfall intensity and composition, and increased duration of occult deposition. 

Additionally, the needle-shaped leaves of the coniferous species often found growing in these

high elevation sites, enhance impaction and retention of PM delivered by all three deposition

modes (CD, pp. 4-29, 4-44).

In order to establish exposure-response profiles useful in ecological risk assessments, two

types of monitoring networks need to be in place. First, a deposition network is needed that can

track changes in deposition rates of PM stressors (nitrates/sulfates) occurring in sensitive or

symptomatic areas/ecosystems. Secondly, a network or system of networks should be

established that measures the response of key sensitive ecological indicators over time to

changes in atmospheric deposition of PM stressors.

Currently in the U.S., national deposition monitoring networks routinely measure total

wet or dry deposition of certain compounds. Atmospheric concentrations of dry particles began

to be routinely measured in 1986, with the establishment of EPA’s National Dry Deposition

Network (NDDN). After new monitoring requirements were added in the 1990 CAAA, EPA, in

cooperation with the National Oceanic and Atmospheric Association, created the Clean Air

Status and Trends Network (CASTNet) from the NDDN. CASTNet comprises 85 sites and is

considered the nation’s primary source for atmospheric data to estimate concentrations for

ground-level ozone and the chemical species that make up the dry deposition component of total

acid deposition (e.g., sulfate, nitrate, ammonium, sulfur dioxide, and nitric acid), as well as the

associated meteorology and site characteristics data that are needed to model dry deposition

velocities (CD, pg. 4-21; (http://www.epa.gov/castnet/).

To provide data on wet deposition amounts in the U.S., the National Atmospheric

Deposition Program (NADP) was initiated in the late 1970's as a cooperative program between

federal, state, and other public and private groups. By the mid-1980's, it had grown to nearly

200 sites, and it stands today as the longest running national atmospheric deposition monitoring

network (http://nadp.sws.uiuc.edu/).

In addition to these deposition monitoring networks, other networks collect data on

ambient aerosol concentrations and chemical composition. Such networks include the

IMPROVE network, discussed above in section 2.5, and the newly implemented PM2.5 chemical
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Speciation Trends Network (STN) that consists of 54 core National Ambient Monitoring

Stations and approximately 250 State and Local Air Monitoring Stations.

Data from these deposition networks demonstrate that N and S compounds are being

deposited in amounts known to be sufficient to affect sensitive terrestrial and aquatic ecosystems

over time. Though the percentages of N and S containing compounds in PM vary spatially and

temporally, nitrates and sulfates make up a substantial portion of the chemical composition of

PM. In the future, speciated data from these networks may allow better understanding of the

specific components of total deposition that are most strongly influencing PM-related ecological

effects. 

Unfortunately, at this time there are only a few sites where long-term monitoring of

sensitive indicators of ecosystem response to acidic and acidifying deposition is taking place

within the U.S.. Two examples are the Hubbard Brook Experimental Forest research site, that

provides the longest continuous record of precipitation and stream chemistry in the U.S. (Likens

and Bormann, 1995) and EPA’s LTM and TIME projects which monitor changes in surface

water chemistry in the acid sensitive regions of the northern and eastern U.S.. Because the

complexities of ecosystem response make predictions of the magnitude and timing of chemical

and biotic recovery uncertain, it is strongly recommended that this type of long-term surface

water chemistry monitoring network be continued, and that a biological monitoring program be

added. Data from these long-term monitoring sites will be invaluable for the evaluation of the

response of forested watersheds and surface waters to a host of research and regulatory issues

related to acid deposition, including soil and surface water recovery, controls on N retention,

mechanisms of base cation depletion, forest health, sinks for S in watersheds, changes in

dissolved organic carbon and speciation of Al, and various factors related to climate change

(EPA, 2003).

6.3.6 Critical Loads

The critical load (CL) has been defined as a “quantitative estimate of an exposure to one

or more pollutants below which significant harmful effects on specified sensitive elements of the

environment do not occur according to present knowledge” (Lokke et al., 1996). The critical

load framework originated in Europe where the concept has generally been accepted as the basis

for abatement strategies to reduce or prevent injury to the functioning and vitality of forest

ecosystems caused by long-range transboundary chronic acid deposition. The concept is useful

for estimating the amounts of pollutants that sensitive ecosystems can absorb on a sustained

basis without experiencing measurable degradation. The estimation of ecosystem critical loads

requires an understanding of how an ecosystem will respond to different loading rates in the long

term and is a direct function of the level of sensitivity of the ecosystem to the pollutants in

question and its capability to ameliorate pollutant stress.
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Key to the establishment of a critical load is the selection of appropriate ecological

endpoints or indicators that are measurable characteristics related to the structure, composition,

or functioning of ecological systems (i.e., indicators of condition). In Europe, the elements used

in the critical load concept are a biological indicator, a chemical criterion, and a critical value

(CD, p. 4-125). The biological indicator is the organism used to indicate the status of the

receptor ecosystem; the chemical criterion is the parameter that results in harm to the biological

indicator; and the critical value is the value of the chemical criterion below which no significant

harmful response occurs to the biological indicator (Lokke et al., 1996).

A number of different types of indicators and chemical criteria for monitoring ecosystem

status have been proposed. Some examples for evaluating ecosystem nitrogen status include:

foliar nitrogen content, nutrient ratios (N:P, N:cation); foliar nitrate; foliar *15 N; arginine

concentration; soil C:N ratio; NO3

-

 in soil extracts or increased and prolonged NO3

-

 loss below

the main rooting zone and in stream water or in soil solution; and flux rates of nitrogenous trace

gases from soil (Fenn et al., 1998). Seasonal patterns of stream water nitrate concentrations are

especially good indicators of watershed N status. Biological indicators that have been suggested

for use in the critical load calculation in forest ecosystems include mycorrhizal fungi (Lokke

et al., 1996) and fine roots, since they are an extremely dynamic component of below-ground

ecosystems and can respond rapidly to stress. The physiology of carbon allocation has also been

suggested as an indicator of anthropogenic stress (Andersen and Rygiewicz, 1991). Lichen

community composition in terrestrial ecosystems or lichen N tissue levels are also fairly

responsive to changes in N deposition over time (Fenn et al., 2003). In aquatic systems, diatom

species composition can be a good indicator of changes in water chemistry (Fenn et al., 2003). 

It should be kept in mind, however, that the response of a biological indicator is an integration of

a number of different stresses. Furthermore, there may be organisms more sensitive to the

pollutant(s) than the species selected (Lokke et al., 1996; National Science and Technology

Council, 1998) (CD, pp. 4-124 to 126).

Within North America, a number of different groups have recently begun to use or

develop critical loads. As discussed below, these groups include the U.S. Federal Land

Managers (FLMs), such as the National Park Service and the Forest Service, a binational group

known as New England Governors/Eastern Canadian Premiers (NEG/ECP), and several

Canadian Provinces.

Federal Land Managers have hosted a number of meetings over the last few years to

discuss how the CL concept might be used in helping them fulfill their mandate of providing

protection for the lands they manage. In trying to develop a consistent approach to using CL, a

number of issues and considerations have been identified. First, the distinction between critical

loads (which are based on modeled or measured dose-response data) and target loads (which can

be based on political, economic, spatial or temporal considerations in addition to scientific

information) needs to be recognized. When using either the critical or the target load (TL)
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approach, one must indicate the spatial (or geographic) scope, the temporal scope (timeframe to

ecological or ecosystem recovery), and a description of the sensitive receptors (or resource) to be

protected, the sensitive receptor indicators (physical, chemical biological, or social

characteristics of the receptor that can be measured), and the harmful effect on the receptor that

is of concern. Additionally, one would need to specify what is the “desired condition” that the

critical or target load is meant to achieve. For any given location, there may be a range or suite

of possible critical or target loads based on different sensitive receptors and/or receptor

indicators found at that site. Alternatively, one could focus on the most sensitive receptor and

select a single CL or TL for that receptor. Several aspects of the CL approach make it attractive

for use by the FLMs. Specifically, it can provide a quantitative, objective and consistent

approach for evaluating resource impacts in a given ecosystem. In an effort to progress the CL

approach, the Forest Service is testing the applicability of the European protocol to several U.S.

case study sites.

Under the auspices of the NEG/ECP, and other binational efforts, Canadian and U.S.

scientists are involved in joint forest mapping projects. A Forest Mapping Work Group has been

tasked with conducting a regional assessment of the sensitivity of northeastern North American

forests to current and projected sulfur and nitrogen emissions levels, identifying specific forested

areas most sensitive to continued deposition and estimating deposition rates required to maintain

forest health and productivity. They have completed the development of methods, models and

mapping techniques, and identification of data requirements. Some of these data requirements

include: pollution loading to forest landscapes; the interaction of pollutants with forest canopies;

plant nutrient requirements; and the ability of soils to buffer acid inputs and replenish nutrients

lost due to acidification. 

In addition to CL measures, they have also defined a corresponding “deposition index”

for each CL value. The deposition index is the difference between the CL value for a given site

and the current deposition rates at that site. Positive values of the index at a particular forested

location reflect the capacity of that forest ecosystem to tolerate additional acid deposition. 

Negative index values correspond to the reduction in S and N deposition required to eliminate or

deter the development of future nutrient limitations. This allows an assessor to identify areas

where the deposition problems are most severe, and which sites might be under the applicable

CL level currently but not far from reaching or exceeding that level should deposition levels

increase. Currently maps exist for Vermont and Newfoundland, though the goal is to develop

maps that will cover Quebec and the Atlantic provinces of Canada, along with the remaining

New England states. These maps show that 31% of Vermont forests and 23% of Newfoundland

forests are sensitive (e.g., current levels of S and N deposition are causing cation depletion). 

Though these current activities hold promise for using the CL approach in environmental

assessments and in informing management decisions, widespread use of CLs in the U.S. is not

yet possible. The CL approach is very data-intensive, and, at the present time, there is a paucity
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of ecosystem- level data for most sites. However, for a limited number of areas which already

have a long-term record of ecosystem monitoring, (e.g., Rocky Mountain National Park in

Colorado and the Lye Brook Wilderness in Vermont), FLMs may be able to develop sitespecific CLs. Further, in areas already exceeding the applicable CL, it may be difficult to

determine what the management goals are/should be for each mapped area (e.g., what is the

“desired condition” or level of protection) without historic baseline data. More specifically, with

respect to PM deposition, there are insufficient data for the vast majority of U.S. ecosystems that

differentiate the PM contribution to total N or S deposition to allow for practical application of

this approach as a basis for developing national standards to protect sensitive U.S. ecosystems

from adverse effects related to PM deposition. Though atmospheric sources of Nr and acidifying

compounds, including ambient PM, are clearly contributing to the overall excess pollutant load

or burden entering ecosystems annually, insufficient data are available at this time to quantify

the contribution of ambient PM to total Nr or acid deposition as its role varies both temporally

and spatially along with a number of other factors. Thus, it is not clear whether a CL could be

developed just for the portion of the total N or S input that is contributed by PM.

6.3.7 Summary and Conclusions

The above discussions identify a group of ecosystems known to be sensitive to excess N

and S inputs and a list of characteristics that can be used to predict or locate other potentially

sensitive ecosystems within the U.S. Further, exposures of these sensitive ecosystems to

atmospherically derived pollutants (e.g., N and S) have been measured and documented, in some

cases for decades. Clear linkages between reduced atmospheric concentrations of these

pollutants and reduced deposition rates have been made. The mechanisms of environmental and

ecosystem responses to these inputs are increasingly understood, though very complex. 

Fertilization and acidification studies have verified observed ecosystem responses to these

pollutants in the field. Ecosystem-level effects associated with excess N and S inputs are

profound, but in most cases potentially reversible. New assessment and management tools, such

as critical and target loads, are being developed to better characterize the relationship between

deposition loads and ecosystem response. The success of these tools will depend on the

availability of sufficient ecosystem response data, which is currently limited to a few long-term

monitoring networks/sites (e.g., TIME/LTM). The current risk to sensitive ecosystems and

especially sensitive species like the checkerspot butterfly, desert tortoise, epiphytic lichens,

native shrub and forb species, and aquatic diatom communities is high. The loss of species and

whole ecosystem types is adverse and should receive increased protection.

 A number of ecosystem-level conditions (e.g., nitrogen saturation, terrestrial and aquatic

acidification, coastal eutrophication) have been associated with chronic, long-term exposure of

ecosystems to elevated inputs of compounds containing Nr, sulfur and/or associated hydrogen

ions. These ecosystem level changes have profound impacts on almost all of the EEAs
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identified in the EPEC Framework (SAB, 2002) and described in sections 4.2.1 and 4.2.3 of the

CD. These impacted EEAs include Landscape Condition, Biotic Condition, Chemical and

Physical Characteristics, Ecological Processes, and Natural Disturbance Regimes. Given that

humans, as well as other organisms, are dependent on the services ecosystems provide,

ecosystem changes of this magnitude are of concern and can lead to adverse impacts on human

health and welfare.

Based on the information included in the above discussions and Chapters 4 and 9 of the

CD, staff has reached the following conclusions:

• An ecologically-relevant indicator for PM should be based on one or multiple

chemical stressors found in ambient PM (e.g. N or S containing compounds).

• PM, as a contributor to the chronic annual loads of total Nr and/or acidifying

compounds entering sensitive ecosystems, has been associated with numerous

effects on those ecosystems and their associated essential ecological attributes. 

There is no bright line or threshold for these effects, but rather a “syndrome” of

complex changes over time. As levels of inputs of these pollutants are reduced,

ecosystem recovery can occur but may take decades, and may require controls

beyond those already established.

• Excess Nr or acidifying deposition acts in conjunction with other co-occurring

stresses (e.g., invasive species, reduced grazing pressure) that jointly determine

ecological outcomes. Therefore, these pollution-related stresses should not be

considered in isolation. Additionally, all forms of airborne nitrogen and

acidifying compounds need to be considered and managed in harmony.

• Existing ambient air and deposition monitoring networks are not generally

sufficient to characterize the associated ecosystem response. Additional, longterm, targeted ecosystem monitoring is needed (e.g., downwind of large urban

areas in the West).

Unfortunately, our ability to relate ambient concentrations of PM to ecosystem response

is hampered by a number of significant data gaps and uncertainties. First, U.S. monitoring

networks have only recently begun to measure speciated PM. Historically, measurements were

focused only on a particular size fraction such as PM10 and, more recently, PM2.5. An exception

to this is the IMPROVE network, which collects speciated measurements. Additionally, except

for the IMPROVE and some CASTNet sites, much of the PM monitoring effort has focused on

urban or near urban exposures, rather than on those in sensitive ecosystems. Thus, the lack of a

long-term, historic database of annual speciated PM deposition rates precludes establishing

relationships between PM deposition (exposure) and ecosystem response at this time. As a

result, while evidence of PM-related effects clearly exists, there is insufficient information
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available at this time to serve as a basis for a secondary national air quality standard for PM,

specifically selected to protect against adverse effects on vegetation and ecosystems.

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 ambient air concentration to a receptor response, an important factor in being able

to set a national ambient air quality standard. A multitude of factors (e.g., 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 at any given point

in time. Therefore, modeled deposition velocities, used in the absence of monitored data, can be

highly uncertain. 

Third, each ecosystem has developed within a context framed by the topography,

underlying bedrock, soils, climate, meteorology, hydrologic regime, natural and land use history,

species associations that may co-occur only at that location (e.g., soil organisms, plants), that

make it unique from all others. 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, or to predict appropriate “critical loads”

for the vast majority of U.S. ecosystems.

As additional PM speciated air quality and deposition monitoring data become available,

there is much room for productive research into the areas of uncertainty identified above. At this

time, however, staff concludes that there is insufficient information available to recommend for

consideration an ecologically defined secondary standard that is specifically targeted for

protection of vegetation and ecosystems against the adverse effects potentially associated with

the levels of PM-related stressors of nitrate and sulfate found in the ambient air.

6.4 EFFECTS ON MATERIALS

The effects of the deposition of atmospheric pollution, including ambient PM, on

materials are related to both physical damage and impaired aesthetic qualities. The deposition of

PM (especially sulfates and nitrates) 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 concrete and limestone. 

Particles contribute to these physical effects because of their electrolytic, hygroscopic and acidic

properties, and their ability to sorb corrosive gases (principally SO2). As noted in the last

review, only chemically active fine-mode or hygroscopic coarse-mode particles contribute to

these physical effects (EPA 1996b, p. VIII-16).

In addition, the deposition of ambient PM can reduce the aesthetic appeal of buildings

and culturally important articles through soiling. Particles consisting primarily of carbonaceous

compounds cause soiling of commonly used building materials and culturally important items
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such as statues and works of art (CD, p. 4-191). 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. Soiling can be remedied by cleaning or washing, and

depending on the soiled material, repainting (EPA, 1996b, p. VIII-19). 

Building upon the information presented in the last Staff Paper (EPA, 1996b), and 

including the limited new information presented in Chapter 4 (section 4.4) of the CD, the

following sections summarize the physical damage and aesthetic soiling effects of PM on

materials including metals, paint finishes, and stone and concrete.

6.4.1 Materials Damage Effects

Physical damage such as corrosion, degradation, and deterioration occurs in metals, paint

finishes, and building materials such as stone and concrete, respectively. Metals are affected by

natural weathering processes even in the absence of atmospheric pollutants. Atmospheric

pollutants, most notably SO2 and particulate sulfates, can have an additive effect, by promoting

and accelerating the corrosion of metals. The rate of metal corrosion depends on a number of

factors, including the deposition rate and nature of the pollutants; the influence of the protective

corrosion film that forms on metals, slowing corrosion; the amount of moisture present;

variability in electrochemical reactions; the presence and concentration of other surface

electrolytes; and the orientation of the metal surface. Historically, studies have shown that the

rate of metal corrosion decreases in the absence of moisture, since surface moisture facilitates

the deposition of pollutants and promotes corrosive electrochemical reactions on metals (CD, pp.

4-192 to 4-193).

The CD (p. 4-194, Table 4-18) summarizes the results of a number of studies

investigating the roles of particles and SO2 on the corrosion of metals. The CD concludes that

the role of particles in the corrosion of metals is not clear (CD, p. 4-193). While several studies

suggest that particles can promote the corrosion of metals, others have not demonstrated a

correlation between particle exposure and metal corrosion. Although the corrosive effects of

SO2 exposure in particular have received much study, there remains insufficient evidence to

relate corrosive effects to specific particulate sulfate levels or to establish a quantitative

relationship between ambient particulate sulfate and corrosion.

Similar to metals, paints also undergo natural weathering processes, mainly from

exposure to environmental factors such as sunlight, moisture, fungi, and varying temperatures. 

Beyond these natural processes, atmospheric pollutants can affect the durability of paint finishes

by promoting discoloration, chalking, loss of gloss, erosion, blistering, and peeling. Historical

evidence indicates that particles can damage painted surfaces by serving as carriers of more

corrosive pollutants, most notably SO2, or by serving as concentration sites for other pollutants. 

If sufficient damage to the paint occurs, pollutants may penetrate to the underlying surface. A

number of studies available in the last review showed some correlation between PM exposure
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and damage to automobile finishes. In particular, Wolff et al. (1990) concluded that damage to

automobile finishes resulted from calcium sulfate forming on painted surfaces by the reaction of

calcium from dust particles with sulfuric acid contained in rain or dew. In addition, paint films

permeable to water are also susceptible to penetration by acid-forming aerosols (EPA 1996b, p.

VIII-18). The erosion rate of oil-based house paint has reportedly been enhanced by exposure to

SO2 and humidity; several studies have suggested that this effect is caused by the reaction of SO2

with extender pigments such as calcium carbonate and zinc oxide, although Miller et al. (1992)

suggest that calcium carbonate acts to protect paint substrates (CD, p. 4-196).

With respect to damage to building stone, numerous studies discussed in the CD (pp.

4-196 to 4-202; Table 4-19) suggest that air pollutants, including sulfur-containing pollutants

and wet or dry deposition of atmospheric particles and dry deposition of gypsum particles, can

enhance natural weathering processes. Exposure-related damage to building stone results from

the formation of salts in the stone that are subsequently washed away by rain, leaving the surface

more susceptible to the effects of air pollutants. Dry deposition of sulfur-containing pollutants

and carbonaceous particles promotes the formation of gypsum (hydrated calcium sulfate) on the

stone’s surface. Gypsum is a black crusty material that occupies a larger volume than the

original stone, causing the stone’s surface to become cracked and pitted, leaving rough surfaces

that serve as sites for further deposition of airborne particles (CD, p. 4-200).

The rate of stone deterioration is determined by the pollutant mix and concentration, the

stone’s permeability and moisture content, and the pollutant deposition velocity. Dry deposition

of SO2 between rain events has been reported to be a major causative factor in pollutant-related

erosion of calcareous stones (e.g., limestone, marble, and carbonated cement). While it is clear

from the available information that gaseous air pollutants, in particular SO2, will promote the

decay 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 (CD, p.

4-201 , 4-202).

6.4.2 Soiling Effects

Soiling affects the aesthetic appeal of painted surfaces. In addition to natural factors,

exposure to PM may give painted surfaces a dirty appearance. Early studies demonstrated an

association between particle exposure and increased frequency of cleaning painted surfaces. 

More recently, Haynie and Lemmons (1990) conducted a study to determine how various

environmental factors contribute to the rate of soiling on white painted surfaces. They reported

that coarse-mode particles initially contribute more to soiling of horizontal and vertical surfaces

than do fine-mode particles, but are more easily removed by rain, leaving stains on the painted

surface. The authors concluded that the accumulation of fine-mode particles, rather than coarsemode particles, more likely promotes the need for cleaning of the painted surfaces (EPA 1996b,

p. VIII-21-22; CD, pp. 4-202 to 4-204). Haynie and Lemmons (1990) and Creighton et al.
6-53

(1990) reported that horizontal surfaces soiled faster than vertical surfaces and that large

particles were primarily responsible for the soiling of horizontal surfaces not exposed to rainfall. 

Additionally, a study was conducted to determine the potential soiling of artwork in five

Southern California museums (Ligocki, et al., 1993). Findings were that a significant fraction of

fine elemental carbon and soil dust particles in the ambient air penetrates to the indoor

environment and may constitute a soiling hazard to displayed artwork (EPA 1996b, p. VIII-22).

As for stone structures, the presence of gypsum is related to soiling of the stone surface

by providing sites for particles of dirt to concentrate. Lorusso et al. (1997) attributed the need

for frequent cleaning and restoration of historic monuments in Rome to exposure to total

suspended particles (TSP). Further, Davidson et al. (2000) evaluated the effects of air pollution

exposure on a limestone structure on the University of Pittsburgh campus using estimated

average TSP levels in the 1930s and 1940s and actual values for the years 1957 to 1997. 

Monitored levels of SO2 were also available for the years 1980 to 1998. Based on the available

data on pollutant levels and photographs, the authors concluded that soiling began while the

structure was under construction. With decreasing levels of pollution, the soiled areas have been

slowly washed away, the process taking several decades, leaving a white, eroded surface (CD,

pp. 4-203).

6.4.3 Summary and Conclusions

 Damage to building materials results from natural weathering processes that are

enhanced by exposure to airborne pollution, most notably sulfur-containing pollutants. Ambient

PM has been associated with contributing to pollution-related damage to materials, and can

cause significant detrimental effects by soiling painted surfaces and other building materials. 

Available data indicate that particle-related soiling can result in increased cleaning frequency

and repainting, and may reduce the useful life of the soiled materials. However, to date, no

quantitative relationships between particle characteristics (e.g., concentrations, particle size, and

chemical composition) and the frequency of cleaning or repainting have been established. Thus,

staff concludes that PM effects on materials can play no quantitative role in considering whether

any revisions of the secondary PM NAAQS are appropriate at this time.

6.5 EFFECTS ON CLIMATE CHANGE AND SOLAR RADIATION

Atmospheric particles alter the amount of electromagnetic radiation transmitted through

the earth’s atmosphere by both scattering and absorbing radiation. As discussed above in

Chapter 2 (section 2.2.6), most components of ambient PM (especially sulfates) scatter and

reflect incoming solar radiation back into space, thus tending to offset the “greenhouse effect” by

having a cooling effect on climate. In contrast, some components of ambient PM (especially

black carbon) absorb incoming solar radiation or outgoing terrestrial radiation, and are believed

to contribute to atmospheric warming. Lesser impacts of atmospheric particles are associated
6-54

with their role in altering the amount of ultraviolet solar radiation (especially UV-B) penetrating

through the earth’s atmosphere to ground level, where it can exert a variety of effects on human

health, plant and animal biota, and other environmental components (CD, p. 205). The extensive

research and assessment efforts into global climate change and stratospheric ozone depletion

provide evidence that atmospheric particles play important roles in these two types of

atmospheric processes, not only on a global scale, but also on regional and local scales as well.

Information on the role of atmospheric particles in these atmospheric processes and the

effects on human health and the environment associated with these atmospheric processes is

briefly summarized below, based on the information in section 4.5 of the CD and referenced

reports. These effects are discussed below in conjunction with consideration of the potential

indirect impacts on human health and the environment that may be a consequence of climatic

and radiative changes attributable to local and regional changes in ambient PM.

6.5.1 Climate Change and Potential Human Health and Environmental Impacts

As discussed in section 4.5.1 of the CD, particles can have both direct and indirect effects

on climatic processes. The direct effects are the result of the same processes responsible for

visibility degradation, namely radiative scattering and absorption. However, while visibility

impairment is caused by particle scattering in all directions, climate effects result mainly from

scattering light away from the earth and into space. This reflection of solar radiation back to

space decreases the transmission of visible radiation to the surface 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 black carbon,

results in an increase in the heating rate of the lower atmosphere.

In addition to these direct radiative effects, particles can also have a number of indirect

effects on climate related to their physical properties. For example, sulfate particles can serve as

condensation nuclei which alter the size distribution of cloud droplets by producing more

droplets with smaller sizes. Because the total surface area of the cloud droplets is increased, the

amount of solar radiation that clouds reflect back to space is increased. A further important

consequence of this effect on cloud properties is the suppression of rain and potentially major

disruption of hydrological cycles downwind of pollution sources, leading to a potentially

significant alteration of climate in the affected regions (CD, p. 4-218).

The overall radiative and physical effects of particles, both direct and indirect, are not the

simple sum of effects caused by individual classes of particles because of interactions between

particles and other atmospheric gases. As discussed in Section 4.5.1.2 of the CD, the effects of

sulfate particles have been the most widely considered, with globally averaged radiative effects

of sulfate particles generally estimated to have partially offset the warming effects caused by

increases in greenhouse gases. On the other hand, global-scale modeling of mineral dust
6-55

particles suggests that even the sign as well as the magnitude of effects depends on the vertical

distribution and effective particle radius. 

The CD makes clear that atmospheric particles play an important role in climatic

processes, but that their role at this time remains poorly quantified. In general, on a global scale,

the direct effect of radiative scattering by atmospheric particles is to likely exert an overall net

effect of cooling the atmosphere, while particle absorption may lead to warming. The net impact

of indirect effects on temperature and rainfall patterns remains difficult to generalize. However,

deviations from global mean values can be very large even on a regional scale, with any

estimation of more localized effects introducing even greater complexity (CD, p. 216). The CD

concludes that any effort to model the impacts of local alterations in particle concentrations on

projected global climate change or consequent local and regional weather patterns would be

subject to considerable uncertainty (CD, p. 4-240).

More specifically, the CD notes that while current climate models are successful in

simulating present annual mean climate and the seasonal cycle on continental scales, they are

less successful at regional scales (CD, p. 4-207). Findings from various referenced assessments

illustrate well the considerable uncertainties and difficulties in projecting likely climate change

impacts on regional or local scales. For example, uncertainties in calculating the direct radiative

effects of atmospheric particles arise from a lack of knowledge of their vertical and horizontal

variability, their size distribution, chemical composition, and the distribution of components

within individual particles. Any complete assessment of the radiative effects of PM would

require computationally intensive calculations that incorporate the spatial and temporal behavior

of particles of varying composition that have been emitted from, or formed by precursors emitted

from, different sources. In addition, calculations of indirect physical effects of particles on

climate (e.g., related to alteration of cloud properties and disruption of hydrological cycles) are

subject to much larger uncertainties than those related to the direct radiative effects of particles

(CD, p. 4-219). The CD concludes that at present impacts on human health and the environment

due to aerosol effects on the climate system can not be calculated with confidence, and notes that

the uncertainties associated with such aerosol-related effects will likely remain much larger than

those associated with greenhouse gases (CD, p. 4-219). Nevertheless, the CD concludes that

substantial qualitative information available from observational and modeling studies indicates

that different types of atmospheric aerosols (i.e., different components of PM) have both

warming and cooling effects on climate, both globally and regionally. Studies also suggest that

global and regional climate changes could potentially have both positive and negative effects on

human health, human welfare, and the environment.
6-56

6.5.2 Alterations in Solar UV-B Radiation and Potential Human Health and

Environmental Impacts

As discussed in section 4.5.2 of the CD, the effects of particles in the lower atmosphere

on the transmission of solar UV-B radiation have been examined both by field measurements

and by radiative transfer model calculations. Several studies cited in the CD reinforce the idea

that particles can play an important role in modulating the attenuation of solar UV-B radiation,

although none included measurements of ambient PM concentrations, so that direct relationships

between PM levels and UV-B radiation transmission could not be determined. The available

studies, conducted in diverse locations around the world, demonstrate that relationships between

particles and solar UV-B radiation transmission can vary considerably over location, conditions,

and time. While ambient particles are generally expected to decrease the flux of solar UV-B

radiation reaching the surface, any comprehensive assessment of the radiative effects of particles

would be location-specific and complicated by the role of particles in photochemical activity in

the lower atmosphere. Whether the photochemical production of ozone is enhanced, remains the

same, or reduced by the presence of ambient particles will be location-specific and dependent on

particle composition. Also complicating any assessment of solar UV-B radiation penetration to

specific areas of the earth’s surface are the influences of clouds, which in turn are affected by the

presence of ambient particles.

The main types of effects associated with exposure to UV-B radiation include direct

effects on human health and agricultural and ecological systems, indirect effects on human

health and ecosystems, and effects on materials (CD, p. 4-221). The study of these effects has

been driven by international concern over potentially serious increases in the amount of solar

UV-B radiation reaching the earth’s surface due to the depletion of the stratospheric ozone layer

by the release of various man-made ozone-depleting substances. Extensive qualitative and

quantitative characterizations of these global effects attributable to projections of stratospheric

ozone depletion have been periodically assessed in studies carried out under WMO and UNEP

auspices, with the most recent projections being published in UNEP (1998, 2000) and WMO

(1999).

Direct human health effects of UV-B radiation exposure include: skin damage (sunburn)

leading to more rapid aging and increased incidence of skin cancer; effects on the eyes, including

retinal damage and increased cataract formation possibly leading to blindness; and suppression

of some immune system components, contributing to skin cancer induction and possibly

increasing susceptibility to certain infectious diseases. Direct environmental effects include

damage to terrestrial plants, leading to possible reduced yields of some major food crops and

commercially important tress, as well as to biodiversity shifts in natural terrestrial ecosystems;

and adverse effects on aquatic life, including reductions in important components of marine food

chains as well as other aquatic ecosystem shifts. Indirect health and environmental effects are

primarily those mediated through increased tropospheric ozone formation and consequent
6-57

ground-level ozone-related health and environmental impacts. Effects on materials include

accelerated polymer weathering and other effects on man-made materials and cultural artifacts. 

In addition, there are emerging complex issues regarding interactions and feedbacks between

climate change and changes in terrestrial and marine biogeochemical cycles due to increased

UV-B radiation penetration. (CD, p. 4-221, 4-222).

In contrast to these types of negative impacts associated with increased UV-B penetration

to the Earth’s surface, the CD (p. 4-222, 4-223) summarizes research results that are suggestive

of possible beneficial effects of increased UV-B radiation penetration. For example, a number of

studies have focused on the protective effects of UV-B radiation with regard to non-skin cancer

incidence, which proved suggestive evidence that UV-B radiation, acting through the production

of vitamin D, may be a risk-reduction factor for mortality due to several types of cancer,

including cancer of the breast, colon, ovary, and prostate, as well as non-Hodgkin lymphoma.

The various assessments of these types of effects that have been conducted consistently

note that the modeled projections quantitatively relating changes in UV-B radiation (attributable

to stratospheric ozone depletion) to changes in health and environmental effects are subject to

considerable uncertainty, with the role of atmospheric particles being one of numerous

complicating factors. Taking into account the complex interactions between ambient particles

and UV-B radiation transmission through the lower atmosphere, the CD concludes that any

effort to quantify projected indirect effects of variations in atmospheric PM on human health or

the environment due to particle impacts on transmission of solar UV-B radiation would require

location-specific evaluations that take into account the composition, concentration, and internal

structure of the particles; temporal variations in atmospheric mixing heights and depths of layers

containing the particles; and the abundance of ozone and other absorbers within the planetary

boundary layer and the free troposphere (CD, 4-226).

At present, models are not available to take such complex factors into account, nor is

sufficient data available to characterize input variables that would be necessary for any such

modeling. The CD concludes, however, that the outcome of such modeling efforts would likely

vary from location to location, even as to the direction of changes in the levels of exposures to

UV-B radiation, due to location-specific changes in ambient PM concentrations and/or

composition (CD, p. 4-227). Beyond considering just average levels of exposures to UV-B

radiation in general, the CD notes that ambient PM can affect the directional characteristics of

UV-B radiation scattering at ground-level, and thus its biological effectiveness. Also, ambient

PM can affect not only biologically damaging UV-B radiation, but can also reduce the groundlevel ratio of photorepairing UV-A radiation to damaging UV-B radiation. Further, the CD notes

that ambient PM deposition is a major source of PAH in certain water bodies, which can enhance

the adverse effects of solar UV-B radiation on aquatic organisms, such that the net effect of

ambient PM in some locations may be to increase UV-B radiation-related biological damage to

certain aquatic and terrestrial organisms. (CD, p. 4-227).
6-58

6.5.3 Summary and Conclusions

A number of assessments of the factors affecting global warming and climate change as

well as those affecting the penetration of solar UV-B radiation to the earth’s surface clearly

recognize ambient PM as playing various roles in these processes. These assessments, however,

have focused on global- and regional-scale impacts, allowing for generalized assumptions to take

the place of specific, but unavailable, information on local-scale atmospheric parameters and

characteristics of the distribution of particles present in the ambient air. As such, the available

information provides no basis for estimating how localized changes in the temporal, spatial, and

composition patterns of ambient PM, likely to occur as a result of expected future emissions of

particles and their precursor gases across the U.S., would affect local, regional, or global changes

in climate or UV-B radiation penetration – even the direction of such effects on a local scale

remains uncertain. Moreover, similar concentrations of different particle components can

produce opposite net effects. It follows, therefore, that there is insufficient information available

to project the extent to which, or even whether, such location-specific changes in ambient PM

would indirectly affect human health or the environment secondary to potential changes in

climate and UV-B radiation.

Based on currently available information, staff concludes that the potential indirect

effects of ambient PM on public health and welfare, secondary to potential PM-related changes

in climate and UV-B radiation, can play no quantitative role in considering whether any

revisions of the primary or secondary PM NAAQS are appropriate at this time. Even

qualitatively, the available information is very limited in the extent to which it can help inform

an assessment of the overall weight of evidence in an assessment of the net health and

environmental effects of PM in the ambient air, considering both its direct effects (e.g.,

inhalation-related health effects) and indirect effects mediated by other routes of exposure and

environmental factors (e.g., dermal exposure to UV-B radiation).
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Section 6.2 – Visibility Impairment

Abt Associates, Inc. (2001) Assessing Public Opinions on Visibility Impairment Due to Air Pollution: Summary

Report. Prepared for EPA Office of Air Quality Planning and Standards; funded under EPA Contract No.

68-D-98-001. Bethesda, Maryland. January 2001.

Air Resource Specialists, Inc. (2003) WinHaze Air Quality Modeler, version 2.9.0. Available from

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Arizona Department of Environmental Quality. (2003) Visibility Index Oversight Committee Final Report:

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BBC Research & Consulting. (2002) Phoenix Area Visibility Survey. Draft Report. October 4, 2002. 

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California Code of Regulations. Title 17, Section 70200, Table of Standards.

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Chestnut , L. G.; Rowe, R. D. (1991) Economic valuation of changes in visibility: A state of the science assessment. 

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Chestnut, L.G.; Dennis, R. L.; Latimer, D. A. (1994) Economic benefits of improvements in visibility: acid rain

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1990 clean air act amendments. J. Air Waste Manage. Assoc. 47:395-402.

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Environmental Protection Agency. (1982) Review of the National Ambient Air Quality Standards for Particulate

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Environmental Protection Agency. (1993) Air Quality Criteria for Oxides of Nitrogen. Research Triangle Park, NC: 

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Environmental Protection Agency. (2000) Guidelines for Preparing Economic Analyses. Washington, DC: Office of

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Environmental Protection Agency. (2001) National Air Quality and Emissions Trends Report, 1999. Research

Triangle Park, NC: Office of Air Quality Planning and Standards. Report no. EPA/454/R-01-004. March.

Grand Canyon Visibility Transport Commission (1996) Report of the Grand Canyon Visibility Transport

Commission to the United States Environmental Protection Agency.

Hass, G. E.; Wakefield, T.J. (1998) National Parks and the American Public: A National Public Opinion Survey of

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Molenar, J.V.; Malm, W.C.; Johnson, C.E. (1994) Visual Air Quality Simulation Techniques. Atmospheric

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Ambient Air Quality Guidelines: Discussion Document. Air Quality Report No. 16. December. 

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Peacock, B.; Killingsworth, C.; Simon, B. (1998) State and National Economic Impacts Associated with Travel

Related Expenditures by Recreational Visitors to Lands Managed by the U.S. Department of Interior. U.S.

Department of the Interior. January.

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Environment, vol. 30, no. 15, pp. 2705-2716.

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Schmidt, S.M., Mintz, D., Rao, T., and McCluney, L. (2005) Draft analysis of PM ambient air quality data for the

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S.; Malm, W.; Molenar, Jr. (1983) The Economic Benefits of Preserving Visibility in the National

Parklands of the Southwest. Nat. Resour. J. 23: 149-173.

Sisler, J.; Malm, W.; Molenar, J.; Gebhardt, K. (1996) Spatial and Seasonal Patterns and Long Term Variability of

the Chemical Composition of Haze in the U.S.: An Analysis of Data from the IMPROVE Network. Fort

Collins, CO: Cooperative Institute for Research in the Atmosphere, Colorado State University.

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Section 6.3 – Vegetation and Ecosystems

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1 As noted in Chapter 1, 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.”

7-1

7. STAFF CONCLUSIONS AND RECOMMENDATIONS ON

SECONDARY PM NAAQS

7.1 INTRODUCTION

This chapter presents staff conclusions and recommendations for the Administrator to

consider in deciding whether the existing secondary PM standards should be revised and, if so,

what revised standards are appropriate. The existing suite of secondary PM standards, which is

identical to the suite of primary PM standards, includes annual and 24-hour PM2.5 standards and

annual and 24-hour PM10 standards. This existing suite of secondary PM standards is intended to

address visibility impairment associated with fine particles and materials damage and soiling

related to both fine and coarse particles. Each of these standards is defined in terms of four basic

elements: indicator, averaging time, level and form. Staff conclusions and recommendations on

these standards are based on the assessment and integrative synthesis of information related to

welfare effects presented in the CD and on staff analyses and evaluations presented in Chapters 2

and 6 herein.

In recommending a range of secondary standard options for the Administrator to

consider, staff notes that the final decision is largely a public policy judgment. A final decision

must draw upon scientific evidence and analyses about effects on public welfare, as well as

judgments about how to deal with the range of uncertainties that are inherent in the relevant

information. The NAAQS provisions of the Act require the Administrator to establish secondary

standards that, in the judgment of the Administrator, are requisite to protect public welfare1

 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 to eliminate all risk of adverse welfare effects, but rather at a level requisite to protect

public welfare from those effects that are judged by the Administrator to be adverse.

7.2 APPROACH

Similar to the approach discussed in Chapter 5, section 5.2, for the review of the primary

NAAQS, staff's approach here can be framed by a series of questions that may be applicable for

each category of PM-related welfare effects identified in the CD as being associated with the

presence of the pollutant in the ambient air. Staff's review of the adequacy of the current PM

standards for each effects category involves addressing questions such as:
7-2

• To what extent does the available information demonstrate or suggest that PMrelated effects are occurring at current ambient conditions or at levels that would

meet the current standards?

• To what extent does the available information inform judgments as to whether

any observed or anticipated effects are adverse to public welfare?

• To what extent are the current secondary standards likely to be effective in

achieving protection against any identified adverse effects?

To the extent that the available information suggests that revision of the current

secondary standards would be appropriate for an effects category, staff then identifies ranges of

standards (in terms of indicators, averaging times, levels, and forms) that would reflect a range

of alternative policy judgments as to the degree of protection that is requisite to protect public

welfare from known or anticipated adverse effects. In so doing, staff addresses questions such

as:

• Does the available information provide support for considering different PM indicators?

• Does the available information provide support for considering different averaging

times?

• What range of levels and forms of alternative standards is supported by the information,

and what are the uncertainties and limitations in that information?

• To what extent would specific levels and forms of alternative standards reduce adverse

impacts attributable to PM, and what are the uncertainties in the estimated reductions?

Based on the available information, estimated reductions in adverse effects, and related

uncertainties, staff makes recommendations as to ranges of alternative standards for the

Administrator's consideration in reaching decisions as to whether to retain or revise the

secondary PM NAAQS.

In presenting this approach, staff recognizes that for some welfare effects the currently

available information falls short of what is considered sufficient to serve as a basis for a distinct

standard defined specifically in terms of the relationship between ambient PM and those effects. 

In the case of visibility impairment, however, the available information may well provide a basis

for a distinctly defined standard. In either case, staff believes it is appropriate to consider the

extent to which the current or recommended primary standards may afford protection against the

identified welfare effects.

Staff first considers information related to the effects of ambient PM, especially fine

particles, on visibility impairment in section 7.3, and makes recommendations that consideration
7-3

be given to a revised secondary PM2.5 standard defined in terms of that effect. Other PM-related

welfare effects, including effects on vegetation and ecosystems, materials, and global climate

change processes, are addressed in section 7.4. This chapter concludes with a summary of key

uncertainties associated with establishing secondary PM standards and related staff research

recommendations in section 7.5.

7.3 STANDARDS TO ADDRESS VISIBILITY IMPAIRMENT

In 1997, EPA decided to address the effects of PM on visibility by setting secondary

standards identical to the suite of PM2.5 primary standards, in conjunction with the future

establishment of a regional haze program under sections 169A and 169B of the Act (62 FR at

38679-83). In reaching this decision, EPA first concluded that PM, especially fine particles,

impairs visibility in various locations across the country, including multi-state regions, urban

areas, and remote Class I Federal areas (e.g., national parks and wilderness areas). EPA also

concluded that addressing visibility impairment solely through setting more stringent national

secondary standards would not be an appropriate means to protect the public welfare from

adverse impacts of PM on visibility in all parts of the country. As a consequence, EPA

determined that an approach that combined national secondary standards with a regional haze

program was the most appropriate and effective way to address visibility impairment.

In reaching these conclusions in 1997, EPA recognized, based on observations from

available monitoring data, primarily from rural sites in the IMPROVE monitoring network, that

the selection of an appropriate level for a national secondary standard to address visibility

protection was complicated by regional differences in visibility impairment. These differences

were due to several factors, including background and current levels of PM, the composition of

PM, and average relative humidity. As a result of these regional differences, EPA noted that a

national standard intended to maintain or improve visibility conditions in many parts of the West

would have to be set at or below natural background levels in the East; conversely, a national

standard that would improve visibility in the East would permit further degradation in the West. 

Beyond such problems associated with regional variability, EPA also determined that there was

not sufficient information available to establish a standard level to protect against visibility

conditions generally considered to be adverse in all areas.

These considerations led EPA to assess whether the protection afforded by the

combination of the selected primary PM2.5 standards and a regional haze program would provide

appropriate protection against the effects of PM on visibility. Based on such an assessment,

EPA determined that attainment of the primary PM2.5 standards through the implementation of

regional control strategies would be expected to result in visibility improvements in the East at

both urban and regional scales, but little or no change in the West, except in and near certain

urban areas. Further, EPA determined that a regional haze program that would make significant

progress toward the national visibility goal in Class I areas would also be expected to improve
7-4

visibility in many urban and non-Class I areas as well. EPA also noted, however, that the

combined effect of the PM NAAQS and regional haze programs may not address all situations in

which people living in certain urban areas may place a particularly high value on unique scenic

resources in or near these areas. EPA concluded that such situations were more appropriately

and effectively addressed by local visibility standards, such as those established by the city of

Denver, than by national standards and control programs.

As anticipated in the last review, EPA promulgated a regional haze program in 1999. 

That program requires States to establish goals for improving visibility in Class I areas and to

adopt control strategies to achieve these goals. More specifically, States are required to establish

goals for improving visibility on the 20% most impaired days in each Class I area, and for

allowing no degradation on the 20% least impaired days. Since strategies to meet these goals are

to reflect a coordinated approach among States, multi-state regional planning organizations have

been formed and are now developing strategies, to be adopted over the next few years, that will

make reasonable progress in meeting these goals.

7.3.1 Adequacy of Current PM2.5 Standards

In considering the information now available in this review, as discussed in Chapters 2

and 6 (section 6.2), staff notes that, while new research has led to improved understanding of the

optical properties of particles and the effects of relative humidity on those properties, it has not

changed the fundamental characterization of the role of PM, especially fine particles, in visibility

impairment from the last review. However, extensive new information now available from

visibility and fine particle monitoring networks has allowed for updated characterizations of

visibility trends and current levels in urban areas, as well as Class I areas. These new data are a

critical component of the analysis presented in section 6.2.3 that better characterizes visibility

impairment in urban areas.

Based on this information, staff has first considered the extent to which available

information shows PM-related impairment of visibility at current ambient conditions in areas

across the U.S. Taking into account the most recent monitoring information and analyses, staff

makes the following observations:

• In Class I areas, visibility levels on the 20% haziest days in the West are about equal to

levels on the 20% best days in the East. Despite improvement through the 1990's,

visibility in the rural East remains significantly impaired, with an average visual range of

approximately 20 km on the 20% haziest days (compared to the naturally occurring

visual range of about 150 + 45 km). In the rural West, the average visual range showed

little change over this period, with an average visual range of approximately 100 km on

the 20% haziest days (compared to the naturally occurring visual range of about 230 +

40 km).
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• In urban areas, visibility levels show far less difference between eastern and western

regions. For example, based on reconstructed light extinction values calculated from

24-hour average PM2.5 concentrations, the average visual ranges on the 20% haziest days

in eastern and western urban areas are approximately 20 km and 27 km, respectively. 

Even more similarity is seen in considering 4-hour (12:00 to 4:00 pm) average PM2.5

concentrations, for which the average visual ranges on the 20% haziest days in eastern

and western urban areas are approximately 26 km and 31 km, respectively (Schmidt et

al., 2005).

Based on this information, and on the recognition that efforts are now underway to

address all human-caused visibility impairment in Class I areas through the regional haze

program implemented under sections 169A and 169B of the Act, as discussed above, staff has

focused in this review on visibility impairment primarily in urban areas. In so doing, staff has

considered whether information now available can inform judgments as to the extent to which

existing levels of visibility impairment in urban areas can be considered adverse to public

welfare. In so doing, staff has looked at studies in the U.S. and abroad that have provided the

basis for the establishment of standards and programs to address specific visibility concerns in

local areas, as discussed in section 6.2.5. These studies have produced new methods and tools to

communicate and evaluate public perceptions about varying visual effects associated with

alternative levels of visibility impairment relative to varying particle pollution levels and

environmental conditions. As discussed in section 6.2.6, methods involving the use of surveys to

elicit citizen judgments about the acceptability of varying levels of visual air quality in an urban

area have been developed by the State of Colorado, and they have been used to develop a

visibility standard for Denver. These methods have now been adapted and applied in other

areas, including Phoenix, AZ, and the province of British Columbia, Canada, producing

reasonably consistent results in terms of the visual ranges found to be generally acceptable by

the participants in the various studies, which ranged from approximately 40 to 60 km in visual

range.

Beyond the information available from such programs, staff believes it is appropriate to

make use directly of photographic representations of visibility impairment to help inform

judgments about the acceptability of varying levels of visual air quality in urban areas. As

discussed in section 6.2.6, photographic representations of varying levels of visual air quality

have been developed for several urban areas (Appendix 6A, available on EPA’s website at

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html). In considering these images for

Washington, D.C., Chicago, and Phoenix (for which PM2.5 concentrations are reported), staff

makes the following observations:

• At concentrations at or near the level of the current 24-hour PM2.5 standard, scenic views

(e.g., mountains, historic monuments), as depicted in these images around and within the

urban areas, are significantly obscured from view.
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• Appreciable improvement in the visual clarity of the scenic views depicted in these

images occurs at PM2.5 concentrations below 35 to 40 μg/m3, which equates to visual

ranges generally above 20 km for the urban areas considered.

While being mindful of the limitations in using visual representations from a small

number of areas as a basis for considering national visibility-based secondary standards, staff

nonetheless concludes that the observations discussed above support consideration of revising

the current PM2.5 secondary standards to enhance visual air quality, particularly with a focus on

urban areas. Thus, in the sections that follow, staff evaluates information related to indicator,

averaging time, level and form to identify a range of alternative PM standards for consideration

that would protect visual air quality, primarily in urban areas.

7.3.2 Indicators

As discussed in Chapter 2, section 2.8, fine particles contribute to visibility impairment

directly in proportion to their concentration in the ambient air. Hygroscopic components of fine

particles, in particular sulfates and nitrates, contribute disproportionately to visibility impairment

under high humidity conditions, when such components can reach particle diameters up to and

even above 2.5 μm. Particles in the coarse mode generally contribute only marginally to

visibility impairment in urban areas. Thus, fine particles, as indexed by PM2.5, are an appropriate

indicator of PM pollution to consider for the purpose of standards intended to address visibility

impairment.

In analyzing how well PM2.5 concentrations correlate with visibility in urban locations

across the U.S., as discussed above in section 6.2.3 and in more detail in Schmidt et al. (2005),

staff concludes that the observed correlations are strong enough to support the use of PM2.5 as the

indicator for such standards. More specifically, clear correlations exist between 24-hour average

PM2.5 concentrations and reconstructed light extinction (RE), which is directly related to visual

range, and these correlations are similar in eastern and western regions. These correlations are

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). Thus, staff

concludes that it is 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

of daylight hours.

7.3.3 Averaging Times

In considering appropriate averaging times for a standard to address visibility

impairment, staff has considered averaging times that range from 24 to 4 hours, as discussed in

section 6.2.3. Within this range, as noted above, correlations between PM2.5 concentrations and

RE are generally less influenced by relative humidity and more consistent across regions as the
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averaging time gets shorter. Based on the regional and national average statistics considered in

this analysis, staff observes that in the 4-hour time period between 12:00 and 4:00 p.m., the slope

of the correlation between PM2.5 concentrations and hourly RE is lowest and most consistent

across regions than for any other 4-hour or longer time period within a day (Chapter 6, Figure

6-4). Staff also recognizes that these advantages remain in looking at a somewhat wider time

period, from approximately 10:00 am to 6:00 pm. Staff concludes that an averaging time from 4

to 8 hours, generally within the time period from 10:00 am to 6:00 pm, should be considered for

a standard to address visibility impairment.

In reaching this conclusion, staff recognizes that the PM2.5 Federal Reference Method

(FRM) monitoring network provides 24-hour average concentrations, and, in some cases, on a

third- or sixth-day sample schedule, such that implementing a standard with a less-than-24-hour

averaging time would necessitate the use of continuous monitors that can provide hourly time

resolution. Given that the data used in the analysis discussed above are from commercially

available PM2.5 continuous monitors, such monitors clearly could provide the hourly data that

would be needed for comparison with a potential visibility standard with a less-than-24-hour

averaging time. Decisions as to which PM2.5 continuous monitors are providing data of

sufficient quality to be used in a visibility standard would follow protocols for approval of

federal equivalent methods (FEMs) that can provide data in at least hourly intervals. 

Development of the criteria for approval of FEMs to support a visibility standard would be based

upon a data quality objective process that considers uncertainties associated with the

measurement system and the level and form of the standard under consideration.

7.3.4 Alternative PM2.5 Standards to Address Visibility Impairment

In considering alternative short-term (4- to 8-hour) PM2.5 standards that would provide

requisite protection against PM-related impairment of visibility primarily in urban areas, staff

has taken into account the results of public perception and attitude surveys 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. Staff believes that these

sources provide a basis for bounding a range of levels appropriate for consideration in setting a

national visibility standard primarily for urban areas.

As discussed above in section 6.2, public perception and attitude surveys conducted in

Denver, CO and Phoenix, AZ resulted in judgments reflecting the acceptability of a visual range

of approximately 50 and 40 km, respectively. A similar survey approach in the Fraser Valley in

British Columbia, Canada reflected the acceptability of a visual range of 40 to 60 km. Visibility

standards established for the Lake Tahoe area in California and for areas within Vermont are

both targeted at a visual range of approximately 50 km. Staff notes that, in contrast to this

convergence of standards and goals around a visual range from 40 to 60 km, California's longstanding general state-wide visibility standard is a visual range of approximately 16 km. Staff
7-8

believes that consideration should be given to national visibility standards for urban areas across

the U.S. that are somewhat less stringent than local standards and goals set to protect scenic

resources in and around certain urban areas that are particularly highly valued by people living in

those areas, suggesting an upper end of the range of consideration below 40 km.

Staff has also inspected the photographic representations of varying levels of visual air

quality that have been developed for Washington, D.C., Chicago, Phoenix, and Denver

(Appendix 6A, available on EPA’s website at

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_cr_sp.html). Staff observes that scenic views

(e.g., historic monuments, lake front and mountain vistas) depicted in these images (around and

within the three urban areas for which PM2.5 concentrations are reported) are significantly

obscured from view at PM2.5 concentrations of 35 to 40 μg/m3 in Chicago, Washington, D.C.,

and Phoenix, corresponding to reported visual ranges in Washington, D.C. and Phoenix of 12 to

20 km, respectively. Staff also observes that visual air quality appears to be good in these areas

at PM2.5 concentrations generally below 20 μg/m3

, corresponding to reported visual ranges in

Washington, D.C. and Phoenix above approximately 25 to 35 km, respectively. In looking at the

images in Denver, staff observes that visual air quality appears to be generally good, specifically

in terms of the ability to view nearby mountain ranges, at a visual range above 52 km. These

observations are interpreted by staff as suggesting consideration of a national visibility standard

in the PM2.5 concentration range of 30 to 20 μg/m3

. The upper end of this range is below the

levels at which scenic views are significantly obscured, and the lower end is around the level at

which visual air quality generally appeared to be good in these areas. Staff recognizes that the

above observations about visual air quality in urban areas inherently take into account the nature

and location of scenic views that are notable within and around a given urban area, which has

implications for the appropriate design of an associated monitoring network.

Building upon the analysis discussed above in section 6.2.3, staff has characterized the

distributions of PM2.5 concentrations, 4-hour averages in the 12:00 to 4:00 pm time frame, by

region, that correspond to various visual range target levels. The results are shown in Figure 7-1,

panels (a) through (c), for visual range levels of 25, 30, and 35 km, respectively. This figure

shows notable consistency across regions in the median concentrations that correspond to the

target visual range level, with what more variation in regional mean values as well as notable

variation within each region. In focusing on the median values, staff observes that 4-hour

average PM2.5 concentrations of approximately 30, 25, and 20 μg/m3

 correspond to the target

visual range levels of 25, 30, and 35 km, respectively. Thus, a standard set within the range of

30 to 20 μg/m3 can be expected to correspond generally to median visual range levels of

approximately 25 to 35 km in urban areas across the U.S.. Staff notes, however, that a standard

set at any specific PM2.5 concentration will necessarily result in visual ranges that vary somewhat

in urban areas across the country, reflecting in part the less-than-perfect correlation between

PM2.5 concentrations and reconstructed light extinction. Staff also notes that the range of PM2.5
Figure 7-1. Distributions of PM2.5 concentrations for 12 p.m. – 4 p.m.

corresponding to visual ranges of 25 km (panel a), 30 km (panel b),

and 35 km (panel c) – by region. Box depicts interquartile range and 

median; whiskers depict 5th and 95th percentiles; star denotes mean.

Source: Schmidt et al. (2005) 

Visual range = 35km

Visual range = 30km

Northeast Southeast Industrial

Midwest

Upper

Midwest

Southwest Northwest Southern 

California

a

b

c

PM2.5 (μg/m3) PM2.5 (μg/m3) PM2.5 (μg/m3)

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concentrations from 30 to 20 μg/m3

, suggested by staff's analysis and observations of

photographic representations, is generally consistent with a national target visual range below 40

km, the level suggested by the public perception surveys and the local visibility standards and

goals discussed above.

In considering a standard down to 20 μg/m3, staff has again looked to information on

PM2.5 background concentrations, as was done in considering primary PM2.5 standard levels in

Chapter 5, section 5.3.5. In both instances, staff recognizes that a standard intended to provide

protection from man-made pollution should be set above background levels. In considering

background levels in conjunction with a primary standard, staff focused on the 99th percentile of

the distribution of estimated background levels, consistent with consideration of a 98th or 99th

percentile form for a primary standard, concluding in that case that 25 μg/m3 was an appropriate

lower end to the range of 24-hour primary PM2.5 standards for consideration. For reasons

discussed below, staff believes that a range of percentile forms extending below the 98th

percentile would be appropriate to consider for a visibility standard, and thus staff has also

looked to lower percentiles in the distribution of estimated background levels as a basis for

comparison with the lower end of the range of short-term secondary PM2.5 standards for

consideration. As discussed in Chapter 2, section 2.6, staff notes that, while long-term average

daily PM2.5 background levels are quite low (ranging from 1 to 5 μg/m3

 across the U.S.), the

estimated 90th percentile values in distributions of daily background levels, for example, are

appreciably higher, but generally well below 15 μg/m3, with levels below 10 μg/m3 in most

areas, and these levels may include some undetermined contribution from anthropogenic

emissions (Langstaff, 2005). In addition, staff again notes that even higher daily background

levels result from episodic occurrences of extreme natural events (e.g., wildfires, global dust

storms), but levels related to such events are generally excluded from consideration under EPA's

natural events policy, as noted in section 2.6. Taking these considerations into account, staff

believes that 20 μg/m3

 is an appropriate lower end to the range of short-term PM2.5 standards for

visibility protection for consideration in this review if a percentile form ranging down to the 90th

percentile were to be considered. Alternatively, if a percentile form at about the 98th percentile

were considered, then, consistent with conclusions for the primary standard, staff believes that

25 μg/m3

 is an appropriate lower end to the range.

As in the last review, staff believes that a national visibility standard should be

considered in conjunction with the regional haze program as a means of achieving appropriate

levels of protection against PM-related visibility impairment in urban, non-urban, and Class I

areas across the country. Staff recognizes that programs implemented to meet a national

standard focused primarily on 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 rule established for protection of visual air quality in Class I

areas. Staff further believes that the development of local programs continues to be an effective
2 The form of the 1987 24-hour PM10 standard is based on the expected number of days per year (averaged

over 3 years) on which the level of the standard is exceeded; thus, attainment with the one-expected exceedance

form is determined by comparing the fourth-highest concentration in 3 years with the level of the standard.

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and appropriate approach to provide additional protection for unique scenic resources in and

around certain urban areas that are particularly highly valued by people living in those areas. 

Based on these considerations, and taking into account the observations and analysis discussed

above, staff concludes that consideration should be given to a short-term (4- to 8-hour daylight

average) secondary PM2.5 standard in the range of 30 to 20 μg/m3

, depending in part on the form

of the standard, as discussed below, for protection of visual air quality primarily in urban areas.

7.3.5 Alternative Forms of a Short-term PM2.5 Standard

In considering an appropriate form for a short-term PM2.5 standard for visibility, staff has

taken into account the same general factors that were taken into account in considering an

appropriate form for the primary PM2.5 standard, as discussed above in Chapter 5, section 5.3.6. 

In that case, as in the last review, staff has concluded that a concentration-based form should be

considered because of its advantages over the previously used expected-exceedance form2. One

such advantage is that a concentration-based form is more reflective of the impacts posed by

elevated PM2.5 concentrations because it 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 standard. Staff notes that the same advantage would apply for a visibility standard

as to a health-based standard, in that it would give proportionally greater weight to days when

PM-related visibility impairment is substantially higher than to days just above the standard. 

Further, staff recognizes that a concentration-based form has greater stability and, thus,

facilitates the development of more stable implementation programs. Taking these factors into

account, staff concludes that consideration should be given to a percentile-based form for a

visibility standard.

To identify a range of concentration percentiles that would be appropriate for

consideration, staff first has taken into account similar considerations as were discussed in

Chapter 5, section 5.3.6.2, for the primary PM2.5 standard as a basis for identifying an

appropriate upper end to a range of percentile forms. More specifically, staff believes that the

upper end of the range of consideration should be the 98th to 99th percentiles, consistent with the

forms being considered for the 24-hour primary PM2.5 standard. Staff has also considered that

the regional haze program targets the 20% most impaired days for improvements in visual air

quality in Class I areas. If a similar target of the 20% most impaired days were 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 days in this range would be

improved by control strategies intended to attain the standard. A focus on improving the 20%
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most impaired days suggests to staff that the 92nd percentile, which represents the mean of the

distribution of the 20% worst days (Schmidt et al., 2005), would be an appropriate lower end of

the range of forms for consideration.

Based on the factors discussed above, staff concludes that a percentile-based form should

be considered, based on a percentile within a range of the 92nd to about the 98th percentile. Staff

believes that a form selected from within this range could provide an appropriate balance between

adequately limiting the occurrence of peak concentrations and providing for a relatively stable

standard.

To assist in understanding the implications of alternative percentile forms, in conjunction

with alternative levels of a 4-hour secondary PM2.5 standard, staff assessed the percentage of days

estimated to exceed various PM2.5 concentrations in counties across the U.S., as shown in

Appendix 7A, Figure 7A-1. This analysis is based on 2001 to 2003 air quality data from

continuous monitors, using the 4-hour average concentration from 12:00 to 4:00 pm at the

maximum monitor in each county. Staff also assessed (based on the same air quality database)

the percentage of counties, and the population in those counties, that would not likely attain

various PM2.5 secondary standards (Appendix 7A, Table 7A-1). These assessments are intended

to provide some rough indication of the breadth of coverage potentially afforded by various

combinations of alternative standards.

7.3.6 Summary of Staff Recommendations

Staff recommendations for the Administrator's consideration in making decisions on the

secondary PM2.5 standards to address PM-related visibility impairment, together with supporting

conclusions from sections 7.3.1 through 7.3.4, are briefly summarized below. Staff recognizes

that selecting from among alternative standards will necessarily reflect consideration of the

qualitative and quantitative uncertainties inherent in the relevant information. In making the

following recommendations, staff is mindful that the Act requires secondary standards to be set

that, in the judgment of the Administrator, are requisite to protect public welfare from those

known or anticipated effects that are judged by the Administrator to be adverse, such that the

standards are neither more nor less stringent than necessary. The Act does not require that

secondary standards be set to eliminate all risk of adverse welfare effects.

( 1) Consideration should be given to revising the current suite of secondary PM2.5 standards to

provide increased and more targeted protection primarily in urban areas from visibility

impairment related to fine particles. This recommendation reflects the recognition that

programs implemented to meet such a standard can be expected to improve visual air

quality in non-urban areas as well, just as programs now being developed to address the

requirements of the regional haze rule, for protection of visual air quality in Class I areas,

can also be expected to improve visual air quality in some urban areas.
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( 2) The indicator for a fine particle visibility standard should be PM2.5, reflecting the strong

correlation between short-term average PM2.5 in urban areas across the U.S. and light

extinction, which is a direct measure of visibility impairment.

( 3) Consideration should be given to a short-term averaging time for a PM2.5 standard, within

the range of 4 to 8 hours, within a daylight time period between approximately 10:00 am

to 6:00 pm. To facilitate implementation of such a standard, consideration should be

given to the adoption of FEMs for appropriate continuous methods for the measurement of

short-term average PM2.5 concentrations.

( 4) Recommendations on ranges of alternative levels and forms for alternative PM2.5 visibility

standards include:

( a) Staff recommends consideration of a 4- to 8-hour PM2.5 standard within the range

of 30 to 20 μg/m3

, depending in part on the form of the standard. 

( b) Staff also recommends consideration of a percentile-based form for such a

standard, focusing on a range from the 92nd up to the 98th percentile of the annual

distribution of daily short-term PM2.5 concentrations, averaged over 3 years.

Staff judges that a standard within these ranges could provide an appropriate degree of

protection against visibility impairment, generally resulting in a visual range of

approximately 25 to 35 km, primarily in urban areas, as well as improved visual air

quality in surrounding non-urban areas.

7.4 STANDARDS TO ADDRESS OTHER PM-RELATED WELFARE EFFECTS

EPA’s decision in 1997 to revise the suite of secondary PM standards took into account

not only visibility protection, but also materials damage and soiling, the other PM-related welfare

effect considered in the last review. Based on this broader consideration, EPA established

secondary standards for PM identical to the suite of primary standards, including both PM2.5 and

PM10 standards, to provide appropriate protection against the welfare effects associated with fine

and coarse particle pollution (62 FR at 38683). This decision was based on considering both

visibility effects associated with fine particles, as discussed above in section 7.3, and materials

damage and soiling effects associated with both fine and coarse particles. With regard to effects

on materials, EPA concluded that both fine and coarse particles can contribute to materials

damage and soiling effects. However, EPA also concluded that the available data did not provide

a sufficient basis for establishing a distinct secondary standard based on materials damage or

soiling alone. These considerations led EPA to consider whether the reductions in fine and coarse

particles likely to result from the suite of primary PM standards would provide appropriate

protection against the effects of PM on materials. Taking into account the available information
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and the limitations in that information, EPA judged that setting secondary standards identical to

the suite of PM2.5 and PM10 primary standards would provide increased protection against the

effects of fine particles and retain an appropriate degree of control on coarse particles.

In this review, in addition to addressing visibility impairment, the CD has broadened its

scope to include effects on ecosystems and vegetation, discussed in Chapter 6, section 6.3, and

also addresses PM-related effects on materials, discussed in section 6.4, and the role of ambient

PM in atmospheric processes associated with climate change and the transmission of solar

radiation, discussed in section 6.5. In considering the currently available evidence on each of

these types of PM-related welfare effects, staff notes that there is much information linking

ambient PM to potentially adverse effects on materials and ecosystems and vegetation, and on

characterizing the role of atmospheric particles in climatic and radiative processes. However, on

the basis of the evaluation of the information discussed in Chapter 6, which highlighted the

substantial limitations in the evidence, especially with regard to the lack of evidence linking

various effects to specific levels of ambient PM, staff concludes that the available evidence does

not provide a sufficient basis for establishing distinct secondary standards for PM based on any of

these effects alone. These considerations lead staff to address in the following sections whether

the reductions in PM likely to result from the current secondary PM standards, or from the range

of recommended revisions to the primary PM standards and from the recommended secondary

PM2.5 standard to address visibility impairment, would provide appropriate protection against

these other PM-related welfare effects.

7.4.1 Vegetation and Ecosystems

With regard to PM-related effects on ecosystems and vegetation, staff notes that the CD

presents evidence of such effects, particularly related to nitrate and acidic deposition, and

concludes that current PM levels in the U.S. “have the potential to alter ecosystem structure and

function in ways that may reduce their ability to meet societal needs” (CD, p. 4-153). Currently,

however, fundamental areas of uncertainty preclude establishing predictable relationships

between ambient concentrations of particulate nitrogen and sulfur compounds and associated

ecosystem effects. One source of uncertainty hampering the characterization of such

relationships is the extreme complexity and variability that exist in estimating particle deposition

rates. These rates are affected by numerous factors, including particle size and composition,

associated atmospheric conditions, and the properties of the surfaces being impacted. A related

source of uncertainty is establishing what portion of the total nitrogen and sulfur deposition

occurring at a given site is attributable to ambient PM. Though several national deposition

monitoring networks have been successfully measuring wet and dry deposition for several

decades, they often do not distinguish the form (e.g., particle, wet, and dry gaseous) in which a

given chemical species is deposited. Further, it is not clear how well data from monitoring sites
7-15

may apply to non-monitored sites with different surface cover, meteorology, or other depositionrelated factors.

In addition, ecosystems have different sensitivities and capacities to buffer or assimilate

pollutants. Many of the documented ecosystem-level effects only became evident after longterm, chronic exposures to total annual loads of Nr or acidifying compounds that eventually

exceeded the natural buffering or assimilative capacity of the system. In most cases, PM

deposition is not the only contributor to the total load of Nr or acidifying compounds entering the

affected system. Since it is difficult to predict the rate of PM deposition, and thus, the PM

contribution to total deposition at a given site, it is difficult to predict the ambient concentration 

of PM that would likely lead to the observed adverse effects within any particular ecosystem.

Equally difficult is the prediction of recovery rates for areas already affected, if PM deposition

rates of various chemical species were to be reduced. 

Despite these uncertainties, a number of significant environmental effects that either have

already occurred or are currently occurring have been linked to chronic deposition of chemical

constituents found in ambient PM. Staff notes, for example, that the following effects have been

linked with chronic additions of Nr and its accumulation in ecosystems:

• Productivity increases in forests and grasslands, followed by decreases in

productivity and possible decreases in biodiversity in many natural habitats

wherever atmospheric reactive nitrogen deposition increases significantly and

critical thresholds are exceeded;

• Acidification and loss of biodiversity in lakes and streams in many regions,

especially in conjunction with sulfate deposition; and

• Eutrophication, hypoxia, loss of biodiversity, and habitat degradation in coastal

ecosystems.

Staff notes that effects of acidic deposition have been extensively documented, as

discussed in the CD and other reports referenced therein. For example, effects on some species of

forest trees linked to acidic deposition include increased permeability of leaf surfaces to toxic

materials, water, and disease agents; increased leaching of nutrients from foliage; and altered

reproductive processes; all of which serve to weaken trees so that they are more susceptible to

other stresses (e.g., extreme weather, pests, pathogens). In particular, acidic deposition has been

implicated as a causal factor in the northeastern high-elevation decline of red spruce. Although

U.S. forest ecosystems other than the high-elevation spruce-fir forests are not currently

manifesting symptoms of injury directly attributable to acid deposition, less sensitive forests

throughout the U.S. are experiencing gradual losses of base cation nutrients, which in many cases

will reduce the quality of forest nutrition over the long term.

Taking into account the available evidence linking chemical constituents of both fine and

coarse PM to these types of known and potential adverse effects on ecosystems and vegetation,
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staff believes that further reductions in ambient PM would likely contribute to long-term recovery

and to the prevention of further degradation of sensitive ecosystems and vegetation. Staff

recognizes, however, that the available evidence does not provide any quantitative basis for

establishing distinct national standards for ambient PM to address these effects. Further, staff

recognizes that due to site-specific sensitivities to various components of ambient PM, differing

buffering and assimilative capacities, and local and regional differences in the percentage of total

deposition of Nr and acidifying compounds that is likely attributable to ambient PM, national

ambient air quality standards alone may not be an appropriate approach to protect against the

adverse impacts of total Nr and acidifying compounds, partially contributed by ambient PM, on

ecosystems and vegetation in all parts of the country. Nonetheless, staff believes that additional

reductions in fine particles and related precursor emissions likely to result from the current suite

of secondary PM standards, or the range of recommended revisions to the primary PM standards

and to the secondary PM2.5 standard to address visibility impairment, would contribute to

increased protection against PM-related effects on ecosystems and vegetation. Staff recommends

that the potential for increased protection of ecosystems and vegetation be taken into account in

considering whether to revise the current secondary PM standards. Further, staff believes that

any such increased protection should be considered in conjunction with protection afforded by

other programs intended to address various aspects of air pollution effects on ecosystems and

vegetation, such as the Acid Deposition Program and other regional approaches to reducing

pollutants linked to nitrate or acidic deposition.

7.4.2 Materials Damage and Soiling

With regard to PM-related effects on materials, staff notes that the available evidence

continues to support 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 paints and building materials, no quantitative relationships between

ambient particle concentrations and rates of damage have been established.

• Similarly, while soiling associated with fine and coarse particles can result in

increased cleaning frequency and repainting of surfaces, no quantitative

relationships between particle characteristics (e.g., concentrations, particle size,

and chemical composition) and the frequency of cleaning or repainting have been

established.

Staff believes that these observations and the underlying available evidence continue to support

consideration of retaining an appropriate degree of control on both fine and coarse particles. 
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Lacking any specific quantitative basis for establishing distinct standards to protect against PMrelated adverse effects on materials, staff believes that reductions in fine and coarse particles

likely to result from the current suite of secondary PM standards, or the range of recommended

revisions to the primary PM standards and to the secondary PM2.5 standard to address visibility

impairment, would contribute to protection against PM-related soiling and materials damage. 

Staff recommends that the potential for such protection be taken into account in considering

whether to revise the current secondary PM standards.

7.4.3 Climate Change and Solar Radiation

With regard to the role of ambient PM in climate change processes and in altering the

penetration of solar UV-B radiation to the earth’s surface, staff notes that information available in

this review derives primarily from broad-scale research and assessments related to the study of

global climate change and stratospheric ozone depletion. As such, this information is generally

focused on global- and regional-scale processes and impacts and provides essentially no basis for

characterizing how differing levels of ambient PM in areas across the U.S. would affect local,

regional, or global climatic changes or alter the penetration of UV-B radiation to the earth’s

surface. As noted in section 6.5, even the direction of such effects on a local scale remains

uncertain. Moreover, similar concentrations of different particle components can produce

opposite net radiative effects. Thus, staff concludes that there is insufficient information

available to help inform consideration of whether any revisions of the current secondary PM

standards are appropriate at this time based on ambient PM’s role in atmospheric processes

related to climate or the transmission of solar radiation.

7.4.4 Summary of Staff Recommendations

Taking into account the conclusions presented in sections 7.4.1 through 7.4.3 above, staff

makes the following recommendations with regard to PM-related effects on vegetation and

ecosystems and materials damage and soiling:

(1) Consideration should be given to setting secondary PM standards that at a minimum retain

the level of protection afforded by the current PM standards, so as to continue control of

ambient fine and coarse-fraction particles, especially long-term deposition of particles

such as particulate nitrates and sulfates, that contribute to adverse impacts on vegetation

and ecosystems and/or to materials damage and soiling. Any such standards should be

considered in conjunction with the protection afforded by other programs intended to

address various aspects of air pollution effects on ecosystems and vegetation, such as the

Acid Deposition Program and other regional approaches to reducing pollutants linked to

nitrate or acidic deposition.
7-18

(2) While staff recognizes that PM-related impacts on vegetation and ecosystems and PMrelated soiling and materials damage are associated with chemical components in both fine

and coarse-fraction PM, staff also recognizes that sufficient information is not available at

this time to recommend consideration of either an ecologically based indicator or an

indicator based distinctly on soiling and materials damage, in terms of specific chemical

components of PM. Thus, for consistency with the primary standards, staff recommends

that consideration be given to basing secondary standards on the same indicators that are

used as the basis for the suite of primary PM standards.

In making these recommendations, staff has taken into account both the available

evidence linking fine and coarse particles with effects on vegetation and ecosystems and material

damage and soiling, as well as the limitations in the available evidence. In so doing, staff

recognizes that the available information does not provide a sufficient basis for the development

of distinct national secondary standards to protect against such effects beyond the protection

likely to be afforded by the suite of primary PM standards.

7.5 SUMMARY OF KEY UNCERTAINTIES AND RESEARCH RECOMMENDATIONS

RELATED TO SETTING SECONDARY PM STANDARDS

Staff believes it is important to continue to highlight the unusually large uncertainties

associated with establishing standards for PM relative to other single component pollutants for

which NAAQS have been set. Key uncertainties and staff research recommendations welfarerelated topics are outlined below. In some cases, research in these areas can go beyond aiding in

standard setting to aiding in the development of more efficient and effective control strategies. 

Staff notes, however, that a full set of research recommendations to meet standards

implementation and strategy development needs is beyond the scope of this discussion.

With regard to welfare-related effects, discussed in Chapter 4 of the CD and Chapter 6

herein, staff has identified the following key uncertainties and research questions that have been

highlighted in this review of the welfare-based secondary standards:

(1) Refinement and broader application of survey methods designed to elicit citizens’

judgments about the acceptability of varying levels of local visibility impairment could

help inform future reviews of a visibility-based secondary standard. Such research could

appropriately build upon the methodology developed by the State of Colorado and used as

a basis for setting a visibility standard for the city of Denver, which has been adapted and

applied in other areas in the U.S. and abroad.

(2) There remain significant uncertainties associated with the characterization and prediction

of particle deposition rates to natural surfaces, especially with respect to nitrogen and
3 This recommendation is consistent with the views of the National Research Council (NRC) contained in

its recent review of air quality management in the U.S. (NRC, 2004). This report recognizes that for some resources

at risk from air pollutants, including soils, groundwaters, surface waters, and coastal ecosystems, a deposition-based

standard could be appropriate, and identifies “critical loads”as one potential approach for establishing such a

deposition-based standard.

7-19

sulfur deposition. Reduction in these uncertainties, particularly in key acid- and Nrsensitive areas, will be important in developing the capability of quantitatively linking

ambient PM concentrations with environmental exposures. In order to better understand

the contribution of PM to cumulative long-term environmental impacts, more research

needs to be conducted on the percentage of total Nr and acidifying deposition contributed

by PM and where necessary, better monitoring methods and network designs should be

developed.

(3) Atmospheric sources of Nr and acidifying compounds, including ambient PM, are clearly

contributing to the total load of these pollutants entering U.S. ecosystems annually. 

However, the immense variability in ecosystem response to total Nr and acidifying

deposition across the U.S., and the factors controlling ecosystem sensitivity and recovery,

have not been adequately characterized. Data should be collected on a greater variety of

ecosystems over longer time scales to determine how ecosystems respond to different

loading rates over time. Such research, in conjunction with the development of improved

predictive models, could help in future consideration within the U.S. of the “critical loads”

concept, and in determining how much of any given critical load is contributed by

different sources of pollutants.3
7-20

REFERENCES

Langstaff, John E. (2004). Estimation of Policy-Relevant Background Concentrations of Particulate Matter. 

Memorandum to PM NAAQS review docket OAR-2001-0017. January 27, 2005.

National Research Council (NRC) (2004). Air Quality Management in the United States. Committee on Air Quality

Management in the U.S., National Research Council of the National Academy of Science. The National

Academies Press, Washington, D.C. ISBN 0-309-08932-8.

Schmidt et al. (2005) Draft analysis of PM ambient air quality data for the PM NAAQS review. Memorandum to PM

NAAQS review docket OAR-2001-0017. January 31, 2005.
3A-1

Appendix 3A. Mortality and Morbidity Effect Estimates and PM Concentrations from U.S. and Can

for Short-term Exposures to PM10, PM2.5, and PM10-2.5

Original study* Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

MORTALITY:Total (nonaccidental) MortalityIto and Thurston, 1996 Chicago, IL

GAM not used 2.47 (1.26, 3.69) --- --- 

Kinney et al., 1995 Los Angeles, CA

GAM not used 2.47 (-0.17, 5.18) --- --- 

Pope et al., 1992 Utah Valley, UT

GAM not used 7.63 (4.41, 10.95) --- --- 

Schwartz, 1993 Birmingham, AL

GAM not used 5.36 (1.16, 9.73) --- --- 

Schwartz et al., 1996 Boston, MASchwartz, 2003a

GAM StrictGLM NSGLM BSGLM PS

--- 5.3 (3.5, 7.1)

5.7 (3.7, 7.6)5.0 (3.1, 7.0)4.5 (2.5, 6.5)

(

0.7 (-1.9, 3.4)

Schwartz et al., 1996 Knoxville, TNSchwartz, 2003a

GAM StrictGLM NSGLM BSGLM PS

--- 3.1 (0.0, 6.2)

3.0 (-0.3, 6.6)2.8 (-0.5, 6.3)2.6 (-0.8, 6.1) 1.7 (-2.7, 6.3)

Schwartz et al., 1996 St. Louis, MOSchwartz, 2003a

GAM StrictGLM NSGLM BSGLM PS

--- 2.6 (0.9, 4.3)

2.4 (0.6, 4.1)2.6 (0.9, 4.4)2.3 (0.6, 4.1) 0.3 (-2.1, 2.7)
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-2

Schwartz et al., 1996 Steubenville, OHSchwartz, 2003a

GAM StrictGLM NSGLM BSGLM PS

--- 2.4 (-0.4, 5.3)

1.7 (-1.3 4.8)

1.5 (-1.5, 4.6)1.8 (-1.2, 4.9) 5.2 (0.0, 10.7)

Schwartz et al., 1996 Portage, WISchwartz, 2003a

GAM StrictGLM NSGLM BSGLM PS

--- 2.6 (-1.2, 6.6)

0.8 (-3.3, 5.1)1.5 (-2.7, 5.8)1.1 (-3.1, 5.4) 0.7 (-4.0, 5.6)

Schwartz et al., 1996 Topeka, KSSchwartz, 2003a

GAM StrictGLM NSGLM BSGLM PS

--- 1.6 (-5.3, 9.0)

2.7 (-5.0, 10.9)

1.3 (-6.2, 9.3)1.4 (-6.3, 9.6) -3.0 (-8.1, 2.3)

Schwartz et al., 1996 6 Cities, OverallSchwartz, 2003a

GAM StrictGLM NSGLM BSGLM PS

--- 3.5 (2.5, 4.5)

3.3 (2.2, 4.3)3.0 (2.0, 4.0)2.9 (1.8, 4.0)

--- 

Styer et al., 1995 Chicago, IL

GAM not used 4.08 (0.08, 8.24) --- --- 

Samet et al., 2000a,b 90 Largest U.S. CitiesDominici et al. (2003)

GAM strictGLM NS

1.4 (0.9, 1.9)1.1 (0.5, 1.7)

--- --- 

Schwartz, 2000a 10 U.S. citiesSchwartz, 2003b

GAM StrictGLM NS

3.4 (2.6, 4.1)2.8 (2.0, 3.6)

--- --- 

Burnett et al., 2000 8 Canadian Cities Burnett and Goldberg, 2003

GAM StrictGLM NS (6knots/yr)

3.2 (1.1, 5.5)

2.7 (-0.1, 5.5)

2.8 (1.2, 4.4)2.1 (0.1, 4.2)

1.9 (-0.1, 3.9)1.8 (-0.6, 4.4)

Chock et al., 2000 Pittsburgh, PA

GAM not used <75 years 2.6 (-2.0, 7.7)

>75 years 1.5 (-3.0, 6.3)

<75 years 0.7 (-1.7, 3.)

>75 years 1.3 (-1.3, 3.8)
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-3

Clyde et al., 2000 Phoenix, AZ

GAM not used 6 (>0, 11) --- --- 

Fairley, 1999 Santa Clara County, CAFairley, 2003

GAM StrictGLM NS

7.8 (2.8, 13.1)8.3 (2.9, 13.9)

8.1 (1.6, 15.0)7.0 (1.4, 13.0)

4.5 (-7.6, 18.1)3.3 (-5.3, 12.6)

Gamble, 1998 Dallas, TX

GAM not used -3.56 (-12.73, 6.58) --- --- 

Goldberg et al., 2000 Montreal, CANGoldberg and Burnett, 2003

GAM StrictGLM NS

--- 4.2 (p<0.05)

1.5 (p>0.05)

--- 

Klemm and Mason, 2000 Atlanta, GA

GAM not used 8.7 (-5.2, 24.7) 4.8 (-3.2, 13.4) 1.4 (-11.3, 15.9) 

Klemm et al., 2000 Six City reanalysis - St. LouisKlemm and Mason, 2003

GAM StrictGLM NS

2.0 (0.0, 4.1)

1.0 (-1.5, 3.6)

2.0 (0.5, 3.5)

1.3 (-0.5, 3.0)

0.0 (-2.2, 2.3)

-0.5 (-3.0, 2.0)

Klemm et al., 2000 Six City reanalysis -SteubenvilleKlemm and Mason, 2003

GAM StrictGLM NS

2.5 (-1.7, 7.0)1.5 (-1.7, 4.9)

1.5 (-1.6, 4.7)0.5 (-2.7, 3.8)

4.6 (-0.7, 10.1)4.0 (-1.6, 10.0)

Klemm et al., 2000 Six City reanalysis - TopekaKlemm and Mason, 2003

GAM StrictGLM NS

-3.5 (-11.6, 5.4)-5.4 (-14.3, 4.4)

1.5 (-6.5, 10.2)

-0.5 (-9.5, 9.4)

-3.7 (-9.2, 2.1)

-4.7 (-10.8, 1.8)

Klemm et al., 2000 Six City reanalysis - KnoxvilleKlemm and Mason, 2003

GAM StrictGLM NS

6.1 (1.5, 11.0)

5.1 (-0.2, 10.7)

4.3 (0.9, 7.8)

3.8 (-0.1, 7.8)

3.5 (-1.0, 8.2)3.0 (-1.9, 8.2)

Klemm et al., 2000 Six City reanalysis - BostonKlemm and Mason, 2003

GAM StrictGLM NS

6.1 (3.6, 8.8)5.6 (2.8, 8.5)

5.1 (3.3, 6.9)4.0 (1.9, 6.2)

1.3 (-1.1, 3.7)1.8 (-1.0, 4.6)
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-4

Klemm et al., 2000 Six City reanalysis - MadisonKlemm and Mason, 2003

GAM StrictGLM NS

1.0 (-4.6, 7.0)

-1.5 (-7.7, 5.1)

1.5 (-2.7, 5.9)

-1.2 (-5.7, 3.5)

0.0 (-4.8, 5.0)

-1.0 (-6.2, 4.5)

Klemm et al., 2000 Six City reanalysis - overallKlemm and Mason, 2003

GAM StrictGLM NS

3.5 (2.0, 5.1)2.5 (0.8, 4.3)

3.0 (2.0, 4.1)2.0 (0.9, 3.2)

0.8 (-0.6, 2.1)

0.5(-1.0, 2.0)

Laden et al., 2000 Six City reanalysisSchwartz, 2003a

GLM PS --- -5.1 (-13.9, 4.6) crustal

9.3 (4.0, 14.9) traffic

2.0 (-0.3, 4.4) coal

--- 

Levy et al., 1998 King Co., WA

GAM not used 7.2 (-6.3, 22.8) 1.76 (-3.53, 7.34) --- 

Lipfert et al., 2000 Philadelphia, PA

GAM not used 5.99 (p>0.055) 4.21 (p<0.055) 5.07 (p>0.055) 

Lippmann et al., 2000 Detroit, MIIto, 2003

GAM StrictGLM NS

3.3 (-2.0, 8.9)3.1 (-2.2, 8.7)

1.9 (-1.8, 5.7)2.0 (-1.7, 5.8)

3.2 (-1.9, 8.6 )

2.8 (-2.2, 8.1)

Moolgavkar, 2000a Los Angeles, CAMoolgavkar, 2003

GAM StrictGLM NS

2.4 (0.5, 4.2)2.3 (0.5, 4.1)

1.5 (0, 3.0)

1.4 (-0.4, 3.2)

--- 

Moolgavkar, 2000a Cook Co., ILMoolgavkar, 2003

GAM StrictGLM NS

2.4 (1.4, 3.5)2.6 (1.6, 3.6)

--- --- 

Ostro, 1995 San Bernadino and Riverside Counties, CA

GAM not used --- 0 (-1.4, 1.4) --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-5

Schwartz, 2000c Boston, MASchwartz, 2003a

GLM NS --- 5.8 (4.5, 73) (15-day)

9.7 (8.2, 11.2) (60-day)

--- 

Schwartz, 2000 Chicago, ILSchwartz, 2003b

Strict GAM(dist. lag)

5.41 (2.36, 8.56) --- --- 

Schwartz, 2000 Pittsburgh, PASchwartz, 2003b

Strict GAM(dist. lag)

3.14 (0.25, 6.11) --- --- 

Schwartz, 2000 Detroit, MNSchwartz, 2003b

Strict GAM(dist. lag)

6.83 (3.73, 10.02) --- --- 

Schwartz, 2000 Seattle, WASchwartz, 2003b

Strict GAM(dist. lag)

7.46 (3.94, 11.10) --- --- 

Schwartz, 2000 Minneapolis, MNSchwartz, 2003b

Strict GAM(dist. lag)

10.25 (4.67, 16.12) --- --- 

Schwartz, 2000 Birmingham, ALSchwartz, 2003b

Strict GAM(dist. lag)

1.71 (-3.44, 7.13) --- --- 

Schwartz, 2000 New Haven, CTSchwartz, 2003b

Strict GAM(dist. lag)

9.17 (1.04, 17.96) --- --- 

Schwartz, 2000 Canton, OHSchwartz, 2003b

Strict GAM(dist. lag)

8.79 (-4.69, 24.18) --- --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-6

Schwartz, 2000 Spokane, WASchwartz, 2003b

Strict GAM(dist. lag)

5.62 (-0.31, 11.91) --- --- 

Schwartz, 2000 Colorado Springs, COSchwartz, 2003b

Strict GAM(dist. lag)

8.58 (-3.94, 22.73) --- --- 

Tsai et al., 2000  Newark, NJ

GAM not used 5.65 (4.62, 6.70) 4.34 (2.82, 5.89) --- 

Tsai et al., 2000 Camden, NJ

GAM not used 11.07 (0.70, 22.51) 5.65 (0.11, 11.51) --- 

Tsai et al., 2000 Elizabeth, NJ

GAM not used -4.88 (-17.88, 10.19) 1.77 (-5.44, 9.53) --- 

Cause-Specific MortalityCardiorespiratory Mortality:Samet et al., 2000a,b 90 Largest U.S. CitiesDominici et al. (2002)

GLM NS 1.6 (0.8, 2.4) --- --- 

Tsai et al., 2000 Newark, NJ

GAM not used 7.79 (3.65, 12.10) 5.13 (3.09, 7.21) --- 

Tsai et al., 2000  Camden, NJ

GAM not used 15.03 (4.29, 26.87) 6.18 (0.61, 12.06) --- 

Tsai et al., 2000 Elizabeth, NJ

GAM not used 3.05 (-11.04, 19.36) 2.28 (-4.97, 10.07) --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-7

Total Cardiovascular MortalityIto and Thurston, 1996 Chicago, IL

GAM not used 1.49 (-0.72, 3.74) --- --- 

Pope et al., 1992 Utah Valley, UT

GAM not used 9.36 (1.91, 17.36) --- --- 

Fairley, 1999 Santa Clara County, CAFairley, 2003

GAM StrictGLM NS

8.5 (0.6, 17.0)8.9 (1.3, 17.0)

6.3 (-4.1. 17.9)6.7 (-2.5, 16.7)

5.0 (-13.3, 27.3) 

Goldberg et al., 2000 Montreal, CANGoldberg and Burnett, 2003

GAM StrictGLM NS

--- 3.48 (-0.16, 7.26) --- 

Lipfert et al., 2000 Philadelphia, PA (7-county area)

GAM not used 8.0 (3.7, 12.3) 5.0 (2.4, 7.5) 5.4 (-0.4, 11.2) 

Lippmann et al., 2000 Detroit, MI Ito, 2003

GAM StrictGLM NS

5.4 (-2.6, 14.0)4.9 (-3.0, 13.5)

2.2 (-3.2, 7.9)2.0 (-3.4, 7.7)

6.7 (-1.0, 15.0)6.0 (-1.6, 14.3)

Mar et al., 2000 Phoenix, AZMar et al., 2003

GAM StrictGLM NS

9.7 (1.7, 18.3)9.5 (0.6, 19.3)

18.0 (4.9, 32.6)19.1 (3.9, 36.4)

6.4 (1.3, 11.7)6.2 (0.8, 12.0)

Moolgavkar, 2000a Los Angeles, CA Moolgavkar, 2003

GAM StrictGLM NS

4.5 (1.6, 7.5)3.9 (0.6, 7.4)

2.6 (0.4, 4.9)

1.7 (-0.8, 4.3)

--- 

Moolgavkar, 2000a Cook Co., ILMoolgavkar, 2003

GAM StrictGLM NS

2.2 (0.3, 4.1)

1.2 (-0.8, 3.1)

--- --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-8

Ostro et al., 2000 Coachella Valley, CA Ostro et al., 2003

GAM StrictGLM NS

5.5 (1.6, 9.5)5.1 (1.2, 9.1)

9.8 (-5.7, 27.9)

10.2 (-5.3, 28.3)

2.9 (0.7, 5.2)2.7 (0.4, 5.1)

Ostro, 1995 San Bernadino and Riverside Counties, CA

GAM not used --- 0.69 (-0.34, 1.74) --- 

Total Respiratory Mortality:Ito and Thurston, 1996 Chicago, IL

GAM not used 6.77 (1.97, 11.79) --- --- 

Pope et al., 1992 Utah Valley, UT

GAM not used 19.78 (3.51, 38.61) --- --- 

Fairley, 1999 Santa Clara County, CA Fairley, 2003

GAM StrictGLM NS

10.7 (-3.7, 27.2)10.8 (-3.4, 27.1)

11.7 (-9.8, 38.3)13.5 (-3.6, 33.7)

32.1 (-9.1, 92.2) 

Lippmann et al., 2000 Detroit, MIIto, 2003

GAM StrictGLM NS

7.5 (-10.5, 29.2)7.9 (-10.2, 29.7)

2.3 (-10.4, 16.7)

3.1 (-9.7, 17.7)

7.0 (-9.5, 26.5)

6.4 (-10.0, 25.7)

Ostro, 1995 San Bernadino and Riverside Counties, CA

GAM not used --- 2.08 (-0.35, 4.51) --- 

COPD Mortality:Moolgavkar, 2000a Cook Co., ILMoolgavkar, 2003

GAM StrictGLM NS

5.5 (0.2, 11.0)

4.5 (-1.6, 11.0)

--- --- 

Moolgavkar, 2000a Los Angeles, CA Moolgavkar, 2003

GAM StrictGLM NS

4.4 (-3.2, 12.6)6.2 (-3.4, 16.7)

1.0 (-5.1, 7.4)0.5 (-6.8, 8.4)

--- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-9

CARDIOVASCULAR MORBIDITY Total Cardiovascular Hospital Admissions:Samet et al., 2000 14 U.S. Cities (>65 years)Zanobetti and Schwartz (2003b)

strict GAMstrict GAM(dist lag)GLM NSGLM PS

4.95 (3.95 ,5.95))

5.73 (4.27, 7.20)

4.8 (3.55, 6.0)5.0 (4.0, 5.95)

--- --- 

Linn et al., 2000 Los Angeles, CA (>29 years)

GAM not used 3.25 (2.04, 4.47) --- --- 

Moolgavkar, 2000b Cook Co., IL (all ages)Moolgavkar, 2003

strict GAM100df

GLM NS100df

4.05 (2.9, 5.2)4.25 (3.0, 5.5)

--- --- 

Moolgavkar, 2000b Los Angeles, CA (all ages)Moolgavkar, 2003

GAM30df

GAM100dfGLM NS100df

3.35 (1.2, 5.5)

2.7 (0.6, 4.8)

2.75 (0.1, 5.4)

3.95 (2.2, 5.7)

2.9 (1.2, 4.6)

3.15 (1.1, 5.2)

--- 

Stieb et al., 2000 St. John, CAN (all ages)

GAM not used 39.2 (5.0, 84.4) 15.11 (0.25, 32.8) --- 

Burnett et al., 1997 Toronto, CAN (all ages)

GAM not used 12.07 (1.43, 23.81) 7.18 (-0.61, 15.6) 20.46 (8.24, 34.06) 

Ischemic Heart Disease Hospital Admissions:Schwartz and Morris, 1995 Detroit (>65 years)

GAM not used 5.0 (1.9, 8.3) --- --- 

Lippmann et al., 2000 Detroit, MI (>65 years)Ito 2003

Strict GAMGLM NS

8.0 (!0.3, 17.1)

6.2 (!2.0, 15.0)

3.65 (!2.05, 9.7)

3.0 (!2.7, 9.0)

10.2 (2.4, 18.6)

8.1 (0.4, 16.4)
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-10

Dysrhythmias Hospital Admissions:Lippmann et al., 2000 Detroit, MI (>65 years)Ito (2003)

Strict GAM GLM NS

2.8 (!10.9-18.7)

2.0 (!11.7-17.7)

3.2 (!6.6-14.0)

2.6 (!7.1-13.3)

0.1 (!12.4-14.4)

0.0 (!12.5-14.3)

Heart Failure/Congestive Heart Disease Hospital Admissions:Schwartz and Morris, 1995 Detroit (>65 years)

GAM not used 2.8 (0.7, 5.0) --- --- 

Linn et al., 2000 Los Angeles, CA (>29 years)

GAM not used 2.02 (-0.94, 5.06) --- --- 

Lippmann et al., 2000 Detroit, MI (>65 years)Ito, 2003

Strict GAM GLM NS

9.2 (!0.3-19.6)

8.4 (!1.0-18.7)

8.0 (1.4-15.0)6.8 (0.3-13.8)

4.4 (!4.0-13.5)

4.9 (!3.55-14.1)

Morris and Naumova, 1998 Chicago, IL (>65 years)

GAM not used 3.92 (1.02, 6.90) --- --- 

Myocardial Infarction Hospital Admissions:

Linn et al., 2000 Los Angeles, CA (>29 years)

GAM not used 3.04 (0.06, 6.12) --- --- 

Cardiac arrhythmia Hospital Admissions:Linn et al., 2000 Los Angeles, CA (>29 years)

GAM not used 1.01 (-1.93, 4.02) --- --- 

Cerebrovascular Hospital Admissions:Linn et al., 2000 Los Angeles, CA (>29 years)

GAM not used 0.30 (-2.13, 2.79) --- --- 

Stroke Hospital Admissions:Linn et al., 2000 Los Angeles, CA (>29 years)

GAM not used 6.72 (3.64, 9.90) --- --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-11

Lippmann et al., 2000 Detroit, MI (>65 years)Ito, 2003

Strict GAM GLM NS

5.00 (-5.27, 16.38)4.41 (-5.81, 15.74)

1.94 (-5.16, 9.57)0.97 (-6.06, 8.52)

5.00 (-4.59, 15.56)5.63 (-4.02, 16.25)

Other Cardiovascular Effects, Including Physiological Changes or BiomarkersGold et al.,  Boston, MA

GAMstringent

--- (heart rate)

-2.3 (-4.2, -0.3)

(r-MSSD)

-6.3 (10.2, -2.3)

--- 

Peters et al., 2000 Boston, MA

GAM not used (cardiac arrhythmia, 10+

events)

144.6 (-2.8, 515.8)

(cardiac arrhythmia, 10+

events)

75.4 (3.2, 198.2)

--- 

Peters et al., 2001 Boston, MA

GAM not used (myocardial infarction)

132.7 (18.7, 356.3)

(myocardial infarction)

82.8 (16.0, 188.1)

(myocardial infarction)

73.1 (-17.0, 261.1)

Schwartz et al., 2001 U.S. population (NHANES)

GAM not used (fibrinogen)

25,7 (8.8, 42.6)

--- --- 

Pope et al., 1999 Utah Valley, UT

GAM not used (heart rate)

34.5 (3.1, 65.9)

--- --- 

Liao et al., 1999 Baltimore, MD

GAM not used --- (heart rate variability)

-0.1 (-0.18, -0.03)

Levy et al., 2001 Seattle, WA

GAM not used (cardiac arrest)

-30.3 (-53.4, 4.3)

--- --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-12

RESPIRATORY MORBIDITYTotal Respiratory Hospital Admissions:Thurston et al., 1994 Toronto, Canada

GAM not used 23.26 (2.03, 44.49) 15.00 (1.97, 28.03) 22.25 (-9.53, 54.03) 

Linn et al., 2000 Los Angeles, CA (>29 years)

GAM not used 2.89 (1.09, 4.72) --- --- 

Schwartz et al., 1996 Cleveland, OH (>65 years)

GAM not used 5.8 (0.5, 11.4) --- --- 

Burnett et al., 1997 Toronto, CAN (all ages)

GAM not used 10.93 (4.53, 17.72) 8.61 (3.39, 14.08) 12.71 (5.33, 20.74) 

Delfino et al., 1997 Montreal, CAN (>64 years)

GAM not used 36.62 (10.02, 63.21) 23.88 (4.94, 42.83) --- 

Delfino et al., 1998 Montreal, CAN (>64 years)

GAM not used --- 13.17 (-0.22, 26.57) --- 

Stieb et al., 2000 St. John, CAN (all ages)

GAM not used 8.8 (1.8, 16.4) 5.69 (0.61, 11.03) --- 

Pneumonia Hospital Admissions:Schwartz, 1995 Detroit (>65 years)

GAM not used 5.9 (1.9, 10.0) --- --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-13

Samet et al., 2000 14 U.S. Cities (>65 years)Zanobetti and Schwartz (2003b)

Strict GAMStrict GAM(dist. lag)GLM NSGLM PS

8.8 (5.9, 11.8)8.3 (4.9, 12.0)

2.9 (0.2, 5.6)

6.3 (2.5, 10.3)

--- --- 

Lippmann et al., 2000 Detroit, MI (>65 years)Ito 2003

Strict GAMGLM NS

18.1 (5.3, 32.5)

18.6 (5.6, 33.1)

10.5 (1.8, 19.8)10.1 (1.5, 19.5)

9.9 (!0.1, 22.0)

11.2 (!0.02, 23.6)

COPD Hospital Admissions:

Schwartz, 1995 Detroit (>65 years)

GAM not used 10.6 (4.4, 17.2) --- --- 

Samet et al., 2000 14 U.S. Cities (>65 years)Zanobetti and Schwartz (2003b)

Strict GAMStrict GAM(dist. lag)GLM NSGLM PS

8.8 (4.8, 13.0)

13.3 (6.2, 20.9)

6.8 (2.8, 10.8)8.0 (4.3, 11.9)

--- --- 

Linn et al., 2000 Los Angeles, CA (>29 years)

GAM not used 1.5 (-0.5, 3.5) --- --- 

Lippmann et al., 2000 Detroit, MI (>65 years)Ito (2003)

Strict GAMGLM NS

6.5 (!7.8, 23.0)

 4.6 (!9.4, 20.8)

3.0(!6.9, 13.9)

0.3(!9.3, 10.9)

8.7 (!4.8, 24.0)

10.8 (!3.1, 26.5)

Moolgavkar, 2000c Cook Co., IL (all ages)Moolgavkar 2003

Strict GAM:100 df

3.24 (.03, 6.24) --- --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-14

Moolgavkar, 2000c Los Angeles, CA (all ages)Moolgavkar 2003

Strict GAM: 30 dfStrict GAM: 100 dfGLM NS: 100df

7.78 (4.30, 11.38)

5.52 (2.53-8.59)

5.00 (1.22, 8.91)

4.69 (2.06, 7.39)2.87 (0.53, 5.27)

2.59 (-0.29, 5.56)

Asthma Hospital Admissions:

Choudbury et al., 1997 Anchorage, AK Medical Visits (all ages)

GAM not used 20.9 (11.8, 30.8) --- --- 

Jacobs et al., 1997 Butte County, CA (all ages)

GAM not used 6.11 (p>0.05) --- --- 

Linn et al., 2000 Los Angeles, CA (>29 years)

GAM not used 1.5 (-2.4, 5.6) --- --- 

Lipsett et al., 1997 Santa Clara Co., CA (all ages)

GAM not used 9.1 (2.7, 15.9) 

(below 40B F)

--- --- 

Nauenberg and Basu, 1999 Los Angeles, CA (all ages)

GAM not used 20.0 (5.3, 35) --- --- 

Tolbert et al., 2000 Atlanta, GA (<17 years)

GAM not used 13.2 (1.2, 26.7) --- --- 

Sheppard et al., 1999  Seattle, WA (<65 years)Sheppard et al., 2003

Strict GAMGLM NS

10.9 (2.8, 19.6)

8.1 (0.1, 16.7)

8.7 (3.2, 14.4)

6.5 (1.1,12.0)

5.5 (0, 14.0)

5.5 (!2.7, 11.1)
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-15

Respiratory Symptoms Odds Ratio (95% CI) for

50 ug/m3 % increase in

PM10

Odds Ratio (95% CI) for

25 ug/m3 % increase in

PM2.5

Odds Ratio (95% CI) for

25 ug/m3 % increase in

PM10-2.5

Schwartz et al., 1994 6 U.S. cities(children, cough)

GAM not used 1.39 (1.05, 1.85) 1.24 (1.00, 1.54) --- 

Schwartz et al., 1994 6 U.S. cities(children, lower respiratorysymptoms)

GAM not used 2.03 (1.36, 3.04) 1.58 (1.18, 2.10) --- 

Neas et al., 1995 Uniontown, PA(children, cough)

GAM not used --- 2.45 (1.29, 4.64) --- 

Ostro et al., 1991 Denver, CO(adults, cough)

GAM not used 1.09 (0.57, 2.10) --- --- 

Pope et al., 1991 Utah Valley, UT(lower respiratory symptoms,schoolchildren)

GAM not used 1.28 (1.06, 1.56) --- --- 

Pope et al., 1991 Utah Valley, UT(lower respiratory symptoms,asthmatic patients)

GAM not used 1.01 (0.81, 1.27) --- --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-16

Neas et al., 1996 State College, PA(children, cough)

GAM not used NR 1.48 (1.17, 1.88) (1-d) --- 

Neas et al., 1996 State College, PA(children, wheeze)

GAM not used NR 1.59 (0.93, 2.70) (1-d) --- 

Neas et al., 1996 State College, PA(children, cold)

GAM not used NR 1.61 (1.21, 2.17) (0-d) --- 

Ostro et al., 1995 Los Angeles, CA(children, asthma episode)

GAM not used 1.05 (0.64, 1.73) --- --- 

Ostro et al., 1995 Los Angeles, CA(children, shortness of breath)

GAM not used 1.51 (1.04, 2.17) --- --- 

Schwartz and Neas, 2000 Six Cities reanalysis(children, cough)

GAM not used --- 1.28 (0.98, 1.67) 1.77 (1.23, 2.54) P

Schwartz and Neas, 2000 Six Cities reanalysis(children, lower respiratorysymptoms)

GAM not used --- 1.61 (1.20, 2.16) 1.51 (0.66, 3.43) P

Vedal et al., 1998 Port Alberni, CAN(children, cough)

GAM not used 1.40 (1.14, 1.73) --- --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-17

Vedal et al., 1998 Port Alberni, CAN(children, phlegm)

GAM not used 1.40 (1.03, 1.90) --- --- 

Vedal et al., 1998 Port Alberni, CAN(children, nose symptoms)

GAM not used 1.22 (1.00, 1.47) --- --- 

Vedal et al., 1998 Port Alberni, CAN(children, sore throat)

GAM not used 1.34 (1.06, 1.69) --- --- 

Vedal et al., 1998 Port Alberni, CAN(children, wheeze)

GAM not used 1.16 (0.82, 1.63) --- --- 

Vedal et al., 1998 Port Alberni, CAN(children, chest tightness)

GAM not used 1.34 (0.86, 2.09) --- --- 

Vedal et al., 1998 Port Alberni, CAN(children, dyspnea)

GAM not used 1.05 (0.74, 1.49) --- --- 

Vedal et al., 1998 Port Alberni, CAN(children, any symptom)

GAM not used 1.16 (1.00, 1.34) --- --- 
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-18

Lung Function Changes Lung Function change

(L/min) (95% CI) for 50

ug/m3 % increase in

PM10

Lung Function change

(L/min) (95% CI) for 25

ug/m3 % increase in

PM2.5

Lung Function change

(L/min) (95% CI) for 25

ug/m3 % increase in

PM10-2.5

Neas et al., 1995 Uniontown, PA(children)

GAM not used --- -2.58 (-5.33, +0.35) --- 

Thurston et al., (1997)  Connecticut summer camp(children)

GAM not used --- PEFR -5.4 (-12.3, 1.5) 

(15 μg/m3 SO4=)

--- 

Naeher et al., 1999 Southwest VA(adult women)

GAM not used am PEFR -3.65 (-6.79, -

0.51)

pm PEFR -1.8 (-5.03,

1.43)

am PEFR -1.83 (-3.44, -

0.21)

pm PEFR -1.05 (-2.77,

0.67)

am PEFR -6.33 (-12.50,

-0.15)

pm PEFR -2.4 (-8.48,

3.68)

P

Neas et al., 1996 State College, PA(children)

GAM not used --- pm PEFR -0.64 (-1.73,

0.44)

--- 

Neas et al., 1999 Philadelphia, PA(children)

GAM not used am PEFR -8.17 (-14.81,

-1.56)

pm PEFR -1.44 (-7.33,

4.44)

am PEFR -3.29 (-6.64,

0.07)

pm PEFR -0.91 (-4.04,

2.21)

am PEFR -4.31 (-11.44,

2.75)

pm PEFR 1.88 (-4.75,

8.44)

Schwartz and Neas, 2000 Uniontown, PA (reanalysis)(children)

GAM not used --- pm PEFR -1.52, (-2.80, -

0.24)

pm PEFR +1.73 (-2.2,

5.67)

Schwartz and Neas, 2000 State College PA (reanalysis)(children)

GAM not used --- pm PEFR -0.93 (-1.88,

0.01)

pm PEFR -0.28 (-3.45,

2.87)
Original study*

 Study LocationReanalysis study

Analysis

Comments

 % increase (95% CI)

per

 50 μg/m3 PM10

 % increase (95% CI)

per

 25 μg/m3 PM2.5

 % increase (95% CI)

per 

25 μg/m3 PM10-2.5

3A-19

Vedal et al., 1998 Port Alberni, CAN(children)

GAM not used PEF -1.35 (-2.7, -0.05) --- --- 

* Studies in italics available in 1996 CD** mean (minimum, maximum) 24-h PM level shown in parentheses unless otherwise noted.
3B-1

Appendix 3B. Mortality and Morbidity Effect Estimates and PM Concentrations from

U.S. and Canadian Studies for Long-Term Exposures to PM10, PM2.5, and PM10-2.5

 Study Indicator (Increment) Relative Risk (95% CI)

Study

Concentrations

(μg/m3

)

Increased Total Mortality in Adults

Six CityA PM15/10 (20 μg/m3

) 1.18 (1.06, 1.32) NR (18, 47)

PM2.5 (10 μg/m3

) 1.13 (1.04, 1.23) NR (11, 30)

SO4

=

 (15 μg/m3) 1.54 (1.15, 2.07) NR (5, 13)

Six CityB PM15-2.5 (10 μg/m3

) 1.43 (0.83, 2.48)

ACS StudyC

(151 U.S. SMSA)

PM2.5 (10 μg/m3

) 1.07 (1.04, 1.10) 18U (9, 34)

SO4

=

 (15 μg/m3) 1.11 (1.06, 1.16) 11U (4, 24)

Six City ReanalysisD PM15/10 (20 μg/m3

) 1.19 (1.06, 1.34) NR (18, 47)

PM2.5 (10 μg/m3

) 1.14 (1.05, 1.23) NR (11, 30)

ACS Study ReanalysisD PM15/10 (20 μg/m3

) (dichot) 1.04 (1.01, 1.07) 59 (34, 101)

PM2.5 (10 μg/m3

) 1.07 (1.04, 1.10) 20 (10, 38)

PM15-2.5 (10 μg/m3

) 1.00 (0.99, 1.02) 7.1 (9, 42)

ACS Study Extended

AnalysesE

PM2.5 (10 μg/m3) (1979-83)

PM2.5 (10 μg/m3

) (1999-00)

PM2.5 (10 μg/m3

) (average)

1.04 (1.01, 1.08)

1.06 (1.02, 1.10)

1.06 (1.02, 1.11)

21 (9, 34)

14 (5, 20)

18 (7.5, 30)

Southern CaliforniaF PM10 (20 μg/m3

) 1.09 (0.99, 1.21) (males) 51 (0, 84)

PM10 (30 days/year>100 μg/m3

) 1.08 (1.01, 1.16) (males)

PM10 (20 μg/m3

) 0.95 (0.87, 1.03) (females) 51 (0, 84)

PM10 (30 days/year>100 μg/m3

) 0.96 (0.90, 1.02) (females)

Southern CaliforniaH PM2.5 (10 μg/m3

) 1.09 (0.98, 1.21) (males) 32 (17, 45)

PM10-2.5 (10 μg/m3

) 1.05 (0.92, 1.21) (males) 27 (4, 44)

Veterans CohortG PM2.5 (10 μg/m3

) (1979-81) 0.90 (0.85, 0.95) (males) 24 (6, 42)

Increased Cardiopulmonary Mortality in Adults

Six CityA PM15/10 (20 μg/m3

) —V NR (18, 47)

PM2.5 (10 μg/m3

) 1.18 (1.06, 1.32) NR (11, 30)

Six City ReanalysisD PM15/10 (20 μg/m3

) 1.20 (1.03, 1.41) NR (18, 47)

PM2.5 (10 μg/m3

) 1.19 (1.07, 1.33) NR (11, 30)

ACS StudyC PM2.5 (10 μg/m3

) 1.12 (1.07, 1.17) 18U (9, 34)

ACS Study ReanalysisD PM15/10 (20 μg/m3

) (dichot) 1.07 (1.03, 1.12) 59 (34, 101)

PM2.5 (10 μg/m3

) 1.12 (1.07, 1.17) 20 (10, 38)

PM15-2.5 (10 μg/m3

) 1.00 (0.98, 1.03) 7.1 (9, 42)

ACS Study Extended

AnalysesE

PM2.5 (10 μg/m3) (1979-83)

PM2.5 (10 μg/m3

) (1999-00)

PM2.5 (10 μg/m3

) (average)

1.06 (1.02, 1.10)

1.08 (1.02, 1.14)

1.09 (1.03, 1.16)

21 (9, 34)

14 (5, 20)

18 (7.5, 30)

Southern CaliforniaF PM10 (20 μg/m3

) 1.01 (0.92, 1.10) 51 (0, 84)
 Study Indicator (Increment) Relative Risk (95% CI)

Study

Concentrations

(μg/m3

)

3B-2

Southern CaliforniaH PM2.5 (10 μg/m3) 1.23 (0.97, 1.55) (males) 32 (17, 45)

PM10-2.5 (10 μg/m3

) 1.20 (0.87, 1.64) (males) 27 (4, 44)

Increased Lung Cancer Mortality in Adults

Six CityA PM15/10 (20 μg/m3

) **** NR (18, 47)

PM2.5 (10 μg/m3

) 1.18 (0.89, 1.57) NR (11, 30)

Six City ReanalysisD PM15/10 (20 μg/m3

) 1.14 (0.75, 1.74) NR (18, 47)

PM2.5 (10 μg/m3

) 1.21 (0.92, 1.60) NR (11, 30)

ACS StudyC PM2.5 (10 μg/m3

) 1.01 (0.91, 1.12) 18U (9, 34)

ACS Study ReanalysisD PM15/10 (20 μg/m3) (dichot) 1.01 (0.91, 1.11) 59 (34, 101)

PM2.5 (10 μg/m3

) 1.01 (0.91, 1.11) 20 (10, 38)

PM15-2.5 (10 μg/m3

) 0.99 (0.93, 1.05) 7.1 (9, 42)

ACS Study Extended

AnalysesE

PM2.5 (10 μg/m3) (1979-83)

PM2.5 (10 μg/m3

) (1999-00)

PM2.5 (10 μg/m3

) (average)

1.08 (1.01, 1.16)

1.13 (1.04, 1.22)

1.14 (1.05, 1.24)

21 (9, 34)

14 (5, 20)

18 (7.5, 30)

Southern CaliforniaF PM10 (20 μg/m3

) 1.81 (1.14, 2.86) (males) 51 (0, 84)

Southern CaliforniaH PM2.5 (10 μg/m3

) 1.39 (0.79, 2.50) (males) 32 (17, 45)

1.26 (0.62, 2.55) (males) 27 (4, 44)

Increased Bronchitis in Children

Six CityI PM15/10 (20 μg/m3

)

PM2.5 (10 μg/m3

)

1.6 (1.1, 2.5)

1.3 (0.9, 2.0)

NR (20, 59)

NR (12, 37)

24 CityJ SO4

=

 (15 μg/m3)

PM2.1 (10 μg/m3

)

PM10 (20 μg/m3

)

3.02 (1.28, 7.03)

1.31 (0.94, 1.84)

1.60 (0.92, 2.78)

4.7 (0.7, 7.4)

14.5 (5.8, 20.7)

23.8 (15.4, 32.7)

AHSMOGK SO4

=

 (15 μg/m3) 1.39 (0.99, 1.92) —

12 Southern California

communitiesL

(all children)

PM10 (20 μg/m3

)

(1986-1990 data)

0.95 (0.79, 1.15) NR (28.0, 84.9)

12 Southern California

communitiesM

(children with asthma)

PM10 (20 μg/m3

)

PM2.5 (10 μg/m3

)

1.4 (1.1, 1.8)

1.3 (0.9, 1.7)

34.8 (13.0, 70.7)

15.3 (6.7, 31.5) 

Increased Cough in Children

12 Southern California

communitiesL

(all children)

PM10 (20 μg/m3

)

(1986-1990 data)

1.05 (0.94, 1.16) NR (28.0, 84.9)

12 Southern California

communitiesM

(children with asthma)

PM10 (20 μg/m3

)

PM2.5 (10 μg/m3

)

1.1 (0.7, 1.8)

1.2 (0.8, 1.8)

13.0-70.7

6.7-31.5

Increased Airway Obstruction in Adults

AHSMOGK PM10 (20 μg/m3

) 1.19 (0.84, 1.68) NR
 Study Indicator (Increment) Relative Risk (95% CI)

Study

Concentrations

(μg/m3

)

3B-3

Decreased Lung Function in Children

Six CityI PM15/10 (50 μg/m3

) NS Changes NR (20, 59)

24 CityJ SO4

=

 (15 μg/m3)

PM2.1 (10 μg/m3

)

PM10 (20 μg/m3

)

!6.56% (-9.64, -3.43) FVC

!2.15% (-3.34, -0.95) FVC

!2.80% (-4.97, -0.59) FVC

4.7 (0.7, 7.4)

14.5 (5.8, 20.7)

23.8 (15.4, 32.7)

12 Southern California

communitiesP

(all children)

PM10 (20 μg/m3)

(1986-90 data)

!19.9 (-37.8, -2.6) FVC NR (28.0, 84.9)

12 Southern California

communitiesP

(all children)

PM10 (20 μg/m3

)

(1986-1990 data)

!25.6 (-47.1, -5.1) MMEF NR (28.0, 84.9)

12 Southern California

communitiesQ

(4th grade cohort)

PM10 (20 μg/m3)

PM2.5 (10 μg/m3

)

PM10-2.5 (10 μg/m3

)

!0.23 (-0.44, -0.01) FVC %

growth

!0.18 (-0.36, 0.0) FVC %

growth

!0.22 (-0.47, 0.02) FVC %

growth

NR (15, 70)X

NR (10, 35)X

NR

12 Southern California

communitiesQ

(4th grade cohort)

PM10 (20 μg/m3

)

PM2.5 (10 μg/m3

)

PM10-2.5 (10 μg/m3

)

!0.51 (-0.94, -0.08) MMEF %

growth

!0.4 (-0.75, -0.04) MMEF %

growth

!0.54 (-1.0, -0.06) MMEF %

growth

NR (15, 70)X

NR (10, 35)X

NR

12 Southern California

communitiesR

(second 4th grade

cohort)

PM10 (20 μg/m3)

PM2.5 (10 μg/m3

)

!0.12 (-0.26, 0.24) FVC %

growth

!0.06 (-0.30, 0.18) FVC %

growth

NR (10, 80)Y

NR (5, 30)Y

12 Southern California

communitiesR

(second 4th grade

cohort)

PM10 (20 μg/m3)

PM2.5 (10 μg/m3

)

!0.26 (-0.75, 0.23) MMEF %

growth

!0.42 (-0.84, 0.0) MMEF %

growth

NR (10, 80)Y

NR (5, 30)Y

12 Southern California

communitiesR

(second 4th grade

cohort)

PM10 (20 μg/m3)

PM2.5 (10 μg/m3

)

!0.16 (-0.62, 0.30) PEFR %

growth

!0.20 (-0.64, 0.25) PEFR %

growth

NR (10, 80)Y

NR (5, 30)Y

12 Southern California

communitiesS

PM10 (20 μg/m3) -3.6 (-18, 11) FVC growth NR (15.0, 66.2)

12 Southern California

communitiesS

PM10 (20 μg/m3) -33 (-64, -2.2) MMEF growth NR (15.0, 66.2)
 Study Indicator (Increment) Relative Risk (95% CI)

Study

Concentrations

(μg/m3

)

3B-4

12 Southern California

communitiesS

PM10 (20 μg/m3) -70 (-120, -20) PEFR growth NR (15.0, 66.2)

Lung Function Changes in Adults

AHSMOGT (%

predicted FEV1,

females)

PM10 (cutoff of 54.2 days/year

>100 μg/m3

)

+0.9 % (-0.8, 2.5) FEV1 52.7 (21.3, 80.6)

AHSMOGT 

(% predicted FEV1,

males)

PM10 (cutoff of 54.2 days/year

>100 μg/m3

)

+0.3 % (-2.2, 2.8) FEV1 54.1 (20.0, 80.6)

AHSMOGT

(% predicted FEV1,

males whose parents

had asthma, bronchitis,

emphysema)

PM10 (cutoff of 54.2 days/year

>100 μg/m3

)

!7.2 % (-11.5, -2.7) FEV1 54.1 (20.0, 80.6)

AHSMOGT

(% predicted FEV1,

males)

SO4

=

 (1.6 μg/m3) !1.5 % (-2.9, -0.1) FEV1 7.3 (2.0, 10.1)

References:

A Dockery et al. (1993)

B

 EPA (1996a)

C Pope et al. (1995)

D Krewski et al. (2000)

E

 Pope et al. (2002)

F

 Abbey et al. (1999)

G Lipfert et al. (2000b)

H McDonnell et al. (2000)

I

 Dockery et al. (1989)

J

 Dockery et al. (1996) 

K Abbey et al. (1995a,b,c)

L

 Peters et al. (1999a)

M McConnell et al. (1999)

N Berglund et al. (1999)

O Raizenne et al. (1996) 

P

 Peters et al. (1999)

Q Gauderman et al. (2000)

R Gauderman et al. (2002)

S

 Avol et al. (2001)

T

 Abbey et al. (1998)

Note: Study concentrations are presented as mean (min, max), or mean (±SD); NS Changes = No significant changes

(no quantitative results reported); NR=not reported.

U Median

V Results only for smoking category subgroups.

X Estimated from Figure 1, Gauderman et al. (2000)

Y Estimated from figures available in online data supplement to Gauderman et al. (2002)
Table 4A-1. Study-Specific Information for Short-term Exposure PM2.5 Studies in Boston, MA

Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM2.5 Coeff. Low

Bo

Schwartz (2003b) [reanalysis of Schwartz et al. (1996)]

Non-accidental < 800 all

log-linear, GAM (stringent)

none 0 70.8 mean of lag 0 & 1 2-day avg 0.00206 0.00

Schwartz (2003b) [reanalysis of Schwartz et al. (1996)] -- 6 cities

Non-accidental < 800 all

log-linear, GAM (stringent)

none 0 174 mean of lag 0 & 1 2-day avg 0.00137 0.00

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)]

COPD 490-492, 494-496 all log-linear, GAM (stringent) none 0 70.8 0 day 2-day avg 0.00276 -0.0

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)]

Ischemic heart 

disease 410-414 all

log-linear, GAM (stringent)

none 0 70.8 0 day 2-day avg 0.00266 0.00

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)]

Pneumonia 480-487 all

log-linear, GAM (stringent)

none 0 70.8 0 day 2-day avg 0.00573 0.00

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)] -- 6 cities

COPD 490-492, 494-496 all log-linear, GAM (stringent) none 0 174 0 day 2-day avg 0.00227 0.00

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)] -- 6 cities

Ischemic heart 

disease 410-414 all

log-linear, GAM (stringent)

none 0 174 0 day 2-day avg 0.00178 0.00

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)] -- 6 cities

Pneumonia 480-487 all

log-linear, GAM (stringent)

none 0 174 0 day 2-day avg 0.00402 0.00

Schwartz and Neas (2000) -- 6 cities

Lower 

respiratory 

symptoms*

n/a 7-14 logistic none N/A N/A 1 day 1-day avg 0.01901 0.00

Schwartz and Neas (2000) -

- 6 cities cough* n/a 7-14 logistic none N/A N/A 0 day 3-day avg 0.00989 -0.0Schwartz and Neas (2000) -- 6 cities

Lower 

respiratory 

symptoms*

n/a 7-14 logistic PM10-2.5 N/A N/A 1 day 1-day avg 0.01698 0.00

Schwartz and Neas (2000) -

- 6 cities cough* n/a 7-14 logistic PM10-2.5 N/A N/A 0 day 3-day avg 0.00451 -0.0

Observed 

Concentrations 

min. max.

*The C-R functions for lower respiratory symptoms and cough were calculated for the summer period April 1 through August 31

Short-Term Exposure Total Mortality -- Single Pollutant Models

Short-Term Exposure Cause-Specific Mortality -- Single Pollutant Models

Respiratory Symptoms and Illnesses** -- Single Pollutant Models

Respiratory Symptoms and Illnesses** -- Multi-Pollutant Models

4A-1
Table 4A-2. Study-Specific Information for Short-term Exposure PM2.5 Studies in Detroit, MI

Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM2.5 Coeff. 

Ito (2003) [reanalysis of Lippmann et al. (2000)] Non-accidental <800 all

log-linear, GAM (stringent)

none 4 86 3 day 1-day avg 0.00074 

Ito (2003) [reanalysis of Lippmann et al. (2000)] Circulatory 390-459 all

log-linear, GAM (stringent)

none 4 86 1 day 1-day avg 0.00087 

Ito (2003) [reanalysis of Lippmann et al. (2000)] Respiratory 460-519 all

log-linear, GAM (stringent)

none 4 86 0 day 1-day avg 0.00090 

Ito (2003) [reanalysis of Lippmann et al. (2000)] Pneumonia 480-486 65+

log-linear, GAM (stringent)

none 4 86 1 day 1-day avg 0.00398 

Ito (2003) [reanalysis of Lippmann et al. (2000)] COPD 490-496 65+

log-linear, GAM (stringent)

none 4 86 3 day 1-day avg 0.00117 

Ito (2003) [reanalysis of Lippmann et al. (2000)]

Ischemic heart 

disease 410-414 65+

log-linear, GAM (stringent)

none 4 86 2 day 1-day avg 0.00143 

Ito (2003) [reanalysis of Lippmann et al. (2000)]

Congestive heart 

failure 428 65+

log-linear, GAM (stringent)

none 4 86 1 day 1-day avg 0.00307 

Ito (2003) [reanalysis of Lippmann et al. (2000)] Dysrhythmias 427 65+

log-linear, GAM (stringent)

none 4 86 1 day 1-day avg 0.00125 

Observed 

Concentrations 

min. max.

Short-Term Exposure Total Mortality -- Single Pollutant Models

Short-Term Exposure Cause-Specific Mortality -- Single Pollutant Models

Hospital Admissions -- Single Pollutant Models

4A-2
Table 4A-3. Study-Specific Information for Short-term Exposure PM2.5 Studies in Los Angeles, CA

Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM2.5 Coeff. Low

Bou

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GAM (stringent), 100 df none 4 86 0 day 1-day avg 0.00032 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GAM (stringent), 100 df none 4 86 1 day 1-day avg 0.00010 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GAM (stringent), 30 df none 4 86 0 day 1-day avg 0.00054 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GLM, 30 df none 4 86 0 day 1-day avg 0.00040 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GAM (stringent), 100 df none 4 86 0 day 1-day avg 0.00032 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GLM, 100 df none 4 86 0 day 1-day avg 0.00030 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GAM (stringent), 30 df none 4 86 1 day 1-day avg 0.00059 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GLM, 30 df none 4 86 1 day 1-day avg 0.00055 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GAM (stringent), 100 df none 4 86 1 day 1-day avg 0.00010 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GLM, 100 df none 4 86 1 day 1-day avg -0.00001 -0.00

Observed 

Concentrations 

min. max.

Short-Term Exposure Total Mortality -- Single Pollutant Models

4A-3
Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM2.5 Coeff. Low

Bou

Observed Concentrations min. max.

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Cardiovascular 390-429 all log-linear, GAM (stringent), 30 df none 4 86 0 day 1-day avg 0.00099 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Cardiovascular 390-429 all log-linear, GAM (stringent), 100 df none 4 86 0 day 1-day avg 0.00097 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Cardiovascular 390-429 all log-linear, GLM, 100 df none 4 86 0 day 1-day avg 0.00097 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Cardiovascular 390-429 all log-linear, GAM (stringent), 30 df none 4 86 1 day 1-day avg 0.00103 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Cardiovascular 390-429 all log-linear, GAM (stringent), 100 df none 4 86 1 day 1-day avg 0.00080 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Cardiovascular 390-429 all log-linear, GLM, 100 df none 4 86 1 day 1-day avg 0.00069 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GAM (stringent), 30 df CO 4 86 1 day 1-day avg -0.00053 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GAM (stringent), 100 df CO 4 86 1 day 1-day avg -0.00033 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Non-accidental <800 all log-linear, GLM, 100 df CO 4 86 1 day 1-day avg -0.00033 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Cardiovascular 390-429 all log-linear, GAM (stringent), 100 df CO 4 86 0 day 1-day avg 0.00178 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Cardiovascular 390-429 all log-linear, GLM, 100 df CO 4 86 0 day 1-day avg 0.00188 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar 

(2000a)]

Cardiovascular 390-429 all log-linear, GAM (stringent), 100 df CO 4 86 1 day 1-day avg 0.00091 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000a)]

Cardiovascular 390-429 all log-linear, GLM, 100 df CO 4 86 1 day 1-day avg 0.00091 -0.00

Short-Term Exposure Cause-Specific Mortality -- Multi-Pollutant Models

Short-Term Exposure Cause-Specific Mortality -- Single Pollutant Models

Short-Term Exposure Total Mortality -- Multi-Pollutant Models

4A-4
Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM2.5 Coeff. Low

Bou

Observed Concentrations min. max.

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]

Cardiovascular 390-429 65+ log-linear, GAM (stringent), 30 df none 4 86 0 day 1-day avg 0.00158 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]

Cardiovascular 390-429 65+ log-linear, GAM (stringent), 100 df none 4 86 0 day 1-day avg 0.00116 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]

Cardiovascular 390-429 65+ log-linear, GLM, 100 df none 4 86 0 day 1-day avg 0.00126 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]

Cardiovascular 390-429 65+ log-linear, GAM (stringent), 30 df none 4 86 1 day 1-day avg 0.00139 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]

Cardiovascular 390-429 65+ log-linear, GAM (stringent), 100 df none 4 86 1 day 1-day avg 0.00113 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]

Cardiovascular 390-429 65+ log-linear, GLM, 100 df none 4 86 1 day 1-day avg 0.00120 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GAM (stringent), 30 df none 4 86 0 day 1-day avg 0.00167 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GAM (stringent), 100 df none 4 86 0 day 1-day avg 0.00138 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GLM, 100 df none 4 86 0 day 1-day avg 0.00149 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GAM (stringent), 30 df none 4 86 1 day 1-day avg 0.00119 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GAM (stringent), 100 df none 4 86 1 day 1-day avg 0.00075 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GLM, 100 df none 4 86 1 day 1-day avg 0.00077 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GAM (stringent), 30 df none 4 86 2 day 1-day avg 0.00185 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GAM (stringent), 100 df none 4 86 2 day 1-day avg 0.00114 0.000

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GLM, 100 df none 4 86 2 day 1-day avg 0.00103 -0.00

Hospital Admissions -- Single Pollutant Models

4A-5
Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM2.5 Coeff. Low

Bou

Observed Concentrations min. max.

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]

Cardiovascular 390-429 65+ log-linear, GAM (stringent), 100 df CO 4 86 0 day 1-day avg 0.00039 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]

Cardiovascular 390-429 65+ log-linear, GLM, 100 df CO 4 86 0 day 1-day avg 0.00058 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]

Cardiovascular 390-429 65+ log-linear, GAM (stringent), 100 df CO 4 86 1 day 1-day avg 0.00024 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000b)]

Cardiovascular 390-429 65+ log-linear, GLM, 100 df CO 4 86 1 day 1-day avg 0.00027 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GAM (stringent), 100 df NO2 4 86 0 day 1-day avg 0.00042 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GAM (stringent), 100 df NO2 4 86 1 day 1-day avg -0.00004 -0.00

Moolgavkar (2003) [reanalysis of Moolgavkar (2000c)]

COPD+ 490-496 all log-linear, GAM (stringent), 100 df NO2 4 86 2 day 1-day avg 0.00035 -0.00

Hospital Admissions -- Single City, Multi-Pollutant Models

4A-6
Table 4A-4. Study-Specific Information for Short-term Exposure PM2.5 Studies in Philadelphia, P

A

Study* Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM2.5 Coeff. 

Lipfert et al. (2000) -- 7 counties Cardiovascular 390-448 all linear none -0.6 72.6 1 day 1-day avg 0.10440 

Table 4A-5. Study-Specific Information for Short-term Exposure PM2.5 Studies in Phoenix, A

Z

Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM2.5 Coeff. 

Mar (2003) [reanalysis of Mar (2000)] Cardiovascular 390- 448.9 65+ log-linear, GAM (stringent) none 0 42 0 day 1-day avg 0.00371 Mar (2003) [reanalysis of Mar (2000)] Cardiovascular 390- 448.9 65+ log-linear, GAM (stringent) none 0 42 1 day 1-day avg 0.00661 

Table 4A-6. Study-Specific Information for Short-term Exposure PM2.5 Studies in Pittsburgh, PA

Study Health Effect ICD-9 Codes Ages Model Other Pollutants Lag Exposure Metric PM2.5 Coeff. 

Chock et al. (2000) Non-accidental <800 <75 log-linear none 3 86 0 day 1-day avg 0.00101 Chock et al. (2000) Non-accidental <800 75+ log-linear none 3 86 0 day 1-day avg 0.00059 Chock et al. (2000) Non-accidental <800 <75 log-linear

CO, O3, 

SO2, NO2, 

PM10-2.5

3 86 0 day 1-day avg 0.0013 

Chock et al. (2000) Non-accidental <800 75+ log-linear

CO, O3, 

SO2, NO2, 

PM10-2.5

3 86 0 day 1-day avg 0.0004 

Short-term Exposure Cause-Specific Mortality -- Single Pollutant Models

Observed 

Concentrations 

Short-term Exposure Total Mortality -- Single Pollutant Models

Short-term Exposure Total Mortality -- Multi-Pollutant Models

Observed 

Concentrations 

min. max.

*The Lipfert et al. (2000) study does not provide the statistical uncertainties surrounding the PM2.5 non-accidental mortality coefficients and the cardiovascular mortality multi-polluta

Short-Term Exposure Cause-Specific Mortality -- Single Pollutant Models

Observed 

Concentrations 

min. max.

4A-7
Table 4A-7. Study-Specific Information for Short-term Exposure PM2.5 Studies in San Jose, C

A

Study Health Effect ICD-9 Codes Ages Model

Other 

Pollutants 

in Model

Lag Exposure Metric PM2.5 Coeff. 

Fairley (2003) [reanalysis of Fairley (1999)] Non-accidental <800 all

log-linear, GAM (stringent)

none 2 105 0 day 1-day avg 0.00314 

Fairley (2003) [reanalysis of Fairley (1999)] Non-accidental <800 all

log-linear, GAM (stringent)

none 2 105 1 day 1-day avg -0.00153 

Fairley (2003) [reanalysis of Fairley (1999)] Respiratory

11, 35, 472-

519, 710.0, 

710.2, 710.4

all

log-linear, GAM (stringent)

none 2 105 0 day 1-day avg 0.00446 

Fairley (2003) [reanalysis of Fairley (1999)] Cardiovascular 390-459 all

log-linear, GAM (stringent)

none 2 105 0 day 1-day avg 0.00248 

Fairley (2003) [reanalysis of Fairley (1999)] Non-accidental <800 all

log-linear, GAM (stringent)

NO2 2 105 0 day 1-day avg 0.00402 

Fairley (2003) [reanalysis of Fairley (1999)] Non-accidental <800 all

log-linear, GAM (stringent)

CO 2 105 0 day 1-day avg 0.00363 

Fairley (2003) [reanalysis of Fairley (1999)] Non-accidental <800 all

log-linear, GAM (stringent)

O3 - 8hr 2 105 0 day 1-day avg 0.00340 

Table 4A-8. Study-Specific Information for Short-term Exposure PM2.5 Studies in Seattle, WA

Study Health Effect ICD-9 Codes Ages Model

Other 

Pollutants 

in Model

Lag Exposure Metric PM2.5 Coeff. 

Sheppard (2003) [reanalysis of Sheppard et al. (1999)]**

Asthma 493 <65

log-linear, GAM (stringent)

none 2.5 96 1 day 1-day avg 0.0033238 

Observed 

Concentrations 

min. max.

Short-Term Exposure Total Mortalit

y -- Single Pollutant Models

Short-Term Exposure Cause-Specific Mortality -- Single Pollutant Models

Short-Term Exposure Total Mortality -- Multi-Pollutant Models

Observed 

Concentrations 

min. max.

Hospital Admissions - Single Pollutant Models

*Sheppard (2003) [reanalysis of Sheppard et al. (1999)] used daily PM2.5 values obtained from nephelometry measurements rather than from air quality monitors.

4A-8
Table 4A-9. Study-Specific Information for Short-term Exposure PM2.5 Studies in St. Louis, MO

Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM2.5 Coeff. 

Schwartz (2003b) [reanalysis of Schwartz et al. (1996)]

Non-accidental < 800 all

log-linear, GAM (stringent) 

none 0.9 88.9 mean of lag 0 & 1 2-day avg 0.00102 

Schwartz (2003b) [reanalysis of Schwartz et al. (1996)] -- 6 cities

Non-accidental < 800 all

log-linear, GAM (stringent) 

none 0 174 mean of lag 0 & 1 2-day avg 0.00137 

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)]

COPD 490-492, 494-496 all Log-linear, GAM (stringent) none 0.9 88.9 0 day 2-day avg 0.00060 

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)]

Ischemic heart 

disease 410-414 all

Log-linear, GAM (stringent)

none 0.9 88.9 0 day 2-day avg 0.00129 

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)]

Pneumonia 480-487 all

Log-linear, GAM (stringent)

none 0.9 88.9 0 day 2-day avg 0.00109 

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)] -- 6 cities

COPD 490-492, 494-496 all Log-linear, GAM (stringent) none 0 174 0 day 2-day avg 0.00227 

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)] -- 6 cities

Ischemic heart 

disease 410-414 all

Log-linear, GAM (stringent)

none 0 174 0 day 2-day avg 0.00178 

Klemm and Mason (2003) [reanalysis of Klemm et al. (2000)] -- 6 cities

Pneumonia 480-487 all

Log-linear, GAM (stringent)

none 0 174 0 day 2-day avg 0.00402 

Schwartz and Neas (2000) -

- 6 cities

Lower respiratory 

symptoms* n/a 7-14 logistic none N/A N/A 1 day 1-day avg 0.01901 

Schwartz and Neas (2000) -

- 6 cities Cough* n/a 7-14 logistic none N/A N/A 0 day 3-day avg 0.00989 Schwartz and Neas (2000) -- 6 cities

Lower respiratory 

symptoms* n/a 7-14 logistic PM10-2.5 N/A N/A 1 day 1-day avg 0.01698 

Schwartz and Neas (2000) -

- 6 cities Cough* n/a 7-14 logistic PM10-2.5 N/A N/A 0 day 3-day avg 0.00451 

Observed 

Concentrations 

min. max.

*The C-R functions for lower respiratory symptoms and cough were calculated for the summer period April 1 through August 31.

Short-Term Exposure Total Mortality -- Single Pollutant Models

Short-Term Exposure Cause-Specific Mortality -- Single Pollutant Models

Respiratory Symptoms and Illnesses** -- Single Pollutant Models

Respiratory Symptoms and Illnesses** -- Multi-Pollutant Models

4A-9
Table 4A-10. Study-Specific Information for Long-term Exposure PM2.5 Studies 

Krewski et al. (2000) -ACS All cause all 30+ log-linear none 10 38 n/a

annual mean 0.0046

Pope et al. (2002) - ACS extended All cause all 30+ log-linear none 7.5 30 n/a

annual mean 0.0058

Krewski et al. (2000) -ACS Cardiopulmonary 401-440, 460-519 30+ log-linear none 10 38 n/a

annual mean 0.0094

Pope et al. (2002) - ACS extended Cardiopulmonary 401-440, 460-519 30+ log-linear none 7.5 30 n/a

annual mean 0.0086

Pope et al. (2002) - ACS extended Lung cancer 162 30+ log-linear none 7.5 30 n/a

annual mean 0.0131

Krewski et al. (2000) -ACS All cause all 30+ log-linear CO 10 38 n/a

annual mean 0.0067

Krewski et al. (2000) -ACS All cause all 30+ log-linear NO2 10 38 n/a

annual mean 0.0081

Krewski et al. (2000) -ACS All cause all 30+ log-linear O3 10 38 n/a

annual mean 0.0067

Krewski et al. (2000) -ACS All cause all 30+ log-linear SO2 10 38 n/a

annual mean 0.0012

Long-Term Exposure Mortality -- Single Pollutant Models

Long-Term Exposure Mortality -- Multi-Pollutant Models

4A-10
Table 4A-11. Study-Specific Information for PM10-2.5 Studies in Detroit, MI

Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM Coarse Coefficient 

Ito (2003) [reanalysis of Lippmann et al. (2000)]

Pneumonia 480- 486 65+ log-linear, GAM (stringent) none 1 50 1 day 1-day avg 0.0037814 

Ito (2003) [reanalysis of Lippmann et al. (2000)]

COPD+ 490- 496 65+ log-linear, GAM (stringent) none 1 50 3 day 1-day avg 0.0033223 

Ito (2003) [reanalysis of Lippmann et al. (2000)]

Ischemic 

heart disease

410-

414 65+

log-linear, GAM (stringent)

none 1 50 2 day 1-day avg 0.0038954 

Ito (2003) [reanalysis of Lippmann et al. (2000)]

Dysrhythmias 427 65+

log-linear, GAM (stringent)

none 1 50 0 day 1-day avg 0.0000416 

Ito (2003) [reanalysis of Lippmann et al. (2000)]

Congestive 

heart failure 428 65+

log-linear, GAM (stringent)

none 1 50 0 day 1-day avg 0.0017142 

Observed 

Concentrations 

 min. 

Hospital Admissions -- Single Pollutant Models

4A-11
Table 4A-12. Study-Specific Information for PM10-2.5 Studies in Seattle, WA

Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM Coarse Coefficient 

Sheppard (2003) (reanalysis of Sheppard et al. (1999)*

Asthma 493 <65

log-linear, GAM (stringent)

none N/A 88 1 day 1-day avg 0.0021293 

Table 4A-13. Study-Specific Information for Studies in St. Louis, MO

Study Health Effect ICD-9 Codes Ages Model Other Pollutants in Model Lag Exposure Metric PM Coarse Coefficient 

Schwartz and Neas, 2000 -- 6 cities

Lower respiratory symptoms*

N/A 7-14 logistic none 0 121 0 day 3-day avg 0.0163785 

Schwartz and Neas, 2000 -- 6 cities Cough* N/A 7-14 logistic none 0 121 0 day 3-day avg 0.0227902 Schwartz and Neas, 2000 -- 6 cities

Lower respiratory symptoms*

N/A 7-14 logistic PM2.5 0 121 0 day 3-day avg 0.0060988 

Schwartz and Neas, 2000 -- 6 cities Cough* N/A 7-14 logistic PM2.5 0 121 0 day 3-day avg 0.0206893 

*The C-R functions for lower respiratory symptoms and cough were calculated for the summer period April 1 through August 31.

Respiratory Symptoms and Illnesses* -- Single Pollutant Models

Respiratory Symptoms and Illnesses* -- Multi-Pollutant Models

Observed 

Concentrations 

 min. 

*Sheppard (2003) [reanalysis of Sheppard et al. (1999)] used daily PM2.5 values obtained from nephelometry measurements rather than from PM2.5 and PM10 air quality monitors.

Hospital Admissions -- Single Pollutant Models

Observed 

Concentrations 

 min. 

4A-12
Los Angeles, CA, 2003

Annual (μg/m3) Daily (μg/m3) Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=2.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

65, 98th percentile value*** 292 115 58 29

(-37 - 612) (-14 - 240) (-7 - 121) (-4 - 61)

0.0% 0.0% 0.0% 0.0%

40, 98th percentile value 292 115 58 29

(-37 - 612) (-14 - 240) (-7 - 121) (-4 - 61)

0.0% 0.0% 0.0% 0.0%

35, 98th percentile value 269 96 45 22

(-34 - 564) (-12 - 200) (-6 - 94) (-3 - 46)

7.9% 16.5% 22.4% 24.1%

30, 98th percentile value 228 65 26 12

(-28 - 476) (-8 - 135) (-3 - 54) (-2 - 25)

21.9% 43.5% 55.2% 58.6%

25, 98th percentile value 186 39 13 5

(-23 - 389) (-5 - 80) (-2 - 27) (-1 - 11)

36.3% 66.1% 77.6% 82.8%

65, 99th percentile value 292 115 58 29

(-37 - 612) (-14 - 240) (-7 - 121) (-4 - 61)

0.0% 0.0% 0.0% 0.0%

40, 99th percentile value 197 45 16 7

(-25 - 413) (-6 - 94) (-2 - 33) (-1 - 14)

32.5% 60.9% 72.4% 75.9%

35, 99th percentile value 171 30 10 3

(-21 - 358) (-4 - 63) (-1 - 20) (0 - 7)

41.4% 73.9% 82.8% 89.7%

30, 99th percentile value 145 18 5 1

(-18 - 302) (-2 - 37) (-1 - 10) (0 - 3)

50.3% 84.3% 91.4% 96.6%

25, 99th percentile value 118 9 2 0

(-15 - 247) (-1 - 18) (0 - 4) (0 - 1)

59.6% 92.2% 96.6% 100.0%

40, 98th percentile value 269 96 45 22

(-34 - 562) (-12 - 199) (-6 - 93) (-3 - 45)

7.9% 16.5% 22.4% 24.1%

35, 98th percentile value 269 96 45 22

(-34 - 562) (-12 - 199) (-6 - 93) (-3 - 45)

7.9% 16.5% 22.4% 24.1%

30, 98th percentile value 228 65 26 12

(-28 - 476) (-8 - 135) (-3 - 54) (-2 - 25)

21.9% 43.5% 55.2% 58.6%

25, 98th percentile value 186 39 13 5

(-23 - 389) (-5 - 80) (-2 - 27) (-1 - 11)

36.3% 66.1% 77.6% 82.8%

40, 99th percentile value 197 45 16 7

(-25 - 413) (-6 - 94) (-2 - 33) (-1 - 14)

32.5% 60.9% 72.4% 75.9%

35, 99th percentile value 171 30 10 3

(-21 - 358) (-4 - 63) (-1 - 20) (0 - 7)

41.4% 73.9% 82.8% 89.7%

30, 99th percentile value 145 18 5 1

(-18 - 302) (-2 - 37) (-1 - 10) (0 - 3)

50.3% 84.3% 91.4% 96.6%

25, 99th percentile value 118 9 2 0

(-15 - 247) (-1 - 18) (0 - 4) (0 - 1)

59.6% 92.2% 96.6% 100.0%

Table 4B-1. Estimated Annual Mortality Associated with Short-Term Exposure to PM2.5 When Alternative 

Standards Are Just Met, Assuming Various Cutpoint Levels*

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

15

14

4B-1
Annual (μg/m3

) Daily (μg/m3) Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=2.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

40, 98th percentile value 245 77 34 16

(-31 - 513) (-10 - 161) (-4 - 69) (-2 - 33)

16.1% 33.0% 41.4% 44.8%

35, 98th percentile value 245 77 34 16

(-31 - 513) (-10 - 161) (-4 - 69) (-2 - 33)

16.1% 33.0% 41.4% 44.8%

30, 98th percentile value 228 65 26 12

(-28 - 476) (-8 - 135) (-3 - 54) (-2 - 25)

21.9% 43.5% 55.2% 58.6%

25, 98th percentile value 186 39 13 5

(-23 - 389) (-5 - 80) (-2 - 27) (-1 - 11)

36.3% 66.1% 77.6% 82.8%

40, 99th percentile value 197 45 16 7

(-25 - 413) (-6 - 94) (-2 - 33) (-1 - 14)

32.5% 60.9% 72.4% 75.9%

35, 99th percentile value 171 30 10 3

(-21 - 358) (-4 - 63) (-1 - 20) (0 - 7)

41.4% 73.9% 82.8% 89.7%

30, 99th percentile value 145 18 5 1

(-18 - 302) (-2 - 37) (-1 - 10) (0 - 3)

50.3% 84.3% 91.4% 96.6%

25, 99th percentile value 118 9 2 0

(-15 - 247) (-1 - 18) (0 - 4) (0 - 1)

59.6% 92.2% 96.6% 100.0%

40, 98th percentile value 222 61 24 11

(-28 - 464) (-8 - 126) (-3 - 50) (-1 - 23)

24.0% 47.0% 58.6% 62.1%

35, 98th percentile value 222 61 24 11

(-28 - 464) (-8 - 126) (-3 - 50) (-1 - 23)

24.0% 47.0% 58.6% 62.1%

30, 98th percentile value 222 61 24 11

(-28 - 464) (-8 - 126) (-3 - 50) (-1 - 23)

24.0% 47.0% 58.6% 62.1%

25, 98th percentile value 186 39 13 5

(-23 - 389) (-5 - 80) (-2 - 27) (-1 - 11)

36.3% 66.1% 77.6% 82.8%

40, 99th percentile value 197 45 16 7

(-25 - 413) (-6 - 94) (-2 - 33) (-1 - 14)

32.5% 60.9% 72.4% 75.9%

35, 99th percentile value 171 30 10 3

(-21 - 358) (-4 - 63) (-1 - 20) (0 - 7)

41.4% 73.9% 82.8% 89.7%

30, 99th percentile value 145 18 5 1

(-18 - 302) (-2 - 37) (-1 - 10) (0 - 3)

50.3% 84.3% 91.4% 96.6%

25, 99th percentile value 118 9 2 0

(-15 - 247) (-1 - 18) (0 - 4) (0 - 1)

59.6% 92.2% 96.6% 100.0%

*This analysis was performed using Moolgavkar (2003). 

***Current standards.

13

12

**For the cutpoints above policy relevant background, the slope of the concentration-response function has been modified based on a simple hockeystick model (see discussion in 

section 4.3.2.1).

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

4B-2
Philadelphia, PA, 2003

Annual (μg/m3) Daily (μg/m3) Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

65, 98th percentile value*** 367 189 106 57

(175 - 560) (90 - 288) (51 - 162) (27 - 87)

0.0% 0.0% 0.0% 0.0%

40, 98th percentile value 317 143 71 34

(151 - 482) (68 - 218) (34 - 107) (16 - 51)

13.6% 24.3% 33.0% 40.4%

35, 98th percentile value 273 106 45 18

(130 - 416) (50 - 161) (22 - 69) (9 - 28)

25.6% 43.9% 57.5% 68.4%

30, 98th percentile value 230 71 25 7

(110 - 350) (34 - 108) (12 - 38) (3 - 11)

37.3% 62.4% 76.4% 87.7%

25, 98th percentile value 187 41 11 2

(89 - 284) (20 - 63) (5 - 16) (1 - 3)

49.0% 78.3% 89.6% 96.5%

65, 99th percentile value 297 126 58 26

(142 - 451) (60 - 191) (28 - 89) (12 - 40)

19.1% 33.3% 45.3% 54.4%

40, 99th percentile value 176 35 8 1

(84 - 268) (17 - 53) (4 - 12) (1 - 2)

52.0% 81.5% 92.5% 98.2%

35, 99th percentile value 152 22 3 0

(72 - 231) (11 - 34) (2 - 5) (0 - 1)

58.6% 88.4% 97.2% 100.0%

30, 99th percentile value 128 12 1 0

(61 - 195) (6 - 19) (1 - 2) (0 - 0)

65.1% 93.7% 99.1% 100.0%

25, 99th percentile value 104 5 0 0

(49 - 158) (2 - 8) (0 - 0) (0 - 0)

71.7% 97.4% 100.0% 100.0%

40, 98th percentile value 336 160 83 42

(160 - 511) (76 - 243) (40 - 127) (20 - 63)

8.4% 15.3% 21.7% 26.3%

35, 98th percentile value 317 143 71 34

(151 - 482) (68 - 218) (34 - 107) (16 - 51)

13.6% 24.3% 33.0% 40.4%

30, 98th percentile value 273 106 45 18

(130 - 416) (50 - 161) (22 - 69) (9 - 28)

25.6% 43.9% 57.5% 68.4%

25, 98th percentile value 230 71 25 7

(110 - 350) (34 - 108) (12 - 38) (3 - 11)

37.3% 62.4% 76.4% 87.7%

40, 99th percentile value 187 41 11 2

(89 - 284) (20 - 63) (5 - 16) (1 - 3)

49.0% 78.3% 89.6% 96.5%

35, 99th percentile value 176 35 8 1

(84 - 268) (17 - 53) (4 - 12) (1 - 2)

52.0% 81.5% 92.5% 98.2%

30, 99th percentile value 152 22 3 0

(72 - 231) (11 - 34) (2 - 5) (0 - 1)

58.6% 88.4% 97.2% 100.0%

25, 99th percentile value 128 12 1 0

(61 - 195) (6 - 19) (1 - 2) (0 - 0)

65.1% 93.7% 99.1% 100.0%

Table 4B-2. Estimated Annual Mortality Associated with Short-Term Exposure to PM2.5 When Alternative 

Standards Are Just Met, Assuming Various Cutpoint Levels*

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

15

14

4B-3
Annual (μg/m3

) Daily (μg/m3) Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

40, 98th percentile value 104 5 0 0

(49 - 158) (2 - 8) (0 - 0) (0 - 0)

71.7% 97.4% 100.0% 100.0%

35, 98th percentile value 304 132 62 29

(145 - 462) (63 - 200) (30 - 95) (14 - 44)

17.2% 30.2% 41.5% 49.1%

30, 98th percentile value 273 106 45 18

(130 - 416) (50 - 161) (22 - 69) (9 - 28)

25.6% 43.9% 57.5% 68.4%

25, 98th percentile value 230 71 25 7

(110 - 350) (34 - 108) (12 - 38) (3 - 11)

37.3% 62.4% 76.4% 87.7%

40, 99th percentile value 187 41 11 2

(89 - 284) (20 - 63) (5 - 16) (1 - 3)

49.0% 78.3% 89.6% 96.5%

35, 99th percentile value 176 35 8 1

(84 - 268) (17 - 53) (4 - 12) (1 - 2)

52.0% 81.5% 92.5% 98.2%

30, 99th percentile value 152 22 3 0

(72 - 231) (11 - 34) (2 - 5) (0 - 1)

58.6% 88.4% 97.2% 100.0%

25, 99th percentile value 128 12 1 0

(61 - 195) (6 - 19) (1 - 2) (0 - 0)

65.1% 93.7% 99.1% 100.0%

40, 98th percentile value 104 5 0 0

(49 - 158) (2 - 8) (0 - 0) (0 - 0)

71.7% 97.4% 100.0% 100.0%

35, 98th percentile value 272 104 44 18

(130 - 414) (50 - 159) (21 - 68) (9 - 27)

25.9% 45.0% 58.5% 68.4%

30, 98th percentile value 272 104 44 18

(130 - 414) (50 - 159) (21 - 68) (9 - 27)

25.9% 45.0% 58.5% 68.4%

25, 98th percentile value 230 71 25 7

(110 - 350) (34 - 108) (12 - 38) (3 - 11)

37.3% 62.4% 76.4% 87.7%

40, 99th percentile value 187 41 11 2

(89 - 284) (20 - 63) (5 - 16) (1 - 3)

49.0% 78.3% 89.6% 96.5%

35, 99th percentile value 176 35 8 1

(84 - 268) (17 - 53) (4 - 12) (1 - 2)

52.0% 81.5% 92.5% 98.2%

30, 99th percentile value 152 22 3 0

(72 - 231) (11 - 34) (2 - 5) (0 - 1)

58.6% 88.4% 97.2% 100.0%

25, 99th percentile value 128 12 1 0

(61 - 195) (6 - 19) (1 - 2) (0 - 0)

65.1% 93.7% 99.1% 100.0%

*This analysis was performed using Lipfert et al. (2000). 

***Current standards.

13

12

**For the cutpoints above policy relevant background, the slope of the concentration-response function has been modified based on a simple hockeystick model (see discussion in 

section 4.3.2.1).

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

4B-4
Pittsburgh, PA, 2003

Annual (μg/m3) Daily (μg/m3) Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

65, 98th percentile value*** 50 22 10 5

(-108 - 200) (-48 - 87) (-23 - 41) (-11 - 18)

0.0% 0.0% 0.0% 0.0%

40, 98th percentile value 47 19 9 4

(-102 - 189) (-43 - 77) (-19 - 34) (-9 - 15)

6.0% 13.6% 10.0% 20.0%

35, 98th percentile value 41 14 5 2

(-88 - 162) (-31 - 56) (-12 - 21) (-5 - 8)

18.0% 36.4% 50.0% 60.0%

30, 98th percentile value 34 9 3 1

(-74 - 136) (-21 - 37) (-6 - 11) (-2 - 4)

32.0% 59.1% 70.0% 80.0%

25, 98th percentile value 28 5 1 0

(-60 - 110) (-12 - 20) (-3 - 5) (-1 - 2)

44.0% 77.3% 90.0% 100.0%

65, 99th percentile value 50 22 10 5

(-108 - 200) (-48 - 87) (-23 - 41) (-11 - 18)

0.0% 0.0% 0.0% 0.0%

40, 99th percentile value 42 15 6 3

(-92 - 168) (-34 - 61) (-13 - 24) (-6 - 10)

16.0% 31.8% 40.0% 40.0%

35, 99th percentile value 36 11 4 1

(-79 - 145) (-24 - 43) (-8 - 14) (-3 - 5)

28.0% 50.0% 60.0% 80.0%

30, 99th percentile value 31 7 2 1

(-67 - 122) (-15 - 27) (-4 - 7) (-2 - 3)

38.0% 68.2% 80.0% 80.0%

25, 99th percentile value 25 4 1 0

(-54 - 99) (-8 - 14) (-2 - 3) (-1 - 1)

50.0% 81.8% 90.0% 100.0%

40, 98th percentile value 46 18 8 3

(-99 - 182) (-40 - 72) (-17 - 31) (-8 - 13)

8.0% 18.2% 20.0% 40.0%

35, 98th percentile value 41 14 5 2

(-88 - 162) (-31 - 56) (-12 - 21) (-5 - 8)

18.0% 36.4% 50.0% 60.0%

30, 98th percentile value 34 9 3 1

(-74 - 136) (-21 - 37) (-6 - 11) (-2 - 4)

32.0% 59.1% 70.0% 80.0%

25, 98th percentile value 28 5 1 0

(-60 - 110) (-12 - 20) (-3 - 5) (-1 - 2)

44.0% 77.3% 90.0% 100.0%

40, 99th percentile value 42 15 6 3

(-92 - 168) (-34 - 61) (-13 - 24) (-6 - 10)

16.0% 31.8% 40.0% 40.0%

35, 99th percentile value 36 11 4 1

(-79 - 145) (-24 - 43) (-8 - 14) (-3 - 5)

28.0% 50.0% 60.0% 80.0%

30, 99th percentile value 31 7 2 1

(-67 - 122) (-15 - 27) (-4 - 7) (-2 - 3)

38.0% 68.2% 80.0% 80.0%

25, 99th percentile value 25 4 1 0

(-54 - 99) (-8 - 14) (-2 - 3) (-1 - 1)

50.0% 81.8% 90.0% 100.0%

14

Table 4B-3. Estimated Annual Mortality Associated with Short-Term Exposure to PM2.5 When Alternative 

Standards Are Just Met, Assuming Various Cutpoint Levels*

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

15

4B-5
Annual (μg/m3

) Daily (μg/m3) Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

40, 98th percentile value 41 15 6 2

(-90 - 165) (-32 - 58) (-13 - 22) (-5 - 9)

18.0% 31.8% 40.0% 60.0%

35, 98th percentile value 41 14 5 2

(-88 - 162) (-31 - 56) (-12 - 21) (-5 - 8)

18.0% 36.4% 50.0% 60.0%

30, 98th percentile value 34 9 3 1

(-74 - 136) (-21 - 37) (-6 - 11) (-2 - 4)

32.0% 59.1% 70.0% 80.0%

25, 98th percentile value 28 5 1 0

(-60 - 110) (-12 - 20) (-3 - 5) (-1 - 2)

44.0% 77.3% 90.0% 100.0%

40, 99th percentile value 41 15 6 2

(-90 - 165) (-32 - 58) (-13 - 22) (-5 - 9)

18.0% 31.8% 40.0% 60.0%

35, 99th percentile value 36 11 4 1

(-79 - 145) (-24 - 43) (-8 - 14) (-3 - 5)

28.0% 50.0% 60.0% 80.0%

30, 99th percentile value 31 7 2 1

(-67 - 122) (-15 - 27) (-4 - 7) (-2 - 3)

38.0% 68.2% 80.0% 80.0%

25, 99th percentile value 25 4 1 0

(-54 - 99) (-8 - 14) (-2 - 3) (-1 - 1)

50.0% 81.8% 90.0% 100.0%

40, 98th percentile value 37 11 4 1

(-80 - 147) (-25 - 44) (-8 - 15) (-3 - 6)

26.0% 50.0% 60.0% 80.0%

35, 98th percentile value 37 11 4 1

(-80 - 147) (-25 - 44) (-8 - 15) (-3 - 6)

26.0% 50.0% 60.0% 80.0%

30, 98th percentile value 34 9 3 1

(-74 - 136) (-21 - 37) (-6 - 11) (-2 - 4)

32.0% 59.1% 70.0% 80.0%

25, 98th percentile value 28 5 1 0

(-60 - 110) (-12 - 20) (-3 - 5) (-1 - 2)

44.0% 77.3% 90.0% 100.0%

40, 99th percentile value 37 11 4 1

(-80 - 147) (-25 - 44) (-8 - 15) (-3 - 6)

26.0% 50.0% 60.0% 80.0%

35, 99th percentile value 36 11 4 1

(-79 - 145) (-24 - 43) (-8 - 14) (-3 - 5)

28.0% 50.0% 60.0% 80.0%

30, 99th percentile value 31 7 2 1

(-67 - 122) (-15 - 27) (-4 - 7) (-2 - 3)

38.0% 68.2% 80.0% 80.0%

25, 99th percentile value 25 4 1 0

(-54 - 99) (-8 - 14) (-2 - 3) (-1 - 1)

50.0% 81.8% 90.0% 100.0%

*This analysis was performed using Chock et al. (2000), age 75+ model. 

***Current standards.

12

**For the cutpoints above policy relevant background, the slope of the concentration-response function has been modified based on a simple hockeystick model (see discussion in 

section 4.3.2.1).

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

13

4B-6
St. Louis, MO, 2003

Annual (μg/m3) Daily (μg/m3) Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

65, 98th percentile value*** 191 75 29 9

(70 - 311) (28 - 122) (11 - 46) (3 - 14)

0.0% 0.0% 0.0% 0.0%

40, 98th percentile value 191 75 29 9

(70 - 311) (28 - 122) (11 - 46) (3 - 14)

0.0% 0.0% 0.0% 0.0%

35, 98th percentile value 190 75 28 8

(70 - 310) (27 - 121) (10 - 46) (3 - 14)

0.5% 0.0% 3.4% 11.1%

30, 98th percentile value 160 49 14 3

(59 - 260) (18 - 80) (5 - 23) (1 - 4)

16.2% 34.7% 51.7% 66.7%

25, 98th percentile value 130 28 5 1

(48 - 211) (10 - 45) (2 - 8) (0 - 1)

31.9% 62.7% 82.8% 88.9%

65, 99th percentile value 191 75 29 9

(70 - 311) (28 - 122) (11 - 46) (3 - 14)

0.0% 0.0% 0.0% 0.0%

40, 99th percentile value 191 75 29 9

(70 - 311) (28 - 122) (11 - 46) (3 - 14)

0.0% 0.0% 0.0% 0.0%

35, 99th percentile value 172 59 19 5

(63 - 280) (22 - 96) (7 - 31) (2 - 7)

9.9% 21.3% 34.5% 44.4%

30, 99th percentile value 145 38 9 2

(53 - 235) (14 - 62) (3 - 14) (1 - 3)

24.1% 49.3% 69.0% 77.8%

25, 99th percentile value 118 20 3 0

(43 - 191) (7 - 33) (1 - 4) (0 - 1)

38.2% 73.3% 89.7% 100.0%

40, 98th percentile value 175 61 20 5

(64 - 284) (22 - 99) (7 - 33) (2 - 8)

8.4% 18.7% 31.0% 44.4%

35, 98th percentile value 175 61 20 5

(64 - 284) (22 - 99) (7 - 33) (2 - 8)

8.4% 18.7% 31.0% 44.4%

30, 98th percentile value 160 49 14 3

(59 - 260) (18 - 80) (5 - 23) (1 - 4)

16.2% 34.7% 51.7% 66.7%

25, 98th percentile value 130 28 5 1

(48 - 211) (10 - 45) (2 - 8) (0 - 1)

31.9% 62.7% 82.8% 88.9%

40, 99th percentile value 175 61 20 5

(64 - 284) (22 - 99) (7 - 33) (2 - 8)

8.4% 18.7% 31.0% 44.4%

35, 99th percentile value 172 59 19 5

(63 - 280) (22 - 96) (7 - 31) (2 - 7)

9.9% 21.3% 34.5% 44.4%

30, 99th percentile value 145 38 9 2

(53 - 235) (14 - 62) (3 - 14) (1 - 3)

24.1% 49.3% 69.0% 77.8%

25, 99th percentile value 118 20 3 0

(43 - 191) (7 - 33) (1 - 4) (0 - 1)

38.2% 73.3% 89.7% 100.0%

Table 4B-4. Estimated Annual Mortality Associated with Short-Term Exposure to PM2.5 When Alternative 

Standards Are Just Met, Assuming Various Cutpoint Levels*

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

15

14

4B-7
Annual (μg/m3

) Daily (μg/m3) Policy Relevant 

Background Cutpoint** Cutpoint** Cutpoint**

=3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

40, 98th percentile value 158 47 13 3

(58 - 256) (17 - 77) (5 - 21) (1 - 4)

17.3% 37.3% 55.2% 66.7%

35, 98th percentile value 158 47 13 3

(58 - 256) (17 - 77) (5 - 21) (1 - 4)

17.3% 37.3% 55.2% 66.7%

30, 98th percentile value 158 47 13 3

(58 - 256) (17 - 77) (5 - 21) (1 - 4)

17.3% 37.3% 55.2% 66.7%

25, 98th percentile value 130 28 5 1

(48 - 211) (10 - 45) (2 - 8) (0 - 1)

31.9% 62.7% 82.8% 88.9%

40, 99th percentile value 158 47 13 3

(58 - 256) (17 - 77) (5 - 21) (1 - 4)

17.3% 37.3% 55.2% 66.7%

35, 99th percentile value 158 47 13 3

(58 - 256) (17 - 77) (5 - 21) (1 - 4)

17.3% 37.3% 55.2% 66.7%

30, 99th percentile value 145 38 9 2

(53 - 235) (14 - 62) (3 - 14) (1 - 3)

24.1% 49.3% 69.0% 77.8%

25, 99th percentile value 118 20 3 0

(43 - 191) (7 - 33) (1 - 4) (0 - 1)

38.2% 73.3% 89.7% 100.0%

40, 98th percentile value 141 35 8 1

(52 - 229) (13 - 57) (3 - 12) (1 - 2)

26.2% 53.3% 72.4% 88.9%

35, 98th percentile value 141 35 8 1

(52 - 229) (13 - 57) (3 - 12) (1 - 2)

26.2% 53.3% 72.4% 88.9%

30, 98th percentile value 141 35 8 1

(52 - 229) (13 - 57) (3 - 12) (1 - 2)

26.2% 53.3% 72.4% 88.9%

25, 98th percentile value 130 28 5 1

(48 - 211) (10 - 45) (2 - 8) (0 - 1)

31.9% 62.7% 82.8% 88.9%

40, 99th percentile value 141 35 8 1

(52 - 229) (13 - 57) (3 - 12) (1 - 2)

26.2% 53.3% 72.4% 88.9%

35, 99th percentile value 141 35 8 1

(52 - 229) (13 - 57) (3 - 12) (1 - 2)

26.2% 53.3% 72.4% 88.9%

30, 99th percentile value 141 35 8 1

(52 - 229) (13 - 57) (3 - 12) (1 - 2)

26.2% 53.3% 72.4% 88.9%

25, 99th percentile value 118 20 3 0

(43 - 191) (7 - 33) (1 - 4) (0 - 1)

38.2% 73.3% 89.7% 100.0%

*This analysis was performed using Schwartz (2003b). 

***Current standards.

13

12

**For the cutpoints above policy relevant background, the slope of the concentration-response function has been modified based on a simple hockeystick model (see discussion in section 4.3.2.1).

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

4B-8
Los Angeles, CA, 2003

Annual (μg/m3) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3

65, 98th percentile value*** 1507 823 138

(531 - 2587) (290 - 1415) (48 - 237)

0.0% 0.0% 0.0%

40, 98th percentile value 1507 823 138

(531 - 2587) (290 - 1415) (48 - 237)

0.0% 0.0% 0.0%

35, 98th percentile value 1265 553 0

(446 - 2168) (195 - 949) (0 - 0)

16.1% 32.8% 100.0%

30, 98th percentile value 829 65 0

(293 - 1416) (23 - 111) (0 - 0)

45.0% 92.1% 100.0%

25, 98th percentile value 396 0 0

(140 - 675) (0 - 0) (0 - 0)

73.7% 100.0% 100.0%

65, 99th percentile value 1507 823 138

(531 - 2587) (290 - 1415) (48 - 237)

0.0% 0.0% 0.0%

40, 99th percentile value 514 0 0

(182 - 876) (0 - 0) (0 - 0)

65.9% 100.0% 100.0%

35, 99th percentile value 240 0 0

(85 - 408) (0 - 0) (0 - 0)

84.1% 100.0% 100.0%

30, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

25, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

40, 98th percentile value 1259 546 0

(444 - 2158) (192 - 937) (0 - 0)

16.5% 33.7% 100.0%

35, 98th percentile value 1259 546 0

(444 - 2158) (192 - 937) (0 - 0)

16.5% 33.7% 100.0%

30, 98th percentile value 829 65 0

(293 - 1416) (23 - 111) (0 - 0)

45.0% 92.1% 100.0%

25, 98th percentile value 396 0 0

(140 - 675) (0 - 0) (0 - 0)

73.7% 100.0% 100.0%

40, 99th percentile value 514 0 0

(182 - 876) (0 - 0) (0 - 0)

65.9% 100.0% 100.0%

35, 99th percentile value 240 0 0

(85 - 408) (0 - 0) (0 - 0)

84.1% 100.0% 100.0%

30, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

25, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

15

14

Table 4B-5. Estimated Annual Mortality Associated with Long-Term Exposure to PM2.5 When 

Alternative Standards Are Just Met, Assuming Various Cutpoint Levels*

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

4B-9
Annual (μg/m3

) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

40, 98th percentile value 1013 270 0

(358 - 1732) (95 - 463) (0 - 0)

32.8% 67.2% 100.0%

35, 98th percentile value 1013 270 0

(358 - 1732) (95 - 463) (0 - 0)

32.8% 67.2% 100.0%

30, 98th percentile value 829 65 0

(293 - 1416) (23 - 111) (0 - 0)

45.0% 92.1% 100.0%

25, 98th percentile value 396 0 0

(140 - 675) (0 - 0) (0 - 0)

73.7% 100.0% 100.0%

40, 99th percentile value 514 0 0

(182 - 876) (0 - 0) (0 - 0)

65.9% 100.0% 100.0%

35, 99th percentile value 240 0 0

(85 - 408) (0 - 0) (0 - 0)

84.1% 100.0% 100.0%

30, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

25, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

40, 98th percentile value 767 0 0

(271 - 1310) (0 - 0) (0 - 0)

49.1% 100.0% 100.0%

35, 98th percentile value 767 0 0

(271 - 1310) (0 - 0) (0 - 0)

49.1% 100.0% 100.0%

30, 98th percentile value 767 0 0

(271 - 1310) (0 - 0) (0 - 0)

49.1% 100.0% 100.0%

25, 98th percentile value 396 0 0

(140 - 675) (0 - 0) (0 - 0)

73.7% 100.0% 100.0%

40, 99th percentile value 514 0 0

(182 - 876) (0 - 0) (0 - 0)

65.9% 100.0% 100.0%

35, 99th percentile value 240 0 0

(85 - 408) (0 - 0) (0 - 0)

84.1% 100.0% 100.0%

30, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

25, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

*This analysis was performed using Pope et al. (2002) -- ACS extended. 

***Current standards.

12

13

**For the cutpoints above policy relevant background, the slope of the C-R function has been modified based on a simple hockeystick model (see discussion in 

section 4.3.2.1).

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

4B-10
Philadelphia, PA, 2003

Annual (μg/m3) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3

65, 98th percentile value*** 536 338 137

(185 - 943) (116 - 597) (47 - 244)

0.0% 0.0% 0.0%

40, 98th percentile value 408 194 0

(141 - 716) (67 - 341) (0 - 0)

23.9% 42.6% 100.0%

35, 98th percentile value 299 72 0

(104 - 524) (25 - 126) (0 - 0)

44.2% 78.7% 100.0%

30, 98th percentile value 191 0 0

(67 - 334) (0 - 0) (0 - 0)

64.4% 100.0% 100.0%

25, 98th percentile value 84 0 0

(29 - 146) (0 - 0) (0 - 0)

84.3% 100.0% 100.0%

65, 99th percentile value 357 137 0

(124 - 626) (47 - 241) (0 - 0)

33.4% 59.5% 100.0%

40, 99th percentile value 58 0 0

(20 - 101) (0 - 0) (0 - 0)

89.2% 100.0% 100.0%

35, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

30, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

25, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

40, 98th percentile value 456 247 37

(157 - 799) (85 - 435) (13 - 65)

14.9% 26.9% 73.0%

35, 98th percentile value 408 194 0

(141 - 716) (67 - 341) (0 - 0)

23.9% 42.6% 100.0%

30, 98th percentile value 299 72 0

(104 - 524) (25 - 126) (0 - 0)

44.2% 78.7% 100.0%

25, 98th percentile value 191 0 0

(67 - 334) (0 - 0) (0 - 0)

64.4% 100.0% 100.0%

40, 99th percentile value 84 0 0

(29 - 146) (0 - 0) (0 - 0)

84.3% 100.0% 100.0%

35, 99th percentile value 58 0 0

(20 - 101) (0 - 0) (0 - 0)

89.2% 100.0% 100.0%

30, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

25, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

Table 4B-6. Estimated Annual Mortality Associated with Long-Term Exposure to PM2.5 When 

Alternative Standards Are Just Met, Assuming Various Cutpoint Levels*

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

14

15

4B-11
Annual (μg/m3

) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

40, 98th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

35, 98th percentile value 375 157 0

(130 - 657) (54 - 276) (0 - 0)

30.0% 53.6% 100.0%

30, 98th percentile value 299 72 0

(104 - 524) (25 - 126) (0 - 0)

44.2% 78.7% 100.0%

25, 98th percentile value 191 0 0

(67 - 334) (0 - 0) (0 - 0)

64.4% 100.0% 100.0%

40, 99th percentile value 84 0 0

(29 - 146) (0 - 0) (0 - 0)

84.3% 100.0% 100.0%

35, 99th percentile value 58 0 0

(20 - 101) (0 - 0) (0 - 0)

89.2% 100.0% 100.0%

30, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

25, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

40, 98th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

35, 98th percentile value 295 67 0

(102 - 516) (23 - 118) (0 - 0)

45.0% 80.2% 100.0%

30, 98th percentile value 295 67 0

(102 - 516) (23 - 118) (0 - 0)

45.0% 80.2% 100.0%

25, 98th percentile value 191 0 0

(67 - 334) (0 - 0) (0 - 0)

64.4% 100.0% 100.0%

40, 99th percentile value 84 0 0

(29 - 146) (0 - 0) (0 - 0)

84.3% 100.0% 100.0%

35, 99th percentile value 58 0 0

(20 - 101) (0 - 0) (0 - 0)

89.2% 100.0% 100.0%

30, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

25, 99th percentile value 0 0 0

(0 - 0) (0 - 0) (0 - 0)

100.0% 100.0% 100.0%

*This analysis was performed using Pope et al. (2002) -- ACS extended. 

***Current standards.

**For the cutpoints above policy relevant background, the slope of the concentration-response function has been modified based on a simple hockeystick model 

(see discussion in section 4.3.2.1).

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

12

13

4B-12
Pittsburgh, PA, 2003

Annual (μg/m3) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3

65, 98th percentile value*** 403 215 25

(141 - 699) (75 - 373) (9 - 43)

0.0% 0.0% 0.0%

40, 98th percentile value 361 168 0

(126 - 626) (58 - 291) (0 - 0)

10.4% 21.9% 100.0%

35, 98th percentile value 264 59 0

(93 - 456) (21 - 102) (0 - 0)

34.5% 72.6% 100.0%

30, 98th percentile value 168 0 0

(59 - 289) (0 - 0) (0 - 0)

58.3% 100.0% 100.0%

25, 98th percentile value 72 0 0

(25 - 124) (0 - 0) (0 - 0)

82.1% 100.0% 100.0%

65, 99th percentile value 403 215 25

(141 - 699) (75 - 373) (9 - 43)

0.0% 0.0% 0.0%

40, 99th percentile value 287 84 0

(100 - 495) (29 - 145) (0 - 0)

28.8% 60.9% 100.0%

35, 99th percentile value 200 0 0

(70 - 345) (0 - 0) (0 - 0)

50.4% 100.0% 100.0%

30, 99th percentile value 114 0 0

(40 - 197) (0 - 0) (0 - 0)

71.7% 100.0% 100.0%

25, 99th percentile value 29 0 0

(10 - 50) (0 - 0) (0 - 0)

92.8% 100.0% 100.0%

40, 98th percentile value 338 141 0

(118 - 585) (49 - 245) (0 - 0)

16.1% 34.4% 100.0%

35, 98th percentile value 264 59 0

(93 - 456) (21 - 102) (0 - 0)

34.5% 72.6% 100.0%

30, 98th percentile value 168 0 0

(59 - 289) (0 - 0) (0 - 0)

58.3% 100.0% 100.0%

25, 98th percentile value 72 0 0

(25 - 124) (0 - 0) (0 - 0)

82.1% 100.0% 100.0%

40, 99th percentile value 287 84 0

(100 - 495) (29 - 145) (0 - 0)

28.8% 60.9% 100.0%

35, 99th percentile value 200 0 0

(70 - 345) (0 - 0) (0 - 0)

50.4% 100.0% 100.0%

30, 99th percentile value 114 0 0

(40 - 197) (0 - 0) (0 - 0)

71.7% 100.0% 100.0%

25, 99th percentile value 29 0 0

(10 - 50) (0 - 0) (0 - 0)

92.8% 100.0% 100.0%

Table 4B-7. Estimated Annual Mortality Associated with Long-Term Exposure to PM2.5 When 

Alternative Standards Are Just Met, Assuming Various Cutpoint Levels*

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

14

15

4B-13
Annual (μg/m3

) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

40, 98th percentile value 273 68 0

(96 - 471) (24 - 118) (0 - 0)

32.3% 68.4% 100.0%

35, 98th percentile value 264 59 0

(93 - 456) (21 - 102) (0 - 0)

34.5% 72.6% 100.0%

30, 98th percentile value 168 0 0

(59 - 289) (0 - 0) (0 - 0)

58.3% 100.0% 100.0%

25, 98th percentile value 72 0 0

(25 - 124) (0 - 0) (0 - 0)

82.1% 100.0% 100.0%

40, 99th percentile value 273 68 0

(96 - 471) (24 - 118) (0 - 0)

32.3% 68.4% 100.0%

35, 99th percentile value 200 0 0

(70 - 345) (0 - 0) (0 - 0)

50.4% 100.0% 100.0%

30, 99th percentile value 114 0 0

(40 - 197) (0 - 0) (0 - 0)

71.7% 100.0% 100.0%

25, 99th percentile value 29 0 0

(10 - 50) (0 - 0) (0 - 0)

92.8% 100.0% 100.0%

40, 98th percentile value 208 0 0

(73 - 358) (0 - 0) (0 - 0)

48.4% 100.0% 100.0%

35, 98th percentile value 208 0 0

(73 - 358) (0 - 0) (0 - 0)

48.4% 100.0% 100.0%

30, 98th percentile value 168 0 0

(59 - 289) (0 - 0) (0 - 0)

58.3% 100.0% 100.0%

25, 98th percentile value 72 0 0

(25 - 124) (0 - 0) (0 - 0)

82.1% 100.0% 100.0%

40, 99th percentile value 208 0 0

(73 - 358) (0 - 0) (0 - 0)

48.4% 100.0% 100.0%

35, 99th percentile value 200 0 0

(70 - 345) (0 - 0) (0 - 0)

50.4% 100.0% 100.0%

30, 99th percentile value 114 0 0

(40 - 197) (0 - 0) (0 - 0)

71.7% 100.0% 100.0%

25, 99th percentile value 29 0 0

(10 - 50) (0 - 0) (0 - 0)

92.8% 100.0% 100.0%

*This analysis was performed using Pope et al. (2002) -- ACS extended. 

***Current standards.

**For the cutpoints above policy relevant background, the slope of the concentration-response function has been modified based on a simple hockeystick model 

(see discussion in section 4.3.2.1).

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

12

13

4B-14
St. Louis, MO, 2003

Annual (μg/m3) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3

65, 98th percentile value*** 596 311 23

(206 - 1047) (107 - 548) (8 - 40)

0.0% 0.0% 0.0%

40, 98th percentile value 596 311 23

(206 - 1047) (107 - 548) (8 - 40)

0.0% 0.0% 0.0%

35, 98th percentile value 592 306 17

(204 - 1039) (105 - 539) (6 - 30)

0.7% 1.6% 26.1%

30, 98th percentile value 414 107 0

(144 - 726) (37 - 188) (0 - 0)

30.5% 65.6% 100.0%

25, 98th percentile value 239 0 0

(83 - 417) (0 - 0) (0 - 0)

59.9% 100.0% 100.0%

65, 99th percentile value 596 311 23

(206 - 1047) (107 - 548) (8 - 40)

0.0% 0.0% 0.0%

40, 99th percentile value 596 311 23

(206 - 1047) (107 - 548) (8 - 40)

0.0% 0.0% 0.0%

35, 99th percentile value 486 188 0

(168 - 853) (65 - 330) (0 - 0)

18.5% 39.5% 100.0%

30, 99th percentile value 327 8 0

(113 - 571) (3 - 15) (0 - 0)

45.1% 97.4% 100.0%

25, 99th percentile value 168 0 0

(58 - 293) (0 - 0) (0 - 0)

71.8% 100.0% 100.0%

40, 98th percentile value 498 201 0

(172 - 874) (69 - 354) (0 - 0)

16.4% 35.4% 100.0%

35, 98th percentile value 498 201 0

(172 - 874) (69 - 354) (0 - 0)

16.4% 35.4% 100.0%

30, 98th percentile value 414 107 0

(144 - 726) (37 - 188) (0 - 0)

30.5% 65.6% 100.0%

25, 98th percentile value 239 0 0

(83 - 417) (0 - 0) (0 - 0)

59.9% 100.0% 100.0%

40, 99th percentile value 498 201 0

(172 - 874) (69 - 354) (0 - 0)

16.4% 35.4% 100.0%

35, 99th percentile value 486 188 0

(168 - 853) (65 - 330) (0 - 0)

18.5% 39.5% 100.0%

30, 99th percentile value 327 8 0

(113 - 571) (3 - 15) (0 - 0)

45.1% 97.4% 100.0%

25, 99th percentile value 168 0 0

(58 - 293) (0 - 0) (0 - 0)

71.8% 100.0% 100.0%

Table 4B-8. Estimated Annual Mortality Associated with Long-Term Exposure to PM2.5 When 

Alternative Standards Are Just Met, Assuming Various Cutpoint Levels*

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

14

15

4B-15
Annual (μg/m3

) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3

Alternative Standards

Incidence Associated with PM2.5

(95% Confidence Interval)

Percent Reduction in Incidence from Current Standards

40, 98th percentile value 401 92 0

(139 - 702) (32 - 162) (0 - 0)

32.7% 70.4% 100.0%

35, 98th percentile value 401 92 0

(139 - 702) (32 - 162) (0 - 0)

32.7% 70.4% 100.0%

30, 98th percentile value 401 92 0

(139 - 702) (32 - 162) (0 - 0)

32.7% 70.4% 100.0%

25, 98th percentile value 239 0 0

(83 - 417) (0 - 0) (0 - 0)

59.9% 100.0% 100.0%

40, 99th percentile value 401 92 0

(139 - 702) (32 - 162) (0 - 0)

32.7% 70.4% 100.0%

35, 99th percentile value 401 92 0

(139 - 702) (32 - 162) (0 - 0)

32.7% 70.4% 100.0%

30, 99th percentile value 327 8 0

(113 - 571) (3 - 15) (0 - 0)

45.1% 97.4% 100.0%

25, 99th percentile value 168 0 0

(58 - 293) (0 - 0) (0 - 0)

71.8% 100.0% 100.0%

40, 98th percentile value 304 0 0

(106 - 532) (0 - 0) (0 - 0)

49.0% 100.0% 100.0%

35, 98th percentile value 304 0 0

(106 - 532) (0 - 0) (0 - 0)

49.0% 100.0% 100.0%

30, 98th percentile value 304 0 0

(106 - 532) (0 - 0) (0 - 0)

49.0% 100.0% 100.0%

25, 98th percentile value 239 0 0

(83 - 417) (0 - 0) (0 - 0)

59.9% 100.0% 100.0%

40, 99th percentile value 304 0 0

(106 - 532) (0 - 0) (0 - 0)

49.0% 100.0% 100.0%

35, 99th percentile value 304 0 0

(106 - 532) (0 - 0) (0 - 0)

49.0% 100.0% 100.0%

30, 99th percentile value 304 0 0

(106 - 532) (0 - 0) (0 - 0)

49.0% 100.0% 100.0%

25, 99th percentile value 168 0 0

(58 - 293) (0 - 0) (0 - 0)

71.8% 100.0% 100.0%

*This analysis was performed using Pope et al. (2002) -- ACS extended. 

***Current standards.

**For the cutpoints above policy relevant background, the slope of the concentration-response function has been modified based on a simple hockeystick model 

(see discussion in section 4.3.2.1).

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

12

13

4B-16
to Meet Annual Standards Using Design Values Based on Maximum vs. Average of Monitor-Specific Averages*

Detroit, MI, 2003Annual (μg/m3) Daily (μg/m3

) Policy Relevant Background Cutpoint** Cutpoint** Cutpoint** Policy Relevant Background Cutpoint** Cutpoint** 

=3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3 =3.5 μg/m3 =10 μg/m3 =15 μg/m3 

65, 98th percentile value*** 122 54 26 12 150 80 46 

(-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35) (-151 - 442) (-81 - 236) (-47 - 137) 

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

40, 98th percentile value 122 54 26 12 150 80 46 

(-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35) (-151 - 442) (-81 - 236) (-47 - 137) 

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

35, 98th percentile value 122 54 26 12 132 63 33 

(-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35) (-133 - 388) (-64 - 186) (-33 - 97) 

0.0% 0.0% 0.0% 0.0% 12.0% 21.3% 28.3% 

30, 98th percentile value 111 45 20 8 111 45 20 

(-112 - 325) (-45 - 131) (-20 - 58) (-9 - 24) (-112 - 325) (-45 - 131) (-20 - 58) 

9.0% 16.7% 23.1% 33.3% 26.0% 43.8% 56.5% 

25, 98th percentile value 90 28 10 3 90 28 10 

(-91 - 263) (-29 - 82) (-10 - 28) (-4 - 10) (-91 - 263) (-29 - 82) (-10 - 28) 

26.2% 48.1% 61.5% 75.0% 40.0% 65.0% 78.3% 

65, 99th percentile value 122 54 26 12 150 80 46 

(-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35) (-151 - 442) (-81 - 236) (-47 - 137) 

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

40, 99th percentile value 122 54 26 12 139 70 38 

(-123 - 358) (-55 - 159) (-27 - 77) (-12 - 35) (-140 - 409) (-70 - 206) (-39 - 112) 

0.0% 0.0% 0.0% 0.0% 7.3% 12.5% 17.4% 

35, 99th percentile value 120 53 25 11 120 53 25 

(-121 - 352) (-53 - 154) (-26 - 74) (-12 - 33) (-121 - 352) (-53 - 154) (-26 - 74) 

1.6% 1.9% 3.8% 8.3% 20.0% 33.8% 45.7% 

30, 99th percentile value 101 37 15 6 101 37 15 

(-102 - 296) (-37 - 107) (-15 - 42) (-6 - 16) (-102 - 296) (-37 - 107) (-15 - 42) 

17.2% 31.5% 42.3% 50.0% 32.7% 53.8% 67.4% 

25, 99th percentile value 82 22 7 2 82 22 7 

(-83 - 239) (-23 - 65) (-7 - 19) (-2 - 6) (-83 - 239) (-23 - 65) (-7 - 19) 

32.8

% 59.3

% 73.1

% 83.3

% 45.3

% 72.5

% 84.8

% 

40, 98th percentile value 111 45 20 8 137 68 37 

(-112 - 326) (-46 - 132) (-20 - 58) (-9 - 24) (-138 - 403) (-68 - 200) (-37 - 108) 

9.0% 16.7% 23.1% 33.3% 8.7% 15.0% 19.6% 

35, 98th percentile value 111 45 20 8 132 63 33 

(-112 - 326) (-46 - 132) (-20 - 58) (-9 - 24) (-133 - 388) (-64 - 186) (-33 - 97) 

9.0% 16.7% 23.1% 33.3% 12.0% 21.3% 28.3% 

30, 98th percentile value 111 45 20 8 111 45 20 

(-112 - 325) (-45 - 131) (-20 - 58) (-9 - 24) (-112 - 325) (-45 - 131) (-20 - 58) 

9.0% 16.7% 23.1% 33.3% 26.0% 43.8% 56.5% 

25, 98th percentile value 90 28 10 3 90 28 10 

(-91 - 263) (-29 - 82) (-10 - 28) (-4 - 10) (-91 - 263) (-29 - 82) (-10 - 28) 

26.2% 48.1% 61.5% 75.0% 40.0% 65.0% 78.3% 

40, 99th percentile value 111 45 20 8 137 68 37 

(-112 - 326) (-46 - 132) (-20 - 58) (-9 - 24) (-138 - 403) (-68 - 200) (-37 - 108) 

9.0% 16.7% 23.1% 33.3% 8.7% 15.0% 19.6% 

35, 99th percentile value 111 45 20 8 120 53 25 

(-112 - 326) (-46 - 132) (-20 - 58) (-9 - 24) (-121 - 352) (-53 - 154) (-26 - 74) 

9.0% 16.7% 23.1% 33.3% 20.0% 33.8% 45.7% 

30, 99th percentile value 101 37 15 6 101 37 15 

(-102 - 296) (-37 - 107) (-15 - 42) (-6 - 16) (-102 - 296) (-37 - 107) (-15 - 42) 

17.2% 31.5% 42.3% 50.0% 32.7% 53.8% 67.4% 

25, 99th percentile value 82 22 7 2 82 22 7 

(-83 - 239) (-23 - 65) (-7 - 19) (-2 - 6) (-83 - 239) (-23 - 65) (-7 - 19) 

32.8

% 59.3

% 73.1

% 83.3

% 45.3

% 72.5

% 84.8

% 

Table 4B-9. Sensitivity Analysis: Estimated Annual Mortality Associated with Short-Term Exposure to PM2.5 When Alternative Standards Are Just Met, Assuming Various Cutpoint Le

14

Alternative Standards

Incidence Associated with PM2.5 Using an Annual Design Value Based on the Maximum 

of Monitor-Specific Averages**

Incidence Associated with PM2.5 Using an Annual Design Val

Monitor-Specific Averages**

(95% Confidence Interval) (95% Confidence Interval)

Percent Reduction in Incidence from Current Standards Percent Reduction in Incidence from Current 

15

4B-17
Annual (μg/m3) Daily (μg/m3) Policy Relevant Background Cutpoint** Cutpoint** Cutpoint** Policy Relevant Background Cutpoint** Cutpoint** 

=3.5 μg/m3 =10 μg/m3 =15 μg/m3 =20 μg/m3 =3.5 μg/m3 =10 μg/m3 =15 μg/m3 

Alternative Standards

Incidence Associated with PM2.5 Using an Annual Design Value Based on the Maximum 

of Monitor-Specific Averages**

Incidence Associated with PM2.5 Using an Annual Design Val

Monitor-Specific Averages**

(95% Confidence Interval) (95% Confidence Interval)

Percent Reduction in Incidence from Current Standards Percent Reduction in Incidence from Current 

40, 98th percentile value 101 36 14 6 124 56 28 

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16) (-125 - 364) (-57 - 165) (-28 - 81) 

17.2% 33.3% 46.2% 50.0% 17.3% 30.0% 39.1% 

35, 98th percentile value 101 36 14 6 124 56 28 

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16) (-125 - 364) (-57 - 165) (-28 - 81) 

17.2% 33.3% 46.2% 50.0% 17.3% 30.0% 39.1% 

30, 98th percentile value 101 36 14 6 111 45 20 

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16) (-112 - 325) (-45 - 131) (-20 - 58) 

17.2% 33.3% 46.2% 50.0% 26.0% 43.8% 56.5% 

25, 98th percentile value 90 28 10 3 90 28 10 

(-91 - 263) (-29 - 82) (-10 - 28) (-4 - 10) (-91 - 263) (-29 - 82) (-10 - 28) 

26.2% 48.1% 61.5% 75.0% 40.0% 65.0% 78.3% 

40, 99th percentile value 101 36 14 6 124 56 28 

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16) (-125 - 364) (-57 - 165) (-28 - 81) 

17.2% 33.3% 46.2% 50.0% 17.3% 30.0% 39.1% 

35, 99th percentile value 101 36 14 6 120 53 25 

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16) (-121 - 352) (-53 - 154) (-26 - 74) 

17.2% 33.3% 46.2% 50.0% 20.0% 33.8% 45.7% 

30, 99th percentile value 101 36 14 6 101 37 15 

(-101 - 295) (-37 - 106) (-15 - 42) (-6 - 16) (-102 - 296) (-37 - 107) (-15 - 42) 

17.2% 33.3% 46.2% 50.0% 32.7% 53.8% 67.4% 

25, 99th percentile value 82 22 7 2 82 22 7 

(-83 - 239) (-23 - 65) (-7 - 19) (-2 - 6) (-83 - 239) (-23 - 65) (-7 - 19) 

32.8% 59.3% 73.1% 83.3% 45.3% 72.5% 84.8% 

40, 98th percentile value 90 28 10 3 111 45 20 

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10) (-112 - 325) (-45 - 131) (-20 - 58) 

26.2% 48.1% 61.5% 75.0% 26.0% 43.8% 56.5% 

35, 98th percentile value 90 28 10 3 111 45 20 

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10) (-112 - 325) (-45 - 131) (-20 - 58) 

26.2% 48.1% 61.5% 75.0% 26.0% 43.8% 56.5% 

30, 98th percentile value 90 28 10 3 111 45 20 

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10) (-112 - 325) (-45 - 131) (-20 - 58) 

26.2% 48.1% 61.5% 75.0% 26.0% 43.8% 56.5% 

25, 98th percentile value 90 28 10 3 90 28 10 

(-91 - 263) (-29 - 82) (-10 - 28) (-4 - 10) (-91 - 263) (-29 - 82) (-10 - 28) 

26.2% 48.1% 61.5% 75.0% 40.0% 65.0% 78.3% 

40, 99th percentile value 90 28 10 3 111 45 20 

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10) (-112 - 325) (-45 - 131) (-20 - 58) 

26.2% 48.1% 61.5% 75.0% 26.0% 43.8% 56.5% 

35, 99th percentile value 90 28 10 3 111 45 20 

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10) (-112 - 325) (-45 - 131) (-20 - 58) 

26.2% 48.1% 61.5% 75.0% 26.0% 43.8% 56.5% 

30, 99th percentile value 90 28 10 3 101 37 15 

(-91 - 264) (-29 - 82) (-10 - 28) (-4 - 10) (-102 - 296) (-37 - 107) (-15 - 42) 

26.2% 48.1% 61.5% 75.0% 32.7% 53.8% 67.4% 

25, 99th percentile value 82 22 7 2 82 22 7 

(-83 - 239) (-23 - 65) (-7 - 19) (-2 - 6) (-83 - 239) (-23 - 65) (-7 - 19) 

32.8% 59.3% 73.1% 83.3% 45.3% 72.5% 84.8% 

*This analysis was performed using Ito (2003). **For the cutpoints above policy relevant background, the slope of the concentration-response function has been modified based on a simple hockeystick model (see discussion in section 4.3.2.1).***Current standards.

13

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

12

4B-18
to Meet Annual Standards Using Design Values Based on Maximum vs. Average of Monitor-Specific Averages*

Detroit, MI, 2003Annual (μg/m3) Daily (μg/m3

) Cutpoint** Cutpoint** Cutpoint** Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3 =7.5 μg/m3 =10 μg/m3 =12 μg/m3

65, 98th percentile value*** 522 282 41 747 535 322

(181 - 910) (98 - 494) (14 - 72) (259 - 1309) (185 - 941) (111 - 568)

0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

40, 98th percentile value 522 282 41 747 535 322

(181 - 910) (98 - 494) (14 - 72) (259 - 1309) (185 - 941) (111 - 568)

0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

35, 98th percentile value 522 282 41 602 372 140

(181 - 910) (98 - 494) (14 - 72) (209 - 1051) (129 - 652) (48 - 247)

0.0% 0.0% 0.0% 19.4% 30.5% 56.5%

30, 98th percentile value 435 185 0 435 185 0

(151 - 757) (64 - 323) (0 - 0) (151 - 757) (64 - 323) (0 - 0)

16.7% 34.4% 100.0% 41.8% 65.4% 100.0%

25, 98th percentile value 270 0 0 270 0 0

(94 - 468) (0 - 0) (0 - 0) (94 - 468) (0 - 0) (0 - 0)

48.3% 100.0% 100.0% 63.9% 100.0% 100.0%

65, 99th percentile value 522 282 41 747 535 322

(181 - 910) (98 - 494) (14 - 72) (259 - 1309) (185 - 941) (111 - 568)

0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

40, 99th percentile value 522 282 41 659 437 212

(181 - 910) (98 - 494) (14 - 72) (229 - 1153) (151 - 766) (73 - 374)

0.0% 0.0% 0.0% 11.8% 18.3% 34.2%

35, 99th percentile value 507 266 23 507 266 23

(176 - 884) (92 - 465) (8 - 40) (176 - 884) (92 - 465) (8 - 40)

2.9% 5.7% 43.9% 32.1% 50.3% 92.9%

30, 99th percentile value 356 97 0 356 97 0

(124 - 619) (34 - 168) (0 - 0) (124 - 619) (34 - 168) (0 - 0)

31.8% 65.6% 100.0% 52.3% 81.9% 100.0%

25, 99th percentile value 207 0 0 207 0 0

(72 - 358) (0 - 0) (0 - 0) (72 - 358) (0 - 0) (0 - 0)

60.3

% 100.0

% 100.0

% 72.3

% 100.0

% 100.0

%

40, 98th percentile value 438 188 0 642 418 191

(152 - 762) (65 - 328) (0 - 0) (223 - 1123) (144 - 733) (66 - 336)

16.1% 33.3% 100.0% 14.1% 21.9% 40.7%

35, 98th percentile value 438 188 0 602 372 140

(152 - 762) (65 - 328) (0 - 0) (209 - 1051) (129 - 652) (48 - 247)

16.1% 33.3% 100.0% 19.4% 30.5% 56.5%

30, 98th percentile value 435 185 0 435 185 0

(151 - 757) (64 - 323) (0 - 0) (151 - 757) (64 - 323) (0 - 0)

16.7% 34.4% 100.0% 41.8% 65.4% 100.0%

25, 98th percentile value 270 0 0 270 0 0

(94 - 468) (0 - 0) (0 - 0) (94 - 468) (0 - 0) (0 - 0)

48.3% 100.0% 100.0% 63.9% 100.0% 100.0%

40, 99th percentile value 438 188 0 642 418 191

(152 - 762) (65 - 328) (0 - 0) (223 - 1123) (144 - 733) (66 - 336)

16.1% 33.3% 100.0% 14.1% 21.9% 40.7%

35, 99th percentile value 438 188 0 507 266 23

(152 - 762) (65 - 328) (0 - 0) (176 - 884) (92 - 465) (8 - 40)

16.1% 33.3% 100.0% 32.1% 50.3% 92.9%

30, 99th percentile value 356 97 0 356 97 0

(124 - 619) (34 - 168) (0 - 0) (124 - 619) (34 - 168) (0 - 0)

31.8% 65.6% 100.0% 52.3% 81.9% 100.0%

25, 99th percentile value 207 0 0 207 0 0

(72 - 358) (0 - 0) (0 - 0) (72 - 358) (0 - 0) (0 - 0)

60.3

% 100.0

% 100.0

% 72.3

% 100.0

% 100.0

%

Table 4B-10. Sensitivity Analysis: Estimated Annual Mortality Associated with Long-Term Exposure to PM2.5 When Alternative Standards Are Just Met, Assuming Various Cutpoint L

Alternative Standards

Incidence Associated with PM2.5 Using an Annual Design Value Based 

on the Maximum of Monitor-Specific Averages**

Incidence Associated with PM2.5 Using an Annual Design Value Based on the A

Monitor-Specific Averages**

(95% Confidence Interval) (95% Confidence Interval)

Percent Reduction in Incidence from Current Standards Percent Reduction in Incidence from Current Standards

1514

4B-19
Annual (μg/m3) Daily (μg/m3) Cutpoint** Cutpoint** Cutpoint** Cutpoint** Cutpoint** Cutpoint**

=7.5 μg/m3 =10 μg/m3 =12 μg/m3 =7.5 μg/m3 =10 μg/m3 =12 μg/m3

Alternative Standards

Incidence Associated with PM2.5 Using an Annual Design Value Based 

on the Maximum of Monitor-Specific Averages**

Incidence Associated with PM2.5 Using an Annual Design Value Based on the A

Monitor-Specific Averages**

(95% Confidence Interval) (95% Confidence Interval)

Percent Reduction in Incidence from Current Standards Percent Reduction in Incidence from Current Standards

40, 98th percentile value 354 94 0 538 301 61

(123 - 615) (33 - 164) (0 - 0) (187 - 939) (104 - 526) (21 - 107)

32.2% 66.7% 100.0% 28.0% 43.7% 81.1%

35, 98th percentile value 354 94 0 538 301 61

(123 - 615) (33 - 164) (0 - 0) (187 - 939) (104 - 526) (21 - 107)

32.2% 66.7% 100.0% 28.0% 43.7% 81.1%

30, 98th percentile value 354 94 0 435 185 0

(123 - 615) (33 - 164) (0 - 0) (151 - 757) (64 - 323) (0 - 0)

32.2% 66.7% 100.0% 41.8% 65.4% 100.0%

25, 98th percentile value 270 0 0 270 0 0

(94 - 468) (0 - 0) (0 - 0) (94 - 468) (0 - 0) (0 - 0)

48.3% 100.0% 100.0% 63.9% 100.0% 100.0%

40, 99th percentile value 354 94 0 538 301 61

(123 - 615) (33 - 164) (0 - 0) (187 - 939) (104 - 526) (21 - 107)

32.2% 66.7% 100.0% 28.0% 43.7% 81.1%

35, 99th percentile value 354 94 0 507 266 23

(123 - 615) (33 - 164) (0 - 0) (176 - 884) (92 - 465) (8 - 40)

32.2% 66.7% 100.0% 32.1% 50.3% 92.9%

30, 99th percentile value 354 94 0 356 97 0

(123 - 615) (33 - 164) (0 - 0) (124 - 619) (34 - 168) (0 - 0)

32.2% 66.7% 100.0% 52.3% 81.9% 100.0%

25, 99th percentile value 207 0 0 207 0 0

(72 - 358) (0 - 0) (0 - 0) (72 - 358) (0 - 0) (0 - 0)

60.3% 100.0% 100.0% 72.3% 100.0% 100.0%

40, 98th percentile value 271 0 0 435 184 0

(94 - 469) (0 - 1) (0 - 0) (151 - 756) (64 - 322) (0 - 0)

48.1% 100.0% 100.0% 41.8% 65.6% 100.0%

35, 98th percentile value 271 0 0 435 184 0

(94 - 469) (0 - 1) (0 - 0) (151 - 756) (64 - 322) (0 - 0)

48.1% 100.0% 100.0% 41.8% 65.6% 100.0%

30, 98th percentile value 271 0 0 435 184 0

(94 - 469) (0 - 1) (0 - 0) (151 - 756) (64 - 322) (0 - 0)

48.1% 100.0% 100.0% 41.8% 65.6% 100.0%

25, 98th percentile value 270 0 0 270 0 0

(94 - 468) (0 - 0) (0 - 0) (94 - 468) (0 - 0) (0 - 0)

48.3% 100.0% 100.0% 63.9% 100.0% 100.0%

40, 99th percentile value 271 0 0 435 184 0

(94 - 469) (0 - 1) (0 - 0) (151 - 756) (64 - 322) (0 - 0)

48.1% 100.0% 100.0% 41.8% 65.6% 100.0%

35, 99th percentile value 271 0 0 435 184 0

(94 - 469) (0 - 1) (0 - 0) (151 - 756) (64 - 322) (0 - 0)

48.1% 100.0% 100.0% 41.8% 65.6% 100.0%

30, 99th percentile value 271 0 0 356 97 0

(94 - 469) (0 - 1) (0 - 0) (124 - 619) (34 - 168) (0 - 0)

48.1% 100.0% 100.0% 52.3% 81.9% 100.0%

25, 99th percentile value 207 0 0 207 0 0

(72 - 358) (0 - 0) (0 - 0) (72 - 358) (0 - 0) (0 - 0)

60.3% 100.0% 100.0% 72.3% 100.0% 100.0%

*This analysis was performed using Pope et al. (2002) -- ACS extended. **For the cutpoints above policy relevant background, the slope of the concentration-response function has been modified based on a simple hockeystick model (see discussion in section 4.3.2.1).***Current standards.

12

Note: Incidences are rounded to the nearest whole number; percents are rounded to the nearest tenth.

13

4B-20
12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 25 (9-44) deaths/yr/100,000

Incidence: 520 (180-910) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 16 (6-27) deaths/yr/100,000

Incidence: 1500 (530-2600) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 35 (12-62) deaths/yr/100,000

Incidence: 540 (190-940) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 31 (11-55) deaths/yr/100,000

Incidence: 400 (140-700) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 24 (8-42) deaths/yr/100,000

Incidence: 600 (210-1100) deaths/yr

Figure 5A-1(a) Estimated percent reduction in PM2.5-related long-term mortality risk for 

alternative standards (99

th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 7.5 μg/m3). Risk 

associated with meeting current PM2.5 standards, based on ACS extended 

study, is shown in figures in terms of estimated annual incidence rate and 

annual incidence (and 95% confidence ranges).

Detroit Los Angeles

Philadelphia Pittsburgh

St. Louis

5A-1
NOTE: Consistent with Figures 5-1(a), (b), and (c) and Figures 5-2(a), (b), (c),

and (d) that appear in Chapter 5, the figures in this Appendix are based on risk

reductions estimated for various combinations of alternative standards, including

annual standards of 15, 14, 13, and 12 :g/m3 and 24-hour standards of 65, 40, 35,

30, and 25 :g/m3

. Since in most cases the estimated risk reductions were the

same or nearly so for alternative 24-hour standards of 65 and 40 :g/m3, the

interim points between these two levels were filled in so as to better depict the 3-

dimensional surface of risk reductions. In those cases involving the 99th

percentile form of the 24-hour standard where there were appreciable differences

in the estimated risk reductions between alternative 24-standards of 65 and 40

:g/m3, the interim points were not filled in, since the shape of the surface between

these points has not been calculated.
12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 14 (5-24) deaths/yr/100,000

Incidence: 280 (100-490) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 9 (3-15) deaths/yr/100,000

Incidence: 820 (290-1400) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 22 (8-39) deaths/yr/100,000

Incidence: 340 (120-600) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 17 (6-29) deaths/yr/100,000

Incidence: 220 (80-370) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 12 (4-22) deaths/yr/100,000

Incidence: 310 ( 110-550) deaths/yr

Figure 5A-1(b) Estimated percent reduction in PM2.5-related long-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 10 μg/m3). Risk 

associated with meeting current PM2.5 standards, based on ACS extended 

study, is shown in figures in terms of estimated annual incidence rate and 

annual incidence (and 95% confidence ranges).

Detroit Los Angeles

Philadelphia Pittsburgh

St. Louis

5A-2
12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 2 (1-3) deaths/yr/100,000

Incidence: 40 (10-70) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 1 (1-2) deaths/yr/100,000

Incidence: 140 (50-240) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 9 (3-16) deaths/yr/100,000

Incidence: 140 (50-240) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 2 (1-3) deaths/yr/100,000

Incidence: 30 (10-40) deaths/yr]

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence rate: 1 (0-2) deaths/yr/100,000

Incidence: 20 (10-40) deaths/yr]

Figure 5A-1(c) Estimated percent reduction in PM2.5-related long-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 12 μg/m3). Risk 

associated with meeting current PM2.5 standards, based on ACS extended 

study, is shown in figures in terms of estimated annual incidence rate and 

annual incidence (and 95% confidence ranges).

Detroit Los Angeles

Philadelphia Pittsburgh

St. Louis

5A-3
. . . . 28

. . . . 18

. . . . 10

. . . . 0

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 6 (-6 - 17) deaths/yr/100,000

Incidence: 120 (-120 - 360) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 3 (0 -6) deaths/yr/100,000

Incidence: 290 (-40 - 610) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 24 (12 - 37) deaths/yr/100,000

Incidence: 370 (180 - 560) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 4 (-8 - 16) deaths/yr/100,000

Incidence: 50 (-110 - 200) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 8 (3 - 12) deaths/yr/100,000

Incidence: 190 (70 - 310) deaths/yr

Figure 5-2(a) Estimated percent reduction in PM2.5-related short-term mortality risk for

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint equal to policyrelevant background). Risk associated with meeting current PM2.5 standards, 

based on ACS extended study, is shown in figures in terms of estimated annual 

incidence rate and annual incidence (and 95% confidence ranges). Estimated 

policy-relevant background is 3.5 μg/m3in eastern cities and 2.5 μg/m3 in 

western cities.

St. Louis

Detroit

Philadelphia

Los Angeles

Pittsburgh

5A-4
12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 3 (-3 - 8) deaths/yr/100,000

Incidence: 50 (-60 - 160) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 1 (0 - 3) deaths/yr/100,000

Incidence: 120 (-10 - 240) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 12 (6 - 19) deaths/yr/100,000

Incidence: 190 (90 - 260) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 2 (-4 - 7) deaths/yr/100,000

Incidence: 20 (-50 - 90) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 3 (1 - 5) deaths/yr/100,000

Incidence: 80 (30 - 120) deaths/yr

Figure 5-2(b) Estimated percent reduction in PM2.5-related short-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 10 μg/m3). Risk 

associated with meeting current PM2.5 standards, based on ACS extended study, 

is shown in figures in terms of estimated annual incidence rate and annual 

incidence (and 95% confidence ranges).

Philadelphia Pittsburgh

St. Louis

Detroit Los Angeles

5A-5
12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 1 (-1 - 4) deaths/yr/100,000

Incidence: 30 (-30 - 80) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 1 (0 - 1) deaths/yr/100,000

Incidence: 60 (-10 - 120) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 7 (3 - 11) deaths/yr/100,000

Incidence: 110 (50 - 160) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 1 (-2 - 3) deaths/yr/100,000

Incidence: 10 (-20 - 40) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 1 (0 - 2) deaths/yr/100,000

Incidence: 30 (10 - 50) deaths/yr

Figure 5-2(c) Estimated percent reduction in PM2.5-related short-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 15 μg/m3). Risk 

associated with meeting current PM2.5 standards, based on ACS extended study, 

is shown in figures in terms of estimated annual incidence rate and annual 

incidence (and 95% confidence ranges).

Philadelphia Pittsburgh

St. Louis

Detroit Los Angeles

5A-6
12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 1 (-1 - 2) deaths/yr/100,000

Incidence: 10 (-10 - 40) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 0 (0 - 1) deaths/yr/100,000

Incidence: 30 (-4 - 60) deaths/yr

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 4 (2 - 6) deaths/yr/100,000

Incidence: 60 (30 - 90) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 0 (-1 - 1) deaths/yr/100,000

Incidence: 5 (-10 - 20) deaths/yr

 

12

13

14

15

65

60

55

50

45

40

35

30

25

0

20

40

60

80

100

Estimated 

percent 

reduction

Annual 

standard 

(ug/m3) 

24-hour

standard (ug/m3)

Incidence Rate: 0 (0 - 1) deaths/yr/100,000

Incidence: 9 (3 - 14) deaths/yr

Figure 5-2(d) Estimated percent reduction in PM2.5-related short-term mortality risk for 

alternative standards (98th percentile form) relative to risk associated with 

meeting current standards (based on assumed cutpoint of 20 μg/m3). Risk 

associated with meeting current PM2.5 standards, based on ACS extended study, 

is shown in figures in terms of estimated annual incidence rate and annual 

incidence (and 95% confidence ranges).

Philadelphia Pittsburgh

St. Louis

Detroit Los Angeles

5A-7
Table 5B-1(a) Predicted percent of counties with monitors (and percent of population in counties w

likely to meet alternative annual and 24-hour (98th percentile form) PM2.5 standards

Alternative Standards and Levels

(μg/m3)

Percent of counties, total and by region, (and total percent population) not likely to meet stated 

Total

counties (population) Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwes

No. of counties with monitors

(Population, in thousands) 562 (185,780) 83 168 130 49 21 81 

Annual standard only:

15 14 (30) 19 7 29 0 0 4 14 25 (41) 28 21 51 0 5 5 13 40 (55) 47 40 76 4 5 7 12 54 (66) 70 61 89 12 5 12 

Combined annual /24-hour: 

15 / 65 14 (30) 19 7 29 0 0 4 15 / 50 15 (31) 19 7 29 0 0 9 15 / 45 15 (33) 19 7 29 0 10 12 15 / 40 17 (35) 20 7 30 0 10 19 15 / 35 27 (48) 45 8 47 0 10 36 15 / 30 51 (72) 78 29 87 6 19 51 15 / 25 78 (86) 98 77 99 51 43 65 14 / 65 25 (41) 28 21 51 0 5 5 14 / 50 26 (43) 28 21 51 0 5 10 14 / 45 26 (44) 28 21 51 0 10 12 14 / 40 27 (46) 28 21 52 0 10 19 14 / 35 34 (55) 45 22 58 0 10 36 14 / 30 53 (72) 78 33 88 6 19 51 14 / 25 78 (86) 98 77 99 51 43 65 

5B-1
Alternative Standards and Levels

(μg/m3)

Percent of counties, total and by region, (and total percent population) not likely to meet stated 

Total

counties (population) Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwes

No. of counties with monitors

(Population, in thousands) 562 (185,780) 83 168 130 49 21 81 

13 / 65 40 (55) 47 40 76 4 5 7 13 / 50 40 (56) 47 40 76 4 5 10 13 / 45 41 (57) 47 40 76 4 10 12 13 / 40 42 (58) 47 40 76 4 10 19 13 / 35 45 (62) 53 40 77 4 10 36 13 / 30 57 (74) 78 43 90 8 19 51 13 / 25 78 (86) 98 77 99 51 43 65 12 / 65 54 (66) 70 61 89 12 5 12 12 / 50 54 (66) 70 61 89 12 5 12 12 / 45 54 (67) 70 61 89 12 10 14 12 / 40 55 (68) 70 61 89 12 10 20 12 / 35 58 (71) 70 61 89 12 10 36 12 / 30 64 (78) 84 62 94 14 19 51 12 / 25 79 (86) 98 78 99 51 43 65 

* Based on 2001-2003 data for sites with at least 11 samples per quarter for all 12 quarters. As such, these estimates are not based on 

that would be used to determine whether an area would attain a given standard or set of standards. These estimates can only approxim

counties that are likely not to attain the given standards and should be interpreted with caution.** "Outside Regions" includes Alaska, Hawaii, Puerto Rico, and the Virgin Islands.

5B-2
Table 5B-1(b) Predicted percent of counties with monitors (and percent of population in counties w

likely to meet alternative annual and 24-hour (99th percentile form) PM2.5 standards

Alternative Standards and Levels

(mg/m3)

Percent of counties, total and by region, (and total percent population) not likely to meet stated 

Total

counties (population) Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwe

No. of counties with monitors

(Population, in thousands) 562 (185,780) 83 168 130 49 21 81 

Annual only:

15 14 (30) 19 7 29 0 0 4 14 25 (41) 28 21 51 0 5 5 13 40 (55) 47 40 76 4 5 7 12 54 (66) 70 61 89 12 5 12 

Combined annual / 24-hour:

15 / 65 14 (30) 19 7 29 0 0 5 15 / 50 16 (33) 19 7 29 0 10 15 15 / 45 18 (35) 24 7 32 0 10 21 15 / 40 27 (46) 47 9 42 0 10 36 15 / 35 44 (68) 72 17 77 0 19 51 15 / 30 68 (82) 96 54 97 35 38 59 15 / 25 85 (89) 100 86 99 69 48 73 14 / 65 25 (41) 28 21 51 0 5 6 14 / 50 27 (44) 28 21 51 0 10 15 14 / 45 28 (45) 30 21 52 0 10 21 14 / 40 35 (53) 48 23 57 0 10 36 14 / 35 47 (70) 72 27 78 0 19 51 14 / 30 68 (82) 96 54 97 35 38 59 14 / 25 85 (89) 100 86 99 69 48 73 

5B-3
Alternative Standards and Levels

(mg/m3)

Percent of counties, total and by region, (and total percent population) not likely to meet stated 

Total

counties (population) Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwe

No. of counties with monitors

(Population, in thousands) 562 (185,780) 83 168 130 49 21 81 

13 / 65 40 (55) 47 40 76 4 5 9 13 / 50 41 (57) 47 40 76 4 10 15 13 / 45 42 (58) 49 40 76 4 10 21 13 / 40 47 (62) 59 40 77 4 10 36 13 / 35 54 (73) 75 40 85 4 19 51 13 / 30 70 (82) 96 58 97 35 38 59 13 / 25 85 (89) 100 86 99 69 48 73 12 / 65 54 (66) 70 61 89 12 5 12 12 / 50 55 (67) 70 61 89 12 10 16 12 / 45 56 (68) 71 61 89 12 10 22 12 / 40 59 (71) 75 62 89 12 10 36 12 / 35 63 (77) 80 62 92 12 19 51 12 / 30 73 (83) 96 68 98 35 38 59 12 / 25 85 (89) 100 86 99 69 48 73 

* Based on 2001-2003 data for sites with at least 11 samples per quarter for all 12 quarters. As such, these estimates are not based on 

that would be used to determine whether an area would attain a given standard or set of standards. These estimates can only approxim

counties that are likely not to attain the given standards and should be interpreted with caution.** "Outside Regions" includes Alaska, Hawaii, Puerto Rico, and the Virgin Islands.

5B-4
Table 5B-2(a) Percent of counties with monitors (and percent of population in counties with monitors) not 

alternative 24-hour (98th percentile form) UPM10-2.5 standards 

Percent of counties, total and by region, (and total percent population) not likely to meet alternative 2

form) PM10-2.5 standards or current PM10 standards* 

Alternative Levels 

Total Counties 

(population) Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwest 

Number of counties 

with monitors 

(Population, in 

thousands) 

259 (141,859) 44 60 57 18 13 45 

70 7 (9) 2 3 4 0 46 4 65 9 (11) 2 3 5 6 46 9 60 12 (16) 2 5 7 6 62 13 55 13 (18) 5 5 7 17 62 13 50 16 (27) 5 7 9 22 62 16 

* Based on 2001-2003 data for sites with 4, 8, or 12 consecutive quarters with at least 11 samples per quarter. As such, these estimatesame air quality data that would be used to determine whether an area would attain a given standard or set of standards. These estimat

the number of counties that are likely not to attain the given standards and should be interpreted with caution. ** "Outside Regions" includes Alaska, Hawaii, Puerto Rico, and the Virgin Islands. 

5B-5
Table 5B-2(b) Percent of counties with monitors (and percent of population in counties with monitors) not 

alternative 24-hour (99th percentile form) UPM10-2.5 standards 

Percent of counties, total and by region, (and total percent population) not likely to meet alter

(99th percentile form) PM10-2.5 standards or current PM10 standards* 

Alternative Levels 

Total Counties 

(population) Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwest 

Number of counties 

with monitors 

 (Population, in 

thousands) 

259 (141,859) 44 60 57 18 13 45 

85 8 (10) 7 2 4 6 46 11 80 10 (11) 7 3 4 6 54 11 75 12 (14) 7 5 5 6 54 11 70 13 (15) 7 7 5 17 54 13 65 16 (19) 9 8 7 33 54 16 60 19 (27) 11 10 7 39 62 16 

* Based on 2001-2003 data for sites with 4, 8, or 12 consecutive quarters with at least 11 samples per quarter. As such, these estimatesame air quality data that would be used to determine whether an area would attain a given standard or set of standards. These estimat

the number of counties that are likely not to attain the given standards and should be interpreted with caution. ** "Outside Regions" includes Alaska, Hawaii, Puerto Rico, and the Virgin Islands. 

5B-6
Table 5B-2(c) Percent of counties with monitors (and percentage of population in counties with monitors) 

current PM10 standards 

Percent of counties, total and by region, (and total percent population) not meeting the current PM

Database 

Total 

Counties 

(population) 

Northeast Southeast Industrial Midwest Upper Midwest Southwest Northwest Southern CA 

All PM10 sites : 

[Number of counties 

with monitors 

(Population, in 

thousands)]*

585 (170,118) 84 120 115 52 33 142 18 

Percent violating 8 (13) 0 3 3 6 27 10 61 

PM10 sites that meet 

'urban' criteria: 

[Number of counties 

with monitors 

(Population, in 

thousands)]

309 (153,546) 59 70 67 21 17 50 15 

Percent violating 6 (12) 0 1 3 0 29 4 53 

Urban PM10 sites, 

alsoPM10-2.5 : 

[Number of counties 

with monitor 

 (Population, in 

thousands)]

259 (141,859) 44 60 57 18 13 45 sp 

Percent violating 7 (11) 0 2 4 0 38 2 47 

* Based on official EPA design values for 2001-2003; see http://epa.gov/airtrends/values.html. ** "Outside Regions" includes Alaska, Hawaii, Puerto Rico, and the Virgin Islands. 

5B-7
Figure 7A-1. Estimated exceedances (%) of various PM2.5 levels for 12 p

county maximum, 2001-2003.

* Figure 7A-1 can be seen in color at: http://www.epa.gov/ttn/naaqs/standards/pm/s

Percentage of days exceeding level

12 p.m. – 4 p.m. average PM2.5 (μg/m3)

blue = east counties* red = west counties

0

102030405060708090

100

65 60 55 50 45 40 35 30 25 

Source: Schmidt et al. (2005) 

7A-1
7A-2

Table 7A-1. Predicted percent of counties with monitors (and percent of population in counties with monimeet alternative 4-hour (12 p.m. - 4 p.m.) PM2.5 secondary standards.

Alternative Forms and Levels of

Secondary PM2.5 Standard 

Percent of counties, total and by region, (and total percent population) not likely to meet stated 

Total Counties

(population)

Northeast Southeast Industrial

Midwest

Upper

Midwest

Southwest Northwest S

Number of counties with monitors

(Population, in thousands)

168 (78,419) 33 45 30 16 14 25 

PM2.5 standard levels and forms:

20 μg/m3, 92nd percentile 51 (67) 76 36 90 38 29 8 

25 μg/m3, 92nd percentile 27 (46) 52 16 53 0 7 4 30 μg/m3, 92nd percentile 8 (17) 15 0 17 0 7 0 

20 μg/m3, 95th percentile 70 (83) 88 73 97 50 50 24 

25 μg/m3, 95th percentile 47 (67) 79 31 87 19 29 4 30 μg/m3, 95th percentile 24 (43) 52 7 47 0 14 4 20 μg/m3, 98th percentile 85 (96) 100 100 100 63 57 48 25 μg/m3, 98th percentile 70 (81) 94 62 100 50 57 28 30 μg/m3, 98th percentile 56 (73) 85 38 90 19 57 24 

* Based on 2001-2003 data for sites with at least 1 year of complete data. Completeness criteria (per year) = Minimum of 3 hours per d

window), 275+ days per year. As such, these estimates are not based on the same air quality data that would be used to determine whetgiven standard or set of standards. These estimates can only approximate the number of counties that are likely not to attain the given 

interpreted with caution.** "Outside Regions" includes Alaska, Hawaii, Puerto Rico, and the Virgin Islands.
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

 WASHINGTON D.C. 20460 

June 6, 2005 

EPA-SAB-CASAC-05-007 

OFFICE OF THE ADMINISTRATOR 

SCIENCE ADVISORY BOARD 

Honorable Stephen L. Johnson 

Administrator 

U.S. Environmental Protection Agency 

1200 Pennsylvania Avenue, NW 

Washington, DC 20460 

Subject: Clean Air Scientific Advisory Committee (CASAC) Particulate Matter (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); and 

Particulate Matter Health Risk Assessment for Selected Urban Areas: Second 

Draft Report (Second Draft PM Risk Assessment, January 2005) 

Dear Administrator Johnson: 

EPA’s Clean Air Scientific Advisory Committee (CASAC), supplemented by subjectmatter-expert Panelists — collectively referred to as the CASAC Particulate Matter (PM) 

Review Panel (“Panel”) — met in a public meeting held in Durham, NC, on April 5-6, 2005, to 

conduct a peer review of subject documents. The current Panel roster is found in Appendix A of 

this report. 

This meeting was a continuation of the CASAC PM Review Panel’s peer review of the 

Review of the National Ambient Air Quality Standards for Particulate Matter: Policy Assessment 

of Scientific and Technical Information (First Draft PM Staff Paper, August 2003) and a related 

draft technical report, Particulate Matter Health Risk Assessment for Selected Urban Areas (First 

Draft PM Risk Assessment, August 2003). The previous draft of the PM Staff Paper was a 

preliminary version since the Panel has not yet finished its review of the Air Quality Criteria 

Document (AQCD) for PM (which was completed in October 2004). In addition, further risk 

analyses and analyses of alternative forms of the PM standards were included in the Second 

Draft PM Staff Paper and Second Draft PM Risk Assessment. The charge questions provided to 

the Panel by EPA are found in Appendix B to this report. Panelists’ individual review comments 

are provided in Appendix C of this report. 

In its peer review of the Second Draft of the PM Staff Paper, most of the members of the 

CASAC PM Review Panel found the document was generally well-written and scientifically 

1 

ATTACHMENT A
well-reasoned for all but the short term primary PM10-2.5 standard. A majority of the members of 

the Panel were in agreement with the following: the primary PM2.5 24-hour and annual PM 

national ambient air quality standards (NAAQS) should be modified to provide increased public 

health protection. Although the evidence for a standard for coarse-mode particles was weaker 

than for the PM2.5, the Panel agreed that a 24-hour NAAQS for PM10-2.5 was appropriate, 

especially in urban areas, with caveats to make exceptions for those types of rural dusts thought 

to have low toxicity. The Panel recommends that the Agency staff expand and strengthen the 

discussion of the exposure index (size-range plus composition and/or source) and the monitoring 

strategy to be used for the coarse-mode NAAQS, as well as the degree of public health 

protection against thoracic coarse PM expected relative to the protection afforded by the current 

PM10 short-term NAAQS. As discussed below, the CASAC PM Review Panel will need to 

review the final version of the PM Staff Paper before providing a final opinion to EPA on the 

adequacy of a short-term PM10-2.5 NAAQS. 

The approach used to set secondary NAAQS to protect the environment was considered 

appropriate, but it was strongly recommended that, in the future, Agency staff also give serious 

consideration to a shift to the European approach of critical loads to protect vegetation and 

ecosystems in the U.S. In addition, most of the Panel supported Agency staff recommendations 

regarding a standard to address the issue of urban visibility impairment. 

1. Background 

The CASAC, comprised of seven members appointed by the EPA Administrator, was 

established under section 109(d)(2) of the Clean Air Act (CAA or “Act”) (42 U.S.C. § 7409) as 

an independent scientific advisory committee, in part to provide advice, information and 

recommendations on the scientific and technical aspects of issues related to air quality criteria 

and NAAQS under sections 108 and 109 of the Act. Section 109(d)(1) of the CAA requires that 

EPA carry out a periodic review and revision, where appropriate, of the air quality criteria and 

the NAAQS for “criteria” air pollutants such as PM. The CASAC, which is administratively 

located under EPA’s Science Advisory Board (SAB) Staff Office, is a Federal advisory 

committee chartered under the Federal Advisory Committee Act (FACA), as amended, 5 U.S.C., 

App. The CASAC PM Review Panel is comprised of the seven members of the chartered 

(statutory) Clean Air Scientific Advisory Committee, supplemented by fifteen technical experts. 

Under section 108 of the CAA, the Agency is required to establish NAAQS for each 

pollutant for which EPA has issued criteria, including PM. Section 109(d) of the Act 

subsequently requires periodic review and, if appropriate, revision of existing air quality criteria 

to reflect advances in scientific knowledge on the effects of the pollutant on public health and 

welfare. EPA is also to revise the NAAQS, if appropriate, based on the revised criteria. The 

purpose of the Second Draft PM Staff Paper is to evaluate the policy implications of the key 

scientific and technical information contained in a related document, EPA’s revised PM AQCD 

(October 2004), and to identify critical elements that EPA believes should be considered in the 

review of the PM NAAQS. The Staff Paper for PM is intended to “bridge the gap” between the 

scientific review contained in the PM AQCD and the public health and welfare policy judgments 

required of the Administrator in reviewing the PM NAAQS. 

2
This Second Draft PM Staff Paper is based on the information in the final PM AQCD, 

which had been the subject of review by the CASAC PM Review Panel since October 1999. 

(The report from the Panel’s final meeting to review the PM AQCD, dated October 4, 2004, is 

posted on the SAB Web Site at: http://www.epa.gov/sab/pdf/casac05001.pdf. The Agency 

subsequently announced the availability of a final document, Air Quality Criteria for Particulate 

Matter (EPA/600/P-99/002aF, EPA/600/P-99/002bF) on October 29, 2004.) In addition, the 

Second Draft PM Staff Paper builds upon the First Draft PM Staff Paper, which was the subject 

of review by the CASAC PM Review Panel held on November 12-13, 2003. The report from the 

Panel’s previous meeting to review these draft documents, dated February 18, 2004, is posted on 

the SAB Web Site at: http://www.epa.gov/sab/pdf/casac_04004.pdf. The Second Draft PM Staff 

Paper and the Second Draft PM Risk Assessment were made available for public review and 

comment on January 31, 2005 by EPA’s Office of Air Quality Planning and Standards 

(OAQPS), within the Office of Air and Radiation (OAR). The Second Draft PM Risk 

Assessment, which builds upon the Agency’s First Draft PM Risk Assessment, describes the 

methodology and presents the results from an updated PM health risk assessment for health risks 

associated with exposure to fine and thoracic coarse particles in a number of U.S. cities. 

2. CASAC PM Review Panel’s Peer Review of the Second Draft PM Staff Paper and 

Second Draft PM Risk Assessment 

After reviewing the Second Draft PM Staff Paper and written comments from the public, 

and after hearing public comments at the meeting, a majority of the members of the CASAC PM 

Review Panel were in agreement with the following: the primary PM2.5 24-hour and annual 

NAAQS should be modified to provide increased public health protection. The evidence for a 

NAAQS for coarse mode particles is weaker than for PM2.5. The Panel agreed, however, that a 

24-hour NAAQS for PM10-2.5 is appropriate, especially in urban areas and with caveats to make 

exceptions for those types of rural dusts thought to have low toxicity. Before the Panel renders 

its final recommendation concerning a daily PM10-2.5 standard, the Panel recommends that the 

Agency staff expand and strengthen the discussion of the exposure index (size-range plus 

composition and/or source) and the monitoring strategy to be used for this standard, as well as 

the degree of public health protection expected relative to the protection against thoracic coarse 

PM afforded by the current PM10 short-term NAAQS. Accordingly, after the Panel has reviewed 

the Final Staff Paper and Risk Assessment for Particulate Matter following its issuance on June 

30, 2005, the Panel will meet again this summer via a public teleconference to consider the final 

Staff Paper’s recommendations concerning the setting of a coarse PM standard. Subsequent to 

the Panel’s teleconference meeting, we will send you a separate letter providing the Panel’s 

recommendations concerning PM10-2.5 as an indicator together with our views on the averaging 

time, statistical form, and level of any potential daily PM10-2.5 standard. 

The approach used to set secondary standards to protect the environment was considered 

appropriate, but it was strongly recommended that, in the future, Agency staff give serious 

consideration to the European approach of critical loads to protect vegetation and ecosystems in 

the U.S. In addition, most of the Panel supported Agency staff recommendations regarding a 

standard to address the issue of urban visibility impairment. 

In its peer review of the Second Draft of the PM Staff Paper, most of the members of the 

CASAC PM Review Panel found the document was generally well-written and scientifically 

3
well-reasoned. The following represent summaries of advice and recommendations of the Panel 

in response to the charge questions provided by EPA, which are found in Appendix B to this 

report. More detailed responses are provided in the individual review comments of each member 

of the Panel included in Appendix C to this report. 

The CASAC PM Review Panel has reached agreement on the following synopsis of 

advice and recommendations for the Agency: 

AIR QUALITY 

Chapter 2: Characterization of Ambient PM 

Chapter 2 of the Second Draft PM Staff Paper was considered well-written, presenting an 

accurate and concise summary of Chapters 2, 3, and 5 of the PM Air Quality Criteria Document. 

The chapter was acceptable to the Panel reviewers as written, but some improvements were 

suggested in two areas. In the area of measurement methods, the Panel thought there should be 

more discussion of continuous PM monitoring methods in light of the recommended secondary 

fine particle standard based on 4- to 8-hour concentration averages and the likely availability of a 

continuous coarse particle monitor. A more quantitative characterization of PM mass 

measurement errors could be presented, especially for PM10-2.5. Interest was expressed in a 

discussion of alternative PM indicators for future NAAQS considerations related to the source of 

the PM, especially for the potentially more toxic portion of coarse PM. In the area of health and 

visibility assessments, concern was expressed that spatial gradients near major arterials and other 

urban sources are not adequately addressed. It was suggested that spatial heterogeneity within a 

city might better be characterized in terms of departures of individual sites from the metropolitan 

average, in place of this draft’s summary statistics of between-sites comparisons. Some 

members of the Panel expressed concerns about the policy-relevant background (PRB) estimates. 

The true background is not observable and is effectively unknowable. As indicated in the 

summary of Chapter 5 comments, alternative standards should be analyzed in ways that are 

insensitive to estimates of PRB. 

HEALTH-BASED STANDARDS 

Chapter 3: Policy-Relevant Assessment of Health Effects Evidence 

 Chapter 3 addresses each of the health effects issues relevant to PM NAAQS 

reconsideration. Agency staff have adequately reviewed advances in understanding effects from 

studies conducted subsequent to the 1997 NAAQS, as summarized in the latest PM AQCD. 

Overall, EPA staff have done a reasonably good job of summarizing the health effects basis for 

considering revised or new PM standards. However, there are instances where the summary of 

findings and their interpretation are overstated (see individual Panel member review comments, 

particularly on pages C-82 and C-83). Specifically, there was confusion over strength of 

association versus strength of evidence, between confounding and effect modification, and 

between temporality and lag structure. There are some areas where Agency staff have either 

over-interpreted or overstated the extent to which the health data support a particular PM 

indicator variable. These problems can be addressed if EPA staff give heed to the individual 

4
comments of the CASAC PM Review Panel when revising the chapter. The discussion of the 

effect of co-pollutants in interpreting the results of PM health studies would benefit from a 

clearer discussion of EPA staff’s approach to interpreting quantitative results from multipollutant studies. 

Numerous epidemiological studies that are reviewed in this chapter have shown 

statistically significant associations between the concentrations of ambient air PM2.5 and PM10 

(including levels that are lower than the current PM NAAQS) and excess mortality and 

morbidity. Furthermore, the evidence presented indicates that the effects appear to be robust, in 

that inclusion of other environmental variables in regression analyses has not been found to 

materially affect the associations of the adverse health effects with ambient PM concentrations. 

On the other hand, the scientific evidence included in the PM Air Quality Criteria Document and 

draft Staff Paper provides substantially less data derived from controlled exposure studies in 

humans or experimental animals; or from studies of biological mechanisms in animals in vivo or 

cells and tissues in vitro, to support the biological plausibility of the effects of the relatively low 

concentrations found in the human population studies. In the case of controlled human studies, 

this appears to be due to the inherent limitations of such studies, which are largely confined to 

young, healthy subjects exposed for short time periods and the examination of mild, reversible 

effects. In the case of animal studies, it may be due to not having adequate animal models of 

human disease processes and exposures to individual chemical agents rather than realistic 

mixtures. Both types of studies may be inadequate to represent the real-world situation of 

susceptible subpopulations of humans undergoing long-term exposures and occasional peak 

levels of complex mixtures of PM, associated surface coatings of reactive chemicals, and 

gaseous co-pollutants. This apparent discontinuity needs to be addressed in future research. 

The current health effects data base for coarse mode particles (PM10-2.5) is relatively 

weak. Few epidemiology studies have been conducted where PM10-2.5 was measured directly as 

opposed to obtaining an estimate of this indicator variable by subtracting data from collocated 

PM2.5 and PM10 monitors. There is limited evidence that PM10-2.5 may be related to 

cardiovascular mortality as well as to hospital admissions for respiratory diseases. The few 

controlled human studies that have been conducted with concentrated ambient particles have not 

shed any light on the morbidity findings from epidemiological studies. Moreover, animal 

toxicological studies using coarse mode particles are virtually nonexistent; they are difficult to 

perform because rodents are obligate nose breathers and thus few of these particles penetrate to 

the lungs. A further complication with current epidemiological studies of the health effects of 

PM10-2.5 is that most have been conducted in urban areas, and because coarse mode particles from 

urban and rural areas may be markedly different, extrapolating these findings to rural settings 

may be difficult. Considerably more research with PM10-2.5 is needed. 

Chapter 4: Characterization of Health Risks 

One major concern with the current version of the chapter is the clarity of presentation. 

Readers need to struggle through dense prose and jargon-ridden text to identify key aspects of 

the methods and findings. Key terms are sometimes used incorrectly or inconsistently across the 

chapter. The chapter could be substantially shortened, and redundancies need to be addressed. 

Figure 4-1 provides an overall framework for the risk assessment that could be used to shape the 

5
chapter. We suggest that the chapter refer to it repetitively as the risk assessment methods and 

findings are described. Subheadings, such as “assumptions” and “sensitivity analyses,” might be 

more effectively used to guide the reader through the individual sections of the chapter. 

A second concern is with methodological issues. The issue of the selection of 

concentration-response (C-R) relationships based on locally-derived coefficients needs more 

discussion. The Panel did not agree with EPA staff in calculating the burden of associated 

incidence in their risk assessment using either the predicted background or the lowest measured 

level (LML) in the utilized epidemiological analysis. The available epidemiological database on 

daily mortality and morbidity does not establish either the presence or absence of threshold 

concentrations for adverse health effects. Thus, in order to avoid emphasizing an approach that 

assumes effects that extend to either predicted background concentrations or LML, and to 

standardize the approach across cities, for the purpose of estimating public health impacts, the 

Panel favored the primary use of an assumed threshold of 10 μg/m3

. The original approach of 

using background or LML, as well as the other postulated thresholds, could still be used in a 

sensitivity analysis of threshold assumptions. 

The analyses in this chapter highlight the impact of assumptions regarding thresholds, or 

lack of threshold, on the estimates of risk. The uncertainty associated with threshold or 

nonlinear models needs more thorough discussion. A major research need is for more work to 

determine the existence and level of any thresholds that may exist or the shape of nonlinear 

concentration-response curves at low levels of exposure that may exist, and to reduce uncertainty 

in estimated risks at the lowest PM concentrations. 

Chapter 5: Staff Conclusions and Recommendations on Primary PM NAAQS

 The Panel had the following advice and recommendations for the PM 2.5 standard: 

The tack taken by EPA staff in recommending a suite of standards for PM2.5 by using 

both an evidence-based and a risk-based approach, while necessarily ad hoc, was felt to be 

reasonable. Most Panel members favored continued use of the 98th percentile form because it is 

more robust than the 99th percentile form and therefore would provide more stability to prevent 

areas from bouncing in and out of attainment from year to year. Some concern was expressed as 

to whether EPA staff would exclude days on which natural phenomena such as forest fires distort 

the distribution. The Panel felt that such days should be eliminated before standard compliance 

is assessed. The link between the percentile form and the exposure level chosen is wellillustrated in the type of three-dimensional figures created by Dr. Miller at the April meeting 

(based on the data in Figure 5-2 in the 2nd draft PM Staff Paper), which were endorsed by the 

Panel and later provided in expanded form by OAQPS staff. The Panel endorses the inclusion of 

these types of figures in the Staff Paper. It would be helpful if reductions in risk associated with 

different regulatory options were expressed in the form of absolute numbers normalized to a 

fixed population size, in addition to those already expressed as percentage reductions. 

In recommending revisions to the PM2.5 NAAQS, changes to either the annual or the 24­

hour standard, or both, could be recommended. Three arguments were made that support placing 

more emphasis on lowering the 24-hour NAAQS. First, the vast majority of studies indicating 

6
effects of short-term PM2.5 exposures were carried out in settings in which PM2.5 concentrations 

were largely below the current standard of 65 μg/m3

. Second, the amount of evidence on shortterm effects, at least as reflected by the number of reported studies, is greater than for long-term 

effects. Third, toxicological findings largely relate to effects of short-term PM2.5 exposures. 

There was a consensus among the Panel members in agreement with the EPA staff 

recommendations that focused on decreasing PM2.5 concentrations through lowering of the 24­

hour PM standard, but the panel did not endorse the option of keeping the annual standard at its 

present level of 15 μg/m3

. It was appreciated that some cities have relatively high annual PM 

concentrations, but without much variation in concentrations from day to day. Such cities would 

only rarely exceed a 24-hour PM2.5 standard, even if set at levels below the current standard. 

This observation indicates the desirability of lowering the level of the annual PM2.5 standard as 

well. 

Of the options presented by EPA staff for lowering the level of the PM standard, based 

on the above considerations and the predicted reductions in health impacts derived from the risk 

analyses, most Panel members favored the option of setting a 24-hour PM2.5 NAAQS at 

concentrations in the range of 35 to 30 μg/m3 with the 98th percentile form, in concert with an 

annual NAAQS in the range of 14 to 13 μg/m3

. The justification for not moving to the lowest 

staff-recommended levels within these ranges is that these were generally associated with only 

small additional predicted reductions in risk. In addition, the uncertainties associated with 

concentration-response relationships increase greatly below these ranges, as reflected in 

substantial widening of the confidence limits for point estimates. 

The Panel had the following advice and recommendation for the PM10-2.5 standard: 

It was acknowledged that the scientific basis supporting a causal role of PM10-2.5 in an 

array of adverse health effects is weaker than that of PM2.5. Regardless, most of the Panel 

members felt that the evidence that exists supports a causal role for health effects for PM10-2.5. 

Moreover, setting this NAAQS would allow continuation and expansion of the PM10-2.5 

monitoring network that would facilitate collection of data for future exposure assessment and 

epidemiology studies. Because the evidence for the toxicity of PM10-2.5 comes from studies 

conducted primarily in urban areas and is related, in large part, to the re-entrainment of urban 

and suburban road dusts as well as primary combustion products, there is concern that the 

associations of adverse effects with PM10-2.5 may not apply to rural areas where the PM10-2.5 is 

largely composed of less-toxic components of windblown soil or products of agricultural 

operations for which there is either no or limited evidence of health issues. 

Further, although there is some evidence that short-term changes in concentrations of 

PM10-2.5 are associated with changes in mortality, particularly cardiovascular mortality, the 

evidence in support of effects on morbidity, especially respiratory morbidity, is stronger. Most 

Panel members therefore favored not including short-term mortality effects in the health impact 

predictions, in line with the approach taken by EPA staff. The Panel agreed with Agency staff’s 

approach of not setting an annual NAAQS for PM10-2.5 at this time. 

7
One of the major reservations expressed by the Panel in recommending a 24-hour PM10-

2.5 NAAQS related to the non-specificity of the PM10-2.5 mass metric. Given that most evidence 

indicates that the component of the coarse fraction in most rural areas has little or no toxicity at 

environmental concentrations, it was felt important to qualify the PM10-2.5 standard by somehow 

allowing exceptions for regions where the coarse fraction was composed largely of material that 

was not contaminated by industrial- or motor vehicle traffic-associated sources. Options 

discussed by members of the Panel for attempting to achieve this approach included limiting the 

standard to cover “all” urban areas, the judicious siting of monitors with a focus on urban areas, 

or regulatory exceptions for regions where road dust is not an issue or where rural components 

dominate the source. No single option was favored. 

The panel also agreed that there was a need for more research on the health effects of 

PM10-2.5. Such research will require the continuation and expansion of the PM10-2.5 monitoring 

network in both rural and urban areas. The Panel recommends that the Agency staff expand and 

strengthen the discussion of the exposure index (size-range plus composition and/or source) and 

the monitoring strategy to be used for this NAAQS as well as the degree of public health 

protection expected relative to the protection against thoracic coarse PM afforded by the current 

PM10 short-term NAAQS. 

WELFARE-BASED STANDARDS 

Chapters 6 & 7: PM-Related Welfare Effects 

Overall, these chapters are well done. Comments are provided below regarding 

vegetation and ecosystem, materials soiling, and visibility. 

Considering the effects of PM on vegetation and ecosystems, EPA staff are to be 

commended for a well-written and concise reflection of the key science as presented in the final 

PM AQCD. The ecological risk assessment is reasonable given the required “criteria pollutant” 

approach. That being said, the criteria pollutant approach in this case (i.e., PM) has serious 

shortcomings when it comes to ensuring environmental protection of vegetation and ecosystems 

in the U.S. This is illustrated in the following discussion. 

Scientific evidence presented in the PM Staff Paper and the PM AQCD indicates that 

forest ecosystems at a number of locations in the U.S. “are now showing severe symptoms of 

nitrogen saturation.” The Staff Paper makes the point that this is the result of chronic long-term 

additions of reactive nitrogen (Nr) species that have been accumulating over time. The PM Staff 

Paper also makes the point that the issue of forest-ecosystem deterioration is broader and more 

complex than just Nr accumulation. The Staff Paper notes that, “The most significant PMrelated ecosystem-level effects result from long-term cumulative deposition of a given chemical 

species (e.g., nitrate) or mix (e.g., acidic deposition) that exceeds the natural buffering or storage 

capacity of the ecosystem and/or affects the nutrient status of the ecosystem.” A key point 

implied here and elaborated later in the PM Staff Paper text is that PM deposition is only 

partially-responsible for the observed ecosystem-level effects and that the extent of the role of 

PM deposition in these ecosystem-level effects needs to be determined. While this has scientific 

merit, the question must be asked as to whether knowing the role of PM alone will improve the 

8
protection of vegetation and ecosystems in the U.S.? The answer to this question is critical 

because forest ecosystems are responding to the cumulative total load that has resulted from the 

chronic long-term deposition of both PM as well as gases and not to PM alone. 

While EPA staff have done a commendable job within the context of the criteria pollutant 

approach, it is strongly recommended that in the future that Agency staff give serious 

consideration to a philosophical shift from the criteria pollutant approach to the European 

approach of “critical loads” when it comes to ensuring protection of vegetation and ecosystems 

in the U.S. The critical load is defined in the criteria document and is a quantitative estimate of 

an exposure to one or more pollutants below which significant harmful effects on specified 

sensitive elements of the environment do not occur according to present knowledge. The current 

criteria pollutant approach is a significant limitation in the efforts of the Agency staff to address 

the cumulative load of all the pollutant stressors to which ecosystems are responding. 

Considering soiling and materials effects, several of the Panel members specifically 

asked EPA to add some discussion of the welfare effects caused by soiling from coarse particles. 

This may lead to consideration of a secondary PM10-2.5 standard intended to protect against 

adverse welfare effects. 

 Considering visibility effects, most Panel members strongly supported the EPA staff 

recommendation to establish a new, secondary PM2.5 standard to protect urban visibility. 

Overall, the Second Draft Staff Paper visibility sections (Chapters 6 and 7 and the detailed 

technical appendix by Schmidt et al., 2005) are well-conceived and clearly-written. Agency staff 

can also be commended for responsiveness to comments previously submitted by this Panel on 

the PM AQCD and the First Draft PM Staff Paper. The recommended new standard was 

considered by most Panel members to be a reasonable complement to the Regional Haze Rules 

that protect Class I areas. The dissenting view is provided in one Panel member’s individual 

review comments (see pages C-101 and C-102). 

The recommended range of secondary standards includes an indicator (PM2.5 mass), 

averaging time (4 to 8 daylight hours), level (20 to 30 μg/m3

) and form (90th percentile “or 

slightly higher”). The sub-daily averaging time is an innovative approach that strengthens the 

quality of the PM2.5 indicator by targeting the driest part of the day. An indirect but important 

benefit will come from the direct use of — and more intense scrutiny on the quality of —the 

hourly data from the widely deployed continuous PM2.5 mass monitors. The net effect is a 

“responsive” standard that (for the first time) would directly link public perception of air 

pollution (predominantly due to visual effects of light scattering by fine particles in the ambient 

air) to a routinely measured pollutant indicator (i.e., artificially-dried PM2.5 mass). 

The recommended level and form of the standard are more difficult to specify. The draft 

PM Staff Paper employs a “bounding” approach, suggesting a level that is below the “obviously 

adverse” level of the current secondary standard — under which extreme short-term 

concentrations exceeding 100 μg/m3

 have been observed on days when 24-hour concentrations 

do not exceed 65 μg/m3

. Some members of the Panel felt the recommended level (and form) of 

the standard were on the high side, but developing a more specific (and more protective) level in 

future standards would require updated and refined public visibility valuation studies. Agency 

9

Appendix A – Roster of the CASAC Particulate Matter Review Panel 

U.S. Environmental Protection Agency 

Science Advisory Board (SAB) Staff Office 

Clean Air Scientific Advisory Committee (CASAC) 

CASAC Particulate Matter Review Panel* 

CHAIR 

Dr. Rogene Henderson*, Scientist Emeritus, Lovelace Respiratory Research Institute, 

Albuquerque, NM 

MEMBERS 

Dr. Ellis Cowling*, University Distinguished Professor-at-Large, North Carolina State 

University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina 

State University, Raleigh, NC 

Dr. James D. Crapo*, Professor, Department of Medicine, Biomedical Research and Patient

Care, National Jewish Medical and Research Center, Denver, CO 

Dr. Philip Hopke**, Bayard D. Clarkson Distinguished Professor, Department of Chemical 

Engineering, Clarkson University, Potsdam, NY 

Dr. Jane Q. Koenig, Professor, Department of Environmental Health, School of Public Health 

and Community Medicine, University of Washington, Seattle, WA

Dr. Petros Koutrakis, Professor of Environmental Science, Environmental Health , School of 

Public Health, Harvard University (HSPH), Boston, MA 

Dr. Allan Legge, President, Biosphere Solutions, Calgary, Alberta 

Dr. Paul J. Lioy, Associate Director and Professor, Environmental and Occupational Health 

Sciences Institute, UMDNJ - Robert Wood Johnson Medical School, NJ 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York 

University School of Medicine, Tuxedo, NY 

Dr. Joe Mauderly, Vice President, Senior Scientist, and Director, National Environmental 

Respiratory Center, Lovelace Respiratory Research Institute, Albuquerque, NM 

Dr. Roger O. McClellan, Consultant, Albuquerque, NM 

Dr. Frederick J. Miller*, Consultant, Cary, NC 

A-1 
Dr. Gunter Oberdorster, Professor of Toxicology, Department of Environmental Medicine, 

School of Medicine and Dentistry, University of Rochester, Rochester, NY 

Mr. Richard L. Poirot*, Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT 

Dr. Robert D. Rowe, President, Stratus Consulting, Inc., Boulder, CO 

Dr. Jonathan M. Samet, Professor and Chair, Department of Epidemiology, Bloomberg School 

of Public Health, Johns Hopkins University, Baltimore, MD 

Dr. Frank Speizer*, Edward Kass Professor of Medicine, Channing Laboratory, Harvard 

Medical School, Boston, MA 

Dr. Sverre Vedal, Professor of Medicine, School of Public Health and Community Medicine 

University of Washington, Seattle, WA 

Mr. Ronald White, Research Scientist, Epidemiology, Bloomberg School of Public Health, 

Johns Hopkins University, Baltimore, MD 

Dr. Warren H. White, Visiting Professor, Crocker Nuclear Laboratory, University of California 

- Davis, Davis, CA 

Dr. George T. Wolff, Principal Scientist, General Motors Corporation, Detroit, MI 

Dr. Barbara Zielinska*, Research Professor, Division of Atmospheric Science, Desert Research 

Institute, Reno, NV 

SCIENCE ADVISORY BOARD STAFF 

Mr. Fred Butterfield, CASAC Designated Federal Officer, 1200 Pennsylvania Avenue, N.W., 

Washington, DC, 20460, Phone: 202-343-9994, Fax: 202-233-0643 (butterfield.fred@epa.gov) 

(Physical/Courier/FedEx Address: Fred A. Butterfield, III, EPA Science Advisory Board Staff 

Office (Mail Code 1400F), Woodies Building, 1025 F Street, N.W., Room 3604, Washington, 

DC 20004, Telephone: 202-343-9994) 

* Members of the statutory Clean Air Scientific Advisory Committee (CASAC) appointed by the EPA 

Administrator 

**Immediate past CASAC Chair 

A-2 

United States Office of Air Quality Planning and Standards Publication No

Environmental Protection Air Quality Strategies and Standards Division June 2005

Agency Research Triangle Park, NC